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Consumer health informatics and the medically underserved

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Title:
Consumer health informatics and the medically underserved the role of information technology in health information access and health communication in the United States
Creator:
Moore, Susan L. ( author )
Place of Publication:
Denver, CO
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University of Colorado Denver
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English
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Subjects / Keywords:
Medical informatics -- United States ( lcsh )
Diffusion of innovations ( lcsh )
Communication in medicine -- United States ( lcsh )
Communication in medicine ( fast )
Diffusion of innovations ( fast )
Medical informatics ( fast )
United States ( fast )
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bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

Notes

Abstract:
This thesis describes the results of a survey conducted to explore information technology (IT) and health information technology utilization patterns, impact, and the validity of Diffusion of Innovations (DoI) theoretical principles among patients who receive primary health care in an urban safety net setting. Utilization in the surveyed population was similar to national utilization for widely adopted technologies. Less-commonly adopted technology use was also observed, but at rates lagging national levels, confirming the existence of a timeshifted digital divide. IT use was reported by 95% of survey respondents. Cell phone use was significantly higher than computer use (p<0.001), with 93% of respondents reporting cell phone use versus 71% reporting computer use. Significantly more people used technology for health information than for health communication (65% vs. 53%, p<0.001). A self-reported general health status of good or better was significantly associated with health information use (p=0.001). Distinct groups of IT adopters identified within the surveyed population showed no significant difference in population distribution from adoption patterns described under DoI theory. This finding supports both DoI theoretical applicability within a single broad socioeconomic stratum and the potential use of theory-based diffusion modeling to reduce the impact of the digital divide through tailored health informatics solutions.
Thesis:
Thesis (Ph.D)--University of Colorado Denver. Health and behavioral sciences
Bibliography:
Includes bibliographic references.
System Details:
System requirements: Adobe Reader.
General Note:
Department of Health and Behavioral Sciences
Statement of Responsibility:
by Susan L. Moore.

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|University of Colorado Denver
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|Auraria Library
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All applicable rights reserved by the source institution and holding location.
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880144434 ( OCLC )
ocn880144434

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Full Text
CONSUMER HEALTH INFORMATICS AND THE MEDICALLY UNDERSERVED:
THE ROLE OF INFORMATION TECHNOLOGY IN HEALTH INFORMATION
ACCESS AND HEALTH COMMUNICATION IN THE UNITED STATES
by
SUSAN L. MOORE
B.S., University of New Orleans, 1994
M.S.P.H., University of Colorado Denver / Colorado School of Public Health, 2008
A thesis submitted to the
Faculty of the Graduate School of the
University of Colorado in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
Health and Behavioral Sciences
2013


This thesis for the Doctor of Philosophy degree by
Susan L. Moore
has been approved for the
Health and Behavioral Sciences Program
by
Sheana S. Bull, Chair
Edward P. Havranek
Henry H. Fischer
Andrew W. Steele


Moore, Susan L. (Ph.D., Health and Behavioral Sciences)
Consumer Health Informatics and the Medically Underserved:
The Role of Information Technology in Health Information Access and Health
Communication in the United States
Thesis directed by Professor Sheana S. Bull.
ABSTRACT
This thesis describes the results of a survey conducted to explore information
technology (IT) and health information technology utilization patterns, impact, and the
validity of Diffusion of Innovations (Dol) theoretical principles among patients who
receive primary health care in an urban safety net setting. Utilization in the surveyed
population was similar to national utilization for widely adopted technologies. Less-
commonly adopted technology use was also observed, but at rates lagging national levels,
confirming the existence of a timeshifted digital divide. IT use was reported by 95% of
survey respondents. Cell phone use was significantly higher than computer use
(p<0.001), with 93% of respondents reporting cell phone use versus 71% reporting
computer use. Significantly more people used technology for health information than for
health communication (65% vs. 53%, p<0.001). A self-reported general health status of
good or better was significantly associated with health information use (p=0.001).
Distinct groups of IT adopters identified within the surveyed population showed no
significant difference in population distribution from adoption patterns described under
Dol theory. This finding supports both Dol theoretical applicability within a single broad
m


socioeconomic stratum and the potential use of theory-based diffusion modeling to
reduce the impact of the digital divide through tailored health informatics solutions.
The form and content of this abstract are approved. I recommend its publication.
Approved: Sheana S. Bull
IV


ACKNOWLEDGMENTS
No one gets there alone, and I am no exception. This endeavor could not have
been completed without the assistance of a great many people, all of whom have my
unending gratitude.
First and foremost, I thank everyone who contributed to this project as a research
participant, whether by responding to a survey or by joining in focused group discussion.
Research depends on data, and the data they unstintingly provided was the gift that made
this work possible. Thanks are due next to my committee chair, Sheana Bull, for her
expert mentorship, guidance, and research advice, as well as the significant gift of her
time; to the other members of my committee, Ed Havranek, Henry Fischer, and Andy
Steele, for their clinical and research expertise and for their participation; and to Susan
Dreisbach, for acting as my academic advisor until her retirement.
I also wish to thank my colleagues at Denver Health, particularly those in the
Department of Patient Safety and Quality, Division of Health Services Research,
Interpreter Services, and the 21st Century Care evaluation team. Especial thanks go to
Josh Durfee, for his invaluable assistance with risk stratification and statistical analysis;
to Debbie Rinehart, for her advice regarding survey incentives; to Rachel Everhart, for
her help in identifying the survey population within the data warehouse; to Amy Tobin
and Isabel Barrera, for aid with multiple translations; and to Tracy Johnson, for her
suggestions about survey sampling, fielding, and presenting results.
My thanks are given as well to colleagues and friends from other fields, in
particular Michelle Thompson Boyer, for providing both the benefit of her expertise in
instructional design and her tireless assistance in applying thousands of labels and stamps
v


to survey envelopes, and Shannon Granville and M.E. Lasseter, for their professional
academic and editorial review, proofreading, and copyediting. The quality of this work
would have been poorer without their help, and I appreciate it more than I can express.
Last but never least, I would like to thank my family, especially my parents, Larry
Moore and Sandra Crockett Moore, and my sister, Summer Crockett Moore, and the
communities of friends, both online and off, who have supported me throughout. These
include the choirs of St. Johns Cathedral, Denver; the sisters and brothers of that Order
which knows the true value of the lens of science; and those who tell collaborative stories
in and about other worlds than these. Special thanks go to Beth Kerr and Batya
Wittenberg for going above and beyond, to Andrea Lankin for the reminder that al shal
be wel, and al manner of thyng shal be wele, and finally to M.E. Lasseter and Lynne A.
McCullough, for paving the way and walking the path alongside me from start to finish.
Both my life and this world are richer for their presence and kindness, and I am forever
grateful.
vi


TABLE OF CONTENTS
CHAPTER
I. INTRODUCTION..........................................................1
Specific Aims........................................................5
Specific Aim 1....................................................5
Specific Aim 2....................................................8
Specific Aim 3....................................................8
II. BACKGROUND AM) SIGNIFICANCE......................................... 10
Quality through Technology: Establishing a National Context.........10
Consumer Health Informatics and Patient-Centered Care...............13
Consumer Health Informatics and Chronic Disease.....................17
Chronic Disease and the Medically Underserved.......................20
Overcoming the Digital Divide.......................................21
III. THEORETICAL BASIS AND FRAMEWORK......................................23
Marxist Theory......................................................23
Antonio Gramsci: Cultural Hegemony..................................26
Michel Foucault: Knowledge and Power................................27
Diffusion of Innovations............................................29
IV. RESEARCH DESIGN AND METHODS..........................................35
Overview............................................................35
Study Population....................................................35
Inclusion and Exclusion Criteria.................................36
Risk Stratification..............................................37
Survey Procedures...................................................39
Survey Design....................................................39
vii


Survey Pilot Test Focused Group Discussion.........................41
Sampling Methodology.................................................44
Survey Implementation................................................45
Data Collection and Management.......................................48
Analysis Plan............................................................50
Measures of Interest.................................................50
Specific Aim 1, Research Question 1..................................52
Specific Aim 1, Research Question 2..................................52
Specific Aim 1, Research Question 3..................................52
Specific Aim 1, Research Question 4..................................53
Specific Aim 2.......................................................53
Specific Aim 3.......................................................54
V. RESULTS.................................................................55
Survey Response..........................................................55
Weighting and Balancing..................................................62
Analytical Methods.......................................................62
Descriptive Statistics...................................................63
Technology Users, Overall............................................65
Computer Users.......................................................68
Cell Phone Users.....................................................70
Internet Users.......................................................71
Activity Patterns Among IT Users.....................................73
Health Information...................................................77
Health Communication.................................................80
Other Technology Users...............................................82
viii


Technology Nonusers.............................................84
Opinions about Technology.......................................87
Health Status and Information Technology Use.......................87
Population Level................................................88
Tier 1 Level....................................................88
Tier 2 Level....................................................88
Tier 3 Level....................................................89
Diffusion of Innovations: Technology Diffusion Assessment..........89
VI. DISCUSSION..........................................................93
REFERENCES...............................................................107
APPENDIX.................................................................122
A. CHI Survey Instrument (English) ................................122
B. Invitation Letter and Mailing Labels (English).................131
C. Reminder Postcard (English)....................................133
D. CHI Codebook...................................................134
ix


LIST OF TABLES
TABLE
1: Attributes of Innovations in Terms of HIT...................................30
2: Classification of Innovation Adopters.......................................31
3: Five Phases of the Gartner Hype Cycle.......................................33
4: DH Primary Care Patient Demographics, January 7, 2013.......................36
5: DH Tiering Algorithm Assignment, Version 1.0................................39
6: Survey Focus Group Demographics.............................................42
7: Survey Pilot Test Emergent Themes by Content Area...........................43
8: Survey Implementation Timeframe..............................................45
9: Survey Population Demographics, Unadjusted...................................60
10: CDC Healthy Days Measures................................................64
11: CDC Healthy Days Measures by Demographic..................................64
11: CDC Healthy Days Measures by Demographic, continued.......................65
12: Information Technology (IT) User Classification.............................65
12: Information Technology (IT) User Classification, continued..................66
13: IT General User Status by Demographic.......................................66
13: IT General User Status by Demographic, continued............................67
14: IT Users, Technology Utilization by Demographic............................68
15: Utilization Patterns among Computer Users (n=266).......................... 69
16: Utilization Patterns among Cell Phone Users (n=364)........................ 70
16: Utilization Patterns among Cell Phone Users (n=364), continued..............71
17: Utilization Patterns among Internet Users (n=279).......................... 72
18: IT-Based Activities (n=405*)............................................... 74
19: IT-Based Activities by Demographic.........................................75
x


19: IT-Based Activities by Demographic, continued.................................76
20: Utilization Patterns among Health Information Users (n=231)....................77
20: Utilization Patterns among Health Information Users (n=231), continued.........78
21: Health Information Content (n=237*)........................................... 79
22: Utilization Patterns among Health Communicators (n=192).......................80
23: Health Communication Contacts (n=210*)........................................81
24: Other Technology Utilization (n=403*)......................................... 82
25: Other Technology Users, Utilization by Demographic............................83
26: IT and HIT Barriers and Facilitators...........................................85
27: IT and HIT Nonusers by Demographic.............................................86
28: Technology Diffusion Classification, Surveyed Population......................92
xi


LIST OF FIGURES
FIGURE
1: The Chronic Care Model.......................................................19
2: The Digital Divide as a Marxist Construct....................................25
3: The Diffusion of Innovations Distribution Curve..............................31
4: Gartner Hype Cycle...........................................................34
5: Inclusion/Exclusion Criteria Flow Diagram....................................37
6: Survey Results: AAPOR Response Types.........................................55
7: Age Histogram and Box Distribution, Respondent vs. Sampled...................61
8: Age Q-Q plot, Respondent vs. Sampled.........................................61
9: Computer Adoption Duration of Use..........................................90
10: Cell Phone Adoption Duration of Use.......................................90
11: Internet Adoption Duration of Use.........................................91
12: Model and Surveyed Population Distributions.................................92
xii


LIST OF EQUATIONS
EQUATION
1: Flesch-Kincaid Grade Level Readability Formula..........................40
2: Probability Sampling Sample Size Formula..............................44
3: Response Rate...........................................................56
4: Cooperation Rate........................................................57
5: Refusal Rate............................................................58
6: Contact Rate............................................................58
xiii


LIST OF ABBREVIATIONS
Ale
BP
AAPOR
AHRQ
ARRA
ACA
AIM
AOL
CAHPS
CCM
CDC
CDPS
CHI
COMIRB
COPD
CPOE
DH
DM
Dol
eHealth
EHR
EMR
FCC
FPC
FQHC
HbAlc
HIT
HITECH
HRQOL
HRSA
HTN
ICPSR
ICT
IOM
glycosylated hemoglobin; laboratory test that measures average
levels of blood glucose over time and is used to both diagnose
diabetes mellitus and assess a patients degree of diabetes control.
See also HbAlc.
blood pressure
American Association for Public Opinion Research
Agency for Healthcare Research and Quality
American Recovery and Reinvestment Act of 2009
Affordable Care Act (see also PPACA)
AOL Instant Messenger; chat system developed and maintained by
AOL
online service and content provider whose corporate name is the
acronym for the original company name of America Online
Consumer Assessment of Health Providers and Systems
Chronic Care Model
Centers for Disease Control and Prevention
Chronic Illness and Disability Payment System
consumer health informatics
Colorado Multiple Institutional Review Board
chronic obstructive pulmonary disease
computerized provider order entry
Denver Health and Hospital Authority
diabetes mellitus; diabetes
Diffusion of Innovations
electronic health; the use of electronic or information technology
for health and health care
electronic health record
electronic medical record
Federal Communications Commission
finite population correction
federally qualified health center
hemoglobin Ale; glycosylated hemoglobin; laboratory test that
measures average levels of blood glucose over time and is used
both to diagnose diabetes mellitus and assess a patients degree of
diabetes control. See also Ale.
health information technology
Health Information Technology for Economic and Clinical Health
Act
Health Related Quality of Life
Health Resources and Services Administration
hypertension
Inter-university Consortium for Political and Social Research
information and communications technologies
Institute of Medicine
xiv


IRC
IT
LDL
mHealth
MAR
MCAR
MCMC
MU
NHIN
OMB
ONC
PHR
PPACA
SAS
SE
USPS
WHO
Internet Relay Chat; messaging system protocol for real-time
synchronous one-on-one or group text-based communication over
network channels via transmission communication protocol and
security
information technology
low-density lipoprotein; cholesterol lab test used as a measure of a
patients diabetes control
mobile health; the use of mobile technology for health and health
care
missing at random
missing completely at random
Markov chain Monte Carlo
meaningful use
Nationwide Health Information Network
Office of Management and Budget
Office of the National Coordinator for Health and Information
Technology
personal health record
Patient Protection and Affordable Care Act (2010)
Statistical Analysis System; analytical software originally created
at North Carolina State University and now developed and
maintained by SAS Institute, Inc. (Cary, NC)
standard error
United States Postal Service
World Health Organization
xv


CHAPTER I
INTRODUCTION
In 2001, as part of its historic in-depth analysis of health care in the United States,
the Institute of Medicine (IOM) found that information technology (IT) had the potential
to promote the provision of health care that was safe, effective, patient-centered, timely,
and equitable (1). Consumer health was identified as a specific domain in which IT
could be of great benefit. Studies to date support this finding, indicating that consumer
health informatics applications are capable of being used to engage patients, augment
clinical interventions, aid in decision support, promote chronic disease self-management,
and improve both intermediate and longer-term clinical health outcomes (2).
Unfortunately, this approach may result in unintended negative consequences for
those who are already significantly burdened with health disparities. The term digital
divide refers to the disparity between those who have both the access and knowledge
necessary to utilize Internet-based IT and those who do not (3). The digital divide has
been shown to disproportionately affect members of the vulnerable and medically
underserved priority populations (4, 5) traditionally served by the health care safety
net. The safety net refers to the system of care for patients with limited or no health
insurance, offered by health delivery systems and providers who have committed either
under the law or by chosen mission to care for patients regardless of their ability to pay
(6). Safety net providers include public hospitals, federally qualified health centers
(FQHCs), and public health departments, and their patients include racial and ethnic
minorities, low-literacy populations, people of below-average socioeconomic status,
1


people with disabilities, children and the elderly, people with multiple chronic conditions,
and rural populations (7-10).
This dissertation describes the results of a cross-sectional study conducted to
explore the use and value of IT among patients who receive primary health care in an
urban safety net setting, considered as a representative sample of a single broad stratum
of the current societal superstructure in the United States. These concepts were explored
through a social science perspective, with consideration given to social and structural
factors that affect access to and use of technology. Inequality of technological access,
control of information within health care delivery systems, and interactions between
knowledge and power were examined in theoretical contexts and considered within the
Diffusion of Innovations (Dol) framework.
The well-documented phenomenon of the digital divide can be showcased in a
theoretical context through a class-oriented examination of the societal implications of
technology access and use. Karl Marx first recognized the increased availability of
technology to those with greater resources and the subsequent leveraging of technology
to conduct capitalist processes as a way by which the worker could be further distanced
from the means of production (11). Marxs proposal that economic change has direct
political and cultural effects on the societal superstructure led directly to Gramscis
observation that cultural norms are in fact social constructs imposed by the prevailing
class as part of the dominant ideology and accepted by subaltern groups as the natural
state of being (cultural hegemony) (12, 13). As technology is considered to be a
significant element in the structural formation of economic change, it must then also be
considered a key factor in hegemonic domination. The social class-based variation in
2


access to and ability to effectively use technology for health informatics purposes that is
characteristic of the digital divide represents this culturally hegemonic societal
superstructure. This variation is one of measurable structural violencethe systematic
exertion of violence by those who belong to a particular social order upon those who are
members of less-privileged classes (14) exerted here through the restriction of
knowledge and the consequent promotion of health disparities. French historian and
social theorist Michel Foucault (15, 16), whose social and structural critiques of health
systems and organizational discipline have significantly informed philosophical thought
on health and health care, has clearly described this interrelationship between knowledge
and power. Foucaults Birth of the Clinic describes the historical transformation of social
and political landscapes necessary to produce the institution of modern clinical medicine
and introduces the concept of the medical gaze and the need for attentive, objective
observation of patients as central to a modem treatment paradigm (17).
Limited information is currently available on the impact of consumer health
informatics applications on health outcomes among the priority population groups
traditionally served by the safety net (2, 18). Nonetheless, the dual lack of access to
technology and information that is represented by the digital divide should not be
assumed to also represent lack of interest in technology. Priority population groups, as
defined by the Agency for Healthcare Research and Quality (AHRQ) (4), include people
who are traditionally considered to be medically vulnerable, such as those without health
insurance, people with low income or low socioeconomic status, racial and ethnic
minorities, low-literacy populations, people with disabilities, children, the elderly, and
those living in rural areas. A disproportionate share of these groups receive health care
3


through core safety net systems, which the IOM has characterized as health systems with
policies and practices that call for treating patients regardless of their ability to pay (19).
Patients in these settings have shown interest in the idea of technology-based information
sharing with their care providers, and have expressed desire to know more about their
own health information (20-23).
Although the digital divide is an indicator of disparity between social classes in a
societal superstructure, recent technological innovations may be able to help overcome
the traditional structural inequity that this disparity perpetuates. Everett Rogers theory of
the Diffusion of Innovations (Dol) has identified the use of appropriate technological
innovations for specific purposes as a means of closing gaps between groups of
innovation adopters and would-be adopters (24). Rogers has also described an approach
for assessing rates of innovation spread and uptake among populations. The increasing
prevalence of certain types of user-centric technologysuch as cell phones among
priority groups, combined with priority groups interest in using technology to support
information sharing, offers the opportunity to improve health information access and
health communication through use of technological innovations that priority groups have
already self-selected. For example, Blacks and Latinos are more likely than whites both
to own cell phones (87% compared to 80%) and to use them for a wide variety of data
access functions. Two-thirds of both minority populations also use their cell phones to
access the wireless Internet (25). Examining the use of these innovations within a single
socioeconomic stratum in the context of Dol and of a constructivist paradigm (which
assumes that individuals actively gain and create knowledge from their own experiences)
may help identify and develop targeted solutions that bridge the inequity gap and
4


improve shared decision making by empowering patients with health information that
best meets their needs.
Specific Aims
The specific aims of the study were as follows:
Specific Aim 1
To assess and describe current methods and patterns of IT utilization for health
information access and engagement in health communications among adult patients who
receive care in an urban safety net setting.
Research Question 1
How do patterns of IT utilization in general, IT utilization for health information
access, and IT utilization for health communications differ by demographic subgroup?
Demographic Measures (Denver Health clinical systems): age, gender, race,
language
IT-General Utilization Measures (survey data) : computer, cell phone, and Internet
user status; computer, cell phone, and Internet use device type; computer, cell phone, and
Internet use duration; computer, cell phone, and Internet use frequency; computer, cell
phone, and Internet use importance; computer and cell phone ownership; Internet access
speed; and IT activity type.
IT-Health Information Measures (survey data) : health information access user
status; health information access duration; health information access frequency; health
information access importance; and health information access topics.
5


IT-Health Communications Measures (survey data) : health communication user
status; health communication duration; health communication frequency; health
communication importance; and health communication contacts.
Research Question 2
How do patterns of IT utilization in general, IT utilization for health information
access, and IT utilization for health communication differ by health status?
Health Status Measures (survey data, Denver Health clinical systems): Centers
for Disease Control and Prevention (CDC) Healthy Days self-rated general health
status, unhealthy days in past 30 days, and mental health in past 30 days; Denver Health
risk stratification tier (version 1.0).
IT-General Utilization Measures (survey data) : computer, cell phone, and Internet
user status; computer, cell phone, and Internet use device type; computer, cell phone, and
Internet use duration; computer, cell phone, and Internet use frequency; computer, cell
phone, and Internet use importance; computer and cell phone ownership; Internet access
speed; and IT activity type.
IT-Health Information Measures (survey data) : health information access user
status; health information access duration; health information access frequency; health
information access importance; and health information access topics.
IT-Health Communications Measures (survey data) : health communication user
status; health communication duration; health communication frequency; health
communication importance; and health communication contacts.
6


Research Question 3
What are the barriers and facilitators to IT utilization in general, IT utilization for
health information access, and IT utilization for health communication among nonusers?
IT-General Utilization Measures (survey data) : computer, cell phone, and Internet
user status; health information access user status; and health communication user status.
Barrier Measures (survey data) : computer, cell phone, and Internet use barriers;
health information access barriers; and health communication barriers.
Facilitator Measures (survey data) : computer, cell phone, and Internet use
facilitators; health information access facilitators; and health communication facilitators.
Research Question 4
What general opinions about IT and health IT (HIT) are held among adult patients
who receive care in an urban safety net setting?
Opinion Measures (survey data) : Topics and themes emergent from participant
responses to open-ended survey questions.
Objectives
The objectives of this aim were to assess the prevalence of IT use, identify
interest in and familiarity with specific IT modalities and activities, discern patterns of
behavior related to IT use, and identify barriers and facilitators to IT use both in general
and for health information access and health communications among members of priority
populations as represented by adult patients who receive care in an urban safety net
setting. It was anticipated that safety net patients with chronic diseases would have
greater interest in, engagement with and use of IT for health information access and
health communications than would those without chronic illness.
7


Specific Aim 2
To compare the health status of IT users and IT nonusers among adult patients
with chronic disease who receive health care in an urban safety net setting.
Hypothesis
Adult patients with chronic disease who receive care in the safety net and who use
IT to access health information and engage in health communications are predicted to
have better health status than adult patients with chronic disease who receive care in the
safety net and who are IT nonusers.
Objective
The objective of this aim was to examine the potential of consumer health
informatics applications to improve health outcomes among patients with chronic disease
who are members of priority populations.
Specific Aim 3
To evaluate the applicability of traditional Dol theory when used to examine
patterns of adoption and utilization of HIT among adult patients who receive care in an
urban safety net setting.
Hypothesis
Members of priority populations have an interest in using IT to access health
information and to engage in health communications that is equivalent to that reported
among members of more advantaged populations; however, they do not use the same
types of IT in the same manner or to the same extent.
8


Objective
Dol theory describes rates and patterns of identification, adoption, and utilization
for specific technological innovations by groups of adopters who are in part characterized
by their socioeconomic strata. This aim proposed to examine the applicability of Dol
theory within rather than across socioeconomic strata to determine if observed patterns
still persist when barriers to access are considered as a known factor rather than being
used in the definition of a larger subgroup.
9


CHAPTER II
BACKGROUND AND SIGNIFICANCE
Quality through Technology: Establishing a National Context
In 1998, the IOM created the Committee on the Quality of Health Care in
America and charged it to develop an approach to substantially and significantly improve
the quality of health care over the next decade. In 2001, the committees second report,
Crossing the Quality Chasm, called for the health care delivery system to be redesigned
in order to improve healthcare quality as a whole (1). As part of its historic analysis, the
committee identified 6 aims for improvement, proposing that health care should be safe,
effective, patient-centered, timely, and equitable.
In its report, the committee noted the potential for IT to play a critical role in the
transformation of the health system to achieve all 6 specified aims for quality
improvement, and recommended that better integration of IT into health care should be 1
of 4 key areas essential for system transformation. Specific recommendations included
developing a national information infrastructure; promoting the adoption of electronic
medical record (EMR) and computerized provider order entry technology (CPOE);
establishing data standards for health information exchange; and continuing to use and
develop informatics applications for patients, a field which has become known as
consumer health informatics. Informatics is the science of using data, information, and
knowledge to improve both human health and the delivery of health care services;
consumer health informatics is the science of informatics as relates to consumer needs,
with a focus on information structures and processes that empower consumers to manage
their own health (26).
10


In light of the committees recommendations, in 2004 Executive Order 13335
(27) established the position of National Coordinator for Health Information Technology
and charged it with responsibility for developing and leading a strategic action plan to
actively promote HIT implementation in both public and private sectors nationwide (27,
28). Subsequent legislation under the Health Information Technology for Economic and
Clinical Health (HITECH) Act, included in the American Recovery and Reinvestment
Act of 2009 (ARRA), mandated the continuation of the position of the National
Coordinator, established the Office of the National Coordinator for Health and
Information Technology (ONC), provided significant incentives for HIT adoption, and
established an initial framework and a schedule for national HIT infrastructure
development and deployment (2, 29).
In the first year of the HITECH Act, ONC set forth the following initiatives with
specific impact on consumer health informatics (30):
1.) The creation of a Nationwide Health Information Network (NHIN) as a
secure, interoperable health information infrastructure designed to connect
providers, consumers, and other key stakeholders involved in supporting
health and health care. From a consumer health informatics perspective,
NHIN is intended to provide patients with the capability to manage and
control their own personal health records as well as providing them with
access to their health information from clinicians electronic health record
(EHR) systems, while also ensuring security and confidentiality of personal
health data.
11


2. ) The establishment of incentive criteria for the meaningful use of EHRs by
providers who participate in Medicare and Medicaid programs. Meaningful
use was defined to include the electronic capture of health information in
coded formats, the use of such electronically captured information to track
certain key clinical conditions and to assist with the coordination of care, and
the use of health information for public health and clinical quality measure
reporting.
3. ) The creation of an initial set of standards, implementation specifications, and
meaningful use certification criteria for EHRs.
4. ) The development in cooperation with health care organizations and standards-
development organizations of a set of interoperability specifications and
uniform data exchange formats, along with detailed technical specifications
for use, intended to support health information exchange between systems.
Further expansion of the ONC initiatives under the HITECH Act was authorized
in 2010 with the passage of the Patient Protection and Affordable Care Act (PPACA; the
Affordable Care Act) (31). Section 1561 of the Affordable Care Act required the
Department of Health and Human Services, in collaboration with the HIT Policy and HIT
Standards Committees created by ONC, to develop secure, interoperable standards and
protocols to facilitate patients ability to electronically enroll and manage their
participation in federal and state health and human services programs (32, 33). The
HITECH Act also called for an update to ONCs original Federal Health Strategic Plan as
last published in 2008. In January 2012, ONC released the updated plan, which takes into
account changes in HIT in the last several years and establishes 5 broad goals for the
12


future. The fourth goal is to empower individuals with health IT to improve their health
and the health care system, thereby reiterating IOMs recommendation on consumer
health informatics and emphasizing the continuing significance of enabling patients to
access and use HIT effectively (34).
Consumer Health Informatics and Patient-Centered Care
Concomitant with the drive toward an increasingly technological health care
system is a movement toward a health care model that focuses on treating the person, not
the disease. Prior models of health care delivery were largely provider-driven, with the
majority of decisions made by the physician and presented to the patient as directives to
be followed, based on clinical data and provider expertise. The patient-centered approach
involves empowered patients acting in consultation with providers as decision-makers in
their own care experience, with health services designed to accommodate patients
individual preferences and health needs (35).
The advent of the patient-centered care model brought a need for patients to be
able to obtain essential information and resources in order to understand their health-
related care options, to engage in complex decision making, and to receive support for
making good health choices and managing their own health-related behaviors according
to their individual health needs. In response, patients turned in large numbers to online
sources of information. In 2010, searching for health information online was the third
most frequent Internet activity, following email and general search engine use. Eight out
of 10 Internet users in the United States reported searching for health information online,
comprising a full 59% of all Americans (36). Online health information seekers were
13


more likely to be women, white, and college-educated; have higher incomes; and act as
unpaid caregivers for other individuals (10, 36).
To increase the availability of health information to patients, health systems and
health care providers participating in meaningful use programs are required under Stage 1
criteria to offer electronic copies of health information to patients within 3 days of a visit,
and are required under Stage 2 criteria to offer patients access to health information
online within 4 days of a visit. Electronic and online accessmay be offered through Web
sites designated as patient portals, where patients can access their electronic health
records and review their health information, including data such as lab results, scheduled
appointments, and medication refill information (30, 37-40). Early adopters of patient
portals, such as Kaiser Permanente (KPHealthConnect) and Geisinger Health System
(myGeisinger), have reported decreased office visits in tandem with observed
improvements in patient-provider communication and quality of care (41). Despite
concerns expressed about privacy and security of personal health information, patients
continue to express willingness to engage in technology-based health information access
and data sharing (42).
In addition to availing themselves of online resources for health information,
patients are increasingly using the Internet to participate in health-related community
interaction, peer support, social engagement, and health data tracking. Web sites such as
Livestrong, Mindbloom, and SparkPeople offer health promotion through tailored
interfaces to engage users daily in tracking self-provided health data such as weight, body
measurements, and physical fitness activity, along with support for wellness-focused
behavioral change such as tobacco cessation, providing support both directly through the
14


users personalized interface and through online forums and social network channels to
promote interaction and community engagement with other users of the service (43-45).
Sites such as PatientsLikeMe, TuDiabetes, and CancerCare offer condition-based support
for diseases through tailored spaces where patients living with specific conditions can
receive information and peer-to-peer support through interaction with other users facing
similar challenges (46-48). The patient-centered concept of participatory medicine, in
which patients are actively encouraged to use the Internet to seek information on their
own behalf or others and to use their findings to inform and empower their interactions
with their health care providers, is actively promoted both by sites such as e-Patients.net
and through a nonprofit organization and online peer-reviewed journal (49-51). Such
activities have a sizeable audience. Of the 74% of American adults who use the Internet,
62% have reported using social network sites, and 23% of them reported keeping up with
their friends health-related site updates. More than a quarter (27%) of Americans have
reported tracking some sort of health data online, while 18% of all online users and 23%
percent of online users with chronic conditions reported turning to the Internet to seek out
others with similar health conditions (36, 52).
Consumer engagement with health informatics solutions is also not limited to
traditional Web site interfaces. The term Web 2.0 broadly refers to the transition of
Web design and technology from a largely static, server-centric information repository
(where information is distributed in broadcast fashion from the source server across a
network to the client recipient) to an interactive model where server and client
applications engage in synchronized data exchange and service delivery occurs through a
shared platform. The core concept underlying Web 2.0 is one of community-sourced
15


development that utilizes a Web-based platform as infrastructure and shared space for
engagement (53). This shift in conceptualization from the Web site provider as both
content creator and distributor to users as content creators and active contributors to a
community product is accompanied by a growing collective intelligence, in which the
sum of the knowledge value contributed by the community of users is greater than that
which could be attributed to an individual alone (53, 54). The Web 2.0 framework not
only is well suited to both patient-centric participatory medicine and a peer-to-peer
approach to health care through social networking (55), but also provides access to shared
platforms through social media and emergent technologies that are not dependent on
traditional access to desktop client computers for their use. Health 2.0 or Medicine
2.0 refers to the development and upgrade of consumer health informatics applications
and services to be fully interoperable with the Web 2.0 model. These tools are targeted
toward patients and care providers and are designed to promote increased engagement in
social networking, collaborative operations, and apomediation within and between groups
of users (56-58), along with greater personal choice and portability of use, including use
on a variety of mobile devices.
The World Health Organization (WHO) defines the use of mobile technology for
health care, or mHealth, as a rapidly growing subset of eHealth, which encompasses all
health services supported by information and communications technologies (ICT) (59,
60). More than 5 billion mobile phone accounts are currently in operation worldwide,
covering more than 85% of the worlds people (60). In the United States, 82% of all
adults own mobile phones of some kind, and 40% of them use their mobile phones to
access the Internet (25). Moreover, 35% of American mobile phone owners possess
16


smartphones (e.g., Android, BlackBerry, iPhone), and of those users 87% use their
smartphone for Internet access and 25% report that their smartphone is the device they
use for the majority of their online activity (61). The advent of widespread and affordable
mobile and wireless technology has rapidly-increasing potential to both further transform
the way individuals access Internet and other technological resources and to have
significant impact on the delivery of health care, although it remains to be seen whether
this transformation will be sufficient to overcome the impact of the digital divide for
those who do not engage in mobile or high-speed Internet access.
Consumer Health Informatics and Chronic Disease
One particular area where consumer health informatics applications have been
shown to have compelling potential for improving the delivery of patient-centered care is
in the treatment of chronic disease.
The problem of chronic disease is severe. The theory of epidemiologic transition
(62) describes 3 phases of population mortality, with degenerative and man-made
diseases replacing infectious disease as the primary causes of mortality in the third
phase. In April of 2011, the WHO published a report on the global status of chronic
diseases, confirming that they have become the leading cause of death worldwide (63,
64). Fully 63% of all deaths globally in 2008 (a total of 36 million) were attributable to
chronic disease, with the majority due to cardiovascular disease, diabetes, cancer, and
chronic lung disease (63, 64). Global economic costs of chronic disease are estimated to
reach $47 trillion by 2030 (65).
The situation in the United States is no better. As of 2005, almost half of
Americans (133 million people) had at least one chronic disease and 63 million were
17


living with multiple chronic diseases; these numbers are projected to reach 157 million
and 81 million respectively by 2020 (66, 67). Chronic diseases account for 70% of
American deaths and 78% of health care expenditures, with an economic cost of $277
billion in 2003 alone (68, 69). Chronic diseases have also contributed to a lower average
life expectancy for Americans over the last decade relative to gains made in other
nations, with a loss of 33.1 million disability-adjusted life-years annually (65).
In contrast to treatment for infectious diseases and other acute conditions,
improving care and outcomes for chronic disease depends on patients ability to achieve
successful management of their disease over the long term. The Chronic Care Model
(CCM), as developed by Wagner et al, (70-72), describes an interactive approach to
chronic disease management that involves a mobilized community in partnership with a
health system organized for chronic care efforts that incorporate decision support tools;
clinical information systems that provide data for monitoring performance, facilitating
planning, reminding patients and providers of care activities, and sharing information; a
delivery system design that promotes effective, efficient, evidence-based and culturally
appropriate care by health teams; and self-management support to empower and engage
patients in their own care (73, 74).
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Figure 1: The Chronic Care Model. Reprinted from Figure 1, Effective Clinical
Practice, 1998, Vol 1, Chronic Disease Management: What Will It Take to Improve Care
for Chronic Illness? by Wagner EH. Reprinted with permission.
HIT solutions are particularly suited to supporting both health systems and
communities in improving quality of care, process and health outcomes in all four
identified domain elements of the chronic care model (73, 75). Studies of the use of HIT
in chronic disease care have demonstrated reduced costs and improvements in both
process and clinical outcomes, with particular effectiveness associated with EMRs,
personal health records, decision support tools, computerized prompts, electronic
scheduling, disease registries and population management (75-79). Patient-facing
consumer health informatics solutions have also proven effective in chronic disease
management (2), with results that include improvements in blood pressure control among
hypertensive patients (80, 81), improvements in glycemic control among diabetic patients
(82, 83), and improvements in medication adherence among asthmatic patients (84).
Evidence suggests that although persons with chronic disease are less likely to have
online access than those without (68% and 81% respectively) (85), when access is
available, users with chronic disease are more likely than those without to both seek and
use online information (85, 86). Among patients with chronic conditions, 75% reported
19


that online information affected their care decision-making, 69% reported using online
information in discussions with their care providers, and 57% reported changing self-
management behaviors based on information found online (87).
Chronic Disease and the Medically Underserved
The impact of chronic disease is most significant among low income, vulnerable
and medically underserved priority populations (4, 5), including racial and ethnic
minorities, low-literacy populations, people with disabilities, children and the elderly, and
those living in rural areas. On a global scale, almost four-fifths of deaths from chronic
disease occur in low and middle-income countries, including 80% of deaths due to
cardiovascular disease and diabetes. Death comes earlier to these populations as well, as
nearly a third of deaths from chronic disease in low-and middle-income countries occur
before age 60 (63). Similar trends are reflected in the United States, where racial and
ethnic disparities in health care persist, particularly with illnesses such as cardiovascular
disease and cancer (88). Significantly higher prevalence of chronic disease has been
observed among blacks, Latinos, and Asians than among whites, as well as among those
near or below the federal poverty level as compared to those 200% or more above it (65,
67). Disparities in chronic disease treatment are present as well: for example, an analysis
of data between 2002 and 2007 indicated that significantly lower numbers of black and
Latino adults, low-and middle-income adults, and uninsured adults received
recommended diabetes care compared with white, high-income, and insured adults (89).
The problem is further exacerbated by overall disparities in quality of care and access to
care. Blacks and Latinos receive worse care than whites for 40% and 60% of core quality
measures, respectively, and have more difficulty accessing care 33% and 83% of the
20


time, while the poor receive a lower quality of care than those with high incomes for 80%
of core measures and have consistently worse access (89-91).
Although the burden of chronic disease on the traditionally medically underserved
is disproportionate and the ability of HIT to improve chronic disease management
practices and outcomes has already been confirmed, information on the effect of
consumer health informatics applications on health outcomes among these groups
remains limited (2). This additional disparity represents a phenomenon commonly known
as the digital divide.
Overcoming the Digital Divide
The digital divide is the term applied to the disparity between those who have
both the means of access and the knowledge necessary to effectively use Internet-based
information technology and those who have neither (92, 93). The digital divide has been
shown to disproportionately affect the same groups found in medically underserved
priority populations, including racial and ethnic minorities, low-literacy populations,
people of below-average socioeconomic status, people with disabilities, and those living
in rural areas (7, 8, 94, 95).
An example of the digital divide is seen in the distribution of broadband
Internet service, which is defined as Internet access through technologies that support
data transmission at speeds much higher than those available through older, dial-up
technology (96). Broadband capability is considered so important in accessing online
information that the Federal Communications Commission (FCC) was charged in 2009
with developing a National Broadband Plan to ensure that ensure that all Americans have
broadband access to Internet resources (97). Users with broadband access are among the
21


most likely to have sought health information online (36); however, 67% of whites in
2010 had broadband access as compared to 56% of blacks. Only 45% of users with
annual incomes under $30,000 and 67% of those with annual incomes between $30,000
and $50,000 had broadband access, compared to 87% of those with annual incomes over
$75,000 (98). The disparity is so significant that the FCC has proposed comprehensive
reform and modernization of the Lifeline/Link Up program, established in 1996 to
support the provision of basic communications services to low-income Americans as
part of the FCCs universal service mission, to also include affordable access to reliable
broadband (99). This proposal met with significant resistance from the American public,
including 45% of current Internet nonusers (98). Resistance such as this highlights the
importance of considering ways to overcome the digital divide that do not depend on
modifications to overarching technological infrastructure.
Despite the observed resistance to the FCCs proposal, the dual lack of access and
knowledge that characterizes the digital divide does not represent lack of receptivity for
technology-based information sharing. Underserved patients have repeatedly shown
active interest in use of consumer health informatics solutions and engaging in health
information exchange with their care providers (20-22, 100-105).
22


CHAPTER III
THEORETICAL BASIS AND FRAMEWORK
To address the problem of the digital divide and its growing impact upon health
disparities, it is critical to examine systems where the medically underserved risk further
separation from needed medical care and health support that increasingly are being
provided through technological channels. To that end, principles inherent in both
traditional and neo-Marxist theoretical perspectives and in post-structuralist discourse,
where perception is critical to informing the creation of complex meaning, were
examined concerning the relationship between knowledge and power to gain insight into
the systems under consideration.
Marxist Theory
The core of traditional Marxist theory considers labor as the unit of social
organization (11), stemming from Marxs assertion that what humanity ascribes as
value derives from an expressed relationship between the object being valued and the
act of human labor. The connection described by Marx between the productive effort of
labor and the process of production itself is at once both simple and complex. When
reduced to basic terms, labor is merely a unit description of the work expended to create a
product, using that which is termed the means of production in order to achieve product
creation according to the mode of production. The means refers to the inputs utilized
for production, such as raw materials and knowledge, and the mode refers to the
organized process of production itself (11, 106). From this base, Marx describes how
societal organization to facilitate the mode of production led to society being defined in
terms of both its economic base and its overarching superstructure, where the latter
23


comprises all cultural aspects of the society under consideration (107). Marx concludes
that the continuing struggle for control over the means of production and the use and
exchange of the resulting products eventually shapes society through ascribing greater
social and economic powera higher classto those who emerge successful from the
conflict.
Having proposed this impact of production-based societal organization upon
societal superstructure, Marx examines the implications of capitalism by analyzing the
economic and sociopolitical factors affected through the exploitation of the worker by the
owner of the means of production (107). He begins by defining the basic usefulness of a
producta commodityas a use-value, which consisted of the physical properties
attributable to that commodity, and then proceeds to describe how a system of commerce
in which one commodity was traded for another was structured according to exchange-
value, which is the assignation of relative worth between commodities. Exchange-value
does not correspond to a one-to-one relationship between use-values; rather, the
difference between use-value and exchange-value is ascribed to a third type of value,
which is considered as having been added to a commodity through the production
associated with creating the commodity. This third value is the value of the labor (11).
Structural violence is inherent in a capitalist system by virtue of the established
separation between the worker and the means of production. In this division, the workers
labor itself becomes the object of value instead of the commodity, and is traded for wages
instead of for products (106). This reification of the worker into an object that exists as
part of the production process, rather than the producer of the commodity, alienates the
worker not only from the process but also from the perception of the worker as an
24


individual with intrinsic power (107). Alienation provides the opportunity for
exploitation by the addition of surplus value to commodity products. This surplus value,
or profit, is not returned directly to the worker, but instead is retained by the capitalist
who owns the means of productionthe technical resources necessary for the production
processand who pays the wages for the labor.
Application of Marxist theoretical principles to the question at hand demonstrates
that the HIT-based infrastructure of the current American health care system can be
considered analogous to a capitalist exchange. In this construct, the patient represents the
worker, health informatics applications are the means of production, and the conduct of
health communications and the delivery of health information are the equivalent of output
commodities. The digital divide then becomes representative of the capitalist-induced
separation of the worker from the means of production and subsequently from direct
access to the commodity produced. The resultant implication, then, is that the divide can
be overcome if the patient-worker obtains access to the additional value required to
bridge the gapor, in other words, achieves ownership of the technological means.
Patients Stratified Separation
by Social Class between Patients
(Socioeconomic and Health
StatusIResources) Information
Means of Access
to Health
Information &
Communications
Health
Information A
Communications
(EHRIEMR data)
Figure 2: The Digital Divide as a Marxist Construct
25


