ArticleBrain and behavior2022
Estimating longitudinal depressive symptoms from smartphone data in a transdiagnostic cohort.
Article in Brain and behavior, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 3 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 3 syntheses or guidelines pooled it, 24 citations in OpenAlex.
- Use of Mobile Sensing Data for Longitudinal Monitoring and Prediction of Depression Severity: Systematic Review.Journal of medical Internet research · 2025Pooled it
- The integration of geographic methods and ecological momentary assessment in public health research: A systematic review of methods and applications.Social science & medicine (1982) · 2024Pooled it
- A Systematic Review of Location Data for Depression Prediction.International journal of environmental research and public health · 2023Pooled it
- Did you miss me? Making the most of digital phenotyping data by imputing missingness with point process models: observational study.BMJ health & care informatics · 2026Observational
- Digital Phenotyping in Health Research: Scoping Review of Methods, Gaps, and Opportunities.Journal of medical Internet research · 2026Article
- Sensor-Equipped Digital Technologies for Monitoring and Detecting Depressive Disorders: A Systematic Review.Health science reports · 2026Review
- Digital Biomarkers in Depression: Integrating DSM-5-TR Symptomatology and RDoC Psychological Systems.Current psychiatry reports · 2026Review
- Digital tools for assessing bipolar disorder: A scoping review of the current landscape.Neuroscience applied · 2026Article
- Feasibility and accuracy of the ASERT digital questionnaire in mood tracking for a longitudinal research study on bipolar disorder.Journal of mood and anxiety disorders · 2025Article
- Real-World Smartphone Data Predicts Mood After Ischemic Stroke and Transient Ischemic Attack Symptoms and May Constitute Digital Endpoints: A Proof-of-Concept Study.Mayo Clinic proceedings. Digital health · 2025Article
- Uncovering Social States in Healthy and Clinical Populations Using Digital Phenotyping and Hidden Markov Models: Observational Study.Journal of medical Internet research · 2025Observational
- Day-to-day variability in sleep and activity predict the onset of a hypomanic episode in patients with bipolar disorder.Journal of affective disorders · 2025Observational
- Recommendations for selection and adaptation of rating scales for clinical studies of rapid-acting antidepressants.Frontiers in psychiatry · 2023Review
- Smartphone accelerometer data as a proxy for clinical data in modeling of bipolar disorder symptom trajectory.NPJ digital medicine · 2022Article
- Estimating longitudinal depressive symptoms from smartphone data in a transdiagnostic cohort.Brain and behavior · 2022Article
- Datasets of Smartphone Modalities for Depression Assessment: A Scoping Review.IEEE transactions on affective computingArticle
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundPassive measures collected using smartphones have been suggested to represent efficient proxies for depression severity, but the performance of such measures across diagnoses has not been studied.
methodsWe enrolled a cohort of 45 individuals (11 with major depressive disorder, 11 with bipolar disorder, 11 with schizophrenia or schizoaffective disorder, and 12 individuals with no axis I psychiatric disorder). During the 8-week study period, participants were evaluated with a rater-administered Montgomery-Åsberg Depression Rating Scale (MADRS) biweekly, completed self-report PHQ-8 measures weekly on their smartphone, and consented to collection of smartphone-based GPS and accelerometer data in order to learn about their behaviors. We utilized linear mixed models to predict depression severity on the basis of phone-based PHQ-8 and passive measures.
resultsAmong the 45 individuals, 38 (84%) completed the 8-week study. The average root-mean-squared error (RMSE) in predicting the MADRS score (scale 0-60) was 4.72 using passive data alone, 4.27 using self-report measures alone, and 4.30 using both.
conclusionsWhile passive measures did not improve MADRS score prediction in our cross-disorder study, they may capture behavioral phenotypes that cannot be measured objectively, granularly, or over long-term via self-report.
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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.