Citation
Evaluating the Effectiveness of an Intervention Mathematics Class for Low Achieving Middle School Students in Northwest Georgia

Material Information

Title:
Evaluating the Effectiveness of an Intervention Mathematics Class for Low Achieving Middle School Students in Northwest Georgia
Creator:
Coats, Johnnie ( author )
Language:
English
Physical Description:
1 online resource (128 pages) : ;

Subjects

Subjects / Keywords:
Underachievers -- Mathematics -- Georgia ( lcsh )
Learning strategies -- Mathematics -- Georgia ( lcsh )
School improvement programs -- Mathematics -- Georgia ( lcsh )
at-risk
CRCT
intervention
mathematics
middle school
remediation
Education, General
Education, Administration
Education, Bilingual and Multicultural
Education, Curriculum and Instruction
Education, Educational Psychology
Education, Mathematics
Education, Secondary
Education, Sociology of
Education, Tests and Measurements
Bilingual, Multilingual, and Multicultural Education
Curriculum and Instruction
Curriculum and Social Inquiry
Education
Educational Assessment, Evaluation, and Research
Educational Methods
Elementary and Middle and Secondary Education Administration
Junior High, Intermediate, Middle School Education and Teaching
Genre:
bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )
Target Audience:
specialized ( marctarget )

Notes

Abstract:
High-stakes testing has become crucial in public education, requiring students to meet increasingly higher standards, regardless of their ability levels. This causal-comparative study sought to determine the effectiveness of an intervention mathematics course in the middle school setting for at-risk, sixth grade students. The Georgia Criterion Referenced Competency Test (CRCT) math scores of 143 at-risk students enrolled in a remediation mathematics course were compared to scores from a control population of 143 at-risk students who did not participate in the class. Math scores from the 2008 administration of the CRCT test were used as covariates, and comparisons were made using the 2009 math CRCT scores for students in the intervention class against scores from students not taking the class. Results showed that there were no significant gains in the scores of students who took the remediation class, regardless of ethnicity or socioeconomic status. However, statistically significant results were seen for the female population who took the class. These results imply that an extra math remediation class in addition to a regularly scheduled math class did not improve student performance on this particular high stakes test. Thus, alternative treatment formats may be considered, and more research in this field is recommended.
Bibliography:
Includes bibliographical references.
General Note:
Department of Health and Behavioral Sciences
Statement of Responsibility:
Johnnie Coats.

Record Information

Source Institution:
|University of Colorado Denver
Holding Location:
Auraria Library
Rights Management:
All applicable rights reserved by the source institution and holding location.
Resource Identifier:
859260929 ( OCLC )
ocn859260929
Classification:
LC4692.G46 C63 2013eb ( lcc )

Full Text
A
EVALUATING THE EFFECTIVENESS OF AN INTERVENTION
PROMOTING WALKING AND LIFESTYLE PHYSICAL ACTIVITY
IN A PRIMARY CARE PRACTICE
by
Kirsten J. Black
B.A., University of California, Berkeley, 1982
M.P.H., University of California, Berkeley, 1989
A thesis submitted to the
University of Colorado at Denver
in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
Health and Behavioral Sciences
2005


This thesis for the Doctor of Philosophy
degree by
Kirsten J. Black
has been approved
by
Miriam Dickinson
Christine Duclos
-O Wilson Pace
David Tracer


Black, Kirsten Johnson (Ph.D., Health and Behavioral Sciences)
Evaluating the Effectiveness of an Intervention Promoting Walking and Lifestyle
Physical Activity in a Primary Care Practice
Thesis directed by Professor Craig Janes
ABSTRACT
Sedentary behavior is a risk factor for many chronic diseases and
conditions including cardiovascular disease, hypertension, diabetes, obesity,
osteoporosis, colon cancer and depression. Nevertheless, only 25% of US adults
achieve recommended levels of physical activity and 29% report no regular
leisure activities.
This study used a sequential explanatory design to evaluate the
effectiveness of a 13-week intervention to increase lifestyle physical activity
among primary care patients. The primary quantitative outcome was change in
average daily step count over time. Interviews were conducted after the
intervention and 6 months later to explore participants experiences with the
intervention.
Of 83 enrollees, 33 completed the intervention; 26 dropped out after
submitting at least one week of data; and 24 never submitted data. Between
group comparisons were significant for age F(2, 76) = 4.07,/? = .021, BMI F(2,
78) = 3.49,p =.035, race x (1, N =83) = 10.13,p < .001, and baseline step count
t(56.9) = 2.026,/? <.047 (two-tailed). Eight-eight percent (n = 59) of participants
were in the lowest categories of activity (59% sedentary, 29% low activity) at
baseline. Self-reported activity level was poorly correlated with baseline activity
rs (n = 59) = .256,/? = .0502. Comparison of baseline steps with daily steps
averaged over the last 4 weeks of the intervention showed study completers
increased their average daily steps by 1,850.3 (95% Cl = 1,024.2, 2,676.4,/?
<.001).
Wearing step counters increased activity awareness and motivated
behavior change. Positive health benefits, including weight loss, improvements in
chronic disease markers and subjective experiences of feeling better were
associated with attitudinal changes that reinforced behavior change. Although
step counters motivated physical activity behavior change during the intervention,
different patterns of use were observed in the maintenance phase. During the
intervention, participants developed strategies to deal with stable physical activity
barriers, however the maintenance phase was often characterized by emergent
situations that interrupted regular patterns of physical activity.
in


The clinical implications of this study are that personalized information
coupled with educational messages encouraging self-monitoring and realistic goal
setting can motivate physical activity behavior change for sedentary individuals.
This abstract accurately represents the content of the candidates thesis. J_
recommend its publication.
Signed
ring Jarigs
IV


DEDICATION
I dedicate this dissertation with love to
my children, Kevin and Sarah Black.


ACKNOWLEDGEMENT
Many friends and colleagues have been instrumental in supporting me
through my dissertation. First, I would like to thank the members of my
committee, Craig, David, Miriam, Chris and Wilson for giving generously of their
time, sharing their expertise and guiding me through this process.
I would also like to acknowledge the support that I have received from the
Department of Family medicine. If it takes a village to raise a child, the department
has been my village. Thank you Colleen for believing in me and encouraging me
to take this step. Thank you Wilson for your vision, wisdom, and mentorship and
for marshalling resources to help with this project. Thank you Miriam for your
patience in helping me deal with statistics. Mark, thanks for your enthusiasm for
physical activity which was the spark for this endeavor. Thank you Sherry for
guiding me through the administrative details. Thanks also to Deb, Laura, Kristen
and Erica for being supportive friends.
I also appreciate the support that I received from Jim Hill and others at the
Center for Human Nutrition. Thank you Jim for sharing your vision and expertise
during the conception of this research, and for the resources you provided. One of
the most enjoyable aspects of this study was collaborating with Helen, Joanie,
Mary and Martha. Thank you Helen and Joanie for your consistent encouragement.
Thank you Mary for showing me the ropes and for always being available when I
needed someone to talk to. Finally, thank you Martha for teaching me about
qualitative research and for taking the time to help me during the course of this
study.
I am also thankful for the funding that I received from the American
Academy of Family Medicine, (Practice Based Research Network Research
Stimulation Grant G0205PB) that supported the intervention phase of this research.
I


CONTENTS
Figures.....................................................xiii
Tables......................................................xiv
CHAPTER
1. INTRODUCTION...............................................1
Background..............................................1
Significance of the Study...............................3
Methods.................................................6
Research Questions......................................7
Researchers Perspective................................7
2. DEFINITIONS AND LITERATURE REVIEW..........................9
Definitions Used in the Study of Physical Activity......9
Literature Review......................................12
Activity and Obesity.............................13
The Relationship between Weight and Health.......15
Fitness Recommendations Contrasted with
Weight Loss Recommendations......................16
17
Fitness in the Absence of Weight Loss
Fitness as a Public Health Target......
19


Physical Activity Recommendations...................19
Exercise vs. Lifestyle..............................23
Walking for Exercise................................26
Using Step Counters to Promote Physical Activity....29
Colorado on the Move................................30
Establishing Step Counter Activity Guidelines.......32
An Ecological Perspective...........................36
Promoting Physical Activity in Primary Care.........40
Improving Behavioral Interventions in Primary Care..42
RE-AIM Framework....................................43
Theoretical Model..........................................45
Theoretical Issues..................................45
Social Cognitive Theory.............................48
The Transtheoretical Model..........................57
Maintenance of Physical Activity Behavior Change....59
3. RESEARCH DESIGN AND METHODS...................................62
Study Aims.................................................62
Mixed Methods Paradigm Conundrum...........................63
Sequential Explanatory Design..............................66
Research Design............................................67
Overview
67


Human Subjects Review .................................68
Participants and Site..................................68
Dependent and Independent Variables....................70
Phase I: Protocol and Data Collection (Quantitative)..71
Phase II: Protocol and Data Collection (Qualitative)..73
Data Collection, Analysis and Integration.....................74
Quantitative Methods...................................75
Qualitative Methods....................................78
Qualitative Analysis...................................81
Validity...............................................84
Integration of Data....................................85
4. QUANTITATIVE RESULTS..............................................86
Attrition.....................................................86
Definition of Study Completion Groups..................86
Attrition Rates........................................86
Quantitative Analysis.........................................90
Sample Demographics....................................90
Differences between Study Completers,
Non-completers and Non-reporters.......................92
Testing the Validity of Week 1 Data
as a Proxy for Baseline Activity.......................94


Relationships between Demographic Variables
and Baseline Step Counts.............................94
Comparison of Weekly Step Counts between
Completers and Non-completers........................96
Change in Step Counts................................98
No Relationship between Change in Step Counts
and Demographic Variables............................99
Change in Activity Categories.......................100
Relationship between Self-Identified Activity
Level and Baseline Step Count.......................100
Conclusion..........................................101
5. QUALITATIVE RESULTS............................................102
Overview...................................................102
Qualitative Data Sample.............................103
Qualitative Themes.........................................104
Motivators..........................................104
Goal Setting........................................108
Physical Activity Behavior Changes..................116
Barriers to Increasing Physical Activity............122
Facilitators........................................127
Benefits of Being in the Study......................130
Future Plans for Physical Activity..................141
Long Term Impacts...................................142
x


A Conceptual Model.................................146
Intervention Evaluation...................................150
Step Counters......................................151
Intervention Components............................161
Participant Recommendations........................164
Evaluating the Study Using the
RE-AIM Framework...................................167
6. DISCUSSION AND STUDY LIMITATIONS.............................172
Discussion................................................172
Interpreting Quantitative Findings.................172
Reaching People Who are Sedentary..................174
Long-term Outcomes.................................178
Patterns of Step Counter Use Over Time.............180
Theoretical Implications...........................182
Completion Rates...................................185
Clinical Application......................................187
Addressing Practical Questions.....................187
Questions for Future Research.............................192
Study Delimitations and Limitations.......................194
Threats to Validity................................195
Conclusions...............................................201
xi


APPENDIX
A. INTERVIEW GUIDES...................203
B. CODELIST...........................205
C. CROSS TABULATION OF QUALITATIVE
AND QUANITATIVE DATA MATRIX........216
D. STEP COUNTER USE MATRIX............219
REFERENCES........................................222
xii


FIGURES
Figure
3.1 Sequential Explanatory Design...............................67
4.1 Reasons for Dropout by Week.................................89
4.2 Change in Average Daily Step Count Over Time................96
4.3 Amount of Change in Average Daily Steps.....................98
4.4 Percent Change in Average Daily Steps.......................99
5.1 Relationship between Affective Outcomes....................137
5.2 Conceptual Model of Showing Relationships between
Intervention Components....................................149
xin


TABLES
Table
2.1 Linking SCT Constructs with Components of the
Intervention...............................................56
4.1 Participant Characteristics................................ 91
4.2 Baseline Activity Level.....................................93
4.3 Mean Daily Steps During Week 1..............................94
4.4 Comparing Demographic Factors with Baseline
Step Counts................................................95
4.5 Average Weekly Step Counts for Non-completers
and Completers.............................................97
4.6 Change in Activity Categories..............................100
B. 1. Code Definitions...........................................206
C. 1. Cross Tabulation of Qualitative and Quantitative
Data Matrix...............................................217
D. 1. Step Counter Use Matrix....................................220
xiv


CHAPTER 1
INTRODUCTION
Background
Sedentary behavior is a strong risk factor for many chronic diseases and
conditions including cardiovascular disease, hypertension, diabetes, obesity,
osteoporosis, colon cancer and depression (Eden, 2002). Inactivity accounts for
an estimated 200,000 excess deaths each year (Dishman, 1996). Consequently,
Healthy People 2010 lists physical activity as one of five leading health
indicators (United States Department of Health and Human Services [USDHHS],
2000). A review of 44 studies published between 1966 and 2000, reported that
all cause mortality was 20-30% lower in people who met Healthy People 2010
guidelines for physical activity (30 minutes of moderate activity five or more
times per week, or 20 minutes of vigorous activity three or more times per week).
Although the review was based on observational studies and therefore subject to
potential confounding by prior health status as well as other health behaviors, the
data are consistent with other studies that have controlled for these confounders
(Lee, 2001).
During the last twenty years, the average level of physical activity for
Americans declined significantly. Today, most Americans are not sufficiently
1


active to promote health or maintain their weight. Furthermore, because norms
for physical activity have declined, many people are oblivious to their own
inactivity. Not surprisingly, reduced levels of physical activity have been
associated with increasing rates of obesity and obesity related chronic diseases.
According to national data, only 25% of adults in the United States
achieve recommended levels of physical activity and 29% of adults report no
regular activity during leisure time (USDHHS, 1996). In response to these low
levels of activity for adults, Healthy People 2010 targets the following three
objectives. Along with each objective, national statistics from 1997 (HSDHHS,
2000) as well as Colorado data collected in 2001 are reported (Colorado
Department of Public Health and Environment, 2003).
Objective 22-1: Reduce to at least 20% the portion of adults aged 18 and
older who engage in no leisure-time physical activity (U.S. baseline 40%,
Colorado 19%).
Objective 22-2: Increase to at least 30% the proportion of people aged 18
and over who engage in regular, preferably daily, moderate physical
activity for at least 30 minutes per day (U.S. baseline 15%, Colorado
38%).
Objective 22-3. Increase to 30% the proportion of adults who engage in
vigorous physical activity that promotes the development and
2


maintenance of cardiorespiratory fitness, three or more days per week, for
20 or more minutes per occasion (U.S. baseline 23%, Colorado 29%).
Based on data from 2001, Colorado has met two of the objectives and is
very close to meeting the third. In comparison to other states, Colorado has a
smaller percentage of overweight or obese adults. Furthermore, adults in
Colorado report higher levels of activity compared to those in other states.
However these optimistic findings mask the disparities among different
population groups. Both within Colorado and nationally minorities are most at
risk for sedentary lifestyles, and diseases associated with being sedentary.
Nationally, sedentary lifestyle is more prevalent among women (30.7%) than
men (26.5%), individuals with low incomes (41.5% for incomes < $10,000 yr.
compared with 17.8% for incomes > $50,000), and among the less educated
(46.5% for < 12 years of school vs. 17.8% for college graduates). People who
have chronic diseases are also among those who report the lowest levels of
physical activity (USDHHS, 1996).
Significance of the Study
Despite the wide body of evidence demonstrating the physical and mental
health benefits of physical activity, the vast majority of Americans are inactive.
Furthermore, efforts to promote physical activity during the past 15 years have
3


had virtually no impact on physical activity rates in the United States. One of the
reasons for this is that previous approaches to physical activity have promoted
exercise and by so doing have failed to engage the public. For example, on
average half the people who start an exercise program drop out within the first
few months (Dishman, 2001). The failure of traditional approaches to increase
physical activity rates suggests the need for new strategies based on theoretical
models that account for the complexity of behavior associated with physical
activity.
One new strategy for reversing inactivity and improving health in the
United States is to promote lifestyle physical activity (LPA). LPA consists of
activities done within the context of ones everyday life. Promoting LPA
involves encouraging people to find simple ways of incorporating activity into
their lives. Associated with the concepts of LPA, step counters have been
promoted as tools for measuring daily activity. Step counters are relatively
inexpensive devices (costs are approximately $10.00-$30.00) that are worn
inconspicuously on clothing to track the number of steps a person takes each day.
They can be used for assessment as well as for monitoring ambulatory forms of
physical activity.
Step counters have become popular in Colorado due to extensive media
coverage of Colorado on the Move, an initiative targeting obesity through
promotion of lifestyle physical activity and calorie reduction. Since its inception
4


in October of 2002, over 50 work sites and 12 communities across the state have
participated in Colorado on the Move. The success of this initiative has led to
the development of a national organization: America on the Move. In a very
short time, interest in step counters has grown tremendously.
Despite the popularity of step counters there are few published studies
examining peoples experiences using counters either short term or long term.
Tudor-Locke (2001) one of the few published researchers in this field, states that
one of the needs for fixture research is to better understand the experience of
using a step counter:
If pedometers are to be used as motivational or intervention tools,
information concerning participant compliance and experiences
(though qualitative methods such as structured interviews or focus
groups) should be provided to assist with interpretation.
The present study was conceived in early 2002 based on the positive
results and wide public interest in Colorado on the Moves community and work
site interventions. Recognizing the success of these interventions, there was
considerable interest in adapting the intervention strategy for use in primary care.
At that time there was no published research in this area. The purpose of this
study was to both evaluate the effectiveness of using step counters to promote
physical activity in a primary care setting as well as to provide information about
participants subjective experiences.
5


A team consisting of James Hill, PhD and his colleagues at Colorado on
the Move, Wilson Pace, MD and Mark Cucuzzella, MD, two primary care
physicians and myself developed the intervention strategy that was used for this
study. Dr. Hill was instrumental in providing the educational resources used in
the intervention, such as funding for development of the Step Your Way to Better
Health video and supplying Colorado on the Move pamphlets. Dr. Pace
organized the automated data collection activities including the database and
telephony system.
Methods
This study uses mixed methods to evaluate the effectiveness of an
intervention designed to increase lifestyle physical activity among primary care
patients. The 13-week intervention consists of using a step counter to measure
baseline activity level, setting individual goals and self-monitoring. Educational
messages emphasize the value of small changes and incorporating lifestyle
changes to increase physical activity. The primary quantitative outcome is
change in average daily step count. Interviews are used to assess individual
experiences both immediately following the intervention and again 6 months
later. Information gained from this research will help to inform future
interventions utilizing step counters.
6


Research Questions
The study utilizes a sequential explanatory design to address the
following aims:
1. Compare and contrast characteristics of completers and non-completers
of the 13-week intervention.
2. Determine if activity levels change during the 13-week intervention
period and whether changes are maintained at 6 months.
3. Examine the accuracy of self-perceptions of physical activity and
evaluate the extent to which the intervention changes perceptions.
4. Explore the subjective experiences of participants during the intervention
and follow-up phase of the study.
5. Propose intervention strategies for using step counters in a primary care
practice.
Researchers Perspective
As a health educator and dietitian, I bring particular biases to this
research. Foremost among these is a patient centered orientation, which
characterizes my approach to both patient care and research. My training and
clinical experience has taught me to begin with the individual and to tailor
behavior change to their unique circumstances. For me it is difficult to study a
single aspect of a persons life, such as their physical activity without
7


consideration of the larger context in which it occurs. The qualitative portion of
this study was an opportunity to explore individual stories while at the same time
looking for broad themes. I discovered however that the complexity of
individual life stories does not always allow reduction to simple themes.
8


CHAPTER 2
DEFINITIONS AND LITERATURE REVIEW
The field of physical activity research is complex and the terms used may
be misunderstood. Therefore, this chapter begins by clarifying the meanings of
terms and concepts that are used in this thesis. This is followed by a discussion
comparing and contrasting exercise and lifestyle approaches for promoting
physical activity with people who are sedentary. Recognizing that individual
behavior change occurs within a larger social and environmental context, a social
ecological framework is used in the next section to discuss proximal and distal
factors that influence physical activity promotion. The final section explores
some of the difficulties in applying current theories of behavior change to the
study of physical activity.
Definitions Used in the Study of Physical Activity
Physical activity refers to bodily movement produced by the skeletal
muscles that results in energy expenditure that can range from low to high
(Pescatello, 2001). Two subsets of physical activity are exercise and lifestyle.
Exercise is a form of physical activity that is planned and structured. It is usually
9


performed during ones leisure time for the purpose of obtaining or maintaining
fitness. In contrast, lifestyle physical activity (LPA) refers to everyday activities
consisting of leisure, occupation and/or household as well as planned and
unplanned activity. The high degree of variability in types of physical activity
performed makes its assessment difficult (Dishman, 2001).
Exercise is categorized as either resistance or endurance. Although both
types of exercise confer health benefits, endurance activities have been more
widely studied because of their association with cardiovascular disease reduction.
In endurance research, the units used to measure exercise intensity are metabolic
energy equivalents (METs). One MET is defined as the resting metabolic rate
(3.5 ml oxygen per kg body mass per minute). Low intensity activities such as
light house work or leisurely walking have METs < 4; moderate intensity
activities such as walking at a pace of 15-20 min per mile have METs of 4.0-5.9;
and vigorous activities such as running have METs > 6. With increasing fitness
cardiorespiratory efficiency improves. Consequently, people with low fitness
levels experience more exertion and get more benefit from low to moderate
intensity activities compared with those at higher fitness levels.
Physical fitness is an attribute that can be defined variously depending on
whether the outcome goal is health or performance. Higher levels of physical
activity are associated with increased levels of physical fitness. Components of
physical fitness that contribute to health outcomes include: cardiorespiratory
10


endurance, muscular endurance and strength, body composition and flexibility.
Higher levels of physical fitness promote cardiovascular benefits and reduce
health risks. Studies that use physical fitness as their primary endpoint typically
measure changes in cardiorespiratory capacity. The main measures used for this
are maximum oxygen uptake (VO2 max), resting heart rate and endurance.
Sedentary is a term that does not have a specific definition but is
frequently used to refer to individuals who do not meet the recommendations for
physical activity (20 minutes of vigorous activity three days per week, or
accumulating 30 minutes of moderate activity on five or more days of the week).
There are no standard criteria for categorizing people as sedentary based on step
counts, however Tudor-Locke and Bassett (2004) have suggested that sedentary
be defined as achieving <5,000 steps per day.
Sedentary behaviors are defined as those activities that require very little
physical activity to perform. Prevalent sedentary behaviors in the United States
include sleeping and eating, television and video viewing, using the computer,
reading, sitting and talking on the telephone.
Body Mass Index (BMI) is calculated based on a persons height and
weight according to the following formula: weight (kg)/height (m ). The
correlation between BMI and percent body fat is generally good (0.6-0.9).
Higher levels of body fat are associated with increasing health risks; therefore,
BMI is used to predict health risk. Based on health outcome data, BMI
11


categories and their associated level of health risk are: < 18.5 underweight (low),
18.5-24.9 normal (average), 25-29.9 overweight (increased), 30-34.9 obesity I
(moderate), 35-39.9 obesity II (severe), > 40 obesity III (severe).
A step counter is a mechanical device, usually battery operated that
measures the number of steps taken. Although the term step counter is
frequently used interchangeably with the term pedometer, the two are not the
same. A pedometer is a battery operated mechanical device that calculates the
distance traveled by multiplying the number of steps taken by the length of a
persons stride.
Literature Review
Physical activity promotion addresses two distinct public health
problems, namely inactivity and obesity. The inextricable relationship between
physical activity and weight along with societys preoccupation with weight has
resulted in physical activity being viewed as primarily a mediator of weight loss.
This is unfortunate because the beneficial effects of physical activity are broader
than simply weight loss and accrue at lower levels than are typically required for
weight loss. Furthermore, people who are not overweight often do not appreciate
their need for physical activity. Thus, there is a need to promote physical activity
in a way that is appropriate to engage the general public. One potentially
effective strategy emphasizes increasing lifestyle physical activity. In contrast to
12


traditional approaches to increasing physical activity that have promoted regular
exercise, the lifestyle approach lowers the bar so that more people are
encouraged to increase activity. This strategy may also be the most realistic way
of addressing the obesity epidemic. According to Hill (2003), simply
encouraging people to increase their activity by the equivalent of 100 calories a
day would be sufficient to help reduce weight gain. Furthermore, this modest
amount of activity may represent a necessary first step for helping sedentary
individuals increase their fitness and self-efficacy for physical activity.
Activity and Obesity
Physical activity is closely associated with obesity. Low rates of physical
activity coupled with over consumption of energy are believed to be the
underlying causes of the obesity epidemic in the United States (Hill, 1998).
Currently, over 56.4% of Americans are considered overweight or obese (Mokad,
2001). The factors associated with this epidemic are complex and deeply rooted
in our society, however at the individual level, weight gain results from an
imbalance between energy consumption and energy utilization.
Over the last 20 years, the amount of energy required for daily living in
industrial countries has declined by approximately 800 kcal/day (James, 1995).
However, energy consumption has not matched this decline, and as a result the
average American adult gains approximately two pounds per year. While it is
13


