Running head: OVEREATING AND UNPLANNED EATING
A Multilevel Logistic Regression Analysis on
the Likelihood of Overeating and Unplanned Eating
Marisa Kataoka
Adviser: Dr. David Schlundt
Honors Program in Psychological Sciences
Under the direction of Dr. David Schlundt and Dr. Jo-Anne Bachorowski
Vanderbilt University
March 2017
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This paper analyzed three models of emotional eating: the restraint disinhibition model,
the affect-regulation model, and the externality model (Herman and Polivy, 1975; Haedt-Matt &
Keel, 2011; Schachter, 1968). Emotional eating is a risk factor that contributes to the probability
that a person will overeat or have an unplanned meal, which can contribute to weight gain and
obesity (Goldbacher et al., 2012). Using multilevel logistic regression in Hierarchical Linear and
Nonlinear Modeling (HLM) software, two-level models were created with overeating and
unplanned eating as the dependent variables (Version 7; Raudenbush, Bryk, & Congdon, 2010).
All three models provided valid frameworks for understanding the risk factors associated with
overeating and unplanned eating. Level 1 of the models included situational factors (e.g. negative
mood, place, people, and type of meal), which were analyzed to understand how context affected
the probability of overeating and unplanned eating. Level 2 of the models included within-person
characteristics (e.g. BMI, age, race), which were analyzed to understand how individual
demographics affected the probability of overeating and unplanned eating.
Introduction
Survey data from the National Institutes of Health and the Centers for Disease Control
and Prevention show that nearly 70% of American adults are overweight or obese (NIDDK,
2012). Over the past several years, the United States has experienced an increase in the
percentage of individuals who are overweight or obese (NIDDK, 2012). Obesity is correlated
with type 2 diabetes, heart disease, and cancer (NIDDK, 2012). Obesity can also interfere in an
individual’s ability to be productive in their daily work and social lives (Andreyeva, T.,
Luedicke, J., & Wang, Y. C., 2014). According to the CDC, the cost of treating obesity in the
United States is estimated to be $147 billion, annually (2009). Furthermore, roughly 1 in 5
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Americans die of obesity-related diseases (Columbia University Mailman School of Public
Health, 2013).
Emotional eating is one potential risk factor for obesity that has been studied for years
(Herman & Polivy, 1975). However, most research concerning emotional eating has targeted
participants who are being treated for eating disorders such as binge-eating disorder and bulimia
nervosa (Herman & Polivy, 1975). The sample used in this analysis was randomly selected from
a healthy population. Using this community sample, the proposed research aims to: (1) increase
understanding of how negative affect (e.g. negative mood, stress, and boredom) contributes to
higher probabilities of overeating and unplanned eating, (2) examine individual differences that
modify the likelihoods of overeating and unplanned eating, and (3) examine if global mental
health scores, restraint scores, location, people, and type of meal are associated with overeating
and unplanned eating. Three models were tested to understand emotional eating within the
community sample: the restraint disinhibition model, the affect-regulation model, and the
externality model. Results from the multilevel logistic regression model were compared against
the three models in order to determine what model(s) provided the best explanation for emotional
eating.
A Review of Emotional Eating Literature
Studies on emotional eating have shown correlations between increases in episodes of
emotional eating with increases in weight gain (e.g. Goldbacher et al., 2012). Literature mainly
focuses on factors that contribute to overeating; however, both overeating and unplanned eating
are categorized as impulsive eating behaviors (Zunker et al., 2012). Negative emotion has shown
to predict the likelihood of overeating (Kemp, Bui, & Grier, 2013). Using a definition found in
Kemp et al. (2013), emotional eating is a “form of dysfunctional coping” (p. 204). Individuals
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who engage in emotional eating often report feelings of negative affect prior to consuming food.
As such, negative affect includes negative emotions such as being angry, stressed, irritable, or
upset (Patel & Schlundt, 2001). Participants are more likely to overeat in response to both
negative and positive emotions as compared to neutral emotions; however, the literature on how
positive emotions affect the likelihood of overeating is less robust and was not a focus of this
paper (Patel & Schlundt, 2001).
Restraint Disinhibition Model
Herman and Polivy (1975) studied the role of anxiety and overeating in obese versus
normal weight individuals. Using research from Nisbett (1972), Herman and Polivy (1975) noted
that obese individuals are consistently living below their weight set points, and therefore are
continually restraining themselves against overeating. This constant restraint leads to anxiety,
which increases the likelihood that the obese individual will overeat. Herman and Polivy (1975)
hypothesized that normal-weight, restrained eaters would have similar eating habits to obese
participants.
In Herman and Polivy’s (1975) study, forty-two participants were given ice cream and split
into two groups: a low-anxiety group, where participants received a low-level shock, and a highanxiety group, where participants received, a high-level shock. Participants were able to view
where the shock dial was placed before they received they filled out the questionnaire and received
the shock (i.e. in the second highest position for the high-anxiety group). Ice cream was weighed
before and after the experiment. In addition, mood questionnaires were given before and after the
experiment.
Results showed that participants in the low-anxiety group had a mean anxiety of 2.75 and
participants in the high-anxiety group had a mean anxiety of 7.2 prior to eating ice cream. Within
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the high-anxiety group, participants who were categorized as restrained eaters had a mean anxiety
of 9.67 compared to unrestrained eaters in the high-anxiety group, who had a mean score of 5.8.
Researchers found a significant interaction effect between anxiety and restrained individuals; those
who were restrained eaters and were placed in the high-anxiety group ate significantly more than
unrestrained eaters in the low-anxiety group. There was no significant difference in amount of ice
cream consumed between the restrained eaters in the low-anxiety group and restrained eaters in
the high-anxiety group. Using anxiety scores before and after eating ice cream, researchers found
no significant decreases in anxiety for either unrestrained eaters or restrained eaters; however,
researchers did find a significant correlation between amount of ice cream consumed and amount
of anxiety reduction.
