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Hunger and binge eating: a meta-analysis of studies using ecological momentary assessment.

ABSTRACT: Binge eating has been associated with increased hunger, suggesting a role for impaired appetite regulation. Ecological momentary assessment (EMA) is ideally suited to examine whether hunger is a precipitant of binge eating but results from such studies have not been systematically reviewed. This study provides a meta-analysis of EMA studies that have examined hunger as an antecedent of binge eating.Electronic database and manual searches produced seven EMA studies with N = 180 participants. Meta-analyses were conducted to compare: (1) pre-binge eating hunger to average ratings of hunger, and (2) pre-binge eating hunger to hunger before regular eating.Across studies, hunger was significantly greater before binge eating compared with average hunger ratings, but was significantly lower before binge eating compared with before other eating episodes.Excessive hunger does not appear to be a precipitant of binge eating because higher levels of hunger are observed before regular eating episodes. However, lower hunger before food consumption may contribute to the experience of a particular eating episode as a binge.

SUBMITTER: Haedt-Matt AA

PROVIDER: S-EPMC3111846 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature

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Hunger and binge eating: a meta-analysis of studies using ecological momentary assessment.

Haedt-Matt Alissa A AA Keel Pamela K PK

The International journal of eating disorders 20101105 7

<h4>Objective</h4>Binge eating has been associated with increased hunger, suggesting a role for impaired appetite regulation. Ecological momentary assessment (EMA) is ideally suited to examine whether hunger is a precipitant of binge eating but results from such studies have not been systematically reviewed. This study provides a meta-analysis of EMA studies that have examined hunger as an antecedent of binge eating.<h4>Method</h4>Electronic database and manual searches produced seven EMA studie ...[more]

PMID: 21997419

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Project description:BackgroundPrevention of binge eating through just-in-time mobile interventions requires the prediction of respective high-risk times, for example, through preceding affective states or associated contexts. However, these factors and states are highly idiographic; thus, prediction models based on averages across individuals often fail.ObjectiveWe developed an idiographic, within-individual binge-eating prediction approach based on ecological momentary assessment (EMA) data.MethodsWe first derived a novel EMA-item set that covers a broad set of potential idiographic binge-eating antecedents from literature and an eating disorder focus group (n=11). The final EMA-item set (6 prompts per day for 14 days) was assessed in female patients with bulimia nervosa or binge-eating disorder. We used a correlation-based machine learning approach (Best Items Scale that is Cross-validated, Unit-weighted, Informative, and Transparent) to select parsimonious, idiographic item subsets and predict binge-eating occurrence from EMA data (32 items assessing antecedent contextual and affective states and 12 time-derived predictors).ResultsOn average 67.3 (SD 13.4; range 43-84) EMA observations were analyzed within participants (n=13). The derived item subsets predicted binge-eating episodes with high accuracy on average (mean area under the curve 0.80, SD 0.15; mean 95% CI 0.63-0.95; mean specificity 0.87, SD 0.08; mean sensitivity 0.79, SD 0.19; mean maximum reliability of rD 0.40, SD 0.13; and mean rCV 0.13, SD 0.31). Across patients, highly heterogeneous predictor sets of varying sizes (mean 7.31, SD 1.49; range 5-9 predictors) were chosen for the respective best prediction models.ConclusionsPredicting binge-eating episodes from psychological and contextual states seems feasible and accurate, but the predictor sets are highly idiographic. This has practical implications for mobile health and just-in-time adaptive interventions. Furthermore, current theories around binge eating need to account for this high between-person variability and broaden the scope of potential antecedent factors. Ultimately, a radical shift from purely nomothetic models to idiographic prediction models and theories is required.

Project description:BackgroundHealthy behaviors are crucial for maintaining a person's health and well-being. The effects of health behavior interventions are mediated by individual and contextual factors that vary over time. Recently emerging smartphone-based ecological momentary interventions (EMIs) can use real-time user reports (ecological momentary assessments [EMAs]) to trigger appropriate support when needed in daily life.ObjectiveThis systematic review aims to assess the characteristics of smartphone-delivered EMIs using self-reported EMAs in relation to their effects on health behaviors, user engagement, and user perspectives.MethodsWe searched MEDLINE, Embase, PsycINFO, and CINAHL in June 2019 and updated the search in March 2020. We included experimental studies that incorporated EMIs based on EMAs delivered through smartphone apps to promote health behaviors in any health domain. Studies were independently screened. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed. We performed a narrative synthesis of intervention effects, user perspectives and engagement, and intervention design and characteristics. Quality appraisal was conducted for all included studies.ResultsWe included 19 papers describing 17 unique studies and comprising 652 participants. Most studies were quasi-experimental (13/17, 76%), had small sample sizes, and great heterogeneity in intervention designs and measurements. EMIs were most popular in the mental health domain (8/17, 47%), followed by substance abuse (3/17, 18%), diet, weight loss, physical activity (4/17, 24%), and smoking (2/17, 12%). Of the 17 studies, the 4 (24%) included randomized controlled trials reported nonstatistically significant effects on health behaviors, and 4 (24%) quasi-experimental studies reported statistically significant pre-post improvements in self-reported primary outcomes, namely depressive (P<.001) and psychotic symptoms (P=.03), drinking frequency (P<.001), and eating patterns (P=.01). EMA was commonly used to capture subjective experiences as well as behaviors, whereas sensors were rarely used. Generally, users perceived EMIs to be helpful. Common suggestions for improvement included enhancing personalization, multimedia and interactive capabilities (eg, voice recording), and lowering the EMA reporting burden. EMI and EMA components were rarely reported and were not described in a standardized manner across studies, hampering progress in this field. A reporting checklist was developed to facilitate the interpretation and comparison of findings and enhance the transparency and replicability of future studies using EMAs and EMIs.ConclusionsThe use of smartphone-delivered EMIs using self-reported EMAs to promote behavior change is an emerging area of research, with few studies evaluating efficacy. Such interventions could present an opportunity to enhance health but need further assessment in larger participant cohorts and well-designed evaluations following reporting checklists. Future research should explore combining self-reported EMAs of subjective experiences with objective data passively collected via sensors to promote personalization while minimizing user burden, as well as explore different EMA data collection methods (eg, chatbots).Trial registrationPROSPERO CRD42019138739; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=138739.

Dataset Information

Hunger and binge eating: a meta-analysis of studies using ecological momentary assessment.

Publications

Hunger and binge eating: a meta-analysis of studies using ecological momentary assessment.

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