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. Author manuscript; available in PMC: 2022 Apr 1.
Published in final edited form as: J Psychosom Res. 2021 Jan 29;143:110373. doi: 10.1016/j.jpsychores.2021.110373

Descriptives and Baseline Ecological Momentary Assessed Predictors of Weight Change over the Course of Psychological Treatments for Binge Eating Disorder

Tyler B Mason 1, Kathryn E Smith 2, Gail A Williams-Kerver 3, Ross D Crosby 3, Scott G Engel 3, Scott J Crow 4, Stephen A Wonderlich 3, Carol B Peterson 4
PMCID: PMC8857897  NIHMSID: NIHMS1667726  PMID: 33567390

Abstract

Objective:

The objectives were to examine individual variability in weight change across psychological treatments for binge-eating disorder (BED) and to examine baseline predictors (i.e., BED symptoms, affect, and appetite) of weight change using ecological momentary assessment (EMA).

Method:

Adults with BED (N=110) enrolled in a randomized clinical trial in which they received one of two psychological treatments for BED. At baseline, participants completed a 7-day EMA protocol measuring BED symptoms, affect, and appetite. Height and weight were measured at baseline, mid-treatment, end-of-treatment, and follow-up, and body mass index (BMI) was calculated.

Results:

On average, participants evidenced a 2% increase in BMI at end-of-treatment and a 1% increase between end-of-treatment and 6-month follow-up assessments. Although results showed that BMI increased over time, the quadratic term reflected a deceleration in this effect. There were interactions between positive affect and the linear trajectory across time predicting BMI, indicating that individuals reporting higher positive affect at baseline evidenced a flatter trajectory of weight gain. There was a main effect of overeating as assessed by EMA and interactions between overeating and linear and quadratic trajectories across time predicting BMI. Individuals who reported greater overeating at baseline had higher BMI across time. However, the BMI of individuals with lower overeating increased linearly, and increases in BMI among those with average or high rates of overeating appeared to stabilize over time.

Conclusion:

Despite the variability in weight change, baseline positive affect and overeating may be ecological targets for improving weight outcomes in psychological treatments for BED.

Keywords: binge-eating disorder, obesity, ecological momentary assessment, weight trajectory

Obesity is a significant global public health problem [1] and is associated with increased mortality [2] and a constellation of diseases including diabetes, heart disease, and cancer [3]. In addition, obesity is associated with greater health care utilization and spending [4] and reduced quality of life [5]. Binge-eating disorder (BED), which is defined as having, on average, at least one binge eating episode a week for at least three months with associated distress [6], is consistently associated with obesity, as individuals with obesity are over five times more likely to have a lifetime BED diagnosis compared to individuals of normal weight [7]. Evidence-based BED treatments often fail to produce reliable decreases in weight in patients; however, a recent study found wide variability in weight change after psychological treatment for BED [8]. Specifically, in a large cognitive behavioral therapy trial for BED, about a third of patients lost weight, a third maintained their weight, and a third gained weight [8].

Given these variable outcomes, more research is needed to elucidate predictors of weight change in psychological treatments for BED. To date, little research has examined correlates of weight outcomes over the course of BED treatment; yet, identifying baseline factors that may promote or inhibit weight loss in individuals with co-occurring BED and obesity may inform the development of novel targets to enhance weight loss outcomes. Also, elucidating pre-treatment characteristics that predict weight change could be used to personalize treatments including providing complementary treatment material and increasing knowledge of factors that might be associated with poor weight outcome.

Presumably, psychological treatments should lead to weight loss by reducing binge eating and thus lower caloric intake [9]. Consistent with this possibility, one recent study found that greater reductions in objective binge eating frequency from baseline to end of treatment were associated with more weight loss [8]. Further, patients with a greater frequency of baseline objective binge eating episodes or higher disinhibited eating were more likely to gain weight over the course of treatment, while patients with lower frequencies of objective binge eating or disinhibited eating at baseline were more likely to maintain or lose weight [8]. However, weight change was not related to other eating disorder symptoms, coping, self-esteem, and depressive symptoms [8]. Therefore, the severity of some eating-related symptoms at baseline may be key predictors of weight change in psychological BED treatments, with individuals with lower baseline severity more likely to lose weight.

Previous studies [8] utilized traditional self-report and interview questionnaires to assess binge eating, which are limited by retrospective recall biases [10]. Recall biases inherent in self-report and interview measures include recency bias (i.e., recent events are more easily remembered), saliency (i.e., salient events are more easily recalled), effort after meaning (i.e., tendency to reconstruct events so that they are consistent with subsequent events), and aggregation of events (i.e., having to recall or add up all occurrences of events across an extended period of time). Ecological momentary assessment (EMA) is a naturalistic assessment methodology that involves the completion of multiple daily assessments over the course of a designated period of time (e.g., a week). Advantages of EMA are that assessment occurs in people’s natural environments (real-world) and temporally close to actual experiences (real-time), thus limiting retrospective recall biases and maximizing ecological validity. Previous research has found only moderate correlations between interview and EMA measures of negative affect and binge eating [11]. Thus, using EMA in randomized clinical trials for BED and obesity could improve measurement of constructs of interest and may be useful for elucidating baseline predictors of weight change across BED treatment with greater accuracy.

