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

Wang, Rui; Wang, Weichen; daSilva, Alex; Huckins, Jeremy F.; Kelley, William M.; Heatherton, Todd F.; Campbell, Andrew T.

doi:10.1145/3191775

Cited by 340Digital Mental Health InterventionsMental Health Research TopicsEmotion and Mood Recognition

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

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Wang, Rui, Wang, Weichen, daSilva, Alex, Huckins, Jeremy F., Kelley, William M., Heatherton, Todd F., Campbell, Andrew T.·2018·Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

DOI 10.1145/3191775PMC PMC11501090

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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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For the past 30 years, clinician-administered and self-reported questionnaires remain the gold standard in the assessment and diagnosis of depression.
“For the past 30 years, clinician-administered and self-reported questionnaires remain the gold standard in the assessment and diagnosis of depression [3,13].”
From Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study (2021) · cites this paper as [3]
In many individuals with depression, it remains undiagnosed and untreated due to barriers such as social stigma and inaccurate assessment methods.
“Yet, in many individuals with depression, it remains undiagnosed and untreated due to barriers such as social stigma and inaccurate assessment methods [1,3,12,13].”
From Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study (2021) · cites this paper as [3]
PHQ-8 is clinically validated for the assessment of depression severity, has high internal consistency (Cronbach α=.82), and has been used in several previous studies.
“Although the depression assessments were self-reported, the PHQ-8 is clinically validated for the assessment of depression severity, has high internal consistency (Cronbach α=.82), and has been used in several previous studies [3,7,41].”
From Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study (2021) · cites this paper as [3]
Monitoring these biomarkers' fluctuations is essential in the early detection and treatment of mental health disorders.
“Monitoring these biomarkers’ fluctuations is essential in the early detection and treatment of mental health disorders [3,32].”
From Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study (2021) · cites this paper as [3]
The ability to detect early warning signs of depression, continuously and as effortlessly as possible, by extending current assessment methods could have a significant impact in mitigating or addressing depression and its related negative consequences.
“The ability to detect early warning signs of depression, continuously and as effortlessly as possible, by extending current assessment methods could have a significant impact in mitigating or addressing depression and its related negative consequences [3,10,11,14].”
From Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study (2021) · cites this paper as [3]