Antonio Gramsci: Cultural Hegemony
However, bridging the digital divide in the construct described previously is not
as simple as providing the patient-worker with the technological means to the end, as
indicated by the resistance encountered among nearly half of Internet nonusers to the
FCCs affordable access to reliable broadband service initiative. To better understand this
seemingly counterintuitive reaction, it becomes useful to examine the proposed construct
through the lens of cultural hegemony as formulated by Antonio Gramsci, whose Marxist
viewpoint was informed by the German idealist philosophy of Georg Hegel and
associated concepts of the importance of perception in defining meaning. Gramscis
cultural hegemony takes into account the impact of subjective contextual influences
imposed upon patients in addition to more objective effects.
Hegemony itself can be broadly defined as the dominant influence of one political
or social group over another. The concept of cultural hegemony as proposed by Gramsci
refers to the hegemonic domination not by physical force alone, but by a specific set of
ideas and beliefs that are so internalized by those who exist under its influence that they
become incapable of realizing its presence or consequent effect upon both behavior and
thought (107, 108). In his Prison Notebooks, Gramsci identifies the creators of
preeminent ideology as the capitalist entrepreneursthe social ruling class of the
intellectual elitein possession of technical capacity (12). Gramsci argues that the
objective fact of the exploited position of the worker in a capitalist society is due in large
part to the spontaneous consent (12, 108) yielded by workers themselves to the
dominant group. This consent stems from a subjective perception of powerlessness
among the working class, concomitant with an acceptance of the ideological and social
26


authority held by the capitalist entrepreneurs. This culturally hegemonic belief
perpetuates the continuation of exploitation by the intellectual elite maintaining the
dominant ideology. In the case of resistance to the provision of broadband access, those
without broadband are similar to members of the working class; their existence is one of
accepted powerlessness, and they have been conditioned to resist structural change.
Gramsci contends that this state of affairs is not an inflexible one, but instead that
the common understanding of the working class can be altered through a war of
position (109) where ideas are generated among the intellectual elite and disseminated to
the masses through education as the means of preparing and developing intellectuals (12).
The eventual result would be a more egalitarian social structure achieved through
equitable distribution of the power conferred by knowledge. Although he neglects to fully
take into account the effort necessary to overcome the inertial weight of ingrained
structural influence when attempting to achieve social change in complex systems, the
relationship between knowledge and power has since been further confirmed to be of
unquestionable import in such endeavors, and thus becomes a significant consideration
when seeking to cross the digital divide.
Michel Foucault: Knowledge and Power
The complex interdependent relationship between power and knowledge most
applicable within the context of the health care setting is well articulated by Michel
Foucault. Foucault initially describes his conceptualization of power-knowledge relations
in Discipline and Punish: The Birth of the Prison, where he states that power produces
knowledge and that power and knowledge are so directly related that the one implies the
other; therefore, no knowledge exists which does not both assume and establish power,
27


and no power exists without the correlating presence of knowledge (110). The patients in
a health care system are subject to the Foucauldian concept of discipline achieved
through observation, examination and normalizing judgment, in which disciplinary
control is exerted for individuals who fail to meet standards defined as societal norms,
such as established acceptable levels of performance on quality indicators used as proxy
measures for health status or chronic disease control. This is a clear example of the
power-knowledge dyad, in that knowledge of a patients health status then promotes the
use of power to adjust behavior perceived as faulty in maintaining good health or to
pursue treatment (111).
In later interviews, Foucault admitted that his interest in and examination of the
interwoven effects of knowledge and power was bom in part from his more
archaeological, historical work first on madness and psychiatry, then on clinical medicine
in the eighteenth century, both of which he described as having solid scientific
frameworks but at the same time being enmeshed in social structures (16), such that
questions of the relationship between scientific knowledge in each field and the power
conferred upon practitioners by the social systems became of significant concern. The
same relationships and same questions persist today in the construct under current
examination, where the knowledge of health information confers on the patient a degree
of power which has the potential to be used constructively in the patient-centered care
model. Patients can work in consultation with providers to achieve better care through a
shared decision-making approach. Conversely, lack of health information and its
associated health knowledge resulting from insufficient access to and ability to use the
technological infrastructure of consumer health informatics represents the conflict
28


paradigm inherent to the existing societal superstructure, which results in a lesser degree
of power available to those experiencing the digital divide.
It is at this point in the theoretical discussion that it becomes easier to recognize
that the disparity connoted by the digital divide, although significant, is not necessarily an
insurmountable challenge. In addition to the Marxian solution of achieving power
through ownership of the technological means of production and Gramscis suggested
approach to overcoming the deleterious effects of cultural hegemony through the
knowledge granted by education, Foucault describes an economy of power, (16) in
which the effects of power relations are circulated throughout an entire social body
through social production and social service generated by the bodies and actions of
individuals established within a network (17). This concept suggests that disseminating
power-knowledge to the currently disempowered through interaction within social
networks offers a potential solution to the problem of the digital divide.
Diffusion of Innovations
Based on the preceding work of social scientists such as Gabriel Tarde and Georg
Simmel, Dol theory as formulated and described by Everett Rogers has long and
successfully been used to evaluate the spread of technological innovations, and thus holds
promise for use in this construct to assess dissemination of the power-knowledge dyad
through social networks of patients experiencing the digital divide, where the dyad is
represented by the innovation of consumer health informatics technology. Diffusion
refers to the process by which communication takes place through channels over time
among members of a social system in order to spread awareness and use of an innovation
29


(24). Innovation adoption takes place in the following five-stage innovation-decision
process:
1. ) Knowledge. The potential adopter becomes aware of the innovations
existence and information about its use and potential.
2. ) Persuasion. The potential adopter develops a positive perception of the
innovation and discusses the innovation with others.
3. ) Decision. The potential adopter seeks out additional information and forms an
intent to try the innovation.
4. ) Implementation. The potential adopter begins to use the innovation on a trial
basis.
5. ) Confirmation. The adopter, having recognized the benefit of the innovation,
integrates the innovation into regular routine.
An innovations rate of adoption can be predicted based on individuals
perceptions of its 5 key attributes as determined during the innovation-decision process
(24, 112). Table 1 presents the 5 attributes of innovations as potentially applied to HIT.
Table 1: Attributes of Innovations in Terms of HIT
Attribute Definition Sample Patient Concerns
Relative Advantage How much better an innovation is in comparison to its predecessor idea Is HIT really better than memory, writing things down on paper, or other recordkeeping methods?
Compatibility How consistent an innovation is with established values, needs, and experiences Will HIT allow me to do the things I want and need to do? Will it keep my health information safe and private?
Complexity How difficult an innovation is to understand and use How hard is HIT to use? Will I have to spend a lot of time learning how to use HIT and trying to figure out how to make it work for me? Will this lessen time I can spend with my health care provider?
Trialability How much an innovation can be experimented with on a limited basis Can I test HIT before I commit to buying and using it? For how long/how thoroughly? What if I dont like it?
Observability The results of an innovation as visible to/perceived by other potential adopters What do other patients like me think about HIT?
30


Just as not all innovations are created equal, not all potential adopters are the
same. When a successful innovations rate of adoption is examined in terms of
cumulative adopters over time, a signature S-shaped curve appears, which over time
approaches normality; (24) this has been shown to hold true with respect to HIT, such as
with providers adoption of EMR technology (113). As shown in Table 2 and Figure 3,
adopters can be categorized into 5 distinct groups based on where they fall along the
distribution (24).
Table 2: Classification of Innovation Adopters
Category Description
Innovator First to adopt; regarded as venturesome or experimental in nature
Early Adopter Respected by peers; opinion leaders and role models
Early Majority Adopts with careful deliberation, just before the average adopter
Late Majority Skeptical adopters; may often be motivated by peer or systemic pressure
Laggards May be suspicious of change and change agents; characteristically delayed adoption; often related to precarious economic position
Figure 3: The Diffusion of Innovations Distribution Curve. Reprinted with permission
of Simon and Schuster Publishing Group from Figure 7.3, p 281, from the Free Press
edition of Diffusion of Innovations, 5th Edition. Copyright 1995, 2003 by Everett M.
Rogers. Copyright 1962, 1971, 1983 by the Free Press. All rights reserved.
31


Although the diffusion process takes place within a social system, an association
between socioeconomic status and adopter category has been observed due to adoption
across social strata. Innovators and early adopters are far more likely to have greater
access to the resources necessary to complete the innovation-decision process than
adopters who fall into one of the later-stage categories. In fact, those who are most likely
to be affected by the digital divide are also most likely to fall into the laggard adopter
category. This same group is also more likely to discontinue use of an innovation after
having adopted it, often because of dissatisfaction with the innovation (24).
Foucaults economy of power is present within the Dol theoretical context as
well, in that the adoption of an innovation within a social system tends to increase the gap
between earlier, more socioeconomically advantaged adopters and later-stage, lower-
resourced groups. The current adoption of consumer health informatics technology by
groups with more education and socioeconomic resources (power-knowledge) as
compared to the impact of the digital divide among the laggards is an example of this
unintended gap-widening effect. One known approach for closing the gap is to use
targeted channels and tailored, appropriate methods and messages to promote diffusion.
This study will attempt to identify channels and technological methods that could help to
bridge the digital divide by not only identifying use patterns and preferences among
technology users, but also by assessing reasons for nonuse among patients who choose
not to engage with consumer health informatics solutions.
One of the limitations of using Dol theory in applied research is the presence of a
pro-innovation bias. A frequent underlying assumption in diffusion research is that the
innovation in question will bring sufficient value to the new adopters to be worthy of
32


diffusion, and thus that the innovation should not be avoided (24). Similarly, it is
assumed that innovation diffusion is possible beginning from the point of availability and
solely based on the potential adopters interest and desire; it is far less clear how the
innovation-decision process applies when the innovation is extant and interest is present,
but resources to obtain the innovation are themselves a limiting factor to adoption. This
study intends to address both of these limitations in Dol theory by using it as the
framework for assessing barriers to adoption and anti-innovation sentiment toward
consumer health informatics technology, and examining the dissemination and uptake of
consumer health informatics technology as an innovation within a single-stratum low-
socioeconomic population rather than across socioeconomic strata.
The concept of a technology hype cycle, as first characterized by technology
analyst group Gartner, Inc., in 1995, (114) represents the progression of new technology
through 5 phases from initial development through to stability and general acceptance, as
shown in Table 3 and Figure 4 (114):
Table 3: Five Phases of the Gartner Hype Cycle
Phase Description
Technology Trigger Initial innovation development; breakthrough and announcement at the proof of concept or early development stage.
Peak of Inflated Expectations Initial publicity and/or pilot results lead to rapid trialing and growth among innovators and early adopters.
Trough of Disillusionment Innovation is abandoned by those who find insufficient value in it; core technology producers stabilize, while others leave the market.
Slope of Enlightenment Next-generation products and iterations of the technology innovation are refined, leading to increased use by non- abandoning early adopters and additional growth among later adopters.
Plateau of Productivity Innovation stabilizes into a mainstream technology with low perceived risk and widespread adoption.
33


expectations
Figure 4: Gartner Hype Cycle. Reprinted from Hype Cycle Research Methodologies
by Gartner, Inc., 2012. This graphic was published by Gartner, Inc. as part of a larger
research document and should be evaluated in the context of the entire document. Gartner
does not endorse any vendor, product or service depicted in its research publications, and
does not advise technology users to select only those vendors with the highest ratings.
Gartner research publications consist of the opinions of Gartner's research organization
and should not be construed as statements of fact. Gartner disclaims all warranties,
expressed or implied, with respect to this research, including any warranties of
merchantability or fitness for a particular purpose. Reprinted with permission.
Technology that reaches the mainstream and the productivity plateau phase
characteristically is accompanied by reduced cost-to-adopt and a consequently lower
barrier to entry. When considered from a Dol theoretical perspective, the increased
availability of a technology innovation that has achieved the plateau phase supports the
concept of the innovation-decision process as dependent on perceived utility and value in
the innovation alone, such that economic barriers to adoption may no longer be
considered a limiting factor. This study proposed that analysis of diffusion patterns
among members of a single socioeconomic class stratum for a technology innovation that
has achieved the plateau of productivity in the larger population will demonstrate the
expected diffusion curve.
34


CHAPTER IV
RESEARCH DESIGN AND METHODS
Overview
This cross-sectional study was designed to measure the variables of interest
through a self-administered, mixed-mode survey conducted at a single point in time
among 3 risk-stratified groups of adult patients, randomly selected from within a larger
population of adult patients who receive primary health care in an urban safety net
setting.
A multiphase mixed methods approach involving both sequential and concurrent
elements was employed for survey development and data collection (115). An integrated
approach to mixing quantitative and qualitative data (116) was used in analysis to
maximize the generalizability of results while also providing in-depth insight into
patients general opinions of and engagement with IT and patterns of behavior related to
IT use in general, for health information access, and for health communications. The
study was approved by the Colorado Multiple Institutional Review Board (COMIRB) as
protocol #12-1099.
Study Population
Denver Health and Hospital Authority (DH) is an integrated urban safety net
health system whose components include a 477-bed hospital and 8 primary care clinics
that are all FQHCs. The DH system provides health care services to 25% of residents in
the city and county of Denver, Colorado. In 2010, the DH system recorded more than
600.000 outpatient visits, including 335,000 primary care visits, among more than
160.000 patients.
35


A majority of DH patients are members of priority populations, in particular racial
and ethnic minorities, the uninsured, and those living below the poverty line.
Approximately 65% of DH patients are below 185% of the federal poverty level, and
more than 50% of DH patients are uninsured. Table 4 presents demographics of the DH
primary care population at the time of the study.
Table 4: DH Primary Care Patient Demographics, January 7, 2013
Patients (N = 116,999) Percent (%)
Age
< 18 53,759 45.95
18-29 17,231 14.73
30-39 13,331 11.39
40-49 10,746 9.18
50-59 10,813 9.24
60-69 7,027 6.01
70-76 2,287 1.95
>76 1,805 1.54
Race/Ethnicity
White 47,384 40.50
Black 17,069 14.59
Hispanic/Latino 34,354 29.36
Asian 3,648 3.12
Other/Unknown 14,544 12.43
Gender
Male 47,367 40.48
Female 69,632 59.52
Inclusion and Exclusion Criteria
Patients were included in the study population if they were enrolled in primary
care at DH, spoke either English or Spanish as their primary language, and were adults
between the ages of 18 and 76. Enrollment in primary care at DH is defined as those
patients who have had at least 2 clinic visits in the DH system in the previous 18 months.
Age criteria were based on Health Resources and Services Administration Diabetes
Collaborative guidelines for diabetes management.
36


Patients were excluded from the sampling frame for the study population if they
did not have both a mailing address and telephone number on record. No exclusions were
made based on gender, race/ethnicity, or socioeconomic status. Figure 5 presents a flow
diagram illustrating inclusion and exclusion criteria.
Figure 5: Inclusion/Exclusion Criteria Flow Diagram
Risk Stratification
Patients in the sampling frame for the study population were risk stratified into
risk groups, or tiers, according to a process and algorithm developed by DH and used
37


under its 21st Century Care model (117, 118) to tailor health care delivery to patients
according to level of need. Of the four possible tiers (1 low risk; 2 medium risk; 3 -
high risk; 4 very high risk), stratification for this study assigned patients to tiers 1-3
only; patients that would otherwise have been identified as tier 4 were included with tier
3 due to a small sample size limitation identified in the first attempt at classifying the
study population. Version 1.0 of the tiering algorithm was used to risk stratify the
population for this study; however, the tiering process continues to evolve based on
iterative evaluation and criteria refinement. Tier assignment under the version 1.0
algorithm is based on a combination of clinical criteria and a patients Chronic Illness and
Disability Payment System (CDPS) risk adjustment score (119). CDPS was developed to
support the use of ICD-9-CM diagnosis-based burden of illness assessments to estimate
future health expenditures and adjust state reimbursements for Medicaid populations, and
compares well to other models used among Medicaid beneficiaries (120, 121). As such, it
is well-suited for use in analyses involving the safety net population served by DH, which
is predominantly composed of Medicaid-qualified patients and uninsured patients who
will soon qualify for Medicaid coverage under the Affordable Care Act.
Clinical criteria used for tier assignment were established diagnoses of either
diabetes (DM) or hypertension (HTN) combined with degree of chronic disease control
as defined by a patients most recent indicator laboratory values for glycosylated
hemoglobin (Ale) measurements of average blood glucose over time, low-density
lipoprotein (LDL) measurements of cholesterol levels, and blood pressure (BP) tests. Tier
assignment was made first according to clinical criteria, then secondarily by CDPS score
in the absence of defining clinical criteria for assignment, as shown in Table 5 (122).
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Table 5: DH Tiering Algorithm Assignment, Version 1.0
Tier (Risk Group) Patients (%) (N=55,225) Clinical Criteria CDPS Criteria
3 (High) 2,525 (4.57%) Diagnosis of DM and most recent Ale > 10; or most recent systolic BP >= 160; or most recent diastolic BP >= 100 Risk score >= 7.025
2 (Medium) 33,484 (60.63%) Diagnosis of DM and no recent Ale; or diagnosis of DM and no recent LDL; or diagnosis of DM and most recent LDL >= 100; or most recent Ale >= 8 but < 10; or most recent diastolic BP >= 90 but < 100; or most recent systolic BP >= 140 but < 160; or diagnoses of both DM and HTN Risk score >= 0.243932609 but < 7.025
1 (Low) 19,216 (34.80%) No specific clinical criteria Risk score < 0.243932609
Survey Procedures
A survey instrument was created for this study to evaluate patterns of behavior
related to IT use in general and for health information access and health communications;
interest in and familiarity with specific IT modalities and activities in general and for
health information access and health communications; and social, behavioral, and
systemic factors that influence IT adoption and utilization.
Survey Design
To maximize generalizability of results in comparison to national data, survey
items were drawn in part from existing instruments such as AHRQs Consumer
Assessment of Health Providers and Systems (CAHPS) item sets (123), the CDCs
Health Related Quality of Life (HRQOL) Healthy Days core measures (124), and the
Pew Research Centers Internet & American Life Project surveys (125). Additional
survey items developed by the investigator were added to explore topic areas of interest
not adequately addressed by existing instruments. Open-ended elements were included
within questions as options in fixed-choice item sets where appropriate to support
39


identification of additional choices not otherwise addressed in the fixed set. Open-ended
topical and general questions were included in addition to fixed-choice questions in order
to elicit unstructured responses and potentially identify emergent themes that might not
otherwise be captured. To promote readability, survey item language was adapted where
necessary to conform to a sixth-grade literacy level according to the Flesch-Kincaid
Grade Level formula (126, 127), given below in Equation 1:
Equation 1: Flesch-Kincaid Grade Level Readability Formula
GL = .39 (Average Sentence Length) + 11.8 (Syllables/Word) 15.59
where:
Average Sentence Length = (Total # Words / Total # Sentences)
Syllables/Word = (Total # Words / Total # Syllables)
The layout of the survey was structured according to basic visual design
principles of proximity, alignment, repetition, and contrast (128), with attention also
given to creating a common layout that would perform well in both paper and electronic
versions and which would reduce cognitive load for respondents and provide specific
visual cues and aids to guide successful survey completion (129). Response options for
each item were grouped in close proximity with each other and with the use of specific
graphic elements and both vertical and horizontal negative space to distinguish each
subgroup from another, in order to promote answer selection for each item and reduce
question omission (129). Items were aligned in balance with each other and
symmetrically around the optical center of each survey page. Elements such as page
headers and number blocks, horizontal lines used to divide instructional text from
40


question text, vertical lines used to divide survey items into columns and subtly indicate
flow, and stop-sign and arrow graphics used to indicate skip logic and pagination were
repeated from page to page and section to section to promote uniformity of experience
and pattern recognition among respondents. Sans-serif fonts were used in both paper and
electronic formats, with differential text size and bold, italic, and underlining text
elements used to add visual interest and provide emphasis where desired. A consistent
color palette was used throughout, with red-green-blue and hue-saturation values drawn
from DH marketing materials to conform with anticipated patient expectations. Although
the paper version of the survey was printed in grayscale, the color palette used supported
shading emphasis and distinction between elements that were comparable to a full-color
version, without loss of visual information. A storyboarding process was used to craft and
iteratively refine the information flow from item to item and page to page, with question
order structured according to guidelines for creating conversational, logically ordered
surveys (129). An instructional design advisor reviewed both draft and final layout
versions for clarity (130).
Survey items, instructions for completing the instrument, and contextual
explanatory language were translated from English to Spanish by a certified medical
interpreter. Both draft and final versions of the survey were approved by COMIRB prior
to administration. The final print version of the survey is included as Appendix A.
Survey Pilot Test Focused Group Discussion
The draft survey was piloted in a focus group of a homogenously sampled (131)
group of individuals familiar with a wide range of information technologies and
technologically focused activities. A total of 10 participants were recruited from among
41


members of an online community focused on collaborative, role-based interactive
storytelling who were in attendance at an annual gathering in Denver, Colorado.
Participant demographics are presented in Table 6.
Table 6: Survey Focus Group Demographics
Demographic Participants
Age
20-29 6
30-39 2
40-49 2
Gender
Female 9
Male 1
Race/Ethnicity
White, Hispanic or Latino 1
White, non-Hispanic or Latino 9
Geographic Location^
Division 1 1
Division 2 3
Division 5 2
Division 6 1
Division 8 1
Division 9 2
* As defined under Office of Management and Budget (OMB)
Directive 15 (132).
'As defined by the United States Census Bureau (133).
Pilot administration of the survey followed by unstructured group discussion was
held over 1 hour on January 20, 2013, with the principal investigator serving as survey
timer and group facilitator. Average time for survey completion was calculated at 12
minutes and 16 seconds, with a median value of 12 minutes and 6 seconds. Qualitative
data collected included written comments provided on participants copies of survey
instruments, written notes of observations made by the principal investigator, and an
audio recording of group discussion that was transcribed by the principal investigator.
Data were subjected to content analysis using an inductive coding process interspersed
with marginal remarks (134) to allow identification of emergent themes without
42


predefinition. Subjects discussed were classified as themes based on the agreement of
multiple participants with a single concept. Themes identified during analysis and
incorporated into survey refinements are presented in Table 7.
Table 7: Survey Pilot Test Emergent Themes by Content Area
Content Area Themes
Layout and Flow Use written instructions in addition to visual indicator elements Place skip logic instructions at the top of pages, not at the bottom Use different, easily recognized symbols for proceed versus skip (eg, arrow and stop sign) Use SKIP instead of TURN when indicating movement past a page rather titan to the next page Use if you dont language in questions immediately following skip logic in order to reiterate/confinn the skip Use different color and/or shading to make your eyes be not as lazy; several specific recommendations Open-ended questions about other things [you] do with computers and cell phones needs to be on the same page with the list of activities; flipping back and forth is annoying Include definitions and/or introductory explanations at the start of each section, not just at the beginning of the survey Error correction: spelling, numbering, other miscellaneous typographical and editorial recommendations
Wording and Language, General Reconcile similar question wording between sections to be identical rather than differential, and use text element emphasis to draw attention to the focus of a particular section instead Inclusivity recommendations: active voice, specific use of people not patients throughout, less formal language where possible (eg, lessons on versus education/training on) Add clear definitions of meaning and specific examples in context for broadly-encompassing terms or concepts (eg, health, technology, use, regular, health information, health communication, talk to someone about health or health care) Recommendation for considering tried but couldnt in context for use-related questions
Wording and Language, Question- Specific Clarifications on the list of IT activities: dont use term social media, use specific well-known examples of sites/services to provide context for any categorical group (eg, photo sites), add talk on phone or similar for voice calls to the list Clarifications on the list of health information activities: add health insurance, information about doctors, symptoms, use family planning in addition to/instead of birth control Clarification on the list of health communication contacts: religious leader versus church leader
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Sampling Methodology
The number of completed surveys required to represent generalizable results for a
population at a 95% confidence level and with a 5% margin of error was determined by
probability sampling (129) as given in Equation 2:
Equation 2: Probability Sampling Sample Size Formula
N, = _______(N. )(/.>)( 1 p)___
(Np-l)(5/C)2 + (p)(l-^)
where:
Ni = required number of completed surveys (completed sample size needed);
Np = total population size;
p = proportion of population expected to choose one of two response categories
(calculation based on two categories in order to maximize heterogeneity);
B = margin of error
C = Z score associated with the desired confidence level
When calculated for the study population (N=57,030) with a 5% margin of error
and a 95% confidence level:
N,= (57030)(.5)(.5)
(57030 1)(0.05/ 1.96)2 + (,5)(.5)
N,= 382
Therefore, 382 was considered the minimum completed sample size necessary to
be considered representative of the study population.
An oversampling approach was taken to allow for all-cause nonresponse among
intended survey recipients (eg, active refusal, passive refusal, survey not received) yet
still achieve the necessary completed sample size to ensure generalizability. Each patient
in the sampling frame was assigned a random seed generated by SAS software (Cary,
NC; version 9.3). Patients were ranked in numeric order by seed, and the first 650
44


patients in each risk tier were selected in order to minimize sampling error. A total of
1,950 targeted recipients were selected for survey distribution. Each recipient was
assigned a unique survey identifier (ID), which was cross-referenced to the patients
unique DH medical record number and securely maintained in an electronic master file.
Survey Implementation
Survey implementation was conducted based on Dillmans tailored design method
for mixed-mode surveys (129), and had 3 discrete phases: initial invitation to participate,
survey distribution, and follow-up reminder prior to survey closure. Table 8 describes the
overall timeframe for implementation.
Table 8: Survey Implementation Timeframe
Description Date
Invitation letter postal mailing February 12, 2013
Survey postal mailing, English March 5, 2013
Activation of online survey, English March 5, 2013
Survey postal mailing, Spanish March 6, 2013
Activation of online survey, Spanish March 6, 2013
Survey open/data collection start date March 8,2013
Postcard reminder postal mailing March 27, 2013
Deactivation of online survey, English April 19,2013
Deactivation of online survey, Spanish April 19, 2013
Survey closure/data collection end date April 19,2013
Based on estimated postal mail delivery times
The initial invitation to participate was extended as a letter sent by postal mail,
approved by COMIRB prior to distribution. The letter was written to include both
informed consent for research language and specific social exchange elements intended
to establish trust, increase the perceived benefits of response, and reduce the perceived
costs of response (129). The letter contained information about the survey itself and how
the recipient was selected, contact information for the principal investigator and for
COMIRB, assurance that survey completion would incur neither cost nor obligation and
45


that privacy would be maintained if at all possible, and an extension of thanks for
consideration. The invitation letter was printed on DH letterhead in either English or
Spanish, signed by the principal investigator, and individually addressed to the survey
recipient by name both in the letter itself and on the address label. Names and addresses
were automatically generated from a research database created for the study (Microsoft
Access, Redmond WA, v. 11.8), populated with information obtained from the DH data
warehouse. The label design included the DH logo and a color bar across the top with the
return address. Letters were sent in standard #10 envelopes. Sample copies of the
invitation letter and address label are included as Appendix B.
The survey package was sent to recipients 3 weeks following the initial invitation.
It was mailed in a standard white 10 x 13 flat envelope with printed address labels of
the same design as that used for the invitation mailing. The survey package included a
printed version of the survey in either English or Spanish, a pre-addressed standard white
9 x 12 flat return envelope with postage affixed, and a $2 cash incentive. The cover
page of each survey was designed to include informational and social exchange elements
to encourage survey completion. Elements included the survey title, a reminder about the
purpose of the survey and how the recipient had been selected, instructions for either
completing and returning the survey in the enclosed envelope or alternately for
completing the survey online at a specified SurveyMonkey web address, the recipients
unique survey ID handwritten in blue ink for personalization purposes, acknowledgment
of the importance of the recipients opinions, mention of the cash incentive in the context
of thanks extended, the principal investigators name and COMIRB protocol number, and
the DH logo. The return envelope included a label with structural design similar to those
46


used in the outbound mailings, but printed without color to aid recipients in
distinguishing between envelopes. The return label also included the survey ID as a
printed element in order to reduce the chances of unlabeled, orphan surveys being
returned. No identifying information other than the survey ID was included or requested
either on the survey itself or on the return envelope. Postage was affixed in the form of
physical stamps with colorful designs rather than through preprinted permit or the use of
business reply envelopes; this method has been shown to both improve response rates and
post office processing speed (129). The survey package was assembled with careful
attention given to how its presentation should appear to the recipient: the survey was
nested cover-up against the flap of the response envelope, and the cash incentive was
tucked between the flap and the survey itself in order to improve both the visual impact
of the whole and the likelihood of the incentive not being left in the mailing envelope.
Electronic versions of the survey in English and Spanish were activated online the
same day as the postal mailings. Online surveys were securely fielded through
SurveyMonkey, with standard HTML5 tags applied within the SurveyMonkey custom
page options to create text design elements, graphic elements, page layouts, and skip
logic that mimicked the print version of the survey as closely as possible. Online
respondents were required to enter their unique survey ID to complete the online survey.
The follow-up reminder was offered to non-responding survey recipients as a
postcard sent by postal mail 3 weeks following the survey mailing. The postcard was
designed in compliance with United States Postal Service (USPS) specifications for
dimensions, weight, and automation-compatible layout (135), printed in black ink on blue
card stock, and sent in both English and Spanish. It included many of the social exchange
47


elements previously used in the invitation letter and survey package, with variation in
visual design used to catch attention and promote ease of cognitive processing. The
postcard also introduced new social exchange elements through specific description of
the value of the information requested and encouragement to recipients to call the
principal investigator for an additional survey if the previous copy had been misplaced. A
sample copy of the reminder postcard is included as Appendix C.
An option to extend the survey period to allow for telephone call follow-up with
recipients who were continued nonresponders after the reminder postcard mailing was
included in the implementation design. This contact method was to be used if response
rates were insufficient to support meaningful data analysis, but proved unnecessary.
Data Collection and Management
A survey codebook was created from the final version of the print survey
according to Inter-university Consortium for Political and Social Research (ICPSR)
guidelines for codebooks (136). Variable details including name, label, question text,
answer choices, and numeric codes associated with each choice were entered in
structured fashion, along with data entry guidelines such as skip logic, identification of
key questions, specific coding instructions in case of multiple answer entry or other
nonstandard cases, and additional notes where applicable. A copy of the final codebook is
included as Appendix D.
Based on the survey codebook, relational tables, queries, and a custom data entry
form were created within the research database to support survey data collection. The
data entry form was designed to store the numeric code for each question in the
underlying table, but displayed the full wording of answer choices within the form itself
48


to minimize data entry errors. The form was also designed using dropdown boxes and
limit-to-list functionality for each question, such that typographical mistakes would
generate an error message during the data entry process rather than accepting incorrect
data. Both fixed-choice and open-choice data collection were supported by the data entry
form, with free-text responses entered as written. Memo field design was used to underly
free-text response data entry to avoid character limitations associated with text fields.
Coder notes associated with free-text responses were entered in brackets to distinguish
them from respondent data. Responses received in Spanish were entered in Spanish; the
Spanish responses were translated by a certified medical interpreter, with the subsequent
English versions entered following the Spanish version of each response in the
respondents record. The survey ID was used as the unique identifier, and the associated
field was designated as the nonduplicated primary key for the data entry form and
underlying table both, with the result that an error message was generated when an
attempt to enter duplicate data was detected. Data collected within the research database
could be exported to one of several standard formats for import and analysis with SAS or
other analytical software as needed.
The electronic surveys collected through SurveyMonkey were stored securely in
the SurveyMonkey system, with researcher-level account-based secure login access
required to retrieve study data. No identifying information other than the survey ID was
collection from respondents; IP address tracking was disabled for this study. Data could
be viewed within the SurveyMonkey sytem or exported securely to one of several
standard formats for subsequent import to SAS or the research database.
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Data about the mailings themselves were also collected in tables within the
research database. Records were kept of all responses received, including dates of survey
responses, specifics of USPS endorsements for mail returned to sender as undeliverable
(137), records of active refusals and survey opt-out by patients, and records of patient
questions received and answers returned over the course of the study period. Patient
questions received in Spanish were answered in Spanish through the aid of DH
interpreter services.
Mail returned to sender was recorded in the research database, then handled
according to a process established for returned mail treatment. Each envelope was opened
and the contents were removed; letters and surveys were securely shredded, postage-paid
return envelopes were stored in a file in the secure research cabinet for later reclamation
of stamps, and incentives returned were redeposited in the research account. Envelopes
with the official LISPS endorsement label attached were forwarded to the Data Integrity
division of DHs Department of Health Information Management for handling.
Completed paper surveys and envelopes were both marked with the dates of
receipt and of data entry, then stored securely in a locked file cabinet. All research data
will be maintained per Health Insurance Portability and Accountability Act regulations
for a minimum of 7 years following study closure by COMIRB, after which it will be
securely destroyed.
Analysis Plan
Measures of Interest
Measures of interest for this study included the following, presented
alphabetically by category:
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Barrier Measures (survey data) : computer, cell phone, and Internet use barriers;
health information access barriers; health communication barriers
Demographic Measures (DH clinical systems): age, gender, race, language
Facilitator Measures (survey data) : computer, cell phone, and Internet use
facilitators; health information access facilitators; health communication facilitators
Health Status Measures (survey data, DH clinical systems): CDC Healthy Days
self-rated general health status, unhealthy days in past 30 days, and mental health in past
30 days; DH risk stratification tier (version 1.0)
IT-General Utilization Measures (survey data) : computer, cell phone, and Internet
user status; computer, cell phone, and Internet use device type; computer, cell phone, and
Internet use duration; computer, cell phone, and Internet use frequency; computer, cell
phone, and Internet use importance; computer and cell phone ownership; Internet access
speed; IT activity type
IT-Health Communications Measures (survey data) : health communication user
status; health communication duration; health communication frequency; health
communication importance; health communication contacts
IT-Health Information Measures (survey data) : health information access user
status; health information access duration; health information access frequency; health
information access importance; health information access topics
Opinion Measures (survey data) : Topics and themes emergent from participant
responses to open-ended survey questions
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Univariate analyses were conducted for each of the measures of interest among all
survey respondents. Results are described through summary presentation of frequency
data, standard error, and variance for each measure.
Specific Aim 1, Research Question 1
The first specific aim of the study was to assess and describe current methods and
patterns of IT utilization for health information access and engagement in health
communications among adult patients who receive care in an urban safety net setting.
The first research question associated with this specific aim was to discern how patterns
of IT utilization in general, IT utilization for health information access, and IT utilization
for health communications might differ by demographic subgroup. Chi-square analyses
were conducted to assess significance for the measures of interest by age, gender,
race/ethnicity, and primary language.
Specific Aim 1, Research Question 2
The second research question associated with this specific aim was to assess how
patterns of IT utilization in general, IT utilization for health information access, and IT
utilization for health communication may differ by health status. Regression analyses
were conducted to assess significance for each of the measures of interest by self-rated
general health status, unhealthy days in the past 30 days, and mental health status in the
past 30 days. All analyses were adjusted for the effects of race/ethnicity, gender, age, and
primary language.
Specific Aim 1, Research Question 3
The third research question associated with this specific aim was to examine the
impact of identified barriers and facilitators on IT general utilization, IT utilization for
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health information access, and IT utilization for health communication among self-
identified non-users. Regression analyses were conducted to assess significance for each
of the measures of interest, adjusting for race/ethnicity, gender, age, and patients primary
language.
Specific Aim 1, Research Question 4
The fourth research question associated with this specific aim was to determine
what general opinions about IT and HIT are held among adult patients who receive care
in an urban safety net setting. Free-text responses received to survey questions were
subjected to content analysis using an analytic induction strategy in order to identify
emergent themes and topics among responses that may not have been otherwise assessed
by closed-ended survey items. An open, heuristic coding process was used to identify
keywords for the development of a code list, which was used to apply coding to free-text
responses and identify recurring topics and themes. A topic was classified as a theme if a
minimum of 5% of respondents to open-ended items identified it as a subject of interest.
Results of the qualitative analysis were used to improve contextual understanding of the
quantitative results from the current study.
Specific Aim 2
The hypothesis for this specific aim assumed that adult patients with chronic
disease who receive care in the safety net and who use IT to access health information
and engage in health communications were predicted to have better health status than
adult patients with chronic disease who receive care in the safety net and who are IT
nonusers.
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Multiple linear and logistic regression analyses were conducted to assess
significance for health status measures among patients with chronic disease who use IT
for health information access and health communications (intervention group) as
compared to patients with chronic disease who do not use IT for health information
access and health communications (control group). Chronic disease status was
determined by risk group assignment to tier 2 or tier 3. Analyses were conducted across
the population as a whole and within each risk tier. All analyses were adjusted by
race/ethnicity, gender, age, and patients primary language.
Specific Aim 3
The hypothesis for this specific aim assumed that members of priority populations
have an interest in using IT to access health information and engage in health
communications that is equivalent to that reported among members of more advantaged
populations, but do not use the same types of IT in the same manner or to the same
extent.
The applicability of Dol theory within a single stratum of the larger social
superstructure rather than across strata, as traditionally considered, was assessed by
examining of the distribution of IT access duration resulting from the univariate analyses
conducted for Specific Aim 1, both for each IT access type and among the study
population as a whole. The expectation was that the distribution would align with the Dol
adoption curve such that the 5 categories of Dol adopters could be clearly identified
within the single stratum of the larger social superstructure represented by the members
of priority populations participating in this study.
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CHAPTER V
RESULTS
Survey Response
Over the 6-week survey period, responses were received for 829 of 1,950
surveyed individuals. Response types included completed surveys, active refusals, and
USPS returns of undeliverable mail. The vast majority of completed surveys were
received by mail (n=393) versus online (n=21) or by telephone (n=l). No response was
received for 1,121 surveyed individuals. Response status was classified according to
American Association for Public Opinion Research (AAPOR) final disposition codes for
mail surveys of specifically named persons (138). All applicable response types are
summarized in Figure 6 through a modified CONSORT diagram (139).
Figure 6: Survey Results: AAPOR Response Types
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Four standard survey outcome rates (138) were calculated from the final response
disposition results: response rate, cooperation rate, refusal rate, and contact rate. The
selected standard response and cooperation rate formulas count partial completes as
respondents. These formulas were chosen from the available options because observation
during the data entry process indicated that the number of partial completes was very low
and predicated in part on skip logic instructions. Standard refusal and contact rate
formulas were selected from the available options to maximize eligibility criteria
assumptions among recipients, neither eliminating unknowns from the equation nor
making estimates to characterize unknown data.
The survey response rate was calculated according to Equation 3:
Equation 3: Response Rate
RR2 =___________H + P)_________
(I + P) + (R + NC + O) + (UH + UO)
where:
RR2 = response rate;
I = number of complete surveys received (code 1.1);
P = number of partial surveys received (code 1.2);
I + P = total number of surveys received (code 1.0 and subcodes);
R = number of refusals and break-offs; (code 2.1 and subcodes);
NC = number of non-contacts (code 2.20 and subcodes);
O = other (code 2.30 and subcodes);
R + NC + O = total number of non-interviews (code 2.0 and subcodes);
UH = unknown if household/occupied housing unit (code 3.1 and subcodes);
UO = unknown, other (codes 3.2, 3.3, 3.4, 3.9, and subcodes);
UH + UO = total number of cases of unknown eligibility (code 3.0 and subcodes)
56


When calculated for the study population:
RR2 =_______________1415)_______
(415)+ (25)+ (1510)
RR2 = 21.28%
The survey cooperation rate was calculated according to Equation 4:
Equation 4: Cooperation Rate
coop2 =________q + P3
(I + P) + R + O
where:
COOP2 = cooperation rate;
I = number of complete surveys received (code 1.1);
P = number of partial surveys received (code 1.2);
I + P = total number of surveys received (code 1.0 and subcodes)
R = number of refusals and break-offs (code 2.20 and subcodes);
O = other (code 2.30 and subcodes);
When calculated for the study population:
COOP2 =________(4153
(415)+ 18 + 7
COOP2 = 94.32%
57


The refusal rate was calculated according to Equation 5:
Equation 5: Refusal Rate
REF1 =_______________(R)_________
(I + P) + (R + NC + O) + (UH + UO)
where:
REF 1 = refusal rate;
I = number of complete surveys received (code 1.1);
P = number of partial surveys received (code 1.2);
I + P = total number of surveys received (code 1.0 and subcodes);
R = number of refusals and break-offs; (code 2.1 and subcodes);
NC = number of non-contacts (code 2.20 and subcodes);
O = other (code 2.30 and subcodes);
R + NC + O = total number of non-interviews (code 2.0 and subcodes);
UH = unknown if household/occupied housing unit (code 3.1 and subcodes);
UO = unknown, other (codes 3.2, 3.3, 3.4, 3.9, and subcodes);
UH + UO = total number of cases of unknown eligibility (code 3.0 and subcodes)
When calculated for the study population:
REF1 =_________________083
(415)+ (25)+ (1510)
REF 1 = 0.92%
The survey contact rate was calculated according to Equation 6:
Equation 6: Contact Rate
CONI =_________a + P) + R + 0___
(I + P) + R + O + NC + (UH + UO)
where:
CON 1 = contact rate;
I = number of complete surveys received (code 1.1);
58


P = number of partial surveys received (code 1.2);
I + P = total number of surveys received (code 1.0 and subcodes);
R = number of refusals and break-offs; (code 2.1 and subcodes);
NC = number of non-contacts (code 2.20 and subcodes);
O = other (code 2.30 and subcodes);
R + NC + O = total number of non-interviews (code 2.0 and subcodes);
UH = unknown if household/occupied housing unit (code 3.1 and subcodes);
UO = unknown, other (codes 3.2, 3.3, 3.4, 3.9, and subcodes);
UH + UO = total number of cases of unknown eligibility (code 3.0 and subcodes)
When calculated for the study population:
CONI = (4151+18 + 7
(415)+ 18+ 7 + 0 +(1510)
CONI = 22.56%
The number of completed surveys received (N=415) exceeds the minimum
completed sample size threshold (N=382) established through probability sampling as
necessary to be considered representative of the population (95% Cl, 5% error). The
overall response rate (21.28%), while low, is not unexpected based on declining survey
response trends observed over decades (140), and remains comparable with response
rates obtained for national public opinion surveys; for example, typical Pew Research
survey response rates range from 5-15% (141). More important, multiple analyses of
nonresponse impact on survey validity have shown that results compared between
identical surveys fielded according to standard and rigorous methods for maximizing
response are equally statistically valid (142, 143). Similar findings were reported in
comparative analyses conducted for 3 state-level, health-specific surveys (144). An
examination of potential bias among nonresponders using community-level correlates in
59


the state of Illinois identified higher nonresponse in areas of urbanicity and concentrated
areas of either high affluence or high poverty (140); however, these effects can be
presumed to be minimal in the context of this studys survey, fielded both in an urban
setting and among a single socioeconomic stratum.
Table 9 shows the demographics of the sample-eligible study population, the
survey sample, and survey respondents.
Table 9: Survey Population Demographics, Unadjusted
Survey- Eligible (N=55,225) Survey- Eligible (%) Random Sample (N=l,950) Random Sample (%) Survey Completed (N=415) Survey Completed (%)
Age
18-29 16,114 29.18 426 21.85 52 12.53
30-39 12,129 21.96 384 19.69 70 16.87
40-49 9,589 17.36 339 17.38 86 20.72
50-59 9.352 16.93 448 22.97 108 26.02
60-69 6,206 11.24 277 14.21 81 19.52
70-76 1,835 3.32 76 3.90 18 4.34
Gender
Female 38,202 69.18 1,264 64.82 277 66.75
Male 17,023 30.82 686 35.18 138 33.25
Race
White 24,208 43.84 834 42.77 189 45.54
Latino 16,107 29.17 560 28.72 116 27.95
Black 8,250 14.94 343 17.59 62 14.94
Other/Unknown 6,660 12.06 213 10.92 48 11.56
Language
English 39,641 71.78 1,411 72.36 309 74.46
Spanish 15,584 28.22 539 27.64 106 25.54
Tier / Risk Grou 3
Tier 1 / Low 19,216 34.80 650 33.33 142 34.22
Tier 2 / Medium 33,484 60.63 650 33.33 132 31.81
Tier 3 / High 2,525 4.57 650 33.33 141 33.98
Differences between population groups were assessed through chi-square
analysis. Age was found to be significantly different between the eligible population and
the sampled population (p<0.001), between the eligible population and the respondent
population (p<0.001), and between the sampled population and the respondent population
60


(p<0.001). Figures 7 and 8 show histogram, box, and quantile-plotted age distributions
between respondent and sampled populations.
0 20 40 60 80
_________________age_________________
| Normal 1 |
Figure 7: Age Histogram and Box Distribution, Respondent vs. Sampled
Q-Q Plots of age
Quantile Quantile
Figure 8: Age Q-Q plot, Respondent vs. Sampled
61