This paper examined individual variability in weight change (Aim 1) and baseline EMA-assessed predictors of weight change (Aim 2) across two psychological treatments for BED (i.e., integrative cognitive affective therapy [ICAT]) [12] and cognitive behavioral therapy – guided self-help [CBTgsh]) [13]. This is a secondary report of a randomized controlled trial of two psychological BED treatments, which has been previously published [14]. Baseline EMA-assessed measures included BED symptoms (i.e., loss of control eating and overeating), appetite (i.e., hunger and craving), and affect (i.e., positive and negative affect). Predictors were chosen given their relevance to BED treatment outcomes [8,15] as well as their role in the maintenance of BED symptoms [16-19] and weight gain [20]. Overeating and loss of control eating have been shown to independently predict outcomes and thus were used separately [21-23]. It was hypothesized that lower levels of BED symptoms, hunger and craving, and negative affect and higher positive affect would be predictive of decreases in weight.

Method

Participants and Procedure

Participants were 112 adults who met full Diagnostic and Statistical Manual of Mental Disorders-5 criteria for BED [6] and were enrolled in a randomized controlled trial at two cities in the Midwestern United States. Exclusion criteria for the study included severe comorbid psychopathology (i.e., lifetime history of psychotic symptoms or bipolar disorder, substance use disorder within 6 months of enrollment), medical or psychiatric instability (e.g. active suicidality), clinically significant purging behavior, eating or weight loss treatment, or a medical condition affecting eating or weight. Detailed exclusion criteria is available elsewhere [14]. Institutional review board approval for the study was obtained at each site.

Participants were recruited from eating disorder clinics, community advertisements, and social media postings at two cities in the Midwest United States. Following an initial eligibility screening, eligible and interested participants completed written informed consent, a baseline assessment comprised of semi-structured clinical interviews assessing eating disorder symptoms and comorbid psychopathology, and a seven-day EMA protocol to assess BED symptoms, affect, and appetite in the natural environment.

Following completion of the baseline assessment, participants were randomized to ICAT-BED (n=56) or CBTgsh (n=56). ICAT-BED [12] consisted of 21, 50-minute individual face-to-face sessions with a study therapist occurring over the course of 17 weeks. CBTgsh utilized “Overcoming Binge Eating” [13], an evidence-based self-help text emphasizing consistent self-monitoring, the development of regular eating patterns, identification of alternative activities to avoid binge eating, problem-solving, and relapse prevention. Consistent with previous studies of CBT [24], participants also attended ten individual guided self-help sessions (one initial 60-minute session and nine 30-minute sessions) occurring over the course of 17 weeks. See Peterson and colleagues (14) for more detail of the components of the randomized clinical trial and treatments.

ICAT-BED was delivered by doctoral-level psychologists and doctoral students who received an initial all-day didactic training along with weekly group clinical supervision. CBTgsh was delivered by master’s level non-specialist clinicians who received a two-hour didactic training and weekly to biweekly group phone supervision. Supervision was provided by two authors (S.A.W. and C.B.P.) with extensive experience delivering psychotherapy for eating disorders. Participants returned for outcome assessments at the end of treatment and 6-month follow-up. Assessments of height and weight were completed at baseline, 8-weeks, end of treatment, and follow-up. Participants received $150 for assessments following study completion.

Measures

Diagnostic interviews.

The Structured Clinical Interview for DSM-IV Axis I Disorders, Patient Version (SCID-I/P) [25] was used to asses current and lifetime history of DSM-IV Axis I psychiatric disorders and was used to assess psychiatric comorbid diagnoses for study sample description. The Eating Disorder Examination 16.0 (EDE) [26] was administered by trained assessors to establish the DSM-5 BED diagnosis as indicated by at least one objective binge eating episode per week, on average, for the 12 weeks prior to the interview.

Anthropometric assessments.

Height and weight were measured with a stadiometer and scale at four time points. BMI was calculated using the standard BMI formula [27].

EMA measures.

Participants completed a seven-day period of EMA, which utilized both signal and interval contingent recordings. Specifically, participants were prompted to complete assessments throughout the day in response to five semi-random signals, which were distributed around five anchor points between 8 am and 10 pm. In addition, participants completed a final assessment at the end of the day (i.e., bedtime). Overeating and loss of control (LOC) eating were measured at all prompts, and participants responded to these items if they indicated they had eaten since the last assessment. For each recording, participants were asked to rate their current mood and appetite and to report any eating behaviors that had not yet been recorded. Participants indicated the timing of reported eating episodes, in order to locate that eating behavior in time and establish temporality.

BED symptoms.

Overeating was assessed with two items: (a) “To what extent to do you feel that you overate?”, and (b) “To what extent do you feel that you ate an excessive amount of food?”. LOC eating was assessed with four items: (a) “While you were eating, to what extent did you feel a sense of loss of control?”, (b) “While you were eating, to what extent did you feel that you could not resist eating?”, (c) “While you were eating, to what extent did you feel that you could not stop eating once you had started?”, and (d) “While you were eating, to what extent did you feel driven or compelled to eat?”. Items were rated on a scale from 1 (not at all) to 5 (extremely). The two overeating and four LOC eating items were averaged to create composite scores reflecting overeating and LOC eating severity, respectively.

