An explanation for the accuracy of sensor-based measures of energy intake: Amount of food consumed matters more than dietary composition

Understanding and intervening on eating behavior often necessitates measurement of energy intake (EI); however, commonly utilized and widely accepted methods vary in accuracy and place significant burden on users (e.g., food diaries), or are costly to implement (e.g., doubly labeled water). Thus, researchers have sought to leverage inexpensive and low-burden technologies such as wearable sensors for EI estimation. Paradoxically, one such methodology that estimates EI via smartwatch-based bite counting has demonstrated high accuracy in laboratory and free-living studies, despite only measuring the amount, not the composition, of food consumed. This secondary analysis sought to further explore this phenomenon by evaluating the degree to which EI can be explained by a sensor-based estimate of the amount consumed versus the energy density (ED) of the food consumed. Data were collected from 82 adults in free-living conditions (51.2% female, 31.7% racial and/or ethnic minority; Mage = 33.5, SD = 14.7) who wore a bite counter device on their wrist and used smartphone app to implement the Remote Food Photography Method (RFPM) to assess EI and ED for two weeks. Bite-based estimates of EI were generated via a previously validated algorithm. At a per-meal level, linear mixed effect models indicated that bite-based EI estimates accounted for 23.4% of the variance in RFPM-measured EI, while ED and presence of a beverage accounted for only 0.2% and 0.1% of the variance, respectively. For full days of intake, bite-based EI estimates and ED accounted for 41.5% and 0.2% of the variance, respectively. These results help to explain the viability of sensor-based EI estimation even in the absence of information about dietary composition.

Ethical, legal, and social implications of digital health: A needs assessment from the Society of Behavioral Medicine to inform capacity building for behavioral scientists

The ethical, legal, and social implications (ELSIs) of digital health are important when researchers and practitioners are using technology to collect, process, or store personal health data. Evidence underscores a strong need for digital health ELSI training, yet little is known about the specific ELSI topic areas that researchers and practitioners would most benefit from learning. To identify ELSI educational needs, a needs assessment survey was administered to the members of the Society of Behavioral Medicine (SBM). We sought to identify areas of ELSI proficiency and training need, and also evaluate interest and expertise in ELSI topics by career level and prior ELSI training history. The 14-item survey distributed to SBM members utilized the Digital Health Checklist tool (see recode.health/tools) and included items drawn from the four-domain framework: data management, access and usability, privacy and risk to benefit assessment. Respondents (N = 66) were majority faculty (74.2%) from psychology or public health. Only 39.4% reported receiving "formal" ELSI training. ELSI topics of greatest interest included practices that supported participant engagement, and dissemination and implementation of digital tools beyond the research setting. Respondents were least experienced in managing "bystander" data, having discussions about ELSIs, and reviewing terms of service agreements and privacy policies with participants and patients. There is opportunity for formalized ELSI training across career levels. Findings serve as an evidence base for continuous and ongoing evaluation of ELSI training needs to support scientists in conducting ethical and impactful digital health research.

Detecting Eating Episodes From Wrist Motion Using Daily Pattern Analysis

This paper presents new methods to detect eating from wrist motion. Our main novelty is that we analyze a full day of wrist motion data as a single sample so that the detection of eating occurrences can benefit from diurnal context. We develop a two-stage framework to facilitate a feasible full-day analysis. The first-stage model calculates local probabilities of eating P(Ew) within windows of data, and the second-stage model calculates enhanced probabilities of eating P(Ed) by treating all P(Ew) within a single day as one sample. The framework also incorporates an augmentation technique, which involves the iterative retraining of the first-stage model. This allows us to generate a sufficient number of day-length samples from datasets of limited size. We test our methods on the publicly available Clemson All-Day (CAD) dataset and FreeFIC dataset, and find that the inclusion of day-length analysis substantially improves accuracy in detecting eating episodes. We also benchmark our results against several state-of-the-art methods. Our approach achieved an eating episode true positive rate (TPR) of 89% with 1.4 false positives per true positive (FP/TP), and a time weighted accuracy of 84%, which are the highest accuracies reported on the CAD dataset. Our results show that the daily pattern classifier substantially improves meal detections and in particular reduces transient false detections that tend to occur when relying on shorter windows to look for individual ingestion or consumption events.

State-level working memory and dysregulated eating in children and adolescents: An exploratory ecological momentary assessment study

BACKGROUND

Children with loss of control (LOC) eating and overweight/obesity have relative deficiencies in trait-level working memory (WM), which may limit adaptive responding to intra- and extra-personal cues related to eating. Understanding of how WM performance relates to eating behavior in real-time is currently limited.

METHODS

We studied 32 youth (ages 10-17 years) with LOC eating and overweight/obesity (LOC-OW; n = 9), overweight/obesity only (OW; n = 16), and non-overweight status (NW; n = 7). Youth completed spatial and numerical WM tasks requiring varying degrees of cognitive effort and reported on their eating behavior daily for 14 days via smartphone-based ecological momentary assessment. Linear mixed effects models estimated group-level differences in WM performance, as well as associations between contemporaneously completed measures of WM and dysregulated eating.

RESULTS

LOC-OW were less accurate on numerical WM tasks compared to OW and NW (ps < .01); groups did not differ on spatial task accuracy (p = .41). Adjusting for between-subject effects (reflecting differences between individuals in their mean WM performance and its association with eating behavior), within-subject effects (reflecting variations in moment-to-moment associations) revealed that more accurate responding on the less demanding numerical WM task, compared to one's own average, was associated with greater overeating severity across the full sample (p = .013). There were no associations between WM performance and LOC eating severity (ps > .05).

CONCLUSIONS

Youth with LOC eating and overweight/obesity demonstrated difficulties mentally retaining and manipulating numerical information in daily life, replicating prior laboratory-based research. Overeating may be related to improved WM, regardless of LOC status, but temporality and causality should be further explored.

PUBLIC SIGNIFICANCE STATEMENT

Our findings suggest that youth with loss of control eating and overweight/obesity may experience difficulties mentally retaining and manipulating numerical information in daily life relative to their peers with overweight/obesity and normal-weight status, which may contribute to the maintenance of dysregulated eating and/or elevated body weight. However, it is unclear whether these individual differences are related to eating behavior on a moment-to-moment basis.

