The Contributions of Psychosocial, Environmental, and Policy Variables on Meeting 
Physical Activity Recommendations in Working Women 
 
by 
 
Nancy M. Gell 
 
 
 
 
A dissertation submitted to the Graduate Faculty of 
Auburn University 
in partial fulfillment of the 
requirements for the Degree of 
Doctor of Philosophy 
 
Auburn, Alabama 
December 8, 2012 
 
 
 
 
Keywords: Physical Activity, Built Environment, Behavior 
 
Copyright 2012 by Nancy M. Gell 
 
 
Approved by 
 
Danielle D. Wadsworth, Chair, Associate Professor of Kinesiology 
Luke J. Marzen, Professor of Geography 
Leah E. Robinson, Associate Professor of Kinesiology 
Mark G. Fischman, Wayne T. Smith Distinguished Professor of Kinesiology 
 
 
 
 
 
 
ii 
 
 
 
 
 
 
 
Abstract 
 
 
 The purpose of this dissertation was to identify psychosocial, worksite policy, and 
environmental factors that facilitate adherence to physical activity among employed 
women. One hundred three employed women (age 23-65 years) from two neighboring 
towns in the southeastern United States participated in the study.  
 Measures included physical activity levels by accelerometry with location of 
physical activity by global positioning systems. Personal variables, physical activity 
history, self-efficacy, social support for physical activity, outcome expectancies, self-
regulation skills, perception of worksite policies, and perception of the neighborhood 
built environment to support physical activity were assessed by questionnaire. 
 Cases, women who met the minimum physical activity recommendations, had 
significantly lower percentages of sedentary time compared to women who did not 
perform enough physical activity to meet public health recommendations (43.9% vs. 
50%, p<.001). Cases performed an average of 271.0 minutes of moderate to vigorous 
physical activity (MVPA) over one week of monitoring with 1.3 bouts (? 10 minutes) of 
MVPA per day over seven days. The average distance between MVPA locations and 
home and work was 6.6 km and 7.4 km respectively, for all subjects. Cases had 
significantly higher scores for self-efficacy, outcome expectancies, self-regulation, friend 
social support, perception of neighborhood land-use mix diversity, and number of MVPA 
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bouts using the built environment. There was no significant difference between cases and 
controls for perception of worksite policies to support physical activity. Meeting weekly 
physical activity recommendations was associated with higher self-regulation scores, 
perception of higher land-use mix diversity, perception of lower infrastructure and safety 
for walking in the home neighborhood, and use of the built environment for physical 
activity.  
 The results show working women achieve recommended levels of activity 
through use of the built environment, self-regulation skills, and more than one bout of 
MVPA on an average day. Perception of the neighborhood built environment to support 
physical activity is also predictive of consistent engagement in physical activity. Working 
women who meet physical activity recommendations spend less time in sedentary 
behavior compared to women who do not meet recommendations.  Overall the findings 
suggest interventions to promote physical activity for working women may want to 
incorporate strategies to use the built environment, develop self-regulation skills, and aim 
for more than one bout of MVPA on an average day to achieve physical activity 
recommendations. 
  
iv 
 
 
 
 
 
 
 
Acknowledgements 
 
 
 I would like to thank my doctoral advisor, Dr. Danielle D. Wadsworth, for all she 
has taught me about physical activity research, her steady guidance, and her 
encouragement to forge my own path. I appreciate the positive environment for learning 
and research she afforded me during this process. I am grateful to Dr. Luke Marzen for 
patiently teaching me about GIS and a geospatial approach to research. A sincere thank 
you to Dr. Leah Robinson for her scientific and personal contributions as well as the 
equipment needed for my research. Thank you to Dr. Mark Fischman for his editorial and 
scientific expertise and to Dr. Maria Witte for her willingness to serve as the outside 
reader. 
 I would like to thank my family, especially my parents and siblings, for their 
unwavering support during this process and their confidence in my ability to see this to 
the end. I thank my son Tandin Dorji for his incredible example of persistence and 
accomplishment through non-procrastination, and for timely, gentle reminders to "step-it-
up." I am grateful to have such a wonderful husband in Omar Oyarzabal, for his 
encouragement, ever-ready willingness to help, and 100% support of my doctoral 
endeavors. It's never a solo journey, and I am glad to have shared this experience with 
Sam Logan, Kip Webster, Maria Morera, and the best officemate ever, Colleen Daly. In 
particular I would also like to thank Mary Sandage and James McDonald for being ever 
v 
 
positive and supportive. This dissertation was made possible by financial support from 
the Auburn University Graduate School and the College of Education National Advisory 
Council. Finally, I would like to thank the women of the Auburn-Opelika community for 
participating in my research and their willingness to share their amazing stories-this is 
what made it all so motivating and worthwhile. 
  
vi 
 
 
 
 
 
 
Table of Contents 
 
 
Abstract ............................................................................................................................... ii 
Acknowledgments ............................................................................................................. iv 
List of Tables ..................................................................................................................... ix 
List of Figures ......................................................................................................................x 
Chapter 1 Introduction  ........................................................................................................1 
 Statement of Purpose  ..............................................................................................9 
 Definition of Terms................................................................................................11 
 Limitations .............................................................................................................13 
 Delimitations ..........................................................................................................14 
Chapter 2 Review of Literature ..........................................................................................15 
 Social Ecological Theory .......................................................................................15 
 Correlates of Physical Activity ..............................................................................17 
        Personal Correlates of Physical Activity ........................................................18 
                   Psychosocial Correlates of Physical Activity .................................................23 
                   Policy Correlates of Physical Activity ............................................................28 
                   Built Environment Correlates of Physical Activity ........................................29 
 Measurement of Physical Activity .........................................................................36 
 Global Positioning Systems (GPS) ........................................................................40 
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 Summary ................................................................................................................41 
Chapter 3 Method ..............................................................................................................43 
 Research Design.....................................................................................................43 
 Subjects ..................................................................................................................43 
 Instruments .............................................................................................................45 
        Demographics, Health History, and Exercise History ....................................45 
                   Self-efficacy ....................................................................................................45 
                   Self-Regulation ...............................................................................................46 
                   Social support..................................................................................................47 
                   Outcome Expectancy ......................................................................................48 
                   Perceived Neighborhood Environment ...........................................................48 
                   Perception of Worksite Policies ......................................................................50 
                   Accelerometers ...............................................................................................51 
                   Global Positioning System Monitors ..............................................................53 
 Procedure ...............................................................................................................54 
        Data Analysis, Data Validation and Date Reduction .............................................55         
Chapter 4 Results ...............................................................................................................61 
 Sedentary Time ......................................................................................................63  
 Bouts ......................................................................................................................64 
 Distance..................................................................................................................65 
 Personal Factors and Physical Activity..................................................................66 
 Built Environment and Physical Activity ..............................................................68 
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 Perception of the Built Environment .....................................................................69 
 Perception of Worksite Policies .............................................................................70 
 Predictors of Physical Activity ..............................................................................71 
Chapter 5 Discussion .........................................................................................................74 
 Sedentary Time ......................................................................................................74 
 Bouts of Exercise ...................................................................................................78 
 Distance..................................................................................................................81 
 GPS ........................................................................................................................84 
 Personal Factors and Physical Activity..................................................................86  
 Predictors of Physical Activity Adherence ............................................................86 
 Perception of Worksite Policies .............................................................................91 
 Summary ................................................................................................................92 
 Conclusions and Future Research Recommendations ...........................................93 
References   ........................................................................................................................95 
Appendix A: Consent Forms ...........................................................................................122 
Appendix B: Questionnaire .............................................................................................126 
Appendix C: Written Instructions for Accelerometer and GPS ......................................140 
Appendix D: Activity, Accelerometer, and GPS Log  .....................................................144 
Appendix E: Omni Scale for Rating of Perceived Exertion  ...........................................148 
Appendix F: Types of Exercise Performed by Cases  .....................................................149 
Appendix G: Boxplots for Distance between Home, Work and MVPA Locations ........150 
 
ix 
 
 
 
 
 
 
 
List of Tables 
 
 
Table 1. Perception of Built Environment Factors of NEWS-A .......................................50 
 
Table 2. Data Analysis by Research Question ...................................................................60 
 
Table 3. Demographic Measures by Activity Status .........................................................62 
Table 4. Availability of Location Data for All Subjects with Bouts of MVPA.................63 
Table 5. Categorization of Additional Bouts of MVPA ....................................................65 
 
Table 6. Average Distances Between Home and Work to MVPA Bout Locations ..........66 
 
Table 7. Social Cognitive Measures by Group ..................................................................67 
 
Table 8. Physical Activity History by Group.....................................................................68 
 
Table 9. Perception of the Neighborhood Built Environment and Worksite Policies  
              to Support Physical Activity ................................................................................70 
 
Table 10. Perception of Individual Worksite Policies to Support Physical Activity .........71 
 
Table 11. Model to Predict Meeting Physical Activity Recommendations .......................72 
 
  
x 
 
 
 
 
 
 
 
List of Figures 
 
 
Figure 1 Adaption of the Social Ecological Model for Physical Activity .........................17 
Figure 2 Validation Thresholds for Accelerometer Data ...................................................52 
Figure 3 Example of Participant Location by Activity Level. ...........................................58 
Figure 4a and 4b Example of MVPA by location..............................................................59 
    
   
1 
 
 
Chapter 1 
 
Introduction 
 
 Physical activity is associated with multiple health benefits including reduced risk 
for chronic diseases, such as cardiovascular disease (Thompson et al., 2003), Type 2 
diabetes (Knowler et al., 2002), osteoporosis (Vuori, 2001), depression (Pollock, 2001), 
breast cancer (Breslow et al., 2001), colon cancer (Slattery & Potter, 2002) and reduced 
risk for total, ischemic, and hemorrhagic stroke (Lee, Folsom, & Blair, 2003). There is 
evidence that participation in leisure time physical activity affords protection against 
functional limitations associated with aging (Huang et al., 1998), and all-cause mortality 
after controlling for genetic and familial factors (Blair et al., 1989; Kujala et al., 1998). 
Participation in regular physical activity has also been shown to attenuate the risks 
associated with obesity (Blair & Brodney, 1999). 
 In addition to health benefits, there are significant costs associated with physical 
inactivity. Using a conservative definition of inactivity, the absence of leisure time 
physical activity, it has been estimated that the direct costs are approximately 24 billion 
dollars or 2.4% of the health care expenditures in the U.S. (Colditz, 1999). A study 
looking at the direct and indirect costs of sedentary lifestyle among Americans estimated 
the cost to be greater than $150 billion for incidences in 1987 (Pratt et al., 2000). A 2002 
report from the President?s Council on Physical Fitness and Sports calculated that 15% of 
the US healthcare budget is required to address the sequelae of physical inactivity (Booth 
& Chakravarthy, 2002). 
2 
 
 Despite the known benefits associated with regular physical activity, the majority 
of Americans do not achieve recommendations. Adults 18 years or over should engage in 
a minimum of 30 minutes of moderate physical activity five days a week, or 20 minutes 
of vigorous activity three days a week or a combination of the two (Haskell et al., 2007). 
Based on results of the Behavioral Risk Factor Surveillance System (BRFSS) from the 
Centers for Disease Control (CDC) it is estimated that 49.5% of Americans in 2007 met 
the recommended levels for physical activity, with 22.6% reporting no leisure-time 
activity. Additionally, an examination of long-term trends reveals a declining rate in 
physical activity in the U.S. (Brownson, Boehmer, & Luke, 2005), particularly among 
adult women. When the BRFSS data was broken down by sex, only 47% of females met 
recommended levels for physical activity and 24.5% indicated no participation in leisure-
time physical activity. During 2003-2006 the National Health and Nutrition Examination 
Survey (NHANES) study collected data on physical activity from both self-report and 
accelerometer recordings in a subset of participants. Overall, the accelerometer data 
showed that females were less physically active than males and less than 5% of the adults 
monitored achieved minimum recommendations (Troiano et al., 2007). Also, based on a 
sample of 6,329 subjects with NHANES accelerometer data, Matthews et al. (2008) 
found 59.4% of their time was spent in sedentary activities. Breakdown by sex showed 
that females spent significantly more time in sedentary behavior compared to males (7.70 
hours in a day vs. 7.63, p=0.001). These studies show that women are less physically 
active and more sedentary than men, which aligns with previous studies (Casperson & 
3 
 
Merritt, 1995; CDC, 1995; HHS, 1996) but also show that questionnaire data may not 
accurately reflect true measures of physical activity, especially in women. 
 Previous studies have attempted to link specific "life stages" to changes in 
physical activity levels in women. Physical activity levels have been shown to decrease in 
women during the transition from school/college to the workplace (Anderssen et al., 
1986; Caspersen, Pereira, & Curran, 2000; Gordon-Larsen, Nelson, & Popkin, 2004). 
Changes in family such as marriage and having children are also associated with a 
decrease in intensity and structure of physical activity in women under the age of 49 
(Scharff et al., 1999). In studies examining perceived barriers to physical activity among 
working women, the most common reason cited was the time constraints associated with 
fulfilling multiple roles (e.g., employee, caregiver, spouse, parent) and responsibilities 
(Marcus et al., 1994; Tavares, Plotnikoff, & Loucaides, 2009). In 2009, surveillance from 
the Bureau of Labor Statistics found 59% of working age women were employed in the 
civilian labor force with projected increases of 9% by 2018. Previous studies have 
described barriers to physical activity for this population, and the complexities of time 
constraints limiting participation in physical activity. Yet, despite the majority of women 
being engaged in some type of employment, factors that specifically enable this 
population to meet physical activity recommendations have not been described. 
 Initial studies examining barriers and correlates of physical activity adherence in 
women evaluated demographic variables and psychosocial constructs related to behavior 
(Brownson et al., 2000; Sallis, Hovell, & Hofstetter, 1992; Sternfield, Ainsworth, & 
Quesenberry, 1999). Both social support and self-efficacy have emerged most 
4 
 
consistently as positive correlates of physical activity adoption and maintenance in adult 
and older women (Sharpe et al., 2008; Sternfield et al., 1999; Trost et al., 2002). There is 
also some evidence showing a positive association between outcome expectancy (Conn, 
1997; Williams, Anderson & Winett, 2005) and self-regulation (Stadler, Oettingen, & 
Gollwitzer, 2009; Titze, Stronegger, & Owen, 2004) and physical activity behavior in 
women. Despite positive associations, these constructs failed to explain the majority of 
variance in participation in consistent physical activity.  
Based on the relative success of reducing acceptance of tobacco use through 
environmental and policy interventions, researchers in physical activity looked to factors 
outside of demographics and psychosocial constructs to explain physical activity 
behavior in a more comprehensive way. Expanding on the social ecological perspective, 
perception of the built environment, and to a lesser extent the influence of policy, was 
included in studies examining determinants of physical activity (Burton et al., 2005; De 
Bourdeaudhuij et al., 2003; Giles-Corti & Donovan, 2002). The findings from these 
studies showed that although environmental variables were related to physical activity, 
the amount of variance explained by the environmental variables was low. For example, 
De Bourdeaudhuij et al. found that 5-13% of the range of physical activity variance was 
explained by demographic and environmental variables. The model by Burton et al. 
included variables assessing psychological, social, and environmental correlates, which 
explained 43% of the variation in total activity. Giles-Corti and Donovan found the 
influence of the physical environment was secondary to individual and social 
environmental determinants. However, these types of studies were limited by the use of 
5 
 
self-report for quantification of physical activity levels, and questionnaires to assess the 
neighborhood built environment to support physical activity.  
As software for the geospatial analysis of the built environment became more 
accessible, researchers were able to incorporate more objective measures of the built 
environment. Examples of the types of features measured include presence of sidewalks, 
relative location of parks and recreational facilities to subjects? home residences, mixed 
use land development (i.e., proportion of use by residential, commercial, business, and 
institutional entities for a specific area), street connectivity (i.e., the density 
of connections in a road network), and shade cover (McGinn et al., 2007; Rutt et al., 
2005; Witten et al., 2008). While these studies objectively described the built 
environment, the actual location of where subjects participated in physical activity was 
not made available. In other words, the underlying assumption to these studies was that 
physical activity rates would be correlated to the specific features of the residential 
neighborhood built environment. However, as physical activity may occur in multiple 
locations, limiting the analysis to one location, such as the neighborhood of residence, 
may lead to an underestimation of the exposure to multiple environments (Kerr, Duncan, 
& Schipperjin, 2011). 
 More recent research has used objective measures of both physical activity and 
the built environment. A pilot study by Rodriguez, Brown, and Troped (2005) found that 
portable global positioning system (GPS) units were sufficiently precise to track an 
individual's movements and, in combination with accelerometry, to help clarify where 
physical activity occurs. GPS allows for more precise identification of where people 
6 
 
perform physical activity and the particular attributes of those places (e.g., use of built 
environment features for physical activity, access to a park or recreational facility close to 
work or home). Furthermore, with this technology it is possible to more accurately 
identify how regular exercisers meet recommendations (i.e., one bout vs. multiple bouts; 
one mode vs. multiple modes).  
 Currently, few studies have used the combined technology of objective physical 
activity monitoring with GPS to study physical activity. The study of free-living physical 
activity using objective monitoring of physical activity and location has primarily been 
conducted in children (Fjortoft, Kristoffersen, & Sageie, 2009; Mackett et al., 2007). 
Mackett et al. looked at how children?s physical activity behaviors, and in particular 
travel behavior, vary in the presence or absence of adults. Fjortoft et al. investigated how 
6-year old school children utilize outdoor space during school recess in an urban and 
rural environment. The combined use of GPS with accelerometry allowed the researchers 
to see how boys preferred the asphalt areas for running and soccer play whereas girls 
favored a small forested area, though overall physical activity was similar between the 
rural and urban environments. In one of the few studies using GPS with accelerometry to 
study free-living physical activity in adults, Troped et al. (2010) examined associations 
between built environment variables within 50-m and 1-km home and work buffers and 
moderate-to-vigorous physical activity (MVPA). Of particular note, the authors found 
that subjects accumulated a daily average of 61.1 minutes of MVPA, but only an average 
of 14 minutes was completed within the 1-km home buffer. These findings suggest that 
studies measuring only the home neighborhood built environment for correlation with 
7 
 
physical activity may not capture the environment where physical activity is actually 
performed.  
 Social ecological models propose that health behaviors are influenced across 
multiple domains which include the personal, interpersonal, environment, and policies. 
Furthermore, the models posit that these variables from each domain interact across the 
domains to effect behavior. While correlates of physical activity have been identified 
from these multiple domains (Trost et al., 2002), few studies have compared these 
correlates in the study of adults, and fewer still have utilized objective measures of 
physical activity and the built environment.  
Most studies of physical activity adherence draw on previously sedentary people 
who participated in a structured exercise program as part of a research intervention 
(Dunn, Andersen, & Jakicic, 1998; Huberty et al., 2008). A few studies have recruited 
from populations more likely to be regular exercisers, such as users of trails in a park 
(Troped et al., 2010), and women running a "fun run" (Titze, Stronegger, & Owen, 2005). 
Troped et al. targeted users of trails in parks for the purpose of studying associations 
between built environment variables and location-based physical activity within 1 km of 
home and work. The mean amount of MVPA accumulated per day over 4 days of 
monitoring was 61.1 minutes, indicating a fairly active subject pool. Titze et al. studied 
middle-aged women over 2 years to examine individual, social, and environmental 
factors predicting adoption of and regression from regular leisure-time running. While 
this study is commendable for its longitudinal design, by limiting the focus to running, it 
is difficult to extrapolate the findings to women who engage in other modes of physical 
8 
 
activity, and in particular more moderate modes of activity. Britton et al. (2000) recruited 
1,501 female subjects age 20-44 from the control group of a multi-center, population-
based study of breast cancer for the purpose of exploring characteristics associated with 
recreational exercise. While they were able to divide the population based on ?higher? 
versus ?lower? levels of exercise, they did not measure or describe how many subjects 
actually met minimum levels of recommended physical activity; therefore, it is not 
known how much of the population could be classified as regular exercisers.  
Although a majority of the United States adult population is inactive, there is a 
subset of the population who maintains regular exercise. Knowledge of how these 
individuals utilize environmental resources and psychosocial variables may enable 
researchers to better understand regular exercise behavior in a free-living environment 
and implement effective intervention strategies. Further research is needed on women 
who are "regular exercisers" (i.e., have met recommendations for at least 6 months) to 
develop a greater understanding of how they differ from women who do not meet 
physical activity recommendations in terms of psychosocial variables, use of the built 
environment, and perception of the built environment and worksite policies to support 
physical activity. By gaining insight into the variables that contribute to exercise 
adherence in working women, future interventions to promote physical activity may be 
able to incorporate these findings and achieve longer term impact. 
9 
 