Gender and race were found to be significantly different between the eligible and
sampled populations (p<0.0001; p=0.014), but not between the eligible and respondent
populations; therefore, respondents were considered representative of the population as a
whole. Risk tier was significantly different between the eligible and sampled populations
due to survey design considerations (ie, stratification, oversampling), but no difference
was found between sampled and respondent populations.
Weighting and Balancing
Post-stratification weighting was applied to survey response data for tier and age
strata in order to obtain analysis results representative of the population as a whole,
reducing the effects of nonresponse bias and increasing precision (145-148). Weight
computation was accomplished through sample-balancing, or raking, using an iterative
proportional fitting approach (149-151).
Analytical Methods
Quantitative analyses were conducted with survey analysis means, frequency,
regression, and logistic regression procedures (152, 153) in SAS Enterprise 9.3 (Cary,
NC). These procedures incorporate statistical adjustments for stratified or otherwise
complex survey designs, survey weighting, domain analysis, and variance due to missing
data. Both stratification by tier and raked weights were explicitly included in analysis.
Missing data for HRQOL items were imputed prior to HRQOL measure calculation,
using Markov chain Monte Carlo methods for stochastic imputation (145). Missing data
for subgroup classification questions (skip logic questions) were imputed through a
deductive approach based on contextual responses where possible, and otherwise
remained designated as missing and were accounted for in analysis. Variance estimation
62


was calculated using Taylor series linearization, with missing data assumed to be missing
at random (MAR), but not missing completely at random (MCAR). A finite population
correction factor was not incorporated in variance estimates, as the sampling fraction
comprised a small enough percentage of the total population to support the use of infinite
population assumptions.
Qualitative analysis of free-text responses to survey questions was conducted
using an analytic induction strategy to identify emergent themes and topics among
responses. An open, heuristic coding process was used to identify keywords for the
development of a code list, which was subsequently used to code free-text responses and
identify recurring topics and themes. A topic was classified as a theme if a minimum of
5% of respondents to open-ended items identified it as a subject of interest.
Descriptive Statistics
Percentages reported in all tables are based on weighted frequencies rather than
derived from unadjusted response numbers. Both standard error and Taylor series
variance are reported for percentage values.
Table 10 shows the results for self-rated measures of health, including general
(physical and mental) health, mental distress, and unhealthy days in the past 30 days.
Almost three-quarters of the population (73.62%) perceived their general health as being
good or better, and four-fifths (82.15%) reported themselves to be in good mental health.
The average number of physically and/or mentally unhealthy days reported was 9.82 (SE
0.71), with a median of 3.97 days (SE 0.81).
63


Table 10: CPC Healthy Days Measures
Responses (N=415) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
Self-rated general health in past 30 days
Good or better 250 73.62 73.62 2.66 7.07
Fair or poor 165 26.37 26.38 2.66 7.07
Mental health in past 30 days
Good mental health 332 82.14 82.15 2.36 5.59
Frequent mental distress 83 17.85 17.85 2.36 5.59
Unhealthy days in past 30 days
None (all days healthy) 127 35.71 35.72 3.19 10.19
1-7 days (1 week) 93 24.00 24.00 2.84 8.09
8-14 days (2 weeks) 42 8.46 8.46 1.88 3.52
15-21 days (3 weeks) 45 11.02 11.03 2.10 4.41
22-30 days (4+ weeks) 108 20.79 20.79 2.51 6.31
Differences by demographics of age, gender, language, and race/ethnicity were
evaluated for all measures. Significant differences were observed in general health status
by gender (p<0.007), by age (p<0.0001), and by race/ethnicity (p<0.021). Mental health
status was observed to be significantly different by age (p<0.001), by gender (p<0.001),
and by language (p<0.003). Overall unhealthy days were observed to be significantly
different by age (p<0.003) and by language (p<0.001). No other significant differences
were observed by demographic (Table 11).
Table 11: CPC Healthy Days Measures by Demographic
General Health Mental Health Unhealthy Weeks
Good/ Fair/ Good/ Frequent
N=415 Better Poor Stable Distress None 1 wk 2 wk 3 wk 4+ wk
% % % % % % % % %
Age p<0.001 * p<0.001* p=0.003*
18-29 26.54 2.65 26.61 2.57 11.73 9.16 2.35 2.84 3.09
30-39 18.91 3.05 19.50 2.48 10.17 4.47 2.71 2.31 2.32
40-49 12.96 4.40 14.57 2.79 6.81 3.60 1.22 3.18 2.55
50-59 6.46 10.47 10.30 6.63 3.61 3.15 0.98 1.37 7.82
60-69 6.92 4.32 8.19 3.05 2.69 2.85 0.87 0.85 3.98
70-76 1.83 1.49 2.98 0.34 0.71 0.77 0.32 0.48 1.03
Gender p=0.007* p<0.001* p=0.909
Female 59.26 17.34 62.91 13.69 27.84 18.93 6.53 8.33 14.96
Male 14.36 9.04 19.24 4.16 7.88 5.07 1.93 2.69 5.84
64


Table 1
CPC Healthy Days Measures by Demographic, continued
General Health Mental Health Unhealthy Weeks
Good/ Fair/ Good/ Frequent
N=415 Better Poor Stable Distress None 1 wk 2 wk 3 wk 4+ wk
% % % % % % % % %
Race/Ethnicity p=0.021* P= 0.083 p=0.112
White 32.04 11.58 35.20 8.41 13.21 10.23 5.11 5.20 9.86
Hispanic/Latino 20.16 8.13 23.77 4.51 13.20 6.22 1.98 2.18 4.71
Black 9.39 5.65 10.77 4.28 2.77 3.85 1.24 2.65 4.53
Other/U nkno wn 12.04 1.02 12.41 0.65 6.53 3.71 0.13 1.00 1.69
Language p=0.388 P= 0.003* p<0.001 *
English 52.24 17.21 54.13 15.32 18.88 19.52 6.44 9.45 15.17
Spanish 21.38 9.17 28.02 2.53 16.84 4.49 2.02 1.58 5.63
*Significant p-value, 0.05 or less.
Technology Users, Overall
Table 12 presents the IT user status results. Among the overall population,
94.57% of people classified themselves as users of some kind of IT, whether computer,
cell phone, or both. Cell phone use was significantly higher than computer use (p<0.001),
with 92.70% of people reporting cell phone use versus 71.41% reporting computer use.
Almost three-quarters use the Internet (73.62%). Significantly more people use
technology for health information than for health communication (65.25% vs. 52.61%,
pCO.OOl).
Table 12: Information Technology (IT) User Classification
IT user status, general Responses (N=412) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
Computer only 14 1.86 1.87 0.74 0.55
Cell phone only 112 22.63 22.76 2.67 7.13
Both computer and cell phone 252 69.55 69.94 2.86 8.27
Neither computer nor cell phone 34 5.40 5.43 1.22 1.49
Computer use Responses (N=415) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
Uses computer 266 71.41 71.41 2.83 7.99
Does not use computer 149 25.58 28.89 2.83 7.99
Cell phone use Responses (N=412) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
Uses cell phone 364 92.18 92.70 1.42 2.01
Does not use cell phone 48 7.26 7.30 1.42 2.01
65


Table 12: Information Technology (IT) User Classification, continued
Internet use Responses (N=404) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
Uses Internet 279 73.62 76.64 2.65 7.00
Does not use Internet 125 22.44 23.36 2.65 7.00
Health information use Responses (N=410) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
Uses IT for information about health-related topics 231 64.23 65.25 3.04 9.24
Does not use IT for information about health-related topics 179 34.21 34.75 3.04 9.24
Health communication use Responses (N=395) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
Uses IT to talk to others about health-related topics 192 49.56 52.61 3.39 11.47
Does not use IT to talk to others about health-related topics 203 44.64 47.39 3.39 11.47
Differences by demographics of age, gender, language, and race/ethnicity were
assessed for all measures. No one below age 40 was found to be a technology nonuser,
but used either or both computers and cell phones. Very few people used computers but
not cell phonesbelow 5% in all categories. English speakers were significantly more
likely than Spanish speakers to use both computers and cell phones, (78.22% vs 50.83%,
p<0.001), while Spanish speakers were significantly more likely than English speakers to
use cell phones only (38.46% vs. 15.95%, p<0.001). Table 13 shows further results.
Table 13: IT General User Status by Demographic
N=415 Computer % (row%) Cell Phone % (row%) Computer and Cell % (row%) Nonuser % (row%)
Age p=indeterminate
18-29 0.08 (0.26) 3.93 (13.40) 25.33 (86.33) 0(0)
30-39 0.55 (2.51) 5.02 (26.27) 15.73 (71.22) 0(0)
40-49 0.51 (3.00) 3.79 (22.39) 12.34 (72.88) 0.29 (1.74)
50-59 0.22 (1.28) 3.80 (22.36) 10.90 (64.11) 2.08 (12.25)
60-69 0.17 (1.46) 4.45 (39.34) 5.18 (45.86) 1.51 (13.33)
70-76 0.35 (10.35) 0.98 (29.48) 0.46 (13.76) 1.55 (46.41)
Gender p=0.191
Female 0.82 (1.07) 16.48 (21.54) 54.76 (71.59) 4.43 (5.80)
Male 1.05 (4.45) 6.28 (26.71) 15.18 (64.59) 1.00 (4.24)
66


Table 13: IT General User Status by Demographic, continued
N=415 Computer % (row%) Cell Phone % (row%) Computer and Cell % (row%) Nonuser % (row%)
Race/Ethnicity p=indeterminate
White 0.91 (2.09) 8.23 (18.78) 32.25 (73.58) 2.43 (5.55)
Hispanic/Latino 0.63 (2.27) 8.98 (32.11) 15.74 (56.30) 2.61 (9.33)
Black 0(0) 2.67(17.70) 12.37 (81.98) 0.05 (0.33)
Other/Unknown 0.32 (2.43) 2.88 (21.94) 9.59 (73.01) 0.34 (2.62)
Language p<0.001 *
English 0.94 (1.35) 11.13 (15.95) 54.58 (78.22) 3.12 (4.47)
Spanish 0.93 (3.06) 11.63 (38.46) 15.37 (50.83) 2.31 (7.65)
Among IT users, significant differences were found by age for all types of use
(computer, cell phone, internet, health information, and health communication).
Utilization decreased as age increased for all types of use except health communication,
where utilization between ages 30 and 49 was found to be higher than in any other age
bracket (p=0.012). Women were significantly more likely than men to both seek out
health information (p=0.027) and engage in health communication (p<0.001). Three-
quarters of women (70.27%) and half of men (49.11%) looked up health information, and
more than half (56.32%) of women compared to less than half (40.55%) of men engaged
in health communication. Race and ethnicity were found to be significantly different for
all types of use except health communication. Blacks were most likely to use computers
(81.76%, p=0.022), cell phones (99.67%, p=0.024), and the Internet (84.93%, p=0.018).
English speakers were more likely than Spanish speakers to use computers (79.50% vs.
53.03%, p<0.001), the Internet (82.95% vs. 60.65%, p<0.001), and to look up health
information (69.78% vs. 54.41%, p=0.027). Table 14 provides additional details.
67


Table 14: IT Users, Technology Utilization by Demographic
Computer Cell Phone Internet Health Info Health Comm
Yes No Yes No Yes No Yes No Yes No
N=415 % % % % % % % % % %
Age p<0. 001* p<0. 001* p<0. 001* p<0. 001* p=0.012*
18-29 86.60 13.40 99.74 0.26 90.67 9.33 82.39 17.61 47.95 52.05
30-39 73.73 26.27 97.49 2.51 83.03 16.97 71.53 28.47 66.37 33.62
40-49 73.58 26.42 95.27 4.73 83.84 16.16 63.16 36.83 63.79 36.21
50-59 65.28 34.72 86.47 13.53 61.79 38.21 45.75 54.25 45.01 54.99
60-69 47.33 52.67 85.21 14.79 55.17 44.83 53.40 46.60 45.79 54.21
70-76 24.11 75.88 43.24 56.76 24.90 75.01 20.71 79.29 13.76 86.24
Gender p=0.603 p< 0.569 p=0.530 p=0.001 * p=0.027*
Female 72.16 27.84 93.13 6.87 77.46 22.54 70.27 29.73 56.32 43.68
Male 68.96 31.04 91.31 8.69 73.81 26.19 49.12 50.88 40.55 59.45
Race/Ethnicity p=0.022* p=0.024* p=0.018* p=0.002* p=0.133
White 75.62 24.38 92.37 7.63 79.34 20.66 66.16 33.84 51.34 48.66
Hispanic/Latino 57.56 42.44 88.40 11.60 63.89 36.11 52.82 47.18 44.32 55.68
Black 81.76 18.24 99.67 0.33 84.93 15.07 64.83 35.17 56.56 43.44
Other/Unknown 75.44 24.56 94.95 5.05 84.37 15.63 88.65 11.35 69.01 30.99
Language p<0. 001* p=0.121 p<0. 001* p=0.027* p=0.30
English 79.50 20.50 94.18 5.82 82.95 17.05 69.78 30.22 54.92 45.08
Spanish 53.03 46.97 89.29 10.71 60.65 39.35 54.41 45.59 46.87 53.13
*Significant p-value, 0.05 or less.
Computer Users
Table 15 presents utilization pattern results for computer users. Two-thirds of
people use a laptop or notebook computer (67.76%), versus just over half who use a
desktop (56.26%) and a quarter who use a tablet device (23.22). More people use just 1
type of computer (62.63%) than use multiple types (36.54%). The majority of people who
classified themselves as computer users own at least 1 computer (82.42%), use it daily
(62.21%), and believe that computer use is always or usually important (68.41%).
68


Table 15: Utilization Patterns among Computer Users (n=266)
Computer device type (n >266 due to multiple device use) Responses (N=266) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
Desktop 163 40.17 56.26 4.03 16.26
Laptop or notebook 160 48.38 67.76 3.67 13.44
Tablet 53 16.58 23.22 3.55 12.63
Other device 18 3.63 5.08 1.51 2.28
Multiple computer usage Responses (N=266) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
1 type of computer 171 44.72 62.63 3.91 15.26
2 types of computer 66 17.02 23.84 3.34 11.13
3 types of computer 25 8.64 12.11 2.85 8.12
4+ types of computer 4 1.01 0.59 0.82 0.67
Computer use duration Responses (N=266) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
< 1 month 6 2.14 3.00 1.83 3.33
1-6 months 11 3.93 5.50 1.91 3.66
7-12 months 8 1.97 2.76 1.20 1.45
1-2 years 21 4.80 6.73 1.82 3.32
3-5 years 34 11.35 15.90 3.18 10.14
5-10 years 48 13.53 18.94 3.16 9.97
>10 years 138 33.69 47.17 4.00 15.98
Computer use frequency Responses (N=266) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
Daily, multiple times 127 34.54 48.37 4.03 16.27
Daily, once per day 39 9.63 13.84 2.70 7.29
Weekly. 3-5 days per week 35 9.09 12.73 2.58 6.64
Weekly, 1-2 days per week 30 8.26 11.56 2.48 6.17
Monthly, every few weeks 17 5.34 7.47 2.13 4.52
Monthly, once per month or less 18 4.56 6.38 2.19 4.82
Computer use value Responses (N=266) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
Always important 122 32.73 45.84 4.00 16.05
Usually important 55 16.12 22.57 3.49 12.19
Sometimes important 57 15.80 22.13 3.41 11.62
Rarely important 19 4.04 5.65 1.57 2.46
Not important 13 2.72 3.81 1.61 2.62
Computer access Responses (N=266) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
Owns a computer 217 58.85 82.42 3.24 10.49
Does not own a computer 49 12.56 17.58 3.24 10.49
Multiple regression analysis was conducted for each of these measures to examine
the significance of general health status, mental health status, and unhealthy days in the
last month, adjusting for demographic variables of age, gender, race/ethnicity, and
language. Value of computer use was associated with language and all 3 health
indicators: general health status, mental health status, and unhealthy days in the past
69


month. Computer ownership was associated with general and mental health status.
Frequency of computer use was associated with general health status and language.
Duration of computer use was associated with general health status, age, other/unknown
race/ethnicity, and language. Desktop and laptop use were found to be significantly
associated with age and white and Hispanic/Latino race/ethnicity. Tablet use was
associated with Flispanic/Latino race/ethnicity. No other significant associations were
found.
Cell Phone Users
Table 16 presents utilization pattern results for cell phone users. Cell phone use is
split almost evenly between smart phones (50.70%) and regular cell phones (47.40%).
Most people use only 1 type of cell phone (96.98%), and most people who classified
themselves as cell phone users own their cell phones (95.97%), use them daily (87.76%),
and believe that cell phone use is usually or always important (87.65%).
Table 16: Utilization Patterns among Cell *hone Users (n=364)
Cell phone type (n >363 due to multiple device use) Responses (N=363) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
Smart phone 148 46.72 50.70 3.46 11.99
Regular/basic phone 207 43.68 47.40 3.45 11.93
Other cell phone 19 4.22 4.92 1.59 2.54
Multiple phone usage Responses (N=342) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
1 type of cell phone 330 83.08 96.98 1.36 1.84
2 types of cell phone 12 2.59 3.01 1.36 1.84
Cell phone use duration Responses (N=363) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
< 1 month 28 9.81 10.68 2.35 5.52
1-6 months 12 3.24 3.52 1.37 1.89
7-12 months 7 1.05 1.14 0.72 0.51
1-2 years 29 4.68 5.09 1.31 1.72
3-5 years 70 16.57 18.04 2.71 7.33
5-10 years 111 28.24 30.74 3.26 10.62
>10 years 106 28.28 30.79 3.17 10.03
70


Table 16: Utilization Patterns among Cell Phone Users (n=364), continued
Cell phone use frequency Responses (N=364) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
Daily, multiple times 268 73.69 79.94 2.71 7.36
Daily, once per day 41 7.21 7.82 1.87 3.51
Weekly, 3-5 days per week 20 4.43 4.80 1.48 2.19
Weekly, 1-2 days per week 20 4.47 4.85 1.48 2.19
Monthly, every few weeks 9 1.32 1.43 0.61 0.37
Monthly, once per month or less 6 1.07 1.17 0.55 0.30
Cell phone use value Responses (N=362) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
Always important 222 62.40 68.20 3.17 10.07
Usually important 70 17.79 19.45 2.84 8.07
Sometimes important 42 6.46 7.06 1.43 2.04
Rarely important 22 4.15 4.53 1.24 1.54
Not important 6 0.70 0.76 0.41 0.16
Cell phone use access Responses (N=339) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
Owns a cell phone 315 81.39 95.97 1.20 1.43
Does not own a cell phone 24 3.42 4.03 1.20 1.43
Multiple regression analysis was conducted for each of these measures to examine
the significance of general health status, mental health status, and unhealthy days in the
last month, adjusting for demographic variables of age, gender, race/ethnicity, and
language. Unhealthy days in the past month was associated with smartphone use together
with age and language; with regular cell phone use along with gender, age, and language;
and with frequency of cell phone use together with age. Multiple device use was found to
be significantly associated with black and other/unknown race/ethnicity. Duration of cell
phone use was associated with language. Cell phone ownership was associated with
other/unknown race/ethnicity. No other significant associations were found.
Internet Users
Table 17 presents utilization pattern results for Internet users. More people access
the Internet on a portable computer (laptop or tablet, 66.21%) or cell phone (56.53%)
than with a desktop computer (55.10%). Users were more likely to use multiple methods
of access (57.85%) than a single method (42.14%). Most people who classified
71


themselves as Internet users have broadband access (73.35%), go online daily (77.07%),
and believe that Internet access is usually or always important (70.75%).
Table 17: Utilization Patterns among Internet Users [n=279)
Internet access method Responses Frequency Responses Std Err Variance
(n >279 due to multiple method use) (N=279) (weighted) (%) (%) (%)
Desktop computer 168 40.56 55.10 3.97 15.79
Portable computer 166 48.74 66.21 3.70 13.66
Cell phone 132 41.62 56.53 3.86 14.90
Other method 9 1.00 1.36 0.75 0.56
Multiple access methods Responses (N=279) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
1 method 135 31.03 42.14 3.87 14.97
2 methods 92 26.89 36.52 3.86 14.87
3 methods 52 15.70 21.33 3.31 10.96
Internet use duration Responses (N=276) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
< 1 month 10 3.74 5.13 2.05 4.22
1-6 months 13 3.90 5.36 1.84 3.37
7-12 months 14 4.32 5.93 1.82 3.34
1-2 years 23 4.19 5.74 1.55 2.41
3-5 years 26 7.03 9.65 2.37 5.62
5-10 years 71 20.75 28.47 3.75 14.06
>10 years 119 28.95 39.72 3.81 14.52
Internet use frequency Responses (N=277) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
Daily, multiple times 150 43.42 59.40 3.82 14.58
Daily, once per day 44 12.92 17.67 3.20 10.24
Weekly, 3-5 days per week 28 5.20 7.11 1.77 3.14
Weekly, 1-2 days per week 23 6.10 8.35 2.14 4.60
Monthly, every few weeks 13 3.55 4.85 1.53 2.34
Monthly, once per month or less 19 1.92 2.63 0.92 0.84
Internet access value Responses (N=277) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
Always important 132 36.80 50.33 3.96 15.69
Usually important 63 14.93 20.42 3.27 10.66
Sometimes important 57 16.13 22.07 3.42 11.67
Rarely important 17 4.26 5.83 1.86 3.48
Not important 8 0.99 1.36 0.75 0.56
Internet access type Responses (N=276) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
Broadband (high speed) 196 53.46 73.35 3.55 12.63
Non-broadband (low speed) 50 11.20 15.37 2.81 7.89
Unknown 30 8.22 11.28 2.73 7.43
72


Multiple regression analysis was conducted for each of these measures to examine
the significance of general health status, mental health status, and unhealthy days in the
last month, adjusting for demographic variables of age, gender, race/ethnicity, and
language. Duration of Internet use was associated with general health status, age, and
language. Using multiple devices for Internet access was associated with age, language,
and white race/ethnicity. Desktop computer access was associated with age; portable
computer access (laptop, notebook, tablet) was associated with age, language, and white
and Latino race/ethnicity. Cell phone Internet access was associated with age and white
race. Frequency of Internet use was associated with age and language. High-speed access
was associated with language. No other significant associations were found.
Activity Patterns Among IT Users
Utilization pattern results for the use of IT for common activities are presented in
Table 18. Users were significantly more likely to send and receive text messages than to
send and receive email (84.42% vs 72.72%, p<0.001). Users were also significantly more
likely to video chat or Skype with someone than to use direct text-based chat (42.10% vs.
30.39%, p<0.001). Social media was not commonly utilized, with by far the highest
activity being on Facebook (57.10% vs. 25.44% for the next highest).
73


Table 18: IT-Based Activities (n=405*)
Activity Computer Cell %(n) Both % (n) User % (n) Nonuser % (n) SE, % Var, %
Communication
Email, send/receive 29.53 (114) 9.96 (34) 33.23 (HO) 72.72 (258) 27.28 (126) 2.91-3.20 4.55- 10.26
Text, send/receive 0.83 (9) 75.05 (253) 8.54 (34) 84.42 (296) 15.58 (95) 0.46-2.68 0.21-7.21
Voice calls, make/receive 1.73 (12) 74.34 (251) 8.96 (31) 85.03 (294) 14.97 (82) 0.75-2.82 0.57-7.97
Chat, video/Skype 26.39 (74) 7.74 (20) 7.97 (21) 42.10 (115) 57.90 (254) 1.94-3.48 3.76- 12.10
Chat, direct text 17.60 (48) 6.26 (17) 6.54 (25) 30.39 (90) 69.61 (283) 1.59-3.38 2.53- 11.43
Chat, group text 7.42 (32) 4.44 (12) 4.76 (15) 16.62 (59) 83.38 (311) 1.29-2.46 1.66-6.06
Information
News 26.89 (107) 17.36 (47) 20.30 (72) 64.55 (226) 35.45 (157) 2.59-3.23 6.72- 10.45
Information 29.72 (108) 2.88 (35) 3.26 (94) 76.13 (237) 23.87 (113) 2.82-3.41 7.94- 11.65
Multimedia
TV/movies 30.81 (107) 6.21 (22) 13.92 (41) 50.94 (170) 49.06 (202) 1.64-3.43 2.68- 11.78
Videos, watch 23.79 (76) 12.90 (38) 25.29 (77) 61.98 (191) 38.02 (190) 2.51-3.22 6.30- 10.37
Videos, make/post 6.79 (28) 8.93 (25) 8.20 (20) 23.92 (73) 76.08 (301) 1.53-3.01 2.33-9.08
Music, listen/play 20.75 (75) 22.19 (68) 26.64 (80) 69.58 (223) 30.42 (162) 2.82-3.09 7.96-9.57
Photos, online 6.74 (18) 12.04 (30) 7.30 (18) 26.09 (66) 73.91 (310) 1.91-3.27 3.64- 10.72
Gaming, solo 14.38 (58) 20.28 (54) 13.71 (47) 48.37 (159) 51.63 (216) 2.46-3.51 5.09- 12.30
Social media
Facebook 19.97 (70) 15.04 (34) 22.10 (66) 57.10 (170) 42.90 (205) 2.80-3.36 7.86- 11.29
Twitter 4.65 (19) 7.77 (22) 6.02 (20) 18.44 (61) 81.56 (313) 1.37-2.62 1.88-6.89
Pinterest 8.48 (25) 7.70 (20) 8.15 (19) 24.33 (64) 75.67 (307) 2.06-3.21 4.27- 10.35
Blogs, write/post 6.92 (26) 3.44 (8) 4.41 (15) 14.77 (49) 85.23 (323) 1.20-2.44 1.45-5.93
Blogs, read/comment 12.60 (42) 4.87 (14) 7.97 (27) 25.44 (83) 74.56 (290) 1.58-3.06 2.49-9.35
Gaming, network 7.35 (30) 10.25 (26) 6.66 (21) 24.26 (77) 75.74 (294) 1.70-3.01 2.89-9.09
* Total responses varied by item from a low of350 to a high of 391.
Age was the most significant demographic differentiator, affecting all activities
except group texting and blog posting. Use was highest among the youngest people
surveyed (ages 18-29) and overall decreased as age increased. Women were significantly
more likely than men to engage in text messaging (87.10% vs. 75.51%, p=0.015), in
74


voice calls (88.22% vs. 74.46%, p=0.005), and to be active on Facebook (60.50% vs.
45.47%, p=0.040). Blacks (33.26%) were significantly more likely than whites (13.76%),
Latinos (16.26%), or other racial/ethnic groups (8.59%) to participate in group chat
rooms (p=0.022). English speakers were significantly more likely than Spanish speakers
to send email (76.71% vs. 62.78%, p=0.040); to participate in group chat rooms (20.18%
vs. 7.34%, p=0.019); to play games both alone (54.01% vs. 33.66%, p=0.013) and with
others (28.72% vs. 12.86%, p=0.014), to post and view photos online (30.59% vs.
14.59%, p=0.030); and to engage in social media activities such as Twitter (22.06% vs.
8.96%, p=0.015), Pinterest (30.83% vs. 7.01%, p=0.003), and blog reading and
commenting (29.73% vs. 13.45%, p=0.026). No other significant differences were
observed. Additional detail is given in Table 19.
Table 19: IT-Based Activities by Demographic
N=405f Age
Activity 18-29 % 30-39 % 40-49 % 50-59 % 60-76 %
Communication
Email, send/receive (p<0.001*) 86.01 77.10 82.86 55.67 44.33
Text, send/receive (p<0.001*) 98.58 94.51 92.68 71.48 42.77
Voice calls, make/receive (p< 0.001*) 97.11 92.15 91.96 67.59 58.28
Chat, video/Skype (p<0.001*) 61.60 45.65 49.18 21.15 9.68
Chat, direct text (p<0.001 *) 50.66 32.32 27.66 13.16 5.97
Chat, group text (p<0.444) 16.79 13.41 25.91 12.29 15.10
Information
News ('p< 0.001*) 72.41 70.50 78.27 44.10 44.79
Information (p 0.001 *) 94.96 83.73 81.44 49.38 44.98
75


Table 19: IT-Based Activities by Demographic, continued
N=405f Age
Activity 18-29 % 30-39 % 40-49 % 50-59 % 60-76 %
Multimedia
TV/movies (p<0.001*) 61.60 56.92 65.28 25.42 27.95
Videos, watch (p<0.001 *) 83.96 68.89 68.20 38.29 24.78
Videos, make/post (p<0.001 *) 36.51 32.22 21.45 6.23 5.73
Music, listen/play (p< 0.001*) 93.91 76.93 67.36 49.95 28.48
Photos, online (p<0.001*) 48.93 23.68 17.34 12.85 6.70
Gaming, solo (p< 0.001*) 71.34 38.06 44.22 38.16 29.76
Social media
Facebook (p< 0.001*) 79.92 61.74 57.16 36.84 21.99
Twitter (p<0.047*) 25.60 19.82 20.91 13.65 2.56
Pinterest (p< 0.001 *) 42.94 20.98 27.95 6.16 5.49
Blogs, write/post (p<0.074) 20.10 20.63 12.82 7.58 4.72
Blogs, read/comment (p<0.014*) 36.00 26.94 28.73 10.80 13.80
Gaming, network (p<0.001*) 40.38 17.41 26.47 11.50 11.72
* Significantp-value, 0.05 or less. f Total responses varied by item from a low of350 to a high of 391.
Gender Race/Ethnicity Lang
Activity Female | Male White | Latino | Black | Other English Spanish
Communication
Email, send/receive 73.57 69.90 72.33 65.57 77.29 82.67 76.71* 62.78*
Text, send/receive 87.10* 75.51* 85.18 82.57 84.73 85.26 84.57 84.07
Voice calls, make/receive 88.22* 74.46* 86.30 84.16 79.86 88.34 85.28 84.43
Chat, video/Skype 42.18 41.85 45.47 34.35 45.41 41.89 45.67 32.75
Chat, direct text 30.03 31.58 28.57 24.49 38.25 38.61 30.71 29.54
Chat, group text 14.93 22.28 13.76* 16.26* 33.26* 8.59* 20.18* 7.34*
Information
News 64.14 65.93 63.68 61.30 68.23 70.03 65.81 61.50
Information 76.48 75.01 76.36 69.63 83.39 81.76 79.08 69.25
Multimedia
TV/movies 50.66 51.85 48.84 44.19 53.96 67.39 52.53 47.05
Videos, watch 63.49 56.86 63.49 56.06 57.60 73.38 65.11 53.98
Videos, make/post Till 26.02 22.91 23.23 26.22 25.95 24.79 21.70
Music, listen/play 70.44 66.72 67.79 67.46 68.79 80.74 72.11 63.44
Photos, online 25.32 28.62 29.21 21.35 23.37 28.07 30.59* 14.59*
Gaming, solo 49.27 45.30 47.80 45.64 57.25 45.41 54.01* 33.66*
Social media
Facebook 60.50* 45.47* 55.83 55.45 57.62 63.84 59.48 50.95
Twitter 18.18 19.32 15.19 18.92 29.26 15.68 22.06* 8.96*
Pinterest 26.22 17.75 22.53 18.21 28.87 36.92 30.83* 7.01*
Blogs, write/post 13.53 19.13 11.81 17.46 16.38 17.51 15.88 11.86
Blogs, read/comment 26.07 23.36 25.28 19.07 30.20 32.22 29.73* 13.45*
Gaming, network 24.53 23.28 24.85 22.79 24.85 24.50 28.72* 12.86*
* Significant p-value, 0.05 or less. fTotal responses varied by item from a low of350 to a high of 391.
Multiple regression analysis was conducted for each of these measures to examine
the significance of general health status, mental health status, and unhealthy days in the
last month, adjusting for demographic variables of age, gender, race/ethnicity, and
76


language. Playing games with other people was associated with mental health status,
unhealthy days in the past month, language, and age. Talking to groups of people in chat
rooms and watching TV or movies were also associated with unhealthy days in the past
month, language, and age. Voice calls, watching videos, music, blog posting, reading and
commenting, and the use of video chat were associated with general health status and
age; video watching and music were also associated with other/unknown race/ethnicity,
and video chat was also associated with Black race/ethnicity. Email was associated with
White race/ethnicity, age, and language. Facebook use, Twitter, gaming, Pinterest, and
online photos were associated with language and age. Age was associated with all
remaining activities. No other significant associations were found.
Health Information
Table 20 presents utilization pattern results for health information users. Most
health information users search for health information on someone elses behalf as well
as their own (59.27%). Although most searches are infrequent, occurring every few
weeks to once a month or less (68.45%), users place a high value on being able to look up
health information, with 67.68% deeming it always or usually important.
Table 20: Utilization Patterns among Health Information Users (n=231)
Health information focus Responses (N=225) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
Self 94 23.74 37.80 4.21 18.25
Another person 8 1.85 2.94 1.23 1.52
Both self and other 123 37.22 59.27 4.27 17.75
Health information duration Responses (N=226) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
< 1 month 17 4.91 7.78 2.32 5.39
1-6 months 23 7.92 12.56 2.85 8.13
7-12 months 11 2.69 4.26 1.48 2.20
1-2 years 34 9.62 15.25 3.30 10.91
3-5 years 45 13.68 21.68 3.70 13.72
5-10 years 58 15.71 24.90 3.91 15.28
>10 years 38 8.56 13.56 2.59 6.71
77


Table 20: Utilization Patterns among Health Information Users (n=231)
Health information frequency Responses (N=226) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
Daily, multiple times 10 2.64 4.18 1.51 2.29
Daily, once per day 13 2.89 4.58 1.64 2.70
Weekly, 3-5 days per week 25 6.51 10.32 2.87 8.24
Weekly, 1-2 days per week 27 7.87 12.47 2.80 7.82
Monthly, every few weeks 49 13.75 21.79 3.47 12.01
Monthly, once per month or less 102 29.44 46.66 4.39 19.30
Health information value Responses (N=228) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
Always important 104 26.61 41.70 4.23 17.88
Usually important 56 16.58 25.98 3.79 14.37
Sometimes important 54 17.82 27.93 4.12 16.97
Rarely important 11 2.44 3.83 1.39 1.93
Not important 3 0.36 0.57 0.50 0.25
Multiple regression analysis was conducted for each of these measures to examine
the significance of general health status, mental health status, and unhealthy days in the
last month, adjusting for demographic variables of age, gender, race/ethnicity, and
language. Frequency of health information searching was associated with general health
status and unhealthy days in the past month. Gender, age, and language were associated
with the recipient of health information searches (self, other, or both). No other
significant associations were found.
Table 21 describes utilization patterns for some commonly sought topics of health
information. People most frequently looked for information about food, nutrition, or diet
(78.54%); about exercise and physical activity (71.93%); about diseases and illnesses that
either they (71.29%) or someone else (70.68%) had; and about medications they take
(66.24%). People were least likely to look up information about lab test results (25.99%
self, 15.94% other) and clinic visit notes (25.00% self, 13.43% other).
78


Table 21: E ealth Information Cont ent(n=237*)
Health information topic Responses (N) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
Disease, self 182 45.60 71.29 3.98 15.84
Disease, anothers 164 44.49 70.68 3.98 15.83
Surgery, self 85 19.84 32.15 3.96 15.71
Surgery, anothers 101 25.62 41.61 4.27 18.22
Feelings/ symptoms 140 39.62 62.98 4.23 17.88
Medicines, self 159 42.16 66.24 4.02 16.15
Medicines, anothers 112 29.70 47.60 4.38 19.21
Health insurance 112 30.78 49.67 4.41 19.49
Doctors/health care providers 125 34.72 55.60 4.38 19.17
Lab test results, self 67 16.19 25.99 3.76 14.12
Lab test results, anothers 38 9.69 15.94 3.21 10.28
Clinic visit notes, self 56 15.22 25.00 3.98 15.81
Clinic visit notes, anothers 32 8.09 13.43 3.06 9.37
Exercise/physical activity 158 44.13 71.93 3.94 15.52
Food, nutrition, or diet 183 49.57 78.54 3.52 12.38
Birth control/family planning 69 26.80 43.78 4.51 20.37
Behavior change (eg, alcohol/tobacco cessation) 92 21.66 35.15 4.09 16.72
Health topics in the news 134 36.43 60.72 4.45 19.85
* Total responses varied by item from a low of 215 to a high of232.
Multiple regression analyses were conducted for each of these measures to
examine the significance of general health status, mental health status, and unhealthy
days in the last month, adjusting for demographic variables of age, gender, race/ethnicity,
and language. Unhealthy days in the past month together with age and other
race/ethnicity were associated with ones own disease. General health status was
associated together with language for ones own upcoming surgery, for ones own lab test
results, and for behavorial change interest. Language was associated with medicine use
both by self and others and with looking up information about health care providers. Age
and Latino race/ethnicity were associated with looking for information about birth control
and family planning. No other significant associations were found.
79


Health Communication
Table 22 presents utilization patterns for health communicators. Half of those who
engage in health communications have been talking with others about health or health
care for less than 3 years (50.78%). The majority of those who talk with others about
health and health care do so relatively infrequently (77.19%, monthly or less) but believe
such communications to be usually or always important (64.90%).
Table 22: Utilization Patterns among Health Communicators (n=192)
Health communication duration Responses (N=192) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
< 1 month 26 7.80 15.74 3.58 12.78
1-6 months 20 6.43 12.98 3.35 11.21
7-12 months 13 3.59 7.25 2.53 6.40
1-2 years 28 7.34 14.81 3.40 11.57
3-5 years 38 11.07 22.33 4.04 16.29
5-10 years 22 4.36 8.79 2.15 4.63
>10 years 45 8.97 18.10 3.23 10.41
Health communication frequency Responses (N=189) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
Daily, multiple times 11 2.15 4.42 1.78 3.18
Daily, once per day 8 1.86 3.83 1.63 2.64
Weekly, 3-5 days per week 15 2.94 6.04 2.09 4.37
Weekly, 1-2 days per week 18 4.15 8.53 2.71 7.32
Monthly, every few weeks 59 17.56 36.11 4.52 20.43
Monthly, once per month or less 78 19.97 41.08 4.63 21.42
Health communication value Responses (N=192) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
Always important 81 20.51 41.39 4.57 20.92
Usually important 42 11.65 23.51 3.94 15.55
Sometimes important 56 14.91 30.09 4.38 19.17
Rarely important 10 2.41 4.86 2.21 4.90
Not important 3 0.07 0.15 0.09 0.01
Multiple regression analyses were conducted for each of these measures to
examine the significance of general health status, mental health status, and unhealthy
days in the last month, adjusting for demographic variables of age, gender, race/ethnicity,
and language. No health status variables were found to be significantly associated.
Language was associated with health communication duration, and health communication
80


frequency was associated with black race/ethnicity. No other significant associations
were found.
Table 23 describes who people use IT to talk with about health and health care.
The majority of people use IT to communicate with their family (88.32%), friends
(74.54%), and their health care providers (71.42%). Very few communicate with others
online about health and health care (7.24%).
Table 23: Health Communication Contacts (n=2
Health communication contact Responses (N) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
Health care provider 142 36.57 71.42 4.00 16.01
Family 178 46.15 88.32 2.88 8.32
Friends 148 37.43 74.54 3.93 15.45
Online acquaintances 15 3.44 7.24 2.30 5.29
Co-workers 64 16.78 34.74 4.60 21.17
Students/classmates 31 10.18 21.33 4.14 17.14
Neighb ors/community members 53 14.58 29.65 4.49 20.17
Religious leaders 29 5.85 12.49 3.00 9.00
* Total responses varied by item from a low of 178 to a high of202.
0*)
Multiple regression analyses were conducted for each of these measures to
examine the significance of general health status, mental health status, and unhealthy
days in the past month, adjusting for demographic variables of age, gender,
race/ethnicity, and language. Talking about health topics with neighbors and community
members was associated with general health status and unhealthy days in the past month.
Talking about health topics with (fellow) students was associated with age; talking about
health topics with family members was associated with age and language. Talking about
health topics with online acquaintainces was associated with language and Latino and
other/unknown race/ethnicity. Talking about health topics with religious leaders was
81


associated with black and other/unknown race/ethnicity. No other significant associations
were found.
Other Technology Users
Table 24 presents utilization for other types of common technology with
information delivery capacity. Multimedia entertainment technology such as DVD and/or
Blu-ray players (74.51%), cable TV with on-demand features (73.55%), and portable
digital music players (51.02%) were the most frequently used. Just under half of
respondents reported using health-specific technology devices (44.96%). Streaming
media boxes (12.05%) and e-book readers (16.97%) were the least used.
Table 24: Other Technology Utilization (n=403*)
Technology type Responses (N) Frequency (weighted) Responses (%) Std Err (%) Variance (%)
Health-specific technology (eg, glucometer, blood pressure cuff) 215 42.59 44.96 3.34 11.16
E-book reader 62 15.54 16.97 2.61 6.79
MP3/music player 164 47.26 51.02 3.38 11.42
Game console 142 39.82 43.49 3.45 11.92
DVD/Blu-ray player 277 69.46 74.51 2.95 8.72
Cable TV (with on-demand) 282 68.69 73.55 3.01 9.04
Streaming media device 51 10.95 12.05 2.23 4.97
Internet-enabled TV 124 33.05 36.48 3.35 11.22
* Total responses varied by item from a low of372 to a high of395.
Health-specific device use increased in association with age, with the highest use
found in the highest age bracket (ages 60-76, 62.69%, p=0.036). Age was also strongly
associated (p<0.001) with game console use, with the youngest age bracket having the
highest use (65.37%) and utilization decreasing as age increased. English speakers were
significantly more likely than Spanish speakers to use all types of examined technology
except for streaming media boxes. Whites were more likely than all other
races/ethnicities to use DVD/Blu-ray players and cable with on-demand service. Women
82


were slightly more likely than men to use e-book readers (17.13% vs. 16.44%, p<0.013).
Additional detail is shown in Table 25.
Table 25: Other Technology Users, Utilization by Demographic
Health Device
eBook
MP3
Game Console
N=403f
Yes
%
No
%
Yes
%
No
%
Yes
%
No
%
Yes
%
No
%
Age
p=0.036*
p<0.343
p<0.001
p<0.001
18-29
36.16
63.84
13.38
86.62
77.11
22.89
65.37
34.63
30-39
34.94
65.06
15.52
84.48
63.81
36.19
57.27
42.73
40-49
50.50
49.50
27.35
72.35
45.19
54.81
34.87
65.13
50-59
52.14
47.86
18.76
81.24
24.18
75.82
20.96
79.04
60-76
62.69
37.31
11.43
88.57
15.12
84.87
11.84
88.16
Gender
p=0.079
p<0.013

=0.241
=0.860
Female
45.44
54.56
17.13
82.87
53.01
46.99
43.19
56.81
Male
43.44
56.56
16.44
83.56
44.50
55.50
44.49
55.51
Race

-0.603
=0.315
-0.006*
=0.530
White
43.87
56.13
19.15
80.95
54.59
45.41
45.94
54.06
Latino
39.77
60.23
9.54
90.46
34.17
65.83
35.99
64.01
Black
50.71
49.29
23.55
76.45
56.37
43.63
50.84
49.16
Other
52.65
47.35
18.51
81.49
68.29
31.71
42.77
57.23
Language
p=0.001 *
p=0.001 *
p=0.004*
p=0.007*
English
51.99
48.01
21.91
78.09
57.44
42.56
49.29
50.71
Spanish
27.74
72.26
4.80
95.20
35.17
64.83
28.86
71.14
*Significantp-value, 0.05 or less.
fResponses vary by item from a low of379 to a high of403.
DVD/
Blu-ray
Cable w /
On-Demand
Streaming
Media Box
Internet
TV
N=403f
Yes
%
No
%
Yes
%
No
%
Yes
%
No
%
Yes
%
No
%
Age
p=0.031
p=0.390
p=0.013
7=0.044
18-29
80.23
19.77
79.36
20.63
14.70
85.30
48.54
51.46
30-39
81.40
18.60
77.79
22.21
18.82
81.18
39.57
60.43
40-49
80.23
19.77
64.02
35.98
7.81
92.19
33.28
66.72
50-59
59.24
40.76
70.18
29.82
10.18
89.82
24.54
75.46
60-76
61.40
38.60
70.33
29.67
3.24
96.76
23.23
76.77
Gender
p=0.969
p=0.057
p=0.837
-0.519
Female
74.57
25.43
72.74
27.26
11.82
88.18
35.40
64.60
Male
74.33
25.67
76.33
23.67
12.80
87.20
40.01
59.99
Race
p<0.001
-0.001*
JL
=0.954
JL
=0.231
White
82.40
17.60
78.38
21.62
10.72
89.28
37.06
62.94
Latino
56.58
43.42
58.47
41.53
12.23
87.77
29.96
73.04
Black
86.84
13.16
93.34
6.66
14.27
85.73
41.14
58.86
Other
70.59
29.41
68.49
31.51
13.50
86.50
48.54
51.46
Language
p<0.001
p<0.001

-0.161

-0.011*
English
81.35
18.65
81.48
18.52
14.02
85.98
41.93
58.07
Spanish
57.97
42.03
55.25
44.75
7.16
92.84
22.72
72.28
*Significantp-value, 0.05 or less.
fResponses vary by item from a low of379 to a high of403.
83