Affect-Regulation Model
Researchers have also proposed that maladaptive behaviors, such as binge eating, may be
used to decrease negative affect (Haedt-Matt & Keel, 2011). Over time, eating becomes a coping
mechanism and a conditioned response to negative emotions. In a meta-analysis that examined
binge eating in 968 participants, with a mean sample size of 28.7 across 36 studies, Haedt-Matt
and Keel (2011) examined antecedents of binge eating. Results showed that participants
experienced greater negative affect prior to a binge eating episode compared to their general
level of negative affect. Participants also experienced greater negative affect prior to binge eating
as compared to before a regular eating episode.
However, scientists found that participants experienced a significant increase in negative
affect from pre-binge to post-binge eating; findings, which did not support the affect regulation
model. Haedt-Matt and Keel’s (2011) analysis did suggest that negative affect was a potential
trigger or antecedent for binge eating episodes. Researchers concluded that negative emotions
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“represent a proximal antecedent to binge eating” (Haedt-Matt & Keel, 2011, p. 2).
Another model, which relates to the affect-regulation model, is labeled as the escape from
self-awareness model (Heatherton & Baumeister, 1991). Heatherton and Baumeister (1991)
hypothesized that episodes of binge eating were caused by a motivated desire to escape from a
state of self-awareness. Researchers also predicted that participants who engaged in binge eating
would be more likely to hold unrealistically high standards about their body image or future
goals compared to non-binge eaters. Researchers proposed that binge eaters would make
frequent comparisons to these high standards and would be self-aware of their inability to meet
such high standards.
Heatherton and Baumeister (1991) provided evidence that demonstrated that binge eaters
tend to overeat in order to be at a low-level of self-awareness. This shift to a low-level of selfawareness is a cognitive narrowing where only the immediate present exists. Individuals stop
engaging in long-term thinking and rational reasoning, which leads to feelings of disinhibition.
For example, Heatherton and Baumeister (1991) note that women with high career standards are
more likely to be binge eaters and individuals with bulimia nervosa are more likely to have
unrealistic expectations about their day-to-day performance standards compared to individuals
without bulimia nervosa.
Heatherton and Baumeister (1991) also cite evidence that shows that binge eaters are
more likely to have a higher level of self-consciousness when in a public environment as
compared to non-binge eaters. And, binge eaters are more likely to be depressed and anxious.
Their research concluded that the combination of a negative view of the self with the constant
appraisal of the self against unrealistic standards, due to a high level of self-awareness,
contributed to increased levels of negative affect and a desire to disengage from self-aware
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thinking.
Externality Model
Schachter (1968) predicted that participants who were obese were more likely to ignore
or misinterpret physiological hunger cues compared to normal weight participants. Schachter
(1968) hypothesized that overweight individuals tend to rely more heavily on external stimuli in
comparison to normal weight individuals. Schachter conducted a study, where researchers
manipulated the perceived passage of time for normal weight and obese individuals. Obese
individuals ate twice as much when researchers sped up the clock in an experimental room
compared to obese participants in the normal-time room.
In another study by Stunkard (1959), non-obese individuals reported a stronger
correlation between feelings of hunger and stomach contractions compared to obese individuals,
who were more likely to feel hungry even when their stomachs were not contracting. Bruch
(1973) argued that obese individuals born with ‘faulty’ genetic programming that prevents them
from properly recognizing when they are hungry. These findings suggest that the feeling of
hunger can be subjective and independent of internal, physiological cues.
Several other studies provide evidence for the externality model. Research has shown that
food availability affects portion size (Hill & Peters, 1998). In a study at Cornell University, 13
normal weight participants were given four meals over the course of a week (Levitsky & Youn,
2003). The first meal was presented as a buffet and was used to calculate a baseline weight of
food. Experimenters tested to see how much food participants would consume when food was
offered in portions of 100%, 125%, and 150% of the baseline food weight. Results showed that
participants who were offered 25% more food ate, on average, 165 kilocalories greater than
when offered 100% of the baseline amount of food. When participants were offered 150% of
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baseline weight in food, participants ate 105 kilocalories more than when offered the baseline
amount of food.
Another environmental factor that influences eating behavior includes the number of
people present during a mealtime (Wansink, 2004). A greater number of people present during a
mealtime has shown to increase the rate of consumption (Patel & Schlundt, 2001). Studies have
shown that a meal eaten with one other person can increase consumption by 33% and a meal
eating with seven or more people can increase consumption by 96% (de Castro, 2000).
In this paper, evidence for the restraint-disinhibition, affect-regulation, externality model
will be examined using Ecological Momentary Assessment (EMA) data from the Healthy
Weight Monitoring Study. A multilevel modeling approach will be used to examine individual
difference variables (level 2) and variables associated with specific eating episodes (level 1) by
examining two outcomes: overeating and unplanned eating.
Methods
This empirical analysis used data from the Mid-South Clinical Data Research Network
(CDRN), based at Vanderbilt University (https://midsouthcdrn.mc.vanderbilt.edu). CDRN was
funded by a grant from the Patient Center Outcomes Research Institute (PCORI) as part of their
initiative to create PCORINet (www.pcornet.org). During, the mid-south CDRN was one of 13
networks that was funded in the United States to facilitate patient-centered outcomes research
including observational studies and pragmatic clinical trials. During the first year of funding,
each site was required to conduct two projects using their respective data research networks: (1)
a prospective cohort study related to obesity, which formed the Healthy Weight Study and (2) a
sub-study demonstrating the ability to recruit people into research, which involved more
intensive data collection. The Mid-South network includes over two million health records in
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electronic healthcare systems from: (1) the Vanderbilt Health System, (2) the Vanderbilt
Healthcare Affiliated Network (VHAN), and (3) Greenway Medical Technologies.
Recruitment for the Healthy Weight Cohort
The Mid-South Healthy Weight Cohort Study recruited participants via patient portals,
email, phone calls, and face-to-face contact in medical clinics. Cohort participants were required
to: (1) have had at least 2 weight measurements in their medical record since April 2009, (2)
have at least 1 height measurement in their medical record since age 18, (3) be alive, and (4)
have participated in at least 1 clinic visit since April 2009. The Healthy Weight Cohort enrolled
11,776 people. After informed consent was obtained, a 20-minute Research Electronic Data
Capture (REDCap) survey was given to participants (Harris et al., 2009). The survey contained
questions about participants’ daily habits, background, and overall health. Participants were
compensated $10 for completing the survey. Participants also consented to allow researchers to
link survey data with their electronic health records (EHR) back to 2009 and for the next five
years (i.e. until August 2020). Using the EHR, researchers have access to a participant’s BMI,
weight, blood pressure, diagnoses, current medications, and certain laboratory measures (e.g.
blood glucose).
