Citation

Auerbach RP, Srinivasan A, Kirshenbaum JS, Mann JJ, Shankman SA. J. Psychopathol. Clin. Sci. 2022; ePub(ePub): ePub.

Copyright

(Copyright © 2022, American Psychological Association)

DOI

10.1037/abn0000742

PMID

35230855

Abstract

Depression recurrence is debilitating, and there is a pressing need to develop clinical tools that detect the reemergence of symptoms with the aim of bridging patients to treatment before recurrences. At baseline, remitted depressed adults (n = 22) and healthy controls (n = 24) were administered clinical interviews and completed self-report symptom measures. Then, smartphone apps were installed on personal smartphones to acquire geolocation data over 21 days and ecological momentary assessment of positive and negative affect during the initial 14-day period. Compared with healthy controls, remitted depressed adults exhibited reduced circadian routine (regularity of one's daily routine) and lower average daily distance traveled. Further, reduced distance traveled associated with greater daily negative affect after controlling for depression severity; however, this effect was not more pronounced among remitted adults. A least absolute shrinkage and selection operator (LASSO) regression indicated that a linear combination of circadian routine, average distance traveled, and baseline depression severity classified remitted depressed individuals with 72% accuracy; outperforming models restricted to either geolocation or clinical measures alone. Mobile sensing approaches hold enormous promise to improve clinical care for depressive disorders. Although barriers remain, leveraging technological advancements related to real-time monitoring can improve treatment for depressed patients and potentially, reduce high rates of recurrence. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Language: en