Statement of Purpose 
 The purpose of this study was to determine the contributions of potential 
psychosocial, worksite policy, and environmental factors that may facilitate adherence to 
regular physical activity among employed women including: 1) contribution of social 
cognitive constructs to the variance in MVPA, 2) contribution of physical activity 
resources such as parks, neighborhoods, work sites, or private facilities where women 
engage in MVPA with respect to home and work location, 3) associations between 
MVPA and built environment characteristics such as distance from work and home, 
facilities (e.g., open space, courts, exercise equipment), and terrain such as sidewalks or 
trails, and 4) contribution of perceived worksite policies to the variance in MVPA. 
Research Questions and Hypotheses 
Research Question #1: What is the difference in the percentage of sedentary time 
between cases (women who meet recommendations) versus controls (women who do not 
meet recommendations)? 
Hypothesis #1: Cases will have less sedentary time, defined by accelerometer 
counts between 0 and 100, as compared to controls. 
Research Question #2: Public health recommendations allow for discontinuous bouts of 
physical activity to achieve recommended levels. Do employed women achieve 
recommendations through more than one daily bout of physical activity on average? 
 Hypothesis #2: Cases are more likely to perform physical activity in a single 
session, (e.g., ?20 minutes of vigorous activity or ?30 minutes of moderate activity), 
10 
 
rather than two or more bouts of short duration (e.g., 10-15 minute bouts interspersed 
throughout the day). 
Research Question #3: How far from home and work do employed women travel in order 
to perform MVPA? 
 Hypothesis #3: Employed women are more likely to perform MVPA in locations 
close (within 8 KM) to home or work.  
Research Question #4: Are there differences in personal level variables (social cognitive 
measures) between cases and controls? 
Hypothesis #4. Cases will have higher scores for self-efficacy, self-regulation 
strategies, outcome expectancies, and social support than controls. 
Research Question #5: Do employed women meet recommendations by using features of 
the built environment? 
Hypothesis #5: Cases will utilize features of the built environment, such as 
sidewalks, roads, free access or municipal recreational facilities, and open spaces, 
to perform physical activity more than women who do not meet weekly MVPA 
recommendations. 
Research Question #6: Are there differences in perception of the built environment 
between cases and controls?  
Hypothesis #6: Cases are more likely than controls to perceive the neighborhood 
built environment as supportive for physical activity, as identified from the 
Neighborhood Environment Walkability Scale, and the Physical Activity 
11 
 
Neighborhood survey. Supportive environments include perception of low traffic, 
and perception of low crime. 
Research Question #7: Are there differences in perception of worksite policies between 
cases and controls?  
Hypothesis #7: Cases are more likely than controls to perceive worksite policies 
as supportive for physical activity, as identified from the index of employer 
policies modified from the Neighborhood Quality of Life Study (2004). 
Definition of Terms 
Accelerometer counts: Dimensionless units that can be converted by equations 
and cutoffs to predict the energy expenditure and intensity of activity (Troiano 
2006). 
Behavioral Risk Factor Surveillance System (BRFSS): The BRFSS is an on-going 
telephone health survey system, tracking health conditions and risk behaviors in 
the United States yearly since 1984. The System is a state-based system of health 
surveys that collects information on health risk behaviors, preventive health 
practices, and health care access primarily related to chronic disease and injury. 
(http://www.cdc.gov/brfss/about.htm) 
Geographic Information System (GIS): A software program used to integrate data 
from multiple sources (e.g., accelerometer, GPS device, & base maps) for 
capturing, managing, analyzing, and displaying all forms of geographically 
referenced information. 
12 
 
Inactive or sedentary: Physical activity beyond baseline activity (the usual light or 
sedentary activities of daily living). For example, standing and sitting are 
classified as inactive (HHS, 2008). As measured by accelerometry, inactivity or 
sedentary behavior is defined as counts between 0 and 100 (Healy et al., 2007). 
Light-intensity activity: Activities beyond baseline such as standing, walking 
slowly, lifting lightweight objects (HHS, 2008). This is also equivalent to 
activities that are completed at less than 3 METs (Ainsworth et al., 2011). 
Metabolic equivalent (MET): A unit of measurement that describes the energy 
expenditure of different modes of physical activity. For example, one MET is the 
resting metabolic rate during which no activity is performed (Ainsworth et al., 
2011).  
Mixed Land Use: Relative proximity of different land uses within a given area. 
For example a mixed-use neighborhood would include homes, stores, offices, 
parks, and schools. The measure of mixed land use is not standardized (Handy et 
al., 2002). 
Moderate-intensity physical activity: Bodily movement that results in energy 
expenditure of 3-6 METS (Haskell et al., 2007).  
Physical activity: Any bodily movement produced by the contraction of skeletal 
muscle that increases energy expenditure above a basal level. (HHS, 2008). 
Physical activity recommendations: The minimum amount of physical activity 
recommended for adults in order to achieve health benefits. Current 
recommendations included 30 minutes of moderate physical activity 5 days per 
13 
 
week, 20 minutes of vigorous physical activity 3 days per week, or a combination 
of the two (Haskell et al., 2007). 
Street connectivity: the directness and availability of alternative routes from one 
point to another within a street network. It can be measured by the number of 
intersections within a certain area or by the average block length (Handy et al., 
2002). 
Vigorous-intensity physical activity: Bodily movement that results in energy 
expenditure of greater than 6 METS (Haskell et al., 2007). 
 
Limitations 
Limitations associated with this study included: 
1. Previous research has shown that seven days of objective monitoring is sufficiently 
representative of physical activity behavior, however, external events such as 
inclement weather, alterations in the normal work schedule, illness, and holidays may 
alter a subject's typical pattern. 
2. While attempts were made to recruit women from multiple employers and equally 
from throughout Auburn and Opelika, AL, it is possible the sample recruited for the 
study may not be representative of the population of the two cities. 
3. Subjects were specifically recruited from two small cities and therefore the 
findings may not be generalizable to other types of geographic areas (e.g., more rural 
or more urban). 
4. GPS data loss may have occurred due to conditions associated with particular 
subject characteristics (failure to charge batteries, failure to wait for initial fix, signal 
14 
 
drop-out in certain environmental conditions). This particular data loss may result in 
nonrandom missing data which could lead to bias in the study results. 
5. Accelerometers measure whole body and lower extremity movement but do not 
capture upper body movement in isolation or accurately measure energy expenditure 
with strength training or flexibility. Additionally, accelerometers cannot accurately 
measure MVPA in water or on bicycles. 
Delimitations 
The delimitations of this study were: 
1. Women who work primarily in the fitness industry were excluded from 
participating in the study. 
2. Women between the ages of 19 and 65. 
3. Women who work less than 30 hours per week were excluded from participating 
in the study. 
4. Only women who reside in the city limits of Auburn and Opelika, AL were 
included in the study. Women who work within city limits, but commute from 
elsewhere, were excluded from participating in the study. 
5. Only women who are capable of engaging in physically activity were included in 
the study. 
 
 
15 
 
 
Chapter 2 
 
Review of Literature 
 
 The purpose of this dissertation study was to examine associations between 
psychosocial, worksite policy, and built environment enablers of moderate-to-vigorous 
physical activity (MVPA) among employed women. In particular, this study tested the 
ability of built environment features, workplace policies, and psychosocial constructs to 
explain adherence to exercise recommendations in the study population. The purpose of 
this literature review is to describe and provide evidence for the choice of study 
population, the variables of study, and the methodology employed for the study. Topics 
included in this literature review include physical activity determinants including 
personal, psychosocial, policy, geospatial and built environments determinants, and 
measurement of physical activity. 
Social Ecological Theory 
 As described by Sallis et al. (2006) dominant frameworks to describe influences 
on physical activity behavior focused on psychological and social influences. However, 
interventions based on these models have shown small to moderate effect sizes, modest 
recruitment rates, and poor maintenance of behavior change following the interventions. 
As noted by Fleury and Lee (2006) most models and theories do not adequately account 
for social and environmental factors to influence physical activity behavior. Furthermore, 
these models and interventions initially focused on leisure-time physical activity alone. In 
the late 1990s, collaborations between experts in physical activity, as well as, other 
disciplines such as urban designers, community planners, and transportation planners led 
 
16 
 
to a broader concept of physical activity called "active living" that included exercise, 
active transportation, leisure-time activity, occupational, and household activities. Social 
ecological models were adapted for physical activity behavior to encompass personal, 
psychological, social and environmental correlates that contribute to active living. A key 
concept of social ecological models is that behavior has multiple influences (i.e., 
environmental, social, psychological) and these influences interact across different levels 
(e.g., personal, social, organizational, physical, community, and policy; Sallis, Owen, & 
Fisher, 2008). Of note, the social ecological models include many of the same 
components of behavioral frameworks as other psychological models, such as self-
efficacy, and social support along with the addition of environmental and policy factors.  
 One of the early theorists of social ecology, Bronfenbrenner (1994) proposed a 
model for the social ecological theory, as applied to child development, which described 
influences on behavior as a series of embedded layers or systems. Bronfenbrenner 
described the four systems as 1) microsystems: the immediate systems in which people 
live and interact, 2) the mesosystem: the location where microsystems interact, 3) the 
exosystem: the external systems that influence the microsystem, and 4) the macrosystem: 
the larger sociocultural context in which a person resides and which encompasses all 
other systems. As applied to physical activity, theorists have replaced the terminology of 
?systems? with levels which include the individual level, social or interpersonal level, 
organizational level, community level, and public policy level. A model with descriptions 
of these multiple levels as they relate to physical activity is presented in Figure 1. Despite 
different designs or terms for various models, the overriding principles remain the same: 
 
17 
 
1) Individual health behaviors are influenced by multiple factors from the intrapersonal, 
interpersonal, organizational, community, and public policy levels; 2) Factors that 
influence behavior interact across the levels or systems; 3) Effective interventions target 
the specific behavior via multiple levels (Sallis, Owen, & Fisher, 2008). Prior to 
evaluating how the constructs potentially interact across levels to influence behavior, it is 
worthwhile to review how the constructs are associated at the individual levels. The 
following sections describe factors from each of these levels and how they correlate to 
physical activity in working women. 
Figure 1. Adaption of the Social Ecological Model for Physical Activity 
 
Correlates of Physical Activity 
 The benefits of physical activity have been consistently and continuously 
described in the literature (Blair, et al., 1989; Haskell et al., 2007; Thompson, et al., 
Public Policy: 
Development and 
enforcement of 
national, regional, 
and local policies to 
support and promote 
PA  
Organization: 
Policies, practices, 
and physical 
environment of 
individual 
organizations to 
support PA I 
 
Interpersonal: The 
relationships, the 
culture and the 
society with whom 
the individual 
interacts 
Individual: 
Knowledge, 
attitudes, beliefs, 
personal factors 
 
18 
 
2003). Despite diverse efforts to convey this information to the public, the rates of 
physical activity remain relatively low with 22.6% of the U.S. adult population reporting 
no participation in leisure-time physical activity and less than half of the population 
meeting recommended levels for physical activity (CDC, 2007). Rates of participation in 
physical activity are consistently lower in women as compared to men across age groups 
and ethnicities (Matthews et al., 2008; Troiano et al., 2007). Based on longitudinal 
research it has also been shown a large percentage (40%) of adult women will have a 
change in activity status (inactive to active, and active to inactive) indicating a fluidity to 
physical activity adherence and non-adherence (Brown & Trost, 2003). Employed 
women in particular have described barriers to physical activity associated with the 
complexities of managing multiple roles responsibilities as employees, spouses, mothers 
(Marcus et al., 1994; Tavares, Plotnikoff, & Loucaides, 2009). An examination of the 
correlates of physical activity in general and adult women in particular may help to 
elucidate how and why these patterns exist.  
Personal Correlates of Physical Activity 
Sex 
 
Physical activity rates for adult women are consistently lower than rates for adult 
men at all ages. In 2009 findings from the CDC showed that 49% of women met 
minimum physical activity requirements compared to 53% of men. This difference in 
levels between men and women is consistent with findings in Australia, Brazil, Hong 
Kong, and New Zealand as described by Bauman et al. (2009). Longitudinal and 
intervention based studies have also reported higher physical activity rates for males at all 
 
19 
 
ages (Annear, Cushman, & Gidlow, 2009; Burton, Shapiro, & German, 1999; Dowda et 
al., 2003; Salmon et al., 2000). Women have also been shown to spend more time in 
sedentary activities compared to men. Using accelerometer data Matthews et al (2008) 
found that females spent significantly more time in sedentary behavior compared to 
males (7.70 hours in a day vs 7.63, p=0.001). This sex disparity exemplifies the need for 
interventions, policies and programs that facilitate women to engage in regular physical 
activity. 
Age 
 Age consistently has an inverse relationship with physical activity behavior (Trost 
et al., 2002). Studies in the U.S., Europe, Asia, and Australia have all reported declines in 
physical activity with increasing age (Booth et al., 2000; Brownson et al., 2000; Leslie et 
al., 1999; Lian et al., 1999; Sj?str?m et al., 2006). BRFSS data from the CDC in 2009 
showed a linear decline in physical activity with increasing age. Sixty-one percent of 18-
24 year olds reported participating in at least 30 minutes of moderate physical activity 
five or more days per week or 20 minutes of vigorous activity three or more days per 
week compared to 52% of people age 35-44 and 47% in people age 55-64.  
Race/Ethnicity 
 Previous studies have described higher rates of physical activity in whites as 
compared to non-white counterparts with some variation for specific groups (Brownson 
et al. 2000; Clark 1999; Sternfield et al, 1999). Overall 52% of non-Hispanic whites met 
recommended physical activity levels compared to 40% of blacks and 42% of Hispanics. 
A breakdown of the BRFSS 2007 results showed that 50.1% of non-Hispanic white 
 
20 
 
women, 36.3% of black women and 41.8% of Hispanic women met physical activity 
recommendations. Hispanic women and Black women reported similar levels of 
inactivity with 21.9% reporting no leisure time activity, whereas 17.9 % of white women 
reported no leisure-time physical activity. In contrast to the previous data, Hawkins et al. 
(2009) analyzed 2003-2006 NHANES accelerometer data and found that middle age 
Hispanic women (age 40-59 years) were significantly more active than white and black 
women of the same age, otherwise levels followed the same pattern as described by the 
BRFSS survey results. 
Socioeconomic status including education 
 Similar to age and sex, Trost et al. (2002) found socioeconomic status (SES), 
occupational status, and education level to all be consistent correlates of physical activity. 
Low SES has been shown to be negatively associated with physical activity behavior. 
Results from the 2007 BRFSS confirm these findings with rates of 40% meeting physical 
activity recommendations in the group with a median yearly income less than $15,000 
compared to 47% in the $25,000-34,999 income group and 56% in the group with a 
median yearly income greater than $50,000. In a breakdown by education and gender, 
53% of women with a college degree met physical activity recommendations compared 
to 37% who did not complete high school and 44% of those who did obtain a high school 
diploma. Sj?str?m et al. (2006) found similar differences in Europe where individuals 
with 16-19 years of education had a 20% increased chance of meeting recommendations 
compared to those who reported 15 years or less of education. In a longitudinal study of 
youth aged 7-15 recruited in 1985, Cleland et al. (2009) completed a follow-up study 
 
21 
 
after 19.6 years and found that females who improved their socio-economic position were 
38% more likely to increase their physical activity levels than to remain inactive. 
BMI/Obesity 
 Obese weight status has consistently been shown to be negatively associated with 
physical activity levels (Brownson et al, 2000; Ruchlin & Lachs, 1999; Salmon et al., 
2000; Sternfield et al., 1999). CDC data shows 54% of people who are in the normal 
weight range achieve recommended levels of physical activity compared to 41% of 
individuals classified as obese. In a study of physical activity correlates for young women 
in the U.S. age 18-30, Dowda et al. (2004) found a significant negative correlation 
between MVPA and BMI. In a study conducted in the European union, Martinez-
Gonzalez et al. (1999) reported subjects in the highest quintile for leisure-time physical 
activity were approximately 50% less likely to be obese than those in the lowest quintile 
for physical activity, after controlling for age, sex, education, sedentary time, marital 
status, smoking, and education. Kaplan et al. (2001) evaluated a sample of older adults in 
Canada and found frequent participation in physical activity decreased as BMI increased. 
To date, obesity and increased weight status are consistently associated with decreased 
physical activity levels for both men and women and across age groups. 
Marital Status 
 There are mixed findings on the association between marital status and physical 
activity behavior. King et al. (1998) examined a cohort of men and women over ten years 
and did not find any difference in physical activity rates between marital states (i.e. 
married vs. single; 10% changed marital status). The authors did note an initial decrease 
 
22 
 
in physical activity during the "pre-marriage" phase which was followed by a net increase 
in activity compared to remaining single. In other words the transition from a single to a 
married state resulted in significant increases in physical activity. Interestingly, the 
transition from a married state to a single state, as compared to those who remained 
married, did not result in a decrease in physical activity levels. Martinez-Gonzalez et al. 
(2001) reported lower physical activity levels among participants who were married, 
cohabitating, or widowed/divorced compared to their single counterparts. Results from 
the Nurses Health Study showed women who were divorced, widowed, or remained 
single had higher rates of physical activity compared to those who remained married (Lee 
et al., 2004). Scharff et al. (1999) also found becoming married to be associated with a 
decrease in intensity and structure of physical activity in women under the age of 49. 
Studies conducted in Australia (Brown & Trost, 2003), and Poland (Kaleta & Jeiger, 
2007), also showed higher rates of physical inactivity in married women. Despite some 
contrary findings, there is evidence to support an inverse relationship between marriage 
and physical activity.  
Parenthood 
 There is growing evidence to support a consistently negative relationship between 
parenthood and physical activity behavior. The results of a longitudinal study by Brown 
and Trost (2003) showed evidence for increased risk of lower rates of physical activity 
upon having a child among the youngest cohort of women. By examining a national U.S. 
health survey, Nomaguchi and Bianchi (2004) also found that participants with no 
children engaged in more physical activity than those with children. Based on a meta-
 
23 
 
analysis of previous studies, Bellow-Riecken and Rhodes (2008) found a significant 
negative relationship (Cohen's d=0.41 to 0.48) in physical activity involvement in parents 
compared to non-parents. The authors also looked at studies examining the combined 
effect of parenthood status and employment but found conflicting results. For example 
Nomaguchi and Bianchi (2004) found that married people, parents and full-time 
employees spend less time doing physical activity. Sallis et al. (2001) also found 
increases in mothers? physical activity correlated with fewer hours of work. However in a 
longitudinal study among young women looking at physical activity, employment status, 
relationship status, and motherhood, Bell and Lee (2005) found the group least likely to 
be physically active was married mothers who did not work outside the home.  
 The demographic factors of sex, age, race/ethnicity, SES, and BMI have all 
consistently been correlated with physical activity across the majority of populations, and 
to a lesser extent change in life-stage such as marriage and parenthood. However, there 
are subgroups of the population with correlates that are negatively associated with 
physical activity engagement that consistently meet physical activity recommendations. 
Analysis of people from the subgroups who do meet physical activity recommendations 
despite being, for example female, and/or overweight, and/or higher age, may help to 
better understand how to target physical activity messages and interventions for others 
with similar demographics. 
 
Psychosocial Correlates of Physical Activity 
There is a growing body of evidence to support our understanding of psychosocial 
correlates of physical activity. In a review of these correlates Trost et al. (2002) found 
 
24 
 
strong positive associations for physical activity behavior and the enjoyment of exercise, 
self-efficacy, social support, and self-motivation. Described below are some of the more 
consistent psychosocial correlates with physical activity behavior. 
Self-efficacy 
 
 Self-efficacy, defined as ?the conviction that one can successfully execute the 
behavior required to produce the outcomes? (Bandura 1997) is a construct utilized in 
multiple theories of behavior change, including social cognitive theory, theory of planned 
behavior, and the transtheoretical model. Self-efficacy has been shown to be predictive of 
adoption and maintenance of physical activity in adults (Sallis et al., 1986; Sallis et al., 
1992; Strachan et al., 2005) and in women in particular (Kaewthummanukul et al., 2005; 
Sternfield et al, 1999; Wilbur et al., 2003). In a study of employed women with and 
without young children, Tavares et al. (2009) examined constructs from multiple social-
cognitive theories for predicting physical activity behavior. Consistent with other studies, 
self-efficacy emerged as the strongest predictor of physical activity behavior in this 
population. Ainsworth et al. (2003) found a positive correlation between physical activity 
behavior and self-efficacy in a group of African American women. In a study of 
primarily minority women, Eyler et al. (2003) also found self-efficacy to be correlated 
with physical activity levels wherein those who were confident in their ability to be active 
were five times more likely to meet recommendations than those with low confidence. In 
a comparison of correlates of physical activity among African American and white 
women, Sharpe et al. (2008) found self-efficacy to be the only significant correlate 
among both African American and white women. 
 