Technology Nonusers
Barriers and facilitators to IT and HIT use were explored with respondents who
reported not using computers, cell phones, or the internet, or not engaging in health
information seeking or health communications. Detailed results are shown in Table 26.
Knowledge was the most frequent barrier reported for computer (62.69%) and Internet
(61.96%) use, although it was much less of a factor for cell phone use (15.68%). Access
was the second most frequent barrier reported for computer and Internet use, and the
highest barrier to cell phone use (43.76%; 43.27%, Internet; 32.61%, computer). Cost
was the second-highest barrier to cell phone use (31.09%) and the third-highest barrier
for computer and Internet use (25.64%, 31.92%). The highest barrier to the use of health
information and to health communication was identified as preference for personal
interaction with ones health care provider (49.75%, health information; 50.87%, health
communication), with knowledge being identified as the second most frequent (39.24%,
health information; 26.03%, health communication).
Definite interest by nonusers in engaging with IT and HIT was reported across all
categories of nonuse: computer (49.57%), cell phone (34.73%), Internet (51.13%), health
information (45.61%), and health communication (27.99%). Disinterest was higher than
definite interest for health communication (40.74%) and cell phone use (39.95%). Interest
modified by knowledge, access, and cost was reported for all 5 categories of nonuse. Half
the population identified education as a facilitator to computer (50.51%) and Internet use
(50.11%). Reduced pricing (cost) was the most frequent facilitator to cell phone use,
identified by half of cell phone users (50.23%), and the second most frequent for
computer (33.90%) and Internet use (45.24%).
84


Table 26: IT and HIT Barriers and Facilitators
Computer %(n) N=149* Cell %(u) N=48* Internet %(n) N=125* H. Info % (n) N=179* H.Comm %(n) N=203* SE, % Var, %
Barrier
Access 32.61 (53) 43.76 (21) 43.27 (40) N/A N/A 5.24- 10.95 27.46- 119.83
Knowledge 62.69 (95) 15.39 (8) 61.96 (82) 39.24 (70) 26.03 (57) 4.23- 7.71 17.87- 59.37
Need 20.10 (25) 15.68 (10) 15.31 (17) 14.26 (19) 18.28 (29) 3.67- 7.37 13.46- 54.32
Interest 20.52 (27) 26.69 (ID 18.41 (23) 17.96 (24) 23.15 (30) 4.28- 9.53 18.33- 90.83
Difficulty 15.10 (21) 16.81 (7) 13.75 (20) 8.41 (16) 11.96 (20) 3.05- 7.82 9.31- 61.17
Cost 25.64 (40) 31.09 (14) 31.92 (33) N/A N/A 4.82- 10.19 23.24- 104.02
Value 8.44 (9) 8.10 (3) 6.44 (8) N/A N/A 2.92- 5.53 8.52- 30.63
Trust N/A N/A N/A 7.51 (8) N/A 2.87 8.24
Health literacy N/A N/A N/A 8.31 (ID 7.21 (10) 2.60- 2.92 6.78-8.53
Privacy N/A N/A N/A 15.84 (26) 16.40 (30) 3.73- 4.10 13.98- 16.83
Personal interaction N/A N/A N/A 49.75 (84) 50.87 (93) 5.29- 5.43 27.99- 29.51
Isolation N/A N/A N/A N/A 19.28 (31) 4.03 16.23
Facilitator
Education 50.51 (73) 19.37 (8) 50.11 (59) N/A N/A 6.01- 8.87 36.14- 78.74
Pricing 33.90 (58) 50.23 (25) 45.24 (44) N/A N/A 5.30- 11.61 28.09- 134.71
Fluency 22.57 (32) 8.41 (4) 32.91 (29) N/A N/A 4.68- 6.31 21.88- 42.54
Unknown 31.49 (32) 32.47 (9) 29.41 (29) N/A N/A 5.89- 10.95 34.74- 119.91
Interest
Definite 49.57 (73) 34.73 (15) 51.13 (57) 45.61 (75) 27.99 (59) 4.55- 10.66 20.75- 113.61
Moderated, access 32.49 (47) 30.74 (15) 21.66 (28) 20.39 (38) 10.48 (25) 2.77- 10.39 7.65- 107.86
Moderated, cost 23.68 (38) 30.07 (16) 25.34 (27) 20.66 (35) 16.31 (29) 3.87- 8.80 14.99- 77.45
Moderated, knowledge 45.88 (61) 19.41 (8) 35.38 (39) 26.53 (42) 14.95 (28) 3.43- 8.31 11.78- 69.04
Moderated, security N/A N/A N/A 19.17 (30) 19.79 (36) 4.17- 4.21 17.36- 17.69
Uncertain 12.42 (13) 6.43 (3) 5.40 (8) 13.13 (20) 15.73 (22) 2.73- 5.62 7.44- 31.55
None 18.01 (26) 39.95 (16) 36.39 £35) 30.21 (43) 40.74 (67) 4.17- 10.53 17.36- 110.87
* Responses varied by item: 135-142 (computer); 38 -45 (cell); 108-116 (internet); 159 -161 (health info); 176 183 (health commn).
85


Full Text

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CONSUMER HEALTH INFORMATICS AN D THE MEDICALLY UNDERSERVED: THE ROLE OF INFORMATION TECHNO LOGY IN HEALTH INFORMATION ACCESS AND HEALTH COMMUNICATION IN THE UNITED STATES by SUSAN L. MOORE B.S., University of New Orleans, 1994 M.S.P.H., University of Colorado Denver / Colorado School of Public Health, 2008 A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Doctor of Philosophy Health and Behavioral Sciences 2013

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ii This thesis for the Doctor of Philosophy degree by Susan L. Moore has been approved for the Health and Behavioral Sciences Program by Sheana S. Bull, Chair Edward P. Havranek Henry H. Fischer Andrew W. Steele June 20, 2013

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iii Moore, Susan L. (Ph.D., Health and Behavioral Sciences) Consumer Health Informatics a nd the Medically Underserved: The Role of Information Technology in Health Information Access and Health Communication in the United States Thesis directed by Professor Sheana S. Bull. ABSTRACT This thesis describes the results of a survey conducted to explore information technology (IT) and health information technol ogy utilizatio n patterns, impact, and the validity of Diffusion of Innovations (DoI) theoretical principles among patients who receive primary health care in an urban safety net setting. Utilization in the surveyed population was similar to nati onal utilization for widely a dopted technologies. Lesscommonly adopted technology use was also observed, but at ra tes lagging na tional levels, confirming the existence of a timeshifted di gital divide. IT use was reported by 95% of survey respondents. Cell phone use was significantly higher than computer use (p<0.001), with 93% of respondents repor ting cell phone use versus 71% reporting computer use. Significantly more people used technology for health information than for health communication (65% vs. 53%, p<0.001). A self-reported general health status of good or better was significantly associated with health information use (p=0.001). Distinct groups of IT adopters identifie d within the surveyed population showed no significant difference in populat ion distribution from adoptio n patterns described under DoI theory. This finding supports both DoI th eoretical applicability within a single broad

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iv socioeconomic stratum and the potential us e of theory-based diffusion modeling to reduce the impact of the digital divide thr ough tailored health informatics solutions. The form and content of this abstract are approved. I reco mmend its publication. Approved: Sheana S. Bull

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v ACKNOWLEDGMENTS No one gets there alone, and I am no ex ception. This endeavor could not have been completed without the assistance of a great many people, all of whom have my unending gratitude. First and foremost, I thank everyone who cont ributed to this project as a research participant, whether by responding to a survey or by joining in focused group discussion. Research depends on data, and the data they un stintingly provided was the gift that made this work possible. Thanks are due next to my committee chair, Sheana Bull, for her expert mentorship, guidance, and research advi ce, as well as the sign ificant gift of her time; to the other members of my committ ee, Ed Havranek, Henry Fischer, and Andy Steele, for their clinical and research expert ise and for their participation; and to Susan Dreisbach, for acting as my academic advisor until her retirement. I also wish to thank my colleagues at Denver Health, particul arly those in the Department of Patient Safety and Qualit y, Division of Health Services Research, Interpreter Services, and the 21st Century Ca re evaluation team. Es pecial thanks go to Josh Durfee, for his invaluable assistance with risk stratification and statistical analysis; to Debbie Rinehart, for her advice regarding survey incentives; to Rachel Everhart, for her help in identifying the survey populati on within the data warehouse; to Amy Tobin and Isabel Barrera, for aid with multiple translations; and to Tracy Johnson, for her suggestions about survey sampling, fielding, and presenting results. My thanks are given as well to collea gues and friends from other fields, in particular Michelle Thompson Boyer, for pr oviding both the benefit of her expertise in instructional design and her tire less assistance in applying thou sands of labels and stamps

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vi to survey envelopes, and Shannon Granville and M.E. Lasseter, for their professional academic and editorial review, proofreading, a nd copyediting. The quality of this work would have been poorer without their help, and I appreciate it more than I can express. Last but never least, I would like to thank my family, especially my parents, Larry Moore and Sandra Crockett Moore, and my sister, Summer Crockett Moore, and the communities of friends, both online and off, who have supported me throughout. These include the choirs of St. John’s Cathedral, De nver; the sisters and br others of that Order which knows the true value of the lens of scie nce; and those who tell collaborative stories in and about other worlds than these. Special thanks go to Beth Kerr and Batya Wittenberg for going above and beyond, to Andr ea Lankin for the reminder that “al shal be wel, and al manner of thyng shal be wele,” and finally to M.E. Lasseter and Lynne A. McCullough, for paving the way and walking the path alongside me fr om start to finish. Both my life and this world are richer for their presence and kindness, and I am forever grateful.

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vii TABLE OF CONTENTS CHAPTER I. INTRODUCTION .................................................................................................. 1 Specific Aims .......................................................................................................... 5 Specific Aim 1 .................................................................................................. 5 Specific Aim 2 .................................................................................................. 8 Specific Aim 3 .................................................................................................. 8 II. BACKGROUND AND SIGNIFICANCE ............................................................ 10 Quality through Technology: Establishing a National Context ............................ 10 Consumer Health Informatics a nd Patient-Centered Care .................................... 13 Consumer Health Informatic s and Chronic Disease ............................................. 17 Chronic Disease and the Me dically Underserved ................................................. 20 Overcoming the Digital Divide ............................................................................. 21 III. THEORETICAL BASI S AND FRAMEWORK .................................................. 23 Marxist Theory...................................................................................................... 23 Antonio Gramsci: Cultural Hegemony ................................................................. 26 Michel Foucault: Knowledge and Power .............................................................. 27 Diffusion of Innovations ....................................................................................... 29 IV. RESEARCH DESIGN AND METHODS ............................................................ 35 Overview ............................................................................................................... 35 Study Population ................................................................................................... 35 Inclusion and Exclusion Criteria ..................................................................... 36 Risk Stratification ........................................................................................... 37 Survey Procedures ................................................................................................ 39 Survey Design ................................................................................................. 39

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viii Survey Pilot Test Focused Group Discussion .............................................. 41 Sampling Methodology ................................................................................... 44 Survey Implementation ................................................................................... 45 Data Collection and Management ................................................................... 48 Analysis Plan ........................................................................................................ 50 Measures of Interest ........................................................................................ 50 Specific Aim 1, Research Question 1 ............................................................. 52 Specific Aim 1, Research Question 2 ............................................................. 52 Specific Aim 1, Research Question 3 ............................................................. 52 Specific Aim 1, Research Question 4 ............................................................. 53 Specific Aim 2 ................................................................................................ 53 Specific Aim 3 ................................................................................................ 54 V. RESULTS ............................................................................................................. 55 Survey Response ................................................................................................... 55 Weighting and Balancing ...................................................................................... 62 Analytical Methods ............................................................................................... 62 Descriptive Statistics ............................................................................................. 63 Technology Users, Overall ............................................................................. 65 Computer Users .............................................................................................. 68 Cell Phone Users ............................................................................................. 70 Internet Users .................................................................................................. 71 Activity Patterns Among IT Users .................................................................. 73 Health Information .......................................................................................... 77 Health Communication ................................................................................... 80 Other Technology Users ................................................................................. 82

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ix Technology Nonusers ..................................................................................... 84 Opinions about Technology ............................................................................ 87 Health Status and Information Technology Use ................................................... 87 Population Level ............................................................................................. 88 Tier 1 Level ..................................................................................................... 88 Tier 2 Level ..................................................................................................... 88 Tier 3 Level ..................................................................................................... 89 Diffusion of Innovations: Technol ogy Diffusion Assessment .............................. 89 VI. DISCUSSION ....................................................................................................... 93 REFERENCES ............................................................................................................... 107 APPENDIX ..................................................................................................................... 122 A. CHI Survey Instrument (English) ................................................................. 122 B. Invitation Letter and Mail ing Labels (English) .............................................. 131 C. Reminder Postcard (English) ......................................................................... 133 D. CHI Codebook ............................................................................................... 134

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x LIST OF TABLES TABLE 1: Attributes of Innovations in Terms of HIT ................................................................... 30 2: Classification of I nnovation Adopters .......................................................................... 31 3: Five Phases of the Gartner Hype Cycle ........................................................................ 33 4: DH Primary Care Patient Demographics, January 7, 2013 .......................................... 36 5: DH Tiering Algorithm A ssignment, Version 1.0 ......................................................... 39 6: Survey Focus Group Demographics ............................................................................. 42 7: Survey Pilot Test Emergent Themes by Content Area ................................................. 43 8: Survey Implementation Timeframe .............................................................................. 45 9: Survey Population Demographics, Unadjusted ............................................................ 60 10: CDC “Healthy Days” Measures ................................................................................. 64 11: CDC “Healthy Days” Meas ures by Demographic ...................................................... 64 11: CDC “Healthy Days” Measures by Demographic, continued .................................... 65 12: Information Technology (IT) User Classification ...................................................... 65 12: Information Technology (IT) User Classification, continued .................................... 66 13: IT General User Status by Demographic .................................................................... 66 13: IT General User Status by Demographic, continued .................................................. 67 14: IT Users, Technology Utilization by Demographic .................................................... 68 15: Utilization Patterns among Computer Users (n=266)................................................. 69 16: Utilization Patterns among Cell Phone Users (n=364) ............................................... 70 16: Utilization Patterns among Cell P hone Users (n=364), continued ............................. 71 17: Utilization Patterns among Internet Users (n=279) .................................................... 72 18: IT-Based Activities (n=405*) ..................................................................................... 74 19: IT-Based Activities by Demographic ......................................................................... 75

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xi 19: IT-Based Activities by Demographic, continued ....................................................... 76 20: Utilization Pattern s among Health Information Users (n=231) .................................. 77 20: Utilization Patterns am ong Health Information Users (n=231), continued ................ 78 21: Health Information Content (n=237*) ........................................................................ 79 22: Utilization Patterns among H ealth Communicators (n=192) ..................................... 80 23: Health Communication Contacts (n=210*) ................................................................ 81 24: Other Technology Utilization (n=403*) ..................................................................... 82 25: Other Technology Users, Ut ilization by Demographic .............................................. 83 26: IT and HIT Barriers and Facilitators ........................................................................... 85 27: IT and HIT Nonusers by Demographic ...................................................................... 86 28: Technology Diffusion Classifi cation, Surveyed Population....................................... 92

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xii LIST OF FIGURES FIGURE 1: The Chronic Care Model. ............................................................................................. 19 2: The Digital Divide as a Marxist Construct ................................................................... 25 3: The Diffusion of Innovati ons Distribution Curve......................................................... 31 4: Gartner Hype Cycle. ..................................................................................................... 34 5: Inclusion/Exclusion Criteria Flow Diagram ................................................................. 37 6: Survey Results: AAPOR Response Types ................................................................... 55 7: Age Histogram and Box Distri bution, Respondent vs. Sampled .................................. 61 8: Age Q-Q plot, Respondent vs. Sampled ....................................................................... 61 9: Computer Adoption – Duration of Use......................................................................... 90 10: Cell Phone Adoption – Duration of Use ..................................................................... 90 11: Internet Adoption – Duration of Use .......................................................................... 91 12: Model and Surveyed P opulation Distributions ........................................................... 92

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xiii LIST OF EQUATIONS EQUATION 1: Flesch-Kincaid Grade Level Readability Formula ....................................................... 40 2: Probability Sampling – Sample Size Formula .............................................................. 44 3: Response Rate .............................................................................................................. 56 4: Cooperation Rate .......................................................................................................... 57 5: Refusal Rate ............................................................................................................... ... 58 6: Contact Rate ............................................................................................................... ... 58

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xiv LIST OF ABBREVIATIONS A1c glycosylated hemoglobin; labora tory test that measures average levels of blood glucose over time and is used to both diagnose diabetes mellitus and assess a patientÂ’s degree of diabetes control. See also HbA1c. BP blood pressure AAPOR American Association for Public Opinion Research AHRQ Agency for Healthcare Research and Quality ARRA American Recovery and Reinvestment Act of 2009 ACA Affordable Care Act (see also PPACA) AIM AOL Instant Messenger; chat sy stem developed and maintained by AOL AOL online service and content provi der whose corporate name is the acronym for the original compa ny name of America Online CAHPS Consumer Assessment of Health Providers and Systems CCM Chronic Care Model CDC Centers for Disease Control and Prevention CDPS Chronic Illness and Disability Payment System CHI consumer health informatics COMIRB Colorado Multiple Institutional Review Board COPD chronic obstructive pulmonary disease CPOE computerized provider order entry DH Denver Health and Hospital Authority DM diabetes mellitus; diabetes DoI Diffusion of Innovations eHealth electronic h ealth; the use of electronic or information technology for health and health care EHR electronic health record EMR electronic medical record FCC Federal Communications Commission FPC finite population correction FQHC federally qualified health center HbA1c hemoglobin A1c; glycosylated hemoglobin; laboratory test that measures average levels of blood glucose over time and is used both to diagnose diabetes mellitus and assess a patientÂ’s degree of diabetes control. See also A1c. HIT health information technology HITECH Health Information Technology for Economic and Clinical Health Act HRQOL Health Related Quality of Life HRSA Health Resources and Services Administration HTN hypertension ICPSR Inter-university Consortium for Political and Social Research ICT information and communications technologies IOM Institute of Medicine

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xv IRC Internet Relay Chat; messaging system protocol for real-time synchronous one-on-one or group text-based communication over network channels via transmi ssion communication protocol and security IT information technology LDL low-density lipoprotein; cholestero l lab test used as a measure of a patientÂ’s diabetes control mHealth mobile health; the use of m obile technology for health and health care MAR missing at random MCAR missing comp letely at random MCMC Markov chain Monte Carlo MU meaningful use NHIN Nationwide Health Information Network OMB Office of Management and Budget ONC Office of the National Coordi nator for Health and Information Technology PHR personal health record PPACA Patient Protection and Affordable Care Act (2010) SAS Statistical Analysis System; anal ytical software originally created at North Carolina State University and now developed and maintained by SAS Institute, Inc. (Cary, NC) SE standard error USPS United States Postal Service WHO World Health Organization

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1 CHAPTER I INTRODUCTION In 2001, as part of its historic in-depth analysis of health care in the United States, the Institute of Medicine (IOM) found that information technology (IT) had the potential to promote the provision of health care that was “safe, effective, patient-centered, timely, and equitable” (1). Consumer health was iden tified as a specific domain in which IT could be of great benefit. St udies to date support this findi ng, indicating that consumer health informatics applications are capable of being used to engage patients, augment clinical interventions, aid in decision support, promote chr onic disease self-management, and improve both intermediate and longer-t erm clinical health outcomes (2). Unfortunately, this approach may result in unintended negative consequences for those who are already significantly burdened with health disparitie s. The term “digital divide” refers to the disparity between those who have both the access and knowledge necessary to utilize Internet-b ased IT and those who do not (3). The digital divide has been shown to disproportionately affect members of the vulnerable and medically underserved “priority populations” (4, 5) tradit ionally served by the health care “safety net.” The safety net refers to the system of care for patients with limited or no health insurance, offered by health delivery systems and providers who have committed either under the law or by chosen mission to care for patients regardless of their ability to pay (6). Safety net providers include public hos pitals, federally qua lified health centers (FQHCs), and public health departments, a nd their patients include racial and ethnic minorities, low-literacy populations, people of below-average socioeconomic status,

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2 people with disabilities, child ren and the elderly, people with multiple chronic conditions, and rural populations (7-10). This dissertation describes the results of a cross-sectional study conducted to explore the use and value of IT among patie nts who receive primary health care in an urban safety net setting, considered as a repr esentative sample of a single broad stratum of the current societal superstr ucture in the United States. These concepts were explored through a social science perspec tive, with consideration give n to social and structural factors that affect access to and use of t echnology. Inequality of technological access, control of information within health care delivery systems, and interactions between knowledge and power were examined in theore tical contexts and considered within the Diffusion of Innovations (DoI) framework. The well-documented phenomenon of the di gital divide can be showcased in a theoretical context through a cl ass-oriented examination of th e societal implications of technology access and use. Karl Marx first re cognized the increased availability of technology to those with greater resources and the subseque nt leveraging of technology to conduct capitalist processes as a way by which the worker could be further distanced from the means of production (11). MarxÂ’s pr oposal that economic change has direct political and cultural effects on the societal superstructure led directly to GramsciÂ’s observation that cultural norms are in fact social constructs imposed by the prevailing class as part of the dominant ideology a nd accepted by subaltern groups as the natural state of being (cultural hegemony) (12, 13) As technology is considered to be a significant element in the structural formati on of economic change, it must then also be considered a key factor in hegemonic domin ation. The social cla ss-based variation in

PAGE 18

3 access to and ability to effectiv ely use technology for health informatics purposes that is characteristic of the digital divide repr esents this culturally hegemonic societal superstructure. This variation is one of measurable structur al violence—the systematic exertion of violence by those who belong to a particular social order upon those who are members of less-privileged classes (14) —e xerted here through the restriction of knowledge and the consequent promotion of h ealth disparities. French historian and social theorist Michel Foucau lt (15, 16), whose social and st ructural critiq ues of health systems and organizational discipline have significantly informed philosophical thought on health and health care, has clearly descri bed this interrelationship between knowledge and power. Foucault’s Birth of the Clinic describes the historical transformation of social and political landscapes necessary to produce the institution of modern clinical medicine and introduces the concept of the medical g aze and the need for attentive, objective observation of patients as central to a modern treatment paradigm (17). Limited information is currently availa ble on the impact of consumer health informatics applications on health outc omes among the priority population groups traditionally served by the safety net (2, 18). Nonetheless, the dual lack of access to technology and information that is represente d by the digital divide should not be assumed to also represent lack of intere st in technology. Prior ity population groups, as defined by the Agency for Healthcare Research and Quality (AHRQ) (4), include people who are traditionally considered to be medically vulnerable, such as those without health insurance, people with low income or lo w socioeconomic status, racial and ethnic minorities, low-literacy populations, people with disabilities, children, the elderly, and those living in rural areas. A disproportiona te share of these groups receive health care

PAGE 19

4 through core safety net systems, which the IO M has characterized as health systems with policies and practices that call for treating patie nts regardless of their ability to pay (19). Patients in these settings have shown intere st in the idea of technology-based information sharing with their care providers, and have expressed desire to know more about their own health information (20-23). Although the digital divide is an indicator of disparity be tween social classes in a societal superstructure, recent technological innovations may be able to help overcome the traditional structural inequi ty that this disparity perpetua tes. Everett RogersÂ’ theory of the Diffusion of Innovations (DoI) has identif ied the use of appropriate technological innovations for specific purposes as a mean s of closing gaps between groups of innovation adopters and would-be adopters (24). Rogers has al so described an approach for assessing rates of innovation spread and uptake among populations. The increasing prevalence of certain types of user-centric technology such as cell phones among priority groups, combined with priority gr oupsÂ’ interest in usin g technology to support information sharing, offers the opportunity to improve health information access and health communication through use of technologi cal innovations that pr iority groups have already self-selected. For example, Blacks a nd Latinos are more likely than whites both to own cell phones (87% compared to 80%) and to use them for a wide variety of data access functions. Two-thirds of both minority populations also use their cell phones to access the wireless Internet (25). Examining th e use of these innovations within a single socioeconomic stratum in the context of Do I and of a constructiv ist paradigm (which assumes that individuals activ ely gain and create knowledge from their own experiences) may help identify and develop targeted solu tions that bridge the inequity gap and

PAGE 20

5 improve shared decision making by empowering patients with health information that best meets their needs. Specific Aims The specific aims of the study were as follows: Specific Aim 1 To assess and describe current methods a nd patterns of IT utilization for health information access and engagement in hea lth communications among adult patients who receive care in an ur ban safety net setting. Research Question 1 How do patterns of IT utilization in genera l, IT utilization for health information access, and IT utilization for health co mmunications differ by demographic subgroup? Demographic Measures (Denver Health clinical systems) : age, gender, race, language IT-General Utilization Measures (survey data) : computer, cell phone, and Internet user status; computer, cell phone and Internet use device type; computer, cell phone, and Internet use duration; computer, cell phone, and Internet use frequency; computer, cell phone, and Internet use importance; computer and cell phone ownership; Internet access speed; and IT activity type. IT-Health Information Measures (survey data) : health information access user status; health information access duration; health information access frequency; health information access importance; and health information access topics.

PAGE 21

6 IT-Health Communications Measures (survey data) : health communication user status; health communication duration; h ealth communication frequency; health communication importance; and h ealth communication contacts. Research Question 2 How do patterns of IT utilization in genera l, IT utilization for health information access, and IT utilization for health communication differ by health status? Health Status Measures (survey data, Denver Health clinical systems) : Centers for Disease Control and Prevention (CDC) “Healthy Days” self-rated general health status, unhealthy days in past 30 days, and me ntal health in past 30 days; Denver Health risk stratification tier (version 1.0). IT-General Utilization Measures (survey data) : computer, cell phone, and Internet user status; computer, cell phone and Internet use device type; computer, cell phone, and Internet use duration; computer, cell phone, and Internet use frequency; computer, cell phone, and Internet use importance; computer and cell phone ownership; Internet access speed; and IT activity type. IT-Health Information Measures (survey data) : health information access user status; health information access duration; health information access frequency; health information access importance; and health information access topics. IT-Health Communications Measures (survey data) : health communication user status; health communication duration; h ealth communication frequency; health communication importance; and h ealth communication contacts.

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7 Research Question 3 What are the barriers and facilitators to IT utilization in general, IT utilization for health information access, and IT utilizat ion for health communication among nonusers? IT-General Utilization Measures (survey data) : computer, cell phone, and Internet user status; health information access user st atus; and health communication user status. Barrier Measures (survey data) : computer, cell phone, and Internet use barriers; health information access barriers; and health communication barriers. Facilitator Measures (survey data) : computer, cell phone, and Internet use facilitators; health informa tion access facilitators; and hea lth communication facilitators. Research Question 4 What general opinions about IT and health IT (HIT) are held among adult patients who receive care in an urban safety net setting? Opinion Measures (survey data) : Topics and themes emergent from participant responses to open-ended survey questions. Objectives The objectives of this aim were to as sess the prevalence of IT use, identify interest in and familiarity with specific IT modalities and activities, discern patterns of behavior related to IT use, a nd identify barriers and facilitato rs to IT use both in general and for health information access and health communications among members of priority populations as represented by a dult patients who receive care in an urban safety net setting. It was anticipated that safety net patients with chronic diseases would have greater interest in, engagement with and us e of IT for health information access and health communications than would those without chronic illness.

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8 Specific Aim 2 To compare the health status of IT us ers and IT nonusers among adult patients with chronic disease who receive health care in an urban safety net setting. Hypothesis Adult patients with chronic disease who r eceive care in the safety net and who use IT to access health information and engage in health communications are predicted to have better health status than adult patients with chronic disease who receive care in the safety net and who are IT nonusers. Objective The objective of this aim was to examine the potential of consumer health informatics applications to improve health outcomes among patients with chronic disease who are members of priority populations. Specific Aim 3 To evaluate the applicability of tradit ional DoI theory when used to examine patterns of adoption and utiliz ation of HIT among adult patie nts who receive care in an urban safety net setting. Hypothesis Members of priority populations have an interest in using IT to access health information and to engage in health communicat ions that is equivale nt to that reported among members of more advantaged populati ons; however, they do not use the same types of IT in the same manner or to the same extent.

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9 Objective DoI theory describes rates and patterns of identification, adop tion, and utilization for specific technological innovations by groups of adopters who are in part characterized by their socioeconomic strata. This aim propos ed to examine the applicability of DoI theory within rather than acr oss socioeconomic strata to de termine if observed patterns still persist when barri ers to access are considered as a known factor rather than being used in the definition of a larger subgroup.

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10 CHAPTER II BACKGROUND AND SIGNIFICANCE Quality through Technology: Establishing a National Context In 1998, the IOM created the Committee on the Quality of Health Care in America and charged it to develop an approach to substantially and significantly improve the quality of health care over the next decade. In 2001, the committee’s second report, Crossing the Quality Chasm called for the health care deliv ery system to be redesigned in order to improve healthcare quality as a whol e (1). As part of its historic analysis, the committee identified 6 aims for improvement, pr oposing that health care should be “safe, effective, patient-centered, timely, and equitable.” In its report, the committee noted the potential for IT to play a critical role in the transformation of the health system to achieve all 6 specified aims for quality improvement, and recommended that better integr ation of IT into health care should be 1 of 4 key areas essential for system tran sformation. Specific recommendations included developing a national information infrastructu re; promoting the adop tion of electronic medical record (EMR) and computerized provider order entr y technology (CPOE); establishing data standards for health inform ation exchange; and continuing to use and develop informatics applications for patie nts, a field which has become known as consumer health informatics. Informatics is the science of using data, information, and knowledge to improve both human health a nd the delivery of health care services; consumer health informatics is the science of informatics as relates to consumer needs, with a focus on information structures and pr ocesses that empower consumers to manage their own health (26).

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11 In light of the committeeÂ’s recomme ndations, in 2004 Executive Order 13335 (27) established the position of National Coordinator for H ealth Information Technology and charged it with responsibility for devel oping and leading a stra tegic action plan to actively promote HIT implementation in both public and private se ctors nationwide (27, 28). Subsequent legislation under the Health Information Technology for Economic and Clinical Health (HITECH) Act, included in the American Recovery and Reinvestment Act of 2009 (ARRA), mandated the continua tion of the position of the National Coordinator, estab lished the Office of the National Coordinator for Health and Information Technology (ONC), provided sign ificant incentives for HIT adoption, and established an initial framework and a schedule for national HIT infrastructure development and deployment (2, 29). In the first year of the HITECH Act, ONC set forth the following initiatives with specific impact on consumer health informatics (30): 1.) The creation of a Nationwide Health Information Network (NHIN) as a secure, interoperable hea lth information infrastructure designed to connect providers, consumers, and other key stakeholders involved in supporting health and health care. From a consum er health informatics perspective, NHIN is intended to provide patients with the capability to manage and control their own personal health record s as well as providing them with access to their health information from cliniciansÂ’ electronic health record (EHR) systems, while also ensuring secu rity and confidentia lity of personal health data.

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12 2.) The establishment of incentive criteria for the “meaningful use” of EHRs by providers who participate in Medicare and Medicaid programs. Meaningful use was defined to include the electroni c capture of health information in coded formats, the use of such electr onically captured information to track certain key clinical conditi ons and to assist with th e coordination of care, and the use of health information for public health and clinical quality measure reporting. 3.) The creation of an initial set of standards, implemen tation specifications, and meaningful use certifica tion criteria for EHRs. 4.) The development in cooperation with h ealth care organizations and standardsdevelopment organizations of a set of interoperability specifications and uniform data exchange formats, along w ith detailed technical specifications for use, intended to support health in formation exchange between systems. Further expansion of the ONC initiatives under the HITECH Act was authorized in 2010 with the passage of the Patient Prot ection and Affordable Care Act (PPACA; the Affordable Care Act) (31) Section 1561 of the Affordable Care Act required the Department of Health and Human Services, in collaboration with the HIT Policy and HIT Standards Committees created by ONC, to deve lop secure, interope rable standards and protocols to facilitate patients’ ability to electronically enroll and manage their participation in federal a nd state health and human services programs (32, 33). The HITECH Act also called for an update to ONC’s original Federal Health Strategic Plan as last published in 2008. In January 2012, ONC released the updat ed plan, which takes into account changes in HIT in the last several years and estab lishes 5 broad goals for the

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13 future. The fourth goal is to “empower individua ls with health IT to improve their health and the health care system,” thereby re iterating IOM’s recommendation on consumer health informatics and emphasizing the conti nuing significance of enabling patients to access and use HIT effectively (34). Consumer Health Informatics and Patient-Centered Care Concomitant with the drive toward an in creasingly technological health care system is a movement toward a health care model that focuses on treating the person not the disease Prior models of health care delivery were largely provider-driven, with the majority of decisions made by the physician an d presented to the patient as directives to be followed, based on clinical data and provide r expertise. The patien t-centered approach involves empowered patients act ing in consultation with providers as decision-makers in their own care experience, with health se rvices designed to accommodate patients’ individual preferences and health needs (35). The advent of the patient-centered care model brought a need for patients to be able to obtain essential information and re sources in order to understand their healthrelated care options, to enga ge in complex decision maki ng, and to receive support for making good health choices and managing thei r own health-related behaviors according to their individual health needs. In response, patients turned in large numbers to online sources of information. In 2010, searching fo r health information online was the third most frequent Internet activity, following ema il and general search e ngine use. Eight out of 10 Internet users in the Un ited States reported searching for health information online, comprising a full 59% of all Americans (36) Online health information seekers were

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14 more likely to be women, white, and college -educated; have higher incomes; and act as unpaid caregivers for othe r individuals (10, 36). To increase the availability of health in formation to patients, health systems and health care providers participating in mean ingful use programs ar e required under Stage 1 criteria to offer electronic copies of health information to patie nts within 3 days of a visit, and are required under Stage 2 criteria to offer patients access to health information online within 4 days of a visit. Electroni c and online accessmay be offered through Web sites designated as “patient portals,” where patients can access their electronic health records and review their health information, including data such as lab results, scheduled appointments, and medication refill informa tion (30, 37-40). Early adopters of patient portals, such as Kaiser Permanente (KPH ealthConnect) and Geisinger Health System (myGeisinger), have reported decreased office visits in tandem with observed improvements in patient-provider communica tion and quality of care (41). Despite concerns expressed about priv acy and security of personal health information, patients continue to express willingne ss to engage in technology-base d health information access and data sharing (42). In addition to availing themselves of online resources for health information, patients are increasingly using the Internet to participate in health-related community interaction, peer support, social engagement, and health data tracking. Web sites such as Livestrong, Mindbloom, and SparkPeople offe r health promotion through tailored interfaces to engage users daily in tracking se lf-provided health data such as weight, body measurements, and physical fitness activity, along with support for wellness-focused behavioral change such as tobacco cessation, providing supp ort both directly through the

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15 user’s personalized interface and through onlin e forums and social network channels to promote interaction and community engagement with other users of the service (43-45). Sites such as PatientsLikeMe, TuDiabetes, and CancerCare offer condition-based support for diseases through tailored spaces where pa tients living with sp ecific conditions can receive information and peer-to-peer support through interaction with other users facing similar challenges (46-48). The patient-centere d concept of “participa tory medicine,” in which patients are actively encouraged to us e the Internet to seek information on their own behalf or others’ and to use their findings to inform and empower their interactions with their health care providers, is actively promoted both by sites such as e-Patients.net and through a nonprofit organization and onlin e peer-reviewed journal (49-51). Such activities have a sizeable audience. Of the 74% of American adults who use the Internet, 62% have reported using social network sites, and 23% of them reported keeping up with their friends’ health-related site updates. Mo re than a quarter (27%) of Americans have reported tracking some sort of health data online, while 18% of a ll online users and 23% percent of online users with chronic conditions reported turn ing to the Internet to seek out others with similar health conditions (36, 52). Consumer engagement with health inform atics solutions is also not limited to traditional Web site interfaces. The term “Web 2.0” broadly refers to the transition of Web design and technology from a largely sta tic, server-centric information repository (where information is distributed in broad cast fashion from the source server across a network to the client recipi ent) to an interactive mode l where server and client applications engage in synchronized data exch ange and service delive ry occurs through a shared platform. The core concept unde rlying Web 2.0 is one of community-sourced

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16 development that utilizes a Web-based platfo rm as infrastructure and shared space for engagement (53). This shift in conceptual ization from the Web site provider as both content creator and distributor to users as content creators and active contributors to a community product is accompanied by a growin g “collective intelligence,” in which the sum of the knowledge value contributed by the community of users is greater than that which could be attributed to an indivi dual alone (53, 54). The Web 2.0 framework not only is well suited to both pa tient-centric participatory me dicine and a peer-to-peer approach to health care through social networ king (55), but also provides access to shared platforms through social media and emergent technologies that are not dependent on traditional access to desktop client computer s for their use. “Health 2.0” or “Medicine 2.0” refers to the development and upgrade of consumer health informatics applications and services to be fully interoperable with the Web 2.0 model. These tools are targeted toward patients and care providers and are de signed to promote increased engagement in social networking, collaborative operations, and apomediation within and between groups of users (56-58), along with greater personal c hoice and portability of use, including use on a variety of mobile devices. The World Health Organization (WHO) de fines the use of mobile technology for health care, or mHealth, as a rapidly growing subset of eHealth, which encompasses all health services supported by information and communications technologies (ICT) (59, 60). More than 5 billion mobile phone account s are currently in operation worldwide, covering more than 85% of the world’s peopl e (60). In the United States, 82% of all adults own mobile phones of some kind, a nd 40% of them use their mobile phones to access the Internet (25). Moreover, 35% of American mobile phone owners possess

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17 smartphones (e.g., Android, BlackBerry, iPhone), and of those users 87% use their smartphone for Internet access and 25% report th at their smartphone is the device they use for the majority of their online activity (6 1). The advent of wide spread and affordable mobile and wireless technology has rapidly-increasi ng potential to both further transform the way individuals access Inte rnet and other technologica l resources and to have significant impact on the delivery of health care, although it remains to be seen whether this transformation will be sufficient to overcome the impact of the digital divide for those who do not engage in mobile or high-speed Internet access. Consumer Health Informatics and Chronic Disease One particular area where consumer health informatics applications have been shown to have compelling potential for improvi ng the delivery of patient-centered care is in the treatment of chronic disease. The problem of chronic disease is severe The theory of epidemiologic transition (62) describes 3 phases of population mort ality, with “degenerative and man-made diseases” replacing infectious disease as the primary causes of mortality in the third phase. In April of 2011, the WHO published a report on the global status of chronic diseases, confirming that they have become the leading cause of death worldwide (63, 64). Fully 63% of all deaths globally in 2008 (a total of 36 million) were attributable to chronic disease, with the majority due to ca rdiovascular disease, diabetes, cancer, and chronic lung disease (63, 64) Global economic costs of chr onic disease are estimated to reach $47 trillion by 2030 (65). The situation in the United States is no better. As of 2005, almost half of Americans (133 million people) had at leas t one chronic disease and 63 million were

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18 living with multiple chronic diseases; thes e numbers are projected to reach 157 million and 81 million respectively by 2020 (66, 67). Chronic diseases account for 70% of American deaths and 78% of health care expenditures, with an economic cost of $277 billion in 2003 alone (68, 69). Chronic diseases have also contribute d to a lower average life expectancy for Americans over the last decade relative to gains made in other nations, with a loss of 33.1 million disabili ty-adjusted life-years annually (65). In contrast to treatment for infecti ous diseases and other acute conditions, improving care and outcomes for chronic diseas e depends on patientsÂ’ ability to achieve successful management of their disease ove r the long term. The Chronic Care Model (CCM), as developed by Wagner et al, (70-72) describes an intera ctive approach to chronic disease management that involves a mobilized community in partnership with a health system organized for chronic care efforts that inco rporate decision support tools; clinical information systems that provide da ta for monitoring performance, facilitating planning, reminding patients and providers of ca re activities, and sharing information; a delivery system design that promotes effectiv e, efficient, evidence-based and culturally appropriate care by health teams; and self -management support to empower and engage patients in their own care (73, 74).

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P c o i d i n p r p s c c o m h y ( 8 E o n a v u Fi g ure 1 P ractice 19 9 f HIT s o mmunities d entified do m n chronic di s r ocess and c ersonal heal c heduling, d o nsumer he a m anagement y pertensive 8 2, 83), and E vidence su g n line access v ailable, us e se online in f : The Chro n 9 8, Vol 1, C h f or Chronic I olutions are in improvi n m ain eleme n s ease care h a c linical outc o th records, d d isease regis t a lth informa t (2), with re s patients (80 improveme n g gests that al than those w e rs with chr o f ormation ( 8 n ic Care M h ronic Dise a I llness? by W particularl y n g quality of n ts of the ch r a ve demons t o mes, with p d ecision sup p t ries and po p t ics solutio n s ults that in c 81), impro v n ts in medic though pers o w ithout (68 % o nic disease 8 5, 86). Am o odel. Repri n a se Manage m W agner EH y suited to s u care, proce s r onic care m t rated reduc e p articular ef f p ort tools, c o p ulation ma n n s have also p c lude impro v v ements in g a tion adher e o ns with ch r % and 81% r are more li k o ng patients w n ted from Fi g m ent: What W Reprinted w u pporting bo t s s and healt h m odel (73, 7 5 e d costs and f ectiveness a o mputerize d n agement (7 5 p roven effe c v ements in b g lycemic co n e nce among r onic diseas e r espectively ) k ely than th o with chroni c gure 1, Effe c W ill It Take w ith permis s t h health sy s h outcomes i 5 ). Studies o f improveme n a ssociated w d prompts, e l 5-79). Patie n c tive in chro n lood pressu r n trol among asthmatic p a e are less li k ) (85), whe n o se without t o c conditions c tive Clinic a to Improve s ion. s tems and i n all four f the use of H n ts in both w ith EMRs, l ectronic n t-facing nic disease r e control a m diabetic pa t a tients (84). k ely to have n access is o both seek 75% repor t 19 a l Care H IT m ong t ients and t ed

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20 that online information affected their car e decision-making, 69% reported using online information in discussions with their car e providers, and 57% reported changing selfmanagement behaviors based on information found online (87). Chronic Disease and the Medically Underserved The impact of chronic disease is most significant among low income, vulnerable and medically underserved priority populat ions (4, 5), including racial and ethnic minorities, low-literacy populations, people with disabilities, children and the elderly, and those living in rural areas. On a global scale, almost four-fifths of deaths from chronic disease occur in low and mi ddle-income countries, includ ing 80% of deaths due to cardiovascular disease and diab etes. Death comes earlier to these populations as well, as nearly a third of deaths from chronic dis ease in low-and middle-in come countries occur before age 60 (63). Similar trends are refl ected in the United States, where racial and ethnic disparities in health care persist, partic ularly with illnesses such as cardiovascular disease and cancer (88). Significantly higher prevalence of chronic disease has been observed among blacks, Latinos, and Asians than among whites, as well as among those near or below the federal poverty level as comp ared to those 200% or more above it (65, 67). Disparities in chronic disease treatment ar e present as well: for example, an analysis of data between 2002 and 2007 indicated that significantly lower numbers of black and Latino adults, low-and middle-income a dults, and uninsured adults received recommended diabetes care compared with wh ite, high-income, and insured adults (89). The problem is further exacerbated by overall di sparities in quality of care and access to care. Blacks and Latinos receive worse care th an whites for 40% and 60% of core quality measures, respectively, and have more di fficulty accessing care 33% and 83% of the

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21 time, while the poor receive a lower quality of care than t hose with high incomes for 80% of core measures and have cons istently worse access (89-91). Although the burden of chronic disease on the traditionally medically underserved is disproportionate and the ability of HIT to improve chronic disease management practices and outcomes has already been confirmed, information on the effect of consumer health informatics applicati ons on health outcomes among these groups remains limited (2). This additional dispar ity represents a phenomenon commonly known as the digital divide. Overcoming the Digital Divide The digital divide is the term applied to the disparity between those who have both the means of access and the knowledge nece ssary to effectively use Internet-based information technology and those who have neith er (92, 93). The digital divide has been shown to disproportionately affect the same groups found in medically underserved priority populations, including racial a nd ethnic minorities, low-literacy populations, people of below-average socioeconomic status people with disabilities, and those living in rural areas (7, 8, 94, 95). An example of the digital divide is se en in the distribution of “broadband” Internet service, which is defined as In ternet access through technologies that support data transmission at speeds much higher th an those available th rough older, “dial-up” technology (96). Broadband capability is cons idered so important in accessing online information that the Federal Communicat ions Commission (FCC) was charged in 2009 with developing a National Broadband Plan to en sure that ensure that all Americans have broadband access to Internet resources ( 97). Users with broadband access are among the

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22 most likely to have sought health informa tion online (36); however, 67% of whites in 2010 had broadband access as compared to 56 % of blacks. Only 45% of users with annual incomes under $30,000 and 67% of t hose with annual incomes between $30,000 and $50,000 had broadband access, compared to 87% of those with annual incomes over $75,000 (98). The disparity is so significant that the FCC has proposed comprehensive reform and modernization of the Lifelin e/Link Up program, established in 1996 to support the provision of “basic communications services” to low-in come Americans as part of the FCC’s universal service mission, to also include affordable access to reliable broadband (99). This proposal met with signifi cant resistance from the American public, including 45% of current Inte rnet nonusers (98). Resistance such as this highlights the importance of considering ways to overcom e the digital divide that do not depend on modifications to overarching t echnological infrastructure. Despite the observed resistance to the F CC’s proposal, the dual lack of access and knowledge that characterizes the digital divide does not represent lack of receptivity for technology-based information sharing. Unders erved patients have repeatedly shown active interest in use of consumer health in formatics solutions and engaging in health information exchange with their care providers (20-22, 100-105).