Appetite.

Participants rated their current level of hunger on a scale from 1 (none) to 5 (extreme), and they indicated whether they were currently craving food on a scale from 1 (not at all) to 5 (completely). These measures are similar to previous EMA research [28-29].

Affect.

Five items (i.e., afraid, nervous, upset, ashamed, and hostile) from the Positive and Negative Affect Schedule (PANAS) [30] were used to measure momentary NA, and five items were used to assess PA (i.e., alert, inspired, determined, attentive, and active). At each recording, participants indicated their current affect on a scale from 1 (not at all) to 5 (extremely). NA and PA items were averaged to create composite measures of NA and PA intensity at each signal. The internal consistency for NA and PA were .80 and .88, respectively.

Statistical Analyses

EMA assessments were averaged across all EMA recordings, which provides trait-based measures of BED symptoms, appetite, and affect. Descriptive statistics were examined to assess variability in BMI change and percent change in BMI, which was calculated as (BMI at assessment – pre-treatment BMI)/pre-treatment BMI. The first aim examined the trajectory of BMI change using a linear mixed model that included the linear and quadratic terms of time (days) as a predictor of BMI. The second aim was assessed by adding pre-treatment predictors to each model as well as the two-way interactions between the predictor (i.e., overeating, LOC eating, craving, hunger, positive affect, and negative affect) and the linear and quadratic time terms. Models were examined separately for each predictor, and each model included treatment group as a covariate. All participants with at least one data point were included in analyses, which is handled well by mixed models. Supplementary analyses were conducted examining interactions by treatment group, given possible differences by treatment group.

Results

Two people were removed (one outlier whose initial BMI increased more than 50% and one missing baseline EMA), leaving 110 patients for analysis. Twenty participants dropped out (received less than half of sessions), and three participants were partial completers (received more than half of session but did not finish). At end of treatment, there were 82 participants who completed assessments, and 90 participants completed assessment at follow-up. The average number of BMI assessments per person was 3.34 (SD=1.03) with 66% having all four assessments. At baseline, the mean age of the sample used in analyses was 39.84 years (SD=13.45; range: 18-64), and the mean BMI was 35.10 kg/m2 (SD=8.69; range: 21.41-62.03). Most participants had a BMI in the overweight (25≤BMI<30: 20%) or obesity (BMI≥30: 66%) range. The majority of the sample was female (82%) and White (93%); had a college degree (69%); were currently employed full-time (59%) or part-time (12%); and had never been married (40%) or currently married (46%). Lifetime rates of Axis I disorders were 12% for mood disorders, 38% for anxiety disorders, and 39% for substance dependence. Baseline EMA compliance was 74%. Missingness was not related to demographics, history of mood/substance use/anxiety disorder, or eating disorder psychopathology.

Descriptive statistics are shown in Table 1, and the percent change in BMI between pre-treatment and end-of-treatment assessments for each participant is depicted by Figure 1. Of the 82 participants for whom percent change in BMI at end-of-treatment could be calculated, 24 participants (29%) lost weight, 7 participants (9%) evidenced no change in weight, and 51 participants (62%) gained weight (Figure 1). On average, participants evidenced a 1.93% increase in BMI over the course of treatment (range: −17% to 14%) and had an additional 0.62% increase between end-of-treatment and follow-up assessments (range: −8% to 13%). With respect to Aim 1, the results of the linear mixed model indicated that the linear and quadratic time terms were both significant, demonstrating that BMI increased over time, although the quadratic term reflected a deceleration in this effect (Table 2; Figure 2).

Table 1.

Descriptive statistics and change in BMI.

N M SD Minimum Maximum
Pre-treatment BMI 110 35.10 8.69 21.41 62.03
Week 8 BMI 85 35.17 9.15 21.05 63.88
EOT BMI 82 34.79 8.70 20.88 59.12
Follow-up BMI 90 35.62 8.73 22.09 61.62
Pre-treatment to Week 8 percent change 85 0.01 0.03 −0.07 0.10
Pre-treatment to EOT percent change 82 0.02 0.05 −0.17 0.14
Pre-treatment to follow-up percent change 90 0.03 0.05 −0.14 0.17
EOT to follow-up percent change 79 0.01 0.04 −0.08 0.13
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Note. BMI=body mass index; EOT=end of treatment. Percent change reflects change in BMI ([BMI at assessment – pre-treatment BMI]/pre-treatment BMI).

Figure 1.

Figure 1.

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Percent change in body mass index (BMI) between pre-treatment and end of treatment assessments. Each vertical line represents one participant.

Table 2.

Estimated fixed effects of linear mixed models of BMI change over time.