25 
 
Social support 
 
 Social support has also emerged as a consistent correlate of physical activity 
behavior for women (Eyler et al., 2002; Kahn et al., 2002; Sternfield et al., 1999). Social 
support has been defined as the functional content of relationships that can be categorized 
by four types of supportive behavior including emotional support, instrumental support, 
informational support, and appraisal support (Heaney & Israel, 2008). Of note, social 
support is distinguished from other aspects of social relationships by always being 
intentionally helpful, as perceived by the provider, and consciously provided. An 
example of this would be, a woman encouraging a friend to exercise as opposed to 
criticizing for not being physically active. There is evidence to show a positive 
association with social support and physical activity behavior across age groups (Kaplan 
et al., 2001; Leslie et al., 1999; Plotnikoff et al., 2004 ), SES (Kamphuis et al., 2008; 
Sternfield et al., 1999) and racial/ethnic groups (Castro et al., 1999; Eyler et al., 2003; 
Wilcox et al., 2000). 
Self-regulation  
 
 Self-regulation, defined as skills used to carry out exercise intentions and to 
overcome personal and situational barriers, has been shown to be associated with 
physical activity behavior in multiple studies. Anderson et al. (2006) used structural 
equation modeling to test social cognitive determinants of physical activity in adults 
(66% female). Self-regulation was found to exert the strongest effect on physical activity. 
In a work-site physical activity intervention based on social cognitive theory variables, 
Hallam and Petosa (2004) found that exercise in the intervention group was mediated by 
 
26 
 
self-regulation. A randomized control trial for women aged 30-50 of cognitive-behavioral 
strategies found that the women who learned self-regulation were significantly more 
physically active compared to those women who received information only (Stadler et al., 
2009). In a work-site study, Petosa found significant associations between regular 
physical activity and the constructs of self-regulation including time-management, goal-
setting, relapse prevention, self-monitoring, reinforcement, and social support (Petosa, 
1993). 
 Outcome Expectancies 
 Fewer studies have considered the role of outcome expectancies on physical 
activity behavior though there is mixed evidence of a positive correlation with physical 
activity. In a review of the construct of outcome expectancy in physical activity research, 
Williams, Anderson, and Winett (2005) found positive outcome expectancy to be more 
predictive of physical activity behavior in older adults than middle-aged and young 
adults. They also found evidence of relations between outcome expectancy and other 
theoretical constructs such as stage of change and self-efficacy. Carels et al. (2005) 
evaluated the relationship of self-monitoring, outcome expectancies, and difficulties with 
exercise on physical activity in a behavioral weight loss program in obese sedentary 
participants. Findings included greater weight loss during weeks that participants 
reported more positive outcome expectancies and greater difficulty with exercise. In a 
study of adults age 18-92 (66% female) Anderson et al. (2006) did not find evidence to 
support the inclusion of outcome expectancies in a structural equation model for physical 
 
27 
 
activity levels. No studies to date have evaluated for an association between outcome 
expectancy and physical activity for employed, adult women. 
Health Behaviors 
 
 Participation in physical activity as an adult has been linked with other health 
behaviors such as a past history of engagement in sports or exercise and history of 
smoking. Multiple studies have shown a positive association between adoption and 
maintenance of physical activity with previous exercise participation ( Bozionelos & 
Bennett, 1999; Brenes, Strube, & Storandt, 1998; Oman & King, 1998). A longitudinal 
study conducted in Finland found that participation in sports at least two times a week 
during adolescence was associated with engaging in physical activity in adulthood 
(Tammelin et al., 2003). A five year longitudinal study conducted in the U.S. found those 
who participated in organized sports in high school reported higher weekly participation 
in physical activity during adulthood regardless of SES or sex. 
 For the most part previous research has shown a negative association between 
smoking and participation in physical activity across age groups (Johnson et al., 1998; 
Paavola, Vartiainen, & Haukkala, 2004; Poortinga, 2007; Ruchlin & Lachs, 1999). One 
study looking at smoking and physical activity behavior in women found a similar pattern 
(Brownson et al., 2000), while another study of a "diverse group" of women did not have 
findings to support this association (Sternfield et al., 1999). 
 Previous studies and reviews provide consistent evidence for an association 
between certain psychosocial constructs and adoption and maintenance of regular 
 
28 
 
physical activity, in particular self-efficacy, social support, self-regulation, and to a lesser 
extent outcome expectancies and other health behaviors. In addition, most of these 
constructs are included in multiple theories of health behavior lending further support for 
their inclusion in studies of physical activity in adults. Despite their consistency in being 
correlated to physical activity, they still do not fully explain the variance in adherence to 
physical activity recommendations. Therefore, while there is sufficient evidence to 
include these constructs in future studies, it is also necessary to build upon these 
constructs in search of additional evidence that explains the factors that allow adults, and 
employed women in particular, to adhere to regular physical activity. 
 Policy Correlates of Physical Activity 
Despite the inherent difficulties of isolating the effects of organizational or 
community policy from other correlates there is some evidence to suggest that policy 
strategies may be beneficial in the promotion of physical activity. A review by Matson-
Koffman et al. (2005) found interventions with the strongest influence on physical 
activity utilized prompts for stair-use, provided access to places and opportunities for 
physical activity or, in combination with comprehensive programs of health promotion, 
provided education, employee and peer support for physical activity and incentives. In a 
qualitative study of African American women ages 20-50 in a rural community in 
Alabama, Sanderson et al. (2002) identified "inflexible work environments" as a primary 
barrier to participation in physical activity and attributed this barrier to worksite policy. 
Brownson et al. (2001) conducted a study of environmental and policy determinants of 
physical activity in the U.S. and found a majority of subjects (71%) believed employers 
 
29 
 
should provide time during the workday for physical activity, however, no assessment of 
worksites with this type of policy was included. In a study of worksite physical activity 
policies and employee physical activity, Crespo et al. (2011) found those subjects who 
met minimum physical activity recommendations had a higher mean worksite promotion 
index (i.e. more supportive worksite policies) than those who did not meet 
recommendations for MVPA. While not worksite related, it is worth noting national 
policies that have influenced physical activity levels. For example in countries such as the 
Netherlands, Germany, and Denmark there are specific policies and programs to promote 
and support bicycling for transportation. This has resulted in equivalent cycling rates for 
males and females and across age and income groups. Additional evidence for the effect 
of these particular polices is evident in cycling rates: in Denmark and the Netherlands 
18% and 27% of trips are taken by bicycle respectively, versus 1% in the U.S., Australia, 
and the U.K. (Pucher & Buehler, 2008).  
 Built Environment Correlates of Physical Activity 
 Early research on determinants of physical activity focused on demographic and 
psychosocial factors however, for the past 15 years there has been more inclusion of built 
environment determinants of physical activity. Features of the built environment related 
to physical activity include, but are not limited to, presence and accessibility of 
recreational facilities such as parks, trails, bike paths, sidewalks, and fitness centers, 
safety, aesthetics, traffic, land use, residential density, and street connectivity. 
Sidewalks 
 
 
30 
 
There is mixed evidence to support the theory that the presence of sidewalks 
promotes physical activity for adults. In a study of predominantly females (67%), 
Brownson et al. (2001) found a positive association between the presence of sidewalks 
and MVPA. In a study of Belgian adults Bourdeauhuij et al. (2003) found that 
performance of walking and moderate physical activity, but not vigorous physical 
activity, was associated with the quality of neighborhood sidewalks. Similarly, in a study 
of Belgian and Portuguese adults, Bourdeauhuij et al. (2005) found a significant 
association between availability of sidewalks and walking in leisure time. In a multi-site 
study of women from diverse racial/ethnic groups, investigators evaluated personal, 
social and physical environment correlates of physical activity (Eyler et al., 2003). The 
presence of sidewalks was found to be significantly associated with meeting physical 
activity recommendations in only one of the seven sites (African American women in 
mixed rural/urban areas in South Carolina). In a study of predominantly Hispanic (79%) 
females (71%) Rutt and Coleman (2005) utilized structural equation modeling to evaluate 
relationships between the built environment and physical activity. Sidewalks, as 
identified by aerial photographs, did not contribute to participation in light, moderate or 
vigorous physical activity. Bengoechea et al. (2005) compared perceived environmental 
correlates of physical activity in men and women. After adjusting for age, income, 
education, and place of residence there was no correlation between presence of sidewalks 
and participation in leisure-time physical activity among women. Interestingly, in a 
review of 47 publications for potential environmental determinants of physical activity in 
 
31 
 
adults, Wendel-Vos et al. (2007) found that availability of sidewalks was found to be 
positively associated with physical activity in men, but not for women. 
Given that physical activity using sidewalks is primarily limited to walking and 
running, it may be worthwhile to consider studies that have primarily looked at walking 
and running rather than the broader category of MVPA that may not be able to be 
performed on sidewalks (i.e. biking, swimming, group fitness classes etc). Addy et al. 
(2004) evaluated association of perceived social and physical environmental supports 
with walking behavior and found access to sidewalks to be positively associated with 
regular walking. In a study looking at correlates of walking for transportation versus 
recreation purposes, Lee and Moudon (2006) found that presence of sidewalks was 
associated with recreational walking but not walking for transportation. In a review of 
built environment correlates of walking, Saelens and Handy (2008) also found evidence 
for a correlation between sidewalks and walking. While the evidence is mixed for an 
association between MVPA and presence of sidewalks in the neighborhood of residence, 
it is more positive for leisure-time walking and sidewalks. This suggests it may be 
important to consider the type or mode of exercise subjects perform in evaluating for 
associations with the built environment rather than just generalized MVPA. Additionally 
it may be worthwhile to assess if and how sidewalks assist with accumulation of 
additional physical activity for those who utilize other resources such recreational 
facilities, group classes, or parks.  
Aesthetics  
 
 
32 
 
 Humpel et al. (2002) performed a review of environmental factors associated with 
adult participation in physical activity and, of the four studies reviewed that included 
perception of neighborhood aesthetics or attractiveness, all reported a significant 
association with physical activity. Additional studies published after the review 
corroborate this finding of the perception of "attractiveness" of a neighborhood or public 
open space contributing to participation in physical activity (Bourdeauhuij et al., 2005; 
Brownson et al., 2001; Burton et al., 2005; Giles-Corti et al., 2005; Saelens et al., 2003). 
Pikora et al. (2006) evaluated built environment correlates of walking near home and 
found no association between aesthetics and self-reported walking for leisure or 
transportation near home (400m radius). In a review of environmental influences of 
walking, Owen et al. (2004) cites three studies that found a significant relationship 
between "aesthetic pleasantness" and walking for exercise or recreation, with one of those 
studies noting the relationship held for men but not for women. In the same review, two 
studies evaluated did not find a significant association between neighborhood aesthetics 
and walking to get to and from places while three studies describe a significant 
relationship for total walking time and neighborhood aesthetics. In summary, there is 
more evidence for neighborhood aesthetics and recreational walking than walking for 
purpose or transportation. 
Spatial Access 
  
 Multiple studies have evaluated the association between physical activity and 
spatial access to recreational facilities, parks, open space, and areas with mixed land use 
(residential, commercial, business, institutional). The majority of studies evaluating 
 
33 
 
access with relation to subjects' residences and subjects' perception of proximity of 
facilities found a positive association (Giles-Corti et al., 2002; Giles-Corti et al., 2005; 
Kirtland et al., 2005; Troped et al., 2011). In a study that utilized objective measures of 
proximity to recreational facilities, Diez Roux et al. (2007) also found associations 
between availability of recreational resources within 1 and 5 miles of the home residence 
and physical activity levels. In a study with predominantly female subjects (71%) Rutt 
and Coleman (2004) found that proximity to recreational facilities, as measured utilizing 
GIS, was positively correlated with vigorous physical activity but not for moderate 
physical activity. By contrast, in a study from New Zealand, Witten et al. (2008) did not 
find any correlation between physical activity and access to open spaces (parks and 
beaches) as minutes taken by a car, calculated using GIS. Duncan and Mummery (2004) 
utilized GIS to objectively measure distance from home residence to nearest footpath and 
found those with a footpath network more than 400m from home were 69% less likely to 
walk in the previous week as compared to those who did have a footpath within the same 
distance from home. 
Urban Features 
 
 Certain features of the built environment associated with urban areas have been 
evaluated for association with physical activity in multiple studies. These features include 
mixed land-use, street connectivity, and relative proximity to shopping and public 
transportation. Studies have shown varying degrees of a positive correlation between 
physical activity and mixed land-use (Bourdeaudhui et al. 2003; Bourdeaudhui et al. 
2007; Li et al., 2008; Saelens et al., 2003; Troped et al., 2011), street connectivity 
 
34 
 
(Bourdeaudhui et al. 2005; Li et al. 2008; Saelens et al. 2003) and proximity to shopping 
and public transportation (Bourdeaudhui et al., 2003; Li et al., 2008; Troped et al., 2011). 
It should be noted that to date, evaluation of these features in particular have been 
predominantly carried out in urban environments only. While it may be worthwhile to 
evaluate if these features contribute in areas other than large cities, they would not be 
expected to be the principal correlates of physical activity in small towns or rural areas. 
These particular features of the built environment, being associated with urban and 
metropolitan areas, are not predominant features of towns, small cities, rural areas, and in 
many instances suburban areas; therefore they are only available to a small percentage of 
the population who live in these types of areas.  
Suburban Features  
 
 While more is known about access to physical activity resources in urban areas 
and to a lesser extent in rural areas, there has been little research on access in ?sub-urban? 
or small cities, characterized as having a population of 50,000-999,000. A case study by 
Nicholls (2001) examining equity to open spaces and outdoor recreation in a small city 
(population 65,660) found that while access for all neighborhoods was low, there was no 
inequity of access for any particular type of neighborhood. In a study examining 
differences in availability and accessibility to physical activity resources by 
neighborhood SES in a small city (population 133,046), Estabrooks et al. (2003) found 
that low and medium SES neighborhoods had significantly fewer free-for-use facilities 
compared to high SES neighborhoods. Duncan and Mummery (2004) examined 
psychosocial and environmental factors associated with physical activity with walking in 
 
35 
 
a small city (population ~75,000) and found self-efficacy, social support, perception of 
neighborhood tidiness, and having a higher number of active people within a 1-km radius 
of home to be strong predictors of attaining recommended levels of physical activity. 
Contrary to common findings of studies in urban areas, this study showed that the people 
with parks beyond a distance of 0.6 km from home and those with "unacceptable" route 
directness to the nearest park were 41% more likely to achieve recommended physical 
activity levels. The authors hypothesized that this unexpected finding could be due to the 
ability of those who engage in regular physical activity to overcome barriers such as 
distance to recreational facilities. Another possibility is that these subjects who do 
participate in regular physical activity may use facilities other than parks and open space. 
The authors recommended future research include characteristics on the types of 
facilities, such as a park, gym, or trail, where people engage in physical activity, the type 
of activity performed and distance to these facilities to add clarification to these findings. 
Furthermore, significant predictors of recreational walking included social support, 
having a footpath within 0.4km from home, and agreeing that the neighborhood footpaths 
were not in good condition. Explanations offered for this unexpected finding about 
footpath condition was that people who use footpaths on a regular basis may be more 
cognizant of their condition or that these same people use areas outside of the 
neighborhood for recreational walking. This highlights the limitations of correlating 
residential neighborhood conditions with recreational walking without knowledge of 
where the subjects actually perform physical activity.  
 
36 
 
 While features of the built environment have shown some consistent correlations 
with physical activity the effect sizes have been modest. Part of the difficulties in 
evaluating the built environment for influence on physical activity has been the primary 
use of the neighborhood of residence as the location for built environment evaluation. 
This does not take into consideration other places where people engage in physical 
activity such as the work environment and neighborhood or facilities along frequently 
traveled commutes. There have also been inconsistencies in the definition of "home 
neighborhood" in terms of extent and direction for different studies with no real 
consensus on how to define neighborhood boundaries. Future research examining where 
people perform physical activity and types of activity associated with different locations 
will help to better elucidate how the built environment contributes to physical activity 
adherence. Recent advances in new technologies that allow for more objective measures 
of both physical activity and the environment will contribute to this research direction. 
Measurement of Physical Activity 
 Options for measurement of physical activity in adults include surveys and 
mechanical monitors such as accelerometers and pedometers. While other options such as 
calorimetry, use of doubly labeled water, and direct observation exist, these methods are 
limited by financial and time restraints and may not always capture free-living activity or 
differentiate between light, moderate, and vigorous physical activity. The most 
commonly used methods to measure free-living physical activity include questionnaires, 
pedometers, and accelerometers.  
 Numerous questionnaires have been developed to capture short term and long 
 
37 
 
term recall of physical activity. While the majority of studies examining associations 
between the built environment and physical activity utilize questionnaires, these have 
been shown to have less accuracy for moderate intensity activities (Bassett, Cureton, & 
Ainsworth, 2000) and range in validity depending on weight status (Timperio et al. 
2003). A review of 10 questionnaires by Jacobs et al. (1993) found little correlation 
between accelerometer data and questionnaire responses. The authors concluded that 
none of the 10 questionnaires accurately captured physical activity data related to 
occupational activity, one questionnaire had measures correlated to physical activity 
related to household activities, and few captured performance of light or moderate 
activity. In a more recent review of survey instruments for evaluation of physical activity 
(Sallis & Saelens, 2000), the authors conclude that most studies show that self reports do 
not provide accurate data with respect to absolute amounts of physical activity but that 
when used in combination with objective measures, they may provide important 
information related to context and the type of physical activity performed.  
 In addition to the limitations of quantifying physical activity by self report, others 
have proposed that many physical activity questionnaires do not accurately assess the 
physical activity of women (Ainsworth, 2000; Masse et al., 1998). Based on the analysis 
and results of multiple studies these authors argued that women are more likely to under 
report bouts of physical activity that occur as part of housework, occupational, and family 
care activities as surveys historically have only asked about physical activity in relation 
to sports and conditioning activities. Additionally the authors proposed that the 
questionnaires typically used to assess physical activity were not as relevant to minority 
 
38 
 
women.  
 In the last 10 years the use of pedometers in research on physical activity has 
increased as the cost has been reduced and the accuracy and availability of pedometers 
has increased. A study by Tudor-Locke et al. (2001) describes the drawbacks and 
advantages in the use of surveys, accelerometers, and pedometers. Consistent with other 
studies, the authors argue that despite the ease of use and efficiency of questionnaires, the 
data obtained is not always valid whereas accelerometers and pedometers have shown 
consistent validity for the measurement of free-living activity. A study by Leenders et al. 
(2000) compared 4 methods for measuring physical activity simultaneously on 11 
women. Subjects recorded physical activity over a 7 day period using an accelerometer, 
pedometer, activity monitor, and the physical activity recall questionnaire at the end of 
the 7 days. Results showed the Physical Activity Recall (PAR) to be significantly 
correlated with all of the objective measures (range 0.82-0.94, p<0.001). However, in 
contrast to other studies, the subjects in this study overestimated physical activity on the 
PAR compared to the other measures. Based on the results the authors recommend use of 
the accelerometer and activity monitors in subjects who perform physical activity via 
methods other than walking and the pedometer in situations where ambulation is the 
primary mode of physical activity. In describing considerations for device selection, 
Trost et al. (2005) describe the advantages of accelerometers compared to pedometers to 
quantify number of bouts of physical activity over a specific period, as well as, slightly 
higher validity coefficients for multiple axis units as compared to uniaxial models.  
 
