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Article

Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones

1
Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth College, Lebanon, NH 03766, USA
2
Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Lebanon, NH 03766, USA
3
Department of Psychiatry, Geisel School of Medicine, Dartmouth College, Lebanon, NH 03766, USA
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(12), 3572; https://doi.org/10.3390/s20123572
Received: 22 May 2020 / Revised: 15 June 2020 / Accepted: 18 June 2020 / Published: 24 June 2020
(This article belongs to the Special Issue Wearable Sensors for Healthcare)
Prior research has recently shown that passively collected sensor data collected within the contexts of persons daily lives via smartphones and wearable sensors can distinguish those with major depressive disorder (MDD) from controls, predict MDD severity, and predict changes in MDD severity across days and weeks. Nevertheless, very little research has examined predicting depressed mood within a day, which is essential given the large amount of variation occurring within days. The current study utilized passively collected sensor data collected from a smartphone application to future depressed mood from hour-to-hour in an ecological momentary assessment study in a sample reporting clinical levels of depression (N = 31). Using a combination of nomothetic and idiographically-weighted machine learning models, the results suggest that depressed mood can be accurately predicted from hour to hour with an average correlation between out of sample predicted depressed mood levels and observed depressed mood of 0.587, CI [0.552, 0.621]. This suggests that passively collected smartphone data can accurately predict future depressed mood among a sample reporting clinical levels of depression. If replicated in other samples, this modeling framework may allow just-in-time adaptive interventions to treat depression as it changes in the context of daily life. View Full-Text
Keywords: major depressive disorder; digital phenotyping; digital biomarkers; machine learning; ecological momentary assessment major depressive disorder; digital phenotyping; digital biomarkers; machine learning; ecological momentary assessment
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MDPI and ACS Style

Jacobson, N.C.; Chung, Y.J. Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones. Sensors 2020, 20, 3572. https://doi.org/10.3390/s20123572

AMA Style

Jacobson NC, Chung YJ. Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones. Sensors. 2020; 20(12):3572. https://doi.org/10.3390/s20123572

Chicago/Turabian Style

Jacobson, Nicholas C.; Chung, Yeon J. 2020. "Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones" Sensors 20, no. 12: 3572. https://doi.org/10.3390/s20123572

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