Detecting Binge Eating Risk With Naturalistic Data and Machine Learning: A Comparative Observational Study

Negative affect is a primary antecedent for binge eating (BE). Just-in-time adaptive interventions (JITAIs) prompt the use of therapy skills when at risk for maladaptive behaviors. JITAIs show promise for improving emotion regulation skill use and decreasing BE. Identifying momentary negative affect has relied on ecological momentary assessment (EMA). EMA surveys are typically delivered 4-6 times/day via smartphone and are self-report; accordingly, EMA is limited by temporal granularity, participant insight, and adherence. Wearable sensors which continuously, passively measure physiological correlates of affect may be able to detect risk for negative affect-related BE while overcoming limitations of EMA. This study compared machine learning models using EMA- and sensor-measured negative affect to predict BE.

Thirty adults with recurrent BE wore smartwatches to measure heart rate and electrodermal activity and reported affect and BE on EMA for 4 weeks (preregistration: https://www.researchprotocols.org/2023/1/e47098/). Support vector machines, random forest, and neural network models were trained using the first 3 weeks and evaluated using the last week of EMA data, sensor data, and combined EMA and sensor data.

The best-performing EMA-only model for predicting BE had macro-averaged accuracy of 0.64 (AUROC = 0.69, sensitivity = 0.47, specificity = 0.81), the sensor-only model had macro-averaged accuracy of 0.64 (AUROC = 0.78, sensitivity = 0.92, specificity = 0.35), and the combined model had macro-averaged accuracy of 0.61 (AUROC = 0.68, sensitivity = 0.90, specificity = 0.33); model macro-averaged accuracies did not significantly differ.

Psychophysiological sensor data demonstrate comparable accuracy to EMA for predicting BE, setting the stage for low-burden JITAIs targeting negative affect-related BE.