Association of weight and shape concern with weight change and weight-related behaviors in behavioral weight loss treatment

Weight and shape concern (WSC) is a facet of negative body image that is common among individuals with overweight/obesity seeking behavioral weight loss treatment (BWL), but remains understudied. This secondary analysis evaluates associations between WSC, weight change, and weight-related behaviors among individuals in a 24-week BWL. Adults (n = 32) with body mass index 25-50 kg/m2 completed a baseline WSC questionnaire, measured weight at 12 and 24 weeks, measured physical activity via accelerometer, and completed 24-hour dietary recalls. Adherence to self-monitoring dietary intake and weight were assessed. A series of linear mixed models were used to evaluate associations between baseline WSC and weight change, as well as weight-related behaviors. Results revealed no significant effect of WSC on weight change. There were significant WSC x time interactions, such that those rating WSC "very important" decreased self-weighing and the "low importance" group decreased their caloric intake during treatment. The "pretty important" group had greater minutes of activity than the "low importance" group. Findings indicated that WSC may impact weight-related behaviors that contribute to BWL success. This trial was pre-registered on ClinicalTrials.gov (NCT03739151).

A randomized trial examining the effect of yoga on dietary lapses and lapse triggers following behavioral weight loss treatment

Background

Dietary lapses can hinder weight loss and yoga can improve self-regulation, which may protect against lapses. This study examined the effect of yoga on dietary lapses, potential lapse triggers (e.g., affective states, cravings, dietary temptations), and reasons for initiating eating following weight loss treatment.

Methods

Sixty women with overweight/obesity (34.3 ± 3.9 kg/m2) were randomized to a 12 week yoga intervention (2x/week; YOGA) or contact-matched control (cooking/nutrition classes; CON) following a 12-week behavioral weight loss program. Participants responded to smartphone surveys (5x/day) over a 10-day period at baseline, 12, and 24 weeks to assess lapses and triggers.

Results

At 24 weeks, YOGA and CON differed on several types of lapses (i.e., less eating past full, eating more than usual, loss of control when eating, self-identified overeating, difficulty stopping eating in YOGA), and YOGA was less likely to eat to feel better or in response to stress (ps < 0.05). YOGA also reported less stress and anxiety and more positive affect (ps < 0.01); dietary temptations and cravings did not differ from CON.

Conclusion

Yoga resulted in fewer dietary lapses and improved affect among women with overweight/obesity following weight loss. While preliminary, findings suggest that yoga should be considered as a potential component of weight loss treatment to target dietary lapses.

Applying a Social Determinants of Health Framework to Guide Digital Innovations That Reduce Disparities in Chronic Disease

ABSTRACT

Chronic diseases are among the top causes of global death, disability, and health care expenditure. Digital health interventions (e.g., patient support delivered via technologies such as smartphones, wearables, videoconferencing, social media, and virtual reality) may prevent and mitigate chronic disease by facilitating accessible, personalized care. Although these tools have promise to reach historically marginalized groups, who are disproportionately affected by chronic disease, evidence suggests that digital health interventions could unintentionally exacerbate health inequities. This commentary outlines opportunities to harness recent advancements in technology and research design to drive equitable digital health intervention development and implementation. We apply "calls to action" from the World Health Organization Commission on Social Determinants of Health conceptual framework to the development of new, and refinement of existing, digital health interventions that aim to prevent or treat chronic disease by targeting intermediary, social, and/or structural determinants of health. Three mirrored "calls to action" are thus proposed for digital health research: a) develop, implement, and evaluate multilevel, context-specific digital health interventions; b) engage in intersectoral partnerships to advance digital health equity and social equity more broadly; and c) include and empower historically marginalized groups to develop, implement, and access digital health interventions. Using these "action items," we review several technological and methodological innovations for designing, evaluating, and implementing digital health interventions that have greater potential to reduce health inequities. We also enumerate possible challenges to conducting this work, including leading interdisciplinary collaborations, diversifying the scientific workforce, building trustworthy community relationships, and evolving health care and digital infrastructures.

Classification of Lapses in Smokers Attempting to Stop: A Supervised Machine Learning Approach Using Data From a Popular Smoking Cessation Smartphone App

INTRODUCTION

Smoking lapses after the quit date often lead to full relapse. To inform the development of real time, tailored lapse prevention support, we used observational data from a popular smoking cessation app to develop supervised machine learning algorithms to distinguish lapse from non-lapse reports.

AIMS AND METHODS

We used data from app users with ≥20 unprompted data entries, which included information about craving severity, mood, activity, social context, and lapse incidence. A series of group-level supervised machine learning algorithms (eg, Random Forest, XGBoost) were trained and tested. Their ability to classify lapses for out-of-sample (1) observations and (2) individuals were evaluated. Next, a series of individual-level and hybrid algorithms were trained and tested.

RESULTS

Participants (N = 791) provided 37 002 data entries (7.6% lapses). The best-performing group-level algorithm had an area under the receiver operating characteristic curve (AUC) of 0.969 (95% confidence interval [CI] = 0.961 to 0.978). Its ability to classify lapses for out-of-sample individuals ranged from poor to excellent (AUC = 0.482-1.000). Individual-level algorithms could be constructed for 39/791 participants with sufficient data, with a median AUC of 0.938 (range: 0.518-1.000). Hybrid algorithms could be constructed for 184/791 participants and had a median AUC of 0.825 (range: 0.375-1.000).

CONCLUSIONS

Using unprompted app data appeared feasible for constructing a high-performing group-level lapse classification algorithm but its performance was variable when applied to unseen individuals. Algorithms trained on each individual's dataset, in addition to hybrid algorithms trained on the group plus a proportion of each individual's data, had improved performance but could only be constructed for a minority of participants.

IMPLICATIONS

This study used routinely collected data from a popular smartphone app to train and test a series of supervised machine learning algorithms to distinguish lapse from non-lapse events. Although a high-performing group-level algorithm was developed, it had variable performance when applied to new, unseen individuals. Individual-level and hybrid algorithms had somewhat greater performance but could not be constructed for all participants because of the lack of variability in the outcome measure. Triangulation of results with those from a prompted study design is recommended prior to intervention development, with real-world lapse prediction likely requiring a balance between unprompted and prompted app data.

Characterizing emotional eating: Ecological momentary assessment with person-specific modeling

Emotional eating is a topic of clinical importance, with links to weight regulation and wellness. However, issues of concept clarity and measurement can interfere with efforts to understand and intervene on emotional eating. One explanation for prior difficulties in defining emotional eating may be that this construct is not uniform across individuals. The current study critically examined emotional eating by combining ecological momentary assessment (EMA) with an idiographic analytic approach. The study examined the heterogeneity in the emotions and dysregulated eating behaviors often thought to underlie emotional eating, by establishing and comparing latent factor profiles across individuals. Ten community adults with overweight or obesity completed a 21-day EMA protocol, with 5 daily prompts to report on relevant emotions and eating behaviors. P-technique factor analysis was used to examine the data. Results suggested variability across individuals in the number of factors that emerged, the items that loaded on each factor, and the strength of loadings. Dysregulated eating was not found to covary with affective states strongly enough to produce a distinct "emotional eating" factor for any individual, nor did the correlations between factors suggest strong relationships between emotions and dysregulated eating for most participants, even in this sample with 90% of participants self-identifying as "emotional eaters." Findings are consistent with a growing body of literature questioning the validity of the "emotional eating" construct as currently defined and measured, and supports conceptualizing emotional eating as a locally heterogenous construct that varies between people. Combining EMA with an intra-individual modeling technique appears to be a promising approach for understanding emotional eating. Additional work with larger samples is needed to capture the full range in individual profiles.

Combining passive eating monitoring and ecological momentary assessment to characterize dietary lapses from a lifestyle modification intervention

Dietary lapses (i.e., specific instances of nonadherence to recommended dietary goals) contribute to suboptimal weight loss outcomes during lifestyle modification programs. Passive eating monitoring could enhance lapse measurement via objective assessment of eating characteristics that could be markers for lapse (e.g., more bites consumed). The purpose of this study was to evaluate if passively-inferred eating characteristics (i.e., bites, eating duration, and eating rate), measured via wrist-worn device, could distinguish dietary lapses from non-lapse eating. Adults (n = 25) with overweight/obesity received a 24-week lifestyle modification intervention. Participants completed ecological momentary assessment (EMA; repeated smartphone surveys) biweekly to self-report on dietary lapses and non-lapse eating episodes. Participants wore a wrist device that captured continuous wrist motion. Previously-validated algorithms inferred eating episodes from wrist data, and calculated bite count, duration, and rate (seconds per bite). Mixed effects logistic regressions revealed no simple effects of bite count, duration, or eating rate on the likelihood of dietary lapse. Moderation analyses revealed that eating episodes in the evening were more likely to be lapses if they involved fewer bites (B = -0.16, p < .05), were shorter (B = -0.54, p < .05), or had a slower rate (B = 1.27, p < .001). Statistically significant interactions between eating characteristics (Bs = -0.30 to -0.08, ps < .001) revealed two distinct patterns. Eating episodes that were 1. smaller, slower, and shorter than average, or 2. larger, quicker, and longer than average were associated with increased probability of lapse. This study is the first to use objective eating monitoring to characterize dietary lapses throughout a lifestyle modification intervention. Results demonstrate the potential of sensors to identify non-adherence using only patterns of passively-sensed eating characteristics, thereby minimizing the need for self-report in future studies. CLINICAL TRIALS REGISTRY NUMBER: NCT03739151.

Dietary lapses are associated with meaningful elevations in daily caloric intake and added sugar consumption during a lifestyle modification intervention

Objective

Lapses from the dietary prescription in lifestyle modification interventions for overweight/obesity are common and impact weight loss outcomes. While it is expected that lapses influence weight via increased consumption, there are no studies that have evaluated how dietary lapses affect dietary intake during treatment. This study examined the association between daily lapses and daily energy and macronutrient intake during a lifestyle modification intervention.

Methods

This study used an intensive longitudinal design to observe participants throughout a 6-month lifestyle modification intervention. Participants (n = 32) were adults with overweight/obesity (body mass index 25-50 kg/m²) and a diagnosed cardiovascular disease risk factor (e.g., hypertension) with a desire to lose weight. Participants underwent a gold-standard individual in-person lifestyle modification protocol consisting of 3 months of weekly sessions with 3 months of monthly sessions. Each participant's dietary prescription included a calorie target range that was based on their starting weight. Participants completed ecological momentary assessment (EMA; repeated daily smartphone surveys) every other week to self-report on dietary lapses and telephone-based 24-h dietary recalls every 6 weeks.

Results

On days with EMA and recalled intake (n = 210 days), linear mixed models demonstrated significant associations between daily lapse and higher total daily caloric intake (B = 139.20, p < 0.05), more daily grams of added sugar (B = 16.24, p < 0.001), and likelihood of exceeding the daily calorie goal (B = 0.89, p < 0.05). The associations between daily lapse and intake of all other daily macronutrients were non-significant.

Conclusions

This study contributes to literature suggesting that dietary lapses pose a threat to weight loss success. Results indicate that reducing lapse frequency could reduce overall caloric intake and added sugar consumption.

Optimizing a Just-in-Time Adaptive Intervention to Improve Dietary Adherence in Behavioral Obesity Treatment: Protocol for a Microrandomized Trial

BACKGROUND

Behavioral obesity treatment (BOT) is a gold standard approach to weight loss and reduces the risk of cardiovascular disease. However, frequent lapses from the recommended diet stymie weight loss and prevent individuals from actualizing the health benefits of BOT. There is a need for innovative treatment solutions to improve adherence to the prescribed diet in BOT.

OBJECTIVE

The aim of this study is to optimize a smartphone-based just-in-time adaptive intervention (JITAI) that uses daily surveys to assess triggers for dietary lapses and deliver interventions when the risk of lapse is high. A microrandomized trial design will evaluate the efficacy of any interventions (ie, theory-driven or a generic alert to risk) on the proximal outcome of lapses during BOT, compare the effects of theory-driven interventions with generic risk alerts on the proximal outcome of lapse, and examine contextual moderators of interventions.

METHODS

Adults with overweight or obesity and cardiovascular disease risk (n=159) will participate in a 6-month web-based BOT while using the JITAI to prevent dietary lapses. Each time the JITAI detects elevated lapse risk, the participant will be randomized to no intervention, a generic risk alert, or 1 of 4 theory-driven interventions (ie, enhanced education, building self-efficacy, fostering motivation, and improving self-regulation). The primary outcome will be the occurrence of lapse in the 2.5 hours following randomization. Contextual moderators of intervention efficacy will also be explored (eg, location and time of day). The data will inform an optimized JITAI that selects the theory-driven approach most likely to prevent lapses in a given moment.

