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Detecting Depression and Predicting its Onset Using Longitudinal Symptoms Captured by Passive Sensing: A Machine Learning Approach With Robust Feature Selection

Authors:

Prerna Chikersal,

Afsaneh Doryab,

Michael Tumminia,

Daniella K. Villalba,

Janine M. Dutcher,

Kasey G. Creswell,

Jennifer Mankoff,

J. David Creswell,

Anind K. DeyAuthors Info & Claims

ACM Transactions on Computer-Human Interaction (TOCHI), Volume 28, Issue 1

Article No.: 3, Pages 1 - 41

https://doi.org/10.1145/3422821

Published: 20 January 2021 Publication History

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Abstract

We present a machine learning approach that uses data from smartphones and fitness trackers of 138 college students to identify students that experienced depressive symptoms at the end of the semester and students whose depressive symptoms worsened over the semester. Our novel approach is a feature extraction technique that allows us to select meaningful features indicative of depressive symptoms from longitudinal data. It allows us to detect the presence of post-semester depressive symptoms with an accuracy of 85.7% and change in symptom severity with an accuracy of 85.4%. It also predicts these outcomes with an accuracy of >80%, 11–15 weeks before the end of the semester, allowing ample time for pre-emptive interventions. Our work has significant implications for the detection of health outcomes using longitudinal behavioral data and limited ground truth. By detecting change and predicting symptoms several weeks before their onset, our work also has implications for preventing depression.

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Cited By

Khoo LLim MChong CMcNaney R(2024)Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing ApproachesSensors10.3390/s2402034824:2(348)Online publication date: 6-Jan-2024
https://doi.org/10.3390/s24020348
Walschots QZarchev MUnkel MKamperman A(2024)Using Wearable Technology to Detect, Monitor, and Predict Major Depressive Disorder—A Scoping Review and Introductory Text for Clinical ProfessionalsAlgorithms10.3390/a1709040817:9(408)Online publication date: 12-Sep-2024
https://doi.org/10.3390/a17090408
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
Show More Cited By

Index Terms

Detecting Depression and Predicting its Onset Using Longitudinal Symptoms Captured by Passive Sensing: A Machine Learning Approach With Robust Feature Selection
1. Computing methodologies
  1. Machine learning
2. Human-centered computing
  1. Human computer interaction (HCI)

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cover image ACM Transactions on Computer-Human Interaction

ACM Transactions on Computer-Human Interaction Volume 28, Issue 1

February 2021

322 pages

ISSN:1073-0516

EISSN:1557-7325

DOI:10.1145/3447785

Editor:
Kristina Höök
KTH Royal Institute of Technology, Sweden

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Publication History

Published: 20 January 2021

Accepted: 01 August 2020

Revised: 01 June 2020

Received: 01 May 2019

Published in TOCHI Volume 28, Issue 1

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Cited By

Khoo LLim MChong CMcNaney R(2024)Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing ApproachesSensors10.3390/s2402034824:2(348)Online publication date: 6-Jan-2024
https://doi.org/10.3390/s24020348
Walschots QZarchev MUnkel MKamperman A(2024)Using Wearable Technology to Detect, Monitor, and Predict Major Depressive Disorder—A Scoping Review and Introductory Text for Clinical ProfessionalsAlgorithms10.3390/a1709040817:9(408)Online publication date: 12-Sep-2024
https://doi.org/10.3390/a17090408
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
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
https://doi.org/10.2196/47805
Choi AOoi ALottridge D(2024)Digital Phenotyping for Stress, Anxiety, and Mild Depression: Systematic Literature ReviewJMIR mHealth and uHealth10.2196/4068912(e40689)Online publication date: 23-May-2024
https://doi.org/10.2196/40689
Beames JHan JShvetcov AZheng WSlade AIbrahim ORosenberg JO’Dea BKasturi SHoon LWhitton AChristensen HNewby J(2024)Use of Smartphone Sensor Data in Detecting and Predicting Depression and Anxiety in Young People (12-25 Years): A Scoping ReviewSSRN Electronic Journal10.2139/ssrn.4798261Online publication date: 2024
https://doi.org/10.2139/ssrn.4798261
Bidargaddi NLeibbrandt RPaget TVerjans JLooi JLipschitz J(2024)Remote sensing mental health: A systematic review of factors essential to clinical translation from validation researchDIGITAL HEALTH10.1177/2055207624126041410Online publication date: 25-Jul-2024
https://doi.org/10.1177/20552076241260414
Xu XLiu XZhang HWang WNepal SSefidgar YSeo WKuehn KHuckins JMorris MNurius PRiskin EPatel SAlthoff TCampbell ADey AMankoff J(2024)GLOBEM: Cross-Dataset Generalization of Longitudinal Human Behavior ModelingGetMobile: Mobile Computing and Communications10.1145/3686138.368614728:2(23-30)Online publication date: 31-Jul-2024
https://dl.acm.org/doi/10.1145/3686138.3686147
Islam RBae S(2024)FacePsy: An Open-Source Affective Mobile Sensing System - Analyzing Facial Behavior and Head Gesture for Depression Detection in Naturalistic SettingsProceedings of the ACM on Human-Computer Interaction10.1145/36765058:MHCI(1-32)Online publication date: 24-Sep-2024
https://dl.acm.org/doi/10.1145/3676505
Bedmutha MKarumudi SPatrick KRataj HWeibel NKostakos VKay JHoang T(2024)Exploring User Willingness towards Mobile Sensing and Intervention: A Case Study on Mental Health of Undergraduate College StudentsCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing10.1145/3675094.3678422(721-728)Online publication date: 5-Oct-2024
https://dl.acm.org/doi/10.1145/3675094.3678422
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