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A measure of weight management strategies and evidence for its utility

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A measure of weight management strategies and evidence for its utility

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Content Running head: MEASURE OF WEIGHT MANAGEMENT STRATEGIES 1

A Measure of Weight Management Strategies and Evidence for Its Utility
Andrew L. Larsen
University of Southern California

Author Note
Andrew Larsen, Department of Psychology, University of Southern California
Correspondence concerning this article should be addressed to Andrew Larsen,
Department of Psychology, University of Southern California, 3620 S. McClintock Ave, SGM
501, Los Angeles, CA 90089-1061. Email: allarsen@usc.edu
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 2

Table of Contents
Abstract ...........................................................................................................................................4
Introduction ....................................................................................................................................5
General Method ...........................................................................................................................11
Overview ....................................................................................................................................11
Procedures and measures ...........................................................................................................12
Part 1 .......................................................................................................................................12
Part 2 .......................................................................................................................................14
Data analysis ...........................................................................................................................14
Sample size, power, and precision .........................................................................................14
Study 1...........................................................................................................................................15
Methods ......................................................................................................................................15
Results ........................................................................................................................................15
Diet clusters ............................................................................................................................16
Exercise clusters ....................................................................................................................17
Secondary analysis .................................................................................................................18
Discussion ..................................................................................................................................19
Conclusion ..................................................................................................................................21
Study 2...........................................................................................................................................21
Methods ......................................................................................................................................22
Results ........................................................................................................................................23
Diet clusters ............................................................................................................................23
Exercise clusters ....................................................................................................................25
Secondary analysis .................................................................................................................26
Discussion ..................................................................................................................................28
General Discussion .......................................................................................................................29
Conclusion ..................................................................................................................................33
References .....................................................................................................................................35
Tables ............................................................................................................................................42
Table 1: Study 1 strategy clusters...............................................................................................42
Table 2: Study 1 correlations .....................................................................................................44
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 3

Table 3: Study 2 strategy clusters...............................................................................................45
Table 4: Study 2 correlations .....................................................................................................47
Figures ...........................................................................................................................................48
Figure 1: Study 1 dendrogram for all strategies .........................................................................48
Figure 2: Study 2 dendrogram for diet strategies only ...............................................................49
Figure 3: Study 2 dendrogram for exercise strategies only ........................................................50

MEASURE OF WEIGHT MANAGEMENT STRATEGIES 4

Abstract
Weight management strategies that people use are one of several factors that determine a
person’s weight. Strategies may be the most viable mechanism that can be targeted for changing
weight because the strategies people use are flexible, which is generally not true for other factors
influencing weight (e.g., genetics, environment, social groups, etc.,). To date, little research has
investigated the effectiveness of different types of weight management strategies for managing
weight, and a quality measure of types of strategies does not exist. In two studies, the present
research aims to construct a measure of types of weight management strategies and to provide
preliminary evidence for its utility by indicating which types of strategies may be effective for
managing weight and exercise. Study 1 consisted of 96 students and friends of students, and
Study 2 consisted of 129 faculty and staff at the University of Southern California. In both
studies, participants rated the frequency of which they use 48 weight management strategies—
for both diet and exercise—and sorted the strategies into groups based on strategy similarity. We
conducted cluster analysis on participants’ sorted data to create a meaningful categorization of
strategies. The authors determined nine clusters of strategies, four for diet and five for exercise.
We used participants’ reported use of the strategy clusters to predict BMI and exercise behavior.
Several of the clusters show evidence for being effective for managing weight or exercise,
including Social and Motivational Support for Exercise (Study 1), Prioritizing or Planning
Exercise (Study 1 and Study 2), Making Exercise Convenient (Study 2), and Planning Diet
(Study 2). The authors conclude that these relationships should be investigated further in future
research.

MEASURE OF WEIGHT MANAGEMENT STRATEGIES 5

A Measure of Weight Management Strategies and Evidence for Its Utility
The recent rise of obesity in developed countries has been well documented (Flegal,
Graubard, Williamson, & Gail, 2005; Hedley et al., 2004; Ogden et al., 2006). Currently, obesity
and its related diseases are among the largest and most costly health problems in the United
States (Flegal et al., 2005; “Overweight, obesity, and health risk. National Task Force on the
Prevention and Treatment of Obesity,” 2000; Prospective Studies Collaboration, 2009;
Silventoinen et al., 2004). Since 1980, the prevalence of obesity has more than doubled (Hedley
et al., 2004); over 32% of adult Americans are clinically obese and an additional 34% are
overweight with a Body Mass Index (BMI) greater than 30 and greater than 25, respectively
(Ogden et al., 2006).
Many factors play a role in determining someone’s weight—for example, genetics, the
environment, and behavior—but few are under the direct control of the person and thus easily
changed. The behavioral strategies people use to control their weight—that is, the conscious
efforts people make to exert control over their eating or physical activity— may be the most
important factor that could be impacted by intervention. Given the current environment of
abundance within most of Western culture, people must rely on their weight management
strategies to stay healthy. Determining which strategies are most effective could prove valuable
in the fight against obesity. A standard measure of weight management strategies that places
similar strategies into clusters would be beneficial in determining which strategies are most
effective in controlling weight. To date, no such measure exists.
The present research aims to develop a standard measure of weight-management
strategies that can be used to investigate the effectiveness of different types of strategies in a
variety of contexts, including: regulating weight, diet, and physical activity, among others. In
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 6

addition, this research seeks to provide evidence for the utility of the measure by investigating
how differences in frequency of strategy use, as measured by our new scale, differs among
“normal”, “overweight”, and “obese” participants.
The strategies used by a person to manage their weight are a fundamental component of
self-regulation—that is, the way a person manages their behavior in order to achieve a goal
(Bandura, 1995; Karoly et al., 2005). Bandura (2005, p. 2) states that “Self-regulation models
differ somewhat in particulars but they are rooted in three generic subfunctions”, one of which
being the strategies used to reach goals. Managing one’s weight is a prototypical example of self-
regulation, and failure to do so results in overweight and obesity. Genetics alone cannot explain
the rise of obesity seen in recent decades, and ultimately, body fat is determined by the balance
between energy input and energy expenditure (Allison, Heshka, Neale, Lykken, & Heymsfield,
1994; Silventoinen, Rokholm, Kaprio, & Sorensen, 2009; Stunkard, Harris, Pedersen, &
McClearn, 1990). Barring rare exceptions, people have control over what they eat and how
much they exercise via the weight management strategies they choose to use (Bandura, 1997).
Weight management strategies have been linked to weight related health in a variety of
ways. Perhaps the most studied weight management strategy has been planning. Planning
interventions—behavior change interventions designed for participants to specify when, where,
how to act, and how to overcome problems when they arise—have been shown to be effective in
changing health behaviors (Gollwitzer & Sheeran, 2006; Wiedemann, Lippke, Reuter,
Ziegelmann, & Schüz, 2011). A specific example of these plans are Gollwitzer’s (1999)
implementation intentions, which are if-then plans designed to preemptively link a certain
context with a specific action. Planning is believed to be a major factor in turning intentions into
actions (Norman & Conner, 2005; Schwarzer, 1992; Sniehotta, Scholz, & Schwarzer, 2005), and
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 7