RESULTS

The recruitment for the microrandomized trial began on April 19, 2021, and is ongoing.

CONCLUSIONS

This study will optimize a JITAI for dietary lapses so that it empirically tailors the provision of evidence-based intervention to the individual and context. The finalized JITAI will be evaluated for efficacy in a future randomized controlled trial of distal health outcomes (eg, weight loss).

TRIAL REGISTRATION

ClinicalTrials.gov NCT04784585; http://clinicaltrials.gov/ct2/show/NCT04784585.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/33568.

Comparing ecological momentary assessment to sensor-based approaches in predicting dietary lapse

Ecological momentary assessment (EMA; brief self-report surveys) of dietary lapse risk factors (e.g., cravings) has shown promise in predicting and preventing dietary lapse (nonadherence to a dietary prescription), which can improve weight loss interventions. Passive sensors also can measure lapse risk factors and may offer advantages over EMA (e.g., objective, automatic, semicontinuous data collection), but currently can measure only a few lapse predictors, a notable limitation. This study preliminarily compared the burden and accuracy of commercially available sensors versus established EMA in lapse prediction. N = 23 adults with overweight/obesity completed a 6-week commercial app-based weight loss program. Participants wore a Fitbit, enabled GPS tracking, completed EMA, and reported on EMA and sensor burden poststudy via a 5-point Likert scale. Sensed risk factors were physical activity and sleep (accelerometer), geolocation (GPS), and time, from which 233 features (measurable characteristics of sensor signals) were extracted. EMA measured 19 risk factors, lapse, and categorized GPS into meaningful geolocations. Two supervised binary classification models (LASSO) were created: the sensor model predicted lapse with 63% sensitivity (true prediction rate of lapse) and 60% specificity (true prediction rate of non-lapse) and EMA model with 59% sensitivity and 72% specificity. EMA model accuracy was higher, but self-reported EMA burden (M = 2.96, SD = 1.02) also was higher (M = 1.50, SD = 0.94). EMA model accuracy was superior, but EMA burden was higher than sensor burden. Findings highlight the promise of sensors in contributing to lapse prediction, and future research may use EMA, sensors, or both depending on prioritization of accuracy versus participant burden.

Examination of the relationship between lapses and weight loss in a smartphone-based just-in time adaptive intervention

We developed a smartphone-based just-in-time adaptive intervention (JITAI), called OnTrack, that provides personalized intervention to prevent dietary lapses (i.e., nonadherence from the behavioral weight loss intervention diet). OnTrack utilizes ecological momentary assessment (EMA; repeated electronic surveys) for self-reporting lapse triggers, predicts lapses using machine learning, and provides brief intervention to prevent lapse. We have established preliminary feasibility, acceptability, and efficacy of OnTrack, but no study has examined our hypothesized mechanism of action: reduced lapse frequency will be associated with greater weight loss while using OnTrack. This secondary analysis investigated the association between lapse frequency and the weekly percentage of weight loss. Post hoc analyses evaluated the moderating effect of OnTrack engagement on this association. Participants (N = 121) with overweight/obesity (MBMI = 34.51; 84.3% female; 69.4% White) used OnTrack with a digital weight loss program for 10 weeks. Engagement with OnTrack (i.e., EMA completed and interventions accessed) was recorded automatically, participants self-reported dietary lapses via EMA, and weighed weekly using Bluetooth scales. Linear mixed models with a random effect of subject and fixed effect of time revealed a nonsignificant association between weekly lapses and the percentage of weight loss. Post hoc analyses revealed a statistically significant moderation effect of OnTrack engagement such that fewer EMA and interventions completed conferred the expected associations between lapses and weight loss. Lapses were not associated with weight loss in this study and one explanation may be the influence of engagement levels on this relationship. Future research should investigate the role of engagement in evaluating JITAIs.

Internalized weight bias is associated with perceived exertion and affect during exercise in a sample with higher body weight

OBJECTIVE

For individuals with overweight/obesity, internalized weight bias (IWB) is linked to low physical activity (PA). This study used a laboratory-based paradigm to test the hypothesis that IWB moderates the association between heart rate (HR) and perceived exertion and affect during PA.

METHODS

Participants with overweight/obesity completed 30-min of supervised moderate-intensity treadmill walking (65%-75% of age-predicted maximal HR). Body Mass Index (BMI) and Weight Bias Internalization Scale were assessed at baseline. HR was monitored every minute; perceived exertion and affect were assessed every 5 min. Linear mixed models were employed with random effects of time and participant.

RESULTS

The sample (n = 59; 79.7% female, 91.5% white) had an average BMI = 32.1 kg/m2 (SD: 3.3), and age = 47.1 (SD: 10.3) years. There was a main effect of IWB on perceived exertion (greater IWB was associated with greater perceived exertion during exercise; p < 0.001). There was an interaction of IWB and HR on affect (B = -0.01, p < 0.01). For individuals with high IWB, HR elevations were associated with a negative affective response during exercise. For individuals with low IWB, HR elevations were associated with increased positive affect during PA.

CONCLUSIONS

Findings indicate that among individuals of higher body weight, IWB is associated with reporting higher perceived exertion during 30 min of moderate intensity PA. IWB moderated the relationship between increasing HR during exercise and affect. Among individuals with overweight/obesity who report IWB, the initial experience of PA may be harder and more unpleasant, with lasting implications for the adoption of PA.

Identifying behavioral types of dietary lapse from a mobile weight loss program: Preliminary investigation from a secondary data analysis

Success in behavioral weight loss (BWL) programs depends on adherence to the recommended diet to reduce caloric intake. Dietary lapses (i.e., deviations from the BWL diet) occur frequently and can adversely affect weight loss outcomes. Research indicates that lapse behavior is heterogenous; there are many eating behaviors that could constitute a dietary lapse, but they are rarely studied as distinct contributors to weight outcomes. This secondary analysis aims to evaluate six behavioral lapse types during a 10-week mobile BWL program (eating a large portion, eating when not intended, eating an off-plan food, planned lapse, being unaware of caloric content, and endorsing multiple types of lapse). Associations between weekly behavioral lapse type frequency and weekly weight loss were investigated, and predictive contextual characteristics (psychological, behavioral, and environmental triggers for lapse) and individual difference (e.g., age, gender) factors were examined across lapse types. Participants (N = 121) with overweight/obesity (M_BMI = 34.51; 84.3% female; 69.4% White) used a mobile BWL program for 10 weeks, self-weighed weekly using Bluetooth scales, completed daily ecological momentary assessment of lapse behavior and contextual characteristics, and completed a baseline demographics questionnaire. Linear mixed models revealed significant negative associations between unplanned lapses and percent weight loss. Unplanned lapses from eating a large portion, eating when not intended, and having multiple "types" were significantly negatively associated with weekly percent weight loss. A lasso regression showed that behavioral lapse types share many similar stable factors, with other factors being unique to specific lapse types. Results add to the prior literature on lapses and weight loss in BWL and provide preliminary evidence that behavioral lapse types could aid in understanding adherence behavior and developing precision medicine tools to improve dietary adherence.

Combining ecological momentary assessment, wrist-based eating detection, and dietary assessment to characterize dietary lapse: A multi-method study protocol

Objectives

Behavioral obesity treatment (BOT) produces clinically significant weight loss and health benefits for many individuals with overweight/obesity. Yet, many individuals in BOT do not achieve clinically significant weight loss and/or experience weight regain. Lapses (i.e., eating that deviates from the BOT prescribed diet) could explain poor outcomes, but the behavior is understudied because it can be difficult to assess. We propose to study lapses using a multi-method approach, which allows us to identify objectively-measured characteristics of lapse behavior (e.g., eating rate, duration), examine the association between lapse and weight change, and estimate nutrition composition of lapse.

Method

We are recruiting participants (n = 40) with overweight/obesity to enroll in a 24-week BOT. Participants complete biweekly 7-day ecological momentary assessment (EMA) to self-report on eating behavior, including dietary lapses. Participants continuously wear the wrist-worn ActiGraph Link to characterize eating behavior. Participants complete 24-hour dietary recalls via structured interview at 6-week intervals to measure the composition of all food and beverages consumed.

Results

While data collection for this trial is still ongoing, we present data from three pilot participants who completed EMA and wore the ActiGraph to illustrate the feasibility, benefits, and challenges of this work.

Conclusion

This protocol will be the first multi-method study of dietary lapses in BOT. Upon completion, this will be one of the largest published studies of passive eating detection and EMA-reported lapse. The integration of EMA and passive sensing to characterize eating provides contextually rich data that will ultimately inform a nuanced understanding of lapse behavior and enable novel interventions.Trial registration: Registered clinical trial NCT03739151; URL: https://clinicaltrials.gov/ct2/show/NCT03739151.

Evaluation of intervention components to maximize outcomes of behavioral obesity treatment delivered online: A factorial experiment following the multiphase optimization strategy framework

BACKGROUND

Behavioral lifestyle intervention (BLI) is recommended as a first-line treatment for obesity. While BLI has been adapted for online delivery to improve potential for dissemination while reducing costs and barriers to access, weight losses are typically inferior to gold standard treatment delivered in-person. It is therefore important to refine and optimize online BLI in order to improve the proportion of individuals who achieve a minimum clinically significant weight loss and mean weight loss.

STUDY DESIGN

Five experimental intervention components will be tested as adjuncts to an established 12-month online BLI: virtual reality for BLI skills training, interactive video feedback, tailored intervention to promote physical activity, skills for dysregulated eating, and social support combined with friendly competition. Following the Multiphase Optimization Strategy (MOST) framework, the components will first be refined and finalized during Preparation Phase pilot testing and then evaluated in a factorial experiment with 384 adults with overweight or obesity. A priori optimization criteria that balance efficacy and efficiency will be used to create a finalized treatment package that produces the best weight loss outcomes with the fewest intervention components. Mediation analysis will be conducted to test hypothesized mechanisms of action and a moderator analysis will be conducted to understand for whom and under what circumstances the interventions are effective.

CONCLUSION

This study will provide important information about intervention strategies that are useful for improving outcomes of online BLI. The finalized treatment package will be suitable for testing in a future randomized trial in the MOST Evaluation Phase.

Refining an algorithm-powered just-in-time adaptive weight control intervention: A randomized controlled trial evaluating model performance and behavioral outcomes

Suboptimal weight losses are partially attributable to lapses from a prescribed diet. We developed an app (OnTrack) that uses ecological momentary assessment to measure dietary lapses and relevant lapse triggers and provides personalized intervention using machine learning. Initially, tension between user burden and complete data was resolved by presenting a subset of lapse trigger questions per ecological momentary assessment survey. However, this produced substantial missing data, which could reduce algorithm performance. We examined the effect of more questions per ecological momentary assessment survey on algorithm performance, app utilization, and behavioral outcomes. Participants with overweight/obesity (n = 121) used a 10-week mobile weight loss program and were randomized to OnTrack-short (i.e. 8 questions/survey) or OnTrack-long (i.e. 17 questions/survey). Additional questions reduced ecological momentary assessment adherence; however, increased data completeness improved algorithm performance. There were no differences in perceived effectiveness, app utilization, or behavioral outcomes. Minimal differences in utilization and perceived effectiveness likely contributed to similar behavioral outcomes across various conditions.

Randomized controlled trial of OnTrack, a just-in-time adaptive intervention designed to enhance weight loss

This randomized trial demonstrated qualified support for the ability of a machine learning-powered, smartphone-based just-in-time, adaptive intervention to enhance weight loss over and above a commercial weight loss program.

Associations between self-monitoring and weight change in behavioral weight loss interventions

OBJECTIVE

The current study is a secondary analysis of the Live SMART trial, a randomized controlled trial comparing a behavioral weight loss (BWL) condition delivered via smartphone (SMART) to a group-based BWL condition (GROUP) and a control condition (CONTROL). Given the established importance of self-monitoring for weight loss, the aims were to evaluate bidirectional associations between adherence to self-monitoring and weight change and to examine the moderating effect of treatment condition on these associations.

METHOD

Adults with overweight/obesity (n = 276; 83% women; 92.8% White; Mage = 55.1 years; Mbody mass index = 35.2 kg/m2) were instructed to self-monitor dietary intake, daily weight, and physical activity minutes via paper diaries in GROUP and CONTROL and via a smartphone application in SMART. All participants were weighed monthly at the research center. Adherence to self-monitoring was assessed via examination of self-monitoring records.

RESULTS

Generalized linear mixed models revealed that adherence to self-monitoring of dietary intake, self-weighing, and physical activity for each month was associated with weight change throughout that month, such that increased frequency of self-monitoring led to greater weight loss (ps < .001). For the GROUP condition only, poorer weight losses in 1 month were prospectively associated with poor adherence to self-monitoring the following month (ps ≤ .01).