95% Confidence Interval
B SE T P Lower Upper
Intercept 36.36 1.17 31.01 <.001 34.03 38.68
Group −2.51 1.66 −1.51 .13 −5.79 0.77
Time 0.01 <0.01 3.44 .001 <0.01 0.01
Time2 <−0.01 <0.01 −2.03 .043 <−0.01 <−0.01
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Note. Group was coded such that CBT-gsh was the reference category; time indicates days since beginning treatment

Figure 2.

Figure 2.

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Change in body mass index (BMI) over time (days). Gray vertical dashed lines indicate approximate end-of-treatment and follow-up assessment times (days 119 and 287, respectively).

Results of linear mixed models examining predictors of BMI trajectories are shown in Table 3. There were interactions between baseline positive affect and the linear time component (B= −0.01, SE<.01, p=.023) predicting BMI, indicating that individuals who reported lower overall positive affect prior to treatment evidenced greater increases in BMI whereas individuals who reported high overall positive affect prior to treatment had a flat BMI trajectory (Figure 3). There was also a main effect of overeating (B=2.42 SE=1.05, p=.031) and interactions between overeating and time (B= .01, SE<.01, p=.040) and overeating and time2 (B<−.01, SE<.01, p=.048) predicting BMI. As shown in Figure 4, individuals who reported greater overeating prior to treatment evidenced higher BMI across time. However, the BMI of individuals with lower frequencies of overeating appeared to increase in a linear manner, whereas increases in BMI among those with average or high overeating appeared to stabilize between end of treatment and follow-up assessments (i.e., approximately days 119-287). No other predictors were statistically significant. Supplementary analyses showed no interactions with treatment group.

Table 3.

Estimated fixed effects of linear mixed models with predictors of BMI.

B SE t P 95% Confidence Interval
Lower Upper
LOC eating
Intercept 32.95 3.10 10.62 <.001 26.79 39.10
Group −2.03 1.68 −1.21 .23 −5.37 1.30
Time <−0.01 0.01 −0.53 .60 −0.02 0.01
Time2 <0.01 <0.01 0.96 .34 <−0.01 <0.01
Pre-treatment LOC eating 1.18 1.03 1.14 .26 −0.87 3.23
Time X Pre-treatment LOC eating <0.01 <0.01 1.62 .11 <−0.01 0.01
Time2 X Pre-treatment LOC eating <−0.01 <0.01 −1.63 .11 <−0.01 <0.01
OE
Intercept 30.26 2.96 10.22 <.001 24.85 36.37
Group −1.72 1.65 −1.04 .30 −5.13 1.31
Time −0.01 0.01 −0.96 .34 −0.02 0.01
Time2 <0.01 <0.01 1.30 .20 <−0.01 <0.01
Pre-treatment OE 2.42 1.11 2.19 .031 0.18 4.50
Time X Pre-treatment OE 0.01 <0.01 2.07 .040 <0.01 0.01
Time2 X Pre-treatment OE <−0.01 <0.01 −1.99 .048 <−0.01 <−0.01
95% Confidence Interval
Hunger B SE T P Lower Upper
Intercept 32.41 3.23 10.06 <.001 26.02 38.79
Group −2.55 1.67 −1.53 .13 −5.85 0.76
Time 0.01 0.01 0.71 .48 −0.01 0.02
Time2 <−0.01 <0.01 −0.30 .76 <−0.01 <0.01
Pre-treatment hunger 1.99 1.56 1.28 .21 −1.10 5.08
Time X Pre-treatment hunger <0.01 <0.01 0.16 .87 −0.01 0.01
Time2 X Pre-treatment hunger <−0.01 <0.01 −0.22 .83 <−0.01 <0.01
Craving
Intercept 31.77 2.79 11.38 <.001 26.23 37.30
Group −2.32 1.64 −1.41 .16 −5.58 0.94
Time <−0.01 0.01 −0.60 .55 −0.02 0.01
Time2 <0.01 <0.01 0.97 .33 <−0.01 <0.01
Pre-treatment craving 1.89 1.07 1.76 .08 −0.24 4.01
Time X Pre-treatment craving <0.01 <0.01 1.75 .08 <−0.01 0.01
Time2 X Pre-treatment craving <−0.01 <0.01 −1.69 .09 <−0.01 <0.01
NA B SE T P 95% Confidence Interval
Lower Upper
Intercept 37.24 2.68 12.56 <.001 27.20 37.81
Group −2.25 1.65 −1.49 .18 −5.52 1.03
Time <−0.01 0.01 −0.45 .66 −0.02 0.01
Time2 <0.01 <0.01 0.70 .49 <−0.01 <0.01
Pre-treatment NA 2.33 1.51 1.48 .13 −0.66 5.33
Time X Pre-treatment NA 0.01 <0.01 1.56 .13 <−0.01 0.01
Time2 X Pre-treatment NA <−0.01 <0.01 −1.37 .18 <−0.01 <0.01
PA
Intercept 37.25 3.03 12.29 <.001 31.24 43.26
Group −2.21 1.70 −1.31 .20 −5.57 1.15
Time 0.02 0.01 3.14 .002 0.01 0.04
Time2 <−0.01 <0.01 −2.36 .02 <−0.01 <−0.01
Pre-treatment PA −0.47 1.27 −0.38 .71 −2.98 2.04
Time X Pre-treatment PA −0.01 <0.01 −2.28 .023 −0.01 <0.01
Time2 X Pre-treatment PA <0.01 <0.01 1.87 .06 <−0.01 <0.01
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Note. Group was coded such that CBT-gsh was the reference category. NA=negative affect; PA=positive affect; LOC=loss of control; OE=overeating; time indicates days since beginning treatment.