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23 CHAPTER III THEORETICAL BASIS AND FRAMEWORK To address the problem of the digital divide and its growing impact upon health disparities, it is critical to examine syst ems where the medically underserved risk further separation from needed medical care and h ealth support that in creasingly are being provided through technological channels. To that end, prin ciples inherent in both traditional and neo-Marxist theoretical perspectives and in post-structuralist discourse, where perception is critical to informi ng the creation of complex meaning, were examined concerning the relationship between knowledge and power to gain insight into the systems under consideration. Marxist Theory The core of traditional Marxist theory considers labor as the unit of social organization (11), stemming from Marx’s asse rtion that what humanity ascribes as “value” derives from an expressed relationshi p between the object being valued and the act of human labor. The connection described by Marx between the productive effort of labor and the process of pr oduction itself is at once both simple and complex. When reduced to basic terms, labor is merely a unit description of the work expended to create a product, using that which is termed the “mean s of production” in order to achieve product creation according to the “mode of production.” The “means” refers to the inputs utilized for production, such as raw materials and knowledge, and the “mode” refers to the organized process of production itself (11, 106). From this base, Marx describes how societal organization to facil itate the mode of production le d to society being defined in terms of both its economic base and its ove rarching superstructure, where the latter

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24 comprises all cultural aspects of the soci ety under consideration (107). Marx concludes that the continuing struggle for control over the means of produc tion and the use and exchange of the resulting products eventual ly shapes society through ascribing greater social and economic power a higher “class” to those who emerge successful from the conflict. Having proposed this impact of prod uction-based societal organization upon societal superstructure, Marx examines the implications of capitalism by analyzing the economic and sociopolitical factors affected th rough the exploitation of the worker by the owner of the means of production (107). He be gins by defining the basic usefulness of a product a “commodity” as a “use-value,” which consis ted of the physical properties attributable to that commodity, and then pro ceeds to describe how a system of commerce in which one commodity was traded for another was structured according to “exchangevalue,” which is the assignation of relative worth between commodities. Exchange-value does not correspond to a one-to-one relati onship between use-values; rather, the difference between use-value and exchange-value is ascribed to a th ird type of value, which is considered as having been a dded to a commodity through the production associated with creating the commodity. This third value is the valu e of the labor (11). Structural violence is inherent in a cap italist system by virtue of the established separation between the worker and the means of production. In this division, the worker’s labor itself becomes the object of value instead of the commodity, and is traded for wages instead of for products (106). This reification of the worker into an object that exists as part of the production process, rather than the producer of the commodity, alienates the worker not only from the process but also from the perception of the worker as an

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i n e x o r w p r t h c o w h c o s e a c b br n dividual wi x ploitation b r profit, is n o w ho owns th e r ocess an d Appli h at the HITb o nsidered a n w orker, healt h ealth comm u o mmodities e paration of c cess to the e overcome r idge the ga p th intrinsic p b y the additi o o t returned d e means of p d who pays t h cation of M a b ased infras t n alogous to a h informati c u nications a n The digital the worker f commodity if the patie n p or, in ot h Fi g ure p ower (107) o n of surpl u d irectly to t h p roduction h e wages fo r a rxist theor e t ructure of t h a capitalist e c s applicatio n n d the deliv e divide then f rom the m e produced. T n t-worker ob h er words, a c 2: The Di gi Alienation u s value to c o h e worker, b u the technic a r the labor. e tical princi p h e current A e xchange. In n s are the m e ry of healt h becomes re p e ans of prod u T he resultant tains access c hieves ow n i tal Divide a provides th e o mmodity p r u t instead is a l resources p les to the q u A merican he a this constr u m eans of pro d h informatio n p resentative u ction and s u implicatio n to the addit i n ership of th e a s a Marxi s e opportunit y r oducts. Thi retained by necessary f o u estion at h a a lth care sys u ct, the patie d uction, and n are the eq u of the capi t u bsequently n then, is th a ional value r e technolog i s t Construc t y for s surplus va l the capitali s o r the produ c a nd demonst r tem can be nt represent s the conduc t u ivalent of o t alist-induce d from direct a t the divide r equired to i cal means. t 25 l ue, s t c tion r ates s the t of o utput d can

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26 Antonio Gramsci: Cultural Hegemony However, bridging the digital divide in th e construct described previously is not as simple as providing the pa tient-worker with the technol ogical means to the end, as indicated by the resistance en countered among nearly half of Internet nonusers to the FCC’s affordable access to reliable broadband service initiative. To better understand this seemingly counterintuitive reaction, it becomes useful to examine the proposed construct through the lens of cultural hegemony as formulated by Antonio Gramsci, whose Marxist viewpoint was informed by the German idealist philosophy of Georg Hegel and associated concepts of the importance of perception in defining meaning. Gramsci’s cultural hegemony takes into ac count the impact of subjec tive contextual influences imposed upon patients in additi on to more objective effects. Hegemony itself can be broadly defined as the dominant influence of one political or social group over another. The concept of cultural hegemony as proposed by Gramsci refers to the hegemonic domi nation not by physical force alon e, but by a specific set of ideas and beliefs that are so internalized by those who exist under its influence that they become incapable of realizing its presence or consequent effect upon both behavior and thought (107, 108). In his Prison Notebooks Gramsci identifies the creators of preeminent ideology as the capitalist entrep reneurs—the social ruling class of the intellectual elite—in possessi on of technical capacity (12) Gramsci argues that the objective fact of the exploited position of the wo rker in a capitalist so ciety is due in large part to the “spontaneous consent” (12, 108) yielded by workers themselves to the dominant group. This consent stems from a subjective perception of powerlessness among the working class, concomitant with an acceptance of the ideological and social

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27 authority held by the capitalist entreprene urs. This culturally hegemonic belief perpetuates the continuation of exploitati on by the intellectual elite maintaining the dominant ideology. In the case of resistan ce to the provision of broadband access, those without broadband are similar to members of th e working class; their existence is one of accepted powerlessness, and they have been conditioned to resist structural change. Gramsci contends that this state of affairs is not an inflexible one, but instead that the common understanding of the working class can be altered through a “war of position” (109) where ideas are generated among the intellectual elite and disseminated to the masses through education as the means of preparing and developing intellectuals (12). The eventual result would be a more egal itarian social stru cture achieved through equitable distribution of the power conferred by knowledge. A lthough he neglects to fully take into account the effort necessary to overcome the inertial weight of ingrained structural influence when attempting to achieve social change in complex systems, the relationship between knowledge and power has since been further confirmed to be of unquestionable import in such endeavors, and thus becomes a significant consideration when seeking to cross the digital divide. Michel Foucault: Knowledge and Power The complex interdependent relationsh ip between power and knowledge most applicable within the context of the health care setting is well ar ticulated by Michel Foucault. Foucault initially de scribes his conceptualization of power-knowledge relations in Discipline and Punish: The Birth of the Prison where he states that power produces knowledge and that power and knowledge are so directly related that the one implies the other; therefore, no knowledge exists whic h does not both assume and establish power,

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28 and no power exists without th e correlating presence of knowle dge (110). The patients in a health care system are subject to the F oucauldian concept of discipline achieved through observation, examination and normalizing judgment, in which disciplinary control is exerted for individua ls who fail to meet standards defined as societal norms, such as established acceptable levels of performance on quality indicators used as proxy measures for health status or chronic diseas e control. This is a clear example of the power-knowledge dyad, in that knowledge of a pa tientÂ’s health status then promotes the use of power to adjust behavior perceived as faulty in maintaining good health or to pursue treatment (111). In later interviews, Foucault admitted that his interest in and examination of the interwoven effects of knowledge and power was born in part from his more archaeological, historical work first on madne ss and psychiatry, then on clinical medicine in the eighteenth century, both of which he described as having solid scientific frameworks but at the same time being enmesh ed in social structures (16), such that questions of the relationship between scientific knowledge in each field and the power conferred upon practitioners by the social systems became of significant concern. The same relationships and same questions pers ist today in the cons truct under current examination, where the knowledge of health in formation confers on the patient a degree of power which has the potential to be used constructively in the patient-centered care model. Patients can work in consultation with providers to achiev e better care through a shared decision-making approach. Conversel y, lack of health information and its associated health knowledge resulting from in sufficient access to and ability to use the technological infrastructure of consumer health informatics represents the conflict

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29 paradigm inherent to the exis ting societal superstructure, wh ich results in a lesser degree of power available to those e xperiencing the digital divide. It is at this point in the theoretical discussion that it beco mes easier to recognize that the disparity connoted by the digital divide although significant, is not necessarily an insurmountable challenge. In addition to the Marxian solution of achieving power through ownership of the technological m eans of production and Gramsci’s suggested approach to overcoming the deleterious effects of cultural hegemony through the knowledge granted by education, Foucault desc ribes an “economy of power,” (16) in which the effects of power relations are circulated throughout an entire social body through social production and social servic e generated by the bodies and actions of individuals established within a network (17). This concep t suggests that disseminating power-knowledge to the currently disempow ered through interaction within social networks offers a potential solution to the problem of the digital divide. Diffusion of Innovations Based on the preceding work of social scie ntists such as Gabriel Tarde and Georg Simmel, DoI theory as formulated and described by Everett Rogers has long and successfully been used to eval uate the spread of technologi cal innovations, and thus holds promise for use in this construct to asse ss dissemination of the power-knowledge dyad through social networks of patients experien cing the digital divide where the dyad is represented by the innovation of consumer health informatics technology. Diffusion refers to the process by which communicati on takes place through channels over time among members of a social system in order to spread awareness and use of an innovation

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30 (24). Innovation adoption takes place in th e following five-stage innovation-decision process: 1.) Knowledge The potential adopter becomes aware of the innovationÂ’s existence and information a bout its use and potential. 2.) Persuasion The potential adopter devel ops a positive perception of the innovation and discusses the innovation with others. 3.) Decision The potential adopter seeks out a dditional information and forms an intent to try the innovation. 4.) Implementation The potential adopter begins to use the innovation on a trial basis. 5.) Confirmation The adopter, having recognized the benefit of the innovation, integrates the innovation into regular routine. An innovationÂ’s rate of adoption can be predicted base d on individualsÂ’ perceptions of its 5 key attr ibutes as determined during the innovation-decision process (24, 112). Table 1 presents the 5 attributes of innovations as potential ly applied to HIT. Table 1: Attributes of Innovations in Terms of HIT Attribute Definition Sample Patient Concerns Relative Advantage How much better an innovation is in comparison to its predecessor idea Is HIT really better than memory, writing things down on paper, or other recordkeeping methods? Compatibility How consistent an innovation is with established values, needs, and experiences Will HIT allow me to do the things I want and need to do? Will it keep my health information safe and private? Complexity How difficult an innovation is to understand and use How hard is HIT to use? Will I have to spend a lot of time learning how to use HIT and trying to figure out how to make it work for me? Will this lessen time I can spend with my h ealth care provider? Trialability How much an innovation can be experimented with on a limited basis Can I test HIT before I commit to buying and using it? For how long/how thoroughly? What if I donÂ’t like it? Observability The results of an innovation as visible to/perceived by other potential adopters What do other patients like me think about HIT?

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s a c u a p w a d d I n E E L L F Just a a me. When a u mulative a d p proaches n o w ith provide r d opters can b i stribution ( 2 Category n novato r E arly Adopte r E arly Majorit y L ate Majority L aggards F i g ure 3: T h of Simon a edition of D Roger s s not all inn o a successful d opters ove r o rmality; (2 4 r s’ adoption b e categori z 2 4). Tab l First to a Respect e y Adopts w Skeptic a May be adoptio n h e Diffusion a nd Schuste r D iffusion of I s Copyright o vations are innovation’ r time, a sig n 4 ) this has b of EMR tec h z ed into 5 di s l e 2: Classi fi a dopt; regar d e d by peers; o w ith careful d a l adopters; m suspicious o f n ; often relat e of Innovat i Publishing I nnovations, 1962, 19 7 created eq u s rate of ad o n ature S-sha p een shown t h nology (11 s tinct group s fi cation of I n D ed as “ventu r o pinion lead e d eliberation, j m ay often be m f change and c e d to precario u i ons Distri b Group from 5th Edition. 7 1, 1983 by u al, not all p o o ption is ex a p ed curve a p t o hold true w 3). As sho w s based on w n novation A D escription r esome” or e x e rs and role m j ust before th m otivated by change agen t us economic b ution Cur v m Figure 7.3, Copyright the Free Pr e o tential ado p a mined in te r p pears, whi c w ith respect w n in Table 2 w here they f a A dopters x perimental i n m odels e average ad o peer or syste m t s; characteri s posi t ion v e. Reprinte d p 281, fro m 1995, 200 3 e ss. All righ t p ters are the r ms of c h over time to HIT, suc 2 and Figure a ll along the n nature o pte r m ic pressure s tically delay e d with perm i m the Free Pr e 3 by Everet t t s reserved. 31 h as 3, e d i ssion e ss t M.

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32 Although the diffusion process takes place with in a social system, an association between socioeconomic status and adopter ca tegory has been observed due to adoption across social strata. Innovators and early adopters are far more likely to have greater access to the resources necessary to comp lete the innovation-decision process than adopters who fall into one of the later-stage ca tegories. In fact, thos e who are most likely to be affected by the digital divide are also most likely to fall into the “laggard” adopter category. This same group is also more likely to discontinue use of an innovation after having adopted it, often because of di ssatisfaction with the innovation (24). Foucault’s economy of power is present within the DoI theoretical context as well, in that the adoption of an innovation within a social system tends to increase the gap between earlier, more socioeconomically ad vantaged adopters and later-stage, lowerresourced groups. The current adoption of c onsumer health informatics technology by groups with more education and socio economic resources (power-knowledge) as compared to the impact of the digital divide among the “laggards” is an example of this unintended gap-widening eff ect. One known approach for closing the gap is to use targeted channels and tailored, appropriat e methods and messages to promote diffusion. This study will attempt to identify channels a nd technological methods that could help to bridge the digital divide by not only identifying use patt erns and preferences among technology users, but also by assessing reasons for nonuse among patients who choose not to engage with consumer health informatics solutions. One of the limitations of using DoI theory in applied research is the presence of a pro-innovation bias. A frequent underlying assumption in diffu sion research is that the innovation in question will bring sufficient va lue to the new adopters to be worthy of

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33 diffusion, and thus that the innovation shoul d not be avoided (24). Similarly, it is assumed that innovation diffusion is possible be ginning from the point of availability and solely based on the potential adopter’s interest and desire; it is far less clear how the innovation-decision process applie s when the innovation is extant and interest is present, but resources to obtain the innovation are th emselves a limiting fact or to adoption. This study intends to address both of these limitations in DoI theory by using it as the framework for assessing barriers to adop tion and anti-innovation sentiment toward consumer health informatics technology, a nd examining the dissemination and uptake of consumer health informatics technology as an innovation within a single-stratum lowsocioeconomic population rather th an across socioeconomic strata. The concept of a technology “hype cycle, ” as first characterized by technology analyst group Gartner, Inc., in 1995, (114) represents the progression of new technology through 5 phases from initial development through to stability and general acceptance, as shown in Table 3 and Figure 4 (114): Table 3: Five Phases of the Gartner Hype Cycle Phase Description Technology Trigger Initial innova tion development; breakthrough and announcement at the proof of concept or early development stage. Peak of Inflated Expectations Initial publicity and/or pilot resu lts lead to rapid trialing and growth among innovators and early adopters. Trough of Disillusionment Innovation is aba ndoned by those who find insufficient value in it; core technology producers stabilize, while others leave the market. Slope of Enlightenment Next-generation pr oducts and iterations of the technology innovation are refined, leading to increased use by nonabandoning early adopters and additional growth among later adopters. Plateau of Productivity Innovation stabilizes in to a mainstream technology with low perceived risk and widespread adoption.

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F r e d c h b a v c o t h c o a m h e x F i g ure 4: G a b y Gartner e search doc u d oes not end o does not a d Gartner res e and shoul express merch a Tech n h aracteristi c arrier to ent r v ailability o f o ncept of th e h e innovatio n o nsidered a m ong mem b as achieved x pecte d dif fu a rtner H y p e Inc., 2012. u ment and s h o rse any ve n d vise techno l e arch public a d not b e co n ed or implie a ntability or f n ology that r ally is acco m r y. When co f a technolo g e innovatio n n alone, suc h l imiting fac t b ers of a sin g the plateau o fu sion curve. e C y cle. Re p This graphi h ould be ev a n do r produ c l ogy users t o a tions consi s n strued as st a d, with resp e f itness for a r eaches the m m panied by r nsidered fr o g y innovati o n -decision p r h that econ o t or. This stu d g le socioeco n o f producti v p rinted fro m c was publi s a luated in th e c t or service d o select only s t of the opi n a tements of f e ct to this r e particular p u m ainstream a r educed cos t m a DoI the o n that has a c r ocess as de p o mic barrier s d y propose d n omic class v ity in the la r m “Hype Cy c s hed by Gar t e context of depicted in i those vend o n ions of Ga r fact. Gartne r e search, incl u u rpose. Rep a nd the pro d t -to-adopt a n e oretical per s c hieved the p endent on p s to adoptio n d that analys i stratum for r ger populat i c le Research t ner, Inc. as the entire d o i ts research p o rs with the r tner's resea r r disclaims a u ding any w rinted with p d uctivity pla t n d a conseq u s pective, th e plateau pha s p erceived ut i n may no lo n i s of diffusi o a technolog y i on will de m Methodolo g part of a lar g o cument. G a p ublication s highest rati n r ch organiz a a ll warranti e w arranties of p ermission. t eau phase u ently lowe r e increased s e supports t i lity and val u n ger be o n patterns y innovatio n m onstrate th e 34 g ies” g er a rtner s and n gs. a tion e s, r t he u e in n that e

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35 CHAPTER IV RESEARCH DESIGN AND METHODS Overview This cross-sectional study was designed to measure the variables of interest through a self-administered, mixed-mode su rvey conducted at a single point in time among 3 risk-stratified groups of adult patients, randomly sel ected from within a larger population of adult patients who receive prim ary health care in an urban safety net setting. A multiphase mixed methods approach i nvolving both sequential and concurrent elements was employed for survey developmen t and data collection (115). An integrated approach to mixing quantitative and qualitati ve data (116) was used in analysis to maximize the generalizability of results wh ile also providing in-depth insight into patientsÂ’ general opinions of and engagement with IT and patterns of behavior related to IT use in general, for health informati on access, and for health communications. The study was approved by the Colorado Multiple Institutional Review Board (COMIRB) as protocol #12-1099. Study Population Denver Health and Hospital Authority (DH) is an integrated urban safety net health system whose components include a 477-bed hospital and 8 primary care clinics that are all FQHCs. The DH system provides hea lth care services to 25% of residents in the city and county of Denver, Colorado. In 2010, the DH system recorded more than 600,000 outpatient visits, including 335,000 pr imary care visits, among more than 160,000 patients.

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36 A majority of DH patients are members of pr iority populations, in particular racial and ethnic minorities, the uninsured, a nd those living below the poverty line. Approximately 65% of DH patients are belo w 185% of the federal poverty level, and more than 50% of DH patients are uninsured. Table 4 presents demographics of the DH primary care population at the time of the study. Table 4: DH Primary Care Pati ent Demographics, January 7, 2013 Patients (N = 116,999) Percent (%) Age < 1853,759 45.95 18–2917,231 14.73 30–3913,331 11.39 40–4910,746 9.18 50–5910,813 9.24 60–697,027 6.01 70–762,287 1.95 > 761,805 1.54 Race/Ethnicity White47,384 40.50 Black17,069 14.59 Hispanic/Latino34,354 29.36 Asian3,648 3.12 Other/Unknown14,544 12.43 Gender Male47,367 40.48 Female69,632 59.52 Inclusion and Exclusion Criteria Patients were included in the study populati on if they were enrolled in primary care at DH, spoke either English or Spanish as their primary language, and were adults between the ages of 18 and 76. Enrollment in primary care at DH is defined as those patients who have had at least 2 clinic visits in the DH syst em in the previous 18 months. Age criteria were based on Health Resour ces and Services Administration Diabetes Collaborative guidelines for diabetes management.

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37 Patients were excluded from the sampling frame for the study population if they did not have both a mailing address and telephon e number on record. No exclusions were made based on gender, race/ethnicity, or soci oeconomic status. Figure 5 presents a flow diagram illustrating inclusion and exclusion criteria. Figure 5: Inclusion/Exclusion Criteria Flow Diagram Risk Stratification Patients in the sampling frame for the st udy population were risk stratified into risk groups, or “tiers,” according to a process and algorithm developed by DH and used

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38 under its 21st Century Care model (117, 118) to tailor health care de livery to patients according to level of need. Of the four possible tiers (1 – low risk; 2 – medium risk; 3 – high risk; 4 – very high risk), stratification for this study a ssigned patients to tiers 1–3 only; patients that would otherwise have been identified as tier 4 were included with tier 3 due to a small sample size limitation identified in the first attempt at classifying the study population. Version 1.0 of the tiering algo rithm was used to risk stratify the population for this study; howev er, the tiering process continues to evolve based on iterative evaluation and crit eria refinement. Tier assignment under the version 1.0 algorithm is based on a combination of clinical criteria and a patient’s Chronic Illness and Disability Payment System (CDPS) risk ad justment score (119). CDPS was developed to support the use of ICD-9-CM diagnosis-based burden of illness assessments to estimate future health expenditures and adjust state reimbursements for Medicaid populations, and compares well to other models used among Medicaid benefici aries (120, 121). As such, it is well-suited for use in analyses involving the safety net populati on served by DH, which is predominantly composed of Medicaid-qu alified patients and uni nsured patients who will soon qualify for Medicaid coverage under the Affordable Care Act. Clinical criteria used for tier assignmen t were established diagnoses of either diabetes (DM) or hypertension (HTN) combined with degree of chronic disease control as defined by a patient’s most recent indi cator laboratory values for glycosylated hemoglobin (A1c) measurements of averag e blood glucose over time, low-density lipoprotein (LDL) measurements of cholesterol le vels, and blood pressure (BP) tests. Tier assignment was made first acco rding to clinical criteria, then secondarily by CDPS score in the absence of defining clin ical criteria for assignment as shown in Table 5 (122).

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39 Table 5: DH Tiering Algorit hm Assignment, Version 1.0 Tier (Risk Group) Patients (%) (N=55,225) Clinical Criteria CDPS Criteria 3 (High) 2,525 (4.57%) Diagnosis of DM and most recent A1c > 10; or most recent systolic BP >= 160; or most recent diastolic BP >= 100 Risk score >= 7.025 2 (Medium) 33,484 (60.63%) Diagnosis of DM and no recent A1c; or diagnosis of DM and no recent LDL; or diagnosis of DM and most recent LDL >= 100; or most recent A1c >= 8 but < 10; or most recent diastolic BP >= 90 but < 100; or most recent systolic BP >= 140 but < 160; or diagnoses of both DM and HTN Risk score >= 0.243932609 but < 7.025 1 (Low) 19,216 (34.80%) No specific clinical criteria Risk score < 0.243932609 Survey Procedures A survey instrument was created for this study to evaluate patterns of behavior related to IT use in general and for health information access and health communications; interest in and familiarity with specific IT modalities and activities in general and for health information access and health comm unications; and social, behavioral, and systemic factors that influen ce IT adoption a nd utilization. Survey Design To maximize generalizability of results in comparison to national data, survey items were drawn in part from existing instruments such as AHRQÂ’s Consumer Assessment of Health Providers and Syst ems (CAHPS) item sets (123), the CDCÂ’s Health Related Quality of Life (HRQOL) H ealthy Days core measures (124), and the Pew Research CenterÂ’s Internet & Ameri can Life Project surveys (125). Additional survey items developed by the investigator we re added to explore topic areas of interest not adequately addressed by existing instrume nts. Open-ended elements were included within questions as options in fixed-choi ce item sets where appropriate to support

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40 identification of additional choices not otherw ise addressed in the fixed set. Open-ended topical and general questions we re included in addition to fi xed-choice questions in order to elicit unstructured responses and potential ly identify emergent themes that might not otherwise be captured. To promote readability, survey item language was adapted where necessary to conform to a sixth-grade lite racy level according to the Flesch-Kincaid Grade Level formula (126, 127), given below in Equation 1: Equation 1: Flesch-Kincaid Grade Level Readability Formula GL = .39 (Average Sentence Length) + 11.8 (Syllables/Word) – 15.59 where: Average Sentence Length = (Total # Words / Total # Sentences) Syllables/Word = (Total # Words / Total # Syllables) The layout of the survey was structured according to basic visual design principles of proximity, alignment, repetiti on, and contrast (128), with attention also given to creating a common layout that woul d perform well in both paper and electronic versions and which would reduce cognitive lo ad for respondents and provide specific visual cues and aids to guide successful survey completion (129). Response options for each item were grouped in close proximity w ith each other and with the use of specific graphic elements and both vertical and hor izontal negative space to distinguish each subgroup from another, in order to promote answer selection for each item and reduce question omission (129). Items were ali gned in balance with each other and symmetrically around the optical center of each survey page. Elements such as page headers and number blocks, hor izontal lines used to divide instructional text from

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41 question text, vertical lines used to divide survey items into columns and subtly indicate flow, and stop-sign and arrow graphics used to indicate skip logic and pagination were repeated from page to page and section to section to promote uniformity of experience and pattern recognition among respondents. Sans-s erif fonts were used in both paper and electronic formats, with differential text size and bold, italic, and underlining text elements used to add visual interest and provide emphasis where desired. A consistent color palette was used throughout, with redgreen-blue and hue-satu ration values drawn from DH marketing materials to conform with anticipated patient expectations. Although the paper version of the survey was printed in grayscale, th e color palette used supported shading emphasis and distinction between elemen ts that were comparable to a full-color version, without loss of visual information. A storyboarding pr ocess was used to craft and iteratively refine the informa tion flow from item to item a nd page to page, with question order structured according to guidelines for creating conversational, logically ordered surveys (129). An instructional design a dvisor reviewed both dr aft and final layout versions for clarity (130). Survey items, instructions for comp leting the instrument, and contextual explanatory language were tran slated from English to Sp anish by a certified medical interpreter. Both draft and fi nal versions of the survey were approved by COMIRB prior to administration. The final print version of the survey is included as Appendix A. Survey Pilot Test Focused Group Discussion The draft survey was piloted in a focus group of a homogenously sampled (131) group of individuals familiar with a wide range of in formation technologies and technologically focused activiti es. A total of 10 participants were recruited from among

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42 members of an online community focused on collaborative, role-based interactive storytelling who were in a ttendance at an annual gath ering in Denver, Colorado. Participant demographics are presented in Table 6. Table 6: Survey Focus Group Demographics Demographic Participants Age 20–296 30–392 40–492 Gender Female9 Male1 Race/Ethnicity* White, Hispanic or Latino1 White, non-Hispanic or Latino 9 Geographic Location† Division 11 Division 23 Division 52 Division 61 Division 81 Division 92 As defined under Office of Management and Budget (OMB) Directive 15 (132). † As defined by the United States Census Bureau (133). Pilot administration of th e survey followed by unstructured group discussion was held over 1 hour on January 20, 2013, with the pr incipal investigator serving as survey timer and group facilitator. Average time fo r survey completion was calculated at 12 minutes and 16 seconds, with a median value of 12 minutes and 6 s econds. Qualitative data collected included written comments pr ovided on participants’ copies of survey instruments, written notes of observations made by the principal investigator, and an audio recording of group discussion that was transcribed by the prin cipal investigator. Data were subjected to content analysis us ing an inductive coding process interspersed with marginal remarks (134) to allow id entification of emergent themes without

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43 predefinition. Subjects discussed were classi fied as themes based on the agreement of multiple participants with a single concept. Themes identified during analysis and incorporated into survey refine ments are presented in Table 7. Table 7: Survey Pilot Test Emergent Themes by Content Area Content Area Themes Layout and Flow Use written instructions in addition to visual indicator elements Place skip logic instructions at th e top of pages, not at the bottom Use different, easily reco gnized symbols for proceed versus skip (eg, arrow and stop sign) Use “SKIP” instead of “TURN” when indicating movement past a page rather than to the next page Use “if you don’t” language in questions immediately following skip logic in order to reiterate/confirm the skip Use different color and/or shading to “make your eyes be not as lazy”; several specific recommendations Open-ended questions about other things [you] do with computers and cell phones needs to be on the same page with the list of activities; flipping back and forth is annoying Include definitions and/or introductory explanations at the start of each section, not just at the beginning of the survey Error correction: spelling, numbering, other miscellaneous typographical and editorial recommendations Wording and Language, General Reconcile similar question wording between sections to be identical rather than differential, and use text element emphasis to draw attention to the focus of a particular section instead Inclusivity recommendations: active vo ice, specific use of “people” not “patients” throughout, less formal language where possible (eg, “lessons on” versus “education/training on”) Add clear definitions of meaning an d specific examples in context for broadly-encompassing terms or conc epts (eg, “health,” “technology,” “use,” “regular,” “health informa tion,” “health communication,” “talk to someone about health or health care”) Recommendation for considering “tried but couldn’t” in context for use-related questions Wording and Language, QuestionSpecific Clarifications on the list of IT activities: don’t use term “social media,” use specific well-known examples of sites/services to provide context for any categorical group (eg, photo sites), add “talk on phone” or similar for voice calls to the list Clarifications on the list of health information activities: add health insurance, information about doctors, symptoms, use “family planning” in addition to/instead of “birth control” Clarification on the list of health communication contacts: “religious” leader versus “church” leader

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44 Sampling Methodology The number of completed surveys required to represent generalizable results for a population at a 95% confidence level and with a 5% margin of error was determined by probability sampling (129) as given in Equation 2: Equation 2: Probability Sampling – Sample Size Formula Ns = (Np)( p )(1 p ) (Np 1)( B / C)2 + ( p )(1 p ) where: Ns = required number of completed surv eys (completed sample size needed); Np = total population size; p = proportion of population expected to choose one of two response categories (calculation based on two categories in order to maximize heterogeneity); B = margin of error C = Z score associated with th e desired confidence level When calculated for the study population (N =57,030) with a 5% margin of error and a 95% confidence level: Ns = (57030)(.5)( 5) (57030 1)(0.05/ 1.96)2 + (.5)(.5) Ns = 382 Therefore, 382 was considered the mini mum completed sample size necessary to be considered representative of the study population. An oversampling approach was taken to allow for all-cau se nonresponse among intended survey recipients (eg, active refusal, passive refusal, survey not received) yet still achieve the necessary completed sample si ze to ensure generalizability. Each patient in the sampling frame was assigned a random seed generated by SAS software (Cary, NC; version 9.3). Patients were ranked in numeric order by seed, and the first 650

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45 patients in each risk tier were selected in order to minimize samp ling error. A total of 1,950 targeted recipients were selected fo r survey distribution. Each recipient was assigned a unique survey identifier (ID), wh ich was cross-referenced to the patientÂ’s unique DH medical record number and securely maintained in an electronic master file. Survey Implementation Survey implementation was conducted based on DillmanÂ’s tailored design method for mixed-mode surveys (129), and had 3 discrete phases: initial invita tion to participate, survey distribution, and follow-up reminder prior to survey closure. Table 8 describes the overall timeframe for implementation. Table 8: Survey Implem entation Timeframe Description Date Invitation letter postal mailing February 12, 2013 Survey postal mailing, English March 5, 2013 Activation of online survey, English March 5, 2013 Survey postal mailing, Spanish March 6, 2013 Activation of online survey, Spanish March 6, 2013 Survey open/data collection start date* March 8, 2013 Postcard reminder postal mailing March 27, 2013 Deactivation of online survey, English April 19, 2013 Deactivation of online survey, Spanish April 19, 2013 Survey closure/data collection end date April 19, 2013 Based on estimated postal mail delivery times The initial invitation to pa rticipate was extended as a letter sent by postal mail, approved by COMIRB prior to distribution. The letter was written to include both informed consent for research language and sp ecific social excha nge elements intended to establish trust, increase the perceived be nefits of response, a nd reduce the perceived costs of response (129). The letter contained information about the survey itself and how the recipient was selected, contact informa tion for the principal investigator and for COMIRB, assurance that survey completion wo uld incur neither cost nor obligation and

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46 that privacy would be maintained if at al l possible, and an extension of thanks for consideration. The invitation letter was prin ted on DH letterhead in either English or Spanish, signed by the principal investigator, and individually addressed to the survey recipient by name both in the letter itself a nd on the address label. Names and addresses were automatically generated from a research database created for the study (Microsoft Access, Redmond WA, v. 11.8), populated with information obtained from the DH data warehouse. The label design included the DH logo and a color bar across the top with the return address. Letters were sent in sta ndard #10 envelopes. Sample copies of the invitation letter and address la bel are included as Appendix B. The survey package was sent to recipien ts 3 weeks following th e initial invitation. It was mailed in a standard white 10” 13” flat envelope with printed address labels of the same design as that used for the inv itation mailing. The survey package included a printed version of the survey in either Eng lish or Spanish, a pre-ad dressed standard white 9” 12” flat return envelope with postage a ffixed, and a $2 cash incentive. The cover page of each survey was designed to include informational and social exchange elements to encourage survey completion. Elements incl uded the survey title, a reminder about the purpose of the survey and how the recipient had been select ed, instructions for either completing and returning the survey in the enclosed envelope or alternately for completing the survey online at a specified SurveyMonkey web address, the recipient’s unique survey ID handwritten in blue i nk for personalization pur poses, acknowledgment of the importance of the recipient’s opinions, mention of the cash incentive in the context of thanks extended, the princi pal investigator’s name and COMIRB protocol number, and the DH logo. The return envelope included a label with struct ural design similar to those

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47 used in the outbound mailings, but printed without color to aid recipients in distinguishing between envelope s. The return label also included the survey ID as a printed element in order to reduce the chan ces of unlabeled, “orphan” surveys being returned. No identifying information other th an the survey ID was included or requested either on the survey itself or on the return envelope. Postage was affixed in the form of physical stamps with colorful designs rather than through preprinted permit or the use of business reply envelopes; this method has b een shown to both improve response rates and post office processing speed (129). The surv ey package was assembled with careful attention given to how its presentation shoul d appear to the reci pient: the survey was nested cover-up against the flap of the re sponse envelope, and the cash incentive was tucked between the flap and the survey itself in order to improve both the visual impact of the whole and the likelihood of the incentive not being left in the mailing envelope. Electronic versions of the survey in Eng lish and Spanish were activated online the same day as the postal mailings. Online surveys were securely fielded through SurveyMonkey, with standard HTML5 tags applied within the SurveyMonkey custom page options to create text design elements, graphic elements, page layouts, and skip logic that mimicked the print version of the survey as closely as possible. Online respondents were required to en ter their unique survey ID to complete the online survey. The follow-up reminder was offered to non -responding survey recipients as a postcard sent by postal mail 3 weeks followi ng the survey mailing. The postcard was designed in compliance with United States Postal Service (USPS) specifications for dimensions, weight, and automation-compatible layout (135), printed in black ink on blue card stock, and sent in both English and Spanis h. It included many of the social exchange

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48 elements previously used in the invitation le tter and survey packag e, with variation in visual design used to catch attention and promote ease of cognitive processing. The postcard also introduced new so cial exchange elements th rough specific description of the value of the information requested and encouragement to recipients to call the principal investigator for an additional survey if the previous copy had been misplaced. A sample copy of the reminder postcard is included as Appendix C. An option to extend the survey period to allow for telephone call follow-up with recipients who were continued nonresponde rs after the reminder postcard mailing was included in the implementation design. This co ntact method was to be used if response rates were insufficient to support meaningf ul data analysis, but proved unnecessary. Data Collection and Management A survey codebook was created from the final version of the print survey according to Inter-university Consortium fo r Political and Social Research (ICPSR) guidelines for codebooks (136). Variable deta ils including name, label, question text, answer choices, and numeric codes associated with each choice were entered in structured fashion, along with data entry guidelines such as skip logic, identification of key questions, specific coding instructions in case of multiple answer entry or other nonstandard cases, and additional notes where ap plicable. A copy of the final codebook is included as Appendix D. Based on the survey codebook, relational tabl es, queries, and a custom data entry form were created within the research data base to support survey data collection. The data entry form was designed to store th e numeric code for each question in the underlying table, but displayed the full wording of answer choices within the form itself

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49 to minimize data entry errors. The form was also designed using dropdown boxes and limit-to-list functionality fo r each question, such that typographical mistakes would generate an error message duri ng the data entry process rath er than accepting incorrect data. Both fixed-choice and open-choice data collection were supported by the data entry form, with free-text responses entered as written. Memo fi eld design was used to underly free-text response data entry to avoid character limitations as sociated with text fields. Coder notes associated with free-text respons es were entered in br ackets to distinguish them from respondent data. Responses received in Spanish were entered in Spanish; the Spanish responses were translated by a certifie d medical interpreter, with the subsequent English versions entered following the Sp anish version of each response in the respondentÂ’s record. The survey ID was used as the unique identifier, and the associated field was designated as the nonduplicated pr imary key for the data entry form and underlying table both, with the result that an error message was generated when an attempt to enter duplicate data was detected. Data collected within the research database could be exported to one of several standard formats for import and analysis with SAS or other analytical software as needed. The electronic surveys colle cted through SurveyMonkey we re stored securely in the SurveyMonkey system, with researcher-level account-based secure login access required to retrieve study data. No identifyi ng information other than the survey ID was collection from respondents; IP address tracki ng was disabled for this study. Data could be viewed within the SurveyMonkey sytem or exported securely to one of several standard formats for subsequent import to SAS or the research database.

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50 Data about the mailings themselves were also collected in tables within the research database. Records were kept of all responses received, incl uding dates of survey responses, specifics of USPS endorsements for mail returned to sender as undeliverable (137), records of active refusals and survey opt-out by patients, and records of patient questions received and answers returned ove r the course of the study period. Patient questions received in Spanish were answ ered in Spanish through the aid of DH interpreter services. Mail returned to sender was recorded in the research database, then handled according to a process established for returned mail treatment. Each envelope was opened and the contents were removed; letters and surveys were securely shredded, postage-paid return envelopes were stored in a file in th e secure research cabinet for later reclamation of stamps, and incentives returned were re deposited in the research account. Envelopes with the official USPS endorsement label att ached were forwarded to the Data Integrity division of DHÂ’s Department of Health Information Management for handling. Completed paper surveys and envelopes we re both marked with the dates of receipt and of data entry, then stored securely in a locked file cabinet. All research data will be maintained per Health Insurance Po rtability and Accountability Act regulations for a minimum of 7 years following study cl osure by COMIRB, after which it will be securely destroyed. Analysis Plan Measures of Interest Measures of interest for this study included the following, presented alphabetically by category:

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51 Barrier Measures (survey data) : computer, cell phone, and Internet use barriers; health information access barriers ; health communication barriers Demographic Measures (DH clinical systems) : age, gender, race, language Facilitator Measures (survey data) : computer, cell phone, and Internet use facilitators; health information access facil itators; health communication facilitators Health Status Measures (survey data, DH clinical systems) : CDC “Healthy Days” self-rated general health status unhealthy days in past 30 days, and mental health in past 30 days; DH risk stratific ation tier (version 1.0) IT-General Utilization Measures (survey data) : computer, cell phone, and Internet user status; computer, cell phone and Internet use device type; computer, cell phone, and Internet use duration; computer, cell phone, and Internet use frequency; computer, cell phone, and Internet use importance; computer and cell phone ownership; Internet access speed; IT activity type IT-Health Communications Measures (survey data) : health communication user status; health communication duration; h ealth communication frequency; health communication importance; hea lth communication contacts IT-Health Information Measures (survey data) : health information access user status; health information access duration; health information access frequency; health information access importance; he alth information access topics Opinion Measures (survey data) : Topics and themes emergent from participant responses to open-ended survey questions

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52 Univariate analyses were conducted for each of the measures of interest among all survey respondents. Results are describe d through summary presentation of frequency data, standard error, and variance for each measure. Specific Aim 1, Research Question 1 The first specific aim of the study was to assess and describe current methods and patterns of IT utilization for health in formation access and engagement in health communications among adult patients who recei ve care in an urban safety net setting. The first research question associated with this specific aim was to discern how patterns of IT utilization in general, IT utilization fo r health information access, and IT utilization for health communications might differ by demographic subgroup. Chi-square analyses were conducted to assess signi ficance for the measures of interest by age, gender, race/ethnicity, and primary language. Specific Aim 1, Research Question 2 The second research question associated w ith this specific aim was to assess how patterns of IT utilization in general, IT u tilization for health information access, and IT utilization for health comm unication may differ by health status. Regression analyses were conducted to assess significance for each of the measures of interest by self-rated general health status, unhealthy days in the pa st 30 days, and mental health status in the past 30 days. All analyses were adjusted for the effects of race/ethnicity, gender, age, and primary language. Specific Aim 1, Research Question 3 The third research question associated w ith this specific aim was to examine the impact of identified barriers and facilitators on IT general ut ilization, IT u tilization for

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53 health information access, and IT utilizat ion for health communication among selfidentified non-users. Regression analyses we re conducted to assess significance for each of the measures of interest, adjusting for race/ethnicity, gender, age, and patientsÂ’ primary language. Specific Aim 1, Research Question 4 The fourth research question associated with this specific aim was to determine what general opinions about IT and HIT are held among adult patients who receive care in an urban safety net setting. Free-text res ponses received to su rvey questions were subjected to content analysis using an anal ytic induction strategy in order to identify emergent themes and topics among responses th at may not have been otherwise assessed by closed-ended survey items. An open, he uristic coding process was used to identify keywords for the development of a code list, which was used to apply coding to free-text responses and identify recurring topics and them es. A topic was classified as a theme if a minimum of 5% of respondents to open-ended items identified it as a subject of interest. Results of the qualitative anal ysis were used to improve c ontextual understanding of the quantitative results from the current study. Specific Aim 2 The hypothesis for this specific aim assume d that adult patients with chronic disease who receive care in th e safety net and who use IT to access health information and engage in health communica tions were predicted to have better health status than adult patients with chronic disease who receiv e care in the safety net and who are IT nonusers.

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54 Multiple linear and logistic regression analyses were conducted to assess significance for health status measures among patients with chronic disease who use IT for health information access and health communications (intervention group) as compared to patients with chronic disease who do not use IT for health information access and health communications (control group). Chronic disease status was determined by risk group assignment to tier 2 or tier 3. Analyses were conducted across the population as a whole and within each risk tier. All analyses were adjusted by race/ethnicity, gender, age, and patientsÂ’ primary language. Specific Aim 3 The hypothesis for this specific aim assumed that members of priority populations have an interest in using IT to access health information and engage in health communications that is equiva lent to that reported among me mbers of more advantaged populations, but do not use the same types of IT in the same manner or to the same extent. The applicability of DoI theory within a single stratum of the larger social superstructure rather than across strata, as traditionally considered, was assessed by examining of the distribution of IT access duration resulting from the univariate analyses conducted for Specific Aim 1, both for each IT access type and among the study population as a whole. The expectation was that the distribution woul d align with the DoI adoption curve such that the 5 categories of DoI adopters could be clearly identified within the single stratum of the larger social superstructure represented by the members of priority populations pa rticipating in this study.