39 
 
 In a study assessing the validity of accelerometry for assessment of free-living 
physical activity, the findings from Hendelman et al. (2000) supported previous studies 
showing highly correlated linear associations between accelerometry and energy costs. 
Wagner et al. (2011) utilized accelerometry and online daily physical activity diaries to 
assess free-living physical activity following a behavior change intervention. Findings 
included greater accuracy for measuring sedentary behavior with the accelerometer but 
the diaries helped to identify modes of moderate and vigorous physical activity that could 
not be captured by accelerometers such as swimming. 
 The majority of studies validating uniaxial accelerometers (measurement in the 
vertical plane) and defining activity count cut-points for light, moderate, and vigorous 
activity have done so using the modes of walking and running (Freedson, Melanson, & 
Sirard 1998; Nichols et al., 2000; Welk, 2004). The development of the triaxial 
accelerometer (measurement in the vertical, anterior-posterior, medio-lateral planes) has 
added the option to use the vector magnitude of activity counts from all three planes to 
measure physical activity which occurs in more than one plane (e.g., aerobics, tennis, 
dancing). Sasaki, John, and Freedson (2011) recently established cut-points to classify 
moderate and vigorous physical activity using the vector magnitude of all three axes on 
the GT3X+ (Moderate: 2690-6166, Vigorous: 6167-9642 counts/min). It was not 
established if it is more advantageous to use the vertical plane activity counts versus the 
tri-planar vector magnitude activity counts. However, it has been acknowledged that 
triaxial accelerometers are expected to be better able to detect free-living activity better 
than a uniaxial sensor (Butte et al., 2012). Previous studies have also shown higher 
 
40 
 
correlations with energy expenditure for the triaxial accelerometer in adults (Hendelmen 
et al., 2000; Yoshida et al., 1994). The use of objective monitors to assess physical 
activity, in particular accelerometers, allows for clearer differentiation of light, moderate 
and vigorous physical activity than any other option currently available. This unique 
ability to discriminate activity level intensity not only allows for verification of time 
spent in moderate and/or vigorous physical activity but also provides more detailed 
information of how subjects meet activity recommendations (i.e., time of day, 
identification of bouts, and identification of patterns in daily activities).  
Global Positioning Systems (GPS) 
 In recent years GPS has been utilized in physical activity research to objectively 
track locations of individuals. These studies have shown greater accuracy as compared to 
self-reported travel surveys or activity diaries when used in combination with 
accelerometry (Badland et al., 2010; Duncan & Mummery, 2007; Maddison & Mhurchu, 
2009). The addition of this technology to behavior research allows researchers to move 
beyond analysis of specific locations, e.g. the neighborhood of residence, and even single 
behaviors (Kerr et al., 2011). Used in combination with accelerometry it can help identify 
not only the location based physical activity but mode of activity as well, such as biking 
versus running. Quigg et al. (2010) were able to identify the percentage of children's 
physical activity which took place in parks with playgrounds, and Mackett et al. (2007) 
were able to differentiate children's physical activity behavior in the presence of an adult 
compared to behavior in the absence of adults by using accelerometry with GPS. This 
combined type of assessment allowed Troped et al. (2010) to show how only a small 
 
41 
 
percentage of daily MVPA was accumulated within 1-km of home and work. Recent 
improvement in technology has allowed for increased data storage, longer battery life, 
and greater discrimination between indoor and outdoor locations. Limiting factors 
include data loss due to misuse of the device or lack of adherence to the wear time 
protocol by participants, signal dropout, and loss during the initialization period (Krenn et 
al., 2011). Data loss has not been associated with sample size, age group being studied, 
device manufacturer, or incentives for participants. However, a relatively clear 
relationship has been shown between data loss and the GPS measurement period, with the 
longer the measurement period, the greater proportion of data lost (Krenn et al., 2011). 
Summary 
 To date, there is consistent and relatively strong evidence for an association 
between certain psychosocial and personal constructs and physical activity maintenance. 
However, given that these do not explain all of the variance in physical activity levels, 
there has been an increasing interest in examining how the built environment and policies 
may also contribute to physical activity behavior. Few studies have incorporated the use 
of objective measures for measurement of both physical activity and the built 
environment in female adults. Furthermore, many studies have observed subjects who 
were previously sedentary, and then received an intervention to increase physical activity, 
or have evaluated for correlates in those who have exercised for less than six months 
(Huberty et al., 2008). Few studies have specifically recruited female regular exercisers 
to evaluate for correlates of physical activity across multiple levels (i.e. psychosocial, 
environment, policy). Recent advances in technology, such as GPS coupled with 
 
42 
 
accelerometer data, now allow for more objective measures of both location and 
performance of physical activity. Application of these technologies to regular exercisers 
will allow for a greater understanding of the correlates of physical activity adherence. 
Specifically, the use of objective measures will help identify if and how employed, 
female regular exercisers utilize their environment to achieve physical activity 
recommendations. Additional measures will help identify the influence of perceived 
worksite policies, personal correlates such as education and history of participation in 
sports, psychosocial correlates, and perceptions of the built environment on achieving 
regular participation in physical activity. 
  
 
43 
 
Chapter 3 
 
Method    
 
 The purpose of this study was to examine the psychosocial, built environment, 
and worksite policy factors contributing to meeting physical activity recommendations in 
adult employed women. This chapter presents the methodology for the study including an 
overview of research design, participant recruitment and selection criteria, 
instrumentation for data collection, research procedures, and data analysis. 
Research Design  
 This study used a case-control observational design with the cases representing 
subjects who are regular exercisers, and controls representing subjects who are sedentary 
or do not exercise enough to meet physical activity recommendations. The purpose of this 
design was to compare characteristics and identify exposures that may contribute to 
differences in exercise behavior. The study used retrospective and current measures to 
explore the ability of major constructs of the social ecological model to describe how 
employed women adhere to physical activity recommendations. 
Subjects  
 The study population was recruited from the general population of adult working 
women residing in the cities of Auburn, AL and Opelika, AL. Subjects were recruited 
through the following: posted fliers, targeted emails, active recruitment at local 
businesses, fitness facilities, local clubs such as running clubs, tennis courts, recreational 
facilities, postings in e-newsletters through the local chambers of commerce, and 
Facebook notices. Requirements to participate included full time employment (30 or 
 
44 
 
more hours/week). Recruitment targeted two groups in particular: A) regular exercisers 
by the standard of performing moderate exercise for at least 30 minutes 5 days per week, 
or vigorous exercise for at least 20 minutes 3 days per week, or a combination of the 
above for at least the past six months, and B) sedentary or irregular exercisers who did 
not meet the standard described above. It was acceptable to achieve the physical activity 
requirement threshold through leisure time physical activity, work-related physical 
activity, or bouts of physical activity related to family and household duties, as well as 
traditional exercise regimens.  
 Incentives offered to subjects for completing the study included the following: 
1. A comprehensive review of the participant's current exercise regimen with suggestions 
for changes based on the participant's goals. At the completion of the study, sedentary 
subjects were offered a progressive physical activity plan based on equipment and 
facilities available and personal physical activity goals. 2. All subjects were entered into a 
raffle for the chance to win: A) One of two checks for $50, B) One of two free Tigerfit 
fitness assessments (includes blood analysis; body composition; pulmonary volumes and 
function; cardiovascular health and fitness; muscle strength, endurance and flexibility; 
and a complete health and fitness report including CVD risk assessment and an 
individualized exercise prescription), C) One of 10 free body composition assessments 
via Dual X-ray absorptiometry (DEXA). 
 Procedures for the study were approved by the Auburn University Institutional 
Review Board for Research Involving Human Subjects (Protocol # 11-377). As part of 
the consent process a written copy of the informed consent was provided to the subjects 
 
45 
 
who then received a verbal explanation of the study including the purpose, procedures, 
benefits (both individual and general), risks, plan for use of the data, and how 
confidentiality would be maintained. Contact numbers for additional information were 
also provided. Subjects verified they met study requirements. Subjects were made aware 
that participation was completely voluntary and withdrawal from the study was 
acceptable at any time, providing the data was identifiable. If the recruit was in 
agreement and wished to participate in the study, she was asked to sign the informed 
consent. (Refer to Appendix A for consent form). 
Instruments  
Demographics, Health History, and Exercise History  
 Subjects completed a questionnaire regarding age, height, weight, family 
demographics, health history, education level, race, current employment, exercise 
equipment at home, and exercise history including participation in sports and recreational 
activities as elementary, high school, and, if applicable, college students. Additional 
questions assessed self-efficacy to regulate exercise, self-regulation strategies for 
exercise, outcome expectancies for exercise, social support for exercise, stage of change 
for physical activity adherence, and perception of the neighborhood environment to 
perform exercise. Details of these sections are provided below. (See Appendix B for the 
full questionnaire). 
Self-efficacy 
 Self-efficacy, defined as ?the conviction that one can successfully execute the 
behavior required to produce the outcomes? (Bandura, 1997) is a construct utilized in 
 
46 
 
multiple theories of behavior change including social cognitive theory, theory of planned 
behavior, and the transtheoretical model. Previous research has demonstrated positive 
correlations between health behaviors and self-efficacy and in particular self-efficacy has 
been shown to be a predictor of both adoption and maintenance of physical activity 
behavior (Brown et al., 2006; Kaewthummanukul et al., 2006; Sallis et al., 1992; Trost et 
al., 2002). The 12-item "Self-Efficacy and Exercise Habits Survey" was used for this 
study and has established reliability (r=.68), internal consistency (Chronbach's alpha=.85) 
and construct validity (Sallis et al., 1988). Subjects rate how confident they are that they 
can exercise in a variety of situations (e.g., ??when your family is demanding more time 
from you,? and ?when you have excessive demands at work?). Each item is rated on a 
scale of 1 (I know I cannot) to 5 (I know I can). The 12-item scale has two factors that 
were used in the analyses: "sticking to it," based on the mean of 8 items (potential score 
range 1-5), and "making time for exercise," based on the mean of 4 items (potential score 
range 1-5). A higher score indicates higher levels of self-efficacy.   
Self-Regulation 
 Six aspects of self-regulation were measured with the PASR-12 including time-
management, goal-setting, relapse prevention, self-monitoring, reinforcement, and social 
support (Umstattd et al., 2009). The instrument contains 12 measures on a 5 point Likert-
type scale ranging from 1=never to 5=very often. The self-regulation score is calculated 
as the sum of the 12 responses. The minimum possible score is 12 and the maximum 
possible score is 60 with a higher score indicating more frequent use of self-regulation 
strategies for participation in physical activity. This instrument assesses the degree to 
 
47 
 
which self-regulation strategies are used to support participation in regular exercise. Both 
structural and construct validity were established by Umstattd et al. using confirmatory 
factor analysis and Pearson product-moment correlation coefficients against self-efficacy 
and physical activity. Confirmatory factor analysis revealed a good fit with strong factor 
loadings (range: 0.76-0.84, CFI=0.99). Construct validity testing demonstrated 
correlations ranging from .79-.96 for comparison to the original 43-item self-regulation 
instrument developed by Petosa (1993). Moderate-to-strong bivariate relationships were 
found between the PASR12 with self-efficacy (r=.56) and physical activity (r=.60). 
Social support 
 Social support for exercise was measured using the "Social Support and Exercise 
Survey" (Sallis et al., 1987), a 23-item questionnaire to measure three factors: family 
participation, family rewards and punishment, and friend participation. Answers range 
from 1-5 representing none, rarely, a few times, often, and very often. There is also an 
option to choose "does not apply." The potential score ranges from 10-50 for both family 
participation and friend participation, and from 3-15 for family rewards and punishment 
for a total potential score of 23-115. This measure has been used in multiple studies on 
exercise behavior which allows for comparison across studies. Test-retest and internal 
consistency reliability have been established along with criterion-related validity (Sallis 
et al., 1987; Sallis, Hovell, & Hofstetter, 1992; Sallis, Hovell, Hofstetter, & Barrington, 
1992). Factor test-retest reliabilities ranged from 0.55 (for questions related to rewards 
and punishments from family members) to 0.79 (for exercising together with friends) 
with alpha coefficients for internal consistency ranging from 0.61 to 0.91. Criterion-
 
48 
 
related validity was measured by correlation with reported health habits. The correlations 
between the social support factors and vigorous exercise ranged from 0.23 to 0.46. While 
the self-regulation instrument also assesses social support, the Sallis questionnaire 
differentiates between support from family and friends, and measures both 
encouragement and criticism for exercising. By utilizing both surveys, specific forms of 
social support and how they enable physical activity participation can be identified.  
Outcome Expectancy 
 Outcome expectancy was assessed using the ?Expected Outcomes for Habitual 
Exercise? (Steinhardt & Dishman, 1989). This survey measures outcome expectations, 
that is, the anticipated consequences of exercise behavior, and outcome expectancies, 
defined as the value placed on the anticipated consequences. Item responses for outcome 
expectations are based on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 
(strongly agree). The outcome expectancy values span a 3-point scale for low, medium, 
and high. The questionnaire is scored by multiplying the outcome expectations score by 
the outcome expectancy score and then summing the scores for all items for a range of 
scores from 19-285. A higher score indicates positive expectations that health benefits 
will follow as a result of physical activity behavior. Internal consistency coefficients for 
the survey established by Steinhardt & Dishman (1989) ranged from .47 to .78 and test-
retest reliability correlations ranged from .66 to .89. The survey also predicted free-living 
exercise behavior as measured by a seven-day recall questionnaire (R2 = .12 to .24).  
 Perceived Neighborhood Environment 
 Perceptions of the neighborhood environment to support walking was assessed 
 
49 
 
using the Neighborhood Environment Walkability Scale abbreviated version (NEWS-A; 
Cerin et al., 2006). The factorial and criterion validity were assessed with a stratified two-
stage cluster sample from eight high- and eight low-walkable neighborhoods matched on 
socioeconomic status. Walking for transport was positively associated with six scales 
including diversity of destinations, residential density, walking infrastructure, aesthetics, 
traffic safety, and crime, whereas walking for recreation was positively associated with 
aesthetics, mixed destinations, and residential density. A subset of questions from the 
instrument was later validated using GIS measures of the built environment (Adams et 
al., 2009) where significant concordance was found between the NEWS-A and the GIS 
measures. There are seven subsections of the NEWS-A representing seven factors. A 
description of the factors, score range, and interpretation of each score is provided in 
Table 1. Given that the NEWS-A instrument is designed primarily to capture perception 
of the local environment for walking only and not other types of physical activity, 
perception of the built environment was also measured using four questions from the 
Physical Activity Neighborhood Survey (PANES). Both construct validity (association 
with meeting physical activity guidelines ORs = 1.2-1.5) and test-retest reliability (ICC = 
0.55-0.77) have been established for the PANES (Alexander et al., 2006; Sallis et al., 
2009).   
  
 
50 
 
Table 1. Perception of Built Environment Factors of NEWS-A 
Factor Score Range Calculation Interpretation 
Land-Use Mix-
diversity 
1-5 min=5 
6-10 min=4 
11-20 min=3 
21-30 min=2 
31+ min =1 
Mean of 22 
items 
Higher score= 
Higher Walkability 
Land-Use Mix-
Access 
Strongly Disagree=1 
to 
Strongly Agree=5 
Mean of 3 
items 
Higher score= 
Higher Walkability 
Street 
Connectivity 
Strongly Disagree=1 
to 
Strongly Agree=5 
Mean of 2 
items 
Higher score= 
Higher Walkability 
Infrastructure 
and Safety for 
Walking 
Strongly Disagree=1 
to 
Strongly Agree=5 
Mean of 6 
items 
Higher score= 
Higher Walkability 
Aesthetics Strongly Disagree=1 
to 
Strongly Agree=5 
Mean of 4 
items 
Higher score= 
Higher Walkability 
Traffic Hazards Strongly Disagree=1 
to 
Strongly Agree=5 
Mean of 3 
items 
Higher score= 
Lower Walkability 
Crime Strongly Disagree=1 
to 
Strongly Agree=5 
Mean of 3 
items 
Higher score= 
Lower Walkability 
 
Perception of Worksite Policies 
 Perception of employer policies to promote or support physical activity at the 
worksite was assessed using an index modified from the Neighborhood Quality of Life 
Study (2004). The questions used to create the index were adapted from a previous study 
with intraclass correlation coefficients ranging from 0.80 to 1.00 (Oldenburg et al., 2002). 
The current version of the questionnaire has been used in multiple studies which allows 
for comparison across studies (Crespo et al., 2011; Schwartz et al., 2007). Internal 
consistency for the index was assessed by Crespo et al. and found to be acceptable 
(Cronbach?s ? = .78). Individual item responses are Yes (value=1) or No (value=0) for a 
 
51 
 
score range of 0-8 for the full index. Subjects also indicated specific incentive programs 
offered by their employers and whether they had participated in any of those programs in 
the past year.  
Accelerometers 
 Subjects wore an Actigraph GT3X+ accelerometer for seven consecutive days. 
This triaxial accelerometer measures activity counts in three planes in addition to position 
(standing, sitting, lying) and step count. The Actigraph GT3X+ uses similar technology 
as the previously validated CSA activity monitor (Computer Science and Applications, 
Inc.) which had the highest overall reliability and lowest variability compared to three 
other types of accelerometers (Welk, Schaben, & Morrow, 2004). Metcalf et al. (2002) 
found the CSA/actigraph model had a coefficient of variability (CV) of less than 2%. The 
memory of the Actigraph GT3X+ allows for up to 20 days of data storage along with a 
battery life of 20 days. This accelerometer is sealed and cannot be reset or altered 
manually. Subjects were provided with a clip or belt for attaching the accelerometer at 
their right hip/waist. Subjects were instructed to wear the accelerometer during all 
waking hours except during showering and aquatic activities. (See Appendix C for 
Participant Instructions).  
 In contrast to most accelerometers, the GT3X+ stores only raw data which allows 
the epoch to be determined post-data collection. The majority of studies on adults have 
used the 1-minute epoch length, and current "best practice" guidelines recommend the 
use of a 1-minute epoch length for adults (Cain & Geremia, 2012; Ward et al., 2004). 
Therefore, an epoch length of 1-minute was also chosen as the standard for the current 
 
52 
 
study. A relatively new feature with the GT3X+ is the ability to choose the sampling rate 
per second. Traditionally the sampling rate is pre-set at 10-30 Hz, or 10-30 samples per 
second. The GT3X+ allows for a sampling rate between 30 and 100 in 10 Hz increments. 
Currently there are no best practice recommendations for sampling rate determination. 
Given the more common use of 30 Hz in previous studies, the sampling rate for this study 
was also set at 30 Hz. 
Previous studies of accelerometry suggest that 3-5 days of monitoring is sufficient 
for adults (Hart et al., 2011; Matthews et al., 2002; Trost, McIver, & Pate, 2005) to 
achieve 80% reliability for total and moderate-to-vigorous intensity physical activity. As 
described by Matthews et al. (2012) there is relative consensus of a minimum of 10 hours 
per day of wear time needed for sampling "wake-time" behavior. In their summary of 
best practices in accelerometer-based activity research Trost et al. (2005) reported 
optimal placement for the accelerometer is at the hip or lower back. Consistent with other 
studies, "non-wear" time was defined as more than 60 minutes of consecutive zero counts 
(Dunton et al., 2012; Matthews et al., 2008) and excluded from analysis. See Figure 2 for 
a summary of the accelerometer protocol for this study. 
Figure 2. Validation Thresholds for Accelerometer Data 
 
 
 
 
 
? 4 valid days Valid week: 
Valid day: ? 10 hours of wear time 
Valid hour: ? 60 minutes of consecutive zeros 
 
53 
 
Global Positioning System Monitors 
 Subjects also wore a QStarz BT-Q1000XT global position travel recorder. The 
memory of the QStarz GPS monitor allows up to 40 days of data storage and has a 
battery life up to 40 hours. These units provide 66 channels and a tracking sensitivity of 
165 dBm. The device claims accuracy within 3.0 meters and acquisition times of 1-36 
seconds (from hot to cold start respectively). In a study comparing reliability and 
accuracy among GPS units previously used for activity research, the QStarz demonstrated 
relatively high accuracy (ICC = 0.644) and good inter-unit reliability across a range of 
conditions including open sky, urban areas, and under canopies (Duncan, Oliver, & 
MacRae, 2011). Subjects were provided with a range of options for carrying the GPS unit 
including a clip, an arm band holder, and a carrying case with a belt attachment option. 
Subjects were asked to turn the unit to the ?NAV? position at the onset of data collection, 
carry the unit with them throughout the day, and charge the unit each evening. The GPS 
units were pre-configured to stop data collection between the hours of 9:30 pm and 4:30 
am to allow for 7 days of data collection without the need to download data during the 
week of assessment. Subjects were not required to turn the units on or off at any other 
point during data collection, unless they planned to participate in physical activity 
between the hours of 10:30 pm and 4:30 am. Subjects who worked second or third shift 
jobs were instructed to turn the unit to the "LOG" position during all waking hours and to 
turn the unit off while sleeping. The units were configured to record latitude and 
longitude positioning data every 10 seconds. This epoch is consistent with rates used in 
previous studies (Cooper et al., 2010; Cooper et al., 2010; Dunstan et al., 2012) and 
 