necessary to intervene on both sides of the energy balance equation, there are
many who believe that targeting physical activity is essential for success. For
example, Kumanyika (2001) speculates that there is:
... .a certain minimum level of physical activity needed in the
society at large in order for the average person to maintain energy
balance, and that the ability to compensate for low energy
expenditure by maintaining a low level of energy consumption
may be limitedparticularly if the eating pattern contains energy-
dense, high fat foods.
Although I am not aware of any studies demonstrating that a certain
physical activity level is required to maintain energy balance, this hypothesis is
conceptually appealing. From an evolutionary perspective it is clear that
historically humans have been an active species. It is only within the last 100
years that physical activity has declined so significantly. Consequently, it is
likely that our physiological mechanisms are better adapted for activity than
sedentary behavior.
The importance of physical activity to energy balance is also supported
by data from the national weight loss registry. According to these data the
people who are most successful at weight loss and weight loss maintenance are
those who have high levels of physical activity (McGuire, 1998). The exact
mechanisms involved in this relationship are not completely understood,
however it is clear that higher energy expenditure helps to compensate for dietary
indiscretions. People who are sedentary have a very small margin for managing
14


energy balance and this poses a problem since our society promotes energy dense
foods. Thus while interventions aimed at both sides of the energy equation are
necessary, I believe that physical activity may represent a threshold issue for
reducing obesity.
The Relationship between Weight and Health
The association between weight and health risk is moderated by the
percentage and distribution of body fat. Studies that focus on change in weight
or body composition, either alone or in addition to fitness typically measure body
mass index (BMI), abdominal girth and/or percentage of body fat. Although
evidence links each of these measures with mortality and morbidity, taken
together they provide a more comprehensive profile of health risk. For example,
there is a small gradual increase in morbidity and mortality for BMI between 25
and 30. For BMI between 30 and 35 the risks increase more rapidly and above
35 they increase dramatically. These findings are the basis of the current
definitions of overweight and obesity (overweight is defined as BMI 25-30;
obesity class I is BMI between 30-35, class II is 35-40). However for an
individual, BMI is insufficient for determining health risks, especially for people
in the overweight category. This is because the metabolic effects of obesity are
related to the amount, type and distribution of body fat. People who are at
greatest risk from being overweight or obese are those with centrally distributed
15


body fat (apple shape) (Reaven, 2003). Central or visceral fat has unique
metabolic characteristics that increase the risk for type 2 diabetes and heart
disease. For this reason measures of abdominal girth and body fat are important
corollaries to BMI.
When changes in weight or body fat are the outcomes of interest, physical
activity is often measured in terms of calories burned. At the individual level,
calories burned can be calculated from measures of oxygen consumption per
minute, however this is impractical for many studies. Therefore calories burned
are typically estimated based on the persons weight, the intensity of the activity
and the amount of time spent doing the activity. For example, a 150 lb person
who walks at a moderate pace for 18 minutes bums about 100 kcalories (kcals).
Fitness Recommendations Contrasted with Weight Loss
Recommendations
Physical activity recommendations are written in terms of frequency,
duration and intensity. Current recommendations from the Surgeon General and
the American College of Sports Medicine (ACSM) state that Americans should
accumulate 30 minutes or more of moderate intensity activity on most, preferably
all days of the week (USDHHS, 1996). The goal of these recommendations is to
improve fitness; they are not designed to promote weight loss. Although some
people may lose weight at this level of activity, most people will need to do
16


significantly more activity in order to lose weight. Since weight is determined by
both energy intake and energy expenditure, recommendations for weight loss
usually consist of moderate calorie reduction (250-500 kcals a day) combined
with increased energy expenditure (250-500 kcals per day) (Leermakers, 2000).
This equates to approximately 60 minutes of moderate activity, which is
significantly more than is recommended for fitness. In order to achieve the
recommended activity level for weight loss a person needs to have a moderate to
high level of fitness. Consequently, the first step in increasing activity for weight
loss should focus on improving fitness rather than setting exercise goals that may
be unrealistic for a sedentary person. For many people, this first step may be the
only step and so we must consider the value of fitness as a goal separate from
weight loss.
Fitness in the Absence of Weight Loss
Physical fitness is not directly associated with weight, therefore it is
possible to be physically fit yet also overweight or obese. Similarly it is possible
to have a normal body weight and be unfit. Fitness and weight loss are separate
outcomes that independently impact health. In a consensus statement issued by
the ACSM the following benefits of improving fitness in the absence of weight
loss were noted: 1) improves insulin action and reduces insulin resistance in
obese persons, 2) attenuates the progression from impaired glucose tolerance to
17


type 2 diabetes, 3) reduces blood pressure, and 4) reduces the amount of visceral
and abdominal subcutaneous fat (Gundy, 1999).
Two studies performed at the Cooper Center have shown that fitness
protects against mortality for men who are overweight and obese. In one study,
men with BMIs >30 who were moderately or highly fit were compared with
normal weight men who were unfit (measured as in the lowest 20% of their age
group). Death rates for the unfit men were 52 per 10,000 man-years of
observation while death rates for the fit but obese men were 18 per 10,000
(Barlow, 1995). A larger study that followed 21,925 men age 30-83 years over
an 8 year period found similar results (Lee, 1993). According to the studys
authors, Obese men who were fit had a much lower risk of cardiovascular
disease (CVD) and all cause mortality than unfit men in the lowest body fat
group. The authors caution however that these results should not be interpreted
to suggest that physical activity eliminates all the health risks associated with
obesity. For example, cardiovascular fitness will not lessen the effects of weight
on osteoarthritis or sleep apnea.
He and Baker (2004) analyzed data from 7,867 adults aged 51-61 over a
four year time period and found that regular physical activity was associated with
better health outcomes even for people who do not lose weight. The health
benefits of activity were also apparent for people who did light activity. For
example, the rate of decline in overall health for those who reported doing no
18


activity was 20.8% whereas it was 8.4% for those who did light exercise on
average three or more times a week.
Fitness as a Public Health Target
In the same way that a sedentary person must first develop fitness before
s/he can expend sufficient energy to facilitate weight loss, our public health
approach to activity goals for sedentary individuals should begin by emphasizing
fitness rather than weight loss. Although weight loss is an optimum goal, it is
often unattainable. Lack of motivation and inability to attain adequate activity
levels prevent many people from losing weight; however there are also people
who are highly fit yet whose weight is intractable no matter how hard they work
at losing. One of the advantages to promoting fitness is that the amount of
activity necessary for fitness is less than is recommended for weight loss. Most
importantly, there is strong evidence to show that fitness confers significant
health benefits even in the absence of weight loss. Focusing on activity rather
than weight may motivate a greater percentage of the population to change
sedentary behaviors.
Physical Activity Recommendations
Regular physical activity reduces the risk of cardiovascular disease, type
2 diabetes, colon cancer and several major chronic diseases and conditions. It is
19


also provides mental health benefits and is inversely associated with mortality.
These relationships are dose dependentgreater benefits accrue to people with
the highest activity levels (USDHHS, 1996; Lee, 2001). Historically this dose
response relationship has been the basis for recommending high intensity
activity, however in recent years there has been a growing consensus that
physical activity need not be vigorous in order to provide health benefits.
Morris (1958) did some of the earliest research linking moderate physical
activity with health. Following 9,376 male workers over 9 years he demonstrated
a relationship between higher levels of occupational activity and lower mortality.
Although this study has been criticized because of the likelihood of confounding
by other factors associated with occupation such as diet, the relationship has been
corroborated by other epidemiological studies. The Harvard Alumni Study
demonstrated that men who regularly expended 500-35,00 kcal per week had
significant risk reduction for premature mortality from coronary artery disease
(CAD). The relationship between activity and health benefits was dose
dependent, and the study demonstrated that even low levels of activity conferred
health benefits (Paffenbarger, 1986). In the Multiple Risk Factor Intervention
Trial (MRFIT), men who averaged 30 minutes a day of moderate intensity
activity had one third fewer deaths from CAD and a 20% lower overall mortality
rate compared to inactive men (Leon, 1987).
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Two large cohort studies found similar results for women. In the
Womens Health Initiative Observational Study, a diverse sample of 73,743
postmenopausal women (aged 50-79) was followed over a 5.9 year period. In the
study, coronary events were inversely associated with energy expenditure.
Women who reported walking briskly or engaging in vigorous exercise for 2.5
hours per week had a 30% lower risk of coronary events. The Nurses Health
Study followed 80,348 women over a 20 year period (Rockhill, 2001). The
authors report that moderate intensity activities conveyed approximately the
same mortality benefit compared to vigorous activities. They found that
increasing levels of physical activity were inversely associated with mortality.
Interestingly, mortality risk for women in different categories of activity showed
the greatest incremental benefit between women in the lowest level of activity (<
1 hour of activity per week) and those only slightly more active (between 1 hour
and 1.9 hour per week). Thus, for sedentary individuals, even small increases in
activity can provide health benefits.
Lee and Skerrett (2001) reviewed 44 studies to determine whether there
was a linear dose response relationship between the volume of physical activity
or fitness and mortality. Although they used only observational studies, they did
find a dose response relationship. Furthermore, the greatest benefit from
increased activity appeared at the lowest levels of activityat higher levels of
activity there appeared to be a threshold effect. They estimate that minimal
21


adherence to physical activity recommendations leading to energy expenditure of
1000 kcal/week results in decreased all-cause mortality rates with risk reductions
on the order of 20-30%. They also found evidence to suggest that even lower
volumes of activity may have health benefits.
Experimental studies have compared multiple short bouts of physical
activity with an equivalent amount done continuously. DeBusk, Stenestrand,
Sheehan, and Haskell (1990) found that improvements in cardiovascular fitness
were similar for 40 men who were randomized into either short bout or long bout
activity groups. Jakicic, Wing, Butler and Robertson (1995) compared both
fitness improvement and adherence rates between intermittent and continuous
exercisers in a 20-week study. He found that improvement in fitness were
similar although adherence was better for the group assigned to intermittent
exercise (234 min/week compared to 188 min/week).
In 1995, an NIH consensus panel was commissioned to review new
evidence and recommend changes in the Surgeon Generals physical activity
recommendations. The committees report is summarized in the first Surgeon
Generals Report on Physical Activity and Health published in 1996 (USDHHS,
1996). The report made three significant changes from earlier recommendations.
First, it reduced the intensity of activity recommended from 60% VO2 max to
50% VO2 max and recognizes that 40% VO2 max is an appropriate level for
people with low fitness. Second, it increased the frequency of activity by
22


encouraging activity on most if not all days of the week. Third, it included the
option of accumulating activity over the course of a day rather then at one time.
The following section considers two different implementation strategies for
increasing activity among sedentary adults.
Exercise vs. Lifestyle
There are two main approaches to improving physical fitness. The first is
a structured approach that emphasizes regularly planned physical activity
exercise. The second utilizes a lifestyle approach. The lifestyle approach differs
from exercise because it is not based on prescribed regular structured activity.
Instead it emphasizes accumulating 30 minutes of daily activity through
everyday activities. Both methods provide similar health benefits; however,
from a public health perspective, the lifestyle approach has advantages that
suggest it would be a better strategy for increasing activity among people who
are sedentary. This section compares and contrasts these two approaches with
regard to health benefits, motivation and sustainability.
The dominant paradigm for promoting physical activity has historically
been exercise. To promote fitness, people were taught that they must exercise at
60% of maximum age-predicted heart rate. Tables and formulas were published
so people could calculate target heart rates. However this approach has not been
effective for increasing activity among the majority of the population.
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Epidemiological studies show that only 15% of the population is active at this
level, 60% of the population is inadequately active and 25% were completely
non-active (USDHHS, 1996). One of the reasons for these low levels of activity
is that people interpreted the recommendations as promoting vigorous activity,
which many find both unappealing and unrealistic. Even after the
recommendations were broadened in 1996, researchers discovered that most
people were not exercising at the amounts prescribed by the ACSM Exercise
Prescription guideline because of a misperception that vigorous exercise was
their only alternative (Dunn, 1998).
High intensity activity is one of the barriers to exercise that is frequently
identified. Intensity can be a barrier both for initiating as well as sustaining
physical activity. For sedentary people who intended to exercise, but did not the
most common barriers were beliefs that exercise would be too physically
demanding and lack of time (Godin, 1986). And, of the relatively small number
of people who begin a vigorous physical activity program, at least 50% of them
drop out within one year (Dishman, 1988).
Other barriers to exercise include: lack of time, too tiring, lack of social
support, inclement weather, disruptions in routine, and lack of access to facilities
(Dishman, 1994; Trost, 2002). Many of these barriers are minimized or
alleviated by the lifestyle physical activity approach. Moderate rather than
vigorous activity is promoted. People are encouraged to accumulate at least 30
24


minutes of moderate intensity activity each day. This can consist of short bouts
or one long bout. Activities are self-selected. They can consist of everyday
activities such as housework as well as structured activities. The lifestyle
approach therefore offers flexibility in meeting ones goals. Furthermore it
allows people to perform activities that are purposeful. Purposeful activities are
shown to be associated with greater long-term adherence among sedentary
individuals (Morgan, 2001).
Two controlled studies comparing lifestyle physical activity with
structured exercise interventions found that participants in both groups had
similar health outcomes. Dunn, et al. (1999) conducted a two year controlled
trial in which 235 healthy sedentary people aged 35-60 years were randomized
into either lifestyle physical activity or structured activity. For each group there
was a six month intensive intervention period followed by an 18 month less
intensive period. The main findings for the study were that there were similar
changes in physical activity and cardiorespiratory fitness over the 24 months. In
a study by Anderson et al. (1999), 40 sedentary obese women were randomized
into either lifestyle or structured physical activity interventions and were
followed for one year. All women received similar dietary recommendations. At
the end of the study period both groups showed similar improvements in systolic
blood pressure, serum lipids, and lipoprotein levels. After 6 months the exercise
group had slightly higher weight loss (8.3 kg compared with 7.9 kg); however, at
25


one year the amounts were similar because the aerobic group had a higher
amount of weight regain (1.6 kg in the aerobic group compared to 0.08 kg in the
lifestyle group). These studies help to show that lifestyle activity confers similar
health benefits as traditional exercise programs.
Other evidence supporting the value of lifestyle activity comes from a
meta-analysis of 127 physical activity studies by Dishman and Buckworth
(1996). They state that effect sizes for interventions promoting active leisure
(similar to lifestyle physical activity) are larger compared with exercise
programs. Similarly, they found that studies with low intensity activities
reported larger effects compared with studies using higher intensities of
activities. They also note that behavioral intervention studies had better effect
sizes.
Walking for Exercise
Walking is the most popular form of physical activity and it an integral
part of lifestyle physical activity. The following paragraphs will briefly
summarize some of the extensive literature devoted to walking. This research
not only supports the health benefits of walking, but also demonstrates the utility
of promoting walking with sedentary people.
In a review of data from the 1990 Behavioral Risk Factor Surveillance
System (BRFSS), Siegel, Brackbill, and Heath (1995) found that walking was the
26


most popular form of physical activity. Furthermore the relative prevalence of
walking was highest among population subgroups that report the lowest levels of
participation in leisure time physical activity, such as low income, less educated,
minority, elderly and overweight. The authors suggest that promotion of walking
for physical activity might be an underused tool for reaching the most sedentary
groups in the population. These findings were corroborated by Salomon, et al.
(2003) who found that 86% of survey respondents reported high enjoyment of
walking while only 31% reported high enjoyment of structured physical activity.
Henderson and Ainsworth (2003) performed qualitative interviews to
identify perceptions of walking with African American and American Indian
women. They found that attitudes toward walking were positive. Women stated
that it is something that can be done anywhere and most women interviewed did
not consider walking as exercise. The authors state that: walking was noted as
an important physical activity undertaken by a range of women of different ages
and from different economic backgrounds and family situations.
The health benefits of walking have been consistently demonstrated. In
the US Nurses Health Study, walking was inversely associated with coronary
events and the relationship was stable for women irrespective of race, age or
BMI (Manson, 1999). Similar results for men were found in the Honolulu Heart
Study. Men who walked >1.5 miles a day had half the risk of heart attack as
those who walked < 0.25 miles a day (Hakim, 1999).
27


Hartman (2001) summarized the literature on walking in elderly
populations stating:
Walking is especially suitable for older people and can lead to
improved quality of life. Among older people regular walking has
been associated with lower rates of hospitalization, lower plasma
triglycerides and higher bone mineralization.
She also argues that many sedentary people are physically unable to undertake
more vigorous activities because of poor cardiorespiratory fitness and/or excess
weight and that for them walking is sufficiently vigorous to promote fitness.
One of the hypothesized benefits of promoting walking for physical
activity is sustainability. Since moderate intensity activities like walking are
easily incorporated into ones daily routine, there are fewer barriers to
participation, therefore maintenance should be enhanced. In a 32-week study
where 32 sedentary adults were randomized to three groups: 1) 30 minutes of
continuous brisk walking, 2) three 10 minute bouts of walking and 3) any
combination of activity equaling 30 minutes, Coleman, et al. (1999) found that
participants in all groups made similar gains in activity and cardiovascular
improvement. Across all groups those who were able to make walking part of
their daily lifestyle where more successful at meeting activity goals. In
particular, participants reported that to be successful, they needed to develop the
habit of choosing walking instead of sedentary activities. Another aspect of the
study was that participants stated that they were better able to meet their walking
28


goals during the week because they encountered more unexpected time demands
on weekends.
Using Step Counters to Promote Physical Activity
The concept of a device to measure steps may have originated from
drawings by Leonardo de Vinci (Wilson, 1999). Thomas Jefferson is reported to
have purchased a pedometer in France and brought it back to the United States.
Then in the late 1930s a popular radio program named Jack Armstrong offered
hike-o-meters (pedometers) as a promotion. During the promotion,
approximately 70,000 daily orders, requiring 10 cents and 2 Wheaties box tops,
were received (Norms Radio, 2003).
The first large-scale commercial production of pedometers occurred in
Japan in the 1960s. These were given the name manpo-kei, which means ten-
thousand steps meter (Tudor-Locke, 2004). It is likely that the concept of
walking 10,000 steps originated from this name. In Japan, walking 10,000 steps
a day continues to be a popular goal. In the United States, some of the earliest
uses of pedometers in the medical field were to measure ambulation for patients
in post surgery or post injury situations. In the late 1990s, step counters were
reintroduced to the American public as a tool to motivate physical activity.
At the forefront of the current wave of step counter popularity is
Colorado on the Move. The primary objective of Colorado on the Move is
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reducing obesity. James Hill, the founder of this movement believes that the
current obesity epidemic originates from an energy gap. He defines the energy
gap as the extent to which energy balance must be modified to prevent weight
gain (Wyatt, 2004). According to his calculations, using data from residents of
Colorado, he hypothesizes
... .the energy gap could be closed by modifying energy balance
by 100 kcal/d... Assuming 50% efficiency of energy storage by
the body, a reduction of positive energy balance of about 80
kcal/d form the Colorado population would be sufficient to close
the energy gap for most people. (Wyatt, 2004).
The premise of Colorado on the Move is that this amount of energy
expenditure can be obtained from increasing lifestyle physical activity.
Colorado on the Move
Colorado on the Move is a statewide intervention that uses step counters
to encourage physical activity. Jim Hill, PhD at the Department of Human
Nutrition at the University of Colorado Health Sciences Center initiated the
program in 2001 after pilot data showed that people who used step counters
increased their activity levels by an average of 2,000 steps over a 12 week
period. Over 75,000 people throughout the state have participated in Colorado
on the Move interventions in various setting such as worksites, schools, churches
and even whole communities. Participating groups include: the Metro Black
30


Church Initiative, the Colorado Department of Health, Coors Brewing Company,
the city of Peetz, and the Southern Ute Tribe. The success and popularity of
Colorado on the Move has led to its transformation and expansion into a national
programAmerica on the Move.
Results recently published by Colorado on the Move showed that
participants in both work site and church interventions increased their steps by
approximately 2,000 steps per day (Wyatt, 2004). According to Hill and
colleagues, a 2,000 step increase is hypothesized to be sufficient, on a population
level, to help prevent weight gain, however the study did not collect outcome
data on weight.
Although qualitative data has not yet been published, preliminary
summaries of focus group data provide insights into different ways people use
step counters and raise concerns about sustainability (M Tenney, personal
communication, April 2003). Some of the unpublished findings from focus
groups of participants in Colorado on the Move are:
People use step counters different ways. After wearing step counters
each day during the 12 week intervention period, people transitioned to
different patterns of use: every day, intermittent, temporal and non-users.
Despite the fact that people had different ways of utilizing step counters
they agree that the step counter helped them to pay more attention to their
activity level.
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Although the main benefits of the step counters were health related,
people identified non-health related benefits as well such as increasing
moral at work, feeling more in control of their health.
In a community or work site setting, certain factors were identified that
enhanced the program. These included having organizational support,
incentives, and publicity for the program.
When the program is advertised as a 15-week program, participants are
less likely to maintain behavior changes compared to if it is advertised as
a lifestyle program.
These findings reinforce the importance of learning more about peoples
motivations, expectations and actual experiences with the intervention before
getting caught up in the current wave of enthusiasm. Although people are
initially very interested in using a step counter to learn about their activity, one of
the major issues to be addressed is how to keep people motivated over the long
run. Taking the time to explore peoples experiences with step counters is
essential to planning a successful clinical intervention.
Establishing Step Counter Activity Guidelines
Evidence suggests that step counters can help motivate people to increase
their activity, however there are currently no evidence-based guidelines on which
to base step count recommendations. As described above, the recommendation
32


of 10,000 steps per day has historical roots, and there is evidence showing that
10.000 steps is associated with important health outcomes such as lower blood
pressure, and less body fat (Tudor-Locke, 2004). However, studies have also
demonstrated that there are health benefits at step levels below 10,000 steps.
Sugiura, et al. (2002), reported improvements in lipid profiles for middle-aged
women who had steps counts of approximately 9,000 steps/day. Although more
data from studies evaluating the relationship between step counts and health
outcomes are needed, one concern about establishing a single number as the
recommendation is that it would not cover the entire population. Although
10.000 steps/day is believed to be a reasonable goal for healthy adults, it
probably overestimates the amount of activity needed for older adults and those
with chronic disease. It also underestimates the activity needs of children whose
step equivalent for activity has been estimated as 15,000 steps/day (Tudor-Locke,
2004).
One of the difficulties in establishing recommendations for activity based
on steps is determining what method to use. For example, the Surgeon Generals
report recommends that Americans get 30 minutes of physical activity each day.
Thirty minutes of walking at a pace of three miles per hour is equivalent to
approximately 3,000 steps. This amount of activity should be in addition to the
activity associated with daily living. Tudor-Locke (2004) reports that for healthy
adults in the United States, activities of usual daily living account for between
33


6,000-7,000 steps. Adding 3,000 extra steps then boosts activity to 9,000-10,000
steps. This reasoning supports the 10,000-step recommendation, however it is
based on the premise that the populations average activities of daily living are
between 6,000-7,000 steps. Unfortunately, many of the people who are most in
need of physical activity are likely to fall significantly below average. People
whose activities of daily living are 4,000-5,000 steps a day would then need to
increase their steps by 6,000-5,000 in order to achieve 10,000 steps. This would
require about an hour of walking. Although this level of activity would probably
be good for them, it may not be a realistic or sustainable goal and it conflicts with
the Surgeon Generals recommendation.
Another approach to establishing physical activity recommendations is to
encourage people to increase their activity by a specific amount. James Hill
(2003) is a proponent of recommending that people increase their steps by 2,000
over baseline. As discussed above, this recommendation is designed to target the
energy gap. Walking 2,000 steps takes about 20 minutes; therefore this amount
of activity is lower than the Surgeon Generals recommendations. Arguments in
favor of 2,000 steps are that it is probably more realistic and sustainable than
10,000 steps and that it appreciates the fact that people start from different
baselines. One argument against recommending that people increase their steps
by an average of 2,000 steps per day is that people who start out with very low
activity levels (4,000 steps/day) would still only reach 6,000 steps and this may
|
i
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34


not be sufficient to promote health. The two approaches to establishing
recommendations could be combined by encouraging people to increase their
steps gradually by 2,000 step increments until they reached an appropriate long-
term goal. The long-term goal would be individualized based on the persons
age, and health.
Realizing that currently there are no standards to evaluate step amounts,
Tudor-Locke and Bassett (2004) have proposed preliminary indices to use to
classify pedometer-determined physical activity in health adults. The criteria that
she proposes are:
(i)<5 ,000 steps/day may be used as a sedentary lifestyle index;
ii) 5,000-7,499 steps/day is typical of daily activity excluding volitional
sports/exercise and might be considered low active;
iii) 7,500-9,999 likely includes some volitional activities (and/or elevated
occupational activity demands) and might be considered somewhat
active;
iv) > 10,000 steps/day indicates that point that should be used to classify
individuals as active.
v) individuals who take > 12,500 steps/day are likely to be classified as
highly active.
Currently these are the only indices available, consequently they have been
adopted in this thesis.
I
35


An Ecological Perspective
There is an inherent contradiction between promoting lifestyle physical
activity when lifestyle is also hypothesized to be the cause of inactivity. If
lifestyle is to blame for Americans inactivity and obesity, then perhaps changing
ones lifestyle to incorporate everyday activities may not be as easy as it is
thought to be. This question will be addressed by discussing some of the social
ecological factors that influence activity. According to Green (1996):
The ecological or transactional view of behavior holds that the
organisms functioning is mediated by behavior-environment
interactions. This view has two implications for behavioral and
social change:
The environment largely controls or sets limits on the
behavior that occurs in it
That changing environmental variables results in the
modification of behavior.
Although there are a variety of well-founded theories linking
environmental influences with physical activity, the concept of the built
environment is currently receiving a great deal of attention (King, 2002).
Examples of some of the more traditional social-environmental theories include
theories of environmental stress, social disorganization, and availability of
community resources. Environmental stress theory posits that neighborhood
crowding, noise, and traffic congestion negatively affect physical activity.
Theories of social disorganization emphasize the harmful effects of
36


neighborhood crime and violence on social support and health promoting
behaviors such as physical activity. Physical factors that are positively related to
activity include access to open space, side walks, recreation centers, and physical
features such as those that increase social cohesiveness and social capital (Giles-
Corti, 2003).
Employing a perspective from the Healthy Cities movement, researchers
have begun examining the impact of the built environment and city planning on
activity. It is clear that over the last century, changes in urban planning have
paralleled the increase in obesity. Examining the association between obesity
and urban sprawl, Lopez (2004) found that the risk for being overweight
increased by 0.2% and the risk for being obese increased by 0.5% with each
point rise in the urban sprawl index. Furthermore, historians note that during the
last century there was an intentional separation of the environment into living
areas and commerce areas. Although this was done for the purpose of creating
healthier living environments by moving people away from polluted
manufacturing sectors, separating housing from work increased dependence on
the automobile (Perdue, 2003). This has had a number of negative effects on
physical activity. For instance, most people comminute to work by carthey
live too far away to walk or ride a bike. The time they spend commuting takes
away from the time they have for physical activity and commuting itself
frequently increases stress. Even if people want to be more active, there are
37


often environmental barriers since most cities and suburbs are designed to
accommodate cars rather than pedestrians or bicycles.
Studies show that most people who walk do so for either transportation or
recreation (Giles-Corti, 2003). The idea of walking for transportation is closely
linked with lifestyle physical activity. However, the ability to implement
lifestyle physical activity is highly dependent on the built environment. People
who live in communities where they are in close proximity to places where they
need to go can more easily chose to walk or ride a bike compared to those living
in the suburbs where things are too spread out to get to by walking or bike riding.
Although there is increasing recognition of the importance of the built
environment on physical activity (and health in general), little is known about
how to change the environment to enhance physical activity. One
recommendation is that public health perspectives should be included in
community development and planning activities. Also there is the recognition
that implementing changes in the built environment will require changes in
zoning and other legal ordinances. Economic incentives must also be considered
to counteract societal trends favoring sedentary activities (Sturm, 2004). Thus, it
is apparent that there must be a multidisciplinary approach to creating the
environmental changes that are required to support a more active community.
One of the important, although not surprising, findings from focus groups
of Colorado on the Move participants is the role of the micro-environment in
38


supporting behavior change. Participants in work site interventions stated that a
supportive work environment helped to facilitate increased activity. Factors that
were mentioned as helpful included: healthy competitions, incentives, and
creating a step culture. Research has also shown that placing cues to action
such as a sign encouraging the use of stairs can help increase activity (Marcus,
2003).
Creating health-promoting environments is essential for sustainability of
individual level interventions. As a community and work site intervention,
Colorado on the Move has strongly advocated for environmental changes that
facilitate activity. With the programs increasing popularity both locally and
nationally, the leaders of Colorado on the Move are a powerful voice on policy
issues. For example, the group is working with the Stapleton Redevelopment
project to design a walking and activity friendly community. This is in contrast
to typical suburban developments, which tend to accommodate automobiles
rather than walkers. Colorado on the Move is also working to increase the
exposure of walking opportunities such as trails, walking routes and organized
walks.
Colorado on the Move is attempting to impact social interactions by
creating a stepping culture in the state. Some of the ways they are working to do
this are by increasing media exposure and creating partnerships with community
organizations. Colorado on the Move has successfully increased the publics
39


attention and motivation for walking and this is should continue diffusing
through the population.
Promoting Physical Activity in Primary Care
From an ecological perspective, environmental engineering and
community based interventions offer a great potential for increasing physical
activity because of the large number of people they affect. However, clinical
interventions in primary care settings can be an important complement to policy
and community interventions.
Primary care settings provide opportunities for individualized counseling
not often possible in community programs. Since adults typically visit physician
offices several times a year, providers have multiple contacts with patients and
can tailor messages according to each patients stage of change (Whitlock, 2002).
Studies show that the majority of patients expect to receive counseling about
behavior change from providers and that counseling increases patient
satisfaction. Patients who receive counseling are also more likely to initiate
behavior change (Pescatello, 2001). Even when change is not initiated, patients
who receive counseling may be primed so that they are more receptive to the
message at a later time (Whitlock, 2002).
Although evidence supports the role of clinical interventions to promote
physical activity, rates of counseling about physical activity are low. Nationally,
40


rates of counseling have been estimated to be between 26% and 34% (Ma, 2004,
Wee, 1999). Patients who were most likely to receive physical activity
counseling were those younger than 75 years old and those with risk factors for
cardiovascular disease (Ma, 2004). Data collected within a Colorado practice
based research network, CaReNet, revealed that in 2,971 clinic visits with adult
patients, physicians provided exercise counseling to only 20.3% of patients.
Among patients diagnosed as obese, rates of exercise counseling were somewhat
higher37% (unpublished data).
When questioned, providers usually identify lack of time, poor counseling
skills, and little or no reimbursement as the main barriers to behavior change
counseling (Pinto, 1998; Ma, 2004). At a more fundamental level, physicians are
also concerned with the issue of effectiveness. Whitlock, Orleans, Pender and
Allen (2002) note:
.. ..although physicians increasingly agree that most health
promoting behaviors are important to patients health, they report
skepticism about patients willingness to change behaviors and
their own ability to intervene successfully in these areas.
Questions about effectiveness are also raised in the US Preventive
Services Task Force (USPSTF) updated guidelines for physical activity
counseling (Eden, 2002). Although the guidelines recommend physician
counseling for physical activity, the strength of evidence to support this
recommendation is rated C (inconclusive evidence for or against).
41


Improving Behavioral Interventions in Primary Care
In order to provide a stronger evidence base on which to inform future
guidelines, the USPSTF convened The Counseling and Behavioral Interventions
Work Group. In a recent publication, this group proposed two analytic
frameworks to guide research and evaluation of behavioral counseling in clinical
practice (Whitlock, 2002). Analytic framework #1 asks the question: Does
changing individual health behavior improve health outcomes? Analytic
framework #2 asks: Can interventions in the clinical setting influence people to
change their behavior? While there is strong evidence that increasing physical
activity is associated with improved health, there is less evidence to guide
implementation of successful interventions in primary care.
Analytic Framework #2 focuses on design and evaluation criteria for clinical
interventions. Embedded in this model are 13 key questions concerning both
short term behavior change and long term maintenance of behavior change. The
two questions that are most relevant to this research are:
Q6: what are the essential elements of efficacious interventions (what,
how, when, where, to whom, by whom, for how often and for how
long?).
Q9: what type of ongoing assistance or support is needed to achieve or
maintain targeted behavior changes? (Whitlock, 2002).
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RE-AIM Framework
The analytic frameworks suggested by the USPSTF axe designed to guide
research efforts in order to build an evidence base for clinical interventions, but
they do not explain how to go about doing this research. Traditional research
methods have emphasized efficacy trials where interventions are tested under
controlled conditions. According to Glasgow, Lichtenstein and Marcus (2003),
these trials do not address many of the practical issues that are essential for actual
implementation of interventions in clinical practice. They distinguish between
efficacy and effectiveness trials and propose a five-phase model for intervention
research. Early stages are devoted to developing intervention hypotheses (Phase
I) and methodologies (Phase II), and testing these for efficacy (Phase III).
Building on this research, Phase IV trials test the interventions in real life settings
and among populations where results can be generalized to the intended
audience. Finally, Phase V consists of large-scale demonstration projects.
The model proposed to guide effectiveness evaluation is the RE-AIM
framework (Glasgow, 2001; 2003). The RE-AIM framework takes into
consideration factors that operate at the level of both the individual and the
setting. The five evaluation dimensions that compose RE-AIM are Reach,
Efficacy, Adoption, Implementation, and Maintenance. These terms are
described below:
43


Reach refers to the extent to which eligible participants take part in the
intervention and how representative they are.
Efficacy or Effectiveness measures the impact (both positive and
negative) of the intervention on participants who began the program.
Adoption considers the percentage of potential settings that will
participate in the intervention and their representative ness.
Implementation tests the extent to which the intervention was delivered
as intended.
Maintenance is evaluated on both the individual and setting level. At the
individual level, long-term (6-12 months) effects are considered. For
settings, maintenance is defined in terms of the extent to which the setting
continues to provide the intervention and whether or not it becomes
institutionalized.
The RE-AIM framework addresses both external (reach and adoption) and
internal validity (efficacy and implementation) as well as individual and setting
level outcomes. This information obtained from application of RE-AIM should
inform translation of efficacy research into actual clinical settings. Many of these
issues have been previously ignored. While the model provides a useful
framework for conducting evaluation, its breath may be beyond the scope of
smaller research projects such as the one discussed in this paper.
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Theoretical Model
Theoretical Issues
Similar concerns about the transferability of research conducted under
conditions that maximize internal validity that were issues raised by Glasgow are
also under discussion among behavioral science theorists. At the forefront of
these dialogues is Alexander Rothman (2004) who argues that theoretical models
that have been rigorously tested in controlled settings need further development
through application and evaluation as interventional strategies in real life
settings. Rothman states that, If investigators are more receptive to the
opportunities interventions afford for theory testing, there will be a dramatic
increase in data that can reveal the adequacies and inadequacies of a given
theory. He postulates that there is a gap between knowledge produced by
theorists and the knowledge that comes from testing theory in real life situations:
From the perspective of an interventionist, the accuracy of the
relations specified in a theory is an important but not sufficient
determinant of its value. Interventions need theories that are
accurate and applicable; that specify not only the relation between
two constructs, but also whether that relation does or does not
change across contexts (e.g., does the impact of risk perceptions
on behavior differ whether one is examining decisions to test of
radon or to start smoking?
One of the opportunities afforded by intervention testing is the ability to identify
moderators and mediators of behavior change.
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Another theoretical concern that is pertinent to physical activity research
is the extent to which behavior change theories are applicable across different
types of behaviors. Increasingly physical activity researchers have recognized
that physical activity behavior change requires multilevel and multidimensional
models. Most behavior change theories use a fairly linear model and focus at the
level of the individual behavior change. These theories may not be
comprehensive enough for more complex behaviors such as physical activity or
nutrition. Recognizing the deficiency in current theories, one idea is to work
toward better integration of different theoretical approaches (Epstein, 1998; King
2002). Others believe that the inability of traditional theories to guide significant
long-term change in physical activity behavior is evidence that what is really
needed is a complete paradigm shift (Morgan, 2001).
Confusion over the application of theory to physical activity behavior
change is complicated by the fact that:
There are hundreds of behavioral studies on physical activity, with
great diversity in research designs, measurement approaches,
populations studied, theories used, variables tested, and physical
activity outcomes. This diversity makes it difficult to integrate the
findings and summarize the status of the field, thus limiting the
ability of subsequent research to build on previous findings.
(Bauman, 2002)
These issues are particularly relevant to this research study since it
represents a fundamentally different approach to physical activity promotion
compared with the majority of previous studies. The theoretical models that have
46


