Unveiling overeating patterns within digital longitudinal data on eating behaviors and contexts.

ABSTRACT: Overeating contributes to obesity and poses a significant public health threat. The SenseWhy study (2018-2022) monitored 65 individuals with obesity in free-living settings, collecting 2302 meal-level observations (48 per participant), using an activity-oriented wearable camera, a mobile app, and dietitian-administered 24-hour dietary recalls. Micromovements (e.g., bites, chews) were manually labeled from 6343 hours of footage spanning 657 days. Psychological and contextual information was gathered before and after meals through Ecological Momentary Assessments (EMAs). We predicted overeating episodes based on EMA-derived features and passive sensing data (mean AUROC = 0.86; mean AUPRC = 0.84). Using semi-supervised learning on EMA-derived features alone, we identified five distinct overea

SUBMITTER: Shahabi F 

PROVIDER: S-EPMC12443995 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature