Patterns of activity correlate with symptom severity in major depressive disorder patients.

Objective measures, such as activity monitoring, can potentially complement clinical assessment for psychiatric patients. Alterations in rest-activity patterns are commonly encountered in patients with major depressive disorder. The aim of this study was to investigate whether features of activity patterns correlate with severity of depression symptoms (evaluated by Montgomery-Åsberg Rating Scale (MADRS) for depression). We used actigraphy recordings collected during ongoing major depressive episodes from patients not undergoing any antidepressant treatment. The recordings were acquired from two independent studies using different actigraphy systems. Data was quality-controlled and pre-processed for feature extraction following uniform procedures. We trained multiple regression models to predict MADRS score from features of activity patterns using brute-force and semi-supervised machine learning algorithms. The models were filtered based on the precision and the accuracy of fitting on training dataset before undergoing external validation on an independent dataset. The features enriched in the models surviving external validation point to high depressive symptom severity being associated with less complex activity patterns and stronger coupling to external circadian entrainers. Our results bring proof-of-concept evidence that activity patterns correlate with severity of depressive symptoms and suggest that actigraphy recordings may be a useful tool for individual evaluation of patients with major depressive disorder.