%0 Journal Article
%@ 2291-9694
%I JMIR Publications
%V 11
%N 
%P e41513
%T Toward Individualized Prediction of Binge-Eating Episodes Based on Ecological Momentary Assessment Data: Item Development and Pilot Study in Patients With Bulimia Nervosa and Binge-Eating Disorder
%A Arend,Ann-Kathrin
%A Kaiser,Tim
%A Pannicke,Björn
%A Reichenberger,Julia
%A Naab,Silke
%A Voderholzer,Ulrich
%A Blechert,Jens
%+ Department of Psychology, Centre for Cognitive Neuroscience, University of Salzburg, Hellbrunnerstraße 34, Salzburg, 5020, Austria, 43 66280445102, ann-kathrin.arend@plus.ac.at
%K idiographic
%K individualized
%K N of 1
%K Ecological Momentary Assessment (EMA)
%K Just-In-Time Adaptive Intervention (JITAI)
%K binge eating
%K literature research
%K focus group
%K prediction algorithm
%K machine learning
%K Best Items Scales that are Cross-validated, Unit-weighted, Informative and Transparent
%K BISCUIT
%D 2023
%7 23.2.2023
%9 Original Paper
%J JMIR Med Inform
%G English
%X Background: Prevention 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. Objective: We developed an idiographic, within-individual binge-eating prediction approach based on ecological momentary assessment (EMA) data. Methods: We 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). Results: On 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. Conclusions: Predicting 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. 
%M 36821359
%R 10.2196/41513
%U https://medinform.jmir.org/2023/1/e41513
%U https://doi.org/10.2196/41513
%U http://www.ncbi.nlm.nih.gov/pubmed/36821359