been evaluated in studies that promote exercise may not be applicable to those
that promote lifestyle physical activity. In fact one of the gaps in the physical
activity literature is in evaluation of the effect of type and intensity of activity on
adoption and maintenance (Dishman, 2001).
One of the ways that theoretical models have been tested is by evaluating
the extent to which their constructs are associated with physical activity.
Dishman, Sallis and Orenstein (1985) performed a meta analysis of over 300
physical activity studies, to determine the correlates of physical activity.
Commenting on this research Bauman (2002) notes:
.. ..the most remarkable finding was the large number of variables
that were not associated with a specific theory but were still found
to be consistently related to physical activity. No current theory,
or even a combination of theories, accounted for 15 variables that
were associated with physical activity.
These findings suggest both the inadequacy of current theoretical models
of behavior change for the study of physical activity as well as the complexity of
interacting factors that influence physical activity. Authors of this study
conclude that physical activity involves a complex causal web of intrapersonal,
interpersonal, social/cultural, and physical environmental correlates. Such an
interpretation suggests the need for a multilevel model (Bauman, 2002).
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Social Cognitive Theory
While a multilevel model is necessary for understanding the complexity of
interacting variables that influence physical activity, the intervention used in this
study is more narrowly focused at the level of the individual. Social Cognitive
Theory (SCT) is used to guide this research because it appreciates the complexity
of factors affecting behavior while supporting an emphasis on the level of the
individual. Social Cognitive Theory explains human behavior as the result of
triadic, reciprocal determinism between behavior, personal factors and
environment. Within each domain specific constructs are identified that are
associated with behavior change.
Within the personal factor domain, the main constructs are: knowledge,
skills, self-efficacy, outcome expectations and personal goals. Behavioral
constructs include: frequency, consistency and other aspects of behavior that are
specifically relevant to particular circumstances. Environmental constructs
include social, institutional and physical factors.
Studies evaluating physical activity determinants have found strong
support for constructs from social cognitive theory, especially self-efficacy and
social support (Dishman, 1985). Lewis, Marcus, Pate, and Dunn (2002) also
found good support for SCT constructs in a review examining theory based
physical activity interventions. This review looked at studies that measured
change in constructs that were hypothesized to mediate physical activity.
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Unfortunately, although many studies use a theoretical model few specifically
measure the extent to which interventions change mediators. The following
paragraphs outline the various constructs that make up SCT and describe how
they apply to this study.
Personal Factors. The first group of constructs are those categorized as
personal factors in the model.
Knowledge. Correlational studies have not found a relationship between
knowledge about the health outcomes associated with exercise and behavior
change to increase physical activity (Trost, 2002). This may be interpreted to
mean that although people know that they should exercise they dont. If this is
true, then we could hypothesize that knowledge about the health benefits of
exercise is not sufficient to overcome the barriers associated with exercise.
However, the outcome might change if the message substitutes the concept
lifestyle physical activity for exercise.
When applied to studies of physical activity, knowledge is typically
defined as extrinsic knowledge about the health risks associated with lack of
exercise and the health benefits that accrue for increasing vigorous activity.
Information, such as the Surgeon Generals recommendations, is widely
publicized in the media. As with many public health messages, most Americans,
even those who are sedentary, are at least somewhat aware of the health risks and
benefits associated with physical activity. However, because of the
49


predominance of messages about moderate-to-vigorous activity, people who are
sedentary may not be aware of the fact that even small changes in activity at low-
to-moderate intensity can provide health benefits.
Although knowledge has not been correlated with physical activity, it
may have more to do with the educational message rather than the construct. In
this intervention, education about lifestyle physical activity may help motivate
sedentary people because it promotes a level of activity that is more easily
obtained.
In this study, knowledge is conveyed in a variety of ways. The first
opportunity to provide education occurs between patient and provider. Flyers for
the study instruct patients to ask their providers about the study. Also, providers
can use the study as an opportunity to discuss activity with patients. In both
instances, provides can individualize the message to patients, which increases the
credibility of the message. The second place where education is provided is
through watching a nine-minute video produced for healthcare settings by
Colorado on the Move. The video is both motivational and instructional. It
provides information about how to use the step counter (e.g. how to position it,
and re-set it), and ways to increase daily activity (using stairs instead of
elevators, parking farther away from stores, etc.). This information is
supplemented by a small booklet that participants receive. The booklet contains
50


information about increasing activity with the step counter and has a journal in
the back for recording daily steps.
The extrinsic knowledge discussed above is complemented by
individualized knowledge that enables participants to evaluate and monitor their
own activity levels. Having objective information that is both personal and
readily available may empower participants to make sustainable changes in
physical activity behavior.
Skills. The skills required for this study can be categorized in terms of
skills related to 1) performing the activity, and 2) self-regulatory skills such as
problem solving, and self-monitoring. One of the advantages of this study is that
the type of activity that it promotes, namely walking requires very little skill.
However, to be successful with the intervention (e.g., to increase activity)
participants need to develop self-regulatory skills. The step counter is a tool for
self-monitoring because it provides real time feedback about activity. Self-
monitoring throughout the day may lead to increased motivation to make small
changes in activity. However, success with behavior change also requires
development of problem-solving skills. These skills may involve planning time
to take a walk and/or developing the habit of using the stairs instead of the
elevator.
Self-efficacy. Self-efficacy is one of the most consistent correlates of
physical activity behavior (Trost, 2002; Dishman, 1985). Self-efficacy for
51


physical activity refers to ones confidence regarding participating in specific
types of physical activity or specific amounts of physical activity, or both. The
concept of self-efficacy is especially useful for understanding the initial appeal of
this intervention to the target audience. Most people have self-efficacy for
walking and lifestyle physical activity. Efficacy is enhanced by the fact that
people are encouraged to set their own goals. Participants are taught that setting
realistic goals is more important then setting overly ambitious goals. By setting
and achieving realistic goals, participants can increase self-efficacy.
Outcome Expectations. One of the theoretical constructs used to study
determinants of physical activity is outcome expectancy theory. Outcome
expectations are the individuals estimate of the likelihood that performing a
behavior will result in a particular outcome. Researchers have found that there
is a predictive association between participation in physical activity and
perceived outcomes. The most commonly reported expected outcome for
engaging in physical activity is health benefits. Other reported outcomes are
decreased stress, weight control and appearance (Steinhardt, 1989). In a review
of mediators of physical activity, Lewis et al. (2004) report support for the
construct from one study that specifically measured changes in outcome
expectations
Outcome Expectancies. Outcome expectancies are the values that a
person places on a particular outcome. According to Baranowoski, Perry, and
52


Parcel (2002) expectancies influence behavior according to the hedonic
principle: if all other things are equal, a person will choose to perform an activity
that maximizes a positive outcome or minimizes a negative outcome. For
physical activity it is important to realize that short-term outcomes, such as how
you feel after activity can have an effect on behavior apart from long-term
outcomes such as risk reduction. Sallis and Owen (1999) note that: The
punishment of vigorous exertion remains immediate and salient, while the
reinforces of improved health or weight loss are greatly delayed and silent. The
importance of short term expectancies is also demonstrated is by the fact that
enjoyment is consistently found to be a strong correlate of physical activity.
Personal Goals. Personal goal setting is an important aspect of this
intervention. Although participants are not required to state their goal(s), they
receive instruction and encouragement for goal setting. The first week of the
study is devoted to collecting baseline data so that participants can determine
their average weekly step amounts. Using their baseline levels participants are
taught to set realistic goals for themselves. Although many people have heard
that the recommended number of steps per day is 10,000 this study downplays
this recommendation so that people do not become discouraged. For example,
people whose baseline activity is below 3,000-4,000 steps per day are
encouraged to try to increase by 500 to 1,000 steps per day. The goal of 10,000
steps is probably only appropriate for people who are already fairly active (8,000
53


steps). The qualitative part of the study will ask people whether or not they met
their person goals (short term and long term).
Environmental Factors. In SCT, environmental factors are factors that
are external to the individual and can be classified as micro and macro factors.
Macro level factors include the availability of enablers of physical activity such
as walking and bike paths, as well as cues to action. Micro factors include social
support and social learning. As discussed previously, most people in the Denver
area have some awareness of Colorado on the Move and many people know
friends who have participated in this program. This provides a social learning
experience. Another aspect of the intervention that was hoped to encourage
social learning is that the staff at the clinic were given step counters and many
providers purchased them prior to launching the study. It was hoped that staff
and provider experience with step counters would increase enthusiasm for the
study. In fact, there are a number of providers who regularly wear a step counter
and this models the behavior for patients.
Behavior. In SCT, behavior is reciprocally determined by both
environmental and personal factors. Although the components of this study
target personal factors, behavior is also likely to be influenced by environmental
factors because of the impact of Colorado on the Move. In the study the
behavior of interest is physical activity and it will be measured as steps taken per
day over a 13-week period. Unfortunately step counters do not measure all types
54


of activity such as biking or swimming. However it is unlikely that the target
population for this intervention will be active in any of these other forms of
activity since they are primarily sedentary. Nevertheless, they will have an
opportunity to describe other activities that they are (or become) involved in
during the qualitative interviews. Step counts will be used to determine whether
participants increased their walking activity over time. One of the goals of the
intervention is to encourage participants to at least make small lifestyle changes
such as taking the stairs instead of elevator as a means of increasing their
activity. During interviews participants will have the opportunity to describe the
specific behavior changes they made, if any.
Linking SCT Theory with Intervention Components. Each of the
preceding paragraphs discusses a construct from SCT and shows how it applies
to this research intervention. In Table 2.1 this information is reorganized to show
the links between components of the intervention, their relations to theoretical
constructs and the potential mechanism by which they might influence behavior
change.
55


Table 2.1.
Linking SCT Constructs with Components of the Intervention.
SCT Behavior Change Mediator Intervention Objective Intervention Components
Knowledge Teach lifestyle physical activity (LPA) Health benefits of LPA How to increase LPA Teach correct techniques for step counter use Video, booklet, provider-patient communication, study coordinator instruction
Goal setting Collect baseline activity level. Set realistic goals. Step counter, video, booklet
Self-regulatory skills: Self-monitoring Problem solving Develop self-regulatory skills to support behavior change. Use step counter to self-monitor activity. Keep daily activity records. Learn problem-solving strategies for LPA. Video, step count log Step counter Video, booklet, activity journal, weekly reporting
Self-efficacy Setting and accomplishing realistic goals increases self-efficacy. Video, booklet, activity journal
Outcome expectations Develop realistic expectancy of the outcome that will be achieved. Video, booklet
Outcome expectancies People will find that LPA is enjoyable and achievable. Video, booklet
Social variables Behavior modeling Physician and clinic staff model wearing step counters.
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The Transtheoretical Model
One model of behavior change that has been applied to studies on
physical activity is the transtheoretical model (TTM) (Prochaska, 2002). The
TTM is a stage-based model that postulates that people move through specific
stages prior to actually making a behavior change. One of the attractive features
of this model is that it represents behavior change as a dynamic process. The
theory discusses the various cognitive processes that apply to each stage of
change and provides useful constructs for designing interventions at various
stages. In the literature, this model is often combined with SCT for physical
activity studies. Although the model is popular and has received good support in
studies looking at behavior change for exercise, there is evidence suggesting that
it may not be a useful model for studying lifestyle physical activity. In an
evaluation of construct validity by Schumann et al. (2002), stages of change was
found to only be valid for strenuous and moderate intensities of activitiesnot
for moderate to low level activities (except in elderly).
Although this finding argues against the application of TTM to low-
moderate levels of physical activity, the underlying logic of the model is valid.
However, the criteria used to define the various stages need to be flexible and
reflect goals that are realistic for the target population. In most instances,
individuals are categorized as being in an active stage only when they meet
specific criteria. These criteria are typically defined by professionals based on
57


health outcome data. In physical activity studies the criteria usually reflect the
Surgeon Generals recommendations. Anyone who meets these criteria is
considered to be in an action phase, while those who do not meet the criteria are
judged to be contemplative or precontemplative. However, the Surgeon
Generals recommendations may not be a realistic immediate goal for someone
who has been sedentary for his or her entire life. For this person, it might be
more appropriate to define their stage according to what is realistic for them to
accomplish. Although they may not be in the Action Phase with regard to
meeting the Surgeon Generals recommendations, they may be in the Action
Phase with regard to taking a 10-minute walk each day. The utility of the TTM
could be enhanced by taking a less rigid, more individualized approach to
defining the behaviors associated with the various stages of the model. Such an
approach would be consistent with evidence showing the value of individualized
goal setting. Furthermore, physical activity requires a complex assortment of
behavior changes and looking at only a single outcome can obscure the fact that
people have different behavior change trajectories. While there may be argument
about how to apply this theory, the general concepts and principles underlying
TTM have pervaded the field of health promotion. The author recognizes that
TTM concepts have influenced the development of this intervention on one level
even though the primary model on which the intervention is based is SCT.
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Maintenance of Physical Activity Behavior Change
While there are many theories that deal with the initiation (adoption)
phase of behavior change, there are few theories that attempt to cover the
maintenance (adherence) phase. Although maintenance of behavior change is
necessary to achieve health improvements, relapse remains the norm, regardless
of the behavior in question (Orleans, 2000). Many studies of exercise
adherence, especially early ones, typically had a disappointing drop out rate of
50% (Marcus, 2000). In more recent studies using different criteria to measure
maintenance and with different types of interventions, Marcus reports that results
are consistent with earlier conclusions that adherence decays over times. One of
the reasons for poor maintenance of behavior change is that most interventions
focus almost exclusively on strategies to promote adoption. These interventions
assume that short-term change will result in long-term change, but there is no
evidence to support this assumption (Dishman, 1996).
Consistent with the focus of intervention studies on initiation of change,
there has been little attention paid to developing theoretical models of
maintenance. According to Orleans (2000), most past research has tended to
treat maintenance as a fixed or static state. As a result, the concept of
maintenance has not been well studied and many today feel that the theoretical
models that we have are not adequate to guide research in this area (Bauman,
2002; Baranowski, 1998).
59


In a supplemental issue of Health Psychology devoted to the topic of
maintenance of behavior change in cardiorespiratory risk reduction, authors
consistently express the need for better theoretical models to guide research in
the study of long-term behavior change. Rothman (2000) states that:
Given that the repeated application of intervention strategies that
facilitate short-term success does little to improve rates of long-
term success, the premise that there are important differences in
the psychological processes that govern behavioral initiation and
maintenance appears worthy of consideration.
These authors unanimously agree that the theoretical models which have aided
researchers in designing effective programs for initiating change, are insufficient
for providing theoretical knowledge on which to base maintenance strategies
(Orleans, 2000; Rothman, 2000; Marcus, 2000).
Even the Transtheoretical model (TTM), one of the few that explicitly
identifies the stage of maintenance, takes a narrow view of maintenance
conceptualizing it only in terms of relapse prevention. By focusing exclusively
on preventing relapse, the model assumes that maintenance is a final destination
rather than a stage on a longer journey. Applying the metaphor of a journey
suggests that maintenance is itself a process that requires the ability to persevere
with behaviors despite changing attitudes, and circumstances (Orleans, 2000).
Viewed in this way, it is apparent that relapse prevention is an insufficient
construct for studying and intervening in the maintenance phase of behavior
change. Since the concept of maintenance has been understudied, this research
60


uses an exploratory approach to understand the factors that influence long-term
physical activity behavior change.
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CHAPTER 3
RESEARCH DESIGN AND METHODS
This study uses mixed methods to evaluate the effectiveness of an
intervention designed to increase lifestyle physical activity among primary care
patients. The primary quantitative outcome is change in average daily step
count. Interviews are used to assess individual experiences both immediately
following the intervention and again 6 months later.
Study Aims
1. Compare and contrast characteristics of completers and non-completers of
the 13-week intervention.
2. Determine if activity levels change during the 13-week intervention period
and whether changes are maintained.
3. Examine the accuracy of self-perceptions of physical activity and evaluate
the extent to which the intervention changes perceptions.
4. Explore the subjective experiences of participants during the intervention
and follow-up phase of the study.
5. Propose intervention strategies for using step counters in a primary care
practice.
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Mixed Methods Paradigms Conundrum
Mixed methods research designs present unique challenges for discussing
paradigms because they combine research methods that are traditionally
associated with divergent epistemologies. The theoretical and paradigmatic
difficulties implied by mixed methods have lead many mixed methods
researchers to avoid the issue of identifying a particular paradigm of inquiry. In
reviewing mixed methods research in the social science, Greene and Caracelli
(2003) note:
It appears that paradigms are not the primary organizing
framework for mixed methods practice. Rather applied social
inquirers appear to ground inquiry decisions primarily in the
nature of the phenomena being investigated and the contexts in
which studies are being conducted.
These authors caution that despite the apparent discrepancy between
theory and practice, paradigms are important and practitioners must be reflexive
as well as cognizant of the underlying epistemologies associated with the
methods they chose.
In their book, Handbook of Mixed Methods in Social and Behavioral
Research, Tashakkori and Teddlie (2003) identify six perspectives on the issue of
how paradigms should be used in the development of mixed methods research.
The a-paradigmatic viewpoint argues that methods and paradigms are
independent consequently there is no inherent epistemological conflict. A
63


second perspective is that the paradigms associated with quantitative (QUAN)
and qualitative (QUAL) designs are incompatible and therefore mixed methods
designs are impossible. The third perspective, complementary strengths states
that the two paradigms have different strengths, which can only be realized if the
methods are kept separate within the study design. The fourth position argues
that there should be a single universal paradigm that unifies mixed methods
research. The fifth view, named dialectic holds that mixed methods research
designs intentionally utilize multiple paradigms, along with their underlying
assumptions, for the specific purpose of examine (ing) the tensions that emerge
from the juxtaposition of these multiple diverse perspectives (Teddlie, 2003).
The final perspective is similar to the dialectic position except that it prescribes
the paradigms that must be used for different types of inquiry.
Although the supporting arguments for each viewpoint have merit, the
position that mostly closely reflects the purpose for choosing mixed methods in
this study is the dialectic perspective. This perspective recognizes that
different paradigms make unique contributions and that combining multiple
paradigms provides greater understanding of the phenomenon under
investigation. According to Teddlie and Tashakkori (2003),
An important component of this position is the ability to think
dialectically. This involves consideration of opposing viewpoints
and interaction with the tensions caused by their juxtaposition.
The tensions come from the differences in the assumptions of the
different paradigms.
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QUAN and QUAL methods are associated with epistemologies that have
divergent assumptions. Quantitative inquiry is usually identified with a positivist
or post positivist approach to research. Positivists believe that:
.. .social research should adopt scientific method, that this method
is exemplified in the work of modem physicists, and that it
consists of rigorous testing of hypotheses by means of data that
take the form of quantitative measurements. (Atkinson, 1994)
The positivist viewpoint is that truth can be discovered through
experimentation. Post positivists also uphold scientific method, but realize that
the results are only approximations of truth. In contrast, constructivists focus on
meanings that are constructed by individuals or groups. They do not attempt to
discover a single truth, but instead explore peoples differing perceptions of
reality. Constructivism supports qualitative methods of inquiry.
A fundamental difference between QUAN and QUAL methods concerns
the role of the researcher. In QUAN research, the researcher should have
no/minimal impact on the study, whereas QUAL research acknowledges the
researcher as a participant in the research and applies reflexivity as a tool to
guide interpretation
Although theorists argue over the implications of blending
epistemologies, there are many pragmatic advantages to combining QUAN and
QUAL methods. In this study, a mixed methods design was chosen in order to
conduct a broad study of the phenomenon of lifestyle physical activity
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promotion. The QUAN data provides an objective assessment of baseline (week
1) activity and measures change in activity over a 13 week time period. While
this data can help show whether or not the intervention was associated with
increased number of steps, it provides very little insight into the reasons why the
intervention was or was not successful. Nor do QUAN methods fully explain
which types of people are more and less successful with the intervention. These
questions can be more easily addressed with QUAL methods, especially in the
early exploratory phases of the research. In addition to providing insight into the
intervention, combining methods also allows triangulation of data, which
increases the validity of QUAN inquiry.
Sequential Explanatory Design
The study utilizes a sequential explanatory design in which quantitative
data (QUAN) is collected in the first phase of the study and qualitative data
(QUAL) in the second phase. As defined by Creswell, Clark, Gutman, and
Hanson (2003) this design
... .is characterized by the collection and analysis of quantitative
data followed by the collection and analysis of qualitative data.
Priority is typically given to the quantitative data, and the two
methods are integrated during the interpretation phase of the
study.
In this mixed methods study the two strands are independent and each is guided
by a unique yet complementary set of research questions. The data from each
66


part of the study are analyzed separately and integrated to create meta-inferences
as illustrated in Figure 3.1.
Figure 3.1. Sequential Explanatory Design.
Research Design
Overview
The Steps for Health study enrolled 83 participants who agree to wear a
step counter daily for 13 weeks. Participants recorded their step counts each day
and reported these values each week using either an automated telephone system
or e-mail. At enrollment, participants viewed a 9 minute video that educated
them about how to use the step counter, set personal goals and gave ideas for
ways to increase daily physical activity. At the conclusion of the 13 week
67


intervention, telephone interviews were conducted with participants and follow
up interviews were conducted 6 months later.
Human Subjects Review
Human subjects approval was granted from the Colorado Multiple
Institutions Review Board/COMIRB (Protocol #03-281). Two amendments to
the original protocol were filed and approved to enable the author to conduct the
qualitative portions of this study. The research was also approved under
HIPPA guidelines, which were implemented by COMIRB shortly after the study
was approved. The research was submitted to the University of Colorado at
Denver, Human Subjects Committee (protocol #2004-129) and this IRB accepted
the approval granted by COMIRB.
In compliance with the study protocol to protect patients and ensure
confidentiality, written informed consent and HIPPA authorization were obtained
from all participants at the time of enrollment. Signed consent forms were stored
in a locked file cabinet. Five digit randomly selected study numbers were used to
identify study participants. Quantitative data were stored on a secured server.
Participants and Site
Research Site. AF Williams Family Medicine Center, Denver, Colorado.
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Sample Size. The study was approved for enrollment of up to 85 patients.
Sample size was calculated based on data from the Denver Black Church
Initiative (unpublished data from Colorado on the Move; J. Hill, P.I.). It was
determined that a sample size of 60 would provide 80% power to detect a .6
effect size pre-post difference, assuming 33% dropout and a .2 correlation
between baseline and final observations. Since dropout rates for exercise studies
tend to be high, we planned for an initial sample of 85 to accommodate a dropout
rate of 50% or slightly higher and still retain sufficient power to detect a .6 effect
size difference.
Recruitment. Participants were recruited through self, nursing and
provider referral. Posters and flyers were displayed throughout the clinic to
advertise the study. Additionally, providers were asked to discuss the study with
patients. To ensure that the study did not pose a medical risk to participants,
each patients primary care provider (PCP) was asked to complete a medical
waiver. The enrollment period was anticipated to take approximately two
months, however it was necessary to extend it to six months. Participation was
voluntary and participants were free to drop out of the study at any time. The
interviews at 13 weeks and 9 months were optional and only those participants
who consented to be interviewed were contacted.
Inclusion/exclusion Criteria. Participants were required to be over age
18, not pregnant, without medical problems that would be adversely affected by
69


