ArticleTranslational psychiatry2023
Using digital phenotyping to capture depression symptom variability: detecting naturalistic variability in depression symptoms across one year using passively collected wearable movement and sleep data.
Article in Translational psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 3 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The Role of Digital Biomarkers in Physiological Signal-Based Depression Assessment: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Sensor-Based Technologies for the Detection of Unwanted Loneliness in Older Adults: A Systematic Review.Sensors (Basel, Switzerland) · 2026Pooled it
- Key Features of Digital Phenotyping for Monitoring Mental Disorders: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Interpretable machine learning for depression symptom classification in NHANES: Performance in a curated high-confidence corpus and the full survey population.IBRO neuroscience reports · 2026Article
- Digital Phenotyping in Health Research: Scoping Review of Methods, Gaps, and Opportunities.Journal of medical Internet research · 2026Article
- Individual-Level Modeling of Depressive Symptom Severity Using Smartphone and Wearable Data: Time-Aware 1-Year Study With Feature-Group Contributions.JMIR formative research · 2026Article
- Predicting weekly instability in depressive symptoms among individuals diagnosed with Major Depressive Disorder using deep learning and passively-collected movement data.Journal of affective disorders reports · 2026Article
- 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
- A review of the application of digital phenotyping in predicting peripartum depressive symptoms.NPJ digital medicine · 2026Article
- Article
- Variability in self-reported depression symptomology and associated behavioral markers in digital phenotyping.Scientific reports · 2026Article
- A systematic exploration of digital biomarkers for the detection of depressive episodes in bipolar disorder.Npj mental health research · 2026Article
- Digital Biomarkers in Depression: Integrating DSM-5-TR Symptomatology and RDoC Psychological Systems.Current psychiatry reports · 2026Review
- Examining the Association Between Internet Use and Perceived Stress in Adults: Longitudinal Observational Study Combining Web Tracking Data With Questionnaires.Journal of medical Internet research · 2026Observational
- Diagnostic digital phenotyping in schizophrenia-spectrum disorders: a systematic review.NPJ digital medicine · 2025Article
- Prediction of graft loss in living donor liver transplantation during the early postoperative period.PLoS computational biology · 2025Article
- Impact of Digital Phenotypes and Question-Asking on Emotional Disorders in Adolescents: 4-Week Field Study.JMIR human factors · 2025Article
- Personalised modelling of routine variability and affective states.NPJ digital medicine · 2025Article
- Integration of Multi-Modal Biosensing Approaches for Depression: Current Status, Challenges, and Future Perspectives.Sensors (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Major Depressive Disorder (MDD) presents considerable challenges to diagnosis and management due to symptom variability across time. Only recent work has highlighted the clinical implications for interrogating depression symptom variability. Thus, the present work investigates how sociodemographic, comorbidity, movement, and sleep data is associated with long-term depression symptom variability. Participant information included (N = 939) baseline sociodemographic and comorbidity data, longitudinal, passively collected wearable data, and Patient Health Questionnaire-9 (PHQ-9) scores collected over 12 months. An ensemble machine learning approach was used to detect long-term depression symptom variability via: (i) a domain-driven feature selection approach and (ii) an exhaustive feature-inclusion approach. SHapley Additive exPlanations (SHAP) were used to interrogate variable importance and directionality. The composite domain-driven and exhaustive inclusion models were both capable of moderately detecting long-term depression symptom variability (r = 0.33 and r = 0.39, respectively). Our results indicate the incremental predictive validity of sociodemographic, comorbidity, and passively collected wearable movement and sleep data in detecting long-term depression symptom variability.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.