CONCLUSIONS

Results provide evidence of a bidirectional association between self-monitoring and weight change. Better self-monitoring was consistently associated with better weight loss across intervention and tracking modalities. Poorer weight loss was prospectively associated with poorer self-monitoring in group treatment, suggesting that social influences could drive adherence in this form of treatment. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

OnTrack: development and feasibility of a smartphone app designed to predict and prevent dietary lapses

Given that the overarching goal of weight loss programs is to remain adherent to a dietary prescription, specific moments of nonadherence known as "dietary lapses" can threaten weight control via the excess energy intake they represent and by provoking future lapses. Just-in-time adaptive interventions could be particularly useful in preventing dietary lapses because they use real-time data to generate interventions that are tailored and delivered at a moment computed to be of high risk for a lapse. To this end, we developed a smartphone application (app) called OnTrack that utilizes machine learning to predict dietary lapses and deliver a targeted intervention designed to prevent the lapse from occurring. This study evaluated the feasibility, acceptability, and preliminary effectiveness of OnTrack among weight loss program participants. An open trial was conducted to investigate subjective satisfaction, objective usage, algorithm performance, and changes in lapse frequency and weight loss among individuals (N = 43; 86% female; body mass index = 35.6 kg/m2) attempting to follow a structured online weight management plan for 8 weeks. Participants were adherent with app prompts to submit data, engaged with interventions, and reported high levels of satisfaction. Over the course of the study, participants averaged a 3.13% weight loss and experienced a reduction in unplanned lapses. OnTrack, the first Just-in-time adaptive intervention for dietary lapses was shown to be feasible and acceptable, and OnTrack users experienced weight loss and lapse reduction over the study period. These data provide the basis for further development and evaluation.

Differential Programming Needs of College Students Preferring Web-Based Versus In-Person Physical Activity Programs

College students report several barriers to exercise, highlighting a need for university-based programs that address these challenges. In contrast to in-person interventions, several web-based programs have been developed to enhance program engagement by increasing ease of access and lowering the necessary level of commitment to participate. Unfortunately, web-based programs continue to struggle with engagement and less-than-ideal outcomes. One explanation for this discrepancy is that different intervention modalities may attract students with distinctive activity patterns, motivators, barriers, and program needs. However, no studies have formally evaluated intervention modality preference (e.g., web-based or in-person) among college students. The current study sought to examine the relationship between intervention modality preference and physical activity programming needs. Undergraduate students (n = 157) enrolled in psychology courses at an urban university were asked to complete an online survey regarding current activity patterns and physical activity program preferences. Participants preferring web-based physical activity programs exercised less (p = .05), were less confident in their abilities to exercise (p = .01), were less likely to endorse the maintenance stage of change (p < .01) and perceived more barriers to exercising (p < .01) than those who preferred in-person programming. Findings suggest that students preferring web-based programming may require programs that enhance self-efficacy by fostering goal-setting and problem-solving skills. A user-centered design approach may enhance the engagement (and therefore effectiveness) of physical activity promotion programs for college students.

Multi-sensor ecological momentary assessment of behavioral and psychosocial predictors of weight loss following bariatric surgery: study protocol for a multicenter prospective longitudinal evaluation

BACKGROUND

Bariatric surgery is currently the most effective strategy for producing significant and durable weight loss. Yet, not all patients achieve initial weight loss success and some degree of weight regain is very common, sometimes as early as 1-2 years post-surgery. Suboptimal weight loss not fully explained by surgical, demographic, and medical factors has led to greater emphasis on patient behaviors evidenced by clinical guidelines for appropriate eating and physical activity. However, research to inform such guidelines has often relied on imprecise measures or not been specific to bariatric surgery. There is also little understanding of what psychosocial factors and environmental contexts impact outcomes. To address research gaps and measurement limitations, we designed a protocol that innovatively integrates multiple measurement tools to determine which behaviors, environmental contexts, and psychosocial factors are related to outcomes and explore how psychosocial factors/environmental contexts influence weight. This paper provides a detailed description of our study protocol with a focus on developing and deploying a multi-sensor assessment tool to meet our study aims.

METHODS

This NIH-funded prospective cohort study evaluates behavioral, psychosocial, and environmental predictors of weight loss after bariatric surgery using a multi-sensor platform that integrates objective sensors and self-report information collected via smartphone in real-time in patients' natural environment. A target sample of 100 adult, bariatric surgery patients (ages 21-70) use this multi-sensor platform at preoperative baseline, as well as 3, 6, and 12 months postoperatively, to assess recommended behaviors (e.g., meal frequency, physical activity), psychosocial indicators with prior evidence of an association with surgical outcomes (e.g., mood/depression), and key environmental factors (e.g., type/quality of food environment). Weight also is measured at each assessment point.

DISCUSSION

This project has the potential to build a more sophisticated and valid understanding of behavioral and psychosocial factors contributing to success and risk after bariatric surgery. This new understanding could directly contribute to improved (i.e., specific, consistent, and validated) guidelines for recommended pre- and postoperative behaviors, which could lead to improved surgical outcomes. These data will also inform behavioral, psychosocial, and environmental targets for adjunctive interventions to improve surgical outcomes.

TRIAL REGISTRATION

Registered trial NCT02777177 on 5/19/2016.

Application of Machine Learning to Predict Dietary Lapses During Weight Loss

BACKGROUND

Individuals who adhere to dietary guidelines provided during weight loss interventions tend to be more successful with weight control. Any deviation from dietary guidelines can be referred to as a "lapse." There is a growing body of research showing that lapses are predictable using a variety of physiological, environmental, and psychological indicators. With recent technological advancements, it may be possible to assess these triggers and predict dietary lapses in real time. The current study sought to use machine learning techniques to predict lapses and evaluate the utility of combining both group- and individual-level data to enhance lapse prediction.

METHODS

The current study trained and tested a machine learning algorithm capable of predicting dietary lapses from a behavioral weight loss program among adults with overweight/obesity (n = 12). Participants were asked to follow a weight control diet for 6 weeks and complete ecological momentary assessment (EMA; repeated brief surveys delivered via smartphone) regarding dietary lapses and relevant triggers.