participation in the study, not already exceeding the Surgeon Generals
recommendations for activity, able to understand the study protocol, and willing
to fulfill the data reporting requirements. Potential participants completed a brief
health survey that included height, weight, age, gender, and self-assessment of
current level of activity. Patients with medical conditions that in the opinion of
their primary care provider would be adversely affected by participating in this
study were excluded. Originally, patients whose physical conditions severely
limited their mobility (e.g., morbid obesity, severe osteoarthritis, and severe
COPD) were to be excluded however, providers felt that some of these patients
could benefit from being in the study and referred them. Although the inclusion
of people with activity limitations negatively impacted the quantitative results,
these participants made important contributions to the qualitative portion of the
study.
Compensation. Study participants received a complementary step counter
with a safety leash (valued at $15.00) along with a Colorado on the Move
educational booklet (valued at $1.00) when they joined the study. They did not
receive any monetary compensation.
Dependent and Independent Variables
Dependent Variables. The dependent variables are average daily step
count by week and change in activity over 13 weeks.
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Independent Variables. Age, race/ethnicity, sex, BMI, current level of
activity, and average daily step count during week 1 (a proxy for baseline
activity).
Phase I: Protocol and Data Collection (Quantitative-)
Prior to launching the study, an in-service was done with clinic staff to
describe the study, educate them about the importance of physical activity and
distribute step counters to each person who wanted one. The purpose of the in-
service was to create a step counter culture in the clinic in order to increase
enthusiasm for the study and model lifestyle physical activity behaviors.
Patients initiated the enrollment process by either calling the study
coordinator, completing a card provided by the nursing staff or giving their
names to their provider. Patients who were in the office could complete the
health questionnaire at that time. The study coordinator followed up with
patients who expressed an interest in the study but did not complete a health
questionnaire. The study coordinator either interviewed the patient over the
phone using the health questionnaire or mailed the questionnaire for patients to
complete and return. This questionnaire was used for preliminary screening
regarding activity and health status. For those patients who met inclusion
criteria, the patients PCP was asked to review the patients self-reported health
questionnaire and sign the medical waiver. After the PCP cleared the patient for
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participation, the study coordinator contacted the patient and scheduled a time for
them to complete the enrollment process.
At the time of enrollment, patients viewed a brief video, Step Your Way to
Better Health explaining the purpose of the study and requirements for
participation. The video served as an educational and motivational tool. Patients
who choose to participate were given a step counter, a Colorado on the Move
booklet and written instructions for using the telephony system. Each step
counter was checked for accuracy before it was distributed. The study
coordinator also gave verbal instructions regarding how to wear the step counter
and emphasized the importance of recording steps daily and reporting them
weekly.
Participants were told that during the first week of the study they should
not make any behavior changes, but should instead simply assess their baseline
activity. Once they knew their baseline, they were encouraged to set realistic,
goals for increasing their activity. Both the study coordinator and the educational
booklet provided information about goal setting. Although the literature reports
that a baseline can be calculated with as few as three days of data, a full week
was chosen because it seemed easier to understand and it also would allow for
the fact that participants may forget to wear their counters during the first week
(Tudor-Locke, 2001).
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Quantitative Data Collection. At enrollment, participants completed a
short questionnaire that asked about demographic information and self-
perception of current level of activity. Participants were instructed to record their
steps daily in a journal provided and to report these amounts on a weekly basis.
The two methods for reporting data were a computerized telephone system and e-
mail. For participants who used e-mail a weekly reminder was sent. Participants
who neglected to report data were contacted by phone or by e-mail.
Phase II: Protocol and Data Collection (Qualitative)
Participant Interviews. Two sets of interviews were conducted, one
immediately after completion of the study and the other 6 months later. In order
to get good representation, 25-30 initial participant interviews were targeted.
Interviews were conducted over the phone and expected to take about 15
minutes. Since the QUAL portion of the study was added after the QUAN
intervention began, an updated consent form asking about willingness to be
contacted for interviews was mailed to participants who were already involved in
the intervention phase of the study (about 40 people). Participants were
instructed to check the appropriate box indicating their willingness to be
interviewed, sign the form and them return it in the stamped envelope provided.
Those who enrolled later completed updated consent forms.
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The main purpose of the interviews was to gather information about the
intervention. All participants who consented for interviews and who completed
the intervention were contacted. Participants who dropped out of the study
typically were not interviewed although they were contacted to discuss their
reason(s) for dropping out. There were however, three women who dropped out
of the study due to time constraints but intended to continue trying to increase
their activity with their step counters. These women were purposefully chosen
for interviews because their experiences represented a naturalistic application of
the intervention. They were each willing to participate in the interviews.
Data Collection, Analysis and Integration
In this sequential explanatory design study, QUAN data consisting of a
short questionnaire and weekly step reports were collected first. These data were
analyzed after all participants completed the 13-week intervention period (the
intervention period is considered the 13 weeks during which participants agree to
wear the counters and record their steps). Interviews were conducted when each
person completed the intervention phase of the study. Consequently, some people
were in the intervention phase while others were in the follow up (interview) phase
of the study. Thus, data from the QUAN strand of the study could not be used to
generate questions for the initial QUAL portion of the study.
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The findings from each portion of the study were combined in the
following manner. Data from both QUAN and QUAL strands of the study are
triangulated to address the first three specific aims. The remaining specific aims
of the study are addressed using the data from QUAL interviews alone. The
following paragraphs describe the methods for QUAN data collection and
analysis followed by a similar discussion of the QUAL methods.
Quantitative Methods
The first three study aims are listed below along with the QUAN research
question(s) that address the aim. Following this, the specific analytic methods
used to address each question are identified. Statistical analysis was conducted
using SPSS Version 12.0 for Windows.
When this study was designed, a power analysis was conducted based on
data from Colorado on the Move. The drop out rate was estimated at 25%;
however, the actual rate of non-reporting and non-completion was much higher
(60%). Although this rate is higher than anticipated, it is not unusual for similar
studies (Wyatt, 2004). Nevertheless, the low completion rate limits the types of
analysis that can be done.
Study Aim 1. Compare and contrast characteristics of completers and non-
completers of the 13-week intervention.
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QUAN Question 1. Are there significant differences between study
completers, non-completers and non-reporters with regard to BMI, gender, age,
ethnicity, self-reported activity level and baseline activity level?
QUAN Question 2. Are demographic variables associated with significant
differences in baseline steps for completers and non-completers?
Descriptive analysis was used to identify differences between study
completers, non-completers and non-reporters with regard to demographic
variables and baseline steps. The following statistical tests were used to
determine if the differences between groups were significant:
One-way ANOVA and Student Newman-Keuls post-hoc tests at a = .05
were used to test differences between groups for BMI, age, and baseline
activity.
Independent sample t-tests (two tailed) with a = .05 were used to test
differences in baseline steps between completers and non-completers in
QUAN question 1.
Independent sample t-tests (two tailed) with a = .05 with the files split
were used to test for significance differences in baseline steps for QUAN
question 2.
Chi-squared tests with a = .05 were used to evaluate differences in
gender, ethnicity (Caucasian or non-Caucasian) and self-reported activity.
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Study Aim 2. Determine if activity levels change during the 13-week
intervention period and whether changes are maintained.
QUAN Question 3. Descriptive analysis of daily step averages per week with
confidence intervals for completers and non-completers.
QUAN Question 4. For study completers, is there a significant mean change
in step counts during the 13-week study?
QUAN Question 5. For study completers, what is the percent change in step
counts during the 13-week study?
The following methods were used to address the study questions:
Average steps per week with percentiles (2.5,97.5) for completers and non-
completers are presented in a table. This information is also displayed
graphically.
One of the primary QUAN aims of this study is to determine whether
participants increase their activity over the 13 week period. Two different methods
were used to make this assessment.
The first uses a paired sample t-test (two-tailed) with a =.05 to compare
average daily steps at baseline (week 1) with average daily steps at week 13.
An alternative approach to calculating this change compares the mean daily
step count in week 1 with the average of the values during the last four
weeks of the study. This method was used in the article that reported on
two community interventions conducted by Colorado on the Move however
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the reasons for selecting this method were not explained (Wyatt, 2004). It is
used here to allow comparison with their data.
Percent change in average daily step count was calculated for each person
who completed the study using the formula, (average steps in week 13
minus average steps in week 1, divided by average steps in week 1) x 100%.
This information is displayed in a frequency table.
Study Aim 3. Examine the accuracy of self-perceptions of physical activity
and evaluate the extent to which the intervention changes perceptions.
QUAN Question 6. To what extent do participants self-reported activity
levels (sedentary, moderate or active) correlate with their baseline step counts?
Spearmans rank-correlation coefficient (rs) with a two-tailed test for
significance at a = .05
Qualitative Methods
The main objective of this QUAL portion of the research is exploratory. Two
sets of semi-structured interviews were conducted with approximately 25
participants to assess their experiences with the 13 week intervention as well as
explore the long-term impacts of the intervention on behavior change. The
following research questions helped to guide the QUAL portion of this study:
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1. In what ways did the intervention impact attitudes toward physical
activity?
2. What types of behavior change, if any were associated with the
intervention?
3. Were behavior changes maintained 6 months after the intervention
period? Why or why not?
4. What strategies did people use to increase activity while in the study and
did these change over time?
5. Identify the ways in which social and environmental factors influence
physical activity behaviors.
6. What aspects of the intervention were most helpful for participants?
7. Did participants set activity goals and what was this experience like?
Data Collection. Two data collection methods were considered, interviews
and focus groups. The decision to use individual interviews was based on the
following considerations: 1) the unit of analysis would be the individual rather
than a group, 2) interviews can provide more depth about a topic, 3) logistics,
and 4) finances. A semi-structured format was selected to allow specific areas to
be targeted as well as probing of other topics that might emerge. The question
guides used for the interviews are found in Appendix A. Since the qualitative
portion of the study was not funded interviewees were not reimbursed for their
time. In consideration of this, the interviews were designed to be relatively short
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(approximately 10-15 minutes) and convenientthey were conducted by
telephone. Although the interview questions could be easily answered in 10
minutes, most interviews took longer because people wanted to discuss their
views and go into depth talking about their experiences. Handwritten notes were
used to document the interviews. This method was chosen because the
interviewer was comfortable taking notes and this seemed less intrusive
compared with tape recording. The information that was collected was not
particularly sensitive and the benefit of getting complete transcripts of interviews
was less important compared with the potential reluctance of participants to talk
freely because they were being tape recorded over the telephone.
The first set of interviews occurred after participants completed the 13
week intervention and the second 6 months later. Questions for the first set of
interviews are more structured compared with the second set in order to allow
more individualized follow up and discussion of themes that emerged during the
first interview. Interviews were documented using hand written notes and typed
immediately after each interview. During the second interview, the interviewer
read a summary of the previous interview to the participant and asked if the
account was accurate. In this way, member checking was used to verify the
accuracy of the interviewers documentation of the previous interview and to
provide a foundation for discussing changes in activity patterns.
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During the entire study the author kept a study journal in which she
recorded events that occurred, thoughts and perceptions, interactions with
participants. This provided a place to keep track of thoughts and impressions.
Sampling. Participating in interviews was an optional part of the study;
nevertheless only one person opted not to participate. Since the main purpose of
the interviews was to learn about participants experiences with the intervention,
people who did not report any data and most of those who dropped out were not
considered for interviews. However, these people were contacted in order to
ascertain their reason(s) for dropping out. Three women who did not complete
the study were exceptions. These women dropped out of the study stating that
they did not have enough time, but each intended to maintain the principles of the
study. These women were purposefully chosen because they represented a
naturalistic application of the intervention. All participants who completed the
study (n = 33) were considered interview candidates and attempts were made to
contact each. People who participated in the first set of interviews (n = 29) were
contacted for the second set. No one who completed the study was intentionally
excluded from interviews.
Qualitative Analysis
Interview transcripts were analyzed with ATLASti. 4.1 for Windows 95. The
data organization and analysis method that best describes the methods used is
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immersion/crystallization (I/C). According to Borkan (1999),
immersion/crystallization
....consists of cycles whereby the analyst immerse him-or herself
into and experiences the text, emerging after concerned reflection
with intuitive crystallizations, until reportable interpretations are
reached.
He identifies the elements of I/C as:
Initial engagement with the topic/reflexivity
Describing
Crystallization during data collection
Immersion and illumination of emergent insight from
collected data and texts
Explication and creative synthesis
Corroboration/legitimation and consideration of alternative
interpretations
Representing and account/report
One of the main characteristics of I/C is recognizing the researchers
initial engagement/understanding of the topic and addressing this issue through
reflexivity. In addition to acknowledging these biases to the reader it is
important for the researcher to be in a continual reflexive dialogue.
The process whereby analysis occurs involves becoming immersed in the
data and seeing it from multiple perspectives over time. This can begin during
the data collection process when particular patterns are identified. In this study,
these thoughts, interpretations and questions about the data were recorded as
notes following each he interview. Later when the transcripts were entered into
ATLASti. these notes were entered as memos. After the first set of interviews
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was collected, the immersion process began. The author read and reread the
transcripts and began looking for themes using an inductive approach.
While I/C describes the primary qualitative approach for this research, a
template organizing style was used to develop codes. One of the advantages of
the template style is that it allows the researcher to focus on particular aspects of
the text early in the process. The main difference between using a template
approach and an editing approach is that, When using a template, the researcher
defines a template or codes and applies these to the data before proceeding to the
connecting and corroborating/legitimating phases of the analysis process
(Crabtree and Miller, 1999). This was appropriate for this research because of
my familiarity with the subject matter. It was also consistent with the semi-
structured nature of the interview questions.
Both a priori identification of categories as well as reviewing the text
were used to develop a multilevel code list. This code list is found in Appendix
B. Applying axial coding methods, I developed a coding frame from which to
compare and contrast the data looking for relationships and patterns. Within the
context of immersion, this allowed me to discover themes and connections
between concepts.
One of the techniques used to look for relationships between concepts
involved constructing case ordered matrices. According to Miles and Huberman
(1994),
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A case-ordered descriptive matrix is usually a fundamental next
step in understanding whats going on across cases. .. .The matrix
forces you to be grounded in the case data and does not allow
escape into vagueness or premature abstraction.
Examining the cross tabulation of variables at the individual level was a
powerful tool for exploring relationships. This method was also used to compare
and contrast data from both the qualitative and quantitative strands of the study.
Validity
Qualitative research is subject to a number of challenges to validity. The
primary concern for the qualitative portion of this study is that the researcher
worked alone both in collecting and analyzing the data. Some of the techniques
that were employed to reduce bias were peer review, triangulation, field journal,
member checking and reflexivity. Peer review was used in constructing the
interview questions, developing the codebook and during data analysis. Multiple
data sources and different techniques for comparing them allowed for
triangulation. I kept a journal during the course of the study and recorded issues
that came up as well as thoughts and impressions. Member checking was used
with participants to ensure that I had accurately captured the essence of the first
interview. Finally, and most importantly was my own reflexivity during the
entire process.
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Integration of Data
Analysis of QUAN and QUAL data was done separately as described
above. QUAL analysis is used to explain the QUAN findings as well as expand
these findings by giving voice to the diverse experiences of participants. The
results for each portion of the study are presented separately, although QUAN
findings are integrated into the QUAL themes as appropriate. The study was not
specifically designed to implement a RE-AIM evaluation and the scope of this
study does not allow for all RE-AIM questions to be addressed, however a
modified version of RE-AIM is used to help summarize the main QUAN and
QUAL findings of the study.
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CHAPTER 4
QUANTITATIVE RESULTS
Attrition
Definition of Study Completion Groups
The Steps for Health study enrolled 83 patients at AF Williams Family
Medicine Clinic in Denver, Colorado between June 2003 and January 2004. Of
this group, 33 people are considered completers because they completed all 13
weeks of the intervention. Twenty-six people are classified as non-completers
because they began the study and reported at least one week of data but dropped
out prior to the studys completion. Finally, there were 24 people who did not
submit any data and they are classified as non-reporters. Within the completer
group, three peoples data for the last weeks of the study were lost. Where it is
appropriate, sensitivity analysis is used to account for this.
Attrition Rates
Study completion rates (39.8%) were lower than anticipated, although not
significantly different from other studies with similar designs (Wyatt, 2004). The
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