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Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review
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Extraction and Interpretation of Deep Autoencoder-based Temporal Features from Wearables for Forecasting Personalized Mood, Health, and Stress
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Digital Phenotyping of Mental Health using multimodal sensing of multiple situations of interest: A Systematic Literature Review
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Differentiating Higher and Lower Job Performers in the Workplace Using Mobile Sensing
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Psychological inflexibility mediates the relationship between adverse childhood experiences and mental health outcomes
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Psychometric Properties of the Generalized Anxiety Disorder-7 and Generalized Anxiety Disorder-Mini in United States University Students
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Designing for Emotional Well-being: Integrating Persuasion and Customization into Mental Health Technologies
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Digital Phenotyping for Stress, Anxiety, and Mild Depression: Systematic Literature Review
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Can digital data diagnose mental health problems? A sociological exploration of ‘digital phenotyping’
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"Energy is a Finite Resource": Designing Technology to Support Individuals across Fluctuating Symptoms of Depression
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Leveraging Collaborative-Filtering for Personalized Behavior Modeling
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App Usage Predicts Cognitive Ability in Older Adults
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Extended reality for mental health: Current trends and future challenges
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From Classification to Clinical Insights
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Digital Phenotyping and Feature Extraction on Smartphone Data for Depression Detection
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Depression predictions from GPS-based mobility do not generalize well to large demographically heterogeneous samples
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Automatic motion artifact detection in electrodermal activity data using machine learning
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Personality Sensing for Theory Development and Assessment in the Digital Age
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The Relationships of Deteriorating Depression and Anxiety With Longitudinal Behavioral Changes in Google and YouTube Use During COVID-19: Observational Study
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Wearable devices for anxiety & depression: A scoping review
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Identifying Mobile Sensing Indicators of Stress-Resilience
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Predicting physician burnout using clinical activity logs: Model performance and lessons learned
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Detection of Artifacts in Ambulatory Electrodermal Activity Data
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Digital phenotyping of student mental health during COVID-19: an observational study of 100 college students
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Automated Screening for Social Anxiety, Generalized Anxiety, and Depression From Objective Smartphone-Collected Data: Cross-sectional Study
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Social Sensing: Assessing Social Functioning of Patients Living with Schizophrenia using Mobile Phone Sensing
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Internet of Things for Mental Health: Open Issues in Data Acquisition, Self-Organization, Service Level Agreement, and Identity Management
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Longitudinal Relationships Between Depressive Symptom Severity and Phone-Measured Mobility: Dynamic Structural Equation Modeling Study
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Prediction for Retrospection: Integrating Algorithmic Stress Prediction into Personal Informatics Systems for College Students’ Mental Health
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Predicting Subjective Measures of Social Anxiety from Sparsely Collected Mobile Sensor Data
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Ambient Assisted Living and Internet of Things
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Depressed Mood Prediction of Elderly People with a Wearable Band
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Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis
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“I think people are powerful”: The Sociality of Individuals Managing Depression
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Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data
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When phones get personal: Predicting Big Five personality traits from application usage
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Multi-Task Learning for Randomized Controlled Trials
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What reveals about depression level? The role of multimodal features at the level of interview questions
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“It’s About Missing Much More Than the People”: How Students use Digital Technologies to Alleviate Homesickness
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One More Bite? Inferring Food Consumption Level of College Students Using Smartphone Sensing and Self-Reports
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Correlates of Stress in the College Environment Uncovered by the Application of Penalized Generalized Estimating Equations to Mobile Sensing Data
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Fusing Ambient and Mobile Sensor Features Into a Behaviorome for Predicting Clinical Health Scores
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COVID Student Study: A Year in the Life of College Students during the COVID-19 Pandemic Through the Lens of Mobile Phone Sensing
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First-Gen Lens
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Harnessing Consumer Wearable Digital Biomarkers for Individualized Recognition of Postpartum Depression Using the All of Us Research Program Data Set: Cross-Sectional Study
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Passively-sensed Behavioral Correlates of Discrimination Events in College Students
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Utilizing daily mood diaries and wearable sensor data to predict depression and suicidal ideation among medical interns
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Psychometric Properties and Factor Structure of the Patient Health Questionnaire-9 as a Screening Tool for Depression Among Ecuadorian College Students
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A for Effort? Using the Crowd to Identify Moral Hazard in New York City Restaurant Hygiene Inspections
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Toward Future-Centric Personal Informatics: Expecting Stressful Events and Preparing Personalized Interventions in Stress Management
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Smartphone Sensing for the Well-Being of Young Adults: A Review
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Fluctuations in behavior and affect in college students measured using deep phenotyping
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Predicting Brain Functional Connectivity Using Mobile Sensing
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Exploring Context-Aware Mental Health Self-Tracking Using Multimodal Smart Speakers in Home Environments
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Use of smartphone sensor data in detecting and predicting depression and anxiety in young people (12–25 years): A scoping review
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Evaluating predictive artificial intelligence approaches used in mobile health platforms to forecast mental health symptoms among youth: a systematic review
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Predicting mental health using smart-phone usage and sensor data
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Trait-like nocturnal sleep behavior identified by combining wearable, phone-use, and self-report data
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Putting human behavior predictability in context
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A process-oriented approach to respecting privacy in the context of mobile phone tracking
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Medical Brain Drain From Southeastern Europe: Using Digital Demography to Forecast Health Worker Emigration
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A review of detection techniques for depression and bipolar disorder
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Authors: Wang, R., Wang, W., daSilva, A., Huckins, J. F., Kelley, W. M., Heatherton, T. F., & Campbell, A. T.
Affiliations: Dartmouth College (US)
ORCID: Wang, Rui
Wang, Weichen
daSilva, Alex
Huckins, Jeremy F.
Heatherton, Todd F.
Campbell, Andrew T.
Venue: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Publisher: Association for Computing Machinery (ACM)
Published: 2018-03-26
Volume: 2
Issue: 1
Pages: 1-26
ISSN: 2474-9567 (ISSN-L)
DOI: 10.1145/3191775
PMC: PMC11501090
OpenAlex: W2794997027
Cited by: 340
References: 75
Citations by year: 2026: 29 · 2025: 35 · 2024: 49 · 2023: 42 · 2022: 71 · 2021: 48 · 2020: 37 · 2019: 24 · 2018: 5
FWCI: 35.94
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations: Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Institute of Mental Health (R01MH059282)
Topics: Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
Keywords: depression, university students, mobile sensing, wearable sensors, passive sensing, PHQ-8, PHQ-4, depression prediction, mental health monitoring, smartphone sensing, wearable sensing

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

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