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Goals and weight: the interplay of goal perceptions in weight management

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Goals and weight: the interplay of goal perceptions in weight management

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Content Running head: GOALS AND WEIGHT 1

Goals and weight: The interplay of goal perceptions in weight management
Grace S. Lee
University of Southern California
GOALS AND WEIGHT 2
Table of Contents
Abstract……………………………………………………………………………………………3
Goals and weight: The interplay of goal perceptions in weight management…………………….4
Method…………………………………………………………………………………………...11
Participants…………………………………………………………………………….....11
Procedures……………………………………………………………………………......11
Measures……………………………………………………………………………........12
Body Mass Index…………………………………………………………….......12
Taxonomy of human motives………………………………………………........13
Perceptions of conflict with life goals……………………………………….......13
Perceptions of facilitation with life goals…………………………………..........13
Self-regulation…………………………………....................................................14
Self-monitoring……………………………..........................................................14
Self-efficacy……………………………...............................................................14
Results. ………………………………………………………………………………………......15
Preliminary Analyses…………………………………………………………….............15
Multiple regression analyses………………….………………………………….............17
Discussion…..……………………………………………………………………………………18
References………………………………………………………………………………………..25
Tables………………………………………………………………………………………….....28
Appendix A………………………………………………………………………………….…...34
Appendix B………………………………………………………………………………………43
GOALS AND WEIGHT 3
Abstract
This study examined how much of the variance in individuals’ body mass indices (BMIs)
can be predicted by their perception of the amount of conflict and facilitation between their
important life goals and their weights. Our findings can extend towards health care in respect to
the growing problem of obesity and its comorbid diseases, as we explored these conditions from
a behavioral standpoint. Participants recruited from USC psychology courses completed an
online survey that measured constructs of self-regulation, self-monitoring, and self-efficacy in
diet and physical activity, along with rank-ordering a taxonomy of 36 important life goals. We
hypothesized that individuals at healthy weights would perceive more facilitation of their
respective important life goals with being at a healthy BMI (22.5), and more conflict of their
respective important life goals with being at an unhealthy BMI (30). We found that we could
account for 37% of the variance in individuals’ BMIs from their perceptions of
conflict/facilitation of important life goals, which substantiated our predictions that life goals are
associated with weight. Future research should examine the application of life goals in weight
management interventions to maximize the influence of positive motivations for healthy
behavior.

