Objective Features Extracted from Motor Activity Time Series for Food Addiction Analysis Using Machine Learning -- A Pilot Study

Wearable sensors and IoT/IoMT platforms enable continuous, real-time monitoring, but objective digital markers for eating disorders are limited. In this study, we examined whether actimetry and machine learning (ML) could provide objective criteria for food addiction (FA) and symptom counts (SC). In 78 participants (mean age 22.1 +/- 9.5 y; 73.1% women), one week of non-dominant wrist actimetry and psychometric data (YFAS, DEBQ, ZSDS) were collected. The time series were segmented into daytime activity and nighttime rest, and statistical and entropy descriptors (FuzzyEn, DistEn, SVDEn, PermEn, PhaseEn; 256 features) were calculated. The mean Matthews correlation coefficient (MCC) was used as the primary metric in a K-nearest neighbors (KNN) pipeline with five-fold stratified cross-validation (one hundred repetitions; 500 evaluations); SHAP was used to assist in interpretation. For binary FA, activity-segment features performed best (MCC = 0.78 +/- 0.02; Accuracy ~ 95.3% +/- 0.5; Sensitivity ~ 0.77 +/- 0.03; Specificity ~ 0.98 +/- 0.004), exceeding OaS (Objective and Subjective Features) (MCC = 0.69 +/- 0.03) and rest-only (MCC = 0.50 +/- 0.03). For SC (four classes), OaS slightly surpassed actimetry (MCC = 0.40 +/- 0.01 vs 0.38 +/- 0.01; Accuracy ~ 58.1% vs 56.9%). Emotional and restrained eating were correlated with actimetric features. These findings support wrist-worn actimetry as a digital biomarker of FA that complements questionnaires and may facilitate privacy-preserving clinical translation.