Integrating human-smartphone interactions and actigraphy in digital phenotyping of obesity and depression: Social-physical coherence algorithm development and validation study

Circadian misalignment is associated with obesity and depression, yet few tools directly quantify the alignment between physical and social activity. We developed social-physical coherence, a digital-phenotyping measure defined as the magnitude-squared coherence between wrist-actigraphy physical activity and smartphone-derived social activity at the circadian (~24-hour) frequency, indexing how closely the two rhythms are aligned. In 135 adults (mean age 43.8 ± 12.3 years; 65.2% female; 42.2% with a history of major depression; 18.5% with obesity), physical and social activity were recorded for at least four weeks (3,973 person-days), and rest-activity rhythm indices – including interdaily stability (IS) – were computed from both data streams. Social-physical coherence correlated positively with app-derived IS (ISapp; r = 0.51–0.64 across 1–6 weeks). Higher coherence and ISapp were associated with lower body mass index during weeks 2–4 (β = −0.22 to −0.25), remaining significant after Benjamini–Hochberg correction and after adjustment for sleep duration and chronotype; actigraphy-derived IS was not, and among all rhythm indices only ISapp paralleled this relationship. Coherence was uncorrelated with social jetlag, and no measure was associated with depressive symptoms. Social-physical coherence, derived from digital phenotyping of physical and social activity, is inversely associated with obesity but not depression.