Recruitment for the Healthy Weight Monitoring Study
Participants in the Healthy Weight Cohort were randomly sampled to be included in the
Healthy Weight Monitoring Study. Periodically, emails were sent to 250 participants at a time in
the Healthy Weight Cohort. The email invitation included a link to the webpage on the Healthy
Weight Monitoring Study
(https://healthbehavior.psy.vanderbilt.edu/Trak/healthyweight/index.asp). The webpage informed
participants about the study and took them through an online informed consent process. If
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participants consented to join the study another email was sent with more specific instructions on
how to record meals and snacks and included a link to the Healthy Weight Monitoring Study
website (https://healthbehavior.psy.vanderbilt.edu/Trak/healthyweight.asp?id=subjectid).
Participants were asked to record all meals and snacks for 14 days. Before beginning,
participants were notified that at least 30 meals or snacks would have to be completed within 14
days in order to receive a $50 compensation. 418 participants were enrolled in the Healthy
Weight Monitoring Study and 14,169 meals and snacks were recorded in to the system. 75
participants were later dropped from analysis because they documented fewer than 10 meals or
snack or they had no variability meal-to-meal in their mood scores. 343 participants and 13,339
meals and snacks were used in the final analysis. At the end of the 14-day period, participants
were emailed with a short REDcap study on study experiences (REDCap) and were mailed a $50
Visa card.
Datasets
Level 1. Eating data were collected using Ecological Momentary Assessment (EMA), a
form of real-time data collection that utilizes various survey methods to obtain information about
participants’ eating behavior (Shiffman, Stone, & Hufford, 2008). In order to study emotional
eating, scientists use retrospective surveys, which are vulnerable to recall bias as well as
inaccuracies due to participant memory loss (Haedt-Matt & Keel, 2011). EMA data collection
decreases recall bias by assessing how a participant’s behavior changes during different contexts
(Shiffman et al., 2008). The Healthy Weight Monitoring Study website was accessible through
mobile devices and tablet computers.
A dashboard was created to monitor participation and meal and snack entry in real-time.
The dashboard also allowed researchers to send reminders to participants who had stopped
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recording meal and snack data for a few days. Additional reminders were sent to participants
who were close to completing 30 meals or were near the end of their 14 days of self-monitoring.
Meal and snack data were collected using client-side JavaScript code. EMA data were sent via
secure connection to a server, which stored eating episode data in a separate file based on the
participant’s identification code.
The EMA data collection began by asking the date and time when the meal or snack was
consumed by the participant. Participants self-recorded 14 questions in total, which included
emotional, social, situational, and behavioral questions. Some questions asked participants to use
a slider, which had a scale from 0 to 100 and was initially set at the 50 mark (Figure 1). Other
questions asked participants to choose an appropriate answer from a list of options (Figure 2).
The 14 EMA variables can be viewed in Table 1 of the Appendix.
A single SPSS file was created where each record represented one eating episode. Each
participant had a minimum of 10 and a maximum of 80 eating episodes (mean = 38.96, s.d. =
11.52). Upon initial examination of the data, participants were found to utilize varying ranges of
the slider for survey questions. The slider was presented in the ‘middle’ position (i.e. 50 out of
100) to participants at the beginning of every question. While some participants were prone to
use large sections of the slider (e.g. a participant’s mood scores varied from 10 to 80 over the
course of data collection), other participants used limited sections of the slider (e.g. a
participant’s mood scores had no variation or varied from 60 to 70 over the course of data
collection).
In order to control for individual differences in slider usage, participants’ mood, stress,
and boredom scores were standardized into z-scores. Z-scores were calculated by subtracting a
participant’s mean and dividing by that participant’s standard deviation. Standardization
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decreased total differences within participant’s scores, but did not eliminate relative differences
in scores from meal-to-meal. Mood (sad to happy), stress (stressed to calm), and boredom (bored
to engaged) scores were reverse coded, so that higher scores corresponded with higher levels of
sadness, stress, and boredom.
Level 1 included the following z-score predictors: mood (happy to sad), stress (calm to
stressed), boredom (engaged to bored). Level 1 also included non-standardized hunger levels
(full to hungry), people (i.e. friend, family, coworker, alone, other person), type of meal (i.e.
breakfast, lunch, supper, snack, other meal), and place (i.e. work, school, restaurant, social event,
car, other place).
Level 1 of the model included within-person differences from meal-to-meal. Variables in
level 1 covered levels of negative mood, stress, boredom, hunger, as well as people, place, and
type of meal. Variables were both continuous and categorical. Type of meal (e.g. breakfast,
lunch, supper, snack, other meal) was dummy coded with breakfast meals as the control meal.
Social context included eating with friends, family, coworkers, alone, or other person (i.e. a
person who was not a friend, family, or coworker). Social context did not include a control
variable in the model and analysis compared the presence and absence of the type of social
context (i.e. friend was present during a meal compared to no friends present during a meal). The
social context categories were recorded as binary variables and did not measure the total number
of friends, family, coworkers, or other person, only if such people were present or not present
during a meal.
Level 2. The Level 2 dataset was obtained from the Healthy Weight Cohort Study baseline
questionnaire. A separate SPSS file was created for the Level 2 dataset. Age, sex, and race
ethnicity were self-reported by each participant. Due to small numbers in certain groups such as
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American Indian and Asian, race was re-coded into four groups: white/Caucasian (not Hispanic),
black/African-American, Hispanic, and other. BMI (kg/m2) was calculated from self-reported
height and weight. Race was dummied coded with white participants as the implicit comparison
category. The variable measuring global mental health (labeled PROMH in Figure 3) was taken
from a shortened version of the Patient-Reported Outcome Measurement Information System
(PROMIS) global mental health questionnaire (Cella et al., 2010). The survey is a tool used for
measuring general mental health in participants. Selected items from the PROMIS mental health
scale were used to compute a mental health score which was converted into t-scores, with a mean
of 50 and standard deviation of 10.