54 
 
allowed for a reasonable compromise between data storage capabilities and optimal 
sampling of a 1-5 second epoch.   
 To enhance compliance with wearing the GPS unit for a full week, the following 
measures were taken:  
1. Reminder messages were offered and provided to those requesting them to charge the 
GPS and wear both devices (mode of message based on subject's preference for email, 
text message, or phone call). 
2. Subjects were asked to log device charging and wearing (see Appendix C). 
3. Subjects were provided with written and verbal instructions explaining the importance 
of compliance with wear-time in order to meet minimum data collection requirements. 
4. It was recommended to subjects that the GPS devices be charged in a location that 
would be easy to access (near cell phone, bed, coffee maker, or work computer, etc). 
Subjects were also asked to keep the accelerometer and log stored in the same place. 
Procedure 
 Subjects were recruited February through May 2012. Subjects who agreed to 
participate were contacted by email, phone, or in person to establish the initial meeting 
time and place at their convenience. Subjects were given an Actigraph accelerometer 
with a clip or belt, depending on the subject's preference, for wearing the accelerometer at 
the hip. Subjects were also given a Q-Starz GPS unit and the option to carry the unit on a 
belt with the accelerometer, on a clip, in a separate case for wearing at the hip, or an 
armband carrier. The subjects were asked to wear the accelerometer and carry or wear the 
GPS for seven days throughout waking hours. Written and oral instructions were given to 
 
55 
 
each participant regarding wearing and storage of the devices. (See Appendix C for 
written instructions). Subjects were given a charger for the GPS unit and instructed to 
charge the unit once every 24 hours. Subjects were offered the choice of reminder phone 
calls, texts, or emails for charging the GPS unit and wearing the devices. Subjects were 
given a journal sheet to record any times the accelerometer and GPS unit were put on and 
removed each day. Additionally, subjects were asked to record any periods of time the 
accelerometer or GPS units were removed and any activity performed while they were 
removed (See Appendix D). The subjects who self-identified as physically active were 
asked to record the mode of physical activity performed that day (i.e., running, biking, 
class, machines, etc.), or if none was performed. At the end of the seven days the 
principal investigator retrieved the accelerometer and GPS unit. After returning the 
accelerometer, GPS, and log, subjects completed the questionnaire (see Appendix B for 
instruments) which took approximately 30 minutes. Subjects provided height by self-
report. Weight was measured with the Digital Medical Scales (Seca Floor Scale 769, 
SECA Corp. Hanover, MD) or collected by self report if the subject had weighed herself 
in the past seven days. The data stored on the accelerometer and GPS units were 
downloaded using proprietary programs for each device.    
Data Analysis, Data Validation and Data Reduction 
 Initial validation of the accelerometer data was conducted using Actilife software. 
Specific thresholds for inclusion of data in the analysis were presented in Figure 2. 
Accelerometer data cut points, as determined by Sasaki, John, and Freedson (2011), were 
used to identify moderate (2690-6166 counts?min-1), vigorous (6167-9642 counts?min-1), 
 
56 
 
and very vigorous (>9642 counts?min-1) activity for the respective equivalent of 3.0-5.99 
METs, 6.0-8.99 METs, and ?9.0 METs. The cut points were based on the triaxial vector 
magnitude (VM3) calculation of VM3=?(VT2+AP2+ML2) where VT represents counts 
from the vertical axis, AP represents counts from the antero-posterior axis, and ML 
represents counts from the medio-lateral axis. Based on public health physical activity 
recommendations, a minimum bout of 10 minutes at ?2690 counts?  min-1 was required to 
count towards time spent in MVPA. Based on previous recommendations (Heil, Brage, & 
Rothney, 2012; Masse et al., 2005; Ward et al., 2005) an interruption of 1-2 minutes was 
allowed as long as the time-intensity threshold was achieved. The following example 
would classify as a bout of physical activity: subject counts were ?2690 counts?min-1 for 
minutes 1-4, <2690 counts?min-1 for minute 5 and ?2690 counts?min-1 for minutes 6-11. 
Bouts were identified with the Actilife software based on the parameters described above. 
 The procedures for physical activities reported but typically not captured by the 
accelerometer such as swimming, water aerobics, and bicycling included the following: 
1) Subjects were asked to rate the intensity of the activity using the Omni perceived 
exertion scale including ratings for the warm-up, cool-down, and approximate time at the 
highest intensity (See Appendix E). This perceived exertion scale was chosen based on its 
applicability to a wide range of physical activities and populations (Robertson, 2004). 2) 
A bout of MVPA was assigned to activity rated as ?6 on the Omni RPE scale and 
performed for at least 10 continuous minutes.  
Cases were defined as subjects who achieved minimum physical activity levels. A 
subject was determined to meet activity levels by achieving one of the following over the 
 
57 
 
week of monitoring: 1) ?30 minutes of moderate activity ?5 days, 2) ?20 minutes of 
vigorous activity ?3 days, 3) ? 4 days of ?30 minutes of moderate activity or 20 minutes 
of vigorous activity per day, with at least 20 minutes of vigorous activity on one day. 
Percentage of time spent in sedentary activity was calculated based on time periods only 
when the accelerometer was worn. Sedentary behavior was defined as activity counts 
<100, consistent with the definition used in previous studies (Healy et al., 2007; Healy et 
al., 2008; Matthews et al., 2008).  
 The sum of MVPA minutes accumulated within the ?10-minute bouts was 
calculated for all subjects based strictly on accelerometer counts. Minutes of MVPA 
accumulated in the water or on a bicycle that were not captured by accelerometry were 
not estimated nor included in the sum of MVPA minutes. Of note, for the purpose of 
identifying cases and controls, physical activity bouts recorded in the daily log, but not 
captured by accelerometry, were used to determine qualifications as a case or control. 
However, the accelerometry counts for these bouts were not available and therefore not 
included in the sum of MVPA minutes. 
 The Qstarz QTravel program was configured to filter and remove invalid values 
of GPS points such as spurious data points due to poor satellite signal or interference 
from buildings. Home and work latitude and longitude coordinates were identified within 
the QTravel program and confirmed against questionnaire data.  
 Accelerometer and GPS data were combined based on timestamps using the "GPS 
correlator" option within the Actilife software. The output provided latitude and 
longitude coordinates matched to corresponding counts for each minute available. 
 
58 
 
Location for each bout was assigned the latitude and longitude of the first minute of the 
bout and confirmed by a check against the activity log and a visual inspection of the 
geospatial coordinates.  
 The merged dataset of home, work, and MVPA bout locations was exported for 
analysis within a GIS. See Figure 3 for an example of a map with participant location by 
activity level and Figures 4a and 4b for examples of MVPA by location. Analysis of the 
combined accelerometer with GIS data identified specific environmental characteristics 
of the built environment where MVPA took place (e.g., sidewalk, bike lane, free public 
access resources, community recreational facility, open space, municipal tennis courts 
and trails). Additional calculations included the Euclidean distance (straight-line) 
between MVPA bout locations and home and work with subsequent calculation of the 
average distance between MVPA bout locations and home and work. 
Figure 3. Example of Participant Location by Activity Level. 
 
 
59 
 
Figure 4a. Example of MVPA by location
 
Figure 4b. Example of MVPA by Location 
 
Data analyses were conducted using the Statistical Package for the Social 
Sciences, version 18.0 (SPSS, Inc., Chicago, IL) and Stata, version 5.0 (Stata 
Corporation, College Station, TX). Descriptive statistics calculated for each group (cases 
and controls) included: the sum of MVPA minutes by accelerometry, distance from work 
 
60 
 
and home of site(s) where MVPA was performed, number and types of environment 
features utilized to perform MVPA (built environment, home, work, or facility). A 
univariate analysis using t-tests (for continuous variables) or chi square analysis (for 
categorical variables) was performed on subjects by group (meet recommendations, do 
not meet recommendations) comparing them for baseline demographics, exercise history, 
self-efficacy, social support, perception of the neighborhood, self-regulation, outcome 
expectancy, mean sedentary time per day, and built environment features used for 
physical activity. Logistical regression was performed using "meets recommended levels 
of physical activity" as the dependent variable to create a predictive model based on the 
data collected during the study. Specific analyses for each research question are presented 
in Table 2. The alpha level was set at .05 for all analyses. 
Table 2. Data Analysis by research question 
Research Question Analysis Variables 
Differences in 
personal level 
variables case vs. 
control 
Mean comparisons 
(chi square, t-test), 
logistic regression 
Self-efficacy, self- regulation, 
outcome expectancies, social 
support, age, BMI, marital status, 
education, physical activity history 
Differences in 
sedentary time 
t-test Average percentage time spent being 
sedentary 
Are 
recommendations 
met through single 
or multiple bouts? 
Describe number 
using single bouts 
vs. number using 
multiple bouts 
Average number of bouts 
Are features of the 
built environment 
used to meet 
MVPA? 
Logistic Regression Sidewalk, bike lane, free access 
recreational facility, open space,  
Distance from work 
and home where 
MVPA performed 
Describe average 
distance 
Average distance between home and 
work to location where physical 
activity performed 
Are "supportive 
features" correlated 
with MVPA? 
Logistic Regression Perceived worksite policies, 
perception of low traffic, low crime, 
aesthetically pleasing 
 
61 
 
Chapter 4 
Results 
 The purpose of this investigation was to identify potential psychosocial, worksite 
policy, and environmental factors that facilitate adherence to regular physical activity 
among employed women. This chapter presents the results of the study relative to the 
research questions of interest.   
 One hundred fifteen subjects volunteered to participate in the study. Of the 103 
subjects who completed data collection, 41 met minimum exercise recommendations as 
measured through accelerometry (herein referred to as cases), 35 were inactive, and 27 
had insufficient activity to qualify as meeting recommendations. For the purpose of 
analysis, the inactive and insufficiently active were combined for one category as "not 
meeting recommendations" (controls). The mean age of all subjects was 44.4 (SD=11.8, 
range: 23-65) years with a mean BMI of 26.2 (SD=5.5, range: 18.2-43.0). See Table 3 for 
demographic measures by group. Subjects were recruited from 36 different employers 
with a variety in the types of work including teachers/school system staff (7%), 
healthcare workers (24%), custodial workers (3%), university employees (staff 23% and 
faculty 12%), self-employed (2%), bank employees (3%), and others (26%). 
 
 
 
 
 
 
62 
 
Table 3. Demographic Measures by Activity Status. 
 Cases 
n=41 
Controls 
n=62 
p 
Age Mean (SD) 45.1 (11.6) 43.9 (12.0)  0.64 
BMI Mean (SD) 24.2 (4.5) 27.3 (6.6)  0.02 
Average Hours/Week at Work Mean (SD) 43.9 (7.1) 41.9 (5.8)  0.12 
Spouse/Partner (% Yes) 80.5 77.4  0.71 
Children living at home (% Yes) 34.2 38.7  0.64 
Hypertension (% Yes) 14.6 21.0  0.42 
Current Smoker (% Yes) 2.4 6.5  0.35 
Education Level (%) 
High School Diploma or Less  
 
Some College or Associates Degree 
 
Bachelors Degree or Higher 
 
2.4 
 
7.3 
 
90 
 
3.2 
 
32.3 
 
64.5 
 0.01 
Race (%White) 90.2 80.7  0.19 
  
  
 Cases had an average of 271.0 (?105.0, range 81-527) minutes of MVPA during 
the week of monitoring compared to controls with an average of 50.7 (?52.2, range 0-
190) minutes of MVPA (p<.001). The subset of insufficiently active subjects had a mean 
of 91.0 (?49.6, range 0-190) minutes of MVPA. Types of exercise reported by cases 
included running, walking, biking, elliptical, classes (zumba, circuits, body pump, boot 
camp, water aerobics, RPM), swimming, treadmill, recumbent bike, and tennis. A 
complete list of types of exercise performed by the cases and the number of subjects who 
performed each type is available in Appendix F. Based on responses to the "stages of 
change" questions, all of the cases confirmed they had been regularly physically active 
for the past six months. 
 There was an average of 6.44 valid days (range: 4-8 days) of accelerometer data 
for all subjects. Home and work locations were identifiable for all subjects via the GPS 
 
63 
 
output and confirmed by questionnaire responses. Location coordinates were identifiable 
for 80% of all MVPA bouts. Seventeen control subjects had no MVPA bouts and 17 (7 
cases, 10 controls) had some missing location data for MVPA bouts including one 
subject, with a single bout during the week of data collection, with no location data. A 
complete description of the percentage of identifiable locations of MVPA per number of 
subjects is presented in Table 4.  
Table 4. Availability of location data for all subjects with bouts of MVPA 
 
n 
Percent Location Data 
Available  
69 100 
9 80-99 
3 71-75 
4 50 
1 0 
 
Sedentary Time 
Research Question #1: What is the difference in the percentage of sedentary time 
between cases versus controls? 
Hypothesis #1: Cases will have less sedentary time, defined by accelerometer counts 
between 0 and 100, as compared to controls. 
 Subjects who met physical activity recommendations had a mean of 43.9% 
(range: 30-55%) of the day in sedentary behavior (accelerometer counts <100 
counts?min-1) compared to a mean of 50.0% (range 29-64%) for controls. As tested by 
the two-sample t-test this difference was significant at p<.0001 (t=4.007 with equal 
variances, d=.80, r=.37). A comparison of cases to the subset of controls with insufficient 
activity levels but self-identifying as exercisers (n= 27) also showed a statistically 
 
64 
 
significant difference between groups for sedentary time (43.9% vs. 47.6%, p=0.05). The 
hypothesis that cases would have less sedentary time compared to controls was upheld. 
Bouts of Physical Activity 
Research Question #2: Public health recommendations allow for discontinuous bouts of 
physical activity to achieve recommended levels. Do employed women achieve 
recommendations through more than one daily bout of physical activity on average? 
Hypothesis #2: Cases are more likely to perform physical activity in a single session, 
(e.g., ?20 minutes of vigorous activity or ?30 minutes of moderate activity), rather than 
two or more bouts of short duration (e.g., 10-15 minute bouts interspersed throughout the 
day). 
 Among cases, the majority (76%) performed more than one bout of MVPA per 
day for an average of 1.3 bouts/ day (?0.31, range 1-2.3). Over all days measured, 22.8% 
of the days included 2 or more bouts of physical activity. The days with more than one 
bout were compared against the daily log to identify if all the bouts in a day were 
"intentional" (i.e., a bout with a clear description of the activity in the daily log), or if at 
least one of the bouts could be classified as "unrecorded" based on lack of an entry in the 
daily log. A breakdown of descriptions of the bouts is presented in Table 5. The length of 
the unrecorded bouts ranged from 10-16 minutes with the majority at 10-11 minutes. A 
comparison of cases to the subset of subjects with insufficient activity levels also showed 
a statistically significant difference between groups for average number of bouts (1.3 vs. 
1.1 bouts/day, p=0.002). Twenty-six percent of those with insufficient activity had at east 
one day with more than one bout of MVPA compared to 76% of cases (p<.001, r=.37).  
 
65 
 
Table 5. Categorization of Bouts of MVPA from Days with >1 Bout. 
n (%) Description of extra bout 
11 (27%) Intentional extra bout 
11 (27%) Unrecorded 
3 (7%) Both intentional extra bouts and unrecorded extra bouts 
10 (24%) No extra bouts 
3 (7%) No log 
 
The hypothesis that employed women who meet physical activity recommendations are 
more likely to perform physical activity in a single bout was not upheld. 
Distance 
Research Question #3: How far from home and work do employed women travel in order 
to perform MVPA? 
Hypothesis #3: Employed women are more likely to perform MVPA in locations close 
(within 8 KM) to home or work.  
 Results of this analysis are provided with outliers included and excluded. Four 
subjects were identified as outliers for average distance traveled for MVPA (see Boxplots 
in Appendix F). One case traveled a long distance for personal reasons during the week 
of testing and had 2 bouts of MVPA in this location leading to a skewed average distance 
from both home and work locations. The other three subjects (controls) had single bouts 
of MVPA in places traveled to on weekends and outside of work obligations. Upon direct 
questioning, these subjects confirmed that these were singular excursions and not "typical 
behavior." The average distance traveled between home and MVPA locations and work 
and MVPA locations for all subjects, respectively, was 6.6 km (SD=8.1) and 7.4 km 
(SD=7.5) with the outliers excluded and 13.5 (SD=34.1) and 12.7 km (SD=29.9) with the 
 
66 
 
outliers included. Table 6 provides a breakdown of average distances traveled for cases 
compared to controls. There was no significant difference in distances traveled for cases 
compared to controls to MPVA locations. 
Table 6. Average distances between home and work to MVPA bout locations. 
 Cases 
Mean (SD) 
Controls  
Mean (SD) 
 
p 
Home to MVPA, outliers excluded 5.1 (5.4) 8.0 (10.0) 0.11 
Work to MVPA, outliers excluded 6.6 (5.6) 8.3 (8.9) 0.31 
Home to MVPA, outliers included 9.5 (28.2) 17.3 (38.8) 0.29 
Work to MVPA, outliers included 6.6 (5.6) 18.2 (40.4) 0.07 
 
Of note, 35% of cases performed MVPA in locations closer to work than home on 
average. Forty-nine percent of all subjects had MVPA bouts in locations closer to work 
than home on average. The hypothesis that employed women are more likely to perform 
MVPA in locations within 8 KM of work and home was upheld. 
Personal Factors and Physical Activity 
Research Question #4: Are there differences in personal level variables (social cognitive 
measures, physical activity history) between cases and controls? 
Hypothesis #4. Cases will have higher scores for self-efficacy, self-regulation strategies, 
outcome expectancies, social support, and a higher percentage will have a history of 
physical activity than controls. 
 Mean scores for self-efficacy, self-regulation, outcome expectancies, and social 
support are presented in Table 7. There was a significant difference between cases and 
controls with cases scoring higher on all social cognitive measures that were assessed 
except for social support by "family participation" and by "family rewards and 
 
67 
 
punishment." There was no significant difference between cases and controls for history 
of family encouragement to engage in physical activity or history of physical activity 
participation in school (See Table 8). Based on univariate comparisons, the hypothesis 
was upheld for social cognitive measures and including these measures in a logistic 
model is warranted. The hypothesis that cases would differ for physical activity history 
compared to controls was not upheld.  
Table 7. Social Cognitive Measures by Group 
 Cases 
n=41 
Mean (SD) 
Controls  
n=62 
Mean (SD) 
Levene's Test of 
Equality of 
Variances F (sig) 
 
 
p 
 
r 
Self-efficacy:  
Making Time 
for Exercise 
(Range: 1-5) 
 
 
4.3 (0.6) 
 
 
3.9 (0.9) 
 
 
3.0 (.08) 
 
 
0.006 
 
0.27 
Self-efficacy: 
Sticking to It 
(Range: 1-5) 
 
4.2 (0.7) 
 
3.5 (0.8) 
 
1.8 (.18) 
 
<0.001 0.43 
Self-regulation 
(Range: 12-60) 
 
47.1 (7.7) 
 
33.5 (13.8) 
 
11.1 (.001) 
 
<0.001? 
 
0.49 
Outcome 
Expectancies 
(Range:19-285) 
 
160.3 (44.2) 
 
122.2 (45.7) 
 
002 (.97) 
 
<0.001 0.39 
Social Support:  
Family 
Participation 
(Range: 10-50) 
 
 
21.8 (8.2) 
 
 
20.5 (8.0) 
 
 
.17 (.69) 
 
 
0.46 NA 
Social Support: 
Family Rewards 
and Punishment 
(Range: 3-15) 
 
 
4.3 (1.8) 
 
 
3.7 (1.4) 
 
 
5.6 (.02) 
 
 
0.08? NA 
Social Support:  
Friend 
Participation 
(Range: 10-50) 
 
 
25.8 (11.5) 
 
 
20.6 (8.8) 
 
 
6.7 (.01) 
 
 
0.02? 
 
0.27 
?Equal variances not assumed 
 
68 
 
Table 8. Physical Activity History by Group 
 
Built Environment and Physical Activity 
Research Question #5: Do employed women meet recommendations by using features of 
the built environment? 
Hypothesis #5: Cases will utilize features of the built environment, such as sidewalks, 
roads, free access or municipal recreational facilities, and open spaces, to perform 
physical activity more than women who do not meet weekly MVPA recommendations. 
 Cases utilized the built environment for MVPA on 49.7% of all physical activity 
bouts. Looking at use of the built environment over a week, cases used the built 
 Cases 
n=41 
% 
Controls 
n=62 
% 
 
 
p 
Family Encouragement to be Active 
Never/Rarely 
Sometimes/Often 
 
29.3 
70.7 
 
23.0 
77.0 
 
0.47 
Recreational Sports in Elementary School 
Never/Rarely 
Sometimes/Often 
 
34.2 
65.8 
 
33.9 
66.1 
 
0.98 
 
Competitive Sports in Elementary School 
Never/Rarely 
Sometimes/Often 
 
68.3 
31.7 
 
57.4 
42.6 
 
0.27 
Recreational Sports in High School 
Never/Rarely 
Sometimes/Often 
 
26.8 
73.2 
 
34.4 
65.6 
 
0.42 
Competitive Sports in High School 
Never/Rarely 
Sometimes/Often 
 
39.0 
61.0 
 
45.9 
54.1 
 
0.49 
Recreational Sports in College 
Never/Rarely 
Sometimes/Often 
 
51.2 
48.8 
 
59.7 
40.3 
 
0.41 
 
Competitive Sports in College 
Never/Rarely 
Sometimes/Often 
 
86.0 
14.0 
 
85.0 
14.0 
 
0.89 
 
69 
 
environment for physical activity on 57.8% of all the days with any MVPA. For example, 
a subject with seven complete days of accelerometer data used the built environment, on 
average, four out the seven days. Use of the built environment for MVPA. out of all 
physical activity bouts, was significantly higher for cases compared to controls (49.7% 
vs. 28.2%, p=.006, r=0.27). Calculation of the use of the built environment over all days 
measured (as compared to only days with any MVPA) was conducted for the purpose of 
a comparison between groups. Controls used the built environment on 12.2% of all days 
measured with accelerometry and GPS compared to 53.1% for cases (p<.001). Based on 
the univariate analysis, the hypothesis that cases utilize features of the built environment 
more than controls was upheld. 
 