Figure 3.

Figure 3.

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Change in body mass index (BMI) based on pre-treatment levels of positive affect (PA). High=1 SD above sample mean; Low=1 SD below sample mean. Gray vertical dashed lines indicate approximate end-of-treatment and follow-up assessment times (days 119 and 287, respectively).

Figure 4.

Figure 4.

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Change in body mass index (BMI) based on pre-treatment levels of overeating (OE). High=1 SD above sample mean; Low=1 SD below sample mean. Gray vertical dashed lines indicate approximate end-of-treatment and follow-up assessment times (days 119 and 287, respectively).

Discussion

Despite improvements in BED-related symptomology, psychological treatments for BED are largely variable in producing significant weight loss across patients. This study sought to characterize weight change during and following psychological treatments for BED and to examine baseline predictors of weight change in an effort to identify potential treatment targets for facilitating weight loss. Results indicated variability in weight change, in which more than half the sample gained weight through end-of-treatment (62%) while 29% lost weight and 9% had no weight change. This variability in weight change is consistent with previous research on weight change during psychological treatment for BED [8]. Trajectory analyses revealed that weight gain was accelerated through treatment but began to decelerate during the follow-up period. Given that previous research has revealed weight gain trajectories for individuals with BED in the months prior to treatment [31-34], the accelerated weight gain observed during the course of treatment may reflect a continuation of a pre-treatment weight gain effect. Further, the deceleration in weight gain observed between the end-of-treatment and follow-up assessments likely reflects that notion that treatment for BED stabilizes or protects against future weight gain [35].

Although psychological treatments for BED primarily aim to reduce maladaptive eating patterns and do not explicitly focus on weight or energy balance, one psychological factor did emerge as a significant predictor of weight loss in the current study. Individuals with higher baseline levels of momentary positive affect flatter trajectories of weight gain by the end of treatment, which remained lower at follow-up. These results suggest that individuals who initially report greater positive emotions on a moment-to-moment basis are less likely to experience weight gain throughout the course of psychological treatment for BED.

This finding regarding baseline PA and weight maintenance is consistent with the broaden-and-build theory of positive emotion [36], which states that the subjective experience of positive affect expands an individual’s repertoire of thoughts and actions and has been extended to maintenance of BED [37]. While negative affect is thought to narrow an individual’s attentional focus, PA serves to broaden attentional scope and likely promotes engagement in novel behaviors that further engender PA [36]. When applied to weight loss behaviors, previous research suggests that higher trait positive affect is related to greater restraint attempts among adults with obesity [17]. In addition, a large body of research has shown that positive affect facilitates physical activity [38-39] and consumption of healthy foods such as fruits and vegetables in children and adults [40-41]. Thus, individuals with BED who have higher PA prior to treatment may be more open to engaging in new or varied behaviors as promoted by treatment for BED (e.g., restraint, activity, and healthful dietary intake), which is then likely to result in weight maintenance, though perhaps not in a manner that causes reliable weight loss.

Results also indicated that individuals reporting higher levels of overeating at baseline had a higher BMI throughout the course of treatment and follow-up. By definition, overeating should be associated with increased caloric intake, which in turn is presumably associated with higher weight. However, results demonstrated that low levels of overeating at baseline predicted steady increases in BMI over time, but average and higher levels of overeating at baseline predicted a stabilization of BMI between end-of-treatment and follow-up. Given that maladaptive eating behaviors, including overeating, are a crucial target in the psychological treatment of BED, it is possible that individuals with average to severe overeating at baseline had the greatest room for improvement by the end of treatment, thus decreasing the likelihood for continued weight gain in the follow-up.

Interestingly, LOC eating did not emerge as a significant predictor of weight change, suggesting that overeating rather than LOC eating may impact weight in adults with BED. This is particularly notable given that updated diagnostic criteria for BED in ICD-11 [42] emphasizes LOC eating over the size of the eating episode. Thus, while psychological treatment for BED may effectively decrease the subjective cognitions and feelings associated with losing control over one’s eating, energy balance (i.e., limiting the amount of food consumed during an episode of binge eating) might be the more effective target for intervention should weight loss be the primary goal of BED treatment. Also, there were no interactions of baseline predictors and treatment group with trajectory of weight change; however, this could be due to low power to examine these interaction effects.