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55 CHAPTER V RESULTS Survey Response Over the 6-week survey period, responses were received for 829 of 1,950 surveyed individuals. Response types include d completed surveys, active refusals, and USPS returns of undeliverable mail. The vast majority of completed surveys were received by mail (n=393) versus online (n= 21) or by telephone (n=1). No response was received for 1,121 surveyed individuals. Res ponse status was classi fied according to American Association for Public Opinion Re search (AAPOR) final disposition codes for mail surveys of specifically named persons ( 138). All applicable response types are summarized in Figure 6 through a m odified CONSORT diagram (139). Figure 6: Survey Results: AAPOR Response Types

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56 Four standard survey outcome rates (138) were calculated from the final response disposition results: response rate, cooperati on rate, refusal rate, and contact rate. The selected standard response and cooperation rate formulas count partial completes as respondents. These formulas were chosen fr om the available opti ons because observation during the data entry process indicated that th e number of partial completes was very low and predicated in part on skip logic inst ructions. Standard refu sal and contact rate formulas were selected from the available options to maximize eligibility criteria assumptions among recipients, neither el iminating unknowns from the equation nor making estimates to characterize unknown data. The survey response rate was calculated according to Equation 3: Equation 3: Response Rate RR2 = (I + P) (I + P) + (R + NC + O) + (UH + UO) where: RR2 = response rate; I = number of complete surv eys received (code 1.1); P = number of partial surveys received (code 1.2); I + P = total number of surveys received (code 1.0 and subcodes); R = number of refusals and br eak-offs; (code 2.1 and subcodes); NC = number of non-contact s (code 2.20 and subcodes); O = other (code 2.30 and subcodes); R + NC + O = total number of “noninterviews” (code 2.0 and subcodes); UH = unknown if household/occupied hous ing unit (code 3.1 and subcodes); UO = unknown, other (codes 3.2, 3.3, 3.4, 3.9, and subcodes); UH + UO = total number of cases of unknow n eligibility (code 3.0 and subcodes)

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57 When calculated for the study population: RR2 = (415) (415) + (25) + (1510) RR2 = 21.28% The survey cooperation rate was cal culated according to Equation 4: Equation 4: Cooperation Rate COOP2 = (I + P) (I + P) + R + O where: COOP2 = cooperation rate; I = number of complete surv eys received (code 1.1); P = number of partial surveys received (code 1.2); I + P = total number of surveys received (code 1.0 and subcodes) R = number of refusals and br eak-offs (code 2.20 and subcodes); O = other (code 2.30 and subcodes); When calculated for the study population: COOP2 = (415) (415) + 18 + 7 COOP2 = 94.32%

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58 The refusal rate was calculat ed according to Equation 5: Equation 5: Refusal Rate REF1 = (R) (I + P) + (R + NC + O) + (UH + UO) where: REF1 = refusal rate; I = number of complete surv eys received (code 1.1); P = number of partial surveys received (code 1.2); I + P = total number of surveys received (code 1.0 and subcodes); R = number of refusals and br eak-offs; (code 2.1 and subcodes); NC = number of non-contact s (code 2.20 and subcodes); O = other (code 2.30 and subcodes); R + NC + O = total number of “noninterviews” (code 2.0 and subcodes); UH = unknown if household/occupied hous ing unit (code 3.1 and subcodes); UO = unknown, other (codes 3.2, 3.3, 3.4, 3.9, and subcodes); UH + UO = total number of cases of unknow n eligibility (code 3.0 and subcodes) When calculated for the study population: REF1 = (18) (415) + (25) + (1510) REF1 = 0.92% The survey contact rate was cal culated according to Equation 6: Equation 6: Contact Rate CON1 = (I + P) + R + O (I + P) + R + O + NC + (UH + UO) where: CON1 = contact rate; I = number of complete surv eys received (code 1.1);

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59 P = number of partial surveys received (code 1.2); I + P = total number of surveys received (code 1.0 and subcodes); R = number of refusals and br eak-offs; (code 2.1 and subcodes); NC = number of non-contact s (code 2.20 and subcodes); O = other (code 2.30 and subcodes); R + NC + O = total number of “noninterviews” (code 2.0 and subcodes); UH = unknown if household/occupied hous ing unit (code 3.1 and subcodes); UO = unknown, other (codes 3.2, 3.3, 3.4, 3.9, and subcodes); UH + UO = total number of cases of unknow n eligibility (code 3.0 and subcodes) When calculated for the study population: CON1 = (415) + 18 + 7 (415) + 18 + 7 + 0 + (1510) CON1 = 22.56% The number of completed surveys re ceived (N=415) exceeds the minimum completed sample size threshold (N=382) es tablished through probability sampling as necessary to be considered representative of the population (95% CI, 5% error). The overall response rate (21.28%), while low, is not unexpected based on declining survey response trends observed over decades (140) and remains comparable with response rates obtained for national public opinion surv eys; for example, typical Pew Research survey response rates range from 5-15% ( 141). More important, multiple analyses of nonresponse impact on survey validity have shown that results compared between identical surveys fielded according to sta ndard and rigorous methods for maximizing response are equally statistically valid ( 142, 143). Similar findings were reported in comparative analyses conducted for 3 statelevel, health-specific surveys (144). An examination of potential bias among nonresponde rs using community-level correlates in

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60 the state of Illinois identified higher nonrespons e in areas of urbanicity and concentrated areas of either high affluence or high pove rty (140); however, these effects can be presumed to be minimal in the context of this study’s survey, fielded both in an urban setting and among a single socioeconomic stratum. Table 9 shows the demographics of the sample-eligible study population, the survey sample, and survey respondents. Table 9: Survey Population Demographics, Unadjusted SurveyEligible (N=55,225) SurveyEligible (%) Random Sample (N=1,950) Random Sample (%) Survey Completed (N=415) Survey Completed (%) Age 18–29 16,114 29.18 426 21.85 52 12.53 30–39 12,129 21.96 384 19.69 70 16.87 40–49 9,589 17.36 339 17.38 86 20.72 50–59 9.352 16.93 448 22.97 108 26.02 60–69 6,206 11.24 277 14.21 81 19.52 70–76 1,835 3.32 76 3.90 18 4.34 Gender Female 38,202 69.18 1,264 64.82 277 66.75 Male 17,023 30.82 686 35.18 138 33.25 Race White 24,208 43.84 834 42.77 189 45.54 Latino 16,107 29.17 560 28.72 116 27.95 Black 8,250 14.94 343 17.59 62 14.94 Other/Unknown 6,660 12.06 213 10.92 48 11.56 Language English 39,641 71.78 1,411 72.36 309 74.46 Spanish 15,584 28.22 539 27.64 106 25.54 Tier / Risk Group Tier 1 / Low 19,216 34.80 650 33.33 142 34.22 Tier 2 / Medium 33,484 60.63 650 33.33 132 31.81 Tier 3 / High 2,525 4.57 650 33.33 141 33.98 Differences between population groups were assessed through chi-square analysis. Age was found to be significantly different between the eligible population and the sampled population (p<0.001), between th e eligible population and the respondent population (p<0.001), and between the sample d population and the respondent population

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( p b p <0.001). Fi etween resp o Fi g ur e gures 7 and o ndent and s e 7: A g e Hi s Fi g ur e 8 show hist o s ampled po p s to g ram an d e 8: Age QQ o gram, box, p ulations. d Box Distr i Q plot, Res p and quantil e i bution, Re s p ondent vs e -plotted ag e s pondent v s Sampled e distributio n s Sampled 61 n s

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62 Gender and race were found to be significan tly different between the eligible and sampled populations (p<0.0001; p=0.014), but no t between the eligib le and respondent populations; therefore, respondent s were considered representa tive of the population as a whole. Risk tier was significantly different between the eligible and sampled populations due to survey design considerations (ie, stratification, oversampling), but no difference was found between sampled a nd respondent populations. Weighting and Balancing Post-stratification weighting was applied to survey response data for tier and age strata in order to obtain analysis results representative of the population as a whole, reducing the effects of nonresponse bias a nd increasing precision (145-148). Weight computation was accomplished through sample-balancing, or raking, using an iterative proportional fitting approach (149-151). Analytical Methods Quantitative analyses were conducted with survey analysis means, frequency, regression, and logistic regr ession procedures (152, 153) in SAS Enterprise 9.3 (Cary, NC). These procedures incorporate statisti cal adjustments for stratified or otherwise complex survey designs, survey weighting, dom ain analysis, and variance due to missing data. Both stratification by tier and raked wei ghts were explicitly included in analysis. Missing data for HRQOL items were imputed prior to HRQOL measure calculation, using Markov chain Monte Carlo methods fo r stochastic imputation (145). Missing data for subgroup classification questions (“skip logic” questions) were imputed through a deductive approach based on contextual re sponses where possible, and otherwise remained designated as missing and were accoun ted for in analysis. Variance estimation

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63 was calculated using Taylor se ries linearization, with missi ng data assumed to be missing at random (MAR), but not missing completely at random (MCAR). A finite population correction factor was not incorporated in va riance estimates, as the sampling fraction comprised a small enough percentage of the to tal population to support the use of infinite population assumptions. Qualitative analysis of free-text respons es to survey questions was conducted using an analytic induction strategy to id entify emergent themes and topics among responses. An open, heuristic coding process was used to identify keywords for the development of a code list, which was subseque ntly used to code fr ee-text responses and identify recurring topics and th emes. A topic was classified as a theme if a minimum of 5% of respondents to open -ended items identified it as a subject of interest. Descriptive Statistics Percentages reported in all tables are ba sed on weighted frequencies rather than derived from unadjusted response numbers. Both standard error and Taylor series variance are reported for percentage values. Table 10 shows the results fo r self-rated measures of health, including general (physical and mental) health, mental distress, and unhealthy days in the past 30 days. Almost three-quarters of the population (73.62%) perceived their general health as being good or better, and four-fifths (82.15%) reported themselves to be in good mental health. The average number of physically and/or mentally unhealthy days reported was 9.82 (SE 0.71), with a median of 3.97 days (SE 0.81).

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64 Table 10: CDC “Healthy Days” Measures Responses (N=415) Frequency (weighted) Responses (%) Std Err (%) Variance (%) Self-rated general health in past 30 days Good or better 250 73.62 73.62 2.66 7.07 Fair or poor 165 26.37 26.38 2.66 7.07 Mental health in past 30 days Good mental health 332 82.14 82.15 2.36 5.59 Frequent mental distress 83 17.85 17.85 2.36 5.59 Unhealthy days in past 30 days None (all days healthy) 127 35.71 35.72 3.19 10.19 1–7 days (1 week) 93 24.00 24.00 2.84 8.09 8–14 days (2 weeks) 42 8.46 8.46 1.88 3.52 15–21 days (3 weeks) 45 11.02 11.03 2.10 4.41 22–30 days (4+ weeks) 108 20.79 20.79 2.51 6.31 Differences by demographics of age, ge nder, language, and race/ethnicity were evaluated for all measures. Significant differenc es were observed in general health status by gender (p<0.007), by age (p<0.0001), and by race/ethnicity (p<0.021). Mental health status was observed to be significantly di fferent by age (p<0.001), by gender (p<0.001), and by language (p<0.003). Overall unhealthy days were observed to be significantly different by age (p<0.003) and by language (p <0.001). No other significant differences were observed by demographic (Table 11). Table 11: CDC “Healthy Days” Measures by Demographic General Health Mental Health Unhealthy Weeks N=415 Good/ Better % Fair/ Poor % Good/ Stable % Frequent Distress % None % 1 wk % 2 wk % 3 wk % 4+ wk % Age p<0.001* p<0.001* p=0.003* 18–29 26.54 2.65 26.61 2.57 11.73 9.16 2.35 2.84 3.09 30–39 18.91 3.05 19.50 2.48 10.17 4.47 2.71 2.31 2.32 40–49 12.96 4.40 14.57 2.79 6.81 3.60 1.22 3.18 2.55 50–59 6.46 10.47 10.30 6.63 3.61 3.15 0.98 1.37 7.82 60–69 6.92 4.32 8.19 3.05 2.69 2.85 0.87 0.85 3.98 70–76 1.83 1.49 2.98 0.34 0.71 0.77 0.32 0.48 1.03 Gender p=0.007* p<0.001* p=0.909 Female 59.26 17.34 62.91 13.69 27.84 18.93 6.53 8.33 14.96 Male 14.36 9.04 19.24 4.16 7.88 5.07 1.93 2.69 5.84

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65 Table 11: CDC “Healthy Days” Measu res by Demographic, continued General Health Mental Health Unhealthy Weeks N=415 Good/ Better % Fair/ Poor % Good/ Stable % Frequent Distress % None % 1 wk % 2 wk % 3 wk % 4+ wk % Race/Ethnicity p=0.021* p=0.083 p=0.112 White 32.04 11.58 35.20 8.41 13.21 10.23 5.11 5.20 9.86 Hispanic/Latino 20.16 8.13 23.77 4.51 13.20 6.22 1.98 2.18 4.71 Black 9.39 5.65 10.77 4.28 2.77 3.85 1.24 2.65 4.53 Other/Unknown 12.04 1.02 12.41 0.65 6.53 3.71 0.13 1.00 1.69 Language p=0.388 p=0.003* p<0.001* English 52.24 17.21 54.13 15.32 18.88 19.52 6.44 9.45 15.17 Spanish 21.38 9.17 28.02 2.53 16.84 4.49 2.02 1.58 5.63 *Significant p-value, 0.05 or less. Technology Users, Overall Table 12 presents the IT user status results. Among the overall population, 94.57% of people classified themselves as us ers of some kind of IT, whether computer, cell phone, or both. Cell phone us e was significantly higher th an computer use (p<0.001), with 92.70% of people reporti ng cell phone use versus 71.41% reporting computer use. Almost three-quarters use the Internet (73.62% ). Significantly more people use technology for health information than for health communication (65.25% vs. 52.61%, p<0.001). Table 12: Information Technol ogy (IT) User Classification IT user status, general Responses (N=412) Frequency (weighted) Responses (%) Std Err (%) Variance (%) Computer only 14 1.86 1.87 0.74 0.55 Cell phone only 112 22.63 22.76 2.67 7.13 Both computer and cell phone 252 69.55 69.94 2.86 8.27 Neither computer nor cell phone 34 5.40 5.43 1.22 1.49 Computer use Responses (N=415) Frequency (weighted) Responses (%) Std Err (%) Variance (%) Uses computer 266 71.41 71.41 2.83 7.99 Does not use computer 149 25.58 28.89 2.83 7.99 Cell phone use Responses (N=412) Frequency (weighted) Responses (%) Std Err (%) Variance (%) Uses cell phone 364 92.18 92.70 1.42 2.01 Does not use cell phone 48 7.26 7.30 1.42 2.01

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66 Table 12: Information Technology (IT) User Classification, continued Internet use Responses (N=404) Frequency (weighted) Responses (%) Std Err (%) Variance (%) Uses Internet 279 73.62 76.64 2.65 7.00 Does not use Internet 125 22.44 23.36 2.65 7.00 Health information use Responses (N=410) Frequency (weighted) Responses (%) Std Err (%) Variance (%) Uses IT for information about health-related topics 231 64.23 65.25 3.04 9.24 Does not use IT for information about health-related topics 179 34.21 34.75 3.04 9.24 Health communication use Responses (N=395) Frequency (weighted) Responses (%) Std Err (%) Variance (%) Uses IT to talk to others about health-related topics 192 49.56 52.61 3.39 11.47 Does not use IT to talk to others about health-related topics 203 44.64 47.39 3.39 11.47 Differences by demographics of age, ge nder, language, and race/ethnicity were assessed for all measures. No one below ag e 40 was found to be a technology “nonuser,” but used either or both com puters and cell phones. Very fe w people used computers but not cell phones—below 5% in all categories. English speakers were significantly more likely than Spanish speakers to use both computers and cell phone s, (78.22% vs 50.83%, p<0.001), while Spanish speakers were significan tly more likely than English speakers to use cell phones only (38.46% vs. 15.95%, p<0.0 01). Table 13 shows further results. Table 13: IT General User Status by Demographic N=415 Computer % (row%) Cell Phone % (row%) Computer and Cell % (row%) Nonuser % (row%) Age p=indeterminate 18–29 0.08 (0.26) 3.93 (13.40) 25.33 (86.33) 0 (0) 30–39 0.55 (2.51) 5.02 (26.27) 15.73 (71.22) 0 (0) 40–49 0.51 (3.00) 3.79 (22.39) 12.34 (72.88) 0.29 (1.74) 50–59 0.22 (1.28) 3.80 (22.36) 10.90 (64.11) 2.08 (12.25) 60–69 0.17 (1.46) 4.45 (39.34) 5.18 (45.86) 1.51 (13.33) 70–76 0.35 (10.35) 0.98 (29.48) 0.46 (13.76) 1.55 (46.41) Gender p=0.191 Female 0.82 (1.07) 16.48 (21.54) 54.76 (71.59) 4.43 (5.80) Male 1.05 (4.45) 6.28 (26.71) 15.18 (64.59) 1.00 (4.24)

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67 Table 13: IT General User Status by Demographic, continued N=415 Computer % (row%) Cell Phone % (row%) Computer and Cell % (row%) Nonuser % (row%) Race/Ethnicity p=indeterminate White 0.91 (2.09) 8.23 (18.78) 32.25 (73.58) 2.43 (5.55) Hispanic/Latino 0.63 (2.27) 8.98 (32.11) 15.74 (56.30) 2.61 (9.33) Black 0 (0) 2.67 (17.70) 12.37 (81.98) 0.05 (0.33) Other/Unknown 0.32 (2.43) 2.88 (21.94) 9.59 (73.01) 0.34 (2.62) Language p<0.001* English 0.94 (1.35) 11.13 (15.95) 54.58 (78.22) 3.12 (4.47) Spanish 0.93 (3.06) 11.63 (38.46) 15.37 (50.83) 2.31 (7.65) Among IT users, significant differences were found by age for all types of use (computer, cell phone, internet, health in formation, and health communication). Utilization decreased as age increased for a ll types of use except health communication, where utilization between ages 30 and 49 was found to be hi gher than in any other age bracket (p=0.012). Women were significantly more likely than men to both seek out health information (p=0.027) and engage in health communication (p<0.001). Threequarters of women (70.27%) and half of men (49.11%) looked up health information, and more than half (56.32%) of women compared to less than half (40.55%) of men engaged in health communication. Race and ethnicity we re found to be significantly different for all types of use except health communication. Blacks were most likely to use computers (81.76%, p=0.022), cell phones (99. 67%, p=0.024), and the Inte rnet (84.93%, p=0.018). English speakers were more likely than Sp anish speakers to use computers (79.50% vs. 53.03%, p<0.001), the Internet (82.95% vs. 60.65%, p<0.001), and to look up health information (69.78% vs. 54.41%, p=0.027). Ta ble 14 provides additional details.

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68 Table 14: IT Users, Technology Utilization by Demographic Computer Cell Phone Internet Health Info Health Comm N=415 Yes % No % Yes % No % Yes % No % Yes % No % Yes % No % Age p<0.001* p<0.001* p<0.001* p<0.001* p=0.012* 18–29 86.60 13.40 99.74 0.26 90.67 9.33 82.39 17.61 47.95 52.05 30–39 73.73 26.27 97.49 2.51 83.03 16.97 71.53 28.47 66.37 33.62 40–49 73.58 26.42 95.27 4.73 83.84 16.16 63.16 36.83 63.79 36.21 50–59 65.28 34.72 86.47 13.53 61.79 38.21 45.75 54.25 45.01 54.99 60–69 47.33 52.67 85.21 14.79 55.17 44.83 53.40 46.60 45.79 54.21 70–76 24.11 75.88 43.24 56.76 24.90 75.01 20.71 79.29 13.76 86.24 Gender p=0.603 p<0.569 p=0.530 p=0.001* p=0.027* Female 72.16 27.84 93.13 6.87 77 .46 22.54 70.27 29 .73 56.32 43.68 Male 68.96 31.04 91.31 8.69 73. 81 26.19 49.12 50.88 40.55 59.45 Race/Ethnicity p=0.022* p=0.024* p=0.018* p=0.002* p=0.133 White 75.62 24.38 92.37 7.63 79. 34 20.66 66.16 33.84 51.34 48.66 Hispanic/Latino 57.56 42.44 88.40 11. 60 63.89 36.11 52.82 47.18 44.32 55.68 Black 81.76 18.24 99.67 0.33 84. 93 15.07 64.83 35.17 56.56 43.44 Other/Unknown 75.44 24.56 94.95 5.05 84.37 15.63 88.65 11.35 69.01 30.99 Language p<0.001* p=0.121 p<0.001* p=0.027* p=0.30 English 79.50 20.50 94.18 5.82 82 .95 17.05 69.78 30 .22 54.92 45.08 Spanish 53.03 46.97 89.29 10.71 60 .65 39.35 54.41 45 .59 46.87 53.13 *Significant p-value, 0.05 or less. Computer Users Table 15 presents utilization pattern resu lts for computer users. Two-thirds of people use a laptop or notebook computer ( 67.76%), versus just over half who use a desktop (56.26%) and a quarter who use a tabl et device (23.22). More people use just 1 type of computer (62.63%) than use multiple types (36.54%). The majority of people who classified themselves as computer users ow n at least 1 computer (82.42%), use it daily (62.21%), and believe that computer use is always or usually important (68.41%).

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69 Table 15: Utilization Patterns among Computer Users (n=266) Computer device type (n >266 due to multiple device use) Responses (N=266) Frequency (weighted) Responses (%) Std Err (%) Variance (%) Desktop 163 40.17 56.26 4.03 16.26 Laptop or notebook 160 48.38 67.76 3.67 13.44 Tablet 53 16.58 23.22 3.55 12.63 Other device 18 3.63 5.08 1.51 2.28 Multiple computer usage Responses (N=266) Frequency (weighted) Responses (%) Std Err (%) Variance (%) 1 type of computer 171 44.72 62.63 3.91 15.26 2 types of computer 66 17.02 23.84 3.34 11.13 3 types of computer 25 8.64 12.11 2.85 8.12 4+ types of computer 4 1.01 0.59 0.82 0.67 Computer use duration Responses (N=266) Frequency (weighted) Responses (%) Std Err (%) Variance (%) < 1 month 6 2.14 3.00 1.83 3.33 1–6 months 11 3.93 5.50 1.91 3.66 7–12 months 8 1.97 2.76 1.20 1.45 1–2 years 21 4.80 6.73 1.82 3.32 3–5 years 34 11.35 15.90 3.18 10.14 5–10 years 48 13.53 18.94 3.16 9.97 > 10 years 138 33.69 47.17 4.00 15.98 Computer use frequency Responses (N=266) Frequency (weighted) Responses (%) Std Err (%) Variance (%) Daily, multiple times 127 34.54 48.37 4.03 16.27 Daily, once per day 39 9.63 13.84 2.70 7.29 Weekly, 3–5 days per week 35 9.09 12.73 2.58 6.64 Weekly, 1–2 days per week 30 8.26 11.56 2.48 6.17 Monthly, every few weeks 17 5.34 7.47 2.13 4.52 Monthly, once per month or less 18 4.56 6.38 2.19 4.82 Computer use value Responses (N=266) Frequency (weighted) Responses (%) Std Err (%) Variance (%) Always important 122 32.73 45.84 4.00 16.05 Usually important 55 16.12 22.57 3.49 12.19 Sometimes important 57 15.80 22.13 3.41 11.62 Rarely important 19 4.04 5.65 1.57 2.46 Not important 13 2.72 3.81 1.61 2.62 Computer access Responses (N=266) Frequency (weighted) Responses (%) Std Err (%) Variance (%) Owns a computer 217 58.85 82.42 3.24 10.49 Does not own a computer 49 12.56 17.58 3.24 10.49 Multiple regression analysis was conducted for each of these measures to examine the significance of general health status, ment al health status, and unhealthy days in the last month, adjusting for demographic variab les of age, gender, race/ethnicity, and language. Value of computer use was asso ciated with language and all 3 health indicators: general health status, mental hea lth status, and unhealt hy days in the past

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70 month. Computer ownership was associated with general and mental health status. Frequency of computer use was associated with general health status and language. Duration of computer use was associated with general health stat us, age, other/unknown race/ethnicity, and language. Desktop and laptop use were found to be significantly associated with age and white and Hispan ic/Latino race/ethnici ty. Tablet use was associated with Hispanic/Latino race/ethnic ity. No other significant associations were found. Cell Phone Users Table 16 presents utilization pattern resu lts for cell phone users. Cell phone use is split almost evenly between smart phones (50.70%) and regular cell phones (47.40%). Most people use only 1 type of cell phone (96.98%), and most people who classified themselves as cell phone user s own their cell phone s (95.97%), use them daily (87.76%), and believe that cell phone use is us ually or always important (87.65%). Table 16: Utilization Patterns among Cell Phone Users (n=364) Cell phone type (n >363 due to multiple device use) Responses (N=363) Frequency (weighted) Responses (%) Std Err (%) Variance (%) Smart phone 148 46.72 50.70 3.46 11.99 Regular/basic phone 207 43.68 47.40 3.45 11.93 Other cell phone 19 4.22 4.92 1.59 2.54 Multiple phone usage Responses (N=342) Frequency (weighted) Responses (%) Std Err (%) Variance (%) 1 type of cell phone 330 83.08 96.98 1.36 1.84 2 types of cell phone 12 2.59 3.01 1.36 1.84 Cell phone use duration Responses (N=363) Frequency (weighted) Responses (%) Std Err (%) Variance (%) < 1 month 28 9.81 10.68 2.35 5.52 1–6 months 12 3.24 3.52 1.37 1.89 7–12 months 7 1.05 1.14 0.72 0.51 1–2 years 29 4.68 5.09 1.31 1.72 3–5 years 70 16.57 18.04 2.71 7.33 5–10 years 111 28.24 30.74 3.26 10.62 > 10 years 106 28.28 30.79 3.17 10.03

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71 Table 16: Utilization Patterns among Cell Phone Users (n=364), continued Cell phone use frequency Responses (N=364) Frequency (weighted) Responses (%) Std Err (%) Variance (%) Daily, multiple times 268 73.69 79.94 2.71 7.36 Daily, once per day 41 7.21 7.82 1.87 3.51 Weekly, 3–5 days per week 20 4.43 4.80 1.48 2.19 Weekly, 1–2 days per week 20 4.47 4.85 1.48 2.19 Monthly, every few weeks 9 1.32 1.43 0.61 0.37 Monthly, once per month or less 6 1.07 1.17 0.55 0.30 Cell phone use value Responses (N=362) Frequency (weighted) Responses (%) Std Err (%) Variance (%) Always important 222 62.40 68.20 3.17 10.07 Usually important 70 17.79 19.45 2.84 8.07 Sometimes important 42 6.46 7.06 1.43 2.04 Rarely important 22 4.15 4.53 1.24 1.54 Not important 6 0.70 0.76 0.41 0.16 Cell phone use access Responses (N=339) Frequency (weighted) Responses (%) Std Err (%) Variance (%) Owns a cell phone 315 81.39 95.97 1.20 1.43 Does not own a cell phone 24 3.42 4.03 1.20 1.43 Multiple regression analysis was conducted for each of these measures to examine the significance of general health status, ment al health status, and unhealthy days in the last month, adjusting for demographic variab les of age, gender, race/ethnicity, and language. Unhealthy days in the past month wa s associated with smartphone use together with age and language; with regular cell phone use along with gender, age, and language; and with frequency of cell phone use together with age. Multiple device use was found to be significantly associated with black a nd other/unknown race/ethnicity. Duration of cell phone use was associated with language. Ce ll phone ownership was associated with other/unknown race/ethnicity. No other significant associations were found. Internet Users Table 17 presents utilization pattern resu lts for Internet users. More people access the Internet on a portable computer (lap top or tablet, 66.21%) or cell phone (56.53%) than with a desktop computer (55.10%). Users were more likely to use multiple methods of access (57.85%) than a single method (42.14%). Most people who classified

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72 themselves as Internet users have broa dband access (73.35%), go online daily (77.07%), and believe that Internet access is us ually or always important (70.75%). Table 17: Utilization Patterns among Internet Users (n=279) Internet access method (n >279 due to multiple method use) Responses (N=279) Frequency (weighted) Responses (%) Std Err (%) Variance (%) Desktop computer 168 40.56 55.10 3.97 15.79 Portable computer 166 48.74 66.21 3.70 13.66 Cell phone 132 41.62 56.53 3.86 14.90 Other method 9 1.00 1.36 0.75 0.56 Multiple access methods Responses (N=279) Frequency (weighted) Responses (%) Std Err (%) Variance (%) 1 method 135 31.03 42.14 3.87 14.97 2 methods 92 26.89 36.52 3.86 14.87 3 methods 52 15.70 21.33 3.31 10.96 Internet use duration Responses (N=276) Frequency (weighted) Responses (%) Std Err (%) Variance (%) < 1 month 10 3.74 5.13 2.05 4.22 1–6 months 13 3.90 5.36 1.84 3.37 7–12 months 14 4.32 5.93 1.82 3.34 1–2 years 23 4.19 5.74 1.55 2.41 3–5 years 26 7.03 9.65 2.37 5.62 5–10 years 71 20.75 28.47 3.75 14.06 > 10 years 119 28.95 39.72 3.81 14.52 Internet use frequency Responses (N=277) Frequency (weighted) Responses (%) Std Err (%) Variance (%) Daily, multiple times 150 43.42 59.40 3.82 14.58 Daily, once per day 44 12.92 17.67 3.20 10.24 Weekly, 3–5 days per week 28 5.20 7.11 1.77 3.14 Weekly, 1–2 days per week 23 6.10 8.35 2.14 4.60 Monthly, every few weeks 13 3.55 4.85 1.53 2.34 Monthly, once per month or less 19 1.92 2.63 0.92 0.84 Internet access value Responses (N=277) Frequency (weighted) Responses (%) Std Err (%) Variance (%) Always important 132 36.80 50.33 3.96 15.69 Usually important 63 14.93 20.42 3.27 10.66 Sometimes important 57 16.13 22.07 3.42 11.67 Rarely important 17 4.26 5.83 1.86 3.48 Not important 8 0.99 1.36 0.75 0.56 Internet access type Responses (N=276) Frequency (weighted) Responses (%) Std Err (%) Variance (%) Broadband (high speed) 196 53.46 73.35 3.55 12.63 Non-broadband (low speed) 50 11.20 15.37 2.81 7.89 Unknown 30 8.22 11.28 2.73 7.43

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73 Multiple regression analysis was conducted for each of these measures to examine the significance of general health status, ment al health status, and unhealthy days in the last month, adjusting for demographic variab les of age, gender, race/ethnicity, and language. Duration of Internet use was associated with general health status, age, and language. Using multiple devices for Internet access was associated with age, language, and white race/ethnicity. Desktop computer access was associated with age; portable computer access (laptop, notebook, tablet) was a ssociated with age, language, and white and Latino race/ethnicity. Cell phone Internet access was associated with age and white race. Frequency of Internet use was associ ated with age and language. High-speed access was associated with language. No othe r significant associations were found. Activity Patterns Among IT Users Utilization pattern results fo r the use of IT for common activities are presented in Table 18. Users were significantl y more likely to send and recei ve text messages than to send and receive email (84.42% vs 72.72%, p<0. 001). Users were also significantly more likely to video chat or Skype with someone th an to use direct text-based chat (42.10% vs. 30.39%, p<0.001). Social media was not commonl y utilized, with by far the highest activity being on Facebook (57.10% vs 25.44% for the next highest).

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74 Table 18: IT-Based Activities (n=405*) Activity Computer % (n) Cell % (n) Both % (n) User % (n) Nonuser % (n) SE, % Var, % Communication Email, send/receive 29.53 (114) 9.96 (34) 33.23 (110) 72.72 (258) 27.28 (126) 2.91-3.20 4.5510.26 Text, send/receive 0.83 (9) 75.05 (253) 8.54 (34) 84.42 (296) 15.58 (95) 0.46-2.68 0.21-7.21 Voice calls, make/receive 1.73 (12) 74.34 (251) 8.96 (31) 85.03 (294) 14.97 (82) 0.75-2.82 0.57-7.97 Chat, video/Skype 26.39 (74) 7.74 (20) 7.97 (21) 42.10 (115) 57.90 (254) 1.94-3.48 3.7612.10 Chat, direct text 17.60 (48) 6.26 (17) 6.54 (25) 30.39 (90) 69.61 (283) 1.59-3.38 2.5311.43 Chat, group text 7.42 (32) 4.44 (12) 4.76 (15) 16.62 (59) 83.38 (311) 1.29-2.46 1.66-6.06 Information News 26.89 (107) 17.36 (47) 20.30 (72) 64.55 (226) 35.45 (157) 2.59-3.23 6.7210.45 Information 29.72 (108) 2.88 (35) 3.26 (94) 76.13 (237) 23.87 (113) 2.82-3.41 7.9411.65 Multimedia TV/movies 30.81 (107) 6.21 (22) 13.92 (41) 50.94 (170) 49.06 (202) 1.64-3.43 2.6811.78 Videos, watch 23.79 (76) 12.90 (38) 25.29 (77) 61.98 (191) 38.02 (190) 2.51-3.22 6.3010.37 Videos, make/post 6.79 (28) 8.93 (25) 8.20 (20) 23.92 (73) 76.08 (301) 1.53-3.01 2.33-9.08 Music, listen/play 20.75 (75) 22.19 (68) 26.64 (80) 69.58 (223) 30.42 (162) 2.82-3.09 7.96-9.57 Photos, online 6.74 (18) 12.04 (30) 7.30 (18) 26.09 (66) 73.91 (310) 1.91-3.27 3.6410.72 Gaming, solo 14.38 (58) 20.28 (54) 13.71 (47) 48.37 (159) 51.63 (216) 2.46-3.51 5.0912.30 Social media Facebook 19.97 (70) 15.04 (34) 22.10 (66) 57.10 (170) 42.90 (205) 2.80-3.36 7.8611.29 Twitter 4.65 (19) 7.77 (22) 6.02 (20) 18.44 (61) 81.56 (313) 1.37-2.62 1.88-6.89 Pinterest 8.48 (25) 7.70 (20) 8.15 (19) 24.33 (64) 75.67 (307) 2.06-3.21 4.2710.35 Blogs, write/post 6.92 (26) 3.44 (8) 4.41 (15) 14.77 (49) 85.23 (323) 1.20-2.44 1.45-5.93 Blogs, read/comment 12.60 (42) 4.87 (14) 7.97 (27) 25.44 (83) 74.56 (290) 1.58-3.06 2.49-9.35 Gaming, network 7.35 (30) 10.25 (26) 6.66 (21) 24.26 (77) 75.74 (294) 1.70-3.01 2.89-9.09 Total responses varied by item from a low of 350 to a high of 391. Age was the most significant demographic differentiator, affecting all activities except group texting and blog posting. Use was highest among the youngest people surveyed (ages 18–29) and overall decreased as age increased. Women were significantly more likely than men to engage in text messaging (87.10% vs. 75.51%, p=0.015), in

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75 voice calls (88.22% vs. 74.46%, p=0.005), and to be active on Facebook (60.50% vs. 45.47%, p=0.040). Blacks (33.26%) were significan tly more likely than whites (13.76%), Latinos (16.26%), or other racial/ethnic groups (8.59%) to participate in group chat rooms (p=0.022). English speakers were signifi cantly more likely than Spanish speakers to send email (76.71% vs. 62.78%, p=0.040); to pa rticipate in group chat rooms (20.18% vs. 7.34%, p=0.019); to play games both al one (54.01% vs. 33.66%, p=0.013) and with others (28.72% vs. 12.86%, p=0.014), to post and view photos online (30.59% vs. 14.59%, p=0.030); and to engage in social medi a activities such as Twitter (22.06% vs. 8.96%, p=0.015), Pinterest (30.83% vs. 7.01%, p=0.003), and blog reading and commenting (29.73% vs. 13.45%, p=0.026). No ot her significant differences were observed. Additional detail is given in Table 19. Table 19: IT-Based Acti vities by Demographic N=405† Age Activity 18-29 % 30-39 % 40-49 % 50-59 % 60-76 % Communication Email, send/receive (p<0.001*) 86.01 77.10 82.86 55.67 44.33 Text, send/receive (p<0.001*) 98.58 94.51 92.68 71.48 42.77 Voice calls, make/receive (p<0.001*) 97.11 92.15 91.96 67.59 58.28 Chat, video/Skype (p<0.001*) 61.60 45.65 49.18 21.15 9.68 Chat, direct text (p<0.001*) 50.66 32.32 27.66 13.16 5.97 Chat, group text (p<0.444) 16.79 13.41 25.91 12.29 15.10 Information News (p<0.001*) 72.41 70.50 78.27 44.10 44.79 Information (p<0.001*) 94.96 83.73 81.44 49.38 44.98

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76 Table 19: IT-Based Activities by Demographic, continued N=405† Age Activity 18-29 % 30-39 % 40-49 % 50-59 % 60-76 % Multimedia TV/movies (p<0.001*) 61.60 56.92 65.28 25.42 27.95 Videos, watch (p<0.001*) 83.96 68.89 68.20 38.29 24.78 Videos, make/post (p<0.001*) 36.51 32.22 21.45 6.23 5.73 Music, listen/play (p<0.001*) 93.91 76.93 67.36 49.95 28.48 Photos, online (p<0.001*) 48.93 23.68 17.34 12.85 6.70 Gaming, solo (p<0.001*) 71.34 38.06 44.22 38.16 29.76 Social media Facebook (p<0.001*) 79.92 61.74 57.16 36.84 21.99 Twitter (p<0.047*) 25.60 19.82 20.91 13.65 2.56 Pinterest (p<0.001*) 42.94 20.98 27.95 6.16 5.49 Blogs, write/post (p<0.074) 20.10 20.63 12.82 7.58 4.72 Blogs, read/comment (p<0.014*) 36.00 26.94 28.73 10.80 13.80 Gaming, network (p<0.001*) 40.38 17.41 26.47 11.50 11.72 Significant p-value, 0.05 or less. †Total responses varied by item from a low of 350 to a high of 391. Gender Race/Ethnicity Lang Activity Female Male White Latino Black Other English Spanish Communication Email, send/receive 73.57 69.90 72.33 65.57 77.29 82.67 76.71* 62.78* Text, send/receive 87.10* 75.51* 85.18 82.57 84.73 85.26 84.57 84.07 Voice calls, make/receive 88.22* 74.46* 86.30 84.16 79.86 88.34 85.28 84.43 Chat, video/Skype 42.18 41.85 45.47 34.35 45.41 41.89 45.67 32.75 Chat, direct text 30.03 31.58 28.57 24.49 38.25 38.61 30.71 29.54 Chat, group text 14.93 22.28 13.76* 16.26* 33.26* 8.59* 20.18* 7.34* Information News 64.14 65.93 63.68 61.30 68.23 70.03 65.81 61.50 Information 76.48 75.01 76.36 69.63 83.39 81.76 79.08 69.25 Multimedia TV/movies 50.66 51.85 48.84 44.19 53.96 67.39 52.53 47.05 Videos, watch 63.49 56.86 63.49 56.06 57.60 73.38 65.11 53.98 Videos, make/post 23.27 26.02 22.91 23.23 26.22 25.95 24.79 21.70 Music, listen/play 70.44 66.72 67.79 67.46 68.79 80.74 72.11 63.44 Photos, online 25.32 28.62 29.21 21.35 23.37 28.07 30.59* 14.59* Gaming, solo 49.27 45.30 47.80 45.64 57.25 45.41 54.01* 33.66* Social media Facebook 60.50* 45.47* 55.83 55.45 57.62 63.84 59.48 50.95 Twitter 18.18 19.32 15.19 18.92 29.26 15.68 22.06* 8.96* Pinterest 26.22 17.75 22.53 18.21 28.87 36.92 30.83* 7.01* Blogs, write/post 13.53 19.13 11.81 17.46 16.38 17.51 15.88 11.86 Blogs, read/comment 26.07 23.36 25. 28 19.07 30.20 32.22 29.73* 13.45* Gaming, network 24.53 23.28 24.85 22.79 24.85 24.50 28.72* 12.86* Significant p-value, 0.05 or less. †Total responses varied by item from a low of 350 to a high of 391. Multiple regression analysis was conducted for each of these measures to examine the significance of general health status, ment al health status, and unhealthy days in the last month, adjusting for demographic variab les of age, gender, race/ethnicity, and

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77 language. Playing games with other people was associated with mental health status, unhealthy days in the past month, language, and age. Talking to groups of people in chat rooms and watching TV or movies were also associated with unhealthy days in the past month, language, and age. Voi ce calls, watching videos, mu sic, blog posting, reading and commenting, and the use of video chat were a ssociated with genera l health status and age; video watching and musi c were also associated w ith other/unknown race/ethnicity, and video chat was also associated with Black race/ethnicity. Email was associated with White race/ethnicity, age, and language. F acebook use, Twitter, gaming, Pinterest, and online photos were associated with language and age. Age was associated with all remaining activities. No other si gnificant associations were found. Health Information Table 20 presents utilization pattern resu lts for health information users. Most health information users search for health information on someone else’s behalf as well as their own (59.27%). Although most search es are infrequent, occurring every few weeks to once a month or less (68.45%), user s place a high value on being able to look up health information, with 67.68% deemi ng it always or usually important. Table 20: Utilization Patterns among Health Information Users (n=231) Health information focus Responses (N=225) Frequency (weighted) Responses (%) Std Err (%) Variance (%) Self 94 23.74 37.80 4.21 18.25 Another person 8 1.85 2.94 1.23 1.52 Both self and other 123 37.22 59.27 4.27 17.75 Health information duration Responses (N=226) Frequency (weighted) Responses (%) Std Err (%) Variance (%) < 1 month 17 4.91 7.78 2.32 5.39 1–6 months 23 7.92 12.56 2.85 8.13 7–12 months 11 2.69 4.26 1.48 2.20 1–2 years 34 9.62 15.25 3.30 10.91 3–5 years 45 13.68 21.68 3.70 13.72 5–10 years 58 15.71 24.90 3.91 15.28 > 10 years 38 8.56 13.56 2.59 6.71

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78 Table 20: Utilization Patterns among Health Information Users (n=231) Health information frequency Responses (N=226) Frequency (weighted) Responses (%) Std Err (%) Variance (%) Daily, multiple times 10 2.64 4.18 1.51 2.29 Daily, once per day 13 2.89 4.58 1.64 2.70 Weekly, 3–5 days per week 25 6.51 10.32 2.87 8.24 Weekly, 1–2 days per week 27 7.87 12.47 2.80 7.82 Monthly, every few weeks 49 13.75 21.79 3.47 12.01 Monthly, once per month or less 102 29.44 46.66 4.39 19.30 Health information value Responses (N=228) Frequency (weighted) Responses (%) Std Err (%) Variance (%) Always important 104 26.61 41.70 4.23 17.88 Usually important 56 16.58 25.98 3.79 14.37 Sometimes important 54 17.82 27.93 4.12 16.97 Rarely important 11 2.44 3.83 1.39 1.93 Not important 3 0.36 0.57 0.50 0.25 Multiple regression analysis was conducted for each of these measures to examine the significance of general health status, ment al health status, and unhealthy days in the last month, adjusting for demographic variab les of age, gender, race/ethnicity, and language. Frequency of health information sear ching was associated with general health status and unhealthy days in the past mont h. Gender, age, and language were associated with the recipient of health information s earches (self, other, or both). No other significant associations were found. Table 21 describes utilizati on patterns for some commonly sought topics of health information. People most frequently looked fo r information about food, nutrition, or diet (78.54%); about exercise and physical activity (71.93%); about diseases and illnesses that either they (71.29%) or someone else (70.68 %) had; and about medications they take (66.24%). People were least likely to look up information about lab test results (25.99% self, 15.94% other) and clinic vis it notes (25.00% se lf, 13.43% other).

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79 Table 21: Health Information Content (n=237*) Health information topic Responses (N) Frequency (weighted) Responses (%) Std Err (%) Variance (%) Disease, self18245.60 71.29 3.98 15.84 Disease, anotherÂ’s16444.49 70.68 3.98 15.83 Surgery, self8519.84 32.15 3.96 15.71 Surgery, anotherÂ’s10125.62 41.61 4.27 18.22 Feelings/symptoms14039.62 62.98 4.23 17.88 Medicines, self15942.16 66.24 4.02 16.15 Medicines, anotherÂ’s11229.70 47.60 4.38 19.21 Health insurance11230.78 49.67 4.41 19.49 Doctors/health care providers12534.72 55.60 4.38 19.17 Lab test results, self6716.19 25.99 3.76 14.12 Lab test results, anotherÂ’s389.69 15.94 3.21 10.28 Clinic visit notes, self5615.22 25.00 3.98 15.81 Clinic visit notes, anotherÂ’s328.09 13.43 3.06 9.37 Exercise/physical activity15844.13 71.93 3.94 15.52 Food, nutrition, or diet18349.57 78.54 3.52 12.38 Birth control/family planning6926.80 43.78 4.51 20.37 Behavior change (eg, alcohol/tobacco cessation) 9221.66 35.15 4.09 16.72 Health topics in the news13436.43 60.72 4.45 19.85 Total responses varied by item from a low of 215 to a high of 232. Multiple regression analyses were conduc ted for each of these measures to examine the significance of gene ral health status, mental health status, and unhealthy days in the last month, adjusting for demogra phic variables of age, gender, race/ethnicity, and language. Unhealthy days in the past month together with age and other race/ethnicity were associated with oneÂ’ s own disease. General health status was associated together with la nguage for oneÂ’s own upcoming surgery, for oneÂ’s own lab test results, and for behavorial change interest. Language was associated with medicine use both by self and others and w ith looking up information about health care providers. Age and Latino race/ethnicity were associated with looking for information about birth control and family planning. No other si gnificant associations were found.

