ArticleJournal of the American Medical Informatics Association : JAMIA2020
Depression screening using mobile phone usage metadata: a machine learning approach.
Article in Journal of the American Medical Informatics Association : JAMIA, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Digital phenotype of mood disorders: A conceptual and critical review.Frontiers in psychiatry · 2022Pooled it
- Applicable Scenarios, Desired Features, and Risks of AI Psychotherapists in Depression Treatment From the Patient's Perspective: Exploratory Qualitative Study.JMIR formative research · 2026Article
- Impact of the Interaction Between Screen Time and Activity Interests on Adolescent Depression Risk: Construction of a Predictive Model Based on Machine Learning.Actas espanolas de psiquiatria · 2026Article
- Digital phenotyping of affect and stress in emerging adults.Frontiers in digital health · 2026Article
- Observational
- Observational
- Mental Health and Mobile Communication Profiles of Crowdsourced Participants.IEEE journal of biomedical and health informatics · 2024Article
- Mobile sensing-based depression severity assessment in participants with heterogeneous mental health conditions.Scientific reports · 2024Article
- Bayesian Networks for Prescreening in Depression: Algorithm Development and Validation.JMIR mental health · 2024Article
- The Use of Text Messaging as an Adjunct to Internet-Based Cognitive Behavioral Therapy for Major Depressive Disorder in Youth: Secondary Analysis.JMIR formative research · 2024Article
- Differential temporal utility of passively sensed smartphone features for depression and anxiety symptom prediction: a longitudinal cohort study.Npj mental health research · 2024Article
- Improving Depression Severity Prediction from Passive Sensing: Symptom-Profiling Approach.Sensors (Basel, Switzerland) · 2023Article
- Acceptability of Personal Sensing Among People With Alcohol Use Disorder: Observational Study.JMIR mHealth and uHealth · 2023Observational
- Depression Detection Based on Hybrid Deep Learning SSCL Framework Using Self-Attention Mechanism: An Application to Social Networking Data.Sensors (Basel, Switzerland) · 2022Article
- Machine Learning on Early Diagnosis of Depression.Psychiatry investigation · 2022Article
- A Machine Learning Approach for Detecting Digital Behavioral Patterns of Depression Using Nonintrusive Smartphone Data (Complementary Path to Patient Health Questionnaire-9 Assessment): Prospective Observational Study.JMIR formative research · 2022Article
- Design and Preliminary Realization of a Screening and Early Warning Health Management System for Populations at High Risk for Depression.International journal of environmental research and public health · 2022Article
- Digital health tools for the passive monitoring of depression: a systematic review of methods.NPJ digital medicine · 2022Review
- Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions.Journal of personalized medicine · 2021Review
- Evaluation of Changes in Depression, Anxiety, and Social Anxiety Using Smartphone Sensor Features: Longitudinal Cohort Study.Journal of medical Internet research · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveDepression is currently the second most significant contributor to non-fatal disease burdens globally. While it is treatable, depression remains undiagnosed in many cases. As mobile phones have now become an integral part of daily life, this study examines the possibility of screening for depressive symptoms continuously based on patients' mobile usage patterns. MATERIALS AND
methods412 research participants reported a range of their mobile usage statistics. Beck Depression Inventory-2nd ed (BDI-II) was used to measure the severity of depression among participants. A wide array of machine learning classification algorithms was trained to detect participants with depression symptoms (ie, BDI-II score ≥ 14). The relative importance of individual variables was additionally quantified.
resultsParticipants with depression were found to have fewer saved contacts on their devices, spend more time on their mobile devices to make and receive fewer and shorter calls, and send more text messages than participants without depression. The best model was a random forest classifier, which had an out-of-sample balanced accuracy of 0.768. The balanced accuracy increased to 0.811 when participants' age and gender were included. DISCUSSIONS/
conclusionThe significant predictive power of mobile usage attributes implies that, by collecting mobile usage statistics, mental health mobile applications can continuously screen for depressive symptoms for initial diagnosis or for monitoring the progress of ongoing treatments. Moreover, the input variables used in this study were aggregated mobile usage metadata attributes, which has low privacy sensitivity making it more likely for patients to grant required application permissions.
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