This study has several strengths, including a relatively large sample size, objective assessment of height and weight, and use of momentary assessment methodology. Specifically, all predictors in the current study were measured using EMA, therefore increasing ecological validity and reducing retrospective recall. Instead of relying on participants to provide a retrospective approximation of their behaviors and experiences, this research calculated an aggregate score for predictor variables derived from multiple momentary assessments. However, despite these methodological strengths, several limitations should be noted. First, the treatments for BED examined in the current study did not specifically focus on weight loss, which was explicitly communicated to participants prior to their enrollment in treatment. This may have resulted in self-selection bias, such that individuals diagnosed with BED who did not want to lose weight might have comprised the majority of the study sample. Also, a proportion of the sample included individuals who were overweight and may not have been looking to lose significant amounts of weight.

Another limitation is that there may have been reactivity to the baseline EMA, although previous research has shown limited evidence of reactivity to EMA [43-44]. Additional limitations include the nature of the sample, as participants were primarily Caucasian and female. Future research would benefit from exploring possible gender and cultural effects on the relationship between the baseline predictors included in this study and weight outcomes. Also, it is unclear if a seven-day timeframe, which was the extent of the EMA surveys, provides the most accurate timeframe for capturing individuals’ average level of BED pathology.

This study has several practical and clinical applications. First, interventions that increase positive affect may effectively target binge-eating symptoms and reduce weight gain during treatment. In as much as positive affect may reflect more positive expectations about therapy and promote greater learning and openness to new experiences [45-46], working with patients to increase their experience of positive emotions at the onset of treatment (e.g., through specific therapeutic techniques, such as behavioral activation) [47] may facilitate improvements in weight-related behaviors such as diet and physical activity [38-41]. Complementary treatment (e.g., behavioral activation) should be added to psychological therapies for BED for individuals with low positive affect to limit weight gain and possibly decrease weight. Finally, working with patients to reduce the amount of food consumed (with or without LOC over eating) during an episode of binge eating may improve weight loss following psychological treatment for BED.

Overall, this study adds to the emerging literature examining baseline predictors of weight change following BED treatment. Consistent with Pacanowski et al. [8], there was variability in weight change, but more patients gained weight than lost or maintained weight. Thus, this underscores the need for novel interventions to concurrently target weight management and binge-eating symptomatology among individuals with obesity and BED. These results suggest that baseline levels of PA prior to treatment may promote more favorable weight-related outcomes for individuals diagnosed with BED. Future research is warranted to study the mechanisms by which baseline positive affect may promote weight maintenance in patients with BED, as well as identify ways of promoting positive affect during interventions for patients with BED, which may protect against weight gain. Additionally, future research should capitalize on EMA in other ways to examine predictors of weight change, including baseline as well as change or variability in psychological constructs over time.

Highlights.

  • Participants showed increases in BMI at end-of-treatment and follow-up assessments.

  • Increases in BMI were less steep after treatment.

  • Individuals with higher positive affect at baseline had less increases in BMI.

  • Individuals who reported greater overeating at baseline had higher BMI across time.

  • Baseline positive affect and overeating may be ecological targets for improving weight outcomes in BED.

Acknowledgments

This research was supported by grants R34MH099040 and R34MH098995 from the National Institute of Mental Health.

Footnotes

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Conflict of Interest Statement: The authors have no conflicts of interest to disclose.

References

  • [1].Blüher M, Obesity: global epidemiology and pathogenesis. Nat. Rev. Endocrinol 15 (2019) 288–298. doi: 10.1038/s41574-019-0176-8 [DOI] [PubMed] [Google Scholar]
  • [2].Flegal KM, Graubard BI, Williamson DF, Gail MH, Excess deaths associated with underweight, overweight, and obesity. J. Am. Med. Assoc 293 2005. 1861–1867. doi: 10.1001/jama.293.15.1861 [DOI] [PubMed] [Google Scholar]
  • [3].Cecchini M, Sassi F, Lauer JA, Lee YY, Guajardo-Barron V, Chisholm D, Tackling of unhealthy diets, physical inactivity, and obesity: Health effects and cost-effectiveness. Lancet 376 2010. 1775–1784. 10.1016/S0140-6736(10)61514-0 [DOI] [PubMed] [Google Scholar]
  • [4].Sturm R, The effects of obesity, smoking, and drinking on medical problems and costs. Health Aff. 21 2002. 245–253. [DOI] [PubMed] [Google Scholar]
  • [5].Kushner RF, Foster GD, Obesity and quality of life. Nutrition 16 2000. 947–952. doi: 10.1016/s0899-9007(00)00404-4 [DOI] [PubMed] [Google Scholar]
  • [6].American Psychiatric Association, Diagnostic and statistical manual of mental disorders (DSM-5). American Psychiatric Pub, 2013. [Google Scholar]
  • [7].Duncan AE, Ziobrowski HN, Nicol G, The prevalence of past 12-month and lifetime DSM-IV eating disorders by BMI category in US men and women. Eur. Eat. Disord. Rev 25 2017. 165–171. 10.1002/erv.2503 [DOI] [PubMed] [Google Scholar]
  • [8].Pacanowski CR, Mason TB, Crosby RD, Mitchell JE, Crow SJ, Wonderlich SA, Peterson CB, Weight CB Change over the Course of Binge Eating Disorder Treatment: Relationship to Binge Episodes and Psychological Factors. Obesity 26 2018. 838–844. 10.1002/oby.22149 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [9].Masheb RM, Dorflinger LM, Rolls BJ, Mitchell DC, Grilo CM, Binge abstinence is associated with reduced energy intake after treatment in patients with binge eating disorder and obesity. Obesity 24 2016. 2491–2496. 10.1002/oby.21664 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [10].Kikuchi H, Yoshiuchi K, Inada S, Ando T, Yamamoto Y, Development of an ecological momentary assessment scale for appetite. BioPsychoSoc. Med 9 2015. 2. 10.1186/s13030-014-0029-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [11].Wonderlich JA, Lavender JM, Wonderlich SA, Peterson CB, Crow SJ, Engel SG, et al. , Examining convergence of retrospective and ecological momentary assessment measures of negative affect and eating disorder behaviors. Int. J. Eat. Disord 48 2015. 305–311. 10.1002/eat.22352 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [12].Wonderlich SA, Peterson CB, Smith TL, Mitchell JE, Crow SJ, Integrative cognitive-affective therapy for bulimia nervosa: A treatment manual. Guilford, New York, 2015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [13].Fairburn CG, Overcoming binge eating: The proven program to learn why you binge and how you can stop, 2nd ed. New York, NY: Guilford Press, 2013. [Google Scholar]
  • [14].Peterson CB, Engel SG, Crosby RD, Strauman T, Smith TL, Klein M, M., et al. , Comparing integrative cognitive-affective therapy and guided self-help cognitive-behavioral therapy to treat binge-eating disorder using standard and naturalistic momentary outcome measures: A randomized controlled trial. Int. J. Eat. Disord 53 2020. 1418–1427. 10.1002/eat.23324 [DOI] [PubMed] [Google Scholar]
  • [15].Mason TB, Crosby RD, Kolotkin RL, Grilo CM, Mitchell JE, Wonderlich SA, et al. , Correlates of weight-related quality of life among individuals with binge eating disorder before and after cognitive behavioral therapy. Eat. Behav 27 2017. 1–6. 10.1016/j.eatbeh.2017.08.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [16].Ng L, Davis C, Cravings and food consumption in binge eating disorder. Eat. Behav 14 2013. 472–475. 10.1016/j.eatbeh.2013.08.011 [DOI] [PubMed] [Google Scholar]
  • [17].Smith KE, Mason TB, Crosby RD, Engel SG, Crow SJ, Wonderlich SA, Peterson CB, State and trait positive and negative affectivity in relation to restraint and binge eating among adults with obesity. Appetite 120 2018. 327–334. doi: 10.1016/j.appet.2017.09.020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [18].Smith KE, Mason TB, Crosby RD, Engel SG, Wonderlich SA, A multimodal, naturalistic investigation of relationships between behavioral impulsivity, affect, and binge eating. Appetite 136 2019. 50–57. doi: 10.1016/j.appet.2019.01.014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [19].Smith KE, Mason TB, Schaefer LM, Juarascio A, Dvorak R, Weinbach N, et al. , Examining intra-individual variability in inhibitory control and negative affect as predictors of binge eating using ecological momentary assessment. J. Psychiatr. Res 120 2020. 137–143. 10.1016/j.jpsychires.2019.10.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [20].Boswell RG, Kober H, Food cue reactivity and craving predict eating and weight gain: A meta-analytic review. Obesity Rev. 17 2016. 159–177. 10.1111/obr.12354 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [21].Forney KJ, Bodell LP, Haedt-Matt AA, Keel PK, P. K. Incremental validity of the episode size criterion in binge-eating definitions: An examination in women with purging syndromes. Int. J. Eat. Disord 49 2016. 651–662. 10.1002/eat.22508 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [22].Goldschmidt AB, Are loss of control while eating and overeating valid constructs? A critical review of the literature. Obesity Rev. 18 2017. 412–449. 10.1111/obr.12491 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [23].Mason TB, Heron KE, Do depressive symptoms explain associations between binge eating symptoms and later psychosocial adjustment in young adulthood? Eat. Behav 23 2016. 126–130. 10.1016/j.eatbeh.2016.09.003 [DOI] [PubMed] [Google Scholar]
  • [24].Wilson GT, Wilfley DE, Agras WS, Bryson SW, Psychological treatments of binge eating disorder. Arch. Gen. Psychiatry 67 2010. 94–101. doi: 10.1001/archgenpsychiatry.2009.170 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [25].First MB, Spitzer RL, Gibbon M, Williams JBW, Structured Clinical Interview for DSM-IV Axis I Disorders (SCID). New York, New York State Psychiatric Institute. Biometrics Research, 1995. [Google Scholar]
  • [26].Fairburn CG, Cooper Z, O'Connor M, The eating disorder examination. In C.G. Fairburn, Cognitive behavioral therapy and eating disorders. Guilford Press, New York, 2013. [Google Scholar]
  • [27].Garrow JS, Webster J, Quetelet's index (W/H2) as a measure of fatness. Int. J. Obesity 9 1985. 147–153. [PubMed] [Google Scholar]
  • [28].Goldschmidt AB, Crosby RD, Cao L, Pearson CM, Utzinger LM, Pacanowski CR, et al. , Contextual factors associated with eating in the absence of hunger among adults with obesity. Eat. Behav 26 2017. 33–39. 10.1016/j.eatbeh.2017.01.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [29].Goldschmidt AB, Smith KE, Crosby RD, Boyd HK, Dougherty E, Engel SG, et al. , Ecological momentary assessment of maladaptive eating in children and adolescents with overweight or obesity. Int. J. Eat. Disord 51 2018. 549–557. 10.1002/eat.22864 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [30].Watson D, Clark LA, Tellegen A, Development and validation of brief measures of positive and negative affect: The PANAS scales. J. Pers. Soc. Psychol 54 1988. 1063–1070. [DOI] [PubMed] [Google Scholar]
  • [31].Barnes RD, Blomquist KK, & Grilo CM (2011). Exploring pretreatment weight trajectories in obese patients with binge eating disorder. Comprehensive Psychiatry, 52, 312–318. doi: 10.1016/j.comppsych.2010.06.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [32].Blomquist KK, Barnes RD, White MA, Masheb RM, Morgan PT, Grilo CM, Exploring weight gain in year before treatment for binge eating disorder: a different context for interpreting limited weight losses in treatment studies. Int. J. Eat. Disord 44 2011. 435–439. doi: 10.1002/eat.20836 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [33].Ivezaj V, Kalebjian R, Grilo CM, Barnes RD, Comparing weight gain in the year prior to treatment for overweight and obese patients with and without binge eating disorder in primary care. J. Psychosom. Res 77 2014. 151–154. doi: 10.1016/j.jpsychores.2014.05.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [34].Masheb RM, White MA, Grilo CM, Substantial weight gains are common prior to treatment-seeking in obese patients with binge eating disorder. Compr. Psychiatry 54 2013. 880–884. doi: 10.1016/j.comppsych.2013.03.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [35].Yanovski SZ, Binge eating disorder and obesity in 2003: could treating an eating disorder have a positive effect on the obesity epidemic? Int. J. Eat. Disord 34 2003. S117–S120. [DOI] [PubMed] [Google Scholar]
  • [36].Fredrickson BL, The role of positive emotions in positive psychology: The broaden-and-build theory of positive emotions. Am. Psychol 56 2001. 218–226. 10.1037/0003-066X.56.3.218. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [37].Mason TB, Smith KE, Anderson LM, Hazzard VM, Anhedonia, positive affect dysregulation, and risk and maintenance of binge-eating disorder. Int J Eat Disord 2020. 10.1002/eat.23433 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [38].Liao Y, Shonkoff ET, Dunton GF, The acute relationships between affect, physical feeling states, and physical activity in daily life: a review of current evidence. Front. Psychol 6 2015. 1975. 10.3389/fpsyg.2015.01975 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [39].Pasco JA, Jacka FN, Williams LJ, Brennan SL, Leslie E, Berk M, Don't worry, be active: Positive affect and habitual physical activity. Aust. N. Z. J. Psychiatry 45 2011. 1047–1052. 10.3109/00048674.2011.621063 [DOI] [PubMed] [Google Scholar]
  • [40].Jeffers AJ, Mason TB, Benotsch EG, Psychological eating factors, affect, and ecological momentary assessed diet quality. Eat. Weight Disord 25 2020. 1151–1159. 10.1007/s40519-019-00743-3 [DOI] [PubMed] [Google Scholar]
  • [41].Mason TB, O'Connor SG, Schembre SM, Huh J, Chu D, Dunton GF, Momentary affect, stress coping, and food intake in mother–child dyads. Health Psychol. 38 2019. 238. 10.1037/hea0000714 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [42].World Health Organization, International classification of diseases for mortality and morbidity statistics (11th Revision), 2018. Retrieved from https://icd.who.int/browse11/l-m/en. [Google Scholar]
  • [43].Heron KE, Smyth JM, Is intensive measurement of body image reactive? A two-study evaluation using Ecological Momentary Assessment suggests not. Body Image 10 2013. 35–44. 10.1016/j.bodyim.2012.08.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [44].Stein KF, Corte CM, C. M. Ecologic momentary assessment of eating-disordered behaviors. Int J Eat Disord 34 2003. 349–360. 10.1002/eat.10194 [DOI] [PubMed] [Google Scholar]
  • [45].Fitzpatrick MR, Stalikas A, Positive emotions as generators of therapeutic change. J. Psychother. Integ 18 2008. 137–154. 10.1037/1053-0479.18.2.137. [DOI] [Google Scholar]
  • [46].Taylor CT, Knapp SE, Bomyea JA, Ramsawh HJ, Paulus MP, Stein MB, What good are positive emotions for treatment? Trait positive emotionality predicts response to Cognitive Behavioral Therapy for anxiety. Behav. Res. Ther 93 2017. 6–12. doi: 10.1016/j.brat.2017.03.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [47].Jacobson NS, Martell CR, Dimidjian S, Behavioral activation treatment for depression: Returning to contextual roots. Clin Psychol 8 2001. 255–270. 10.1093/clipsy.8.3.255 [DOI] [Google Scholar]

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