research-article

Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing

Authors:

Jeremy F. Huckins,

William M. Kelley,

Todd F. Heatherton,

Andrew T. CampbellAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 2, Issue 1

Article No.: 43, Pages 1 - 26

https://doi.org/10.1145/3191775

Published: 26 March 2018 Publication History

Abstract

There are rising rates of depression on college campuses. Mental health services on our campuses are working at full stretch. In response researchers have proposed using mobile sensing for continuous mental health assessment. Existing work on understanding the relationship between mobile sensing and depression, however, focuses on generic behavioral features that do not map to major depressive disorder symptoms defined in the standard mental disorders diagnostic manual (DSM-5). We propose a new approach to predicting depression using passive sensing data from students' smartphones and wearables. We propose a set of symptom features that proxy the DSM-5 defined depression symptoms specifically designed for college students. We present results from a study of 83 undergraduate students at Dartmouth College across two 9-week terms during the winter and spring terms in 2016. We identify a number of important new associations between symptom features and student self reported PHQ-8 and PHQ-4 depression scores. The study captures depression dynamics of the students at the beginning and end of term using a pre-post PHQ-8 and week by week changes using a weekly administered PHQ-4. Importantly, we show that symptom features derived from phone and wearable sensors can predict whether or not a student is depressed on a week by week basis with 81.5% recall and 69.1% precision.

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Index Terms

Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing
1. Applied computing
  1. Life and medical sciences
2. Human-centered computing
  1. Ubiquitous and mobile computing

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 2, Issue 1

March 2018

1370 pages

EISSN:2474-9567

DOI:10.1145/3200905

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Copyright © 2018 ACM.

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Publication History

Published: 26 March 2018

Accepted: 01 January 2018

Revised: 01 November 2017

Received: 01 May 2017

Published in IMWUT Volume 2, Issue 1

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Hurwitz EButzin-Dozier ZMaster HO'Neil SWalden AHolko MPatel RHaendel M(2024)Harnessing Consumer Wearable Digital Biomarkers for Individualized Recognition of Postpartum Depression Using the All of Us Research Program Data Set: Cross-Sectional StudyJMIR mHealth and uHealth10.2196/5462212(e54622)Online publication date: 2-May-2024
https://doi.org/10.2196/54622
Ahmed MHasan TIslam SAhmed N(2024)Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide StudyJMIR Research Protocols10.2196/5154013(e51540)Online publication date: 24-Apr-2024
https://doi.org/10.2196/51540
McDuff DBarakat AWinbush AJiang ACordeiro FCrowley RKahn LHernandez JAllen N(2024)The Google Health Digital Well-Being Study: Protocol for a Digital Device Use and Well-Being StudyJMIR Research Protocols10.2196/4918913(e49189)Online publication date: 14-May-2024
https://doi.org/10.2196/49189
Mullick TShaaban SRadovic ADoryab A(2024)Framework for Ranking Machine Learning Predictions of Limited, Multimodal, and Longitudinal Behavioral Passive Sensing Data: Combining User-Agnostic and Personalized ModelingJMIR AI10.2196/478053(e47805)Online publication date: 20-May-2024
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