Observational studyJournal of medical Internet research2021
Automated Screening for Social Anxiety, Generalized Anxiety, and Depression From Objective Smartphone-Collected Data: Cross-sectional Study.
Observational study in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 5 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 5 syntheses or guidelines pooled it, 49 citations in OpenAlex.
- The Role of Digital Biomarkers in Physiological Signal-Based Depression Assessment: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Use of Mobile Sensing Data for Longitudinal Monitoring and Prediction of Depression Severity: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Digital Phenotyping for Stress, Anxiety, and Mild Depression: Systematic Literature Review.JMIR mHealth and uHealth · 2024Pooled it
- Studies of Social Anxiety Using Ambulatory Assessment: Systematic Review.JMIR mental health · 2024Pooled it
- Digital phenotype of mood disorders: A conceptual and critical review.Frontiers in psychiatry · 2022Pooled it
- Digital phenotyping with large language models to detect depressive state changes in patients.NPJ digital medicine · 2026Article
- AI in Mental Health: Transforming Diagnosis and Management of Depression and Anxiety.Health science reports · 2026Article
- Digital biomarkers in the diagnosis of social anxiety disorder: a scoping review.BMC psychiatry · 2026Article
- Digital Biomarkers in Depression: Integrating DSM-5-TR Symptomatology and RDoC Psychological Systems.Current psychiatry reports · 2026Review
- Developing personalized algorithms for sensing mental health symptoms in daily life.Npj mental health research · 2025Article
- Development of a Cohesive Predictive Model for Substance Use Disorder Rehabilitation Using Passive Digital Biomarkers, Psychological Assessments, and Automated Facial Emotion Recognition: Protocol for a Prospective Cohort Study.JMIR research protocols · 2025Article
- Relationship Between Problematic Smartphone Use and Graduate Students' Research Self-Efficacy: A Moderated Mediation Model.Behavioral sciences (Basel, Switzerland) · 2024Article
- The Impact of Perceived Discrimination on Mobile Phone Addiction Among Chinese Higher Vocational College Students: A Chain Mediating Role of Negative Emotions and Learning Burnout.Psychology research and behavior management · 2024Article
- Predicting Generalized Anxiety Disorder From Impromptu Speech Transcripts Using Context-Aware Transformer-Based Neural Networks: Model Evaluation Study.JMIR mental health · 2023Article
- The Use of Passive Smartphone Data to Monitor Anxiety and Depression Among College Students in Real-World Settings: Protocol for a Systematic Review.JMIR research protocols · 2022Article
- Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review.Cognitive neurodynamics · 2022Review
- Screening for Generalized Anxiety Disorder From Acoustic and Linguistic Features of Impromptu Speech: Prediction Model Evaluation Study.JMIR formative research · 2022Article
- A Machine Learning Approach for Continuous Mining of Nonidentifiable Smartphone Data to Create a Novel Digital Biomarker Detecting Generalized Anxiety Disorder: Prospective Cohort Study.JMIR medical informatics · 2022Article
- Digital Phenotyping in Health Using Machine Learning Approaches: Scoping Review.JMIR bioinformatics and biotechnology · 2022Article
- Acoustic and Linguistic Features of Impromptu Speech and Their Association With Anxiety: Validation Study.JMIR mental health · 2022Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe lack of access to mental health care could be addressed, in part, through the development of automated screening technologies for detecting the most common mental health disorders without the direct involvement of clinicians. Objective smartphone-collected data may contain sufficient information about individuals' behaviors to infer their mental states and therefore screen for anxiety disorders and depression.
objectiveThe objective of this study is to compare how a single set of recognized and novel features, extracted from smartphone-collected data, can be used for predicting generalized anxiety disorder (GAD), social anxiety disorder (SAD), and depression.
methodsAn Android app was designed, together with a centralized server system, to collect periodic measurements of objective smartphone data. The types of data included samples of ambient audio, GPS location, screen state, and light sensor data. Subjects were recruited into a 2-week observational study in which the app was run on their personal smartphones. The subjects also completed self-report severity measures of SAD, GAD, and depression. The participants were 112 Canadian adults from a nonclinical population. High-level features were extracted from the data of 84 participants, and predictive models of SAD, GAD, and depression were built and evaluated.
resultsModels of SAD and depression achieved a significantly greater screening accuracy than uninformative models (area under the receiver operating characteristic means of 0.64, SD 0.13 and 0.72, SD 0.12, respectively), whereas models of GAD failed to be predictive. Investigation of the model coefficients revealed key features that were predictive of SAD and depression.
conclusionsWe demonstrate the ability of a common set of features to act as predictors in the models of both SAD and depression. This suggests that the types of behaviors that can be inferred from smartphone-collected data are broad indicators of mental health, which can be used to study, assess, and track psychopathology simultaneously across multiple disorders and diagnostic boundaries.
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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.