ArticleJournal of affective disorders2022
Utilizing daily mood diaries and wearable sensor data to predict depression and suicidal ideation among medical interns.
Article in Journal of affective disorders, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches.Sensors (Basel, Switzerland) · 2024Pooled it
- Daily Language as an Objective Indicator of Depressive Affective States: A Two-Week Ecological Momentary Assessment Study in Emotional Labor Workers.Psychiatry investigation · 2026Article
- Digital Phenotyping in Health Research: Scoping Review of Methods, Gaps, and Opportunities.Journal of medical Internet research · 2026Article
- Conceptualization From the Sensors to Suicide-Related Outcomes: Scoping Review Based on Layered Hierarchical Sensemaking Framework.JMIR mHealth and uHealth · 2026Article
- Digital mood phenotyping predicts short- and long-term suicidal ideation across a two-year timescale.Molecular psychiatry · 2026Article
- Feasibility and Acceptability of a Mobile App and Wearable Device for Collecting Mental Health Survey and Passively Sensed Data Among Health Care Workers in Kenya: Mixed Methods Pilot Study.JMIR mHealth and uHealth · 2026Article
- Predicting Momentary Suicidal Ideation From Smartphone Screenshots Using Vision-Language Models: Prospective Machine Learning Study.JMIR mental health · 2026Article
- Low-Burden Detection of Clinical Worsening in Body Dysmorphic Disorder Using Smartphone Sensor and Demographic Data.Behavior therapy · 2026Observational
- Prospective observational study on behavioral monitoring, disease progression assessment, and screening model development for patients with depression using wearable devices and mobile phones: protocol.Frontiers in psychiatry · 2026Article
- Attitudes and barriers to mobile mental health interventions among first-year college students: a mixed-methods study.Journal of American college health : J of ACH · 2025Article
- Understanding resilience in medical interns through ecological momentary assessments, predictive modelling, and topic analysis.Communications medicine · 2025Article
- Smartphone-based text obtained via passive sensing as it relates to direct suicide risk assessment.Psychological medicine · 2025Article
- Machine Learning Models to Identify Individuals With Imminent Suicide Risk Using a Wearable Device: A Pilot Study.Psychiatry investigation · 2025Article
- A systematic review on passive sensing for the prediction of suicidal thoughts and behaviors.Npj mental health research · 2024Article
- Using Large Language Models to Detect Depression From User-Generated Diary Text Data as a Novel Approach in Digital Mental Health Screening: Instrument Validation Study.Journal of medical Internet research · 2024Article
- Smartphone keyboard dynamics predict affect in suicidal ideation.NPJ digital medicine · 2024Article
- The effect of cognitive behavioral therapy text messages on mood: A micro-randomized trial.PLOS digital health · 2024Article
- Effects of frequent assessments on the severity of suicidal thoughts: an ecological momentary assessment study.Frontiers in public health · 2024Article
- Ecological Momentary Assessment and Machine Learning for Predicting Suicidal Ideation Among Sexual and Gender Minority Individuals.JAMA network open · 2023Article
- Using machine learning with intensive longitudinal data to predict depression and suicidal ideation among medical interns over time.Psychological medicine · 2023Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundIntensive longitudinal methods (ILMs) for collecting self-report (e.g., daily diaries, ecological momentary assessment) and passive data from smartphones and wearable sensors provide promising avenues for improved prediction of depression and suicidal ideation (SI). However, few studies have utilized ILMs to predict outcomes for at-risk, non-clinical populations in real-world settings.
methodsMedical interns (N = 2881; 57 % female; 58 % White) were recruited from over 300 US residency programs. Interns completed a pre-internship assessment of depression, were given Fitbit wearable devices, and provided daily mood ratings (scale: 1-10) via mobile application during the study period. Three-step hierarchical logistic regressions were used to predict depression and SI at the end of the first quarter utilizing pre-internship predictors in step 1, Fitbit sleep/step features in step 2, and daily diary mood features in step 3.
resultsPassively collected Fitbit features related to sleep and steps had negligible predictive validity for depression, and no incremental predictive validity for SI. However, mean-level and variability in mood scores derived from daily diaries were significant independent predictors of depression and SI, and significantly improved model accuracy. LIMITATIONS: Work schedules for interns may result in sleep and activity patterns that differ from typical associations with depression or SI. The SI measure did not capture intent or severity.
conclusionsMobile self-reporting of daily mood improved the prediction of depression and SI during a meaningful at-risk period under naturalistic conditions. Additional research is needed to guide the development of adaptive interventions among vulnerable populations.
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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.