Digital Monitoring of Micro- and Macro-Movement Regularity in Psychiatric Inpatients With Depression

Depression involves mood-related behavioral changes typically monitored through subjective reports, which are limited by recall bias and low temporal resolution. Digital mental health tools offer objective, continuous monitoring, but prior studies have focused on outpatients subject to environmental variability. In this preliminary feasibility study, we examined psychiatric inpatients in a controlled setting to assess associations between behavioral regularity and depression severity, highlighting the clinical potential of digital phenotyping.

Thirty-five adults from a closed psychiatric ward were recruited, and data from 10 inpatients with ≥7 days of valid monitoring were analyzed. Depression severity was assessed weekly using the Hamilton Depression Rating Scale (HAMD) and Dysfunctional Self-focus Attributes Scale, yielding 18 samples. Hourly accelerometer and location data from wearable devices and ward sensors were processed to generate digital phenotypes-interdaily stability (IS), intradaily variability (IV), ratio of IS to IV (ISV), entropy (EN), and normalized entropy (NE)-segmented into daytime and nighttime. Linear mixed models assessed group differences, and correlation and multiple regression examined associations with depression.

Patients with asymptomatic/mild depression showed significantly higher IS_day and ISV_day, and lower EN_night, and NE_night (all p<0.05). These four features correlated with HAMD after false discovery rate (all p<0.05) correction. A regression model including IS_day and NE_night explained 60.6% of HAMD variance (p<0.05).

Digital monitoring provides an objective and continuous method to assess depression severity. By capturing macro- and micro-level movement regularity across day and night in an inpatient environment, this approach offers practical relevance for psychiatric care. However, results should be considered preliminary due to the limited sample size.