Perception of the Built Environment 
Research Question #6: Are there differences in perception of the built environment 
between cases and controls?  
Hypothesis #6: Cases are more likely to perceive the neighborhood built environment as 
supportive for physical activity, as identified from the Neighborhood Environment 
Walkability Scale, and the Physical Activity Neighborhood survey. Supportive 
environments include perception of low traffic, and perception of low crime. 
 Results of the independent sample t-tests for perceived neighborhood built 
environment to support physical activity are presented in Table 9. Equality of variances 
for the t-tests were assumed based on non-significant results of the Levene's test on all of 
the built environment variables. There was a significant difference between cases and 
controls for perception of more land-use mix diversity within walking distance of home, 
 
70 
 
such as grocery stores, the post office, banks, parks, and restaurants (effect size r=0.20). 
Of note, all subjects, on average, "disagreed" that their neighborhoods provided 
infrastructure and safety for walking.  
Table 9. Perception of the neighborhood built environment to support physical activity. 
 Cases n=41 
Mean (SD) 
Controls n=62 
Mean (SD) 
 
p 
PANES (0-4) 2.4 (1.1) 2.0 (1.1) 0.08 
Land Diversity (1-5) 2.3 (1.0) 1.9 (0.9) 0.04 
Aesthetics (1-5) 3.1 (0.7) 2.8 (0.8) 0.16 
Land Use Mix-Access (1-5) 2.4 (1.2) 2.0 (1.2) 0.07 
Infrastructure and Safety for Walking (1-5) 2.0 (0.8) 2.1 (0.9) 0.63 
Street Connectivity (1-5) 2.5 (1.0) 2.4 (1.1) 0.56 
Crime (1-5) 2.2 (0.4) 2.2 (0.3) 0.68 
Traffic Hazards (1-5) 2.4 (0.6) 2.3 (0.8) 0.63 
 
Overall, the hypothesis of the neighborhood built environment was not upheld, based on 
univariate comparisons, except for significant differences in perception of land-use mix 
diversity.  
Perception of Worksite Policies 
Research Question #7: Are there differences in perception of worksite policies between 
cases and controls?  
Hypothesis #7: Cases are more likely than controls to perceive worksite policies 
as supportive for physical activity, as identified from the index of employer 
policies modified from the Neighborhood Quality of Life Study (2004). 
The mean score for the worksite policy index was not significantly different between 
cases and controls (2.9 ?2.3 vs. 2.2 ?2.2, p=0.09). Results of the independent sample t-
tests for perceived individual worksite policies to support physical activity are presented 
 
71 
 
in Table 10. There were no significant differences in the perception of individual 
worksite policies between groups. 
Table 10. Perception of individual worksite policies to support physical activity 
 
 Cases n=41 
% Yes 
Controls n=62 
%Yes 
p 
Exercise facilities 29.3 29.0 0.98 
Regular exercise programs 56.1 37.1 0.06 
Shower facilities    34.2 24.2 0.27 
Safe bicycle storage  39.0 24.2 0.11 
An exercise specialist or activity coordinator 
available for employees  
29.3 29.0 0.98 
Policies that encourage exercise or biking  29.3 25.8 0.70 
Employer provides paid time for you to 
exercise  
2.4 3.2 0.82 
Employer offers incentives to be physically 
active 
39.0 25.8 0.16 
 
The hypothesis that there would be differences in the perception of worksite policies to 
support physical activity was not upheld. 
Predictors of Physical Activity 
 Based on the results of the analysis for research questions #4, #5, and #6, logistic 
regression was used to identify differences in cases and controls using demographic, 
psychosocial, perceived worksite policy, and perceived built environment variables as the 
independent variables. A test of the full model against a constant only model was 
statistically significant, indicating that the predictors as a set reliably distinguished 
between cases and controls (chi square  p .001 with df 4). Nagelkerke?s R2 
of .55 indicated a moderately strong relationship between prediction and grouping. 
Prediction success was relatively high overall at 80% (82 % for controls and 78% for 
cases). The Wald criterion demonstrated that four variables, perception of neighborhood 
 
72 
 
land diversity, perception of neighborhood infrastructure and safety for walking, self-
regulation, and use of the built environment for physical activity, made significant 
contributions to prediction. The logistic regression results are presented in Table 11. 
Goodness of fit was shown to be acceptable with a Homer-Lemeshow Test (? 2=5.513, 
df=8, p=.70). There was no indication of multi-collinearity with the lowest tolerance at 
0.53 and the highest variance inflation factor for all variables in the model at 1.90.  
Table 11. Model to Predict Meeting Physical Activity Recommendations 
 B SE Wald Odds 
Ratio 
p Confidence 
Interval 
Self-regulation .13 .03 15.40 1.13 <.001 1.06, 1.21 
Use of Built 
Environment for 
MVPA 
 
2.00 
 
.61 
 
10.66 
 
7.35 
 
.001 
 
2.22, 24.33 
Land Use Mix 
Diversity 
.94 .36 6.97 2.56 .008 1.27, 5.14 
Infrastructure and 
Safety for Walking 
 
-.91 
 
.39 
 
5.37 
 
.40 
 
.02 
 
.19, .87 
 
 The odds of meeting physical activity recommendations increased 13% for every 
1 point increase in the self-regulation scale (range: 12-60). For every one point increase 
in the perception of land-use mix diversity (range:1-5) in the neighborhood of residence a 
subject was 2.6 times more likely to meet physical activity recommendations. 
Conversely, subjects who met recommendations perceived their neighborhoods to have 
lower infrastructure and safety for walking. For every one point increase in the perception 
of safety and infrastructure for walking, the odds of meeting physical activity 
recommendations decreased 60%. Subjects who used the built environment for MVPA 
were 7.4 times more likely to achieve recommended levels of physical activity. The 
model did not change when adjusted for age, BMI, race, and education level.  
 
73 
 
 Hypotheses #4 and #6 were partially upheld by the logistic regression model. Self 
regulation skills were significantly different between cases and controls and were 
significant in the model to predict physical activity adherence. The other social cognitive 
measures including social support, self-efficacy, and outcome expectancy did not 
contribute significantly to the model when holding all of the other predictors constant. 
The perception of increased land mix diversity was significantly higher for cases 
compared to controls and remained as a significant predictor for physical activity 
adherence when holding the other predictors constant. In a simple univariate comparison, 
perception of neighborhood infrastructure for safety and walking was not significantly 
different between cases and controls; however, when holding the other predictors 
constant, this perception was a significant predictor of physical activity adherence. 
Hypothesis #5 was upheld by the logistic regression model. Use of the built environment 
for physical activity is a significant predictor of physical activity adherence when the 
other predictors are held constant.  
  
 
74 
 
Chapter 5 
Discussion 
 Across age groups, women consistently have lower rates of participation in 
physical activity, and in particular MVPA, compared to men (Brownson et al., 2005; 
(Troiano et al., 2008). Women also demonstrate higher rates of sedentary time compared 
to men (Matthews et al., 2008). Previous research has identified barriers to participation 
in physical activity, as well as correlates of women adopting a more physically active 
lifestyle. However, factors associated with long term, consistent participation in physical 
activity among women, and in particular working women, have not been clearly 
identified. Nor have objective measures of MVPA been utilized. A better understanding 
of these factors is needed for program development and intervention design to have 
influence on physical activity levels. The purpose of this investigation was to identify 
potential psychosocial, worksite policy, and environmental factors that facilitate 
adherence to regular physical activity among employed women. This chapter presents an 
interpretation and discussion of the study?s findings along with recommendations for 
future research.  
Sedentary Time 
  
 Studies of sedentary behavior have established associations between time spent 
being sedentary and decreased health outcomes including increased risk for metabolic 
syndrome (Healy, Dunstan et al., 2008; Healy, Wijndaele et al., 2008), cardiometabolic 
risk factors including insulin, glucose, triglyceride and LDL concentrations (Celis-
Morales et al., 2012), hypertension (Pardee et al., 2007), depression (Tychenne et al. 
 
75 
 
2010), and obesity (Sugiyama et al., 2008). A study by the American Cancer society 
found that women who sit more than 6 hours daily during leisure time, compared to those 
who sit less than 3 hours, had a 40% higher all-cause mortality rate, independent of 
leisure time physical activity levels (Patel et al., 2010). Sugiyama et al. found similar risk 
for overweight and obesity in subjects who had sufficient levels of physical activity but 
also spent more time in sedentary behaviors compared to those who were insufficiently 
active but spent less time in sedentary behavior. The results of the current study showed 
that women who meet physical activity recommendations have significantly lower rates 
of sedentary time (43.9% vs. 50.0%) and suggests that women who engage in MVPA on 
a consistent basis are also less likely to engage in sedentary behavior. It cannot be 
deduced from the data if this additional activity above the sedentary level is spontaneous 
or planned. It is possible that women who engage in MVPA on a regular basis experience 
the physical and psychosocial benefits of physical activity and therefore, whether 
consciously or not, seek more opportunities to be less sedentary. It is also likely that the 
time spent in MVPA, predominantly during "leisure-time," reduces the time available for 
sedentary activities. It is worth investigating if interventions that seek to increase physical 
activity have a cumulative effect whereby health benefits are achieved through both 
increasing MVPA and decreasing sedentary time. Further research is needed to 
understand how interventions to reduce sedentary behavior may influence physical 
activity behavior and vice versa, and how each approach can move women along the 
continuums of more activity and less sedentary time, thereby exerting positive benefits to 
overall health. Additionally, research is needed to understand the factors influencing 
 
76 
 
sedentary behavior and specifically how participation in MVPA potentially alters 
sedentary behavior. 
 There has been some evidence to suggest that children may compensate for 
increased periods of physical activity (e.g., school-based interventions to increase 
physical activity) by being more sedentary at other times during the day (Donnelly et al., 
1996; Mallam et al., 2003). King et al. (2007) proposed compensation as a barrier to 
weight loss in adults who adopt or increase physical activity. That is, individuals become 
less active as a response to exercise induced energy expenditure. The present findings, 
however, show that women who meet physical activity recommendations do not 
compensate with more sedentary activities. Therefore, compensation may not occur in 
regular exercisers, and, engagement in physical activity may promote increased activity 
overall. 
 The inaccuracies associated with self-report for sedentary behavior have been 
previously described (Marshall & Ramirez, 2011; Owen et al., 2010; Pate et al., 2008). 
Accelerometry offers an objective means to capture sedentary behavior over the course of 
a full day thereby capturing the full extent of sedentary behavior including work-related 
and leisure-time sedentary activities. However, the current standard of using a cut-off of 
<100 counts?min-1 has limitations. To date the <100 counts?min-1 cut-off is fairly 
standard across studies. It has been argued that sedentary behavior is best operationalized 
as sitting and refers to behavior that entails sitting and energy expenditure less than 1.5 
METs (Marshall & Ramirez, 2011; Pate et al., 2008). At the same time, no other studies 
have reported positional status (i.e., standing, sitting, or lying), in identifying sedentary 
 
77 
 
time. It is certainly feasible for an accelerometer to measure (or a person to accumulate) 
<100 counts?min-1 while a person is standing. In the current study, sedentary time with 
standing time eliminated was calculated on a subset of randomly chosen subjects (n=21, 
20%). The average sedentary time with standing included was 45.8% compared to 37.1% 
with sedentary standing time removed (p=<.001). Therefore, it is necessary to consider if 
sedentary time is being overestimated by not excluding time spent standing at counts of 
<100?min-1. Further research is needed to quantify how to best capture sedentary 
behavior with current technology and differentiate between lack of movement during 
sitting and standing activities. In addition, further research is needed to understand what 
types of sedentary behavior (i.e., sitting vs. standing) contribute to poor health.  
 Overall percentages of sedentary time were lower in the current study sample 
compared to percentages from population-based studies (48% vs. 57% for men and 
women ages 30-97 in Healy et al., 2007, and 55% for men and women in Matthews et al., 
2008). While it is possible the current sample was more active overall, more likely this 
can be attributed to the use of all three axes and the vector magnitude to calculate activity 
levels. Both the NHANES accelerometer studies and the studies by Healy et al. used 
uniaxial accelerometers thereby measuring activity only in the vertical plane. The tri-
axial accelerometer also captures horizontal and perpendicular activity. A subject in the 
current study could have <100 counts?min-1 on Axis 1, (i.e., would be designated as 
sedentary in other studies), but also have counts > 100 on Axis 2 and/or 3, thereby not 
qualifying as sedentary. This difference in measurement does not allow for the sedentary 
behavior of the subjects in the current study to be comparable to other studies, however, 
 
78 
 
the additional information captured by using a triaxial accelerometer may help further 
refine how to identify and capture sedentary time. Just as accelerometers provided a 
substantial improvement in capturing sedentary behavior over self report, triaxial 
accelerometers which include positional status allow for more accurate definition and 
measurement of true sedentary time. 
Bouts of Exercise 
 Evidence has shown that exercise accumulated in bouts of ?10 minutes to achieve 
physical activity recommendations is as beneficial as a single, prolonged bout of similar 
intensity (Haskell et al., 2007). Therefore, current recommendations allow for this 
variation in MVPA duration to achieve the 20-30 minutes of recommended activity. The 
option to achieve the needed minutes in bouts throughout the day is often promoted and 
incorporated into physical activity promotion programs and interventions (e.g., Take10, 
Exercise is Medicine, CDC Physical Activity for Everyone), however, to date it has not 
been reported if women actually utilize this option in order to achieve recommendations. 
In other words, while it is appealing to be able to say, "you don't need a full 30 minutes, 
you just need 10 minutes at three different times over the course of the day" to encourage 
women to be more physically active, it is not known if this is a practical way for people 
to achieve recommendations, nor is it known if it is put into practice by consistent 
exercisers. This study examined how women incorporate physical activity into a daily 
schedule (i.e., number and location of MVPA bouts), to better understand how employed 
women meet physical activity recommendations. 
 
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 The majority of cases (76%) had at least one day with more than one bout of 
activity. The average of 1.3 bouts of activity per day for cases suggests that women who 
achieve physical activity recommendations do perform physical activity in a singular 
prolonged bout the majority of the time, but also have additional bouts on some days, 
interspersed over a week. Given the significant difference in number of MVPA bouts 
between cases and controls, and in particular between cases and the subset of controls 
who were active but not enough meet minimum recommendations (26% vs. 76% with at 
least one day with more than one bout), these additional bouts may play  an important 
role in helping women get sufficient activity. This finding is important for physical 
activity promotion in that additional bouts ?10 minutes of MVPA over the course of a 
week will help to achieve the weekly recommended amount of MVPA needed for health 
benefits. 
 Cases had significantly higher scores for self-efficacy, self-regulation, social 
support from friends, and outcome expectancies for physical activity. These higher scores 
may be reflected in the higher number of average bouts in that those with higher self-
efficacy, social support, and outcome expectancies have the confidence and the interest in 
incorporating physical activity into a day whether through an additional planned bout or 
through a more spontaneous situation, such as walking to get places or playing sports or 
games with their children. The self-monitoring or time management aspect of self-
regulation may "trigger" these cases to engage in extra bouts of MVPA to ensure the 
behavior is accomplished. The cross-sectional study design does not allow for a 
determination of the direction of the relationship, that is, do the psychosocial constructs 
 
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influence the activity levels, or do the higher activity levels influence psychosocial 
outcomes? Regardless of the direction, these additional bouts may also help with 
achieving increased health benefits. There is a dose-response relationship between 
physical activity and health, with increasing amounts of MVPA reducing risk for chronic 
diseases and disabilities (Garber et al., 2011; Haskell et al., 2007). The additional bouts 
performed by the cases in this study not only helped them achieve recommendations but 
allowed them to accumulate additional activity above the suggested minimum. 
 A strength of this study was the use of the 10-minute bout as the threshold to 
count towards MVPA. Previous studies utilizing accelerometry often used a gross sum of 
total MVPA minutes over the course of the day (Saelens et al., 2003; Troped et al., 2010), 
however, the recommendations specifically state that physical activity bouts should reach 
a 10-minute minimum (Garber et al., 2011; Haskell et al., 2007). This aspect of the 
guidelines is based on studies showing improved cardiovascular fitness and reduced 
blood pressure from MVPA bouts of at least 10 minutes with little evidence this occurs 
from bouts of less than 10 minutes (Garber et al., 2011). An exception to the 10-minute 
minimum may be appropriate when measuring the transition from a completely sedentary 
lifestyle to some or any accumulation of moderate or vigorous minutes. While evaluation 
of the total MVPA minutes may be relevant in studies that aim to reduce sedentary 
behavior, the evidence does not currently exist to support using these procedures to 
examine cardiovascular and weight benefits from bouts of less than 10 minutes. The 
practice of using 150 minutes of MVPA over a week to evaluate correlates of physical 
activity may lead to spurious conclusions if the subjects accumulated minutes over the 
 
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course of a day without engaging in any sustained aerobic exercise resulting in an 
increased heart rate. In other words, if these subjects do not engage in purposeful and 
sustained MVPA, can we associate their psychosocial profiles and built environment 
perceptions as correlates of physical activity behavior? As demonstrated by Troiano et al. 
(2007), one of the only accelerometry studies to use the 10-minute bout criterion, less 
than five percent of the U.S. population meets physical activity recommendations by this 
standard. This low percentage makes it more difficult to find subjects who meet physical 
activity recommendations, however it is necessary to move to the stricter criterion of the 
10-minute bout to further develop the knowledge base of correlates of physical activity 
adherence. 
Distance 
 Studies examining the relationship between physical activity and the built 
environment have asked subjects about the perception of their residential neighborhood 
(Giles-Corti et al., 2005; Kirtland et al., 2003), or assessed the built environment within a 
predefined distance of homes and cited associations with physical activity levels as 
indicative of the influence of the built environment on physical activity (Diez Roux et al., 
2007; Duncan & Mummery, 2004; Rutt & Coleman, 2005). This approach may attribute 
any and all physical activity behavior to the neighborhood of residence, without 
measurement of the actual location of the physical activity, thereby obfuscating the 
relationship between the built environment and physical activity. To date, two studies 
have evaluated associations between the worksite neighborhood environment and 
walking for adults (Schwartz et al, 2009; Troped et al., 2010). Relating physical activity 
 
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behavior to only the worksite neighborhood may lead to the same measurement errors as 
the studies looking only at home neighborhoods. Troped et al. used GPS to identify 
physical activity performed within a 1-km network buffer of both work and home and 
found approximately 14% and 25% of MVPA was performed within 1 km of home and 
work, respectively. This finding indicates that evaluation of associations between the 
built environment and physical activity may be better served by considering a larger 
geographical area than an arbitrary home or work neighborhood delineation. In the 
current study, working women, on average, engaged in MVPA 6.6 km from home and 
7.4 km from work. While some activity did occur at or close to home and work locations, 
a significant amount took place in locations well outside the home or work vicinity. This 
finding corroborates the recent call to evaluate the full range of environmental exposures, 
without limit of an arbitrary buffer distance for associations with physical activity 
(Duncan & Mummery, 2004; Hurvitz & Moudon, 2012).  
 One of the underlying assumptions in examining the neighborhood built 
environment is that people will use facilities (i.e., parks, trails, bike paths, recreational 
centers) within walking distance of home for physical activity. While this may have some 
credence in dense metropolitan areas, there is no data to support this assumption in other 
types of areas. The majority of the locations where MVPA occurred in the current study 
were reached by motor vehicle. It is worth considering that in areas such as the cities 
under study (small cities) people typically use a motor vehicle for transportation. A 
"walkable" destination may not be as important to women, as much as characteristics of 
the drive to exercise locations (e.g., on the way home from work, near the location where 
 
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children participate in after school activities, near day care, etc.). The average distances 
of 6.6 and 7.4 km indicate that although the subjects traveled outside of the home or work 
neighborhood to engage in MVPA,  they did not drive long distances from either of these 
locations, except under special circumstances (typically weekend or holiday excursions).  
  Further research is needed to determine how this average distance varies for 
metropolitan, suburban, and rural settings and across populations including men and 
women, varying age groups, and minority populations. This type of information would be 
useful not only for community planning (e.g., new facility locations) but also for physical 
activity interventions. Understanding patterns of consistent exercisers may help women 
who fail to achieve recommended levels of MVPA with adherence. For example, finding 
a location for physical activity closer to work or along the commute path to/from work 
may be more conducive to physical activity adherence than other locations, even those 
closer to home. Alternatively, capitalizing on opportunities to use the built environment 
encountered over the course of a typical day, such as walking between buildings or from 
vehicles to buildings before, during, or after work, and not just the home neighborhood 
built environment, may be useful to include in physical activity promotion. 
 In the current study, distance was calculated by "straight-line" or Euclidean 
measures without accounting for the road network. Apparicio et al. (2008) found strong 
correlations between Euclidean distance and network distances (Pearson correlation > 
0.95) across a metropolitan area, with a decrease in correlation for more suburban areas 
of a major metropolitan area. In a study evaluating of 66,000 locations in the 50 states of 
the United States, Boscoe, Henry and Zdeb (2012) compared straight-line and travel 
 
84 
 
driving distance to hospitals and found the measures to be highly correlated with r2>0.9. 
While the road network distance may provide greater accuracy for the actual distance 
traveled by the study subjects, it still does not account for construction detours and traffic 
patterns. Use of the Euclidean measure allowed for distance calculations outside of the 
area of study and, based on the previous research cited, offers a relatively solid picture of 
the distances working women travel to perform MVPA in a small city. Further research 
on commute patterns and temporospatial patterns between home, work and physical 
activity locations, in the context of a work day, is needed to provide a more detailed 
illustration and understanding of how a subset of working women are able to meet 
physical activity recommendations.  
GPS 
 Previous physical activity studies using GPS and accelerometry have reported 
data loss from GPS devices from 2.5% to 92% (Krenn et al., 2011). Eighty percent of all 
MVPA locations were identified through a combination of GPS with supplementation by 
a daily log, representing a comparatively high rate of data acquisition. A critical feature 
for obtaining such a high rate was utilization of the time programmer on the GPS unit 
which stopped data collection during late evening through early morning hours, except 
for second or third shift workers. Memory space for seven days of data collection was 
preserved, while reducing potential data loss through subject noncompliance with turning 
the unit on and off each day. The addition of a simple log to record wear times, battery 
charging on the GPS, and physical activity performed each day, provided confirmatory 
data for the accelerometer and GPS outputs as well as back-up data collection for GPS 
data loss, despite adding to subject burden. As with other studies, it is difficult to 
 
85 
 
ascertain why some subjects had higher rates of data loss than others. Subjects were 
given multiple options for wearing or carrying the GPS unit which may have influenced 
acquisition rates. This was a planned compromise as many subjects were not willing to 
wear the unit on a belt for seven continuous days. The decision to provide subjects with 
the option of wearing or carrying the unit in a bag or pocket was a calculated risk to 
enhance compliance with a potential risk for loss of data without a standardized plan for 
wearing the units. Unlike some other GPS units, the QStarz BT-Q1000XT does not have 
an external display except for a small light that indicates a satellite fix by flashing. 
Subjects may be more inclined to see if the GPS is acquiring data with an external display 
that provides a map indicating location, speed, and latitude and longitude coordinates. 
Future research is needed to determine if this feature influences compliance with the 
research protocol and location data acquisition. 
 The addition of GPS monitoring for assessment of free-living physical activity 
has significant advantages, in spite of the risk of inconsistent data acquisition across 
subjects and locations. In particular, visual analysis of the GPS data combined with 
accelerometry output was used to eliminate spurious MVPA minutes captured by 
accelerometry based on acceleration in a motor vehicle. GPS also allows researchers to 
capture the types of locations where MVPA occurs, reducing the need for recall or self-
report while illustrating the variety and nuance of where and when MVPA occurs. New 
technologies such as the ability to download GPS information from mobile phones for 
analysis will reduce subject burden for data collection and enhance understanding of the 
geospatial relationships between the physical activity, the built environment, availability 
 
86 
 
of physical activity resources and how home, work, and commutes may influence MVPA 
behavior. 
Personal Factors and Physical Activity 
 Several of the personal factors shown to be correlates of physical activity in 
previous studies, such as age, marriage status, parenthood, education level, history of 
engagement in sports or exercise, and weight status, did not contribute to the predictive 
model, and none except BMI and education level were significantly different between 
groups. A possible explanation as to why these factors did not emerge as correlates is the 
specific population recruited in the current study. By limiting recruitment to employed 
women, the population of the current study may not be comparable to the more general 
adult populations used in other studies. The findings of no differences between groups for 
these personal factors suggests that they need not be considered "barriers" to engagement 
in physical activity. For example, women without a history of engagement in sports or 
women with children living at home can and do meet current physical activity 
recommendations. Interventions to promote physical activity may take these factors into 
consideration for tailoring of programs, but should still target these populations for 
physical activity behavior change. 
Predictors of Physical Activity Adherence 
 The logistic regression model reveals some associations with physical activity that 
have not been identified clearly in past research. Research findings on the association 
between the built environment and physical activity have been mixed. This is the first 
known study to quantify how much women actually use the built environment for MVPA 
 
87 
 
in relation to facilities, home equipment, and work-related MVPA. The finding that 
women who used the built environment were 7.4 times more likely to meet physical 
activity recommendations could indicate that women who meet recommendations are 
doing vigorous activities such as running, biking, tennis, hiking, fast walking, or other 
sports that are typically performed using the built environment. That is, the variable of 
built environment could be a proxy for more intensive or vigorous activities which allows 
these women to meet recommendations more readily. An alternative interpretation is that 
those who use the built environment are more likely to meet recommendations because it 
is more readily available and is accommodating to physical activity. In other words, 
women who use the built environment for physical activity are not dependent on getting 
to a specific facility, or attending an exercise class at a specific time, thereby reducing the 
chances they will not engage in MVPA in a day when events prevent them from getting 
to a facility or class. Similarly, women who plan to use home exercise equipment are 
limited to doing physical activity in a single environment which has competing interests 
such as family and home responsibilities in addition to leisure options. The increased 
chance of meeting physical activity recommendations by use of the built environment is 
important for future policy development and interventions to promote physical activity.  
The results of the current study suggest municipal infrastructure for physical activity, 
including sidewalks, trails, bike paths, recreational facilities such as tennis courts, and 
parks or open space are key components to achieving physical activity recommendations. 
Development, maintenance, and promotion of these options for physical activity may 
help more women with physical activity adherence.  
 
88 
 
 The psychosocial variables that were consistently correlated with MVPA in 
previous studies, including self-efficacy, social support, and to a lesser extent outcome 
expectances, failed to predict exercise participation in the current sample. Cases had 
significantly higher scores on all of the psychosocial constructs except for two measures 
of social support (family participation and family rewards and punishment) with 
univariate analysis. However, when all of the other predictors were held constant these 
relationships did not significantly contribute to the model predicting meeting of physical 
activity recommendations, except for self-regulation. This suggests that the factors that 
predict short term behavior change may not do so in the context of long term behavior 
change and behavior maintenance. For example, social support has consistently been 
regarded as a positive correlate of physical activity, particularly for women (Eyler et al., 
2002; Kahn et al., 2002; Sternfield et al., 1999). However, based on the current results, it 
may be important in supporting the initial behavior change, but once the behavior is 
integrated, then it may have less relevance to behavior maintenance.  
 It was hypothesized that self-efficacy for physical activity would be a significant 
contributor to working women meeting physical activity recommendations based on 
previous studies of adult women (Kaewthummanukul et al., 2005; Sternfield et al., 1999; 
Wilbur et al., 2003). The finding that it did not contribute to the model was unexpected 
but not without precedent. A prospective study by Oman and King (1998) found self-
efficacy to be predictive of early adoption of exercise behavior, but not for early 
maintenance or long-term exercise adherence except in the specific circumstance of 
subjects in a supervised home-based exercise condition. Similar to social support, cases 
 
89 
 
had significantly higher self-efficacy scores compared to controls in a singular 
comparison. However, the lack of significant contribution to the model, in the context of 
the other variables measured, suggests that these other variables such as self-regulation, 
living in an area with land-use mix diversity, and use of the built environment for MVPA 
are more relevant factors for long term exercise adherence compared to self-efficacy.   
Self-regulation was a significant predictor of physical activity adherence in the 
current study. This aligns with previous studies showing associations between self-
regulation and physical activity behavior (Anderson et al., 2006; Hallam & Petosa, 2004) 
and in particular for adult women (Stadler et al., 2009). Self-regulation encompasses six 
skills used to overcome personal and situational barriers for the purpose of carrying out 
exercise intentions including time-management, goal-setting, relapse prevention, self-
monitoring, reinforcement, and social support. Given that other psycho-social constructs 
did not contribute to the predictive model, it appears the women who met physical 
activity recommendations rely on self-regulation skills versus external stimuli (friend and 
family social support) and perceptions (outcome expectancies) for physical activity 
adherence. This is consistent with social cognitive theory (Bandura, 1985) which posits 
that maintenance of behavior change is a result of self-regulation and that motivation and 
willpower are not enough to sustain consistent physical activity behavior over the long-
term without the implementation of self-regulation. Furthermore, the environment 
becomes a key factor in helping to regulate the behavior, which is consistent with the 
results of this study. A closer examination of the self-regulation constructs reveals the 
time-management aspect was a key component in relation to the other skills. Although it 
 
90 
 
is limiting to look at each self-regulation skill because the questionnaire assesses each 
skill with two questions, it is worth noting its predominance in increasing the odds for 
physical activity adherence. The cases have internalized physical activity behavior to the 
level that it is not a question of ?will they exercise?? but ?how will they get it done?? and 
time management is a key aspect of assuring the behavior will be maintained. This 
coincides with Bandura?s (1985) explanation of the final level of self-regulation in which 
rewards and motivation are intrinsic in nature. Further research is needed to determine 
how interventions can target specific aspects of self-regulation, and development of these 
skills, to improve physical activity adherence in working women. 
 The findings of an inverse relationship between perception of the home 
neighborhood infrastructure and safety for walking were not predicted. However, Duncan 
and Mummery (2004) had similar findings in a study of the built environment and 
physical activity, where a significant predictor of recreational walking in adults was the 
perception that footpaths near home (0.4 km) were not in good condition. Presumably, 
people who use the built environment, such as the cases in the present study, are more 
cognizant of the conditions available for physical activity. Additionally, the average 
distance of 5.1 km traveled for exercise indicates the cases did not use the neighborhood 
of residence as the primary location for exercise and therefore their perception of the 
infrastructure does not need to align with physical activity levels. In other words, if they 
do not use the neighborhood environment, their perception of it will not necessarily 
influence physical activity, but being regular exercisers, and in particular users of the 
built environment, makes them more aware of the local built environment. Alternatively, 
 
91 
 
some of the cases do use their neighborhood for physical activity and, through first-hand 
knowledge, know that the infrastructure could be better. 
 The perception of neighborhood land-use mix diversity was predictive of meeting 
physical activity recommendations. The control subjects had an average score on this 
measure of 1.9 (?0.9) which on the five-point scale represents a perceived average time 
to walk to a wide range of facilities (schools, restaurants, parks, stores, etc.) as 21 or 
more minutes. The cases had a mean score of 2.3 (?1.0), which also averages out to a 
walk of 21 or more minutes to the same types of facilities, but with more perceived to be 
within a 20-minute walk of home. There was little indication the cases used active 
transportation (walking, cycling, running) to access the facilities in the neighborhood, 
that is, used the perceived land-use mix diversity to achieve MVPA recommendations, 
however, additional data would be needed to confirm this observation. Further research is 
needed to discern if cases, being more physically active, merely perceive the various 
facilities as being closer to walking distance compared to controls, or if women who live 
in neighborhoods with greater land-use mix diversity are more likely to adhere to 
physical activity guidelines.  
  Perception of Worksite Policies 
 There were no differences in perceptions of worksite policies to support physical 
activity between cases and controls. Overall, the average for the worksite policy index 
was relatively low (2.47 ?2.25) on an eight-point scale. A previous study by Crespo et al. 
(2011) of working men and women, using the same measure, found those who met 
physical activity guidelines had an average index of 3.34 (?2.51) compared to an average 
 
92 
 
index of 2.63 (?2.27) for subjects who did not meet MVPA guidelines (p<.01). This 
suggests that the policies for employees in the current study may not have been sufficient 
to influence physical activity levels; and the lack of variance in the index scores makes it 
difficult to detect worksite policy influences on physical activity. Despite the low 
perception of worksite policies cases met MVPA recommendations, indicating they were 
able to capitalize on other factors, such as self-regulation and the use of built 
environment, to achieve recommended physical activity levels. Given the overall 
perception of a lack of supportive employer policies, the data is not conclusive to 
speculate that worksite policies do or do not influence activity levels. This study did not 
assess employer policies and therefore it is not known if the perceptions of the subjects 
are congruent with what is actually offered to them in the workplace. Additional research 
is needed to determine how worksite policies influence employee participation in MVPA, 
differences between perception of policies and actual policies in place, and how 
discrepancies between perception and reality of policies influence MVPA. 
Summary  
 The results of this study show that working women achieved recommended levels 
of activity through use of the built environment for MVPA, whether as the sole choice of 
environment for MVPA, or in addition to home and private facilities. Additional bouts of 
MVPA over the course of a week were needed to meet minimum physical activity 
recommendations. Among the women who perceived themselves as physically active, 
those who did not achieve the recommended levels had significantly fewer additional 
bouts of MVPA (i.e., more than one bout in a day) compared to those who met 
 
93 
 
recommendations. Self-regulation, and in particular time management, was a significant 
predictor of maintaining exercise adherence in working women. Perception of the 
neighborhood built environment to support physical activity, particularly land-use mix 
diversity, was also associated with consistent engagement in physical activity. However, 
the perception of poorer infrastructure and safety for walking in the home neighborhood 
did not preclude working women from MVPA adherence. The results also indicated that, 
overall, working women travelled outside of their home and work neighborhoods to 
engage in MVPA. Finally, working women who met physical activity recommendations 
were less sedentary compared to those who did not meet recommendations.  
Conclusions and Research Recommendations 
Conclusions 
1. To help women achieve the recommended amount of MPVA, the exercise 
prescription should include recommendations for additional bouts of MVPA 
interspersed over the course of a week.  
 2. Self-regulation is a critical aspect of physical activity adherence. Interventions 
 seeking to promote physical activity, and in particular maintenance of the 
 behavior, will need to address how these skills are developed and implemented.   
 3. The built environment plays a key role in meeting physical activity 
 recommendations. Interventions seeking to promote physical activity can 
 capitalize on this finding in helping working women find ways to include physical 
 activity as a regular behavior.  
 
94 
 
 4. Working women's physical activity behavior is influenced by both psychosocial 
 and environmental factors. Exclusion of either of these factors from a physical 
 activity intervention will limit both effectiveness and reach of the intervention. 
Research Recommendations  
 1. Additional research is needed to understand the dynamic relationship between 
 sedentary behavior and physical activity and how to maximize intervention 
 potential to reduce sedentary behaviors and increase engagement in MVPA. 
 2. Further research is needed to clarify how engagement in consistent physical 
 activity influences perceptions of the built environment and how these perceptions 
 in turn influence behavior maintenance.   
 3. Further research is needed to delineate how self-regulation skills enhance 
 management and use of the external environment to maintain exercise behavior. 
 4. Associations between the built environment and physical activity need to be 
 evaluated in locations beyond the home neighborhood.  
 
 
  
 
95 
 
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122 
 
Appendices 
Appendix A?Consent Forms 
 
123 
 
 
 
 
 
 
124 
 
 
 
125 
 
 
  
 
126 
 
Appendix B: Questionnaire 
1.  Date of Birth:_____________ 
 
2. Height:__________________ 
 
3. Weight:__________________ 
 
4. Do you smoke?  ?Yes ?No 
5. Have you been diagnosed with high blood pressure?  ?Yes ?No 
6. Have you been diagnosed with diabetes?  ?Yes ?No 
 
7. How often do you visit a physician? 
?Once a year    ?Twice a year    ?Three times a year    ?Once a month    
?Other_________ 
 
8. Do you have or have you had any disease or condition requiring medication, regular 
physician's care, surgery, or other treatment? ?Yes ?No 
 
If yes, please list: ___________________________________________________ 
__________________________________________________________________ 
 
9. Do you take any medication(s) on a regular, on-going basis?  ?Yes ?No 
 
If yes, please list: ____________________________________________________ 
________________________________________________________ 
 
10. Occupation:__________________________________________________ 
 
11. Employer:____________________________________________________ 
 
12. Average number of hours/week at work:______________________ 
 
13. Number of years employed:___________________ 
 
14. Spouse/Partner:  ?Yes ?No 
 
15. Do you have children:  ?Yes ?No  If Yes: How Many? ________  
Ages:___________________ 
 
 
127 
 
16. Childcare (Mark all that apply): ? N/A ? Within your home  ? Childcare Facility      
  ? After school program    ?Other:______________________   
17. What is the highest level of education that you have completed? 
 
th grade or less (did not graduate high school)  
 
, but no degree 
-year degree (academic or occupational) 
-year degree 
or Professional Degree  
 
18. In which group do you consider yourself? 
 
-Latino    
 
-American   
 
    
______________________ 
 
19. Were you encouraged by your family to be active while growing up? 
? Never   ? Rarely  ? Sometimes  ? Often 
    
 20. Did you participate in recreational sports/activity programs during elementary 
school years? (For example: Ballet, Gymnastics, and Swimming) 
? Never   ? Rarely  ? Sometimes  ? Often 
 
If Yes, please list the sports/activities: _________________________________________ 
________________________________________________________________________ 
 
 21. Did you participate in competitive sports/activity programs during elementary 
school years? (For example: Soccer, Baseball, Softball, Tennis) 
? Never   ? Rarely  ? Sometimes  ? Often 
 
If Yes, please list the sports/activities: _________________________________________ 
________________________________________________________________________ 
 
 22. Did you participate in recreational sports/activity programs during high school?  
? Never   ? Rarely  ? Sometimes  ? Often 
 
 
128 
 
If Yes, please list the sports/activities: _________________________________________ 
________________________________________________________________________ 
 
 
23. Did you participate in competitive sports/activity programs during high school?  
? Never   ? Rarely  ? Sometimes  ? Often 
 
If Yes, please list the sports/activities: _______________________________________ 
______________________________________________________________________ 
 
24. Did you participate in recreational sports/activity programs during college?  
? Never   ? Rarely  ? Sometimes  ? Often
 ?Not Applicable 
 
If Yes, please list the sports/activities: _________________________________________ 
________________________________________________________________________ 
 
25. Did you participate in competitive sports/activity programs during college?  
     ? Never   ? Rarely  ? 
Sometimes  ? Often ?Not Applicable 
 
If Yes, please list the sports/activities: _________________________________________ 
________________________________________________________________________ 
 
26. Do you currently exercise at a facility outside of home? ? Yes  ? No 
If Yes, what is the name of the facility? ________________________________ 
 
27. Do you use exercise equipment at home?     ? Yes  ? No 
If Yes, please list the exercise equipment you use at home: 
________________________________________________________________________
________________________________________________________________________ 
 
28. Do you use exercise videos/DVDs at home?  ? Yes  ? No 
 
 
 
 
 
 
129 
 
29. What forms of transportation do you use to get to and from work? (mark all that 
apply) and how many days per week do you use each? 
 
? car /auto       _______ days 
? walk       _______ days  
? bicycle      _______ days  
? other please specify __________     _______ days 
 
30. Shops, stores, and markets are within easy walking distance of my home:  
? Yes  ? No 
 
31. My neighborhood has free or low-cost recreation facilities, such as parks, walking 
trails, bike paths, recreation centers, playgrounds, public swimming pools, etc.  
? Yes  ? No 
 
32. My worksite has free or low-cost recreation facilities, such as walking trails, bike 
paths, fitness facilities, recreation centers, etc.  
? Yes  ? No 
 
33. There are sidewalks on most of the streets in my neighborhood: 
? Yes  ? No 
 
34. The crime rate in my neighborhood makes it unsafe to walk at night:  
? Yes ? No 
 
 
 
 
 
 
 
 
 
 
 
 
 
130 
 
For each of the following questions, please mark Yes or No. Physical activity or exercise 
includes activities such as walking briskly, jogging, bicycling, swimming, or any other 
activity in which the exertion is at least as intense as these activities.  
36A. I am currently physically active.   ? Yes  ? No  
36B. I intend to become more physically active in the next six months. ? Yes 
 ? No 
 
For activity to be regular, it must add up to a total of 30 minutes or more per day and be 
done at least five days per week. For example, you could take one 30-minute walk or take 
three 10-minute walks for a total of 30 minutes.  
 