GOALS AND WEIGHT 4
Goals and weight: The interplay of goal perceptions in weight management
Self-motivated behaviors underpin many life decisions, from deciding to drink a bottle of
water to deciding to get married. Furthermore, the specific strategies with which individuals
make decisions may hold implications for self-motivated behaviors, such as the willingness to
stay in one’s career (Talevich, Read, & Walsh, 2011). In light of previous goal perception
literature, the intent of this project was to connect goals with the ways in which individuals make
decisions regarding weight management. In the present study, we measured the influence that
perceptions of life goals can have in weight management behaviors to determine if the variance
in individuals’ BMIs could be explained by their life goal perceptions.
The constructs that we studied in regards to weight management may hold implications
for health care, as obesity is a growing problem in the United States. Obesity is defined as having
an excess amount of body fat that arises when an individual consumes more calories than the
body burns off in metabolism for an extended period of time (NIH, 2011). Health is often
measured in Body Mass Index (BMI), defined by a formula of the ratio between weight and
height. A BMI within the range of 17.5-25 is considered as healthy BMI. A BMI between 25-30
is considered overweight, 30-40 is considered as obese, and any BMI above 40 is considered as
morbidly obese. In 2011, the National Institutes of Health reported that 68% of U.S. adults (20
years old and over) are overweight, and that obesity prevalence has increased by 22% since the
1960s to a level of 32% in the adult population (NIH, 2011).
Obesity holds major negative ramifications for health care because this condition has
high comorbidity rates with heart disease, hypertension, diabetes, and other diseases that are
significant contributors to total U.S. mortality rates. For example, an estimated 112,000 deaths
due to cardiovascular disease per year are associated with obesity. In fiscal terms, obese
GOALS AND WEIGHT 5
individuals on average pay 42% more in health care than individuals who are at healthy weights.
However, obesity is not only financially draining at an individual level; medical insurance
programs on average pay about $1300 more for each obese beneficiary (NIH, 2011).
In order to address the fiscal, physical, and emotional consequences of obesity, we
believe that researches should be targeting prevention strategies that will be most effective in
combating future escalation of this condition. Mokdad et. al (2001) examined how physical
activity and diet are major predictors in weight, which leads us to measure these constructs in the
present study (Mokdad, Ford, Bowman, Dietz, Vinicor, Bales, & Mark, 2001). However, we
diverge from previous research in that we did not implement physical activity and diet programs,
but we rather examined individuals’ self-regulation, self-monitoring, and self-efficacy in
conjunction with their life goal perceptions regarding weight management strategies in an effort
to identify major variables that might explain the variance between people who have healthy and
unhealthy weights in the U.S.
Previous research has shown how positive perceptions of health can predict health, as
strong beliefs in pro-health statements were associated with higher frequency of healthy weight
management behaviors and healthy BMIs (Reser, Larsen, & Walsh, 2011). Reser et. al (2011)
shed further light on the role of beliefs in goal-related behaviors, such as staying healthy, as they
found evidence that individuals’ beliefs may predict their current BMIs. Thus, beliefs that
emphasize the importance of health maintenance behaviors also may be associated with life
goals, such as being healthy.
Health beliefs can be extrapolated to perceptions on concepts such as exercise. Previous
research has shown that exercise perceptions are important in successfully implementing
exercise goals. Karoly et. al (2005) found that self-regulatory or inhibitory perceptions were
GOALS AND WEIGHT 6
significant predictors of regular exercise habits, as regular exercisers report higher levels of self-
monitoring, value, planning, positive arousal, and other factors that culminated to achieve
exercise goals. These individuals may have engaged in these behaviors because such practices
were compatible with their self-regulatory perceptions that facilitated regular exercise habits
(Karoly, Ruehlman, Okun, Lutz, Newton & Fairholme 2005). This study also found that
individuals who exercised regularly gained more pleasure from more pursuits than those who did
not exercise regularly, which implies that goal categorizations can be reorganized in order to be
achieved by self-regulatory and self-monitoring mechanisms. These findings can be related to
weight management because internal motivations enhanced by perceptions may influence the
decisions to execute pro-weight management behaviors.
Such self-regulatory mechanisms described above appear to be affected by internal
motivations like self-determination. Mata, Silva, Vieria, Carraça, & Andrade (2011) examined
how exercise-induced motivation in obese and overweight women influenced eating regulation
behaviors in weight management through a general health intervention. They found that intrinsic
exercise motivations such as higher self-determination and more self-derived motivation to
exercise predicted better eating self-regulation. Thus, this study exhibits a foundation for which
we examined motivational factors in decision-making that involves self-regulatory strategies in
the current study.
Along with self-regulation, Wing, Fava, Phelan, McCaffery, Papandonatos, Gorin, &
Tate (2008) implemented an intervention with individuals who had lost about twenty pounds of
body weight to foster self-monitoring weight management strategies. Individuals who received
the intervention in person rather than the Internet and who reported higher frequencies of
checking weight showed less weight regain. Therefore, they found that frequency of self-
GOALS AND WEIGHT 7
monitoring in the form of self-weighing and physical activity played an influential motivational
role in the intervention, which resulted in less weight regain. Self-monitoring can thus foster
adherence to weight-management goals, and in conjunction with self-regulation we believed that
these factors would contribute to prediction of individuals’ BMIs. These are some of the
variables we investigated in the present research.
Self-determined motivations for exercise are associated with goal process cognition.
Lutz, Karoly, & Okun (2008) emphasized goal process representation, or thinking about and
feeling fulfillment in the journey to achieve a certain goal, through which they found that self-
determination in exercise influenced the frequency of exercise in individuals. Positive arousal for
individuals’ exercise goals was a powerful predictor of frequency of exercise. Therefore, they
provided evidence that goal planning, regulatory measures, and self-monitoring were
interrelated, and the interaction between self-monitoring and goal planning and perceptions could
be extrapolated to predict health.
While goal planning can affect behavior through self-regulating and self-monitoring,
there also exists interplay between goals and conflict between current endeavors in decision-
making. Emmons & King (1988) found that higher levels of conflict and ambivalence towards
individuals’ self-reported strivings were associated with greater physical illnesses and negative
affect, including depression and neuroticism. Thus, greater conflict with personal strivings
exhibited a positive correlation with the frequency of hospital visits and illnesses the participants
suffered. Additionally, conflict and ambivalence were negatively correlated with the amount of
time the participant thought about a striving and the amount that the participant pursued that
striving. Perceived conflict thus significantly influences not only psychological, but physical
well-being. In light of the current study, we see evidence that individuals are less likely to
GOALS AND WEIGHT 8
partake in activities that conflict with their goals, thus if an individual’s goal is to be at a healthy
weight, activities that are not conducive to being at a healthy weight would not be pursued, and
activities that facilitate being at a healthy weight would be pursued instead.
Goal pursuit as described above can be conceptualized in a framework proposed by
Beach and Mitchell (1987) called Image Theory. Image Theory proposes that an individual’s
self-directed motivation can be understood in terms of four images, or cognitive schemata that
the individual uses to organize their goal pursuits. The first of the four Images Beach and
Mitchell propose is the Value image, (sometimes referred to as the Self image) which represents
the underlying reasons and principles that guide a person’s selection of goals, such as their
personal morals, values, ethics, beliefs. The Trajectory image (which is directed by the Value
Image) is a future-oriented series of life goals that an individual has selected to work towards
achieving. These selected goals can be concrete or intangible goals. The Action Image comprises
the plans, procedures and strategies to achieve the goals represented in the Trajectory Image.
These plans are the medium through which the “action” of achieving a goal is accomplished.
The Projected Image is the prediction that the individual makes dependent on the present plan.
This is what the individual thinks will likely occur in the future if a new plan is adopted or a
previous plan is continued.
This hierarchy of images can be related back to weight management because goal
perceptions, as we conceptualize them in our research, result from an interaction between the
Value Image, the Trajectory Image and the Projected Image. Health behaviors that either
promotes a healthy or obese weight are subsequent actions that affect the likelihood of achieving
goals previously selected for the Trajectory Image. The change in likelihood of accomplishing
previously selected life goals would be represented in the Projected Image. Beach & Mitchell
GOALS AND WEIGHT 9
(1987) also discussed the concept of adoption decisions in decision-making. Adoption decisions
require compatibility between the Value Image and new goal options to be included in the
Trajectory Image. Thus, we predict that goal perceptions will be guiding influences in weight
management behaviors, and that they should predict people’s current BMIs.
In the context of Image Theory, Brougham and Walsh (2007) examined the influence of
the Trajectory Image in making the decision to either retire or continue working in people who
were of retirement-age. In examining individuals’ perceptions of incompatibility, facilitation, or
cost-benefit between their important life goals and working or retiring, they found that the degree
of goal incompatibility was the strongest predictor of retirement intent, above facilitation and
cost-benefit predictabilities. Greater goal incompatibility was associated with less intent to retire,
while less goal incompatibility was associated with greater intent to retire, which can illustrate
how incompatibility with life goals may affect individuals’ life goal perceptions.
In a more recent study that used image theory as its foundation and examined similar
constructs as Brougham and Walsh (2007), Talevich et. al (2012) examined the effectiveness of a
decision-making measure based on goal perceptions that predicted whether people would leave
their current jobs and look for new sources of employment. The findings were significant in
favoring the influence of goal impact on intent to stay in the current job, as 39% of the variance
in the intentions to leave a job was predicted by the perceived facilitation or conflict of the
individuals’ life goals. This finding is also substantiated by Bargh et. al (2001), who explored the
automatic origins of goals to conclude that goals guided goal-related behavior after activation.
They advocated the notion that goals can be established without conscious choice through
various studies that provided evidence for goal-related behavior in response to primed goals.
GOALS AND WEIGHT 10
Thus, such findings suggest that goal facilitation or conflict may be significant predictors in
weight management, as it was in job intent.
In the current study, we employed a 36 Goal Cluster solution that was identified in a
replication of the Chulef, Read, and Walsh (2001) study by Talevich Read, Walsh, Chopra, and
Iyer (2012) (see Appendix B). However, the outcome of weight management as represented by
current BMI was the dependent variable in the present study in contrast to intent to retire as in
the Brougham and Walsh (2007) study. We utilized the work of Talevich et. al (2012) to identify
the goals to be studied, as they examined how important human goals could be further
categorized into smaller groups by asking individuals to sort 164 goals into semantically similar
categories. Cluster analysis applied to these 164 goals was used to identify different levels of
taxonomic abstraction. We chose a 36 cluster level solution for the present study that included
goals that, fall into three major groups: (1) family/marriage/sex/romance, (2) interpersonal goals
and interacting with people, and (3) intrapersonal goals. Thus, the current study uses general goal
taxonomy in order to operationalize goal perceptions. In respect to life goals, we examined the
amount of conflict and facilitation participants perceived with attaining their life goals associated
with being at a particular weight that corresponded to both a healthy and unhealthy BMI.
Therefore, in light of the previous literature and our aims for this study, we hypothesized
that perception of conflict in achieving important life goals at BMIs of 22.5 and 30 would be
significant predictors of individuals’ actual BMI. We also hypothesized that perception of
facilitation in achieving important life goals at BMIs of 22.5 and 30 would be significant
predictors of actual BMI in participants. Finally, we hypothesized that goal perceptions favorable
to a healthy BMI would be significantly associated with self-regulation, self-monitoring, and
self-efficacy.
GOALS AND WEIGHT 11
Method
Participants
Participants were 441 people recruited through an undergraduate statistics course at the
University of Southern California. Students in the class were first recruited to complete the
survey for course extra credit, and they then recruited their family members to complete the
survey for additional extra credit. We intended to study college students and their parents to
capture a broad spectrum of life goals across age cohorts, along with a representative range of
BMI values across individuals.
Procedure
Participants anonymously completed an online survey through the Qualtrics system (see
Appendix A). Participants first reported their height in feet and inches and their weight in
pounds, both of which were used by the server to calculate their BMIs. They also reported their
perceived ideal weights in pounds, how much they liked their current weight, their desire and
intention to lose weight, and to predict the likelihood on a scale of 0-100% of being at weights
corresponding to BMIs of 25 and 30 in the next six months, in the next twelve months, and in the
next two years. Participants then organized a set of 36 goals from the most important to least
important by grouping them into seven subgroups composed of three most important, three least
important, next five most important, next five least important, and next six most important, and
next six least important goals. The remaining eight goals were automatically ranked as of
intermediate importance by our online system and required no response by participants (see
Appendix B).
Following ranking the goals, participants then reported their perception of the conflict or
facilitation that a weight, personally computed for them, corresponding to a healthy BMI of 22.5
GOALS AND WEIGHT 12
would create in achieving each of the 36 life goals they had previously ranked. They rated their
perceived conflict or facilitation through an eleven-point scale ranging from “extremely hard” to
“extremely easy” to attain the particular goal at a weight that corresponded to the participant’s
individual BMI at 22.5. They repeated this analysis with a weight corresponding to an obese
BMI of 30. Participants then answered questions regarding their own self-regulation, self-
monitoring, and self-efficacy behaviors, i.e. food intake, exercise habits, weight-monitoring
strategies, etc. These questions also included self-reports on how confident the participants felt in
maintaining healthy diets and active lifestyles when confronted with difficult situations. Lastly,
participants reported their demographic information, i.e. gender, ethnicity, education level,
political views, and religious affiliations.
Measures
Body Mass Index. Body Mass Index (BMI) infers an individual’s percentage of body fat,
or adiposity (Eknoyan 2008). BMI is calculated by the following equation:
BMI =
!"##! !"
!"#$!%!!
!
.
Mass in kilograms can be converted to pounds, and height in meters can be converted to inches
by multiplying the entire equation by a factor of 703. BMI was calculated in our study by asking
the participants to report their heights and weights. The height data was entered into an algebraic
variation of the BMI equation that allowed us to compute the weight in pounds that would
corresponded to BMIs of 22.5 and 30 for each participant, given their height. These values
looped back from the computer server, and were plugged into the questions each participant
completed regarding conflict and facilitation with the 36 goals.
GOALS AND WEIGHT 13
Taxonomy of human motives. Talevich et. al (2012) identified 36 goals through cluster
analysis, which we used in order to measure the perceptions of life goals by our participants
through an online “Q-sort” program that was integrated into the Qualtrics survey instrument.
Perceptions of conflict with life goals. We used an eleven-point scale of conflict or
facilitation developed by Brougham & Walsh (2007) in order to determine participants’
perceptions of goal achievement (-5 is “Extremely difficult to achieve,” and +5 is ‘Extremely
easy to achieve”) at a healthy and unhealthy BMI. Perceptions of conflict comprised the lower 5
choices on the scale whereas the middle choice was a neutral standpoint on the ability to pursue a
particular goal. We later separated analyses for conflict and facilitation. The conflict analyses
included only response scores that ranged from -5 to -1. All other response scores (0, and +1 to
+5) were treated as “0.” We then performed the opposite analysis when we analyzed for
facilitation, as we only included response scores that ranged from +1 to +5 while treating all
other response scores as “0.” (see Appendix A).
Perceptions of facilitation with life goals. Each goal was assessed for the participant on
the same eleven-point scale of conflict or facilitation described earlier. Perceptions of facilitation
comprised the upper 5 choices on the scale. Again, we did not analyze the conflict and
facilitation scores together in order to determine predictability because any opposite-signed
scores may have biased the data to show less individual conflict or facilitation scores. For
example, an individual who indicated +5 and -5 scores in rating two goals would have shown to
have a summated conflict/facilitation score of “0,” which would have given the impression that
there was neither conflict nor facilitation, when in fact the individual had bipolar perceptions
towards the goals (see Appendix A).
GOALS AND WEIGHT 14
Self-regulation. Kruger, Yore, & Kohl (2008) found that those who actively attempt to
lose weight engage in more physical activity than those who are not trying to lose weight.
Conversely, they found that overweight individuals do not participate in as much regulatory
exercise as individuals who actively attempt to lose weight do. Self-regulation in weight
management involves implementing and executing strategies to manage diet and physical
activity. However, there is no established measurement for self-regulation that encompasses both
exercise and diet measures that would have been applicable to the present study. Thus, in the
present study, self-regulation was measured through questions that asked participants how often
they attempted to incorporate more physical activity in daily life and how they managed their
food intake (see Appendix A).
Self-monitoring. Boutelle & Kirschenbaum (1998) examined how self-monitoring
strategies in cognitive-behavioral treatments for people attempting to lose weight resulted in
greater weight loss in the individuals. Weight loss in individuals was predicted by self-
monitoring strategies regarding food intake. Self-monitoring strategies involve individuals
keeping themselves accountable to their goals. In the present study, we measured self-monitoring
through questions that asked participants to report how often they weigh themselves and how
often they make conscious attempts to eat more healthy foods (see Appendix A).
Self-efficacy. Increasing self-efficacy along with dissatisfaction with present efforts can
drive individuals towards making a stronger effort towards achieving a goal (Bandura & Cervone
1983). Self-efficacy in health behaviors refers to the intensity or drive with which an individual
strives towards achieving a particular goal. Thus, in the context of weight management, self-
efficacy can be defined as the drive of an individual to achieve or maintain a healthy weight by
engaging in pro-health behaviors. In the present study, we measured self-efficacy through
GOALS AND WEIGHT 15
questions that asked participants how confident they feel in being able to eat healthy foods and
exercise or incorporate appropriate physical activity (see Appendix A).
Results
Preliminary Analyses
We first examined the distributions for the variables for their approximations to
normality. Only the distributions of conflict and facilitation ratings deviated substantially from
normality. The primary purpose of our study was to examine these goal perception ratings as
predictors of participant’s current BMIs using multiple regression, a statistical tool that assumes
normality of the independent and dependent variables. In order to solve this problem, we carried
out a log transformation of the original conflict and facilitation ratings to create quasi-normal
distributions. All of the analyses reported below used these log-transformed scores for conflict
and facilitation perceptions.
Table 1 shows the first order correlations among most of the variables obtained by
Pearson correlation analyses. A common level of statistical significance with p < .01 was used
for all analyses. We examined self-regulation of activity and diet measures, self-efficacy of
activity and diet measures and self-monitoring of weight measures in relation to one another and
in relation to BMI. Table 1 shows that all of the self-regulation, self-efficacy and self-monitoring
measures were correlated with each another, (correlations range from .85 to .99) suggesting that
these measures tapped a single underlying dimension or factor. However, Table 1 also shows that
none of these variables were significantly, or meaningfully correlated with BMI (range from -.01
to -.06).
In addition to self-regulation, self-efficacy, and self-monitoring, we examined the simple
correlations between perceived goal facilitation and conflict with BMI. As Table 1 shows, all
GOALS AND WEIGHT 16
four correlations of goal conflict and facilitation perceptions with BMI were significantly
correlated (range r = -.27 to +.48).
We then used four different sets of multiple regression analyses so that we could compare
the amount of variance in BMI that could be accounted for by perceptions of conflict or
facilitation in the 36 life goals. We use the R and R
2
values from the multiple regression analyses
to determine how much of the variance in individuals’ BMIs can be explained by their
perceptions of goal facilitation/conflict with healthy and unhealthy BMIs.
In these multiple regression analyses, we coded the goals that individuals ranked by
establishing a numerical hierarchy of the top three most important goals being coded as a “7,”
the next five most important goals as a “6,” next 6 most important goals as a “5,” the middle 8
unsorted goals as a “4,” the six less important goals as a “3,” the five less important goals as a
“2,” and the lowest 3 goals as a “1.” We used actual BMI as the dependent variable, which was
predicted by four independent variables: perceived goal conflict at a BMI of 22.5, perceived goal
facilitation at a BMI of 22.5, perceived goal conflict at a BMI of 30, and perceived goal
facilitation at a BMI of 30.
The goal conflict/facilitation scale was split in half at the middle, or “neither hard nor
easy,” (the “0” point on the eleven-point scale that ranged from -5 to +5). Thus, -5 to 0
comprised conflict only scores, whereas +5 to 0 comprised facilitation only scores. We
aggregated conflict and facilitation scores separately for both 22.5 and 30 BMIs across all 36
goals. We then ran multiple regression analyses using the conflict only and facilitation only
scores separately as our independent variables. As stated above, we used logarithmic
transformations of the goal facilitation and conflict scores for both 22.5 and 30 BMIs in order to
produce more normalized score distributions. The purpose of separating the analyses for conflict
GOALS AND WEIGHT 17
and facilitation was to compartmentalize the effect sizes of conflict and facilitation in order to
determine how such perceptions contributed to the prediction of BMI.
Multiple regression analyses
To examine the hypotheses that perceptions of conflict and facilitation in achieving
important life goals at BMIs of 22.5 and 30 would predict actual BMI, we performed a stepwise
regression analysis to examine the predictive nature of goal perceptions on actual BMI (Table 2).
The perceptions of goal conflict at a BMI of 30 entered first into the stepwise multiple
regressions and predicted 23.7% of the variance in actual BMI values (β = -.44, p < .01). The
perceptions of goal facilitation at a BMI of 22.5 entered second and accounted for an additional
11.2% of the variance in BMI, for a total R-square of 34.9% (β = .27, p < .01). In the third step,
perceptions of goal facilitation at a BMI of 30 entered and accounted for and additional 1.3% of
the variance in BMI, for a total R-square of 36.2% (β = .14, p < .01). On the fourth step,
perceptions of goal conflict at a BMI of 22.5 predicted another 1.1% of the variance in BMI for a
total R-square of 37.3% (β = -.11, p < .01). Thus, the total amount of variance in BMI explained
by goal perceptions (R
2
= .37) is the accumulation of the separate conflict and facilitation scores
collected from participants as they imagined they were at weights corresponding to BMIs of 22.5
and 30, which was individually computed for them based on their height.
In addition to examining the separate facilitation and conflict perceptions as independent
variables to predict BMI, we also conducted analyses that allowed conflict for one goal to be
compensated for by facilitation of another goal. These analyses used only two independent
variables, which were the algebraically combined totals of facilitation and conflict. Thus, we
added all conflict and facilitation scores (-5 to +5) across the 36 goals (1) for goal perceptions at
a BMI of 22.5 and (2) for goal perceptions at a BMI of 30 to see what their aggregated

GOALS AND WEIGHT 18
contribution towards predicting BMI would be. We used logarithmic transformations of these
scores for both 22.5 and 30 BMIs in order to produce more normalized score distributions (Table
3). The combined goal conflict and facilitation scores at a BMI of 30 predicted 19.8% of the
variance in actual BMI values (β = .40, p < .01), and the entry of perceptions of goal conflict and
facilitation at a BMI of 22.5 accounted for an additional 7.2% of the variance in BMI for a total
R-square of 27.0% (β = .27, p < .01). Thus, the total amount of variance in BMI explained by
goal perceptions (R
2
= .27) is the accumulation of the combination, or “compensatory” scores of
conflict and facilitation at BMIs of 22.5 and 30.
We examined the hypothesis that goal perceptions favorable to a healthy BMI would be
associated with more self-regulation, self-monitoring, and self-efficacy by looking at the first
order Pearson correlations between these variables, shown in Table 1. The only significant
association found was that self-efficacy regarding food was negatively related to the perceived
goal facilitation at BMI of 22.5 (r = -.21, p < .01).
Discussion
The current study supports the concepts proposed in Beach and Mitchell’s image theory,
as the perceived conflict or facilitation in achieving one’s life goals at a particular BMI were
strong, statistically significant predictors of the individual’s actual BMI. We found
overwhelmingly strong evidence for the mechanisms of image theory working in the role of
goals in weight management through the amount of predictability perceptions of goal conflict
and facilitation held over actual BMI. As explained previously, we believe that Value Image,
Trajectory Image and Projected Image interact to influence health. Our findings thus support the
role of goal perceptions, as conceptualized by Image Theory, as our results show a substantial
amount of variance in individuals’ BMIs being accounted for by goal perceptions. Thus, we
GOALS AND WEIGHT 19
believe that Image Theory can be utilized as a useful framework for understanding personal
weight management behavior.
Our hypotheses that perceptions of conflict and facilitation in achieving important life
goals at BMIs of 22.5 and 30 would predict actual BMI in participants were confirmed because
37.3% of the total amount of variance in individuals’ BMIs was accounted for by goal
perceptions. Furthermore, we found that all four conflict and facilitation scores (related to
perceptions at BMIs of 22.5 and 30) were separate, statistically significant contributors to the
total variance explained. In light of the results, we feel our hypotheses that goal perceptions of
conflict and facilitation separately can predict individuals’ BMIs are strongly confirmed. We
believe that goal perceptions can effectively predict weight (as measured by BMI) because of the
psychological concepts that Image Theory proposes, which we discussed in the introduction.
In examining the compensatory scores that aggregated conflict and facilitation scores
algebraically into a single score, we found that we lost substantial predictability in BMI in
contrast to computing separate scores for conflict and facilitation. This may be because one
strong facilitation score with an important goal can mask the conflict effects of other less
important goals when algebraically summed into a single score, and vice versa. Thus, more
important goals may have more “psychological reality” in driving behavior than a summed
composite of conflict and facilitation across many goals. Image Theory proposes such a selective
view towards behavior, and it also proposes that conflict alone is the driving influence behind
choice. However, our data shows that facilitation contributes towards choice, diverging from
Image Theory in that regard.
Our hypothesis that BMIs were significantly correlated with self-regulation, self-
monitoring, and self-efficacy was disconfirmed. Likewise, goal perceptions in association with
GOALS AND WEIGHT 20
BMI were not correlated with self-regulation and self-monitoring. However, self-efficacy
regarding food was negatively correlated to the perceived goal facilitation at a healthy BMI,
which may indicate that the confidence an individual has in maintaining a certain weight can
have significant influence on goal perceptions of being at a BMI of a certain weight. This may
have been due to the presence of many slender women in the sample, as a BMI of 22.5 may have
been greater than their actual BMI, thus the healthy BMI scenario may have been unfavorable.
However, an unexpected finding was that we found a positive beta weight value between
perception of goal facilitation at a BMI of 22.5 and actual BMI (β = .27, p < .01). This suggests
that the less one perceives goal facilitation at a BMI of 22.5, the lower their actual BMIs. This
may be explained by the fact that our sample contained many women who may have been at
BMIs below 22.5, as the healthy BMI range is from 17.5-25. Thus the thought of achieving goals
at a heavier weight might have resulted in little perceived facilitation. Therefore, although some
participants may have been at healthy weights that corresponded to a BMI less than 22.5, the
goal perceptions may have changed when given a scenario that was at a BMI that was higher
than theirs. Thus, in light of these findings, we proceeded to examine the differences between
genders regarding BMI being related to goal perceptions of being at healthy and unhealthy
BMIs.
Interested in the difference between the perceptions of women and men towards the
hypothetical BMI scenarios we posed in the study, we further examined the conflict and
facilitation scores at BMIs of 22.5 and 30 separately for males and females. First, from the
descriptive data we found that women had an average BMI of 23.8 with a standard deviation of
5.14, which indicated that as many as 40% of the female participants were at BMIs below 22.5.
Furthermore, we found a positive beta weight for the relationship between facilitation at 22.5 and
GOALS AND WEIGHT 21
BMI for the women that entered in the second step of the stepwise multiple regressions for
women only (β = 4.08, p < .01). Thus, this positive beta value weight may have been due to the
fact that 40% of the women in our sample were below a BMI of 22.5 and thereby perceived
being at a BMI of 22.5 as an undesirable weight gain, as it was for our self-efficacy and
facilitation at BMI of 22.5 finding. Finally, we found that the total amount of variance in BMI
explained by goal perceptions in females was 52.5% (R
2
= .52), which was the accumulation of
the conflict and facilitation scores at BMIs of 22.5 and 30 (Table 4).
Men had an average BMI of 24.7 with a standard deviation of 4.36, which indicated that
far less than 40% of the men had BMIs below 22.5. We also found that perception of goal
facilitation at a BMI of 22.5 did not enter in their separate stepwise regression analysis. Thus, the
positive beta weight of .27 for the overall analysis between perception of goal facilitation at a
BMI of 22.5 and actual BMI may be due largely to the female proportion of our sample. The
total amount of variance in BMI explained by goal perceptions in males only was 18.9% (R
2
=
.18), which was the accumulation of the conflict and facilitation scores at a BMI of 30 (Table 5).
Given that the relationship in women is almost three times as strong as that of men, we see a
tremendously different trend in goal perceptions and BMI between genders. As stated above, we
believe this may be largely due to the discrepancy between recommended “healthy” BMI values
for women and their actual BMIs. This holds implications for future work that involves weight
loss interventions that may differ between men and women. BMI may also not be the best
measure for healthy weight as there is such a variable range of weights among women due to
factors that contrast with men, such as less muscle mass. Thus, future studies should be more
sensitive to gender differences when choosing measures for weight and health.
GOALS AND WEIGHT 22
Another result that conflicted with our initial hypotheses was that we also found that self-
regulation and self-monitoring constructs are not significant predictors of BMI. In fact, most of
the correlations between these constructs and BMI were close to zero and accounted for no
variance in participants’ BMI. In contrast, goal perceptions (facilitation or conflict) accounted
for 37% of the variance in participants’ BMI. As self-regulation, self-monitoring, and self-
efficacy had very strong correlations to one another and nearly zero correlations to BMI, we
conclude that there is no relationship between self-reported self-regulation, self-monitoring, and
self-efficacy and actual BMI in our data. Although previous studies have established such
variables to be significant predictors of weight loss, these studies were limited to dealing with
solely obese populations. Within an obese population, more self-regulation does correlate with
more weight loss. However, the present study captured a wide range of backgrounds and BMIs,
and showed no correlation between such variables and current BMI. Measuring goal perceptions
did not affect the measurement of self-regulation, self-monitoring, and self-efficacy, as the
analyses in such variables alone show zero variance accounted for in BMI.
Limitations of this study included that we used an online survey to measure goal
perceptions, self-regulation, and BMI concurrently. We did not manipulate any variables, as we
did not incorporate any experimental elements into our study design, thus we could not make any
inferences on causality as the basis for the relationships we found between goal perceptions and
BMI. Future directions for examining similar variables would be to incorporate an interventional
design that places participants in varying conditions of being more immersed in the hypothetical
BMI scenarios and formulating an instructional program educating participants on life goals and
their relation to health. We believe that such manipulations would be able to further elucidate the
GOALS AND WEIGHT 23
relationships between goal perceptions and weight. Thus, such experimental methods would
strengthen the findings that we have accumulated in the present study.
Another limitation was that we did not use a standardized scale for measuring self-
regulation, self-monitoring, or self-efficacy. While we are aware of formalized exercise and diet
questionnaires distributed by governmental agencies, these measures did not include items that
measured the dimensions we were interested in studying. We believe that more research can be
conducted in order to determine a standardized measurement scale for such constructs so that
explanatory power of such variables may be further examined in future studies. However, in this
particular study, we did not find any correlation between self-regulation, self-monitoring, and
self-efficacy measures with BMI, although they correlated very closely with one another, which
could mean that they may measure a single underlying dimension or factor.
We also did not instruct participants to think about their 22.5 or 30 BMI situations in
regards to being at a weight of a BMI of 22.5 or less, or a weight of a BMI of 30 or more, which
may have led participants to think of their goal facilitation/conflict choices in terms of a BMI
exactly at 22.5 or 30. As we mentioned in our separate gender analyses, many slender young
women today consider a weight that corresponds to a BMI of 22.5 as overweight for their
standards (i.e. a 22-year-old, 5’5 female may believe that a weight of 135 that corresponds to a
22.5 BMI is actually overweight in contrast to her current weight of 115). Therefore, our
wording in the survey may have led some participants to become confused on how to interpret
their goal facilitation and conflict perceptions. Future research will work towards elucidating
such wording and ensuring full understanding of participants.
In conclusion, the current study has provided a foundation for future studies on goal
perceptions regarding weight-management; especially future studies that may seek to formulate a
GOALS AND WEIGHT 24
goal-directed intervention for weight loss. We found that goal perceptions were strong predictors
of current weight/BMI, and that self-regulation, self-monitoring, and self-efficacy had no
relationship to current BMI. Collecting goal perception measures did not affect the prediction of
self-regulation and self-monitoring variables, which suggests that such constructs have poor
predictability in understanding individual differences in current BMI. Therefore, we believe that
future studies will benefit substantially by incorporating measures of motivation in order to
better understand the behavioral bases for weight management.

GOALS AND WEIGHT 25
References
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GOALS AND WEIGHT 26
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GOALS AND WEIGHT 27
(2008). Maintaining large weight losses: The role of behavioral and psychological
factors. Journal of Consulting and Clinical Psychology, 76(6), 1015-1021.

GOALS AND WEIGHT 28
Table 1. Correlations Among the Variables (N=441)

1

2

3

4

5

6

7

8

9

10
1. BMI

-- -.01 -.02 -.04 -.05 -.02 .30
*
.30
*
-.47
*
-.27
*

2. Self-regulation
food
-- .97
*
.84
*
.86
*
.87
*
.06 .05 .06 .03
3. Self-regulation
exercise
-- .87
*
.88
*
.89
*
.08 .07 -.05 -.06
4. Self-monitoring -- .98
*
.97
*
-.03 .06 -.06 .05
5. Self-efficacy
food
-- .98
*
-.21
*
.01 -.08 -.03
6. Self-efficacy
exercise
-- .05 .06 -.06 .01
7. Goal facilitation
at 22.5 BMI
-- .37
*
.03 -.09
!
GOALS AND WEIGHT 29
8. Goal facilitation
at 30 BMI
-- -.10 .10
9. Goal conflict at
30 BMI
-- .32
*

10. Goal conflict at
22.5 BMI
--
*p < .01
GOALS AND WEIGHT 30
Table 2. Predicting BMI from Goal Perceptions (N=441).

Independent Variables Total R
2
Incremental R
2
β ΔF

Step 1: Goal conflict score at a BMI of 30 .23 .23 -.44 92.06
Step 2: Goal facilitation score at BMI of 22.5 .34 .11 .27 50.75
Step 3: Goal facilitation score at BMI of 30 .36 .02 .14 5.94
Step 4: Goal conflict at BMI of 22.5 .37 .01 -.11 5.26

GOALS AND WEIGHT 31
Table 3. Predicting BMI from Combined Conflict and Facilitation Perceptions.

Independent Variables Total R
2
Incremental R
2
β ΔF

Step 1: Facilitation and conflict scores at
BMI of 30
.19 .19 .40 73.07
Step 2: Facilitation and conflict scores at
BMI of 22.5
.27 .07 .27 28.98

GOALS AND WEIGHT 32
Table 4. Predicting BMI from Goal Perceptions for Females Only.
Independent Variables Total R
2
Incremental R
2
β ΔF

Step 1: Goal conflict score at BMI of 30 .32 .32 -.44 83.92
Step 2: Goal facilitation score at BMI of 22.5 .48 .16 .34 56.10
Step 3: Goal conflict score at BMI of 22.5 .51 .02 -.17 7.59
Step 4: Goal facilitation at BMI of 30 .52 .01 .12 5.11

GOALS AND WEIGHT 33
Table 5. Predicting BMI from Goal Perceptions for Males Only.
Independent Variables Total R
2
Incremental R
2
β ΔF

Step 1: Goal conflict score at BMI of 30 .14 .14 -.37 19.22
Step 2: Goal facilitation score at BMI of 30 .18 .04 .22 6.98

GOALS AND WEIGHT 34
Appendix A
Survey items without the 36 goal sorting screen:
Height and weight:

Ideal weight:

GOALS AND WEIGHT 35
Current perceptions of weight:

GOALS AND WEIGHT 36
Rating goal facilitation or conflict at a BMI of 22.5 (based on a sample height and weight input):

GOALS AND WEIGHT 37
Rating goal facilitation or conflict at a BMI of 30 (based on a sample height and weight input):

Self-regulation regarding diet reporting:

GOALS AND WEIGHT 38
Self-regulation regarding exercise reporting:

Self-monitoring reporting:

GOALS AND WEIGHT 39
Self-efficacy regarding food reporting:

Self-efficacy regarding exercise reporting:

GOALS AND WEIGHT 40
Demographics regarding gender and race:

Demographics regarding educational background:

GOALS AND WEIGHT 41
Demographics regarding political views:

Demographics regarding political views on issues:

GOALS AND WEIGHT 42
Demographics regarding religiosity:

GOALS AND WEIGHT 43
Appendix B
List of 36 goals used in the survey:
1. Wisdom & Serenity e.g. being in harmony; finding higher meaning and wisdom.
2. Technological Competence e.g. understand mechanical, physical, and technical systems;
able to synthesize information.
3. Stability & Safety e.g. having a safe, stable, conventional life; being taken care of and
mentored.
4. Social Life & Friendship e.g. being part of a social group; having close friends; having
others to be with and rely on.
5. Smart & Rational e.g. being thoughtful, and aware; being rational, logical, and showing
common-sense.
6. Sexual Intimacy e.g. attracting a sexual partner; having sexual experiences.
7. Self-Reliant e.g. being confident in one's own judgment; independent, self-sufficient, in
control.
8. Self-Regulated e.g. being disciplined, self-controlled, responsible and reliable.
9. Religion e.g. engaging in religious traditions; maintaining religious faith; pleasing God.
10. Psychological Security e.g. avoiding anxiety, stress, guilt, and regrets.
11. Playful & Exciting e.g. being spontaneous, adventurous, and playful; having an exciting
life, living for today.
12. Physical Health e.g. being active, physically fit, eating a nutritious diet and maintaining a
healthy weight.
13. Personal Growth e.g. knowing yourself; experiencing personal growth; being true to self.
GOALS AND WEIGHT 44
14. Personal Appearance & Control e.g. being clean, neat, attractive, fashionable; being
active, controlling one's environment.
15. Parent's Family e.g. obey and respect parents and elders; live close to family and accept
their help.
16. Own Family e.g. have a stable family life: have a good marriage, be close to children, and
a good parent.
17. Organized & Efficient e.g. to plan, manage well, and keep things organized; to do things
quickly and correctly.
18. Occupational Success e.g. being successful in an occupation that I like and I know well.
19. Leadership e.g. leading, influencing, and persuading others; holding others accountable.
20. Interpersonal Effectiveness e.g. being more assertive; communicate and share my
feelings with others.
21. Intellectual Growth e.g. earn advanced degrees; have intellectual conversations and
experiences; be intelligent.
22. Inspiring & Teaching Others e.g. being respected by and inspiring others; teaching and
setting a good example.
23. Helping Others e.g. listening, pleasing, and helping others; being selfless and
empathetic.
24. Happiness e.g. feeling satisfied; feeling good about one's self, being happy.
25. Financial Security e.g. achieving financial security, providing for family, leaving money
for heirs.
26. Ethics e.g. being an ethical person; honest, humble and loyal; having firm values.
27. Emotional Intimacy e.g. being emotionally close to a partner: being in love.
GOALS AND WEIGHT 45
28. Curiosity & Passion e.g. being curious, unique and different; taking risks, pursuing
ideals, being flexible.
29. Control of Others e.g. decide for and control others; have others give me things.
30. Contributions to Society e.g. having a commitment to a cause; working for peace,
equality, and justice.
31. Competition e.g. be better than others, beat them in competition.
32. Avoiding Social Rejection e.g. avoid rejection, conflict, and criticism from others; avoid
hurting others.
33. Avoiding Social Demands e.g. keeping to oneself, avoiding others, avoiding notice.
34. Avoiding Personal Effort e.g. procrastinating; avoiding effort or work; avoiding
responsibility.
35. Appreciating Beauty e.g. experiencing a world of beauty, fine design; learning about the
arts.
36. Achievement e.g. being ambitious and competent; accomplishing difficult things;
mastery & perfection.
!

Abstract (if available)

Abstract This study examined how much of the variance in individuals’ body mass indices (BMIs) can be predicted by their perception of the amount of conflict and facilitation between their important life goals and their weights. Our findings can extend towards health care in respect to the growing problem of obesity and its comorbid diseases, as we explored these conditions from a behavioral standpoint. Participants recruited from USC psychology courses completed an online survey that measured constructs of self-regulation, self-monitoring, and self-efficacy in diet and physical activity, along with rank-ordering a taxonomy of 36 important life goals. We hypothesized that individuals at healthy weights would perceive more facilitation of their respective important life goals with being at a healthy BMI (22.5), and more conflict of their respective important life goals with being at an unhealthy BMI (30). We found that we could account for 37% of the variance in individuals’ BMIs from their perceptions of conflict/facilitation of important life goals, which substantiated our predictions that life goals are associated with weight. Future research should examine the application of life goals in weight management interventions to maximize the influence of positive motivations for healthy behavior.

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Asset Metadata

Creator Lee, Grace S. (author)

Core Title Goals and weight: the interplay of goal perceptions in weight management

School College of Letters, Arts and Sciences

Degree Master of Arts

Degree Program Psychology

Publication Date 04/30/2013

Defense Date 03/25/2013

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

Tag BMI,goal perceptions,goals,Motivation,OAI-PMH Harvest,weight

Format application/pdf (imt)

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Advisor Walsh, David A. (committee chair), Farver, Jo Ann M. (committee member), Read, Stephen A. (committee member)

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