Group Involvement 1
Abstract
The purpose of this study was to investigate the effect of involvement in weight loss and
fitness groups on performance of weight loss and fitness behaviors in adults trying to lose weight
or maintain weight loss. The predictive, secondary analysis was guided by Social Cognitive
Theory, which proposes that group involvement affects behavior performance. Weight loss and
fitness involvement was measured with four dimensions: extensive (size of group social
network), affective (importance that significant others give to involvement), duration, and
frequency. Performance of weight loss and fitness behaviors was measured with three factors:
diet and exercise balancing behaviors, food avoidance behaviors, and dietary tracking behaviors.
Gender, age, and body mass index were controlled throughout all multiple regressions. Results
indicated that extensive involvement significantly predicted diet tracking behaviors and affective
and frequency of involvement significantly predicted diet and behavior balancing behaviors.
Therefore, as social networks within the group increase, the performance of diet tracking
behaviors increases. As the importance significant others place on weight loss and fitness
involvement increases and as frequency of involvement increases, so do diet and exercise
balancing behaviors. Since the sample was 91% Caucasian, future studies should examine other
ethnic groups to see if these results apply.
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Running Head: GROUP INVOLVEMENT AND BEHAVIORS
Predictors of the Performance of Weight Loss and Fitness Behaviors
Emily Kreuz
The University of Akron
March, 2011
8200
Group Involvement 3
Acknowledgements
First, I would like to thank my sponsor, Dr. Graor, for supporting me and assisting me
throughout the entire process of this paper. No matter how frustrated I felt on my way to our
meetings, every time I left your office I felt encouraged and motivated to keep going and knew
that I could do it! Thank you to Dr. Mugler and The University of Akron Honors College, as well
as the College of Nursing for assisting me financially to attend the 2011 MNRS research
conference. I truly appreciate the support to make the most of this opportunity. Again, thank you
College of Nursing for assisting with production of my poster to present at CUGSR at the
University of Akron and the MNRS conference. Thanks to my readers, Mrs. Horning and Dr.
Murrock, for taking time to read my paper and give me feedback; I truly appreciate it.
I thank my family and friends for supporting me throughout these past two years. Thanks
for the listening ear when I was frustrated. Thanks for sharing in my excitement. And thanks for
pushing me to be all that I can be. I could not have made it without you all.
Group Involvement 4
The growing prevalence of those overweight and obese is a significant concern in the
United States and many developed countries (BBC, 2008). In the U.S., 66% of noninstitutionalized adults are overweight or obese (CDC, 2009). The concerns related to this
growing prevalence are numerous. For example, obesity is associated with many health
complications, such as Type II diabetes, cardiovascular problems, and certain cancers (CDC,
2010). Therefore, it may be important for overweight or obese adults to lose weight and maintain
a healthy weight in order to achieve optimal health.
Despite the health issues associated with the increasing overweight problem in the U.S.,
researchers have found that losing weight and maintaining weight loss can be difficult (Elfhag &
Rossner, 2005). Although some weight loss groups are free or supported based on member
donations, such as Overeaters Anonymous (Overeaters Anonymous, 2010), it is often costly to
invest in a weight loss program or to join a health and fitness center. The cost to lose 30 pounds
can easily exceed $300, not including the cost of healthy foods that are also necessary
(Bankrate.com, 2011). Researchers, however, have found that encouraging involvement in
weight loss and fitness groups may be a cost-effective means for health care providers to support
weight loss in their clients (Turner, Thomas, Wagner, & Moseley, 2008). In general, support
groups contribute to relevant health related outcomes. Researchers have studied the effect of
group support on alcoholics and drug addicts (Gossup, Stewart, & Marsden, 2007), imprisoned
pregnant women (Ferszt & Erikson-Owens, 2008), patients with cardiovascular problems (Paul
& Sneed, 2004; Sher, Bellg, Braun, Domas, Rosenson, & Canar, 2002), diabetics (Beverly &
Wray, 2008), and smokers who are trying to quit (Froelicher & Li, 2008). None, however,
examined the effect of weight loss and fitness involvement dimensions on the performance of
weight loss and fitness behaviors. The purpose of this study was to examine if dimensions of
Group Involvement 5
involvement in weight loss and fitness groups predict the performance of weight loss and fitness
behaviors. The following research questions were addressed: Does frequency of involvement in
weight loss and fitness groups predict performance of behaviors? Does duration of involvement
in weight loss and fitness groups predict performance of behaviors? Does extensiveness of
involvement in weight loss and fitness groups predict the performance of behaviors? Does
affective involvement in weight loss and fitness group predict the performance of behaviors?
Literature Review
Researchers who have examined the relationship between involvement in support groups
and weight related changes have consistently found that involvement is related to behavior
changes (Beverly & Wray, 2008; Bowden, Shaul, & Bennett, 2004; Dawson, Tracey, & Berry,
2008; Felton, Saunders, Ward, Dishman, Dowda, & Pate, 2005; Turner et al., 2008). Dawson,
Tracey, and Berry (2008) compared the effects of an internet-based intervention and a face-toface intervention on confidence levels to overcome barriers and on exercising in adults trying to
lose weight in the work place. They found higher confidence levels and more frequent exercise
in those in the face-to-face groups compared to those in the internet intervention group. Turner,
Thomas, Wagner, and Moseley (2008) examined the effect of a no-cost wellness program on
advancing readiness to make behavior changes, lose weight, and improve overall health in a
diverse adult population. They found that the support received within these programs promoted
readiness to change and positive health outcomes.
Extensive involvement refers to the scope of social connection within a group. No studies
found investigated the effects of extensive involvement on behavior change. Although not
examining the effects of extensive involvement, Bowden and colleagues (2004) evaluated the
effects of counseling sessions based on health risk behaviors on rural adults in their attempts to
Group Involvement 6
make health related changes. Overall, there was a lack of behavior change for those participating
in the counseling sessions. The qualitative aspect of this study found that “[t]here was a general
consensus among participants that lack of regularly scheduled support groups was a barrier to
individual behavior change” (page 415). In this instance, the lack of support groups was
identified as a hindrance in achieving behavior changes and suggests that an increase in
extensive involvement would promote behavior change.
Affective involvement is the importance that significant others place on group
involvement. Although no studies were found that investigated the relationship between the
importance significant others placed on weight loss and fitness involvement and behavior
performance, researchers have investigated the effect of spousal support on involvement in an
exercise program by adults with chronic diseases, such as Type 2 diabetes (Beverly and Wray,
2008) and coronary artery disease (Sher et al., 2002). The results of both studies suggest a
positive relationship between the support received within the everyday environment from the
spouse and involvement in an exercise program. This may be due in part to the meaning and
benefit the health-related change makes for both of the partners, i.e., the people that matter the
most to each other (Sher el al., 2002).
Researchers have studied the effect of group involvement frequency on behavioral
outcomes, but no studies were found that examined the effect of group involvement duration on
behavioral outcomes. Turner and colleagues (2008) compared how diet and frequency of
involvement in a group affected weight loss in adult patients who were attempting to make
changes in diet and physical activity. They found that increased frequency of attendance at the
exercise classes was a greater predictor of weight loss than diet. In addition, Gossup et al. (2007)
examined the effect of frequency of involvement in Alcoholics Anonymous on abstinence of
Group Involvement 7
drinking in alcoholics and found that frequency of attendance at Alcoholics Anonymous had a
positive effect on abstinence in alcoholics.
The findings of the above studies are limited. Many focused on the effects of group
involvement in those beginning to make a behavior change (Bowden et al., 2004; Felton et al.,
2005; Gance-Cleveland & Mays, 2008; and Paul & Sneed, 2004). There are also a
disproportionate amount of studies that either looked specifically at women (Felton et al., 2005;
Ferszt & Erikson-Owens, 2008; Li & Froelicher, 2008) or that had a significantly greater number
of female participants (Bowden et al., 2008; Dawson et al., 2008; Gance-Cleveland & Mays,
2008; and Turner et al., 2008).
Theoretical Framework
The Social-Learning Theory guides this project. The theory was developed by Albert
Bandura who proposed that there is an interaction between environment, cognition, and behavior
(Santrock, 2008). This theory proposes that the environment affects learning, which affects
behavior. Through group involvement, the environment changes in a positive, supportive way,
leading to changes in the cognition of the members. Being surrounded by people going through
the same or similar situations makes a person feel less alone. Group members give each other
advice and support that may lead others to feel that they can make changes. Seeing the success of
others may also make members feel that if someone else can be successful and make a change,
then they can too. As people’s thinking changes and they believe that they can make a change,
they then begin to change their behavior in a positive, lasting way.
Based on this theory, the researcher predicts that increased involvement in weight loss
and fitness groups (the environment) will result in cognitive changes, which will result in
increased weight loss and fitness behavior performance. Therefore, involvement will predict
Group Involvement 8
weight loss and fitness behavior performance. Four dimensions of involvement will be
measured: frequency of involvement, duration of involvement, affective involvement, and
extensive involvement. Affective involvement is the importance that significant others place on
involvement in weight loss and fitness activities. Extensive involvement is the size of the social
network within the weight loss and fitness group.
Methods
The design of this quantitative study is predictive. This project is a secondary data
analysis of data originally collected in 2006 to examine if weight loss maintenance and
subculture involvement predict weight related meanings (i.e. behavior meanings, self-meanings,
and identity meanings) (Graor, 2008). The cross sectional data were collected with a selfadministered survey, which was available both online and in paper form. Data from a sub-sample
examined in this analysis are described based on gender, age, race and ethnicity, marital status,
and highest education degree obtained. All participants were adults trying to lose weight nonsurgically or maintain non-surgical weight loss.
Sample
Data were collected from a convenience sample. Inclusion criteria included adults (18
years or older) trying to lose weight non-surgically or to maintain non-surgical weight loss. Data
collection occurred from June 2006 to January 2007 after the study was approved by university
and hospital IRB review boards.
Sampling and Data Collection Procedures
Participants were recruited locally with announcements posted at weight loss and fitness
centers located in the North central section of a Midwestern state. Participants came from six
large health and fitness centers and 32 private weight loss and fitness centers. Online,
Group Involvement 9
participants were recruited with announcements posted on 43 fitness and weight loss internet
websites and chat rooms and on 112 internet weight loss and fitness blogging web sites. Surveys
were completed either online or in paper form, and the survey was piloted with a focus group.
Measures
Duration and frequency of involvement in weight loss and fitness experiences were
measured with six items. Participants self-reported duration by writing the number of days,
weeks, months or years they have been involved with group experiences; duration was coded
into number of months. Participants self-reported frequency of involvement by writing how often
they were involved per day, week, month, or year; frequency of involvement is coded into
frequency per month. Weight loss and fitness experiences included:
(1) Memberships and participation in a weight control program, health and/or fitness
center, “work-out” individual or group sessions
(2) The attendance at weight and fitness-related meetings, workshops, or conferences
(3) Weight/fitness internet web site involvement (on-line groups, chat-rooms, message
boards, reading of blog entries, etc.); or reading or listening to weight/fitness media, such
as newsletters, books, audiotapes, CDs, DVDs, videotapes, or magazines.
Affective involvement was measured as the importance that participants felt significant
others placed on involvement in weight loss and fitness activities. Participants responded to the
three affective involvement items on 1 to 9 point Likert scales, with 1 = very unimportant, 5 =
neutral, and 9 = very important. The affective involvement survey items were:
(1) How important is it to your intimate (girlfriend, boyfriend, partner or spouse) that you
participate in weight management kinds of efforts or activities, such as but not limited to
using weight and fitness related programs, centers, meetings, workshops, conferences,
group or individual “workouts,” on-line websites, magazines, newsletters, books,
audiotapes, CDs, DVDs, or videotapes?
(2) How important is it to your family members, excluding your spouse or partner, that
you participate in weight management kinds of efforts or activities, such as but not
limited to using weight and fitness related programs, centers, meetings, workshops,
conferences, group or individual “workouts,” on-line websites, magazines, newsletters,
books, audiotapes, CDs, DVDs, or videotapes?
Group Involvement 10
(3) How important is it to friends that you participate in weight management kinds of
efforts or activities, such as but not limited to using weight and fitness related programs,
centers, meetings, workshops, conferences, group or individual “workouts,” on-line
websites, magazines, newsletters, books, audiotapes, CDs, DVDs, or videotapes?
Extensive involvement was measured as the size of participants’ social network within
the weight loss and fitness group. Participants responded to the following four survey items on 1
to 9 point Likert scales with 1 = none, 2 = 1 – 2, 3 = 3 – 5, 4 = 6 – 10, 5 = 11 – 15, 6 = 16 – 20, 7
= 21 – 25, 8 = 26 – 39, and 9 = more than 40.
(1) What is the number of friends that you have made as a result of your involvement in
weight and fitness related efforts or activities?
(2) What is the number of meaningful relationships you would lose if you were no
longer involved in weight and fitness related efforts or activities?
(3) What is the number of people you would miss, including friends, exercising partners,
team mates, dieting partners, internet chat connections, weight loss program staff,
etc., if you no longer participated in weight and fitness related efforts or activities?
(4) What is the number of people you would no longer see if you no longer participated
in weight and fitness related efforts or activities?
Thirteen items were used to measure the performance of weight loss and fitness
behaviors. Performance was self reported as the frequency of maintaining a general daily caloric
intake, maintaining a general daily fat intake, keeping track of food intake, eating at least five
servings of fruits and vegetables a day, eating smaller portions sizes of food, avoiding certain
kinds of foods, avoiding specific foods, keeping track of weight, making dietary and exercise
adjustments based on weight changes, keeping track of daily exercise and physical activity,
engaging in moderately intense physical activity at least thirty minutes a day, adjusting dietary
intake or exercise to maintain a particular balance, and taking “breaks” from eating or exercise
routines for special occasions. Subjects rated frequency of performing each behavior by marking
their responses on a 1 to 9 point Likert scale (1 = never and 9 = always). These thirteen items
were reduced to three factors: diet tracking behaviors, diet and exercise balance behaviors, and
Group Involvement 11
food avoidance behaviors. In addition to the above, the following demographics were measured:
gender, age, race, marital status, highest educational degree obtained, employment, personal
income, and family income.
Data Analysis
Descriptive statistics were used to determine percentages, means, and standard
deviations, based on the level of the variable measure. Multiple regression was used to
determine the predictability of involvement dimensions on behavior performance factors.
Gender, age, and body mass index were controlled throughout all analyses. Factor analysis
(principal-component factor analysis and orthogonal varimax rotation methods) was used to
reduce the performance of behaviors items and to identify underlying structure of the 13 items.
Based on Kaiser’s rule and scree plot, three factors, which accounted for 57% of the variance,
were retained. Appendix A shows rotated factor loadings for the pattern matrix with all behavior
performance items. Factor loadings range from .91 to .49. Factor scores were used to determine
behavior performance values. As a result, all involvement and performance values were
centered. Level of significance was set at p values less than 0.05.
Results
Sample Characteristics
The 305 participants in this study ranged from 18 to 80 years with an average of 42.16
(SD=12.15). The majority of the participants were female (83%), married (61.5%), white (91%),
and had a bachelor’s degree (28.7%), some college or vocational training (24.4%), or a Master’s
degree (21%). Over half of the participants (51%) made from $20,000- $59,999 annually and
47.45% of participants had annual family incomes of $30,000 to $79,999. Detailed demographics
are shown in Appendix B.
Field Code Changed
Group Involvement 12
Group Involvement and Performance of Behaviors
Multiple regressions were used to determine if involvement predicted performance of behaviors,
and gender, age, and BMI were consistently controlled. Analysis showed that age, gender, and BMI did
not predict diet tracking behaviors. When extensive group involvement was stepped into the regression, it
was found to predict diet tracking behaviors (β = .13, SE = .06, p = 0.02). No other dimensions of group
involvement were related to diet tracking behaviors. When all dimensions of involvement were included
in the regression, extensive involvement predicted diet tracking behaviors (β = .13, SE = .06, p = 0.03),
although only 2% of the variance in diet tracking behaviors was explained. Therefore as social networks
within the group increase, the performance of diet tracking behaviors increases.
Multiple regressions were used to determine if group involvement predicted performance of food
avoidance behaviors. When age, gender, and BMI were controlled, BMI predicted food avoidance
behaviors (β = -.02, SE = .01, p = 0.02). Therefore, as BMI decreased, food avoidance behaviors
increased. When dimensions of involvement were stepped into the regressions, none were found to
predict performance of food avoiding behaviors although BMI remained a predictor of food avoidance
behaviors throughout (β = -.02, SE = .01, p = 0.01, adjusted R-squared = .02). Therefore, no dimensions
of involvement predicted the performance of food avoidance behaviors.
Multiple regressions were then used to determine if group involvement predicted diet and
exercise balancing behaviors. When age, gender, and BMI were controlled, gender and BMI predicted
the balancing behaviors (gender: β = .33, SE = .16, p = 0.04; BMI: β = -.02, SE = .01, p = .006; adjusted
R-squared = 0.03). The results suggested that female participants were more likely to use diet and
exercise balancing behaviors than male participants, and that as BMI decreased, tracking behaviors
increased.
When dimensions of involvement were separately stepped into the regression, affective
involvement and frequency of involvement were found to predict diet and behavior balancing behaviors
(affective: β = .12, SE = .05, p = .03; frequency: β = .45, SE = .11, p < .000). Affective and frequency
involvement remained predictors of diet and behavior balancing behaviors when all dimensions were
controlled (affective: β = .12, SE = .05, p = 03; frequency: β = .43, SE = .11, p < .000), explaining 9% of
Group Involvement 13
the variance in diet and exercise balancing behaviors. BMI remained a predictor of the performance of
balancing behaviors throughout (β = -.02, SE = .01, p = 0.02). Therefore, as affective and frequency of
involvement increased, so did diet and exercise balancing behaviors.
Discussion
Obesity continues to be a major health concern in the United States and the health risks
associated with this issue are undeniable. However, making the necessary behavior changes to
decrease this problem is often difficult. This study aimed to examine if group involvement in
weight loss and fitness groups predicted weight loss and fitness behaviors by analyzing survey
data about four dimensions of involvement: frequency, duration, affective, and extensive. When
age, gender, and BMI were controlled, the dimension of extensive involvement predicted diet
tracking behaviors. As the size of social networks within the group increased, so did the use of
diet tracking behaviors, e.g., the frequency of maintaining a general daily caloric intake,
maintaining a general daily fat intake, keeping track of food intake, eating at least five servings
of fruits and vegetables a day, eating smaller portions sizes of food. Affective involvement and
frequency of involvement were found to predict diet and exercise balancing behaviors, e.g.,
keeping track of weight, making dietary and exercise adjustments based on weight changes,
keeping track of daily exercise and physical activity, engaging in moderately intense physical
activity at least thirty minutes a day, adjusting dietary intake or exercise to maintain a particular
balance, and taking “breaks” from eating or exercise routines for special occasions. The greater
the importance significant others placed on group involvement and the more frequent the group
involvement, the more often diet and exercise balancing behaviors were used. Duration of
involvement was the only dimension not found to predict any of the weight loss and fitness
behaviors. In addition, BMI predicted food avoidance behaviors, e.g., avoiding certain kinds of
foods, avoiding specific foods: as BMI decreased, the use of food avoidance behaviors increased.
Group Involvement 14
In line with Turner and colleagues (2008) who found that increased attendance to an
exercise group was related with increased weight loss, the findings show that increased
frequency of involvement predicted increased frequency of diet and behavior balancing
activities, which in turn may have a role in weight loss. Along with frequency, affective
involvement was predicted diet and behavior balancing activities. Previous studies have shown
the positive effect spouses have on support group involvement when persons suffer from a
chronic disease (Beverly and Wray, 2008; Sher et al., 2002). Although affective involvement
does not have to be a spouse, the majority of the participants in this study were married. This,
along with the previous studies, suggests that the majority of affective involvement may come
from spouses, and plays a significant role in behaviors. Extensive involvement predicted diet
tracking behaviors, e.g., maintaining a general daily caloric intake, maintaining a general daily
fat intake, keeping track of food intake, eating at least five servings of fruit and vegetables a day,
and eating smaller portion sizes of food. None of the research found directly measured this
dimension of involvement. However, Felton and colleagues (2005) studied teen girls in a
physical education class and found that social network within the school and class had a positive
impact on behavior. However, the study was done among teen age girls required to participate in
a physical education class rather than in adults actually attempting to lose or maintain weight
loss.
The findings were in line with the predictions based on the Social-Learning theory.
Social-Learning theory proposes that the environment affects learning and cognition, which in
turn affect behavior (Santrock, 2008). The findings showed that as extensive involvement in a
weight loss and fitness group increased, performance of diet tracking behaviors increased. In
addition, as affective involvement and frequency of involvement increased, diet and behavior
Group Involvement 15
balancing behaviors increased. Duration of involvement was the only involvement dimension
that failed to significantly affect any behavior items. This could be due to the fact that the social
support received due to affective involvement, extensive involvement, and the frequency of
involvement had lasting positive effects on behavior apart from duration of time spent in the
group. Thus, actually being involved has a greater effect than simply belonging to a group. The
sample was predominately female, white, married, affluent and educated. This could represent
the social pressures in the U.S. for women to be thin and present an image of unattainable
“perfection” (Biordi, 2009). Access to resources, e.g., income, may affect involvement in weight
loss and fitness activity, and thus those with money and the means to utilize resources may be the
ones to participate in efforts to lose weight (Biordi, 2009). It is no secret that being a part of
many support groups costs money that people with less income and from lower socioeconomic
classes do not have.
Limitations
This study was limited in terms of the sample. A convenience sample was used, and
while having the benefit of being relatively inexpensive and “convenient,” this method
constructs a sample of participants who are readily available and does not control who
participates. For this reason, participants may have similar factors that made them readily
available to participate in the study, and thus bias the results. In this study, the majority of the
participants were female, as were most of the participants in other related studies. Studies that
evaluate a greater variety of social and cultural groups would be beneficial in attaining a greater
understanding of the dynamics surrounding the benefits of involvement in weight loss and fitness
groups on weight loss and fitness behaviors. Although three of the involvement dimensions
Group Involvement 16
predicted weight loss and fitness behaviors, only two of the three behavior dimensions were
predicted.
Implications for Practice
Knowing that there may be benefits to affective involvement, extensive involvement, and
frequency of involvement may inform nurses of the importance of group involvement for those
trying to lose weight. Practitioners often are responsible for disseminating information about
health promotion and behaviors as well as educating clients on ways to achieve health related
goals, specifically related to weight loss. It would therefore be beneficial for practitioners to
provide information about the importance of support in maintaining efforts to achieve or
maintain desired weight loss. In addition, it may be particularly important to stress the
importance of affective involvement, extensive involvement, and frequency of involvement. This
study found that it was the frequency of involvement rather than duration of involvement that
predicted weight loss and fitness behavior performance. The friends made by attending group
activities also predicted weight loss and fitness behavior performance. For these reasons, it is
important for practitioners to encourage patients to get involved in a group rather than simply
belonging to a group. Having the support of family, friends, and particularly significant others
may be important, as shown by the predictability of affective involvement on behavior
performance. It is not enough, however, to simply give information without being supporters
themselves. Practitioners can take an active role in giving support to clients, as well as
connecting them with local weight loss support groups that fit the individual’s needs.
Implications for Future Studies
Future studies should further investigate how group involvement dimensions predict
behaviors cross culturally and in men. More focused studies should focus on specific groups to
Group Involvement 17
see if these results still apply. Future studies could explore additional weight loss and fitness
behaviors associated with group involvement, and investigate factors contributing to the
performance of weight loss and fitness behaviors either within or outside of the group.
Conclusion
This study examined if dimensions of involvement in weight loss and fitness groups
predicted the performance of weight loss and fitness behaviors. Previous research supported the
effect of group involvement on positive behavior outcomes; however, the research did not
examine dimensions of involvement in adults who were trying to lose weight or to maintain
weight loss. This study found frequency of involvement, affective involvement, and extensive
involvement predict behavior performance, while duration of involvement did not. The findings
suggest that behavioral performance may be related to social processes, as suggested by the
Social Learning theory. These findings were from a sample of predominately white, married,
affluent women, which may suggest limitations. However, the sample also may accurately
portray the targeted group of weight loss groups and products, as well as support previous
studies’ findings of increased social pressures on this group of women to be thin. Future studies
should more closely examine other ethnic groups to determine if these results apply across
culture.
Group Involvement 18
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EBSCOhost database.
Group Involvement 21
Appendix A
Field Code Changed
Behavior Performance Factor Loadings
Behavior Performance Variables
Diet and
exercise
balancing
behaviors
Factor Loadings
Specific
food
avoidance
behaviors
Dietary
tracking
behaviors
Maintaining a general daily caloric intake
.79
Maintaining a general daily fat intake
.76
Keeping track of food intake
.62
Eating at least five servings of fruits and vegetables
a day
.50
Eating smaller portions sizes of food
.49
Avoiding certain kinds of foods
.91
Avoiding specific foods
.90
Keeping track of weight
.57
Making dietary and exercise adjustments based on
weight changes
.63
Keeping track of daily exercise and physical activity
.71
Engaging in moderately intense physical activity at
least thirty minutes a day
.62
Adjusting dietary intake or exercise to maintain a
particular balance
.67
Taking “breaks” from eating or exercise routines for
.51
special occasions
Note: Extraction Method: Principal-Component Factor Analysis. Rotation Method:
Orthogonal Varimax.
Group Involvement 22
Appendix B
Demographics
Sample: n=305
Gender
Female
Male
85.9%
14.1%
Mean-42.16
SD-12.15
Age
Ethnicity
White
Black
Asian
Hispanic
Other
91.15%
4.92%
1.31%1.64%
1.64%
0.98%
Marital Status
Single
Married
Separated
Divorced
Widowed
Other
18.09%
61.51%
1.32%
8.55%
2.62%
7.89%
Education
Less than high school degree
High School Degree
Some college/vocational training
Vocational or Associates Degree
Bachelor’s Degree
Master’s Degree
Professional Degree(dentistry,
medicine, law, etc)
Doctoral Degree
Other
0.33%
8.25%
24.42%
8.58%
28.71%
21.12%
$0-$19,999
$20,000-$39,999
$40,000-$59,999
$60,000-$79,999
$80,000-$99,999
$100,000-$119,999
$120,000 and up
25.17%
24.49%
26.53%
13.26%
3.40%
3.06%
4.08%
Personal Income
3.63%
3.96%
0.99%
Group Involvement 23
Family Income
$0-$19,999
$20,000-$39,999
$40,000-$59,999
$60,000-$79,999
$80,000-$99,999
$100,000-$119,999
$120,000 and up
8.47%
13.22%
17.62%
21.69%
9.83%
11.18%
17.79%