research has shown that planning interventions can lead to greater fruit and vegetable
consumption (Kreausukon, Gellert, Lippke, & Schwarzer, 2011; Van Osch et al., 2009),
decreased snacking (Van Osch et al., 2009), decreased saturated fat intake (Soureti, Hurling, van
Mechelen, Cobain, & ChinAPaw, 2011), and more physical activity (De Bruijn et al., 2007;
Schwarzer, Luszczynska, Ziegelmann, Scholz, & Lippke, 2008; Sniehotta, Schwarzer, Scholz, &
Schüz, 2005).
Furthermore, strategies have been linked directly to weight loss and weight loss
maintenance. In a randomized controlled trial, Wing, Crane, Thomas, Kumar, and Weinberg
(2010) found that including strategies as part of an online weight loss intervention more than
doubled the amount of weight lost after 12 weeks compared to a control group not receiving
strategies as part of the intervention (3.5 kg lost versus 1.4 kg). Tinker and Tucker (1997)
compared the strategy use of people who successfully lost a large amount of weight to the
strategy use of people who were unsuccessful in losing weight, and found that several strategies
were more frequently used during successful weight loss attempts, including: exercising,
reducing fat intake, increasing fruit, fiber, and vegetable intake, reducing snacking, and eating
slower. More recently, Sciamanna et al., (2011) report evidence that some weight management
strategies are effective for weight loss, but not necessarily weight loss maintenance, and vice
versa. In addition, weight management strategies have been shown to guard against weight gain
in college students, specifically monitoring weight (Levitsky, Garay, Nausbaum, Neighbors, &
DellaValle, 2006).
Because people have control over the strategies they use, and because strategies can
affect body weight and health, determining the effectiveness of different strategies could be vital
in reducing the prevalence of obesity through educational interventions. A standard measure that
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 8

identifies meaningful clusters of weight management strategies would be useful for a variety of
reasons. It could: (1) distinguish between different types of strategies, allowing researchers to
compare the effectiveness of the different strategies in managing weight, diet, and physical
activity; (2) provide researchers a conceptual framework for investigating strategies; (3) enable
comparisons of strategies across contexts, populations, and intentions of the strategy user (i.e.,
losing weight, maintaining weight loss, avoiding initial weight gain); and (4) create a common
framework for comparison across studies that could support future meta-analyses on weight
management strategies.
Furthermore, investigating the effectiveness of types of weight management strategies
has important practical implications. For instance, strategies that are determined to be effective
for managing weight could be utilized in educational weight loss interventions. On a smaller
scale, it is important to understand the relationship between types of strategies and the behaviors
they are meant to regulate. For example, how effective different types of diet strategies are at
helping people eat a healthy diet, and likewise for exercise strategies and peoples’ physical
activity. This is an important concept because a person’s weight is not always a good indicator of
their overall health (Romero-Corral et al., 2008). Eating a healthier diet or increasing physical
activity can have profound effects on a person’s health, and these effects are not always mirrored
by a loss in weight (Blair & Morris, 2009). Even small increases in physical activity can have
large effects on overall health (Caudwell, Hopkins, King, Stubbs, & Blundell, 2009; King,
Hopkins, Caudwell, Stubbs, & Blundell, 2009). In accordance with these findings, researchers
have pushed for scientists to focus more on studying healthy behaviors and other health
indicators rather than height and weight alone (Ernsberger & Koletsky, 1999). An obvious
example of why this is important is that people have different body compositions, (i.e, different
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 9

levels of body fat versus lean muscle tissue), and two people of the same height and weight could
have very different proportions of body fat and muscle.
Past research examining weight management strategies has generally not sought to create
a standard measure of strategies. In two studies, the present research aims to construct such a
measure, and provide initial evidence for its reliability as well as its utility. We feel we will
improve upon past research in at least three main ways.
First, we have compiled a broad list of strategies for regulating both diet and physical
activity. Past research has typically utilized a limited number of strategies, with the bulk of the
work focusing on diet strategies only. Most weight management strategies research that includes
exercise strategies contains only a single item for exercise, such as “Increase exercise” (French,
Perry, Leon, & Fulkerson, 1995). There are multiple strategies that can be used to manage
physical activity—such as planning activity in advance, monitoring exercise, or seeking out
activity in daily routines—and some may be more effective than others. It could be valuable to
evaluate a variety of strategies for exercise rather than group them all into a singular item.
A second improvement is that our list of strategies can be answered by people of all
weight ranges (i.e., it is not weight-group specific). This is an improvement as typically weight
management research is done on people who are either (a) overweight or obese, or (b) have lost a
significant amount of weight. Thus, it ignores most of the healthy weight population,
approximately one third of the United States (Ogden et al., 2006). Many healthy weight people
(though probably not all) must regulate their diet and exercise on a daily basis to avoid gaining
weight, and assessing their strategy use could provide evidence for which strategies effectively
guard against weight gain in the first place. Weight loss registries have utilized a similar
approach—comparing successful weight losers and weight loss maintainers to their unsuccessful
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 10

counterparts—and have been the source of much of what we know about weight loss and weight
loss maintenance (Klem, Wing, McGuire, Seagle, & Hill, 1997; Thomas, Bond, Hill, & Wing,
2011; Wing & Phelan, 2005).
A third improvement of this research is using an empirically driven classification system
to classify strategies into meaningful clusters, each cluster representing a “type” of strategy, that
allows for investigating which types of strategies are most effective for managing weight. In the
present research, Participants sorted 48 weight management strategies that we identified in
existing literature and pilot research (23 for diet; 25 for exercise) into similar clusters based on
their judgment of the semantic meaning of each strategy (i.e., how similar one strategy is judged
to be relative to another). Cluster analysis was used on participants sorted strategies to produce
clusters of strategies representing different “types” of strategies.
To our knowledge, cluster analysis has not been used in weight management research to
identify groups of highly similar strategies.. However, cluster analysis has been used
productively in other research to identify commonalities among human goals that serve as strong
predictors of behavior. For example, Chulef, Read, and Walsh (2001) improved upon past
research investigating life-goals by using cluster analysis applied to lay peoples semantic
judgments of similarity among a universe of 165 goals identified in the literature. The goal
clusters that were identified by Chulef et al have been shown to predict 21% of the variance in
people’s retirement intentions (Brougham and Walsh, 2007), 44% of the variance in their job
turnover (Talevich, Read, & Walsh, under review) and 38% of the variance in BMI (Lee,
Talevich, Lee, Larsen, Read, & Walsh, in preparation). We feel this approach could also be
utilized in classifying weight management strategies into conceptually meaningful clusters that
might prove to be strong predictors of eating and exercise behaviors.
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 11

To summarize, in the two studies reported in the present research, our primary goal is to
develop a broad, inclusive measure of weight management strategies that contains multiple
strategies for managing diet and exercise. In addition, the measure should be applicable to people
of all weight ranges (i.e., healthy, overweight, and obese people). We will use cluster analysis to
classify strategies into conceptually meaningful clusters. Finally, a secondary goal of the present
research is to evaluate the utility of the measure by examining its ability to predict participants’:
(a) BMI and (b) weekly exercise activity. We also (c) compare differences in reported strategy
use between normal weight (BMI < 25) and overweight/obese participants (BMI > 25)
We hypothesize that cluster analysis of participants sorted data will generate a
meaningful and replicable set of weight management strategy clusters. Lastly, we hypothesize
that frequency of reported use of different strategy clusters will predict BMI and weekly
exercise, and that frequency of strategy use across clusters will differ between healthy and
overweight/obese participants.
General Method:
Overview
Both studies were conducted online. Data on participants’ demographics, height, weight,
exercise behaviors, diet behaviors, and other weight-management behaviors were collected on
Qualtrics.com®. In addition, participants sorted 48 weight management strategies into groups
based on their judgment of how similar the strategies were to each other using Websort.net®.
All participants were 18 or older. Participants who reported being diagnosed with a
disease affecting their weight, taking medication affecting their weight, having a medical
condition influencing their diet or physical activity, or currently/recently pregnant participated in
the sorting procedure only. Since these conditions are unlikely to affect their understanding of
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 12

the semantic meaning of strategies, although they are likely to affect their eating and activity
behaviors, they sorted the weight management strategies only and provided no data on their
behaviors. For their participation, participants were entered into a lottery for a chance to win one
of two $50.00 prizes.
Procedure and Measures:
Part 1:
Demographics and weight management behaviors. This part of the survey consisted of
demographic questions (age, sex, education level, and race), general questions related to
participants’ weight management behaviors (height in inches, weight in pounds, frequency of
self-weighing, weekly exercise, and the length of a typical work out session), and questions
regarding how frequently participants use 48 weight management strategies. Also, participants
reported subjective ratings of their difficulty in managing weight, diet, and physical activity, and
how often they think about managing their weight, diet, and physical activity, but these items
were not used in the analysis and are not discussed further. In addition, participants responded to
several yes/no questions, including: if they were ever diagnosed with a disease affecting their
weight; if they were taking any medication affecting their weight; if they suffered from a medical
condition affecting their weight; and if they had currently or recently been pregnant. Answering
“Yes” to any of these questions moved participants directly to Part 2 of the study (the sorting
procedure). BMI was computed from participants’ self-reported height and weight. Using self-
reported height and weight is a limitation of the study, but we feel it is a small one. Self-reported
weight has been shown to be fairly accurate in past studies, and it should have only a small effect
on the results (Larsen et al., 2006; Lowe, Kral, & Miller-Kovach, 2008; Lowe, Miller-Kovach, &
Phelan, 2001; McAdams, Van Dam, & Hu, 2007). Furthermore, a bias in self-reported height or
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 13

weight tends to lead to a slight decrease in BMI (due to people under reporting weight and over
reporting height), which would likely decrease the power of the statistical tests in detecting
significance. Therefore, the significant findings found in the present research would presumably
hold, or could have even been stronger, had we obtained participants’ real heights and weights.
Finally, the complete anonymity of participants’ responses should have limited the intentional
biasing of height and weight as much as possible.
Diet strategies. Participants rated the proportion of times they use 23 strategies to manage
their diet on a nine point scale ranging from 1 (never) to 9 (100% of the time). A proportion scale
was used, rather than a frequency scale, because it creates a common basis of comparison across
strategies that would be absent if frequency of use was employed (e.g., the percentage of times a
person restricts their food purchases to healthy foods is more useful than the frequency because
frequency relies on how often someone goes shopping, and some people shop more often than
others). To conclude this section, participants reported any other strategies they use to help
manage their diet.
Exercise strategies. Participants were presented 25 strategies a person might use to
manage their physical activity. For 16 of the strategies, participants rated the proportion of times
they use those strategies on the same nine-point scale used for the diet strategies (a proportion
scale was used for the same reason described above). For the remaining nine strategies,
participants reported the frequency they use each strategy on a nine point scale ranging from 1
(never) to 9 (four times a day or more). To conclude this section, participants reported any other
strategies they use to manage their physical activity.
Reported proportions and frequencies of each diet and exercise strategy were
standardized due to the different nine point scales (a proportion and a frequency scale).
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 14

Participants who completed Part 1 of the study (demographics, diet strategy, and exercise
strategy questions) in less than three minutes were removed from the analysis, as we believe it
would be impossible for someone to attentively complete the survey in that time.
Part 2:
Strategy sorting. In the final section of the study, participants sorted the 48 diet and
exercise strategies into groups based on how semantically similar the strategies are to each other.
The instructions read: “There is no limit to the number of groups you can make, or to how many
statements you can place in each group. Please place all statements into categories you feel form
a meaningful, similar cluster of items, and please label each category you have created with a
brief descriptive label when you are done.” Participants were dropped from the analysis if they
did not sort all of the items, completed the task too quickly (completing in less than six minutes
in Study 1, or four minutes in Study 2), or if they sorted all of the items into two groups or less
indicating a lack of effort in completing the sorting routine.
Data Analysis
An Average Linkage Cluster Analysis algorithm, carried out by Websort.net®, was used
on participants’ sorted data. This type of Cluster Analysis groups items based on the “average
level of similarity with all current members of the cluster” (Punj & Stewart, 1983, p. 139; italics
in original). Strategies were partitioned visually into clusters using a dendrogram derived from
the analysis. Composite scores were created by summing the items within each cluster. All other
analyses were done using the Statistical Package for the Social Sciences (SPSS) version 17.0.
Sample size, power, and precision
Our primary goal was to construct a standard measure of weight-management strategies
using cluster analysis. The necessary sample size for cluster analysis has not been well defined
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 15

(Dolnicar, 2003). It has been noted that when natural clusters exist in the items under
investigation, and items are highly correlated--which we hypothesize is the case with diet and
exercise strategies—cluster analysis is more stable across sample sizes and partitioning
procedures (Aldenderfer & Blashfield, 1984; Dolnicar, 2003). Based on this rationale, sample
sizes ranging from 70-100 should provide adequate power for the cluster analyses we propose to
conduct.
Study 1
Methods
Participants were 96 students (52 females) and friends of those students from USC, who
participated for extra credit in their psychology classes. Participants were entered into a lottery to
win one of two $50.00 prizes. The sample had a mean age of 24.96 (SD = 11.5) and a mean BMI
of 24.04 (SD = 4.78).
For the sorting task, participants sorted all 48 diet and exercise strategies at once.
However, to make the task cognitively simpler, all 23 diet strategies were listed first followed by
the 25 exercise strategies, and the diet strategies were listed in lower-case letters while the
exercise strategies were listed in upper-case letters.
Results
One hundred and two participants completed the sorting procedure, 31 of whom were
removed from the analysis for completing it too quickly (less than six minutes), leaving a final
sample of 71 participants for the cluster analysis. The average linkage cluster analysis on
participants’ sorted data produced the dendrogram presented in figure 1. Based on the
dendgrogram, the authors partitioned the weight management strategies into nine clusters, four
for diet and five for exercise. The diet and exercise clusters and their labels are shown in Table 1.
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 16

Below, we describe the individual strategies that clustered together to form the four diet and five
exercise clusters found.
Diet clusters
Avoiding Food or Using Cognition to Limit Consumption cluster. The first diet strategies
cluster consisted of seven items. Although these strategies seem diverse, they all attempt to meet
the goal of limiting consumption. The items are: “Limiting your consumption by perceiving
foods as unappetizing”, “Not eating late in the day”, “Avoiding situations that could lead to
eating unhealthily”, “Avoiding situations that lead to overeating”, “Distracting yourself to avoid
excess eating”, “Stopping yourself from impulsive eating”, and “Using will power to avoid
overeating”. We further evaluated the cohesiveness of this cluster by computing the Cronbach’s
alpha on the standardized ratings of use of the strategies by participants. The Cronbach’s alpha
for participants’ reported use of items in this cluster was 0.88. This value, and those Cronbach
alphas that follow, are also shown in Table 1.
Limiting the Volume of Food Eaten cluster. The second diet strategies cluster contained
five items: “Preparing less food than it would take to fill you”, “Ordering less food than it would
take to fill you”, “Stop eating before you are full”, “Eating slowly to limit food intake”, and
“Filling up with water to limit room for food”. The Cronbach’s alpha for participants’ reported
use of items in this cluster was 0.81.
Monitoring and Planning Diet cluster. The third diet strategies cluster contained seven
items: “Reading food labels”, “Consider the calories of foods you order in restaurants”, “Plan the
content of your meals in advance”, “Measuring serving sizes of food”, “Counting calories of
foods”, “Buying only the food I need”, and “Avoid buying unhealthy foods”. The Cronbach’s
alpha for participants’ reported use of items in this cluster was 0.86.
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 17

Eating Healthily cluster. The last diet strategies cluster contained four items: “Using
healthy ingredients instead of unhealthy ones in recipes”, “Eating only small portions of
unhealthy snacks”, “Eating healthy foods for snacks”, and “Filling up with fruits or vegetables to
limit your room for food”. The Cronbach’s alpha for participants’ reported use of items in this
cluster was 0.79.
Exercise clusters
Prioritizing Exercise cluster. The first exercise strategies cluster contained three items:
“Making exercise a top priority”, “Exercising even when you would rather do something else”,
and “Exercising before your daily shower” (“Going to a gym to exercise” was originally in this
cluster, but decreased Cronbach’s alpha substantially and was dropped). The Cronbach’s alpha
for participants’ reported use of items in this cluster was 0.73.
Planning and Monitoring Exercise cluster. The second exercise strategies cluster
contained nine items: “Choosing a convenient time to exercise”, “Exercising at the same time
everyday”, “Scheduling regular exercise periods”, “Planning the details of your exercise in
advance”, “Wearing a pedometer”, “Timing the length of your exercise sessions”, “Keeping
records of your exercise”, “Monitoring your heart rate when exercising”, and “Measuring the
calories you burn when exercising”. The Cronbach’s alpha for participants’ reported use of items
in this cluster was 0.81.
Social and Motivational Support for Exercise cluster. The third exercise strategies cluster
contained three items: “Finding recreational activities that require exercise”, “Exercising with
friends”, and “Doing physical activity you consider fun.” The Cronbach’s alpha computed on
participants’ standardized ratings of proportion and frequency of use for these strategies was
0.70.
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 18

Making Exercise Convenient cluster. The fourth exercise strategies cluster contained four
items: “Doing a short work out when you are too busy for a full one”, “Going for walks”,
“Exercising around your neighborhood”, and “Exercising at home”. The Cronbach’s alpha for
participants’ reported use of items in this cluster was 0.49.
Creating Exercise From Everyday Activities cluster. The last exercise strategies cluster
contained five items: “Avoiding inactivity”, “Parking your car further away from your
destination to walk further”, “Using stairs rather than elevators”, “Using routine activities at
work or home to create exercise”, and “Getting up and moving around at regular intervals”. The
Cronbach’s alpha for participants’ reported use of items in this cluster was 0.57.
Secondary analysis
The goal of the secondary analysis was to provide correlational evidence examining
whether the strategies clusters might be effective for managing weight by (a) testing for
differences in use of strategy clusters between normal (BMI < 25) and overweight/obese (BMI >
25) participants and (b) examining the correlations between the strategy clusters and BMI and
the relationship of these clusters to total amount of weekly exercise. Participants’ responses to
the items in each strategy cluster were summed to create composite scores for their total use of
each cluster, and the resulting composite scores were standardized.
Bivariate correlations (pairwise deletion, n = 91-96) between BMI, weekly exercise, self-
weighing, and the nine strategy clusters are reported in Table 2. BMI and frequency of self-
weighing were not significantly correlated with any of the other variables, including weekly
exercise. Weekly exercise was positively associated with all five exercise strategy clusters.
Independent samples t-tests comparing those participants with BMIs below 25with those
with BMIs above 25 found no significant differences in reported use of strategy clusters.
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 19

Similarly, multiple regression with BMI as the dependent variable and the strategy clusters
entered together as predictors did not yield any statistically significant findings (p-value < .79).
To determine if the exercise strategy clusters could predict weekly exercise, a forward
entry step-wise multiple regression with weekly exercise as the dependent variable and the
exercise strategy clusters as independent variables was used. Two clusters significantly predicted
weekly exercise; Prioritizing Exercise and Social and Motivational Support for Exercise. The
Prioritizing Exercise cluster entered first, R
2
= 0.25, F(1, 88) = 27.8, p < .001, B = 0.13, 95% CI
[.08, .18], and the Social and Motivational Support for Exercise cluster entered second, R
2
=
0.13, F(2, 87) = 25.28, p < .001, B = 0.14, 95% CI [.08, .21], with a total R
2
= 0.38.
Discussion
Using the dendrogram derived from the average linkage cluster analysis, the authors
identified nine strategy clusters: four for diet and five for exercise. The four diet clusters were
labeled “Avoiding Food or Using Cognition to Limit Consumption”, “Limiting the Volume of
Food Eaten on a Single Occasion”, “Eating Healthily”, and “Monitoring and Planning Diet”, and
had standardized Cronbach’s alphas ranging from 0.79-0.86. The five exercise clusters were
labeled “Prioritizing Exercise”, “Planning and Monitoring Exercise “,“Social and Motivational
Support for Exercise “, “Making Exercise Convenient“, and “Creating Exercise From Everyday
Activities”, and had standardized Cronbach’s alphas ranging from 0.49-0.81.
Two of the exercise clusters, “Prioritizing Exercise” and “Social and Motivational
Support for Exercise”, significantly predicted weekly exercise. These findings suggest that these
two strategy clusters may be effective for managing exercise. Exercise is a known factor in
determining body weight and provides many other benefits beyond simple calorie exertion
(Annesi, 2011; Blair & Morris, 2009; Fox & others, 1999; Warburton, Nicol, & Bredin, 2006).
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 20

Strategies that are effective in managing exercise should be emphasized in weight loss
interventions and communicated to the general public. Future research should continue to
investigate the relationship between exercise and these two weight management strategy clusters,
as well as others.
BMI was not significantly correlated with other variables in this sample. The statistical
tests may have suffered from low power due to: (a) known limitations resulting from using BMI
as a measure of health, and (b) several characteristics of the sample. One known limitation is the
substantial amount of measurement error involved in using BMI as a health measure. The World
Health Organization defines obesity as an excess amount of body fat. BMI is highly correlated
with body fat percent, but it is incapable of discriminating between body fat and lean muscle
mass. In particular, problems arise in the middle of the BMI scale—that is, BMIs between 23-27
(Romero-Corral et al., 2008). In this range of BMIs there can be (a) healthy people with high
amounts of lean muscle mass and low body fat and (b) unhealthy people with low amounts of
lean muscle mass and high body fat; and BMI cannot distinguish between them. Most of the
participants in our sample fall into or close to this medium range of BMI where substantial
amounts of measurement error exist, thus creating low power for our statistical tests to detect
significance.
Another issue grows from the BMI of our sample having low variability. The median
BMI of our sample was 23.3, and the 75
th
percentile was 26.6, which are much lower than the
population at large. This is likely due to the fact that our sample was relatively young (median
age = 21). Because weight accumulates slowly over the course of several years, variance in BMI
of a young sample will likely be substantially less than the variance in BMI of an older one.
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 21

Because there was relatively little variance in BMI in our sample, the statistical tests likely had
low power for predicting variance within BMI.
A third factor that could have reduced power was a weakened effect size of strategy
usage on BMI caused by the sample being fairly homogenous. The sample was a group of young
college students. College students from a single university are likely to have more characteristics
in common than a more traditional sample, which could have diminished the effects of strategy
use on body weight. The effects of strategy use would likely be stronger in a more heterogeneous
sample.
Almost all of these issues of low power can be corrected for by recruiting an older, more
diverse sample. An older sample will likely have a larger variance in BMI, which increases the
power of statistical tests while simultaneously attenuating the measurement error involved in
using BMI as a measure of health. Furthermore, an older sample would be more heterogeneous,
which would introduce more variability in strategy use, and thus could increase the strength of
the effect of weight management strategies.
Conclusion
Study 1 lays the framework for a measure of weight management strategies. The nine
clusters of strategies derived from the cluster analysis should be replicated in a new sample. In
addition, a sample with a larger variance in BMI should be recruited in order to mitigate many of
the problems resulting from using BMI as a dependent variable in analyses. These are the
changes that were made for Study 2.
Study 2
The primary goal of Study 2 was to replicate the findings from Study 1. A secondary goal
was to recruit a sample with a larger variance of BMIs to increase the power of statistical tests
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 22

using BMI as a dependent variable. This was accomplished by targeting an older population for
recruitment.
Methods
Participants included 129 (91 female) staff and faculty from USC, recruited via email. All
participants were entered into a lottery for a chance to win one of two $50.00 prizes. Participants
were excluded from the secondary analysis (but not the cluster analysis) if they reported having a
disease affecting their weight, taking medication affecting their weight, having a medical
condition influencing their diet or physical activity, being pregnant or recently being pregnant, or
if they completed the first part of the study in less than three minutes. The sample’s mean age
was 44.59 (SD = 12.70) and it had a mean BMI of 26.08 (SD = 5.55).
A few changes were made to the procedures for Part 2 (sorting task) of the study. First,
the sorting procedure was split into two, one for diet and one for exercise. This was done to
reduce the cognitive complexity of the task to make it faster and simpler, which we felt would
make people who start the sorting routine more likely to finish, and ultimately, provide better
and more reliable results. Also, the instructions were rewritten for clarification. Participants were
told to sort the strategies into groups based on how similar they felt the strategies were to each
other. They were also told that they could make as many or as few categories as they wished, and
could place as many or as few strategies into each category. In addition, participants were
explicitly told not to group strategies based on whether or not they used them, or on whether or
not they felt they were “good” or “bad” strategies. Participants were excluded from the cluster
analysis if they did not sort all of the strategies, they completed the sorting task in less than four
minutes, or they sorted all the strategies into two groups or less, indicating a lack of effort in
completing the task.
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 23

Of the 129 participants: 101 were included in the diet strategies cluster analysis, 94 were
included in the exercise strategies cluster analysis, and sample sizes ranged from 104-129 for the
secondary analysis.
Results
One hundred and two participants completed the sorting procedure for the diet strategies,
one of whom was removed for completing the task too quickly (in less than three minutes),
leaving a final sample size of 101. The average linkage cluster analysis on participants’ sorted
data for the diet strategies produced the dendrogram that is presented in Figure 2. One hundred
and eight participants completed the sorting procedure for the exercise strategies, 14 of whom
were removed for completing the task too quickly (in less than three minutes), leaving a final
sample size of 94. The average linkage cluster analysis on participants’ sorted data for the
exercise strategies produced the dendrogram that is presented in Figure 3. Based on the two
dendrograms, the authors partitioned the weight management strategies into nine clusters, four
for diet and five for exercise. The diet and exercise clusters and their labels are shown in Table 3.
Below, we describe the differences in findings between Study 1 and Study 2, and also cluster
results from Study 2. We believe that the minor differences found between Study 1 and Study 2
were likely a result of more clearly written instructions and a more motivated sample, as the
sample from Study 2 was older and did not participate for extra credit. Therefore, we advocate
the cluster solution from Study 2 as the final cluster solution for our measure.
Diet clusters
Comparing differences in diet strategy clusters between Study 1 and Study 2. Although
the results in Study 1 and Study 2 were highly similar, there were two differences. First, the
“Monitoring and Planning Diet” cluster from Study 1 split into two separate clusters in Study 2;
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 24

“Monitoring Diet” and “Planning Diet.” The other difference was the “Eating Healthily” cluster
in Study 1 split in half; one half going to the “Limiting the Volume of Food Eaten” cluster from
Study 2, and the other half going to the “Planning Diet” cluster from Study 2.
Avoiding Food or Using Cognition to Limit Consumption cluster. The first diet strategies
cluster contained six items: “limiting your consumption by perceiving foods as unappetizing”,
“Avoiding situations that could lead to eating unhealthily”, “Avoiding situations that lead to
overeating”, “Distracting yourself to avoid excess eating”, “Stopping yourself from impulsive
eating”, and “Using will power to avoid overeating”. We further evaluated the cohesiveness of
this cluster by computing the Cronbach’s alpha on the standardized ratings of proportion and
frequency of use by participants. The Cronbach’s alpha for participants’ reported use of items in
this cluster was 0.77. This value, and those Cronbach alphas that follow, are also shown in Table
3.
Limiting the Volume of Food Eaten cluster. The second diet strategies cluster contained
eight items: “Preparing less food than it would take to fill you”, “Ordering less food than it
would take to fill you”, “Stop eating before you are full”, “Eating slowly to limit food intake”,
“Filling up with water to limit room for food”, “Not eating late in the day”, “Eating only small
portions of unhealthy snacks”, and “Filling up with fruits or vegetables to limit your room for
food”. The Cronbach’s alpha for participants’ reported use of items in this cluster was 0.77.
Planning Diet cluster. The third diet strategies cluster contained six items: “Reading food
labels”, “Plan the content of your meals in advance”, “Buying only the food I need”, “Avoid
buying unhealthy foods”, “Eating healthy foods for snacks”, and “Using healthy ingredients
instead of unhealthy ones in recipes”. The Cronbach’s alpha for participants’ reported use of
items in this cluster was 0.80.
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 25

Monitoring Diet cluster. The last diet strategies cluster contained three items: “Consider
the calories of foods you order in restaurants”, “Measuring serving sizes of food”, and “Counting
calories of foods”. The Cronbach’s alpha for participants’ reported use of items in this cluster
was 0.68.
Exercise clusters
Comparing differences in exercise strategy clusters between Study 1 and Study 2.
Although the results in Study 1 and Study 2 were highly similar, there were two differences. The
first difference was the Planning Exercise items in the Monitoring and Planning Exercise cluster
from Study 1 were combined with the Prioritizing Exercise cluster from Study 1, which led to
the Prioritizing Exercise cluster to be renamed Planning or Prioritizing Exercise and the
Monitoring and Planning Exercise cluster to be renamed Monitoring Exercise in Study 2. The
other difference was the item “Going for walks” moved from the Making Exercise Convenient
cluster to the Creating Exercise From Everyday Activities cluster.
Planning or Prioritizing Exercise cluster. The first exercise strategies cluster contained
eight items: “Making exercise a top priority”, “Exercising even when you’d rather do something
else”, “Exercising before your daily shower”, “Planning the details of your exercise in advance”,
“Choosing a convenient time to exercise”, “Scheduling regular exercise periods”, and
“Exercising at the same time everyday”. (“Going to a gym to exercise” was originally in this
cluster, but decreased Cronbach’s alpha substantially and was dropped.) The Cronbach’s alpha
for participants’ reported use of items in this cluster was 0.86.
Monitoring Exercise cluster. The second exercise strategies cluster contained five items:
“Wearing a pedometer”, “Timing the length of your exercise sessions”, “Keeping records of your
exercise”, “Monitoring your heart rate when exercising”, and “Measuring the calories you burn
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 26

when exercising”. The Cronbach’s alpha for participants’ reported use of items in this cluster
was 0.65.
Social and Motivational Support for Exercise cluster. The third exercise strategies cluster
contained three items: “Finding recreational activities that require exercise”, “Exercising with
friends”, and “Doing physical activity you consider fun.” The Cronbach’s alpha for participants’
reported use of items in this cluster was 0.34.
Making Exercise Convenient cluster. The fourth exercise strategies cluster contained
three items: “Doing a short work out when you are too busy for a full one”, “Exercising around
your neighborhood”, and “Exercising at home”. The Cronbach’s alpha for participants’ reported
use of items in this cluster was 0.31.
Creating Exercise From Everyday Activities cluster. The last exercise strategies cluster
contained six items: “Avoiding inactivity”, “Parking your car further away from your destination
to walk further”, “Using stairs rather than elevators”, “Using routine activities at work or home
to create exercise”, “Getting up and moving around at regular intervals”, and “Going for walks”.
The Cronbach’s alpha for participants’ reported use of items in this cluster was 0.42.
Secondary analysis
The goal of the secondary analysis was to (a) test for differences in use of strategy
clusters between normal (BMI < 25) and overweight/obese (BMI > 25) participants, and (b) to
examine the correlations between the strategy clusters and BMI and the relationship of these
clusters to total amount of weekly exercise. The items in each strategy cluster were summed to
create composite scores for total use of each cluster, and the resulting composite scores were
standardized.
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 27

Bivariate correlations (pairwise deletion, n = 112-129) between BMI, weekly exercise,
self-weighing, and the nine strategy clusters are reported in Table 4. BMI was significantly
negatively correlated with weekly exercise (r = -.26, p < .003) and two strategy clusters:
Planning or Prioritizing Exercise (r = -0.24, p < .009) and Planning Diet (r = -0.27, p < .003),
indicating that the more people used these two strategy clusters, the lower their BMI. Frequency
of self-weighing was significantly correlated with Monitoring Food (r = 0.18, p < .046). Weekly
exercise was significantly correlated with four of the five exercise strategy clusters: Social and
Motivational Support for Exercise (r = 0.25, p < .006), Monitoring Exercise (r = 0.33, p < .001),
Planning or Prioritizing Exercise (r = 0.64, p < .001), and Making Exercise Convenient (r = 0.58,
p < .001; only Creating Exercise From Daily Activities was not significantly correlated with
weekly exercise).
Participants with BMIs below 25, compared with those with BMIs above 25, reported
more use of the Planning Diet cluster, t(118) = -2.06, p = .042, d = -3.88, 95% CI [-7.61, -.14],
Making Exercise Convenient cluster, t(114) = -2.65, p = .009, d = -2.34, 95% CI [-4.08, -0.59],
and Planning or Prioritizing Exercise cluster, t(114) = -2.14, p = .034, d = -5.79, 95% CI [-11.14,
-0.44], . No strategy clusters were reported to be used more frequently by overweight and obese
participants compared with normal weight participants.
Multiple regressions were conducted using BMI as the dependent variable and the
strategy clusters as predictors. All nine strategy clusters entered together accounted for 19.2% of
the total variance in BMI, F(9, 94) = 2.48, p < .014. Next, all four diet clusters were entered in a
forward entry step-wise multiple regression predicting BMI, and the same was done for the five
exercise clusters in a separate multiple regression. Of the four diet clusters, Planning Diet
significantly predicted BMI, F(1, 116) = 8.3, p < .005, B = -.14, 95% CI [-.23, -.04], and of the
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 28

five exercise clusters, Planning or Prioritizing Exercise significantly predicted BMI, F(1, 107) =
9.9, p < .01, B = -.10, 95% CI [-.17, -.04]. Finally, Planning Diet and Planning or Prioritizing
Exercise were entered together in a forward step-wise multiple regression predicting BMI to see
if they have additive predictability of BMI. Both strategy clusters entered significantly, with
Planning Diet entering first, F(1, 111) = 8.48, p < .004, B = -.14, 95% CI [-.23, -.04], and
Planning or Prioritizing Exercise entering second, F(1, 110) = 5.6, p < .02, B= -.08, 95% CI [-
.14, -.01], predicting a total of 11.6% of the variance in BMI.
Finally, to test the relationship between exercise strategy clusters and the amount of
weekly exercise reported, we conducted a forward entry step-wise multiple regression with
weekly exercise as the dependent variable and the exercise strategy clusters entered as predictors.
Two of the clusters significantly predicted weekly exercise: Planning or Prioritizing Exercise
entered first, F(1, 107) = 69.0, p < .001, B = .07, 95% CI [.05, .08], and Making Exercise
Convenient entered second, F(1, 106), p < .001, B = .13, 95% CI [.08, .18], explaining a total of
51.3% of the variance in weekly exercise.
Discussion
The primary goal of Study 2 was to replicate the findings from Study 1. Similar to Study
1, the cluster analysis from Study 2 revealed a nine strategy cluster solution: four for diet and
five for exercise. The four diet clusters were labeled: Avoiding Food or Using Cognition to Limit
Consumption, Limiting the Volume of Food Eaten on a Single Occasion, Monitoring Diet, and
Planning Diet, and had standardized Cronbach’s alphas ranging from 0.68-0.80. The five
exercise clusters were labeled: Planning or Prioritizing Exercise, Monitoring Exercise, Social
and Motivational Support for Exercise, Making Exercise Convenient, and Creating Exercise
From Everyday Activities, and had standardized Cronbach’s alphas ranging from 0.31-0.86.
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 29

Three of the strategy clusters stood out as possible effective weight management
strategies in the secondary analysis for Study 2: Planning Diet, Planning or Prioritizing Exercise,
and Making Exercise Convenient. Planning Diet and Planning or Prioritizing Exercise were both
negatively correlated with BMI, and both significantly predicted BMI. In addition, normal
weight people reported using Planning Diet and Planning or Prioritizing Exercise, as well as
Making Exercise Convenient, more frequently than overweight/obese participants. Finally,
Planning or Prioritizing Exercise and Making Exercise Convenient explained approximately half
of the variance in reported hours of weekly exercise. Although these data were collected with
questionnaires and thus cannot be used to infer causality, they nonetheless suggest that these
three strategy clusters may be effective for weight management interventions and should be
explored in future research. Future research should be designed to experimentally manipulate
the use of these strategy clusters with the goal of observing their affect on diet, exercise and
BMI.
General Discussion
The clusters that emerged from Study 1 and Study 2 were highly similar. There were two
differences between the four diet strategy clusters from Study 1 and Study 2. First, the
Monitoring and Planning Diet strategy cluster from Study 1 split into two separate clusters in
Study 2; Monitoring Diet and Planning Diet. The other difference was the Eating Healthily
cluster in Study 1 was split in half; one half going to the Limiting the Volume of Food Eaten on a
Single Occasion cluster and the other going to the Planning Diet cluster. There were also two
differences between the five exercise strategy clusters from Study 1 and Study 2. First, the
Planning Exercise items in the Monitoring and Planning Exercise cluster in Study 1 were
combined with the Prioritizing Exercise cluster from Study 1, which led to this cluster being
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 30

renamed Planning or Prioritizing Exercise and the Monitoring and Planning Exercise cluster to
be renamed Monitoring Exercise in Study 2. The other difference was that the item “Going for
walks” moved from the Making Exercise Convenient cluster in Study 1 to the Creating Exercise
From Everyday Activities cluster in Study 2.
Although there were minor differences between the cluster analyses from Study 1 and
Study 2, the majority of the sorting structure remained consistent across studies. We believe the
differences were likely a result of more clearly written instructions and a more motivated sample
in Study 2, as this sample was older volunteers and not students participating for extra credit. We
believe the sorting results from Study 2 are likely a better product, and we advocate this cluster
solution as the final cluster solution for our measure.
Several of the strategy clusters showed evidence for being effective weight management
strategies. In Study 2, Planning Diet and Planning or Prioritizing Exercise significantly predicted
BMI, and Planning or Prioritizing Exercise and Making Exercise Convenient significantly
predicted weekly exercise. These findings jibe with previous research showing that planning is
an effective strategy for changing healthy behaviors (Gollwitzer, 1999; Schwarzer, 1992;
Wiedemann et al., 2011). Social and Motivational Support for Exercise predicted weekly
exercise in Study 1 but not Study 2, and Making Exercise Convenient predicted weekly exercise
in Study 2 but not Study 1. This could have resulted from the difference in age of the samples
between Study 1 and Study 2 (college students versus adults, respectively), and should be
investigated further.
We believe our measure offers several advantages to previous research investigating
weight management strategies. Our measure contains a broad list of weight management
strategies, for both diet and exercise, and it reliably categorizes those strategies into nine
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 31

conceptually meaningful clusters of strategies: four for diet and five for exercise. This
differentiation allows for evaluating how effective each of the nine clusters are at managing
weight, diet, and physical activity. We believe that our measure could be used to study strategy
effectiveness across most weight ranges in the population, including: healthy, overweight, and
obese people. Also, we believe our measure could be used to study strategy effectiveness in
population sub-groups (e.g., young adults, the elderly, women, men, etc.,) as well as across
varying intentions of the people using the strategies (e.g., people attempting to lose weight,
maintain weight loss, or avoid initial weight gain). Although our list of strategies is not complete
(and a complete list of strategies would likely be too long and complex to be useful), we feel that
most weight management strategies people use could easily be added to one of our nine clusters.
In addition, future research could add additional clusters that include items specifically related to
sub-populations, such as weight losers and weight loss maintainers. Our measure will also
provide researchers with a common conceptual framework for investigating weight management
strategies, which opens up the possibility for comparison of strategy effectiveness across studies
with techniques such as meta-analysis. And finally, our measure allows for evaluating the
effectiveness of these clusters for managing different types of behaviors, such as diet and
physical activity, which is important because BMI is not always an accurate predictor of overall
health (Romero-Corral et al., 2008). For instance, in the present research Social and Motivational
Support for Exercise (Study 1) and Making Exercise Convenient (Study 2) failed to predict BMI,
but these strategy clusters significantly predicted weekly exercise. The fact that these strategies
predicted weekly exercise could still indicate that these are effective strategies because exercise
has been linked to a number of positive health outcomes, and even small amounts of regular
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 32

physical activity can lead to improved health (Blair & Morris, 2009; Caudwell et al., 2009; King
et al., 2009).
One possible issue with our measure could be relatively low internal reliabilities for some
of the exercise clusters. The Cronbach’s alphas for three of the exercise strategy clusters in Study
1 and Study 2 were relatively low, but we do not believe this represents a major weakness in our
measure. Cronbach’s alpha is a measure of similar patterns of responding by participants to
separate items making up a composite, and we believe it was not a coincidence that the three
clusters with low Cronbach’s alphas were exercise clusters. This is because most Americans
exercise very few times per week and thus have a limited number of opportunities to use exercise
strategies, much less show common repeated use for all strategies in a cluster . In other words,
people can only use exercise strategies when they are going to exercise, and most people exercise
only a few times a week or less (Nelson, Story, Larson, Neumark-Sztainer, & Lytle, 2008). In
contrast, people make food choices regularly throughout the day (Wansink & Sobal, 2007), thus
they can use a large number of different diet strategies within a single day, and the Cronbach’s
alphas for the diet clusters in our studies were all relatively high.
Furthermore, our clusters represent semantically similar ways of achieving the same end
for diet or exercise outcomes, not a set of items that are meant to measure a common attribute.
We would not necessarily expect that participants would use all items within a cluster to achieve
the same end. For example, participants could exclusively use one strategy within a cluster as
long as they use it frequently enough to accomplish that cluster’s goal (e.g., making sure to
exercise everyday accomplishes the goal of Planning or Prioritizing Exercise). We believe this
could be tested by altering the response format for our clusters. Participants could rate the
proportion of times they use any, or all, of the individual strategies that define a cluster, and use
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 33

that single score as our measure for the use of that strategy cluster. For example, on a single
scale participants can rate “How often do you monitor your exercise (e.g., time the length of your
exercise sessions, measure the calories you burn, keeping records of your exercise).” This
approach makes the problem of low internal reliability among some of the strategy clusters
irrelevant, and could ultimately create a more useful independent variable for statistical analyses.
Future studies should explore this as well as other options.
Conclusion
The primary purpose of these studies was to create a universal measure of weight
management strategies. A secondary purpose was to provide data to support the utility of the
measure. The cluster analyses from Study 1 and Study 2 revealed highly similar strategy clusters.
The authors recommend using results from the Study 2 cluster analysis because of improved
methods that likely yielded a more reliable product. The final strategy clusters are: “Avoiding
Food or Using Cognition to Limit Consumption”, “Limiting the Volume of Food Eaten on a
Single Occasion”, “Monitoring Diet”, “Planning Diet”, “Planning or Prioritizing Exercise”,
“Monitoring Exercise”, “Social and Motivational Support for Exercise”, “Making Exercise
Convenient”, and “Creating Exercise From Everyday Activities”. Several of these clusters
showed evidence in the secondary analyses for being effective weight management strategies,
including: Planning Diet, Planning or Prioritizing Exercise, Social and Motivational Support for
Exercise, and Making Exercise Convenient.
Future studies should replicate these findings and further explore the utility of the
measure by recruiting larger and more diverse samples. In addition, future studies should utilize
different types of biological and behavioral dependent variables related to health that may be
more useful than BMI, such as body fat percentage, hip and waist measurements, cholesterol
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 34

measurements, food intake, etc. Each additional measure would provide information that BMI
alone cannot. Finally, these data are observational, thus we cannot infer causation. Future studies
should investigate a causal link between these weight management strategies and various health
outcomes. Emphasizing different types of strategies in weight management interventions and
measuring change scores across a variety of variables could accomplish this goal. Another
approach would be to predict changes in weight and health related variables across time, from
earlier reports of strategy usage.

MEASURE OF WEIGHT MANAGEMENT STRATEGIES 35

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MEASURE OF WEIGHT MANAGEMENT STRATEGIES 42

Table 1
Study 1 strategy clusters and Cronbach’s alpha

Diet Strategies

Cluster Label Strategies α
1. Avoiding Food or
Using Cognition to Limit
Consumption
 Limiting your consumption by perceiving foods as
unappetizing.
 Not eating late in the day.
 Avoiding situations that could lead to eating
unhealthily
 Avoiding situations that lead to overeating
 Distracting yourself to avoid excess eating
 Stopping yourself from impulsive eating
 Using will power to avoid overeating

0.88
2. Limiting the Volume of
Food Eaten on a Single
Occasion
 Preparing less food than it would take to fill you
 Ordering less food than it would take to fill you
 Stop eating before you are full
 Eating slowly to limit food intake
 Filling up with water to limit room for food

0.81
3. Monitoring and
Planning Diet
 Reading food labels
 Consider the calories of foods you order in
restaurants
 Plan the content of your meals in advance
 Measuring serving sizes of food
 Counting calories of foods
 Buying only the food I need
 Avoid buying unhealthy foods

0.86
4. Eating Healthily  Using healthy ingredients instead of unhealthy ones
in recipes
 Eating only small portions of unhealthy snacks
 Eating healthy foods for snacks
 Filling up with fruits or vegetables to limit your room
for food
0.79

Exercise Strategies

1. Prioritizing Exercise  Making exercise a top priority
 Exercising even when you would rather do something
else
0.73
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 43

 Exercising before your daily shower

2. Planning and
Monitoring Exercise
 Choosing a convenient time to exercise
 Exercising at the same time everyday
 Scheduling regular exercise periods
 Planning the details of your exercise in advance
 Wearing a pedometer
 Timing the length of your exercise sessions
 Keeping records of your exercise
 Monitoring your heart rate when exercising
 Measuring the calories you burn when exercising

0.81
3. Social and Motivational
Support for Exercise
 Finding recreational activities that require exercise
 Exercising with friends
 Doing physical activity you consider fun

0.70
4. Making Exercise
Convenient
 Doing a short work out when you are too busy for a
full one
 Exercising around your neighborhood
 Exercising at home
 Going for walks

0.49
5. Creating Exercise From
Everyday Activities
 Avoiding inactivity
 Parking your car further away from your destination
to walk further
 Using stairs rather than elevators
 Using routine activities at work or home to create
exercise
 Getting up and moving around at regular intervals
0.57
Note: α = Cronbach’s alpha.

Running head: MEASURE OF WEIGHT MANAGEMENT STRATEGIES 44

Table 2
Study 1 Correlation matrix between BMI, weekly exercise, self-weighing, and the nine strategy clusters (sample size in parenthesis)
1 2 3 4 5 6 7 8 9 10 11 12
1. BMI

--
2. Weekly exercise

-.08
(96)
--
3. Self-weighing

.03
(96)
-.02
(96)
--
4. Avoiding Food or Using
Cognition to Limit Consumption
.07
(95)
.08
(95)
.05
(95)
--
5. Limiting the Volume of Food
Eaten on a Single Occasion
.01
(94)
.09
(94)
.07
(94)
.78**
(93)
--
6. Monitoring and Planning Diet -.01
(92)
.23*
(92)
-.06
(92)
.72**
(91)
.63**
(91)
--
7. Eating Healthily -.07
(95)
.23*
(95)
.00
(95)
.76**
(94)
.64**
(94)
.79**
(92)
--
8. Prioritizing Exercise .08
(96)
.47**
(96)
-.06
(96)
.30**
(95)
.37**
(94)
.38
(92)
.30
(95)
--
9. Planning and Monitoring
Exercise
.05
(95)
.40**
(95)
-.02
(95)
.44**
(94)
.43**
(93)
.48**
(91)
.36**
(94)
.77**
(95)
--
10. Social and Motivational
Support for Exercise
.01
(95)
.47**
(95)
.01
(95)
.01
(94)
.07
(93)
-.10
(91)
.05
(94)
.16
(95)
.12
(94)
--
11. Making Exercise Convenient -.11
(94)
.27**
(94)
.03
(94)
.32**
(93)
.21*
(92)
.30**
(91)
.34**
(93)
.35**
(93)
.48**
(93)
.24*
(93)
--
12. Creating Exercise From
Everyday Activities
.00
(94)
.45**
(94)
-.06
(94)
.39**
(93)
.39**
(92)
.33**
(90)
.35**
(93)
.48**
(94)
.52**
(93)
.40**
(93)
.41**
(93)
--
Note. The numbers across the first row correspond to the variables in the first column of the table. BMI = body mass index.
* p < 0.05
** p < 0.01
Running head: MEASURE OF WEIGHT MANAGEMENT STRATEGIES 45

Table 3
Study 2 strategy clusters and Cronbach’s alpha

Diet Strategies

Cluster Label Strategies α
1. Avoiding Food or
Using Cognition to Limit
Consumption
 Limiting your consumption by perceiving foods as
unappetizing.
 Avoiding situations that could lead to eating
unhealthily
 Avoiding situations that lead to overeating
 Distracting yourself to avoid excess eating
 Stopping yourself from impulsive eating
 Using will power to avoid overeating

0.77
2. Limiting the Volume of
Food Eaten on a Single
Occasion
 Preparing less food than it would take to fill you
 Ordering less food than it would take to fill you
 Stop eating before you are full
 Eating slowly to limit food intake
 Filling up with water to limit room for food
 Not eating late in the day
 Eating only small portions of unhealthy snacks
 Filling up with fruits or vegetables to limit your room
for food

0.77
3. Planning Diet  Reading food labels
 Plan the content of your meals in advance
 Buying only the food I need
 Avoid buying unhealthy foods
 Eating healthy foods for snacks
 Using healthy ingredients instead of unhealthy ones
in recipes

0.80
4. Monitoring Diet  Consider the calories of foods you order in
restaurants
 Measuring serving sizes of food
 Counting calories of foods

0.68

Exercise Strategies

1. Planning or Prioritizing
Exercise
 Making exercise a top priority
 Exercising even when you would rather do something
0.86
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 46

else
 Exercising before your daily shower
 Planning the details of your exercise in advance
 Choosing a convenient time to exercise
 Scheduling regular exercise periods
 Exercising at the same time everyday

2. Monitoring Exercise  Wearing a pedometer
 Timing the length of your exercise sessions
 Keeping records of your exercise
 Monitoring your heart rate when exercising
 Measuring the calories you burn when exercising

0.65
3. Social and Motivational
Support for Exercise
 Finding recreational activities that require exercise
 Exercising with friends
 Doing physical activity you consider fun

0.34
4. Making Exercise
Convenient
 Doing a short work out when you are too busy for a
full one
 Exercising around your neighborhood
 Exercising at home

0.31
5. Creating Exercise From
Everyday Activities
 Avoiding inactivity
 Parking your car further away from yoru destination
to walk further
 Using stairs rather than elevators
 Using routine activities at work or home to create
exercise
 Getting up and moving around at regular intervals
 Going for walks
0.42
Note. α = Cronbach’s alpha.

Running head: MEASURE OF WEIGHT MANAGEMENT STRATEGIES 47

Table 4
Study 2 Correlation matrix between BMI, weekly exercise, self-weighing, and the nine strategy clusters (sample size in parenthesis)
1 2 3 4 5 6 7 8 9 10 11 12
1. BMI

--
2. Weekly exercise

-.26**
(129)
--
3. Self-weighing

-.04
(123)
-.135
(129)
--
4. Avoiding Food or Using
Cognition to Limit Consumption
-.09
(123)
.21*
(123)
.14
(123)
--
5. Limiting the Volume of Food
Eaten on a Single Occasion
-.06
(122)
.15
(122)
.08
(122)
.72**
(122)
--
6. Monitoring Diet

.01
(122)
.07
(122)
.18*
(122)
.67**
(121)
.60**
(120)
--
7. Planning Diet

-.27**
(120
.25**
(120)
.06
(120)
.65**
(120)
.59**
(120)
.51**
(118)
--
8. Planning or Prioritizing
Exercise
-.24**
(116)
.64**
(116)
-.04
(116)
.18
(115)
.21*
(115)
.17
(114)
.18
(113)
--
9. Monitoring Exercise .01
(114)
.33**
(114)
-.12
(114)
.16
(113)
.19*
(112)
.28**
(112)
.08
(110)
.50**
(113)
--
10. Social and Motivational
Support for Exercise
.02
(118)
.25**
(118)
-.03
(118)
.18
(117)
.12
(116)
.03
(116)
.12
(114)
.15
(116)
.05
(114)
--
11. Making Exercise Convenient -.21*
(116)
.58**
(116)
-.06
(116)
.15
(115)
.17
(114)
.14
(114)
.28**
(112)
.47**
(114)
.25**
(112)
.22*
(116)
--
12. Creating Exercise from
Everyday Activities
-.02
(116)
.14
(116)
.14
(116)
.20*
(115)
.17
(114)
.13
(114)
.20*
(112)
.05
(114)
.12
(112)
.16
(116)
.13
(114)
--
Note. The numbers across the first row correspond to the variables in the first column of the table. BMI = body mass index.
* p < 0.05
** p < 0.01

Running head: MEASURE OF WEIGHT MANAGEMENT STRATEGIES 48

Figure 1. Study 1 dendrogram for all strategies.

Figure 1. Study 1 Dendrogram for all strategies derived from the average linkage cluster
analysis. A full description of each strategy can be found in Table 1.
MEASURE OF WEIGHT MANAGEMENT STRATEGIES 49

Figure 2. Study 2 dendrogram for diet strategies only.

Figure 2. Study 2 dendrogram for diet strategies derived from the average linkage cluster
analysis. A full description of each strategy can be found in Table 3.

MEASURE OF WEIGHT MANAGEMENT STRATEGIES 50

Figure 3. Study 2 dendrogram for exercise strategies only.

Figure 3. Study 2 dendrogram for exercise strategies derived from the average linkage cluster
analysis. A full description of each strategy can be found in Table 3.

Abstract (if available)

Abstract Weight management strategies that people use are one of several factors that determine a person’s weight. Strategies may be the most viable mechanism that can be targeted for changing weight because the strategies people use are flexible, which is generally not true for other factors influencing weight (e.g., genetics, environment, social groups, etc.,). To date, little research has investigated the effectiveness of different types of weight management strategies for managing weight, and a quality measure of types of strategies does not exist. In two studies, the present research aims to construct a measure of types of weight management strategies and to provide preliminary evidence for its utility by indicating which types of strategies may be effective for managing weight and exercise. Study 1 consisted of 96 students and friends of students, and Study 2 consisted of 129 faculty and staff at the University of Southern California. In both studies, participants rated the frequency of which they use 48 weight management strategies—for both diet and exercise—and sorted the strategies into groups based on strategy similarity. We conducted cluster analysis on participants’ sorted data to create a meaningful categorization of strategies. The authors determined nine clusters of strategies, four for diet and five for exercise. We used participants’ reported use of the strategy clusters to predict BMI and exercise behavior. Several of the clusters show evidence for being effective for managing weight or exercise, including Social and Motivational Support for Exercise (Study 1), Prioritizing or Planning Exercise (Study 1 and Study 2), Making Exercise Convenient (Study 2), and Planning Diet (Study 2). The authors conclude that these relationships should be investigated further in future research.

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Asset Metadata

Creator Larsen, Andrew L. (author)

Core Title A measure of weight management strategies and evidence for its utility

School College of Letters, Arts and Sciences

Degree Master of Arts

Degree Program Psychology

Publication Date 11/10/2012

Defense Date 11/09/2012

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag OAI-PMH Harvest,obesity,strategies,weight management

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Walsh, David A. (committee chair), Monterosso, John R. (committee member), Wood, Wendy (committee member)

Creator Email allarsen@usc.edu,larsena3@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-109358

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Rights Larsen, Andrew L.

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

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