Observational studyJournal of medical Internet research2024
Predicting and Monitoring Symptoms in Patients Diagnosed With Depression Using Smartphone Data: Observational Study.
Observational study in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Digital Markers for Passive Remote Monitoring of Bipolar Disorder: Systematic Review.JMIR mental health · 2026Pooled it
- The Role of Digital Biomarkers in Physiological Signal-Based Depression Assessment: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Smartphone-based digital markers and clinical symptoms during therapy for Borderline Personality Disorder.Internet interventions · 2026Article
- Digital Phenotyping in Health Research: Scoping Review of Methods, Gaps, and Opportunities.Journal of medical Internet research · 2026Article
- Passive Screening for Depressive Symptoms Using Daily Wrist Actigraphy and Deep Learning: Model Development and Validation Study.JMIR mHealth and uHealth · 2026Article
- Modeling Short-Term Symptom Changes and Behavioral Subtypes of Depression and Anxiety in the General Population: Observational Study Using Smartphone Data.JMIR formative research · 2026Observational
- Digital phenotyping with large language models to detect depressive state changes in patients.NPJ digital medicine · 2026Article
- Contribution of Longitudinal Mobile Health Measures in the Dynamic Track of Patients With Major Depressive Disorder: Multiple Centers, Prospective Cohort Study Using Functional Data Analysis and Machine Learning.JMIR mHealth and uHealth · 2026Article
- Variability in self-reported depression symptomology and associated behavioral markers in digital phenotyping.Scientific reports · 2026Article
- Digital tools for assessing bipolar disorder: A scoping review of the current landscape.Neuroscience applied · 2026Article
- Construction of a depression risk prediction model for hepatitis B patients based on machine learning strategy.PloS one · 2026Article
- ASYM: multimodal depression recognition via mamba-enhanced attentive feature fusion.Frontiers in psychiatry · 2026Article
- Multimodal Sleep Measurement and Alignment Analysis in Outpatients With Major Depressive Episode: Observational Study.JMIR mHealth and uHealth · 2025Observational
- Early diagnosis of bipolar disorder.World journal of psychiatry · 2025Review
- Passive Sensing for Mental Health Monitoring Using Machine Learning With Wearables and Smartphones: Scoping Review.Journal of medical Internet research · 2025Article
- Passive Smartphone Sensors for Detecting Psychopathology.JAMA network open · 2025Article
- Observational
- Digital phenotyping using smartphones could help steer mental health treatment.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Multimodal Digital Phenotyping Study in Patients With Major Depressive Episodes and Healthy Controls (Mobile Monitoring of Mood): Observational Longitudinal Study.JMIR mental health · 2025Observational
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClinical diagnostic assessments and the outcome monitoring of patients with depression rely predominantly on interviews by professionals and the use of self-report questionnaires. The ubiquity of smartphones and other personal consumer devices has prompted research into the potential of data collected via these devices to serve as digital behavioral markers for indicating the presence and monitoring of the outcome of depression.
objectiveThis paper explores the potential of using behavioral data collected with smartphones to detect and monitor depression symptoms in patients diagnosed with depression. Specifically, it investigates whether this data can accurately classify the presence of depression, as well as monitor the changes in depressive states over time.
methodsIn a prospective cohort study, we collected smartphone behavioral data for up to 1 year. The study consists of observations from 164 participants, including healthy controls (n=31) and patients diagnosed with various depressive disorders: major depressive disorder (MDD; n=85), MDD with comorbid borderline personality disorder (n=27), and major depressive episodes with bipolar disorder (n=21). Data were labeled based on depression severity using 9-item Patient Health Questionnaire (PHQ-9) scores. We performed statistical analysis and used supervised machine learning on the data to classify the severity of depression and observe changes in the depression state over time.
resultsOur correlation analysis revealed 32 behavioral markers associated with the changes in depressive state. Our analysis classified patients who are depressed with an accuracy of 82% (95% CI 80%-84%) and change in the presence of depression with an accuracy of 75% (95% CI 72%-76%). Notably, the most important smartphone features for classifying depression states were screen-off events, battery charge levels, communication patterns, app usage, and location data. Similarly, for predicting changes in depression state, the most important features were related to location, battery level, screen, and accelerometer data patterns.
conclusionsThe use of smartphone digital behavioral markers to supplement clinical evaluations may aid in detecting the presence and changes in severity of symptoms of depression, particularly if combined with intermittent use of self-report of symptoms.
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