A measure of dietary restraint (labeled RESTRAIN in Figure 3) was created from four
items on the baseline questionnaire: (1) Do you think you are: underweight, normal weight, or
overweight, (2) Have you attempted to lose weight in the past?: No or Yes, (3) What is your
ideal weight?, and (4) How interested in losing weight now?: Currently trying to lose weight,
Planning to lose weight in the future, or Not interested in losing weight. These four scores were
combined into an overall weight dissatisfaction score. A factor analysis was conducted, which
produced one principle component. The principle component was used to calculate a factor score
and the factor score was used as a measure of dietary restraint. Level 2 of the model used
individual characteristics (e.g. sex, age, BMI, and race) to measure between-person differences
that might predict the likelihoods of overeating and unplanned eating.
Outcome variables. The dependent variables were overeating and unplanned eating. Overeating
was coded as 0 = no overeating and 1 = overeating. Unplanned eating was coded as 0 = meal was
planned and 1 = meal was unplanned.
Multilevel Logistic Regression Model
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The data analysis was completed using a two-level mixed model in which eating episodes
were nested within participants. Using Hierarchical Linear and Nonlinear Modeling (HLM)
software, a two-level model was created to analyze the hypotheses (Version 7; Raudenbush,
Bryk, & Congdon, 2010). The dependent variables were overeating and unplanned eating; and, a
Bernoulli distribution was used in the model. Hypotheses were tested using two multilevel
logistic regression models, one for overeating and another for unplanned meals. Hypotheses,
based on the three emotional eating models, were tested in two ways: (1) level 2 variables that
significantly predicted the intercept for either overeating or unplanned eating and (2) level 1
variables that significantly predicted the dependent variables (i.e. the restraint disinhibition
model).
The complete multilevel model for overeating as it appears in HLM is displayed in Figure
3 of the Appendix. An identical model with an outcome variable for unplanned eating was used
for the second model. This model shows how level 1 and level 2 variables are combined into a
single, two-level logistic regression model.
Results
Participant Characteristics
343 participants were included in the analysis. A majority of participants were female
(77%) and white (82.4%) (see Table 2). 79.8% of male participants and 59.9% of female
participants were overweight, obese, or morbidly obese. 73.1% of all participants had college
degrees or greater. Most males (73.4%) and roughly half of the females (55.3%) were married at
the time of the study. A majority of male participants (55.1%) had household incomes of $75,000
or more. Ages varied widely with the largest group falling between 26 to 35 years for both males
and females.
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Level 1 Descriptive Statistics
Of the 13,339 meals in the sample, 13% included episodes of overeating and 23% were
unplanned meals (see Table 3). A majority of meals were eaten at home (59%) while 15% were
eaten at work and 12% were eaten in restaurants. 46% of meals were eaten alone and 40% of
meals were eaten in the presence of at least one family member. Meals were eaten as a variety of
types (i.e. breakfast, lunch, supper, snacks). Most meals were consumed when participants were
happy, calm, engaged, and hungry (see Table 3). Overall, the 13,339 meals encompass a wide
range of contextualized eating episodes.
Multilevel Model of Overeating
Predictors in level 2 of the model that predicted the likelihood of overeating included:
sex, age, BMI, race, restraint, and the PROMIS mental health score. For every year increase in
age, participants were 1% less likely (p = 0.019) to overeat at any given meal (see Table 4).
Increases in a participant’s restraint factor increased the odds of a participant overeating by
21.9% (p = 0.017). Increased mental health scores decreased the likelihood of overeating by
2.5% (p < 0.001). Sex, BMI, and race did not significantly predict the likelihood of overeating.
Predictors in level 1 of the model included levels of negative mood, stress, boredom,
hunger, as well as people present, type of meal, and location of the meal (see Table 4). Negative
mood, stress and boredom were significant predictors of overeating. For every standard deviation
increase in negative mood, there was an 8.7% increase in the odds of overeating (p = 0.007). And
for every standard deviation increase in stress, participants had a 9.2% increase in the likelihood
of overeating. One standard deviation increase in boredom predicted a 6.8% increase in the
likelihood of overeating. Hunger levels did not predict the odds of overeating.
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Additionally, several situational factors were significant predictors of overeating (see
Table 4). The presence of friends, family members, and coworkers increased the probability of
overeating compared to the presence of no friends, no family members, and no coworkers,
respectively. Meals eating with friends increased the likelihood of overeating by 44.9% (p <
0.001). When family members were present during a meal, participants had a 73.6% (p < 0.001)
increase in odds of overeating compared to when no family members were present during a meal.
Similarly, coworkers increased the likelihood of overeating by 61.7% (p = 0.003) compared to
when they were not present during a meal. Eating alone versus not alone was not a significant
predictor for overeating.
Compared to breakfast, all other meals (i.e. lunch, supper, snack, and other meal) had
higher probabilities of overeating (see Table 4). Results showed that lunch meals increased the
odds of overeating by 221.6% in comparison to breakfast meals (p < 0.001). Supper meals and
snacks increased odds by 466.5% (p < 0.001) and 373.2% (p < 0.001), respectively. When
participants were eating an ‘other’ meal, they were 594% (p < 0.001) more likely to overeat
compared to breakfast meals.
Eating meals at work decreased the likelihood of overeating by 29.9% (p = 0.011)
compared to meals eaten at home. Eating meals at school compared to meals at home also
decreased the odds of overeating (odds ratio = 0.27, p = 0.004). Meals eaten at restaurants and
social events increased the likelihood of overeating compared to meals at home; restaurants
increased odds by 147% (p < 0.001) and social events increased odds by 153.6% (p < 0.001).
Meals eaten in cars and other places were not significant predictors of overeating in comparison
to meals eaten at home.
