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

Area of Science:

• Digital health
• Psychiatry
• Behavioral science

Background:

• Subjective depression reports have limitations like recall bias.
• Digital phenotyping offers objective, continuous monitoring.
• Previous studies focused on outpatients, not controlled inpatient settings.

Purpose of the Study:

• Assess associations between behavioral regularity and depression severity in psychiatric inpatients.
• Explore the clinical potential of digital phenotyping in a controlled environment.
• Evaluate digital monitoring as an objective method for depression assessment.

Main Methods:

• Recruited 35 adults from a psychiatric ward; analyzed data from 10 inpatients with ≥7 days of monitoring.
• Collected hourly accelerometer and location data from wearable devices and ward sensors.
• Generated digital phenotypes (IS, IV, ISV, EN, NE) for daytime and nighttime; assessed depression with HAMD and Dysfunctional Self-focus Attributes Scale.

Main Results:

• Asymptomatic/mild depression linked to higher daytime stability (IS_day, ISV_day) and lower nighttime entropy (EN_night, NE_night).
• Four digital features (IS_day, ISV_day, EN_night, NE_night) significantly correlated with HAMD scores.
• A regression model using IS_day and NE_night explained 60.6% of HAMD variance.

Conclusions:

• Digital monitoring offers an objective, continuous method for assessing depression severity in inpatients.
• Movement regularity patterns captured by digital phenotyping have practical relevance for psychiatric care.
• Results are preliminary due to the limited sample size but highlight digital phenotyping's potential.