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80 Health Communication Table 22 presents utilization patterns for health communicators. Half of those who engage in health communications have been talking with others a bout health or health care for less than 3 years (50.78%). The major ity of those who talk with others about health and health care do so relatively infrequently (77.19%, monthly or less) but believe such communications to be usuall y or always important (64.90%). Table 22: Utilization Patterns among Health Communicators (n=192) Health communication duration Responses (N=192) Frequency (weighted) Responses (%) Std Err (%) Variance (%) < 1 month 26 7.80 15.74 3.58 12.78 1–6 months 20 6.43 12.98 3.35 11.21 7–12 months 13 3.59 7.25 2.53 6.40 1–2 years 28 7.34 14.81 3.40 11.57 3–5 years 38 11.07 22.33 4.04 16.29 5–10 years 22 4.36 8.79 2.15 4.63 > 10 years 45 8.97 18.10 3.23 10.41 Health communication frequency Responses (N=189) Frequency (weighted) Responses (%) Std Err (%) Variance (%) Daily, multiple times 11 2.15 4.42 1.78 3.18 Daily, once per day 8 1.86 3.83 1.63 2.64 Weekly, 3–5 days per week 15 2.94 6.04 2.09 4.37 Weekly, 1–2 days per week 18 4.15 8.53 2.71 7.32 Monthly, every few weeks 59 17.56 36.11 4.52 20.43 Monthly, once per month or less 78 19.97 41.08 4.63 21.42 Health communication value Responses (N=192) Frequency (weighted) Responses (%) Std Err (%) Variance (%) Always important 81 20.51 41.39 4.57 20.92 Usually important 42 11.65 23.51 3.94 15.55 Sometimes important 56 14.91 30.09 4.38 19.17 Rarely important 10 2.41 4.86 2.21 4.90 Not important 3 0.07 0.15 0.09 0.01 Multiple regression analyses were conduc ted for each of these measures to examine the significance of gene ral health status, mental health status, and unhealthy days in the last month, adjusting for demogra phic variables of age, gender, race/ethnicity, and language. No health status variables we re found to be significantly associated. Language was associated with health comm unication duration, and health communication

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81 frequency was associated with black race/e thnicity. No other si gnificant associations were found. Table 23 describes who people use IT to ta lk with about health and health care. The majority of people use IT to communi cate with their family (88.32%), friends (74.54%), and their health care providers ( 71.42%). Very few communicate with others online about health a nd health care (7.24%). Table 23: Health Communica tion Contacts (n=210*) Health communication contact Responses (N) Frequency (weighted) Responses (%) Std Err (%) Variance (%) Health care provider14236.57 71.42 4.00 16.01 Family17846.15 88.32 2.88 8.32 Friends14837.43 74.54 3.93 15.45 Online acquaintances153.44 7.24 2.30 5.29 Co-workers6416.78 34.74 4.60 21.17 Students/classmates3110.18 21.33 4.14 17.14 Neighbors/community members 5314.58 29.65 4.49 20.17 Religious leaders295.85 12.49 3.00 9.00 Total responses varied by item from a low of 178 to a high of 202. Multiple regression analyses were conduc ted for each of these measures to examine the significance of gene ral health status, mental health status, and unhealthy days in the past month, adjusting for demographic variables of age, gender, race/ethnicity, and language. Talking about health topics with neighbors and community members was associated with general health status and unhealthy days in the past month. Talking about health topics with (fellow) st udents was associated with age; talking about health topics with family members was asso ciated with age and language. Talking about health topics with online ac quaintainces was associated with language and Latino and other/unknown race/ethnicity. Talking about he alth topics with religious leaders was

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82 associated with black and other/unknown race/e thnicity. No other significant associations were found. Other Technology Users Table 24 presents utilization for ot her types of common technology with information delivery capacity. Multimedia en tertainment technology such as DVD and/or Blu-ray players (74.51%), cable TV with “ on-demand” features (73.55%), and portable digital music players (51.02%) were the most frequently used. Just under half of respondents reported using health-specifi c technology devices (44.96%). Streaming media boxes (12.05%) and e-book reader s (16.97%) were the least used. Table 24: Other Technology Utilization (n=403*) Technology type Responses (N) Frequency (weighted) Responses (%) Std Err (%) Variance (%) Health-specific technology (eg, glucometer, blood pressure cuff) 215 42.59 44.96 3.34 11.16 E-book reader 62 15.54 16.97 2.61 6.79 MP3/music player 164 47.26 51.02 3.38 11.42 Game console 142 39.82 43.49 3.45 11.92 DVD/Blu-ray player 277 69.46 74.51 2.95 8.72 Cable TV (with “on-demand”) 282 68.69 73.55 3.01 9.04 Streaming media device 51 10.95 12.05 2.23 4.97 Internet-enabled TV 124 33.05 36.48 3.35 11.22 Total responses varied by item from a low of 372 to a high of 395. Health-specific device use increased in asso ciation with age, with the highest use found in the highest age bracket (ages 6076, 62.69%, p=0.036). Age was also strongly associated (p<0.001) with game console us e, with the youngest age bracket having the highest use (65.37%) and utiliza tion decreasing as age increased. English speakers were significantly more likely than Spanish speaker s to use all types of examined technology except for streaming media boxes. White s were more likely than all other races/ethnicities to use DVD/Blu-ray players and cable with on-demand service. Women

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83 were slightly more likely than men to us e e-book readers (17.13% vs. 16.44%, p<0.013). Additional detail is shown in Table 25. Table 25: Other Technology Users, Utilization by Demographic Health Device eBook MP3 Game Console N=403† Yes % No % Yes % No % Yes % No % Yes % No % Age p=0.036* p<0.343 p<0.001* p<0.001* 18–29 36.16 63.84 13.38 86.62 77.11 22.89 65.37 34.63 30–39 34.94 65.06 15.52 84.48 63.81 36.19 57.27 42.73 40–49 50.50 49.50 27.35 72.35 45.19 54.81 34.87 65.13 50–59 52.14 47.86 18.76 81.24 24.18 75.82 20.96 79.04 60–76 62.69 37.31 11.43 88.57 15.12 84.87 11.84 88.16 Gender p=0.079 p<0.013* p=0.241 p=0.860 Female 45.44 54.56 17.13 82 .87 53.01 46.99 43.19 56.81 Male 43.44 56.56 16.44 83. 56 44.50 55.50 44.49 55.51 Race p=0.603 p=0.315 p=0.006* p=0.530 White 43.87 56.13 19.15 80. 95 54.59 45.41 45.94 54.06 Latino 39.77 60.23 9.54 90. 46 34.17 65.83 35.99 64.01 Black 50.71 49.29 23.55 76. 45 56.37 43.63 50.84 49.16 Other 52.65 47.35 18.51 81. 49 68.29 31.71 42.77 57.23 Language p=0.001* p=0.001* p=0.004* p=0.007* English 51.99 48.01 21.91 78 .09 57.44 42.56 49.29 50.71 Spanish 27.74 72.26 4.80 95 .20 35.17 64.83 28.86 71.14 *Significant p-value, 0.05 or less. †Responses vary by item from a low of 379 to a high of 403. DVD/ Blu-ray Cable w/ On-Demand Streaming Media Box Internet TV N=403† Yes % No % Yes % No % Yes % No % Yes % No % Age p=0.031* p=0.390 p=0.013 p=0.044 18–29 80.23 19.77 79.36 20.63 14.70 85.30 48.54 51.46 30–39 81.40 18.60 77.79 22.21 18.82 81.18 39.57 60.43 40–49 80.23 19.77 64.02 35.98 7.81 92.19 33.28 66.72 50–59 59.24 40.76 70.18 29.82 10.18 89.82 24.54 75.46 60–76 61.40 38.60 70.33 29.67 3.24 96.76 23.23 76.77 Gender p=0.969 p=0.057 p=0.837 p=0.519 Female 74.57 25.43 72.74 27 .26 11.82 88.18 35.40 64.60 Male 74.33 25.67 76.33 23. 67 12.80 87.20 40.01 59.99 Race p<0.001* p=0.001* p=0.954 p=0.231 White 82.40 17.60 78.38 21. 62 10.72 89.28 37.06 62.94 Latino 56.58 43.42 58.47 41. 53 12.23 87.77 29.96 73.04 Black 86.84 13.16 93.34 6.66 14.27 85.73 41.14 58.86 Other 70.59 29.41 68.49 31. 51 13.50 86.50 48.54 51.46 Language p<0.001* p<0.001* p=0.161 p=0.011* English 81.35 18.65 81.48 18 .52 14.02 85.98 41.93 58.07 Spanish 57.97 42.03 55.25 44 .75 7.16 92.84 22.72 72.28 *Significant p-value, 0.05 or less. †Responses vary by item from a low of 379 to a high of 403.

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84 Technology Nonusers Barriers and facilitators to IT and HIT use were explored with respondents who reported not using computers, ce ll phones, or the internet, or not engaging in health information seeking or health communications Detailed results are shown in Table 26. Knowledge was the most frequent barrier re ported for computer ( 62.69%) and Internet (61.96%) use, although it was much less of a factor for cell phone use (15.68%). Access was the second most frequent barrier reporte d for computer and Internet use, and the highest barrier to cell phone use (43.76% ; 43.27%, Internet; 32.61% computer). Cost was the second-highest barrier to cell phone use (31.09%) and the third-highest barrier for computer and Internet use (25.64%, 31.92%). The highest barrier to the use of health information and to health communication was identified as preference for personal interaction with oneÂ’s health care provider (49.75%, h ealth information; 50.87%, health communication), with knowledge being identified as the se cond most frequent (39.24%, health information; 26.03% health communication). Definite interest by nonusers in engaging with IT and HIT was reported across all categories of nonuse: computer (49.57%), ce ll phone (34.73%), Intern et (51.13%), health information (45.61%), and health communica tion (27.99%). Disinterest was higher than definite interest for health communicati on (40.74%) and cell phone us e (39.95%). Interest modified by knowledge, access, and cost was repo rted for all 5 categories of nonuse. Half the population identified education as a facil itator to computer ( 50.51%) and Internet use (50.11%). Reduced pricing (cost) was the most frequent facilitato r to cell phone use, identified by half of cell phone users ( 50.23%), and the second most frequent for computer (33.90%) and In ternet use (45.24%).

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85 Table 26: IT and HIT Barriers and Facilitators Computer % (n) N=149* Cell % (n) N=48* Internet % (n) N=125* H. Info % (n) N=179* H.Comm % (n) N=203* SE, % Var, % Barrier Access 32.61 (53) 43.76 (21) 43.27 (40) N/A N/A 5.2410.95 27.46119.83 Knowledge 62.69 (95) 15.39 (8) 61.96 (82) 39.24 (70) 26.03 (57) 4.237.71 17.8759.37 Need 20.10 (25) 15.68 (10) 15.31 (17) 14.26 (19) 18.28 (29) 3.677.37 13.4654.32 Interest 20.52 (27) 26.69 (11) 18.41 (23) 17.96 (24) 23.15 (30) 4.289.53 18.3390.83 Difficulty 15.10 (21) 16.81 (7) 13.75 (20) 8.41 (16) 11.96 (20) 3.057.82 9.3161.17 Cost 25.64 (40) 31.09 (14) 31.92 (33) N/A N/A 4.8210.19 23.24104.02 Value 8.44 (9) 8.10 (3) 6.44 (8) N/A N/A 2.925.53 8.5230.63 Trust N/A N/A N/A 7.51 (8) N/A 2.87 8.24 Health literacy N/A N/A N/A 8.31 (11) 7.21 (10) 2.602.92 6.78-8.53 Privacy N/A N/A N/A 15.84 (26) 16.40 (30) 3.734.10 13.9816.83 Personal interaction N/A N/A N/A 49.75 (84) 50.87 (93) 5.295.43 27.9929.51 Isolation N/A N/ A N/A N/A 19.28 (31) 4.03 16.23 Facilitator Education 50.51 (73) 19.37 (8) 50.11 (59) N/A N/A 6.018.87 36.1478.74 Pricing 33.90 (58) 50.23 (25) 45.24 (44) N/A N/A 5.3011.61 28.09134.71 Fluency 22.57 (32) 8.41 (4) 32.91 (29) N/A N/A 4.686.31 21.8842.54 Unknown 31.49 (32) 32.47 (9) 29.41 (29) N/A N/A 5.8910.95 34.74119.91 Interest Definite 49.57 (73) 34.73 (15) 51.13 (57) 45.61 (75) 27.99 (59) 4.5510.66 20.75113.61 Moderated, access 32.49 (47) 30.74 (15) 21.66 (28) 20.39 (38) 10.48 (25) 2.7710.39 7.65107.86 Moderated, cost 23.68 (38) 30.07 (16) 25.34 (27) 20.66 (35) 16.31 (29) 3.878.80 14.9977.45 Moderated, knowledge 45.88 (61) 19.41 (8) 35.38 (39) 26.53 (42) 14.95 (28) 3.438.31 11.7869.04 Moderated, security N/A N/A N/A 19.17 (30) 19.79 (36) 4.174.21 17.3617.69 Uncertain 12.42 (13) 6.43 (3) 5.40 (8) 13.13 (20) 15.73 (22) 2.735.62 7.4431.55 None 18.01 (26) 39.95 (16) 36.39 (35) 30.21 (43) 40.74 (67) 4.1710.53 17.36110.87 Responses varied by item: 135 – 142 (computer); 38 –45 (cell); 108 –116 (internet); 159 –161 (health info); 176 – 183 (health commn).

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86 Demographic differences among nonuser s were examined by age, gender, race/ethnicity, and language. Race was str ongly significant across all categories of nonuse (p<0.001). Latinos were most likely to be computer nonusers (41.99%) and Internet nonusers (42.16%), while whites we re most likely to be cell phone nonusers (45.83%), health information nonusers ( 42.63%), and health communication nonusers (46.45%). Women were signifi cantly more likely than men to be nonusers in all categories. People ages 50–59 were most likely to be cell phone nonusers (31.51%, p=0.040) and health information nonusers (26.80%, p=0.047), while people ages 18–29 were most likely to be health communi cation nonusers (31.98%, p<0.001). English speakers were more likely than Spanish sp eakers to look up health information (61.31% vs. 38.69%, p=0.034) and engage in h ealth communication (67.86% vs. 32.14%, p<0.001). Additional detail is shown in Table 27. Table 27: IT and HIT Nonusers by Demographic Computer % (n=149) Cell Phone % (n=48) Internet % (n=125) Health Info % (n=179) Health Comm % (n=203) Age p=0.388 p=0.040* p=0.121 p=0.047* p<0.001* 18–29 13.68 1.06 11.96 15.02 31.98 30–39 20.18 7.58 15.59 18.27 15.68 40–49 16.04 10.98 11.45 17.65 12.35 50–59 20.57 31.51 28.56 26.80 20.34 60–69 20.71 22.91 21.32 14.57 13.23 70–76 8.81 25.96 11.11 7.69 6.41 Gender p<0.001* p=0.018* p<0.001* p=0.002* p<0.001* Female 74.58 72.01 74.73 65.24 70.50 Male 25.41 27.99 25.27 34.76 29.50 Race/Ethnicity p<0.001* p<0.001* p<0.001* p<0.001* p<0.001* White 37.19 45.83 39.20 42.63 46.45 Hispanic/Latino 41.99 44 .41 42.16 37.58 31.72 Black 9.60 0.67 9.72 15.47 12.82 Other/Unknown 11.22 9.09 8.92 4.33 9.01 Language p=0.097 p=0.578 p=0.701 p=0.034* p<0.001* English 49.81 55.64 52.32 61.31 67.86 Spanish 50.19 44.36 47.68 38.69 32.14 *Significant p-value, 0.05 or less.

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87 Opinions about Technology Both technology nonusers and technology user s expressed significant interest in HIT and reported perceived value of IT both in general and for HIT purposes. Many identified specific HIT solutions of interest and offered suggestions for the health system, such as requesting the ability to make appointments online, by email, or by text; to communicate with providers by email or te xt; and to access their information through patient portals or similar solutions. Barriers to IT and HIT use we re noted repeatedly, particularly in terms of cost to obtain, use, and repair cell phones and computers; in terms of knowledge and the need for training to better make use of existing available technology; and in terms of access, specifically with regard to DH systems not supporting either IT-based access to patientsÂ’ health information or the ability to use IT to communicate with health care providers. Respo ndents also made fre quent reference to their perceptions of advances in IT as an overall years-long trend th at they expected to continue. Some expressed concerns about IT and/or HIT, including concern about the trustworthiness of h ealth information found online and concern about the isolating or distancing effects of technology, along with fears that IT so lutions might be used to replace face-to-face or personal c ontact with health care providers. Health Status and Information Technology Use Multiple regression analyses were used to assess the impact of health information user status and health comm unications user status on CDC HRQOL measures of general health status (good, very good, or excellent self-rated health vers us fair or poor self-rated health), mental health status as assessed by the presence or absence of frequent mental distress, and the number of unhealthy days repor ted in the past 30 days, while controlling

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88 for demographic variables of age, gender, race, and primary language. Analysis was conducted at the population level and within ea ch risk tier subgroup. Model fit was was assessed by convergence criterion satisf action, the difference between Akaike Information Criterion, Schwarz Criterion, a nd negative two times the log-likelihood values for intercept-only and interceptwith-covariates and by examining global null hypothesis testing results (154). Population Level General health status of good or better wa s significantly associ ated with health information use (p=0.001), with age, with black and other/unknown race/ethnicity, and with language in the presence of black a nd other/unknown race/ethnic ity. General health status was not associated with health communication. Mental health status was associated w ith age and language, but not with health information use or health communication. Unhealthy days in the past month was associated with age, but not with health information us e or health communication. Tier 1 Level General health status of good or better was associated with language. Mental health status was associated with gend er and other/unknown race/ethnicity, and number of unhealthy days in the past month was asso ciated with gender. None of the 3 were associated with health informa tion use or health communication. Tier 2 Level General health status of good or better was associated with health information use (p=0.025), age, gender, and other/unknown r ace/ethnicity. Mental health status was associated with age and language. Number of unhealthy days in the past month was

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89 associated with age and white race/ethnicity. Neither mental health status nor unhealthy days in the past month were associated with health information use, and none of the 3 health measures were associated with health communication. Tier 3 Level No significant associations were found between general health status, mental health status, or unhealthy da ys in the past month and either health measures or demographic measures. Examination of the num ber of observations in this tier (141 read; 133 used) and the associated post-stratifica tion weights used in analysis (sum of 4.569542 read; sum of 4.327224 used) led to consid eration of the possibility that there was insufficient power to detect measurable results in this stratum. A secondary examination through unweighted analysis was conducted, on the basis that tier weighting was not applicable in a single-stratum anal ysis and that regres sion techniques would adjust for potential disproportionate impact of age. The findings from the unweighted analysis were concordant with the we ighted analysis, confirming the results. Diffusion of Innovations: T echnology Diffusion Assessment Computers, cell phones, and the Internet were selected as examples of IT innovations that have achieved the “plateau of productivity” because of the length of time between their inception and widespread use. A technology can be deemed to have entered the plateau once 20-30% of potential users have adopted it (155). A combination of user versus nonuser status and duration of technology use reported for each of these 3 technologies were used to categorize users into 5 groups in potential alignment with the 5 categories of innova tion adopters descri bed in DoI theory. Group distribution in the surveyed populat ion was then compared to a reference

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p t h c o opulation o f h e DoI theo r Figur e o mputers, c e f 200 model r etical popul a e s 9, 10, an d e ll phones, a Fi g u r Fi g ur e users classi f a tion distrib u d 11 show d u a nd the Inter n r e 9: Comp u e 10: Cell P h f ied into ad o u tion (Figu r u ration of te c n et in the su u ter Adopti o h one Adopt i o pter catego r r e 3). c hnology us e u rveyed pop u o n – Durati i on – Dura t r ies in strict e (ie, adopti o u lation. i on of Use t ion of Use alignment w on) for 90 w ith

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t e c o p t h t h t h c l i n f o n Surv e e chnologies o mputers, s m otential earl y h ey had ado p h an 10 years h ree technol o l assified as p n novators o r o r between 5 ot already b e Fi g u r yed users w and were us m artphones, y adopters i f p ted all 3 te c Users wer e o gies or if t h p otential ea r r early adopt e 5 and 10 ye a e en classifi e r e 11: Inter n ere classifie d ing the new e and broadb a f they had n o c hnologies, a e classified a h ey had not a r ly majority i e rs and if th e a rs. Users w e e d and they h n et Adopti o d as potenti a e st or most a a nd internet o t already b e a nd if they h a s potential l a dopted bot h i f they had n e y had been e re classifie d h ad been usi n o n – Durati o a l innovator s a dvanced te c access). Us e e en classifie d h ad been usi n l aggards if t h h computers n ot already b using one o d as potenti a n g 1 or mor e o n of Use s if they ha d c hnology in e rs were cla s d as potenti a n g each tec h h ey had ado p and the Int e b een classifi e o r more of t h a l late major i e of the 3 te c d adopted all each class ( t s sified as a l innovator s h nology for m p ted none o f e rnet. Users e d as potent i h e 3 technol o i ty if they h a c hnologies f 91 3 t ablet s if m ore f the were i al o gies a d f or up

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t o p s h o 5 years. T a opulation m Tab Ado p Earl y Earl y Lat e No si g h ows the di s a ble 28 sho w odel. le 28: Tech n p ter Categor y Innovator (1 ) y Adopte r (2 ) y Majority (3 ) e Majority (4 ) Laggards (5 ) g nificant di f s tribution fo r Figure 12 : w s the result i n olo gy Diff u Survey e N=415 y (p=0.754) ) 13 ) 49 ) 130 ) 105 ) 118 f ference bet w r both refere : Model an d i ng classific a u sion Class i e d Surv e % ( w 3.4 0 11. 8 36. 1 28. 1 20. 4 w een popula t nce (group 1 d Surve y ed a tion in co m i fication, S u e yed w gt) M N = 0 8 4 2 1 1 6 1 6 6 4 9 3 t ions was fo 1 ) and surve Population m parison to t h u rve y ed Po p M odel = 200 M 5 2 7 68 68 32 o und (p=0.7 5 e yed (group 2 Distributi o h e reference p ulation M odel % 2.50 13.50 34.00 34.00 16.00 5 4). Figure 1 2 ) populatio n o ns 92 1 2 n s.

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93 CHAPTER VI DISCUSSION This study is believed to be the first to conduct a detailed examination of IT utilization patterns and characteristics bot h in general and when used for health informatics purposes among the vulnerable popula tions traditionally served by the health care safety net, as well as the first to propose and examine the applicability of DoI theoretical principles to a single socioeconomic stratum. The results of this study are anticipated to be useful for developing a ppropriate HIT-based, patient-centered health service delivery approaches and potentially in formative for HIT policy and infrastructure development. Limitations of this study include th e assumption for study purposes that the population surveyed for this study accurately represents the homeless or other groups without a mailing address or telephone numbe r as fixed forms of contact, given that survey outreach was conducted by postal mailing. Although the impact of these limitations is expected to have been minima l, given the low number of people excluded from the sampling frame under these criteria, fu ture studies might a ddress this concern by dedicating resources to outreach conducted at the point of care for these individuals. It is also noteworthy that fewer responses were re ceived from people between the ages of 18 and 29 than in older age brackets, especia lly considering that technology adoption and use is significantly higher in younger populations Consideration should be given to the potential for improving survey response among technology users by supporting targeted online methods for survey invitation and pa rticipation in compar ison to paper-based versions. Consideration should also be give n to the possibility of technology nonuser

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94 underrepresentation among respondents. While pos t-stratification weighting was used to adjust for age differences and nonresponse bi as was deemed to have low probability given the close matching of eligible and respondent populations on demographics of gender, race/ethnicity, and language, future st udies might incorporate alternate ways to reach and engage these specific groups in survey response. The findings from this study further suppor t the IOMÂ’s assertion that information technology can be used to improve consumer h ealth (1), and also extend the applicability of that recommendation to specifically incl ude priority populations among those who can benefit from consumer health informatics so lutions. Given the obser ved effectiveness of HIT solutions for health care in all four domains of the Chronic Care Model (73, 75), this is of particular importance considering that the higher pr evalence and greater burden of chronic disease previously reported among pr iority populations (65, 67) was observed in the study population as well. Two-thirds ( 65.2%) of the study popula tion were stratified into higher-risk tiers based on current chronic disease status and elev ated chronic illness (CDPS) risk score, compared to a reported pr evalence of chronic disease in half the U.S. population overall (66). Self-repor ted health status was poorer in the study population as well, with 26.4% reporting fair or poor ge neral health, compared to 15.9% nationwide; 18% reporting frequent mental distress, compared to 10% nationwide; and a mean of 10 unhealthy days per month compared to 6 unhealthy days per month nationwide (156). The first specific aim of this study was to assess and describe current methods and patterns of IT utilization in general and fo r health information access and engagement in health communications among adult patients wh o receive care in an urban safety net setting. Specific attention was given to how pa tterns of general utiliz ation, utilization for

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95 health information access, and utilizati on for health communication might differ by demographic subgroup or health st atus, as well as to barriers and facilitators encountered by patients with regard to IT and HIT use a nd to opinions held by patients about IT and HIT. It was anticipated that safety net patien ts with chronic diseases would have greater interest in, engagement with a nd utilization of IT for health information access and health communications than would thos e without chronic illness. For technologies that have reached the point of widespread, relatively inexpensive access, utilization in the study population wa s similar to utilization reported both nationwide and in similar populations. Inte rnet use among the surveyed population was reported by 74%, comparable to previously-r eported nationwide use (74%) and to use assessed separately among medically unde rserved groups (72%) and primary care patients (78%) (157-160) .The increasing av ailability of lower-cost mobile technology appears to contribute to higher adoption and utilization in the study population than nationally in some categorie s. For example, 93% of t hose surveyed use cell phones, compared to 87% of adults nationally, and 51% of those surveyed reported smartphone use, compared to 45% of adults nationally ( 125). This finding is similar to findings of mobile “leapfrogging” occurring among prior ity populations (161, 162), referring to the generally-higher adoption of mobile technology in preference to older, more traditional methods (eg, personal computers). In additi on to its observance here, higher cell phone utilization among health care consumers ha s been reported among those with chronic illness (163) and those enrolled in substance abuse treatment (91%; (164)). Evidence of the beneficial impact of IT and HIT on health was observed in the study population as well. Computer ownership and computer use-value were found to be

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96 associated with general and mental hea lth status, cell phone use was found to be associated with unhealthy days in the past month, duration of Inte rnet use was found to be associated with general health status, a nd general health status of good or better was significantly associated with health informa tion use (p=0.001). These findings not only uphold the IOM’s claim, but also give creden ce to the importance of the fourth goal of the ONC’s Federal Health Strategic Plan under the HITECH Act, which is to empower individuals with HIT in order to improve both the individuals’ health and the health care system overall (34) – a goal which implicit ly acknowledges the rele vance of Foucault’s power-knowledge dyad. In addition to their micro-level, applie d Foucaldian significance, examining these findings in the macro-level context of th e previously-described Marxist healthcare construct shown in Figure 2, whic h considers the societal supers tructure of health care in the U.S. as a capitalist system in which prio rity population patients are representative of the worker class, gives additional meaning to the observed associations between IT, HIT, and health status. These associations can be recognized as illustrative of the patientworker having overcome alienation from access to the commodity of health information through achieving ownership of the means of production in the form of technological capacity. Furthermore, the fact that the tec hnological means are selected by the patientworkers themselves according to their own pr eferences rather than imposed upon them by ruling-class capitalist entrepreneurs allows for emergence from beneath the dominant pressure of Gramsci’s cultural hegemony. The problem of the patient-workers’ spontaneous consent to their own exploitati on is solved through the patient-workers’

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97 adoption and use of technologica l means that have already undergone dissemination from the elite to the masses and been accepted as such. Additional examination of detailed st udy findings in accordance with these theoretical contexts cautions ag ainst pursuing a strictly stru cturalist, one-size-fits-all infrastructure-based approach to HIT solutio ns and supports the importance of addressing issues of cultural hegemony through patie nt-centered methods. The predominance of mobile methods of IT use – notebook (68%) ove r desktop (56%) computers as well as the 93% prevalence of cell phones – suggests that solutions designed to take advantage of mobile capacities and limitations will be best-suited for the study population. Smartphone use among half (51%) the cell phone-using population indicates that health app development may be a worthwhile invest ment. Significant differences observed among IT activity patterns, such as the preferen ce for text messaging (84%) over email (73%), the preference for video chat (42%) over text ch at (30%), and the fact that over half the study population engages in video-watching (62%) and is on Facebook (57%) offer possibilities for tailoring health messaging to appropriate communication channels. The significant age-based differences in prevalence of IT use suggest that technology-focused solutions may be more quickly accepted among younger patients; at the same time, the fact that half of patients 60 and above report technology use indicates that health care providers may find value in HI T solutions that take into consideration the needs of geriatric populations as well. This finding is also supported by studies indicating disparity in older users’ t echnology use (165-167) as compar ed to that of younger users. The spike of higher health communication use in the 30s and 40s (66% and 64% respectively) implies the existence of a pe riod during which patients may have greater

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98 interest in engaging in HIT activities in th is domain, and thus a potential timeframe for intervention. This finding is supported by similar observations from the Health Information National Trends Survey, in whic h health information exchange was found to be more highly valued by users ag e 45 to 54 than by younger users (168). Significant differences observed by race/e thnicity clearly indicate that HIT solutions need to be culturally sensitive in order to be successful. For example, black patients had the highest IT utilization of all racial/ethnic groups, acr oss all types of IT: computers (82%), cell phones (99%), and the In ternet (85%), suggesti ng that a variety of HIT solutions for health concerns that more strongly affect black patients and communities may be well-accepted, where soluti ons targeted toward Hispanic/Latino patients and communities may need to be confined to more widely-adopted channels. Evidence indicates that culturally sensitive approaches toward HIT solutions have been found useful; for example, findings from th e MenÂ’s Prostate Aware ness Church Training Project, targeted at church-based health promotion for black men, indicate potential for incorporating HIT support through these channels (169), while user-centric design studies among black youth identified the cu lture-specific need for inclusion of trust components in their informatics solutions (170). In a ddition, Spanish speakers were significantly less likely than English speakers to engage in health information seeking and in textdependent activies such as email, social media, and blog reading and commenting. This suggests that current Spanish-language IT-bas ed print resources may be limited and that visual or audio HIT solutions may be more appropriate for this group, especially when considered in light of findings that indicat e health literacy challe nges are encountered when seeking health information online (171, 172).

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99 The second aim of this study was to compare the health status of IT users and IT non-users among adult patients with chronic disease who receive health care in an urban safety net setting. Adult patients with chronic di sease in this setting who use IT to access health information and engage in health co mmunications were pred icted to have better health status than adult patients with ch ronic disease and who were non-IT users. Findings at the population level indicated a significant, positive association between health information seeking and general health status, as did findings within Tier 2, where chronic disease was present but better-controlled than among patients in Tier 3. No significant associations with health inform ation were seen in other tiers, nor were significant associations with health communica tions found to exist. Where health information seeking is concerned, findings about popular topics among the study population offer insight into pote ntial areas of interest where health care providers could effectively engage with their patients. Interest in food, nutrition, and diet (79%) and in exercise and physical activity (72%) were widespread both among the study population and nationwide (173); patients were also interested in their own medications (66%) and their own (71%) and othersÂ’ (7 1%) diseases and illnesses. Given the population-level association betw een general health status of good or better and health information use, pursuing HIT solutions in th is area may offer particular value to both patients and providers. Similar indications are observed in findings from a national health IT consumer survey, in which 55% of a dults expressed interest in HIT web sites or applications, including specific interest in tracking information about chronic illnesses (42%), tracking diet and nutri tion (36%), tracking exercise and physical activity (33%), and for medication reminders (30%) (174).

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100 Within-tier findings regarding associations between health status measures and health information use are of interest as well, given that risk stra tification was based on CDPS risk scores and chronic disease status for diabetes and hypertension, and thus indicative both of present and projected futu re health outcomes. Tier 1, the lowest-risk and healthiest tier, showed no association be tween health status measures and health information or communication; neither did Tier 3, where health status measures were significantly skewed toward the unhealthier e nd of the spectrum. While the former might be due to lack of interest related to patient s in the lower-risk tier having a generally-better overall health status or to there being insu fficient power to detect smaller effects in a healthier subpopulation, the latter finding is somewhat surp rising. One possibility is that patients in Tier 3 may have already reached a point where the increased power and increased agency associated with patien ts’ knowledge of health information in a Foucaldian framework may be insufficient to effect significan t change on health outcomes in the face of other f actors. If true, this suggests that HIT-based interventions with this higher-risk populati on may be of more value when involving active engagement between patients and health providers in th e context of using health information to improve coordination of care than approaches that depend on patients’ own ability to translate information into knowledge and put it into practice unaided. In addition, the finding of a within-stratum a ssociation between good general health status and use of health information in Tier 2 – the intermedia te-health tier – indicates the existence of a possible intervention point where HIT may be effectively used to either delay or ultimately avoid poor health outcomes. This potential opportunity for increased effectiveness should be taken into consider ation when designing patient-centered HIT-

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101 based health interventions. Additional benefit may be gained by conducting follow-up research to examine tier-specific results in greater depth through collecting comparable data from a larger sample of the population w ithin each risk tier. Another approach would be to examine the longer-term effects of IT and HIT use in a cohort over time. Despite the observation of defined and significant patterns of IT and HIT utilization among the study population, the findi ngs from this study do not disprove the continued existence of the digi tal divide; instead, they affirm it. The digital divide was observed to exist between the study and nati onal populations, taking th e overall shape of “timeshifted” IT adoption rather than t echnology nonuse. For example, 31% of people nationally use tablet computers (125), versus 23% in the study population; 26% nationally use e-readers, versus 17% in th e study population; and 67% nationally are on Facebook (175), versus 57% in the study population. Adoption of IT devices and activities in the study population can be theorized to lag national adoption, increasing over time as price or other barriers to use decrease. For example, examination of the cell phone adoption timeline (Figure 10) shows a spike in cell phone use of less than a month’s duration. It is possible that this increased recent adoption may be attributable in part to the comparatively new availability of free and reduced-price, subsidized Lifeline wireless service to qualifying recipients in the state of Colorado, lowering barriers to access and enabling patient-workers to obtain this particular t echnological means of production. The divide was clearly seen within th e surveyed population as well. Use was observed to be significantly higher among younger people than among seniors, representing the existence of the “gray gap” in technology use (125, 166, 167). Primary

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102 language was also observed to be a barrier, as English speakers were significantly more likely than Spanish speakers to use computers, the Internet, health information, and most other types of other common technology w ith information delivery capacity. Race/ethnicity and gender were also significan t factors affecting use and nonuse, with Hispanic/Latino patients less likely to use co mputers and the Internet, whites less likely to use cell phones, health information, and health communication, and women less likely to use IT or HIT solutions than men in general. In addition, technology nonusers consistently reported barriers of knowledge, acce ss, and cost that hindered their ability to utilize IT and HIT, while at the same tim e expressing interest in overcoming these barriers and crossing the divide – findings that reflect simila r observations both in safety net settings (176) and nationally (94, 95, 173). These hindrances are far from insignifi cant in light of the finding that the Foucauldian power-knowledge dyad was obs erved to be appli cable through the population-level and Tier 2 associations be tween accessing health information and good general health status. These findings indica te support for the premise that people can assume greater agency over their own health status and improve their health outcomes in the presence of health information, which consequently indicates the importance of assisting patients to obtain such agency in th e first place. The health inequity represented by the impact of the digital divide on acces sing health information and achieving the associated better health may we ll be measured in the cost of care, in morbidity, and in mortality. One possible approach to overcoming the de leterious effects of the digital divide is to utilize the concept of the Foucauld ian economy of power to disseminate power-

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103 knowledge across the divide through the influence of social networks. In order to assess the potential to conduct such dissemination activ ities within this popul ation, the third aim of this study was to evaluate the applicabil ity of traditional DoI theory when used to examine patterns of adoption and utilization of health IT among adult patients who receive care in an urban safe ty net setting. It was hypothesize d that members of priority populations would have an interest in using IT to access health information and to engage in health communications that is equivalent to that re ported among members of more advantaged populations, but would not use the same types of IT in the same manner or to the same extent. Health communication findings from this study indicate that patients consider their health-related social networks to primar ily include their families (88%), their friends (75%), and their health care providers (71%). The identi fication of distinct adopter categories within the study population whic h show no significant difference in their population distribution from that of the Do I model population supports the use of DoI theory within this single broad population stratum as well as across population strata. This offers the opportunity to use diffusion research principles with this population, including the chance for DH health care provi ders to consciously act as leaders and “change agents” within patients’ health-related social networks. The role of health care provider as change agent is additionally s upported by studies indica ting that patients are more likely to engage with and have confid ence in IT systems that providers use or recommend (174, 177, 178). In addition to addressing the challeng es of cultural hegemony through patientcentered approaches to health informatic s solutions as discussed above and through

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104 current initiatives to actively collect info rmed consent and patient-provided preferences for IT-based communication methods and topics DH could act at the level of the Marxist capitalist system to alleviate the separa tion between patient-workers and the output commodity of their health information. Usi ng the meaningful use criteria established under the HITECH Act and the recommended sta ndards and solutions under the ACA in combination with the Chronic Care Model as a guide, the operational infrastructure could be altered through implementing changes to DHÂ’s electronic medical record, clinical information and decision support systems to lo wer specific barriers to access in ways that are particularly applicable to and supportive of patient needs. Themes emerging from the qualitative results for this study suggest areas of potential opportunity for such system redesign that are of interest to DH patients, such as making information available through patient portals, providing patients with the ab ility to schedule appoi ntments online or by email or text, and establishing electronic ch annels of communication between patients and their care providers, such as by ema il and text message. DH is also uniquely positioned to represent the needs of priority pop ulations in a leadership role in certain areas on a larger scale; for example, despit e patientsÂ’ expressed preferences and granting of informed, explicit permissions, the implem entation of the HIPAA Privacy and Security Final Rule in January, 2013 limits what he alth information can be exchanged through widely accessible electronic methods such as text messages (179). By working in partnership with its patient population to de velop patient-centered informatics solutions that are tailored and appropriate to populati on needs, DH may be able to provide insights that have the potential to effect needed cha nge within the greater societal superstructure.

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105 In a more immediate, interpersonal sense, DH providers could interact as change agents with interested patients to overcom e knowledge barriers through such means as the provision of technology tr aining classes, conducted si milarly to group education sessions held on topics such as nutrition a nd weight management. Providers and care teams could be trained and engaged as we ll to overcome knowledge gaps and bias inherent in possible misperception of patientsÂ’ technological capabili ties, and could then work appropriately in partners hip with patients at the point of care to identify low-cost, accessible channels for IT and HIT use as well as to address patient concerns about IT and HIT, such as concerns about the privacy and validity of health information obtained through IT and the perceived potential of IT solutions to be impersonal and distancing from their health care providers. Additional future work to inform this area might involve conducting detailed analyses to compare populat ion-specific Bass model coefficients of innovation and imitation (113, 180) for known technologies to th e coefficients observed to exist in the larger population. If equivalence between co efficients is observed, diffusion modeling could then be conducted using the larger-popul ation coefficients to reliably predict technology adoption in vulnerable populations. This predictive modeling could be used to refine IT and HIT development strategies to improve targeting a nd delivery of patientcentered, tailored appropriate health informa tics solutions which c ould be designed in a timeframe that would support use at the timesh ifted point when such technologies hit the productivity plateau in the larger U.S. population and begin to move up the adoption curve in the study population. By actively creating patient-centered solutions for implementation across the timeshift, the DH h ealth care system and its patient population

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106 thus have the chance to work together in partnership to re duce the impact of IT-related health disparities and close th e gap of the digital divide.