37A. I currently engage in regular physical activity.   ? Yes  ? No 
37B. I have been regularly physically active for the past six months.   ? Yes  ? No 
 
131 
 
Part B. EXERCISE CONFIDENCE SURVEY  
Below is a list of things people might do while trying to increase or continue regular 
exercise. We are interested in exercises like running, swimming, brisk walking, bicycle 
riding, or aerobics classes.  
 
Whether you exercise or not, please rate how confident you are that you could really 
motivate yourself to do things like these consistently, for at least six months.  
 
Please circle one number for each question.  
 
How sure are you that you can do these 
things? 
I 
know 
I 
cannot 
 Maybe 
I can 
 I 
know 
I can 
Does not 
apply 
38. Get up early, even on weekends, to exercise 
 1 2 3 4 5 N/A 
39. Stick to your exercise program after a long, 
tiring day at work 
 
1 2 3 4 5 N/A 
40. Exercise even though you are feeling 
depressed 
 
1 2 3 4 5 N/A 
41. Set aside time for a physical activity 
program; that is, walking, jogging, swimming, 
biking, or other continuous activities for at least 
30 minutes, 3 times per week 
 
1 2 3 4 5 N/A 
42. Continue to exercise with others even though 
they seem too fast or too slow for you 
 
1 2 3 4 5 N/A 
43. Stick to your exercise program when 
undergoing a stressful life change (e.g., divorce, 
death in the family, moving) 
 
1 2 3 4 5 N/A 
44. Attend a party only after exercising 
 1 2 3 4 5 N/A 
45. Stick to your exercise program when your 
family is demanding more time from you 
 
1 2 3 4 5 N/A 
46. Stick to your exercise program when you 
have household chores to attend to 
 
1 2 3 4 5 N/A 
47. Stick to your exercise program even when 
you have excessive demands at work 
 
1 2 3 4 5 N/A 
48. Stick to your exercise program when social 
obligations are very time consuming 
 
1 2 3 4 5 N/A 
49. Read or study less in order to exercise more 1 2 3 4 5 N/A 
 
  
 
132 
 
Part C. People use various techniques to help them exercise on a regular basis. 
Recalling your exercise activities performed in the last four (4) weeks, please answer the 
following questions regarding techniques you may have used to help you exercise. If you 
did not exercise during this time period, select ?never?. 
On the scale provided next to each item, circle the number that best represents how often 
you used the specified technique in the past four (4) weeks. 
 
 
Never Rarely 
Some 
times Often Very Often 
50. I mentally kept track of my 
exercise activities. 1 2 3 4 5 
51. I mentally noted specific things 
that helped me exercise. 
 
 
1 
 
2 
 
3 
 
4 
 
5 
52. I established short term goals 
(daily or weekly) related to how 
often I exercise. 
 
 
1 
 
2 
 
3 
 
4 
 
5 
53. I established exercise goals that 
focused on my health (e.g. 
improved fitness). 
 
 
1 
 
2 
 
3 
 
4 
 
5 
54. I asked someone for advice or 
demonstration of exercise 
activities. 
 
 
1 
 
2 
 
3 
 
4 
 
5 
55. I asked an exercise 
expert/health professional for 
advice or demonstration of 
exercise activities. 
 
 
1 
 
2 
 
3 
 
4 
 
5 
56. After I exercised, I focused on 
how good I felt. 
 
1 
 
2 
 
3 
 
4 
 
5 
57. I reminded myself of positive 
health benefits of exercise (e.g. 
lose weight, tone body). 
 
 
1 
 
2 
 
3 
 
4 
 
5 
58. I mentally schedule my time 
periods to exercise. 
 
 
1 
 
2 
 
3 
 
4 
 
5 
59. I rearranged my schedule of 
other activities to ensure I had time 
to exercise. 
 
 
1 
 
2 
 
3 
 
4 
 
5 
60. I purposely planned ways to 
exercise when I was on trips away 
from home. 
 
 
1 
 
2 
 
3 
 
4 
 
5 
61. I purposely planned ways to 
exercise during bad weather. 
 
1 
 
2 
 
3 
 
4 
 
5 
 
 
133 
 
Part D 
Please complete the phrase I exercise to . . . for each item.  Please circle a number that 
represents how often (FREQUENCY) and the value (IMPORTANCE) for each item: 
 
EXAMPLE: FREQUENCY IMPORTANCE 
I exercise to:  never  rarely some times  often very often low value med value high value 
Build muscle 
strength 1 2 3 4 5 1 2 3 
 FREQUENCY IMPORTANCE 
I exercise to:  
never 
 
rarely 
some 
times 
 
often 
very 
often 
low 
value 
med 
value 
high 
value 
62. Improve my 
health 
 
1 2 3 4 5 1 2 3 
63. Stay in shape 1 2 3 4 5 1 2 3 
64. Release 
tension 
 
1 2 3 4 5 1 2 3 
65. Maintain/lose 
weight 
 
1 2 3 4 5 1 2 3 
66. Enhance my 
self-image and 
appearance 
 
1 2 3 4 5 1 2 3 
67.Improve my 
physical 
attractiveness 
 
1 2 3 4 5 1 2 3 
68. Feel a 
positive 
psychological 
effect 
 
1 2 3 4 5 1 2 3 
69. Experience a 
sense of 
accomplishment 
 
1 2 3 4 5 1 2 3 
70. Enjoy the 
activity 1 2 3 4 5 1 2 3 
71. Improve 
metal alertness 1 2 3 4 5 1 2 3 
72. Cope with 
life?s pressures 
 
1 2 3 4 5 1 2 3 
73. Have 
fun/enjoyment 1 2 3 4 5 1 2 3 
 
134 
 
 FREQUENCY IMPORTANCE 
I exercise to:  never  rarely some times  often very often low value med value high value 
74. Feel younger 1 2 3 4 5 1 2 3 
75. Spend time 
with friends 
 
1 2 3 4 5 1 2 3 
76. Be a member 
of a team 
 
1 2 3 4 5 1 2 3 
77. To earn the 
respect of others 
for my skills 
 
1 2 3 4 5 1 2 3 
78. Spend time 
with family 
 
1 2 3 4 5 1 2 3 
79. Feel the thrill 
of victory 
 
1 2 3 4 5 1 2 3 
80. Feel the thrill 
of competition 
 
1 2 3 4 5 1 2 3 
You are MORE than half-way done. Great Job!
 
135 
 
Part E. SOCIAL SUPPORT AND EXERCISE SURVEY 
Below is a list of things people might do or say to someone who is trying to exercise 
regularly. Please rate each question twice.  Under family, rate how often anyone living in 
your household has said or done what is described during the last three months. Under 
friends, rate how often your friends, acquaintances, or coworkers have said or done what 
is described during the last three months.  
Please write one number from the following rating scale in each space:  
  
None Rarely A few times Often Very often Does not apply 
1 2 3 4 5 NA 
         
During the past three months, my family (or members of my household) or friends:  
  Family Friends 
 81. Exercised with me 
 
  
 82. Offered to exercise with me 
 
  
 83. Gave me helpful reminders to exercise  
("Are you going to exercise tonight?) 
 
  
 84. Gave me encouragement to stick with my exercise program 
 
  
 85. Changed their schedule so we could exercise together 
 
  
 86. Discussed exercise with me 
 
  
 87. Complained about the time I spend exercising 
 
  
 88. Criticized me or made fun of me for exercising 
 
  
 89. Gave me rewards for exercising 
(Bought me something or gave me something I like) 
 
  
 90. Planned for exercise on recreational outings 
 
  
 91. Helped plan activities around my exercise 
 
  
 92. Asked me for ideas on how they can get more exercise 
 
  
 93. Talked about how much they like to exercise 
 
  
 
 
136 
 
Part F. Please indicate which of these items are available at your work.  Please circle an 
answer for each item.  
 Yes No Don't 
Know 
94. Exercise facilities  
(e.g. workout room/gym, exercise equipment, walking path/PAR 
course)  
 
? ? ? 
95. Regular exercise programs  
(e.g. aerobic classes, team sports, walking groups, etc.)  
 
? ? ? 
96. Shower facilities that you can use       
  
? ? ? 
97. Safe bicycle storage  
 
? ? ? 
98. An exercise specialist or activity coordinator available for 
employees   
 
? ? ? 
99. Policies that encourage exercise or biking  
 
? ? ? 
100. Employer provides paid time for you to exercise   
 
? ? ? 
101. Employer offers incentives to be physically active 
 
? ? ? 
 
    
102. If you answered yes to the previous question, please check the following programs 
that are offered to you and which of these programs you have used or participated in, in 
the past year:  
          ? Not Applicable         
 
 Program 
Offered 
    Program 
Used  
 
Reduced health insurance premiums ? ? 
Subsidized gym membership ? ? 
Seasonal programs ? ? 
Programs with cash or other prize incentives ? ? 
Other: ___________________________ ? ? 
 
Almost There!! Keep it up ? 
 
 
137 
 
Part G. We would like to find out more information about the way that you perceive or 
think about your neighborhood.  Please answer the following questions about your 
neighborhood and yourself.  Please answer as honestly and completely as possible and 
provide only one answer for each item.  There is no right or wrong answer and your 
information is kept confidential. 
 
Part 1. Stores, facilities, and other things in your neighborhood  
About how long would it take to get from your home to the nearest businesses or 
facilities listed below if you walked to them?  Please put only one check mark (?) for 
each business or facility.                
   
 1-5 
min         
6-10 
min 
11-20 
min       
21-30 
min        
31+ 
min       
don?t 
know 
example:  gas station               ?   
103. convenience/small           
104. grocery store           
105. supermarket         
106. laundry/dry 
cleaners     
      
107. hardware store           
108. clothing store           
109. fruit/vegetable 
market    
      
110. post office            
111. library       
112. elementary school          
113. other schools          
114. book store           
115. fast food 
restaurant    
      
116.coffee place           
117. bank/credit union          
118. non-fast food 
restaurant     
      
119. video store           
120. pharmacy/drug 
store    
      
121. your job       
122. park       
123. recreation center           
124. gym or fitness 
facility   
      
 
138 
 
Access to services: Please mark the answer that best applies to you and your 
neighborhood.  Both local and within walking distance mean within a 10-15 minute walk 
from your home.   
  
 1 2 3 4 
 strongly 
disagree 
somewhat 
disagree 
somewhat 
agree 
strongly 
agree 
125. Stores are within easy walking distance of my 
home.  
   
? ? ? ? 
126. There are many places to go within easy walking 
distance of my home 
 
? ? ? ? 
127. The streets in my neighborhood are hilly, making 
my neighborhood difficult to walk in.  
 
? ? ? ? 
Streets in my neighborhood: Please mark the answer that best applies to you and your neighborhood. 
 
 1 2 3 4 
 strongly 
disagree 
somewhat 
disagree 
somewhat 
agree 
strongly 
agree 
128. The distance between intersections in my 
neighborhood is usually short (100 yards or less; the 
length of a football field or less).   
 
? ? ? ? 
129. There are many alternative routes for getting from 
place to place in my neighborhood.  (I don't have to go 
the same way every time.)    
 
? ? ? ? 
 
Places for walking and cycling: Please mark the answer that best applies to you and your neighborhood. 
 1 2 3 4 
 strongly 
disagree 
somewhat 
disagree 
somewhat 
agree 
strongly 
agree 
130. There are sidewalks on most of the streets in my 
neighborhood.   
 
? ? ? ? 
131. My neighborhood streets are well lit at night. 
 
? ? ? ? 
132. Sidewalks are separated from the road/traffic in 
my neighborhood by parked cars.  
 
? ? ? ? 
133. There is a grass/dirt strip that separates the streets 
from the sidewalks in my neighborhood.  
? ? ? ? 
 
 
Neighborhood surroundings: Please mark the answer that best applies to you and your neighborhood  
 1 2 3 4 
 strongly 
disagree 
somewhat 
disagree 
somewhat 
agree 
strongly 
agree 
134. There are trees along the streets in my 
neighborhood.   
? ? ? ? 
 
139 
 
135. There are many interesting things to look at while 
walking in my neighborhood.  
? ? ? ? 
136. My neighborhood is generally free from litter. ? ? ? ? 
137. There are many attractive natural sights in my 
neighborhood (such as landscaping, views).   
 
? ? ? ? 
138. There are attractive buildings/homes in my 
neighborhood.   
? ? ? ? 
 
Safety from traffic: Please mark the answer that best applies to you and your neighborhood.   
 
 1 2 3 4 
 strongly 
disagree 
somewhat 
disagree 
somewhat 
agree 
strongly 
agree 
139. There is so much traffic along nearby streets that 
it makes it difficult or unpleasant to walk in my  
Neighborhood 
 
? ? ? ? 
140. The speed of traffic on most nearby streets is 
usually slow (30 mph or less).   
 
? ? ? ? 
141. Most drivers exceed the posted speed limits while 
driving in my neighborhood.  
 
? ? ? ? 
142. There are crosswalks and pedestrian signals to 
help walkers cross busy streets in my neighborhood.    
 
? ? ? ? 
 
Safety from crime: Please mark the answer that best applies to you and your neighborhood.  
 1 2 3 4 
 strongly 
disagree 
somewhat 
disagree 
somewhat 
agree 
strongly 
agree 
143. There is a high crime rate in my neighborhood. ? ? ? ? 
144. The crime rate in my neighborhood makes it 
unsafe to go on walks during the day.  
 
? ? ? ? 
145. The crime rate in my neighborhood makes it 
unsafe to go on walks at night.  
? ? ? ? 
  
 
140 
 
Appendix C?Written Instructions for Accelerometer and GPS  
Instructions for wearing the Accelerometer 
 
This small activity meter records general movement and allows us to get a 
better idea of your overall activity level. We will not be able to tell what kind of specific 
activity is happening. At first, the belt may feel slightly awkward, but after a few hours, you 
will probably get used to it and not notice it as much. It is extremely important for our study 
that you wear the accelerometer properly. Please follow these instructions carefully: 
 
1. Wear the meter attached to the belt around your waist, just above your right hipbone. 
You can wear it either underneath or on top of your clothing.  
 
 
 
 
2. Wear the meter snug against your body. If you have to, you can adjust the belt by 
pulling the end of the strap to make it tighter. Or, to loosen the belt, push more of the 
strap through the loop. Wear the belt tight enough so that the meter does not 
move when you are being active.  
 
3. Please put it on first thing in the morning -- either just after you get out of bed or 
just after you shower or take a bath in the morning.  
 
4. Keep the activity meter on all day (unless swimming or in the water).  
 
5. At night, take it off right before you go to bed.  
 
6. Do not let anyone else wear it.  
 
7. There is no ?ON? or ?OFF? switch that you need to worry about turning on or off 
every day. The activity meter runs on a battery and is programmed to run 
continuously without you needing to turn it on. Please do not try to open the 
activity meter. 
8. Do not wash the monitor or the belt. The belts will be cleaned when they are 
returned.  
 
141 
 
Questions? Call or text (334) 559-4321 If there is no answer, please leave a 
message and your call will be returned.  
 
Instructions for wearing and charging the GPS 
 
GPS refers to Global Positioning System. The GPS unit will continuously record your 
position on the face of the earth as you move about during the day. We are asking you to 
wear this unit so that we can learn more about what environments people are physically 
active in. Here is a list of things that will help you remember what to do:  
1. The Auburn Researcher will assist you to ensure that your device is turned on and 
working at each of your scheduled appointments.  
2. Take the unit with you wherever you go.  
3. Clip the unit directly to your clothing or movement meter belt, or carry it in your pocket, 
backpack or purse.  
4. Don?t let the GPS device get wet or bumped hard.  
You must charge the GPS battery each evening.  
1. Charge the GPS unit near your cell phone so that you remember to pick it up in the 
morning.  
2. You can leave the GPS device in its pouch while charging.  
3. Use the cable provided.  
4. Plug the cable into the slot on the side of the GPS device (there is only 1 slot).  
 
142 
 
                  
5. A green light indicates the battery is in charging mode 
6. You do not need to move any switches on the device while charging.  
 
 
143 
 
 
 
144 
 
 
 
Appendix D?Activity, Accelerometer, and GPS Log 
 
Device and Activity Log 
Wear both devices for seven (7) consecutive days. If you are unable to wear both devices 
for seven (7) consecutive days, add additional days at the end of the week. Please fill out the 
log daily. An example entry is provided. If you take the accelerometer or GPS off for more 
than 5 minutes, such as showering, record when you take it off and put it back on, and any 
activity you performed while not wearing.  
 
Questions? Just call or text: 334-559-4321 or email: gellnan@auburn.edu 
Example 
Time On: Time Off: Activity while not 
wearing: 
Location: Charge 
GPS? 
(?) 
6:00 am 
 
7:00 am Showered and 
changed after 
walking in a.m. 
home 9-
11am 
7:30 9:30 pm 
 
  
 
 
   
 
 
   
 Exercise performed: 
 
walked 
 
 
 
 
Day 1 
Time On: Time Off: Activity while not 
wearing: 
Location: Charge 
GPS? 
(?) 
 
 
    
 
 
   
 
 
   
 
 
   
 Exercise performed: 
 
 
 
 
145 
 
 
 
 
Day 2 
Time On: Time Off: Activity while not 
wearing: 
Location: Charge 
GPS? 
(?) 
 
 
    
 
 
   
 
 
   
 
 
   
 Exercise performed: 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Day 3 
Time On: Time Off: Activity while not 
wearing: 
Location: Charge 
GPS? 
(?) 
 
 
    
 
 
   
 
 
   
 
 
   
 Exercise performed: 
 
 
 
 
146 
 
 
 
Day 4 
Time On: Time Off: Activity while not 
wearing: 
Location: Charge 
GPS? 
(?) 
 
 
    
 
 
   
 
 
   
 
 
   
 Exercise performed: 
 
 
 
 
You're more than half way! Great job-just 3 more days to go 
Day 5 
Time On: Time Off: Activity while not 
wearing: 
Location: Charge 
GPS? 
(?) 
 
 
    
 
 
   
 
 
   
 
 
   
 Exercise Performed: 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
147 
 
 
 
 
 
 
 
Day 6 
Time On: Time Off: Activity while not 
wearing: 
Location: Charge 
GPS? 
(?) 
 
 
    
 
 
   
 
 
   
 
 
   
 Exercise performed: 
 
 
 
 
 
Day 7 
Time On: Time Off: Activity while not 
wearing: 
Location: Charge 
GPS? 
(?) 
 
 
    
 
 
   
 
 
   
 
 
   
 Exercise performed: 
 
 
 
 
 
 
Great Job! All Done ? 
 
  
 
148 
 
Appendix E. Omni Scale for Rating of Perceived Exertion 
 
 
 
  
 
149 
 
 
Appendix F. Types of Exercise Performed by Cases (n=41)  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Exercise Number of Cases 
Walking 25 
Running/Jogging 17 
Weight Lifting 11 
Elliptical 9 
RPM/Spin Class 6 
Tennis 5 
Yoga 5 
Circuits 5 
Yard work 5 
Treadmill 4 
Zumba 4 
Body Pump 4 
Recumbent bike 3 
Swim 3 
Outdoor cycling 3 
Body Combat 3 
Boot Camp 2 
Water Aerobics 2 
Personal Training 2 
Pilates 2 
Stair Climbing 2 
"Ripped" class 1 
Rowing Machine 1 
Step Aerobics 1 
Hiking 1 
Hula Hoop 1 
Kettlebells 1 
Kickball 1 
 
150 
 
 
Appendix G 
 
Boxplot of Average Distance between Home and MVPA locations for All Cases and 
Controls 
 
 
  
 
 
 
 
 
 
Controls Cases 
 
Dist
anc
e (
KM
) 
 
151 
 
 
 
 
 
Boxplot of Average Distance between Home and MVPA Locations with Outliers 
Removed 
 
 
 
 
 
 
 
 
Controls Cases 
 
Dist
anc
e (
KM
) 
 
152 
 
 
 
 
 
Boxplot of Average Distance between Work and MVPA Locations for All Cases and 
Controls 
 
 
 
 
 
 
 
 
 
 
Controls Cases 
 
Dist
anc
e (
KM
) 
 
153 
 
 
 
 
 
 
 
 
 
Boxplot of Average Distance between Work and MVPA locations with Outliers 
Removed 
 
  
 
 
Controls Cases 
 
Dist
anc
e (
KM
)