Multilevel Model of Unplanned Eating
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Predictors in level 2 of the model that predicted the likelihood of unplanned eating were
consistent with the overeating multilevel model and included: sex, age, BMI, race, restraint, and
the PROMIS mental health score. For every year increase in age, participants were 1.1% less
likely (p = 0.04) to engage in unplanned eating at any given meal (see Table 4). The remaining
level 2 factors were insignificant predictors of unplanned eating, including the restraint factor
and global mental health scores.
Predictors in level 1 of the model are consistent to the overeating model (see Table 4).
Negative mood, boredom, and hunger were significant predictors of unplanned eating. For every
standard deviation increase in negative mood, there was an 13.1% increase (p < 0.001) in the
probability of unplanned eating. One standard deviation increase in boredom increased odds of
unplanned eating by 17.5% (p < 0.001). Increases in hunger also increased the likelihood of
participants engaging in unplanned eating (odds ratio = 1.006, p < 0.001).
In comparison to the overeating model, the presence of a family member was the only
significant social factor in the unplanned eating model (see Table 4). Meals and snacks eaten in
the presence of family members decreased odds by 34.1% (p = 0.002) compared to meals
consumed in the presence of no family members. Meals and snacks consumed with friends,
coworkers, and other people were not significant predictors (p = 0.713, 0.226, 0.924) of
unplanned eating. The odds of eating alone versus eating with at least one other person of any
type did not significantly (p = 0.439) predict the likelihood of unplanned eating.
Compared to breakfast, lunch meals had an increased probability of unplanned eating by
55.4% (p <0.001) (see Table 4). Snacks and meals categorized as ‘other’ increased odds by
2,766.2% times (p < 0.001) and 1,628.4% (p < 0.001) in comparison to breakfast meals,
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respectively. Supper meals were not significant predictors of unplanned eating compared to
breakfast meals (p = 0.06).
All location variables, excluding meals consumed at social events, were significant
predictors of unplanned eating compared to meals and snacks consumed at home (see Table 4).
Meals and snacks eaten at work and school decreased the likelihood of unplanned eating by
39.1% (p < 0.001) and 65.0% (p = 0.007), correspondingly, compared to meals at home. In
contrast, meals eaten at restaurants, in cars, and in locations classified as ‘other’ increased odds
of unplanned eating by 59.4% (p < 0.001), 116% (p < 0.001), and 51.2%, respectively.
Discussion
The analysis utilized a large dataset, which allowed for sophisticated models to be tested
for this paper. Multilevel logistic regression models were used to test three emotional eating
models using two separate dependent variables, overeating and unplanned eating. The multilevel
model for overeating supported the restraint disinhibition, affect-regulation, and externality
hypotheses (Herman and Polivy, 1975; Haedt-Matt & Keel, 2011; Schachter, 1968). For the
restraint disinhibition hypothesis, higher levels of restraint increased odds of overeating (see
Table 4).
Considering the affect-regulation hypothesis, higher levels of negative mood, stress, and
boredom increased the probability of overeating (see Table 4). Though the data cannot discern
participant motives, if any, prior to an episode of overeating, one explanation may indicate that
participants overate in order to decrease negative mood, stress, and boredom. In addition,
participants with worse mental health scores were at a greater risk of overeating (see Table 4).
Hunger was an insignificant (p = 0.315) predictor of the likelihood overeating, while
nearly all people, place, and type of meal variables were significant predictors of the odds of
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18
overeating (see Table 4). These results suggest that situational factors influenced the probability
of overeating rather than hunger levels, and that participants were influenced by the context in
which they consumed their meals and snacks rather than internal physiological cues. These
findings support the externality hypothesis (Schachter, 1968). Additionally, the multilevel
models produced additive effects and independent predictors; therefore, a supper meal consumed
with a friend while stressed produced much greater odds of overeating compared to a supper
meal consumed without a friend while less stressed.
The multilevel model for unplanned eating supported some aspects of the affectregulation and externality hypotheses (Haedt-Matt & Keel, 2011; Schachter, 1968). Increases in
negative mood and boredom increased the probability of unplanned eating (see Table 5).
However, stress and mental health scores were insignificant factors, which may indicate that the
affect-regulation model is a less robust framework for explaining the causes of unplanned eating
compared to overeating. Hunger was a significant factor in the odds of unplanned eating, which
does not provide evidence for the externality hypothesis proposed by Schachter (1968). Though,
it is worth noting that Schachter (1968) focused on overeating rather than unplanned eating in his
analysis of the externality model of emotional eating. Nonetheless, participants’ situational cues
(i.e. people, place, and type of meal) also predicted significant effects on the probability of
unplanned eating. Finally, the model did not demonstrate evidence for the restraint disinhibition
hypothesis (Herman and Polivy, 1975). Again, the restraint disinhibition model by Herman and
Polivy (1975) focused on disinhibited eating which is more similar to the overeating outcome
variable.
Age was the sole significant predictor of overeating and unplanned eating out of the
individual demographic variables (see Tables 4 and 5). Increases in age decreased the likelihoods
OVEREATING AND UNPLANNED EATING
19
of both overeating and unplanned eating. Sex, race, and BMI were not significant predictors of
either overeating or unplanned eating.
Limitations
Limitations surrounding this analysis arise from the validity and reliability of EMA data.
Participants in the Healthy Weight Monitoring Study were instructed to complete survey data
immediately or soon-after every meal consumed within a two-week period. However, many
participants recorded data significantly after (i.e. several hours) a meal was consumed. This may
have contributed to increased recall bias in the data. During the course of data collection,
identical EMA surveys were administered within a short period. Participants may have been
inclined to record repetitive answers independent of their actual experiences, which would have
produced little to no variability within their eating data. New technologies, such as smart
watches, should be utilized in future studies to decrease recall bias and encourage participants to
record data as soon as possible after meal consumption.
Another limitation of this analysis was that it did not include interaction effects. The
models only accounted for intercepts as outcomes and not slopes as outcomes. It is possible that
variables like sex, age, restraint, or mental health might predict differences in the strength of
predictors such as people, place, type of meal, and mood for overeating and unplanned eating.
Interaction effects may provide greater understanding about the relationships between negative
affect, people, type of meal, location, and likelihoods of overeating and unplanned eating. For
example, the effect of negative mood might interact with social context such as negative moods
becoming most polarized when a person is alone.
Future analyses should account for potential interaction effects. Future studies should
also aim to include a more diverse set of participants in order to better understand how sex,
OVEREATING AND UNPLANNED EATING
20
race/ethnicity, and socio-economic status influence eating habits. The sample in the Healthy
Weight Monitoring Study consisted mostly of white women and a majority of the participants
were above-normal weight, married, and educated. New studies should continue to include nontargeted, random samples in order to strengthen models of overeating and unplanned for
community populations.
Conclusion
The analysis in this paper builds off of previous research on emotional and contextualized
eating. By understanding the factors that lead to increased probabilities of overeating and
unplanned eating, this empirical analysis aimed to increase understanding of three models: the
restrain disinhibition model, the affect-regulation model, and the externality model. While all
three models provide valid explanations for overeating, the findings demonstrated that the three
models were less robust for unplanned eating. This analysis has demonstrated that negative affect
affects how likely an individual is at-risk for overeating and unplanned eating even after
controlling for individual characteristics. Moreover, people, type of meal, and location all
influence eating behaviors.
As the United States, and the rest of the globe, continue to fight against rising obesity
rates, the impact of emotional and contextualized eating factors must be integrated into solutions
targeted at obesity. Treatment programs must be broadly based in order to be effective, due to the
fact that eating behaviors are sensitive to a wide range of factors. It is imperative that treatment
programs account for meal-to-meal differences in an individual’s emotions, location, type of
meal, and social context. Programs that are not broadly based run the risk of ignoring important
factors that increase high-risk eating behaviors such as overeating and unplanned eating. I hope
OVEREATING AND UNPLANNED EATING
that the findings in this paper help readers to gain a greater understanding of the complexity of
everyday eating behaviors.
21
OVEREATING AND UNPLANNED EATING
22
References
Adult Obesity Facts. (2015). Retrieved February 02, 2016, from
http://www.cdc.gov/obesity/data/adult.html
Andreyeva, T., Luedicke, J., & Wang, Y. C. (2014). State-Level Estimates of ObesityAttributable Costs of Absenteeism. Journal of Occupational and Environmental
Medicine, 56(11), 1120-1127. doi:10.1097/jom.0000000000000298
Bruch, H. (1973). Eating Disorders: Obesity, Anorexia Nervosa, and the Person Within. New
York, NY: Basic Books.
Cella, D., Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S., Amtmann, D., Bode, R.,
Buysse, D. J., Choi, S. W., Cook, K. F., DeVellis, R., DeWalt, D., Fries, J. F., Gershon,
R., Hahn, E., Pilkonis, P., Revicki, D., Rose, M., Weinfurt, K., & Hays, R. D. on behalf
of the PROMIS Cooperative Group. (2010). Initial item banks and first wave testing of
the Patient–Reported Outcomes Measurement Information System (PROMIS) network:
2005–2008. Journal of Clinical Epidemiology, 63(11), 1179-94.
de Castro, J. (2000). Eating behavior: lessons from the real world of humans. Ingestive Behav.
Obes. 16, 800–13.
Geliebter, A. & Aversa, A. (2003). Emotional eating in overweight, normal weight, and
underweight individuals. Eating Behaviors, 3, 341-347.
Goldbacher, E., Grunwald, H., Lagrotte, C., Klotz, A., Oliver, T., Musliner, K., . . . Foster, G.
(2012). Factor structure of the Emotional Eating Scale in overweight and obese adults
seeking treatment. Appetite, 59(2), 610-615.
OVEREATING AND UNPLANNED EATING
23
Haedt-Matt, A. A., & Keel, P. K. (2011). Revisiting the affect regulation model of binge eating:
A meta-analysis of studies using ecological momentary assessment. Psychological
Bulletin, 137(4), 660-681. doi:10.1037/a0023660
Harris, P., Taylor, R., Thielke, R., Payne, J., Gonzalez, N., & Conde, J. (2009). Research
electronic data capture (REDCap) - A metadata-driven methodology and workflow
process for providing translational research informatics support. J Biomed Inform, 42(2),
377-81.
Heatherton, T. & Baumeister, R. (1991). Binge Eating as Escape from Self-Awareness.
Psychological Bulletin, 110, 86-108.
Herman, C. P., & Polivy, J. (1975). Anxiety, restraint, and eating behavior. Journal of Abnormal
Psychology,84(6), 666-672. doi:10.1037/0021-843x.84.6.666
Hill, J. & Peters, J. (1998). Environmental Contributions to the Obesity Epidemic. Science, 280,
1371-1374.
Kemp, E., Bui, M., & Grier, S. (2013). When food is more than nutrition: Understanding
emotional eating and overconsumption. Journal of Consumer Behaviour J. Consumer
Behavior, 12(3), 204-213.
Levitsky, D. & Youn, T. (2004). The More Food Young Adults Are Served, the More They
Overeat. American Society for Nutritional Sciences, 134, 2546-2549.
Mid-South Clinical Data Research Network. (n.d.). Vanderbilt University. Retrieved April 16,
2016, from https://midsouthcdrn.mc.vanderbilt.edu
Nisbett, R. (1972). Hunger, Obesity, and the Ventromedial Hypothalamus. Psychological
Review, 79, 433 – 453.
OVEREATING AND UNPLANNED EATING
24
Obesity Kills More Americans Than Previously Thought. (2013). Retrieved January 28, 2017,
from https://www.mailman.columbia.edu/public-health-now/news/obesity-kills-moreamericans-previously-thought
Overweight and Obesity Statistics. (2012). Retrieved February 02, 2016, from
http://www.niddk.nih.gov/health-information/health-statistics/Pages/overweight-obesitystatistics.aspx
Patel, K., & Schlundt, D. (2001). Impact of moods and social context on eating behavior.
Appetite, 36(2), 111-118.
The Patient Centered Outcomes Research Institute (PCORI). The national patient-centered
clinical research network: Clinical Data Research Networks (CDRN).
Raudenbush, S.W., Bryk, A.S, & Congdon, R. (2010). HLM 7 for Windows [Computer
software]. Skokie, IL: Scientific Software International, Inc.
Schachter, S. (1968). Obesity and Eating. American Association for the Advancement of Science,
161, 751-756.
Shiffman, S., Stone, A., & Hufford, M. (2008). Ecological Momentary Assessment. Annual
Review of Clinical Psychology, 4, 1-32. doi: 10.1146/annurev.clinpsy.3.022806.091415
Stunkard, A. & Messick, S. (1985). The Three-Factor Eating Questionnaire to Measure Dietary
Restraint, Disinhibition and Hunger. Journal of Psychosomatic Research, 29, 71-83.
Wansink, B. (2004). Environmental Factors That Increase The Food Intake And Consumption
Volume Of Unknowing Consumers. Annual Review of Nutrition, 455-479.
Wansink, B. & Sobal, J. (2007) Mindless Eating. Environment and Behavior, 39, 106-123.
OVEREATING AND UNPLANNED EATING
25
Zunker, C., Karr, T., Saunders, R., & Mitchell, J. E. (2012). Eating Behaviors Post-Bariatric
Surgery: A Qualitative Study of Grazing. Obesity Surgery, 22(8), 1225-1231.
doi:10.1007/s11695-012-0647-7
Appendix
Measure
Date
Type of Meal
Survey Question
Pick a date.
Type of meal? (i.e. breakfast, lunch,
supper, snack, other)
Location
Where did you eat? (i.e. home, work,
school, restaurant, social event, car, other)
Social Context
Who did you eat with? (i.e. friend, family,
coworker, alone, other)
Mood*
Rate your mood. (i.e. sad to happy)
Stress*
Rate your stress. (i.e. stressed to
calm/relaxed)
Interest*
Rate how interested/engaged you are. (i.e.
bored to engaged)
Energy*
Rate your energy/fatigue. (i.e. tired to
energetic)
Hunger*
How hungry did you feel before eating?
(i.e. very hungry to full)
Planned
Was this meal planned (i.e. is this a
habitual meal) or unplanned (i.e. were you
not?
Overeat
Did you overeat? (i.e. did you eat more
than you planned to)
Healthy
How healthy were your food choices (i.e.
using your own standards how healthy
were your food choices?)
Food Groups
What kinds of foods did you eat? (i.e.
fruits/fruit juices, vegetables,
bread/cereal/rice, meats, eggs/beans/nuts,
milk/cheese/yogurt, salty snacks,
desserts/sweets, sugared beverage,
dressing/butter/spreads)
Loss of Control*
Overall, how much control did you feel
like you had over your eating at this meal
or snack? (i.e. in control to out of control)
*These measures asked participants to self-rate using a slider and had a range from 0 to 100.
Table 1. EMA survey measures used in the Healthy Weight Monitoring Study
OVEREATING AND UNPLANNED EATING
26
Male
Frequency
%
Female
Frequency
%
Age
18 to 25
26 to 35
36 to 45
46 to 55
56 to 65
66 to 75
75+
Total
3
18
17
17
13
10
1
79
3.8%
22.8%
21.5%
21.5%
16.5%
12.7%
1.3%
24
82
51
46
41
20
0
264
9.1%
31.1%
19.3%
17.4%
15.5%
7.6%
0.0%
Underweight
Normal
Overweight
Obese
Morbidly obese
Total
1
15
38
15
10
79
1.3%
19.0%
48.1%
19.0%
12.7%
7
99
68
44
46
264
2.7%
37.5%
25.8%
16.7%
17.4%
HS Graduate or GED
Some College or 2year degree
College degree
More than college
degree
Total
5
6.5%
11
4.2%
15
19.5%
61
23.1%
22
28.6%
65
24.6%
35
45.5%
127
48.1%
BMI
Education
(not in model)
Household
Income
(not in model)
Marital Status
(not in model)
77
264
less than $10,000
$10,000 to $19,999
$20,000 to $34,999
$35,000 to $49,999
$50,000 to $74,999
$75,000 to $99,999
$100,000 or more
Total
5
3
7
7
13
11
32
78
6.4%
3.8%
9.0%
9.0%
16.7%
14.1%
41.0%
7
5
35
37
53
49
62
248
2.8%
2.0%
14.1%
14.9%
21.4%
19.8%
25.0%
Now Married
Living with a partner
/significant other
Widowed
58
73.4%
146
55.3%
8
10.1%
29
11.0%
0
0.0%
2
0.8%
OVEREATING AND UNPLANNED EATING
Divorced
Separated
Never married
Total
27
0
0
13
79
0.0%
0.0%
16.5%
27
3
57
264
10.2%
1.1%
21.6%
25
11
215
11
262
9.5%
4.2%
82.1%
4.2%
Race/ethnicity
Black
3
3.8%
Hispanic
2
2.6%
White
65
83.3%
Other
8
10.3%
Total
78
Table 2. Level 2 Descriptive Statistics
Mean
SD
Overeat
Unplanned
0.13
0.23
0.34
0.42
Mood
68.34
20.43
Stress
Boredom
Hunger
62.62
64.62
37.88
24.07
22.44
19.12
Home
Work
School
Restaurant
Social Event
Car
Other Place
0.59
0.15
0.00
0.12
0.02
0.05
0.06
0.49
0.36
0.07
0.32
0.15
0.23
0.23
Friend
Family
Coworker
Alone
Other
0.11
0.40
0.06
0.46
0.02
0.00
0.28
0.25
0.31
0.49
0.24
0.50
0.15
0.00
0.45
0.44
Outcome Variables
Emotion Variables
(non-standardized,
not reverse coded)
Place
Social Context
Type of Meal
Breakfast
Lunch
OVEREATING AND UNPLANNED EATING
28
Supper
0.26
Snack
0.20
Other Meal
0.01
Table 3. Level 1 Descriptive Statistics
0.44
0.40
0.10
Fixed
Effect
Coefficient
t-ratio
p-value
Odds
Ratio
Confidence
Interval
Intercept
-2.377
-4.341
<0.001
0.093
(0.032,0.273)
Sex
-0.031
-0.217
0.829
0.969
(0.728,1.290)
Age
-0.010
-2.366
0.019
0.990
(0.982,0.998)
BMI
0.012
1.095
0.274
1.012
(0.990,1.035)
Black
-0.025
-0.126
0.9
0.976
(0.665,1.432)
Hispanic
-0.155
-0.72
0.472
0.856
(0.560,1.309)
Other
-0.406
-1.928
0.055
0.666
(0.440,1.009)
Restraint
0.198
2.404
0.017
1.219
(1.037,1.434)
Mental
Health
-0.025
-3.396
<0.001
0.975
(0.961,0.989)
Negative
Mood
0.084
2.691
0.007
1.087
(1.023,1.155)
Stress
0.088
2.694
0.007
1.092
(1.024,1.163)
Boredom
0.066
2.101
0.036
1.068
(1.004,1.136)
Hunger
0.002
1.004
0.315
1.002
(0.998,1.006)
Friend
0.371
3.292
<0.001
1.449
(1.162,1.808)
Family
0.551
4.48
<0.001
1.736
(1.364,2.209)
Coworker
0.480
2.971
0.003
1.617
(1.178,2.220)
Alone
0.254
1.924
0.054
1.289
(0.995,1.671)
Other
Person
0.101
0.605
0.545
1.107
(0.797,1.536)
Level 2
Level 1
OVEREATING AND UNPLANNED EATING
29
Lunch
1.168
10.634
<0.001
3.216
(2.593,3.989)
Supper
1.734
17.125
<0.001
5.665
(4.645,6.910)
Snack
1.554
14.213
<0.001
4.732
(3.819,5.863)
Other Meal
1.937
9.24
<0.001
6.940
(4.601,10.46
7)
Work
-0.356
-2.545
0.011
0.701
(0.533,0.921)
School
-1.309
-2.891
0.004
0.270
(0.111,0.656)
Restaurant
0.904
9.303
<0.001
2.470
(2.041,2.988)
Social
Event
0.930
6.099
<0.001
2.536
(1.880,3.420)
Car
-0.056
-0.396
0.692
0.946
(0.717,1.247)
Other Place
0.141
1.1
0.271
1.151
(0.896,1.479)
Table 4. Multilevel Logistic Regression - Predicting Likelihood of Overeating from Individual
Characteristics and Situational Eating Factors.
Fixed
Effect
Coefficient
t-ratio
p-value
Odds
Ratio
Confidence
Interval
-1.476
-0.146
-0.011
0.008
0.028
0.104
-0.268
0.204
-1.746
-0.801
-2.064
0.516
0.146
0.189
-0.877
1.764
0.082
0.423
0.04
0.606
0.884
0.85
0.381
0.079
0.229
0.864
0.989
1.008
1.028
1.110
0.765
1.226
(0.043,1.207)
(0.604,1.237)
(0.978,0.999)
(0.978,1.039)
(0.708,1.493)
(0.375,3.287)
(0.419,1.396)
(0.977,1.538)
-0.012
-1.016
0.31
0.988
(0.965,1.011)
0.123
3.496
<0.001
1.131
(1.055,1.211)
0.005
0.161
0.006
0.043
-0.417
0.157
5.235
3.353
0.368
-3.056
0.875
<0.001
<0.001
0.713
0.002
1.005
1.175
1.006
1.043
0.659
(0.945,1.069)
(1.106,1.248)
(1.003,1.010)
(0.832,1.309)
(0.504,0.861)
Level 2
Intercept
Sex
Age
BMI
Black
Hispanic
Other
Restraint
Mental
Health
Level 1
Negative
Mood
Stress
Boredom
Hunger
Friend
Family
OVEREATING AND UNPLANNED EATING
30
Coworker
Alone
Other
Person
Lunch
Supper
Snack
-0.209
-0.111
-1.211
-0.774
0.226
0.439
0.811
0.895
(0.578,1.138)
(0.676,1.185)
-0.018
-0.096
0.924
0.982
(0.678,1.422)
0.441
0.183
3.356
4.977
1.882
25.932
<0.001
0.06
<0.001
1.554
1.201
28.662
(1.306,1.849)
(0.992,1.454)
(22.241,36.937)
Other Meal
2.850
10.926
<0.001
17.284
(10.366,28.818)
Work
School
Restaurant
Social
Event
Car
-0.496
-1.048
0.466
-3.847
-2.719
4.214
<0.001
0.007
<0.001
0.609
0.350
1.594
(0.473,0.784)
(0.165,0.746)
(1.283,1.980)
0.107
0.564
0.573
1.112
(0.768,1.611)
0.770
5.589
<0.001
2.160
(1.649,2.829)
Other Place
0.414
3.484
<0.001
1.512
(1.198,1.909)
Table 5. Multilevel Logistic Regression - Predicting Likelihood of Unplanned Eating from
Individual Characteristics and Situational Eating Factors.
Figure 1. Screenshot of dashboard during survey collection.
OVEREATING AND UNPLANNED EATING
Figure 2. Screenshot of dashboard during survey collection.
Level 1:
Prob(OVEREATij=1|βj) = ϕij
ηij = β0j + β1j*(MDREVLNij) + β2j*(STREVLNij) + β3j*(BOREVLNij) + β4j*(HUNGERij)
+ β5j*(NFRIENDij) + β6j*(NFAMILYij) + β7j*(NCOWORKEij) + β8j*(NALONEij)
+ β9j*(NOTHERij) + β10j*(LUNCHij) + β11j*(SUPPERij) + β12j*(SNACKij)
+ β13j*(OTHERMEAij) + β14j*(WORKij) + β15j*(SCHOOLij) + β16j*(RESTAURAij)
+ β17j*(SOCIAL_Eij) + β18j*(CARij) + β19j*(OTHERPLAij)
Level 2:
β0j = γ00 + γ01*(SEXj) + γ02*(AGEj) + γ03*(BMIj) + γ04*(BLACKj)
+ γ05*(HISPANICj) + γ06*(OTHERj) + γ07*(RESTRAINj) + γ08*(PROMHj) + u0j
Figure 3. The multilevel logistic regression model for overeating.
31