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R AP R EMINDE R PENDIX C R POSTCA R C R D (ENGL ISH) 133

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134 APPENDIX D CHI CODEBOOK Part 1: Your Health in General Variable Name: CHI-Q1 Variable Label: CDC HRQOL – 4 item 1 Question Text: Would you say that in general your health is: Values & Labels: 1 – Excellent 2 – Very good 3 – Good 4 – Fair 5 – Poor 7 – Don’t know/not sure 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q2 Notes: Questions and coding: http://www.cdc.gov/hrqol/hrqol14_measure.htm#1 Methods, measures, and SAS syntax: http://www.cdc.gov/hrqol/methods.htm Variable Name: CHI-Q2 Variable Label: CDC HRQOL – 4 item 2 Question Text: Now thinking about your phys ical health, which includes physical illness and injury, for how many days during the past 30 days was your physical health not good? Values & Labels: __ – number of days 88 – None 77 – Don’t know/not sure 99 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q3 Notes: Questions and coding: http://www.cdc.gov/hrqol/hrqol14_measure.htm#1 Methods, measures, and SAS syntax: http://www.cdc.gov/hrqol/methods.htm

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135 Variable Name: CHI-Q3 Variable Label: CDC HRQOL – 4 item 3 Question Text: Now thinking about your me ntal health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good? Values & Labels: __ – number of days 88 – None 77 – Don’t know/not sure 99 – Refused (no answer) Skip Pattern/ Default Next: SKIP to CHI-Q5 if CHI-Q2 AND CHI-Q3 both equal NONE (88); Else CHI-Q4 Notes: Questions and coding: http://www.cdc.gov/hrqol/hrqol14_measure.htm#1 Methods, measures, and SAS syntax: http://www.cdc.gov/hrqol/methods.htm Variable Name: CHI-Q4 Variable Label: CDC HRQOL – 4 item 4 Question Text: During the past 30 days for about how many days did poor physical or mental health keep you from doing your usual activities, such as self-care, work, or recreation? Values & Labels: __ – number of days 88 – None 77 – Don’t know/not sure 99 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q5 Notes: Questions and coding: http://www.cdc.gov/hrqol/hrqol14_measure.htm#1 Methods, measures, and SAS syntax: http://www.cdc.gov/hrqol/methods.htm

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136 Part 2: Using Computers and Cell Phones Variable Name: CHI-Q5 Variable Label: Computer use assessment Question Text: Do you use a computer, at least sometimes? Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: SKIP to CHI-Q11 if 2 (No); Else CHI-Q6 Notes: Modified from Pew Internet & American Life Project – Digital Divisions Survey Topline (2005) Variable Name: CHI-Q6 Variable Label: Computer type Question Text: What kind of computer do you use? Please mark all that apply. Values & Labels: See CHI-Q6 subset items Skip Pattern/ Default Next: CHI-Q6a Notes: Variable Name: CHI-Q6a Variable Label: Computer type = desktop Question Text: Desktop Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q6b Notes: Variable Name: CHI-Q6b Variable Label: Computer type = portable, non-tablet Question Text: Laptop or notebook Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q6c Notes:

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137 Variable Name: CHI-Q6c Variable Label: Computer type = tablet Question Text: Tablet (lik e an iPad or Galaxy) Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q6d Notes: Variable Name: CHI-Q6d Variable Label: Computer type = other Question Text: Another kind Values & Labels: 1 – Yes (f ree-text value provided) 2 – No (blank) Skip Pattern/ Default Next: If CHI-Q6d = yes, then CHI-Q6e; Else CHI-Q7 Notes: Variable Name: CHI-Q6e Variable Label: Other computer type – detail Question Text: _________________________ Values & Labels: Free text narrative content to be coded based on content analysis of responses Skip Pattern/ Default Next: CHI-Q7 Notes: Variable Name: CHI-Q7 Variable Label: Computer use duration Question Text: How long have you been using computers? Values & Labels: 1 – Less than 1 month 2 – From 1 to 6 months 3 – 7 months to a year 4 – 1 or 2 years 5 – 3 to 5 years 6 – 5 to 10 years 7 – more than 10 years 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q8 Notes: Modified from Pew Internet & American Life Project – Chronic Disease Fall Tracking Survey Topline (2008)

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138 Variable Name: CHI-Q8 Variable Label: Com puter use frequency Question Text: About how often do you use a computer? Values & Labels: 1 – Several times a day 2 – About once a day 3 – 3 to 5 days a week 4 – 1 or 2 days a week 5 – Every few weeks 6 – Once a month or less often 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q9 Notes: Modified from Pew Internet & American Life Project – Chronic Disease Fall Tracking Survey Topline (2008) Variable Name: CHI-Q9 Variable Label: Computer use value Question Text: How important is it to you to be able to use a computer? Values & Labels: 1 – Very or always important 2 – Usually or mostly important 3 – Sometimes important 4 – Rarely or only a little important 5 – Not important 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q10 Notes: Modified from Pew Internet & American Life Project – Chronic Disease Fall Tracking Survey Topline (2008) Variable Name: CHI-Q10 Variable Label: Computer ownership Question Text: Do you have a computer of your own? Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q14 Notes:

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139 Variable Name: CHI-Q11 Variable Label: Computer use barriers Question Text: If you don’t use a computer why not? Please mark all that apply. Values & Labels: See CHI-Q11 subset items Skip Pattern/ Default Next: CHI-Q11a Notes: Variable Name: CHI-Q11a Variable Label: Barrier = access Question Text: I don’t have one, and th ere’s not one anywhere I can use Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q11b Notes: Variable Name: CHI-Q11b Variable Label: Barrier = knowledge Question Text: I don’t know how to Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q11c Notes: Variable Name: CHI-Q11c Variable Label: Barrier = need Question Text: I don’t need to Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q11d Notes:

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140 Variable Name: CHI-Q11d Variable Label: Barrier = interest Question Text: I don’t want to, or I’m not interested Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q11e Notes: Variable Name: CHI-Q11e Variable Label: Barrier = difficulty Question Text: It’s too hard or I get frustrated by it Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q11f Notes: Variable Name: CHI-Q11f Variable Label: Barrier = cost Question Text: It’s too expensive Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q11g Notes: Variable Name: CHI-Q11g Variable Label: Barrier = value Question Text: It’s a waste of time Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q11h Notes:

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141 Variable Name: CHI-Q11h Variable Label: Barrier = other Question Text: Some other r eason (please tell us why) Values & Labels: 1 – Yes (fr ee text response provided) 2 – No (blank) Skip Pattern/ Default Next: If CHI-Q11h = yes, then CHI-Q11i; Else CHI-Q12 Notes: Variable Name: CHI-Q11i Variable Label: Other barrier – detail Question Text: _________________________ Values & Labels: Free text narrative content to be coded based on content analysis of responses Skip Pattern/ Default Next: CHI-Q12 Notes: Variable Name: CHI-Q12 Variable Label: Computer use interest (nonusers) Question Text: If you don’t use a computer, would you like to? Pl ease mark all that apply. Values & Labels: See CHI-Q12 subset items Skip Pattern/ Default Next: CHI-Q12a Notes: Variable Name: CHI-Q12a Variable Label: Interest = definite Question Text: Yes Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q12b Notes:

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142 Variable Name: CHI-Q12b Variable Label: Interest = none Question Text: No Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q12c Notes: Variable Name: CHI-Q12c Variable Label: Interest = moderated by access Question Text: Maybe, if I had one Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q12d Notes: Variable Name: CHI-Q12d Variable Label: Interest = moderated by cost Question Text: Maybe, if it didn’t cost too much Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q12e Notes: Variable Name: CHI-Q12e Variable Label: Interest = moderated by knowledge Question Text: Maybe, if I knew how Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q12f Notes:

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143 Variable Name: CHI-Q12f Variable Label: Interest = uncertain Question Text: I don’t know / I’m not sure Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q13 Notes: Variable Name: CHI-Q13 Variable Label: Computer use barrier mediation (nonusers) Question Text: If you don’t use a computer, what would make it easier for you to use a computer? Please mark all that apply. Values & Labels: See CHI-Q13 subset items Skip Pattern/ Default Next: CHI-Q13a Notes: http://psych.wisc.edu/henriques/mediator.html “The classic reference on this topic is Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual strategic, and statistical considerations. Journal of Pers onality and Social Psychology, 51, 1173-1182.” Variable Name: CHI-Q13a Variable Label: Mediator = education Question Text: Lessons on how to use computers Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q13b Notes: Variable Name: CHI-Q13b Variable Label: Mediator = pricing Question Text: Lower cost, so that I could afford one Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q13c Notes:

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144 Variable Name: CHI-Q13c Variable Label: Mediator = fluency Question Text: Having computer programs and information in my native language Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q13d Notes: Variable Name: CHI-Q13d Variable Label: Mediator = unknown Question Text: I don’t know Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q13e Notes: Variable Name: CHI-Q13e Variable Label: Mediator = other Question Text: Something el se (please tell us what) Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: If CHI-Q13e = yes, then CHI-Q13f; Else CHI-Q14 Notes: Variable Name: CHI-Q13f Variable Label: Other mediator – detail Question Text: _________________________ Values & Labels: Free text narrative content to be coded based on content analysis of responses Skip Pattern/ Default Next: CHI-Q14 Notes:

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145 Variable Name: CHI-Q14 Variable Label: Cell phone use assessment Question Text: Do you use a cell phone, at least sometimes? Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: SKIP to CHI-Q20 if 2 (No); Else CHI-Q15 Notes: Modified from Pew Internet & American Life Project – Digital Divisions Survey Topline (2005) Variable Name: CHI-Q15 Variable Label: Cell phone type Question Text: What kind of cell phone do you use? Please mark all that apply. Values & Labels: See CHI-Q15 subset items Skip Pattern/ Default Next: CHI-Q15a Notes: Variable Name: CHI-Q15a Variable Label: Cell phone type = smart Question Text: A “smart” phone (like an iPhone or Android) Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q15b Notes: Variable Name: CHI-Q15b Variable Label: Cell phone type = regular Question Text: A regular or basic phone Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q15c Notes:

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146 Variable Name: CHI-Q15c Variable Label: Cell phone type = other Question Text: Another kind Values & Labels: 1 – Yes (f ree-text value provided) 2 – No (blank) Skip Pattern/ Default Next: If CHI-Q15c = yes, then CHI-Q15d; Else CHI-Q16 Notes: Variable Name: CHI-Q15d Variable Label: Other cell phone type – detail Question Text: _________________________ Values & Labels: Free text narrative content to be coded based on content analysis of responses Skip Pattern/ Default Next: CHI-Q16 Notes: Variable Name: CHI-Q16 Variable Label: Cell phone use duration Question Text: How long have you been using cell phones? Values & Labels: 1 – Less than 1 month 2 – From 1 to 6 months 3 – 7 months to a year 4 – 1 or 2 years 5 – 3 to 5 years 6 – 5 to 10 years 7 – more than 10 years 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q17 Notes: Modified from Pew Internet & American Life Project – Chronic Disease Fall Tracking Survey Topline (2008)

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147 Variable Name: CHI-Q17 Variable Label: Cell phone use frequency Question Text: About how often do you use a cell phone? Values & Labels: 1 – Several times a day 2 – About once a day 3 – 3 to 5 days a week 4 – 1 or 2 days a week 5 – Every few weeks 6 – Once a month or less often 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q18 Notes: Modified from Pew Internet & American Life Project – Chronic Disease Fall Tracking Survey Topline (2008) Variable Name: CHI-Q18 Variable Label: Cell phone use value Question Text: How important is it to you to be able to use a cell phone? Values & Labels: 1 – Very or always important 2 – Usually or mostly important 3 – Sometimes important 4 – Rarely or only a little important 5 – Not important 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q19 Notes: Modified from Pew Internet & American Life Project – Chronic Disease Fall Tracking Survey Topline (2008) Variable Name: CHI-Q19 Variable Label: Cell phone ownership Question Text: Do you have a cell phone of your own? Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q23 Notes:

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148 Variable Name: CHI-Q20 Variable Label: Cell phone use barriers Question Text: If you don’t us e a cell phone, why not? Plea se mark all that apply. Values & Labels: See CHI-Q20 subset items Skip Pattern/ Default Next: CHI-Q20a Notes: Variable Name: CHI-Q20a Variable Label: Barrier = access Question Text: I don’t have one, and th ere’s not one anywhere I can use Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q20b Notes: Variable Name: CHI-Q20b Variable Label: Barrier = knowledge Question Text: I don’t know how to Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q20c Notes: Variable Name: CHI-Q20c Variable Label: Barrier = need Question Text: I don’t need to Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q20d Notes:

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149 Variable Name: CHI-Q20d Variable Label: Barrier = interest Question Text: I don’t want to, or I’m not interested Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q20e Notes: Variable Name: CHI-Q20e Variable Label: Barrier = difficulty Question Text: It’s too hard or I get frustrated by it Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q20f Notes: Variable Name: CHI-Q20f Variable Label: Barrier = cost Question Text: It’s too expensive Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q20g Notes: Variable Name: CHI-Q20g Variable Label: Barrier = value Question Text: It’s a waste of time Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q20h Notes:

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150 Variable Name: CHI-Q20h Variable Label: Barrier = other Question Text: Some other r eason (please tell us why) Values & Labels: 1 – Yes (fr ee text response provided) 2 – No (blank) Skip Pattern/ Default Next: If CHI-Q20h = yes, then CHI-Q20i; Else CHI-Q21 Notes: Variable Name: CHI-Q20i Variable Label: Other barrier – detail Question Text: _________________________ Values & Labels: Free text narrative content to be coded based on content analysis of responses Skip Pattern/ Default Next: CHI-Q21 Notes: Variable Name: CHI-Q21 Variable Label: Cell phone use interest (nonusers) Question Text: If you don’t us e a cell phone, would you lik e to? Please mark all that apply. Values & Labels: See CHI-Q21 subset items Skip Pattern/ Default Next: CHI-Q21a Notes: Variable Name: CHI-Q21a Variable Label: Interest = definite Question Text: Yes Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q21b Notes:

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151 Variable Name: CHI-Q21b Variable Label: Interest = none Question Text: No Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q21c Notes: Variable Name: CHI-Q21c Variable Label: Interest = moderated by access Question Text: Maybe, if I had one Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q21d Notes: Variable Name: CHI-Q21d Variable Label: Interest = moderated by cost Question Text: Maybe, if it didn’t cost too much Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q21e Notes: Variable Name: CHI-Q21e Variable Label: Interest = moderated by knowledge Question Text: Maybe, if I knew how Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q21f Notes:

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152 Variable Name: CHI-Q21f Variable Label: Interest = uncertain Question Text: I don’t know / I’m not sure Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q22 Notes: Variable Name: CHI-Q22 Variable Label: Cell phone use barrier mediation (nonusers) Question Text: If you don’t us e a cell phone, what would make it easier for you to use a cell phone? Please mark all that apply. Values & Labels: See CHI-Q22 subset items Skip Pattern/ Default Next: CHI-Q22a Notes: http://psych.wisc.edu/henriques/mediator.html “The classic reference on this topic is Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual strategic, and statistical considerations. Journal of Pers onality and Social Psychology, 51, 1173-1182.” Variable Name: CHI-Q22a Variable Label: Mediator = education Question Text: Lessons on how to use cell phones Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q22b Notes: Variable Name: CHI-Q22b Variable Label: Mediator = pricing Question Text: Lower cost, so that I could afford one Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q22c Notes:

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153 Variable Name: CHI-Q22c Variable Label: Mediator = fluency Question Text: Having cell phone programs a nd information in my native language Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q22d Notes: Variable Name: CHI-Q22d Variable Label: Mediator = unknown Question Text: I don’t know Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q22e Notes: Variable Name: CHI-Q22e Variable Label: Mediator = other Question Text: Something el se (please tell us what) Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: If CHI-Q22e = yes, then CHI-Q22f; Else CHI-Q23 Notes: Variable Name: CHI-Q22f Variable Label: Other mediator – detail Question Text: _________________________ Values & Labels: Free text narrative content to be coded based on content analysis of responses Skip Pattern/ Default Next: CHI-Q23 Notes:

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154 Variable Name: CHI-Q23 Variable Label: IT activities Question Text: Do you use a computer a nd/or a cell phone to do any of the following things, at least sometime s? Please mark all that apply. Values & Labels: See CHI-Q23 subset items Skip Pattern/ Default Next: CHI-Q23a Notes: Modified from Pew Internet & American Life Project – Chronic Disease Fall Tracking Survey Topline (2008) Variable Name: CHI-Q23a Variable Label: Activity = email Question Text: Send or receive email Values & Labels: 1 – Computer 2 – Cell phone 3 – Both 4 – Neither 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q23b Notes: Variable Name: CHI-Q23c Variable Label: Activity = text Question Text: Send or receive text messages Values & Labels: 1 – Computer 2 – Cell phone 3 – Both 4 – Neither 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q23c Notes:

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155 Variable Name: CHI-Q23c Variable Label: Activity = voice calls Question Text: Make or receive voice calls Values & Labels: 1 – Computer 2 – Cell phone 3 – Both 4 – Neither 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q23d Notes: Variable Name: CHI-Q23d Variable Label: Activity = consume videos Question Text: Watch videos (like on YouTube) Values & Labels: 1 – Computer 2 – Cell phone 3 – Both 4 – Neither 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q23e Notes: Variable Name: CHI-Q23e Variable Label: Activity = create videos Question Text: Make and post your own videos (like on YouTube) Values & Labels: 1 – Computer 2 – Cell phone 3 – Both 4 – Neither 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q23f Notes:

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156 Variable Name: CHI-Q23f Variable Label: Activity = music Question Text: Play or listen to music Values & Labels: 1 – Computer 2 – Cell phone 3 – Both 4 – Neither 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q23g Notes: Variable Name: CHI-Q23g Variable Label: Activity = Facebook Question Text: Read, post, or comment on Facebook Values & Labels: 1 – Computer 2 – Cell phone 3 – Both 4 – Neither 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q23h Notes: Variable Name: CHI-Q23h Variable Label: Activity = Twitter Question Text: Read or post to Twitter Values & Labels: 1 – Computer 2 – Cell phone 3 – Both 4 – Neither 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q23i Notes:

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157 Variable Name: CHI-Q23i Variable Label: Activity = solo gaming Question Text: Play games yourself Values & Labels: 1 – Computer 2 – Cell phone 3 – Both 4 – Neither 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q23j Notes: Variable Name: CHI-Q23j Variable Label: Activity = social gaming Question Text: Play games with other people Values & Labels: 1 – Computer 2 – Cell phone 3 – Both 4 – Neither 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q23k Notes: Variable Name: CHI-Q23k Variable Label: Activity = create blogs Question Text: Write and post to your own blog or online journal Values & Labels: 1 – Computer 2 – Cell phone 3 – Both 4 – Neither 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q23l Notes:

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158 Variable Name: CHI-Q23l Variable Label: Activity = consume blogs Question Text: Read or comment on other people’s blogs or online journal posts Values & Labels: 1 – Computer 2 – Cell phone 3 – Both 4 – Neither 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q23m Notes: Variable Name: CHI-Q23m Variable Label: Activity = Pinterest Question Text: Read, post, or share things on websites like Pinterest Values & Labels: 1 – Computer 2 – Cell phone 3 – Both 4 – Neither 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q23n Notes: Variable Name: CHI-Q23n Variable Label: Activity = social pictures Question Text: Look at or post pi ctures on Instagram or Flickr Values & Labels: 1 – Computer 2 – Cell phone 3 – Both 4 – Neither 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q23o Notes:

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159 Variable Name: CHI-Q23o Variable Label: Activity = video chat Question Text: Talk to people with video chat or Skype Values & Labels: 1 – Computer 2 – Cell phone 3 – Both 4 – Neither 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q23p Notes: Variable Name: CHI-Q23p Variable Label: Activity = direct text chat Question Text: Talk to people one on on e, like with AIM, Yahoo!Messenger, or Google Chat Values & Labels: 1 – Computer 2 – Cell phone 3 – Both 4 – Neither 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q23q Notes: Variable Name: CHI-Q23q Variable Label: Activity = group text chat Question Text: Talk to groups of people in chat rooms Values & Labels: 1 – Computer 2 – Cell phone 3 – Both 4 – Neither 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q23r Notes:

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160 Variable Name: CHI-Q23r Variable Label: Activity = news Question Text: Read news Values & Labels: 1 – Computer 2 – Cell phone 3 – Both 4 – Neither 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q23s Notes: Variable Name: CHI-Q23s Variable Label: Activity = broadcast media Question Text: Watch TV shows or movies Values & Labels: 1 – Computer 2 – Cell phone 3 – Both 4 – Neither 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q23t Notes: Variable Name: CHI-Q23t Variable Label: Activity = information seeking Question Text: Look up information Values & Labels: 1 – Computer 2 – Cell phone 3 – Both 4 – Neither 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q23u Notes:

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161 Variable Name: CHI-Q23u Variable Label: Activ ity = computer other Question Text: Other things with computers? Please tell us what: Values & Labels: 1 – Yes (true) 2 – No (false/no answer) Skip Pattern/ Default Next: If CHI-Q23u = yes, then CHI-Q23v; Else CHIQ23w Notes: Variable Name: CHI-Q23v Variable Label: Computer other activity = detail Question Text: _______________________ Values & Labels: Free text narrativ e to be coded based on responses Skip Pattern/ Default Next: CHIQ23w Notes: Variable Name: CHI-Q23w Variable Label: Activity = cell phone other Question Text: Other things with cell phones? Please tell us what: Values & Labels: 1 – Yes (true) 2 – No (false/no answer) Skip Pattern/ Default Next: If CHI-Q23w = yes, then CHI-Q23x; Else CHIQ24 Notes: Variable Name: CHI-Q23x Variable Label: Cell phone other activity = detail Question Text: _______________________ Values & Labels: Free text narrativ e to be coded based on responses Skip Pattern/ Default Next: CHIQ24 Notes:

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162 Variable Name: CHI-Q24 Variable Label: Health IT – information Question Text: Do you use a computer, cell phon e, or other device to keep track of information about your health, at least sometimes? Health information can include things like what medicines you take, when your medical appointments are, l ooking up things like lab test results or information about a disease, and home monitoring of things like your blood pressure, blood sugar, how you are feeling, and how much you exercise. Please mark all that apply. Values & Labels: See CHI-24 subset questions Skip Pattern/ Default Next: CHIQ24a Notes: Variable Name: CHI-Q24a Variable Label: HIT information – computer Question Text: Computer Values & Labels: 1 – Yes (true) 2 – No (false/no response) Skip Pattern/ Default Next: CHIQ24b Notes: Variable Name: CHI-Q24b Variable Label: HIT information – cell phone Question Text: Cell phone Values & Labels: 1 – Yes (true) 2 – No (false/no response) Skip Pattern/ Default Next: CHIQ24c Notes: Variable Name: CHI-Q24c Variable Label: HIT information – other Question Text: Another device Values & Labels: 1 – Yes (true) 2 – No (false/no response) Skip Pattern/ Default Next: CHIQ24d Notes: Variable Name: CHI-Q24d

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163 Variable Label: HIT information – none Question Text: I don’t do this Values & Labels: 1 – Yes (true) 2 – No (false/no response) Skip Pattern/ Default Next: CHIQ25 Notes: Variable Name: CHI-Q25 Variable Label: Health IT – communication Question Text: Do you use a computer, cell phon e, or other device to talk to other people about health or health car e, at least sometimes? This is called health communication, and can include things like calling a provider to ask questions between visits, calling, emailing or texting people you know to talk a bout health or h ealth care, and posting comments, questions, or stories about health online for other people to see and reply to. Please mark all that apply. Values & Labels: See CHI-25 subset questions Skip Pattern/ Default Next: CHIQ25a Notes: Variable Name: CHI-Q25a Variable Label: HIT co mmunication – computer Question Text: Computer Values & Labels: 1 – Yes (true) 2 – No (false/no response) Skip Pattern/ Default Next: CHIQ25b Notes: Variable Name: CHI-Q25b Variable Label: HIT communication – cell phone Question Text: Cell phone Values & Labels: 1 – Yes (true) 2 – No (false/no response) Skip Pattern/ Default Next: CHIQ25c Notes:

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164 Variable Name: CHI-Q25c Variable Label: HIT communication – other Question Text: Another device Values & Labels: 1 – Yes (true) 2 – No (false/no response) Skip Pattern/ Default Next: CHIQ25d Notes: Variable Name: CHI-Q25d Variable Label: HIT communication – none Question Text: I don’t do this Values & Labels: 1 – Yes (true) 2 – No (false/no response) Skip Pattern/ Default Next: CHIQ26 Notes: Variable Name: CHI-Q26 Variable Label: Other IT use – computer Question Text: Are there things you would like to use computers to do, but can’t? Please tell us what and why. Values & Labels: 1 – Yes (true) 2 – No (false/no response) Skip Pattern/ Default Next: If CHI-Q26 = yes, then CHI-26a; Else CHI-Q27 Notes: Variable Name: CHI-Q26a Variable Label: Other IT computer use – detail Question Text: __________________ Values & Labels: Free text narrative to be coded depending on responses Skip Pattern/ Default Next: CHI-Q27 Notes:

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165 Variable Name: CHI-Q27 Variable Label: Other IT use – cell phone Question Text: Are there things you would like to use cell phones to do, but can’t? Please tell us what and why. Values & Labels: 1 – Yes (true) 2 – No (false/no response) Skip Pattern/ Default Next: If CHI-Q27 = yes, then CHI-27a; Else CHI-Q28 Notes: Variable Name: CHI-Q27a Variable Label: Other IT cell phone use – detail Question Text: __________________ Values & Labels: Free text narrative to be coded depending on responses Skip Pattern/ Default Next: CHI-Q28 Notes: Part 3: Using the Internet Variable Name: CHI-Q28 Variable Label: Internet use Question Text: Do you use the Internet (“go online”), at least sometimes? Values & Labels: 1 – Yes 2 – No 9 – Refused (no response) Skip Pattern/ Default Next: If CHI-Q28 = no, then CHIQ34; Else CHI-Q29 Notes: Modified from Pew Internet & American Life Project – Digital Divisions Survey Topline (2005) Variable Name: CHI-Q29 Variable Label: Internet access type Question Text: How do you access the Internet (go online)? Please mark all that apply. Values & Labels: See CHI-Q9 subset items Skip Pattern/ Default Next: CHI-Q29a Notes:

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166 Variable Name: CHI-Q29a Variable Label: Access type = computer, desktop Question Text: Desktop computer Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q29b Notes: Variable Name: CHI-Q29b Variable Label: Access t ype = computer, portable Question Text: Laptop, notebook, or tablet computer Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q29c Notes: Variable Name: CHI-Q29c Variable Label: Access type = cell phone Question Text: Cell phone Values & Labels: 1 – Yes (f ree-text value provided) 2 – No (blank) Skip Pattern/ Default Next: CHI-Q29d Notes: Variable Name: CHI-Q29d Variable Label: Access type = other Question Text: Another way Values & Labels: 1 – Yes (f ree-text value provided) 2 – No (blank) Skip Pattern/ Default Next: If CHI-Q29d = yes, then CHI-Q29e; Else CHI-Q30 Notes:

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167 Variable Name: CHI-Q29e Variable Label: Other access type – detail Question Text: _________________________ Values & Labels: Free text narrative content to be coded based on content analysis of responses Skip Pattern/ Default Next: CHI-Q30 Notes: Variable Name: CHI-Q30 Variable Label: In ternet use duration Question Text: How long have you been using the Internet? Values & Labels: 1 – Less than 1 month 2 – From 1 to 6 months 3 – 7 months to a year 4 – 1 or 2 years 5 – 3 to 5 years 6 – 5 to 10 years 7 – more than 10 years 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q31 Notes: Modified from Pew Internet & American Life Project – Chronic Disease Fall Tracking Survey Topline (2008)

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168 Variable Name: CHI-Q31 Variable Label: Internet use frequency Question Text: About how of ten do you use the Internet? Values & Labels: 1 – Several times a day 2 – About once a day 3 – 3 to 5 days a week 4 – 1 or 2 days a week 5 – Every few weeks 6 – Once a month or less often 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q32 Notes: Modified from Pew Internet & American Life Project – Chronic Disease Fall Tracking Survey Topline (2008) Variable Name: CHI-Q32 Variable Label: Internet use value Question Text: How important is it to you to be able to use the Internet (go online)? Values & Labels: 1 – Very or always important 2 – Usually or mostly important 3 – Sometimes important 4 – Rarely or only a little important 5 – Not important 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q33 Notes: Modified from Pew Internet & American Life Project – Chronic Disease Fall Tracking Survey Topline (2008)

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169 Variable Name: CHI-Q33 Variable Label: Broadband access Question Text: Do you have broadband or high-speed Internet access? Values & Labels: 1 – Yes 2 – No 3 – I don’t know 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q37 Notes: Variable Name: CHI-Q34 Variable Label: Internet use barriers Question Text: If you don’t us e the Internet (go online) why not? Please mark all that apply. Values & Labels: See CHI-Q34 subset items Skip Pattern/ Default Next: CHI-Q34a Notes: Variable Name: CHI-Q34a Variable Label: Barrier = access Question Text: I don’t have access to it at home, and there’s anywhere else I can use it Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q34b Notes: Variable Name: CHI-Q34b Variable Label: Barrier = knowledge Question Text: I don’t know how to Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q34c Notes:

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170 Variable Name: CHI-Q34c Variable Label: Barrier = need Question Text: I don’t need to Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q34d Notes: Variable Name: CHI-Q34d Variable Label: Barrier = interest Question Text: I don’t want to, or I’m not interested Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q34e Notes: Variable Name: CHI-Q34e Variable Label: Barrier = difficulty Question Text: It’s too hard or I get frustrated by it Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q34f Notes: Variable Name: CHI-Q34f Variable Label: Barrier = cost Question Text: It’s too expensive Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q34g Notes:

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171 Variable Name: CHI-Q34g Variable Label: Barrier = value Question Text: It’s a waste of time Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q34h Notes: Variable Name: CHI-Q34h Variable Label: Barrier = other Question Text: Some other r eason (please tell us why) Values & Labels: 1 – Yes (fr ee text response provided) 2 – No (blank) Skip Pattern/ Default Next: If CHI-Q34h = yes, then CHI-Q34i; Else CHI-Q35 Notes: Variable Name: CHI-Q34i Variable Label: Other barrier – detail Question Text: _________________________ Values & Labels: Free text narrative content to be coded based on content analysis of responses Skip Pattern/ Default Next: CHI-Q35 Notes: Variable Name: CHI-Q35 Variable Label: Internet use interest (nonusers) Question Text: If you don’t use the Internet (go online), would you like to? Please mark all that apply. Values & Labels: See CHI-Q35 subset items Skip Pattern/ Default Next: CHI-Q35a Notes:

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172 Variable Name: CHI-Q35a Variable Label: Interest = definite Question Text: Yes Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q35b Notes: Variable Name: CHI-Q35b Variable Label: Interest = none Question Text: No Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q25c Notes: Variable Name: CHI-Q35c Variable Label: Interest = moderated by access Question Text: Maybe, if I had access Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q35d Notes: Variable Name: CHI-Q35d Variable Label: Interest = moderated by cost Question Text: Maybe, if it didn’t cost too much Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q35e Notes:

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173 Variable Name: CHI-Q35e Variable Label: Interest = moderated by knowledge Question Text: Maybe, if I knew how Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q35f Notes: Variable Name: CHI-Q35f Variable Label: Interest = uncertain Question Text: I don’t know / I’m not sure Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q36 Notes: Variable Name: CHI-Q36 Variable Label: Internet us e barrier mediation (nonusers) Question Text: If you don’t use the Internet (go online), wh at would make it easier for you to use the Internet? Pl ease mark all that apply. Values & Labels: See CHI-Q36 subset items Skip Pattern/ Default Next: CHI-Q36a Notes: http://psych.wisc.edu/henriques/mediator.html “The classic reference on this topic is Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual strategic, and statistical considerations. Journal of Pers onality and Social Psychology, 51, 1173-1182.”

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174 Variable Name: CHI-Q36a Variable Label: Mediator = education Question Text: Lessons on how to use the Internet Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q36b Notes: Variable Name: CHI-Q36b Variable Label: Mediator = pricing Question Text: Lower cost, so that I could afford access to the Internet Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q36c Notes: Variable Name: CHI-Q36c Variable Label: Mediator = fluency Question Text: Having information available in my native language Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q36d Notes: Variable Name: CHI-Q36d Variable Label: Mediator = unknown Question Text: I don’t know Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q36e Notes:

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175 Variable Name: CHI-Q36e Variable Label: Mediator = other Question Text: Something el se (please tell us what) Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: If CHI-Q36e = yes, then CHI-Q36f; Else CHI-Q37 Notes: Variable Name: CHI-Q36f Variable Label: Other mediator – detail Question Text: _________________________ Values & Labels: Free text narrative content to be coded based on content analysis of responses Skip Pattern/ Default Next: CHI-Q37 Notes: Part 4: Learning About Health Information Through Technology Variable Name: CHI-Q37 Variable Label: HIT use – information Question Text: Do you use a computer, ce ll phone, or other device to look up health information, at least sometimes? Values & Labels: 1 – Yes 2 – No 9 – Refused (no response) Skip Pattern/ Default Next: If CHI-Q37 = no, then CHIQ43; Else CHI-Q38 Notes: Modified from Pew Internet & American Life Project – Chronic Disease Fall Tracking Survey Topline (2008)

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176 Variable Name: CHI-Q38 Variable Label: HIT information – object Question Text: Do you look up health info rmation for yourself or for someone else? Please mark all that apply. Values & Labels: 1 – Self 2 – Other 3 – Both 9 – Refused (no response) Skip Pattern/ Default Next: CHI-Q39 Notes: Variable Name: CHI-Q39 Variable Label: HIT information – duration Question Text: How long have you been looking up health information? Values & Labels: 1 – Less than 1 month 2 – From 1 to 6 months 3 – 7 months to a year 4 – 1 or 2 years 5 – 3 to 5 years 6 – 5 to 10 years 7 – more than 10 years 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q40 Notes: Modified from Pew Internet & American Life Project – Chronic Disease Fall Tracking Survey Topline (2008)

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177 Variable Name: CHI-Q40 Variable Label: HIT information – frequency Question Text: About how often do you look up health information? Values & Labels: 1 – Several times a day 2 – About once a day 3 – 3 to 5 days a week 4 – 1 or 2 days a week 5 – Every few weeks 6 – Once a month or less often 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q41 Notes: Modified from Pew Internet & American Life Project – Chronic Disease Fall Tracking Survey Topline (2008) Variable Name: CHI-Q41 Variable Label: HIT information – value Question Text: How important is it to you to be able to look up health information? Values & Labels: 1 – Very or always important 2 – Usually or mostly important 3 – Sometimes important 4 – Rarely or only a little important 5 – Not important 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q42 Notes: Modified from Pew Internet & American Life Project – Chronic Disease Fall Tracking Survey Topline (2008) Variable Name: CHI-Q42 Variable Label: HIT information activities Question Text: What kinds of health information do you look up, at least sometimes? Please mark all that apply. Values & Labels: See CHI-Q42 subset items Skip Pattern/ Default Next: CHI-Q42a Notes: Modified from Pew Internet & American Life Project – Chronic Disease Fall Tracking Survey Topline (2008)

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178 Variable Name: CHI-Q42a Variable Label: Activity = disease, self Question Text: About a dise ase or illness you have Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q42b Notes: Variable Name: CHI-Q42b Variable Label: Activity = disease, other Question Text: About a disease or illness someone you know has Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q42c Notes: Variable Name: CHI-Q42c Variable Label: Activity = surgery, self Question Text: About surgery you are going to have Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q42d Notes: Didn’t include past surgeries in framing. Oops.

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179 Variable Name: CHI-Q42d Variable Label: Activity = surgery, other Question Text: About surgery someone you know is going to have Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q42e Notes: Didn’t include past surgeries in framing. Oops. Variable Name: CHI-Q42e Variable Label: Activity = symptoms Question Text: About how you are feeling Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q42f Notes: Variable Name: CHI-Q42f Variable Label: Activity = medication, self Question Text: About medicines you take Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q42g Notes:

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180 Variable Name: CHI-Q42g Variable Label: Activity = medication, other Question Text: About medicines someone you know takes Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q42h Notes: Variable Name: CHI-Q42h Variable Label: Activity = insurance Question Text: About health insurance Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q42i Notes: Variable Name: CHI-Q42i Variable Label: Activity = provider Question Text: About doctors or other health care providers Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q42j Notes:

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181 Variable Name: CHI-Q42j Variable Label: Activity = labs, self Question Text: Lab test results for yourself Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q42k Notes: Variable Name: CHI-Q42k Variable Label: Activity = labs, other Question Text: Lab test results for someone you know Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q42l Notes: Variable Name: CHI-Q42l Variable Label: Activity = visit notes, self Question Text: Notes from your last appoi ntment with my health care provider Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q42m Notes: Pronoun disagreement included in survey as grammatical error

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182 Variable Name: CHI-Q42m Variable Label: Activity = visit notes, other Question Text: Notes from someone else’s appointment with their health care provider Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q42n Notes: Variable Name: CHI-Q42n Variable Label: Activity = exercise Question Text: About exerci se or physical activity Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q42o Notes: Variable Name: CHI-Q42o Variable Label: Activity = nutrition Question Text: About f ood, nutrition, or diet Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q42p Notes:

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183 Variable Name: CHI-Q42p Variable Label: Activity = reproductive planning Question Text: About birth c ontrol or family planning Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q42q Notes: Variable Name: CHI-Q42q Variable Label: Activity = habitual behavior change Question Text: About habits you want to change (like drinking or smoking) Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q42r Notes: Variable Name: CHI-Q42r Variable Label: Activity = health news Question Text: About health topics in the news Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q42s Notes:

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184 Variable Name: CHI-Q42s Variable Label: Activity = other Question Text: Other kinds of health information (please tell us what) Values & Labels: 1 – Yes (free text response) 2 – No (no answer) Skip Pattern/ Default Next: If CHI-Q42s = yes, then CHI-Q42t; Else CHI-Q45 Notes: Variable Name: CHI-Q42t Variable Label: Other activity – detail Question Text: _______________________ Values & Labels: Free text response to be coded based on content analysis of results Skip Pattern/ Default Next: CHI-Q45 Notes: Variable Name: CHI-Q43 Variable Label: HIT information barriers Question Text: If you don’t use a computer cell phone, or othe r device to look up health information, why not? Pl ease mark all that apply. Values & Labels: See CHI-Q43 subset items Skip Pattern/ Default Next: CHI-Q43a Notes: Variable Name: CHI-Q43a Variable Label: Barrier = knowledge Question Text: I don’t know how to Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q43b Notes:

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185 Variable Name: CHI-Q43b Variable Label: Barrier = need Question Text: I don’t need to Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q43c Notes: Variable Name: CHI-Q43c Variable Label: Barrier = interest Question Text: I don’t want to, or I’m not interested Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q43d Notes: Variable Name: CHI-Q43d Variable Label: Barrier = difficulty Question Text: It’s too hard or I get frustrated by it Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q43e Notes: Variable Name: CHI-Q43e Variable Label: Barrier = health literacy Question Text: I don’t understand th e health information I find Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q43f Notes:

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186 Variable Name: CHI-Q43f Variable Label: Barrier = trust Question Text: I don’t trust th e health information I find Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q43g Notes: Variable Name: CHI-Q43g Variable Label: Barrier = personal interaction Question Text: I would rather ask my health care provider in person Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q43h Notes: Variable Name: CHI-Q43h Variable Label: Barrier = privacy concerns Question Text: I am worried about my privacy Values & Labels: 1 – Yes 2 – No Skip Pattern/ Default Next: CHI-Q43i Notes: Variable Name: CHI-Q43i Variable Label: Barrier = other Question Text: Some other r eason (please tell us why) Values & Labels: 1 – Yes (fr ee text response provided) 2 – No (blank) Skip Pattern/ Default Next: If CHI-Q43i = yes, then CHI-Q43j; Else CHI-Q44 Notes:

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187 Variable Name: CHI-Q43j Variable Label: Other barrier – detail Question Text: _________________________ Values & Labels: Free text narrative content to be coded based on content analysis of responses Skip Pattern/ Default Next: CHI-Q44 Notes: Variable Name: CHI-Q44 Variable Label: HIT inform ation interest (nonusers) Question Text: If you don’t use a computer cell phone, or othe r device to look up health information, would you like to? Please mark all that apply. Values & Labels: See CHI-Q44 subset items Skip Pattern/ Default Next: CHI-Q44a Notes: Variable Name: CHI-Q44a Variable Label: Interest = definite Question Text: Yes Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q44b Notes: Variable Name: CHI-Q44b Variable Label: Interest = none Question Text: No Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q44c Notes:

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188 Variable Name: CHI-Q44c Variable Label: Interest = moderated by access Question Text: Maybe, if I had a way to Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q44d Notes: Variable Name: CHI-Q44d Variable Label: Interest = moderated by cost Question Text: Maybe, if it didn’t cost too much Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q44e Notes: Variable Name: CHI-Q44e Variable Label: Interest = moderated by knowledge Question Text: Maybe, if I knew how Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q44f Notes: Variable Name: CHI-Q44f Variable Label: Interest = moderated by security Question Text: Maybe, if I was sure my pe rsonal information was safe and private Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q44g Notes:

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189 Variable Name: CHI-Q44g Variable Label: Interest = uncertain Question Text: I don’t know / I’m not sure Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q45 Notes: Part 5: Talking With Others About Health Through Technology Variable Name: CHI-Q45 Variable Label: HIT use – communication Question Text: Do you use a computer, cell phon e, or other device to talk to other people about health or health care, at least sometimes? Values & Labels: 1 – Yes 2 – No 9 – Refused (no response) Skip Pattern/ Default Next: If CHI-Q45 = no, then CHIQ51; Else CHI-Q46 Notes: Modified from Pew Internet & American Life Project – Chronic Disease Fall Tracking Survey Topline (2008) Variable Name: CHI-Q46 Variable Label: HIT co mmunication – duration Question Text: How long have you been talki ng to others about health or health care this way? Values & Labels: 1 – Less than 1 month 2 – From 1 to 6 months 3 – 7 months to a year 4 – 1 or 2 years 5 – 3 to 5 years 6 – 5 to 10 years 7 – more than 10 years 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q47 Notes: Modified from Pew Internet & American Life Project – Chronic Disease Fall Tracking Survey Topline (2008)

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190 Variable Name: CHI-Q47 Variable Label: HIT co mmunication – frequency Question Text: About how often do you talk to others about health or health care this way? Values & Labels: 1 – Several times a day 2 – About once a day 3 – 3 to 5 days a week 4 – 1 or 2 days a week 5 – Every few weeks 6 – Once a month or less often 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q48 Notes: Modified from Pew Internet & American Life Project – Chronic Disease Fall Tracking Survey Topline (2008) Variable Name: CHI-Q48 Variable Label: HIT communication – value Question Text: How important is it to you to be able to look up health information? Values & Labels: 1 – Very or always important 2 – Usually or mostly important 3 – Sometimes important 4 – Rarely or only a little important 5 – Not important 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q49 Notes: Modified from Pew Internet & American Life Project – Chronic Disease Fall Tracking Survey Topline (2008) Variable Name: CHI-Q49 Variable Label: HIT communication contacts Question Text: Who do you talk to about h ealth or health care through computers, cell phones, or other devices? Pl ease mark all that apply. Values & Labels: See CHI-Q49 subset items Skip Pattern/ Default Next: CHI-Q49a Notes:

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191 Variable Name: CHI-Q49a Variable Label: Contact = provider Question Text: Your health care provider Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q49b Notes: Variable Name: CHI-Q49b Variable Label: Contact = family Question Text: Your family Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q49c Notes: Variable Name: CHI-Q49c Variable Label: Contact = friends Question Text: Your friends Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q49d Notes:

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192 Variable Name: CHI-Q49d Variable Label: Contact = online acquaintances Question Text: People you met online Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q49e Notes: Variable Name: CHI-Q49e Variable Label: Contact = colleagues Question Text: People you work with Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q49f Notes: Variable Name: CHI-Q49f Variable Label: Contact = students Question Text: People you go to school with Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q49g Notes:

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193 Variable Name: CHI-Q49g Variable Label: Contact = community Question Text: People in your neighborhood or community Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q49h Notes: Variable Name: CHI-Q49h Variable Label: Contact = religious Question Text: Your priest, preach er, or other religious leader Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q49i Notes: Variable Name: CHI-Q49i Variable Label: Contact = other Question Text: Someone else/other pe ople you know (pleas e tell us who) Values & Labels: 1 – Yes (free text response) 2 – No (no answer) Skip Pattern/ Default Next: If CHI-Q49i = yes, then CHI-Q49j; Else CHI-Q50 Notes: Variable Name: CHI-Q49j Variable Label: Other contact – detail Question Text: ______________________ Values & Labels: Free text response to be coded based on content analysis of results Skip Pattern/ Default Next: CHI-Q50 Notes:

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194 Variable Name: CHI-Q50 Variable Label: HIT commun ication – user barriers Question Text: Are there people you would like to talk to this way, but can’t? Please tell us who they are and why you can’t. Values & Labels: Free text response to be coded based on content analysis of results Skip Pattern/ Default Next: CHI-Q53 Notes: Variable Name: CHI-Q51 Variable Label: HIT communi cation – barriers (nonusers) Question Text: If you don’t use a computer, ce ll phone, or other de vice to talk with others about health or health care, why not? Please mark all that apply. Values & Labels: See CHI-Q51 subset items Skip Pattern/ Default Next: CHI-Q51a Notes: Variable Name: CHI-Q51a Variable Label: Barrier = knowledge Question Text: I don’t know how to Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q51b Notes: Variable Name: CHI-Q51b Variable Label: Barrier = need Question Text: I don’t need to Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q51c Notes:

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195 Variable Name: CHI-Q51c Variable Label: Barrier = isolation Question Text: I don’t know anyone to talk to Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q51d Notes: Variable Name: CHI-Q51d Variable Label: Barrier = interest Question Text: I don’t want to, or I’m not interested Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q51e Notes: Variable Name: CHI-Q51e Variable Label: Barrier = difficulty Question Text: It’s too hard or I get frustrated by it Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q51f Notes: Variable Name: CHI-Q51f Variable Label: Barrier = health literacy Question Text: I don’t understand what they tell me that way Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q51g Notes:

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196 Variable Name: CHI-Q51g Variable Label: Barrier = personal interaction Question Text: I would rather ask my health care provider in person Values & Labels: 1 – Yes 2 – No (blank) Skip Pattern/ Default Next: CHI-Q51h Notes: Variable Name: CHI-Q51h Variable Label: Barrier = privacy concerns Question Text: I am worried about my privacy Values & Labels: 1 – Yes 2 – No Skip Pattern/ Default Next: CHI-Q51i Notes: Variable Name: CHI-Q51i Variable Label: Barrier = other Question Text: Some other r eason (please tell us why) Values & Labels: 1 – Yes (fr ee text response provided) 2 – No (blank) Skip Pattern/ Default Next: If CHI-Q51i = yes, then CHI-Q51j; Else CHI-Q52 Notes: Variable Name: CHI-Q51j Variable Label: Other barrier – detail Question Text: _________________________ Values & Labels: Free text narrative content to be coded based on content analysis of responses Skip Pattern/ Default Next: CHI-Q52 Notes:

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197 Variable Name: CHI-Q52 Variable Label: HIT commun ication interest (nonusers) Question Text: If you don’t use a computer, ce ll phone, or other de vice to talk with others about health or health ca re, would you like to? Please mark all that apply. Values & Labels: See CHI-Q52 subset items Skip Pattern/ Default Next: CHI-Q52a Notes: Variable Name: CHI-Q52a Variable Label: Interest = definite Question Text: Yes Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q52b Notes: Variable Name: CHI-Q52b Variable Label: Interest = none Question Text: No Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q52c Notes: Variable Name: CHI-Q52c Variable Label: Interest = moderated by access Question Text: Maybe, if I had a way to Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q52d Notes:

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198 Variable Name: CHI-Q52d Variable Label: Interest = moderated by cost Question Text: Maybe, if it didn’t cost too much Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q52e Notes: Variable Name: CHI-Q52e Variable Label: Interest = moderated by knowledge Question Text: Maybe, if I knew how Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q52f Notes: Variable Name: CHI-Q52f Variable Label: Interest = moderated by security Question Text: Maybe, if I was sure my pe rsonal information was safe and private Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q52g Notes: Variable Name: CHI-Q52g Variable Label: Interest = uncertain Question Text: I don’t know / I’m not sure Values & Labels: 1 – Yes (true) 2 – No (false/blank) Skip Pattern/ Default Next: CHI-Q53 Notes:

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199 Part 6: Your Opinions About Technology Variable Name: CHI-Q53 Variable Label: Other IT use Question Text: Have you ever used any of th ese other things, at least sometimes? Please mark all that apply. Values & Labels: See CHI-Q53 subset items Skip Pattern/ Default Next: CHI-Q53a Notes: Variable Name: CHI-Q53a Variable Label: Device = home health monitoring Question Text: A special device just for keeping track of your health, like a blood sugar tester (glucometer), a blood pr essure cuff, or a scale to weigh yourself on? Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q53b Notes: Variable Name: CHI-Q53b Variable Label: Device = ereader Question Text: An e-book reader, like aKindle or Nook? Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q53c Notes:

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200 Variable Name: CHI-Q53c Variable Label: Device = music Question Text: An MP3 or music player, like an iPod? Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q53d Notes: Variable Name: CHI-Q53d Variable Label: Device = game Question Text: A game console, like a Xbox, Playstation, or Nintendo? Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q53e Notes: Variable Name: CHI-Q53e Variable Label: Device = DVD Question Text: A DVD play er or Blu-Ray player? Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q53f Notes:

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201 Variable Name: CHI-Q53f Variable Label: Device = cable Question Text: Cable TV that lets you watch movies or TV shows you pick “on demand”? Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q53g Notes: Variable Name: CHI-Q53g Variable Label: Device = streaming media box Question Text: A special box that lets you watch movies or TV shows online, like a Roku, Boxee, or Apple TV? Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q53h Notes: Variable Name: CHI-Q53h Variable Label: Device = internetTV Question Text: Special features on a TV th at let you watch movies or TV shows online? Values & Labels: 1 – Yes 2 – No 9 – Refused (no answer) Skip Pattern/ Default Next: CHI-Q54 Notes:

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202 Variable Name: CHI-Q54 Variable Label: Comments Question Text: Do you have any thought s, opinions, or comments about information technology (like comput ers and cell phones) either in general or when used for health or health care that you would like to share? Values & Labels: 1 – Yes (free text response) 2 – No (blank/no answer) Skip Pattern/ Default Next: If CHI-Q54 = yes, then CHI-Q54a; Else END Notes: Variable Name: CHI-Q54a Variable Label: Comments – detail Question Text: _________________________ Values & Labels: Free text response to be coded based on content analysis of results Skip Pattern/ Default Next: END Notes: