Trial reportDepression and anxiety2019
The accuracy of passive phone sensors in predicting daily mood.
Trial report in Depression and anxiety, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
55 citing papers in PubMed, 3 syntheses or guidelines pooled it, 122 citations in OpenAlex.
- Dropout, Attrition, Adherence, and Compliance in Mood Monitoring and Ambulatory Assessment Studies for Depression and Bipolar Disorder: Systematic Review and Meta-Analysis.JMIR mental health · 2026Pooled it
- A Systematic Review of Location Data for Depression Prediction.International journal of environmental research and public health · 2023Pooled it
- Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible Limitations.JMIR mHealth and uHealth · 2019Pooled it
- Predicting daily cognition and lifestyle behaviors for older adults using smart home data and ecological momentary assessment.The Clinical neuropsychologist · 2026Article
- Low-Burden Detection of Clinical Worsening in Body Dysmorphic Disorder Using Smartphone Sensor and Demographic Data.Behavior therapy · 2026Observational
- Digital phenotyping of affect and stress in emerging adults.Frontiers in digital health · 2026Article
- A Dual In-Person and Remote Assessment Approach to Developing Digital End Points Relevant to Autism and Co-Occurring Conditions: Protocol for a Multisite Observational Study.JMIR research protocols · 2025Article
- Fourier Transform Analysis of GPS-Derived Mobility Patterns for Diagnosis and Mood Monitoring of Bipolar and Major Depressive Disorders: Prospective Study.Journal of medical Internet research · 2025Article
- Personalized prediction of negative affect in individuals with serious mental illness followed using long-term multimodal mobile phenotyping.Translational psychiatry · 2025Article
- Why do individuals with generalized anxiety disorder and depression engage in worry and rumination? A momentary assessment study of positive contrast enhancement.Journal of anxiety disorders · 2025Article
- Forecasting mental states in schizophrenia using digital phenotyping data.PLOS digital health · 2025Article
- Identifying Digital Markers of Attention-Deficit/Hyperactivity Disorder (ADHD) in a Remote Monitoring Setting: Prospective Observational Study.JMIR formative research · 2025Observational
- Comparative Assessment of Multimodal Sensor Data Quality Collected Using Android and iOS Smartphones in Real-World Settings.Sensors (Basel, Switzerland) · 2024Article
- Exploring User Experiences of the Mom2B mHealth Research App During the Perinatal Period: Qualitative Study.JMIR formative research · 2024Article
- Identifying Person-Specific Drivers of Depression in Adolescents: Protocol for a Smartphone-Based Ecological Momentary Assessment and Passive Sensing Study.JMIR research protocols · 2024Article
- Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data.Npj mental health research · 2024Article
- Activity space during treatment with medication for opioid use disorder: Relationships with personality, mood, and drug use.Journal of substance use and addiction treatment · 2024Observational
- Differential temporal utility of passively sensed smartphone features for depression and anxiety symptom prediction: a longitudinal cohort study.Npj mental health research · 2024Article
- Acceptance of smart sensing, its determinants, and the efficacy of an acceptance-facilitating intervention in people with diabetes: results from a randomized controlled trial.Frontiers in digital health · 2024Article
- Enhancing the acceptance of smart sensing in psychotherapy patients: findings from a randomized controlled trial.Frontiers in digital health · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundSmartphones provide a low-cost and efficient means to collect population level data. Several small studies have shown promise in predicting mood variability from smartphone-based sensor and usage data, but have not been generalized to nationally recruited samples. This study used passive smartphone data, demographic characteristics, and baseline depressive symptoms to predict prospective daily mood.
methodDaily phone usage data were collected passively from 271 Android phone users participating in a fully remote randomized controlled trial of depression treatment (BRIGHTEN). Participants completed daily Patient Health Questionnaire-2. A machine learning approach was used to predict daily mood for the entire sample and individual participants.
resultsSample-wide estimates showed a marginally significant association between physical mobility and self-reported daily mood (B = -0.04, P < 0.05), but the predictive models performed poorly for the sample as a whole (median R
conclusionsPassive smartphone data with current features may not be suited for predicting daily mood at a population level because of the high degree of intra- and interindividual variation in phone usage patterns and daily mood ratings. Personalized models show encouraging early signs for predicting an individual's mood state changes, with GPS-derived mobility being the top most important feature in the present sample.
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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.