RESULTS

WEKA decision trees were used to predict lapses with an accuracy of 0.72 for the group of participants. However, generalization of the group algorithm to each individual was poor, and as such, group- and individual-level data were combined to improve prediction. The findings suggest that 4 weeks of individual data collection is recommended to attain optimal model performance.

CONCLUSIONS

The predictive algorithm could be utilized to provide in-the-moment interventions to prevent dietary lapses and therefore enhance weight losses. Furthermore, methods in the current study could be translated to other types of health behavior lapses.

Return of the JITAI: Applying a Just-in-Time Adaptive Intervention Framework to the Development of m-Health Solutions for Addictive Behaviors

PURPOSE

Lapses are strong indicators of later relapse among individuals with addictive disorders, and thus are an important intervention target. However, lapse behavior has proven resistant to change due to the complex interplay of lapse triggers that are present in everyday life. It could be possible to prevent lapses before they occur by using m-Health solutions to deliver interventions in real-time.

METHOD

Just-in-time adaptive intervention (JITAI) is an intervention design framework that could be delivered via mobile app to facilitate in-the-moment monitoring of triggers for lapsing, and deliver personalized coping strategies to the user to prevent lapses from occurring. An organized framework is key for successful development of a JITAI.

RESULTS

Nahum-Shani and colleagues (2014) set forth six core elements of a JITAI and guidelines for designing each: distal outcomes, proximal outcomes, tailoring variables, decision points, decision rules, and intervention options. The primary aim of this paper is to illustrate the use of this framework as it pertains to developing a JITAI that targets lapse behavior among individuals following a weight control diet.

CONCLUSION

We will detail our approach to various decision points during the development phases, report on preliminary findings where applicable, identify problems that arose during development, and provide recommendations for researchers who are currently undertaking their own JITAI development efforts. Issues such as missing data, the rarity of lapses, advantages/disadvantages of machine learning, and user engagement are discussed.

Promising technological innovations in cognitive training to treat eating-related behavior

One potential reason for the suboptimal outcomes of treatments targeting appetitive behavior, such as eating and alcohol consumption, is that they do not target the implicit cognitive processes that may be driving these behaviors. Two groups of related neurocognitive processes that are robustly associated with dysregulated eating and drinking are attention bias (AB; selective attention to specific stimuli) and executive function (EF; a set of cognitive control processes such as inhibitory control, working memory, set shifting, that govern goal-directed behaviors). An increasing body of work suggests that EF and AB training programs improve regulation of appetitive behaviors, especially if trainings are frequent and sustained. However, several key challenges, such as adherence to the trainings in the long term, and overall potency of the training, remain. The current manuscript describes five technological innovations that have the potential to address difficulties related to the effectiveness and feasibility of EF and AB trainings: (1) deployment of training in the home, (2) training via smartphone, (3) gamification, (4) virtual reality, and (5) personalization. The drawbacks of these innovations, as well as areas for future research, are also discussed. The above-mentioned innovations are likely to be instrumental in the future empirical work to develop and evaluate effective EF and AB trainings for appetitive behaviors.

Are individuals with loss-of-control eating more prone to dietary lapse in behavioural weight loss treatment? An ecological momentary assessment study

OBJECTIVE

Individuals with overweight/obesity and loss-of-control eating (LOC) may experience poorer outcomes from behavioural weight loss due to reactivity to internal (e.g., affective and physical) states that impact treatment adherence (e.g., dietary lapses). This study examined (a) whether the presence of LOC increased risk for dietary lapses and (b) the moderating role of LOC on the relation between internal states and dietary lapses.

METHOD

Individuals (n = 189) with overweight and obesity completed ecological momentary assessment early in behavioural weight loss.

RESULTS

LOC was positively associated with dietary lapse. LOC did not moderate the relation between momentary changes in internal states and dietary lapses. However, the effect of average levels of internal states on lapses was attenuated for those with LOC.

DISCUSSION

Results suggest that those with LOC are at higher risk of dietary lapse, whereas elevated average levels of internal states may contribute to early inadherence for those without LOC.

Ecological Momentary Assessment of Dietary Lapses Across Behavioral Weight Loss Treatment: Characteristics, Predictors, and Relationships with Weight Change

BACKGROUND

Adherence to dietary prescriptions is critical for successful weight loss and weight loss maintenance. However, research on specific instances of inadherence (lapses) is limited, and findings regarding the frequency, nature, and causes of lapses are mixed. Additionally, no studies have examined lapses over the course of a weight loss program.

PURPOSE

In the context of a reduced calorie diet prescribed as part of a behavioral treatment, we aimed to characterize lapse occurrence, examine lapse frequency across treatment, examine predictors of lapses, and assess the relationship between lapses and weight loss.

METHODS

Adults (n = 189) enrolled in a 12-month behavioral weight loss program completed ecological momentary assessment (EMA) at baseline, mid-treatment, and end of treatment. At each EMA survey, participants indicated whether a lapse had occurred, and responded to questions assessing situational, environmental, and affective states.

RESULTS

Lapse frequency showed a curvilinear relationship over time, such that frequency first decreased and then increased. Lapse frequency at baseline was negatively associated with early and overall weight loss. Lapses most often occurred at home, in the evenings, on the weekends, and entailed eating a forbidden food. Greater overall levels of assessed affective and environmental triggers predicted lapses, and greater momentary hunger and deprivation, and the presence of palatable food, also prospectively predicted lapses.

CONCLUSIONS

In addition to characterizing lapse frequency, the current study identified prospective predictors of lapses across treatment. These findings support the importance of lapses to weight control and provide insight for potential targets of intervention to prevent lapse occurrence.

Project HELP: a Remotely Delivered Behavioral Intervention for Weight Regain after Bariatric Surgery

BACKGROUND

Weight regain following bariatric surgery is common and potentially compromises the health benefits initially attained after surgery. Poor compliance to dietary and physical activity prescriptions is believed to be largely responsible for weight regain. Patients may benefit from developing specialized psychological skills necessary to engage in positive health behaviors over the long term. Unfortunately, patients often face challenges to physically returning to the bariatric surgery program for support in developing and maintaining these behaviors. Remotely delivered interventions, in contrast, can be conveniently delivered to the patient and have been found efficacious for a number of health problems, including obesity. To date, they have received little attention with bariatric surgery patients. The study aimed to evaluate a newly developed, remote acceptance-based behavioral intervention for postoperative weight regain.

METHODS

Patients at least 1.5 years out from surgery who experienced postoperative weight regain were recruited to receive the 10-week intervention. Participants were assessed at baseline, mid-treatment, post-treatment, and at 3-month follow-up.

RESULTS

Support for the intervention's feasibility and acceptability was achieved, with 70 % retention among those who started the program and a high mean rating (4.7 out of 5.0) of program satisfaction among study completers. On average, weight regain was reversed with a mean weight loss of 5.1 ± 5.5 % throughout the intervention. This weight loss was maintained at 3-month follow-up. Significant improvements in eating-related and acceptance-based variables also were observed.

CONCLUSIONS

This pilot study provides initial support for the feasibility, acceptability, and preliminary efficacy of a remotely delivered acceptance-based behavioral intervention for postoperative weight regain.

Ecological momentary assessment of self-attitudes in response to dietary lapses

OBJECTIVE

To examine whether self-attitudes and self-efficacy after dietary lapses relate to lapse frequency or predict risk for lapsing again on the same day.

METHOD

Adults with overweight/obesity (n = 91) completed ecological momentary assessment for 14 days at the start of a lifestyle modification program. At each survey, participants reported whether they had experienced a dietary lapse, and, if so, reported their self-attitudes (i.e., self-criticism, self-forgiveness, self-regard) and self-efficacy. The relationships between participants' typical (i.e., average level for each participant across lapses) self-attitudes/self-efficacy after lapsing and lapse frequency were examined using correlations. Generalized estimating equations examined whether participants' typical (average across lapses; between-person effect) self-attitudes/self-efficacy or momentary (i.e., level of each variable at a particular lapse relative to one's typical level; within-person effect) self-attitudes/self-efficacy predicted same-day lapse occurrence.

RESULTS

Lower typical self-efficacy and more negative typical self-regard related to greater lapse frequency. Additionally, lower momentary self-criticism predicted greater likelihood of same-day lapse occurrence. There also was a quadratic relationship between typical self-regard and risk of same-day lapse occurrence, such that individuals with either more negative or more positive typical self-regard were more likely to lapse on the same day.

CONCLUSION

Findings provide preliminary support for the relevance of self-attitudes and self-efficacy to lapses during early lifestyle modification. While greater typical self-efficacy and more positive typical self-regard are associated with fewer lapses, lower momentary self-criticism and very positive or negative typical self-regard may confer risk for same-day lapses. (PsycINFO Database Record

Acceptance-based versus standard behavioral treatment for obesity: Results from the mind your health randomized controlled trial

OBJECTIVE

To evaluate the efficacy, as well as potential moderators and mediators, of a revised acceptance-based behavioral treatment (ABT) for obesity, relative to standard behavioral treatment (SBT).

METHODS

Participants with overweight and obesity (n = 190) were randomized to 25 sessions of ABT or SBT over 1 year. Primary outcome (weight), mediator, and moderator measurements were taken at baseline, 6 months, and/or 12 months, and weight was also measured every session.

RESULTS

Participants assigned to ABT attained a significantly greater 12-month weight loss (13.3% ± 0.83%) than did those assigned to SBT (9.8% ± 0.87%; P = 0.005). A condition by quadratic time effect on session-by-session weights (P = 0.01) indicated that SBT had a shallower trajectory of weight loss followed by an upward deflection. ABT participants were also more likely to maintain a 10% weight loss at 12 months (64.0% vs. 48.9%; P = 0.04). No evidence of moderation was found. Results supported the mediating role of autonomous motivation and psychological acceptance of food-related urges.

CONCLUSIONS

Behavioral weight loss outcomes can be improved by integrating self-regulation skills that are reflected in acceptance-based treatment, i.e., tolerating discomfort and reduction in pleasure, enacting commitment to valued behavior, and being mindfully aware during moments of decision-making.

Slowing down and taking a second look: Inhibitory deficits associated with binge eating are not food-specific

Poor inhibitory control may contribute to the maintenance of binge eating (BE) among overweight and obese individuals. However, it is unknown whether deficits are general or specific to food (versus other attractive non-food stimuli), or whether observed deficits are attributable to increased depressive symptoms in BE groups. In the current study, we hypothesized that individuals with BE would display inhibitory control deficits, with more pronounced deficits occurring when food stimuli were used. Overweight or obese participants with (n = 25) and without (n = 65) BE completed a Stop Signal Task (SST) with distinct task blocks featuring food-specific stimuli, positive non-food stimuli, or neutral stimuli. The BE group exhibited poorer inhibitory control across SST stimuli types (p = .003, ηp(2)=.10), but deficits did not differ by stimuli type (p = .68, ηp(2) < .01). Including depression as a covariate did not significantly alter results. Results suggest individuals with BE display inhibitory control deficits compared to controls; however, deficits do not appear to be specific to stimuli type. Furthermore, inhibitory control deficits do not appear to be associated with mood disturbance in the BE group. Replication and further research is needed to guide treatment targets.

Review of smartphone applications for the treatment of eating disorders

mHealth tools may be a feasible modality for delivering evidence-based treatments and principles (EBPs), and may enhance treatment for eating disorders (EDs). However, research on the efficacy of mHealth tools for EDs and the extent to which they include EBPs is lacking. The current study sought to (i) review existing apps for EDs, (ii) determine the extent to which available treatment apps utilize EBPs, and (iii) assess the degree to which existing smartphone apps utilize recent advances in smartphone technology. Overall, existing ED intervention apps contained minimal EBPs and failed to incorporate smartphone capabilities. For smartphone apps to be a feasible and effective ED treatment modality, it may be useful for creators to begin taking utilizing the abilities that set smartphones apart from in-person treatment while incorporating EBPs. Before mHealth tools are incorporated into treatments for EDs, it is necessary that the feasibility, acceptability, and efficacy be evaluated.

Relationship of dieting and restrained eating to self-reported caloric intake in female college freshmen

Evidence indicates that restrained eaters do not eat less than unrestrained eaters in the natural environment. However, no study has examined caloric intake in those who are currently dieting to lose, or avoid gaining, weight. The current study examined caloric intake using 24-hour food recalls among individuals dieting to lose weight, dieting to avoid weight gain, restrained nondieters, and unrestrained nondieters. Participants were 246 female college students participating in a weight gain prevention trial. The predicted significant difference in caloric intake across the four groups was found for beverage but not for food intake. Results reinforce past literature indicating that dieting/restraint status does not reflect hypo-caloric intake in naturalistic settings.