ArticleBJPsych open2022
Digital phenotyping correlations in larger mental health samples: analysis and replication.
Article in BJPsych open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The Relation Between Passively Collected GPS Mobility Metrics and Depressive Symptoms: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Digital Phenotyping for Stress, Anxiety, and Mild Depression: Systematic Literature Review.JMIR mHealth and uHealth · 2024Pooled it
- Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches.Sensors (Basel, Switzerland) · 2024Pooled it
- Personality Constructs Predictions Beyond FFM/Big5: A Digital Phenotyping-Based Exploration.Journal of personality · 2026Article
- A multilevel meta-analysis of passive smartphone sensing of adolescent mental health.NPJ digital medicine · 2026Article
- LINC: a framework for maintaining high-quality passive data in digital phenotyping studies.Scientific reports · 2026Article
- Digital Phenotyping for Adolescent Mental Health: Feasibility Study Using Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data.Journal of medical Internet research · 2026Article
- Smartphone Apps and Digital Tools for Managing Schizophrenia and Psychotic Disorders: A Clinical Review.Focus (American Psychiatric Publishing) · 2025Review
- Towards clinical subtypes in schizophrenia: integrating cognitive, functional, and digital phenotyping assessments.Molecular psychiatry · 2025Article
- Understanding the game experiences and mental health of youth: protocol for the Game-in-Action Quebec cohort study.BMJ open · 2025Article
- Harnessing Digital Phenotyping for Early Self-Detection of Psychological Distress.Healthcare (Basel, Switzerland) · 2025Article
- Digital health technologies in the accelerating medicines Partnership® Schizophrenia Program.Schizophrenia (Heidelberg, Germany) · 2025Article
- The evolving field of digital mental health: current evidence and implementation issues for smartphone apps, generative artificial intelligence, and virtual reality.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2025Article
- Designing smartphone-based cognitive assessments for schizophrenia: Perspectives from a multisite study.Schizophrenia research. Cognition · 2025Article
- Assessing Health Technology Literacy and Attitudes of Patients in an Urban Outpatient Psychiatry Clinic: Cross-Sectional Survey Study.JMIR mental health · 2024Article
- Predicting Depressive Symptoms Using GPS-Based Regional Data in Germany With the CORONA HEALTH App During the COVID-19 Pandemic: Cross-Sectional Study.Interactive journal of medical research · 2024Article
- Predicting Mood Based on the Social Context Measured Through the Experience Sampling Method, Digital Phenotyping, and Social Networks.Administration and policy in mental health · 2024Article
- Differential temporal utility of passively sensed smartphone features for depression and anxiety symptom prediction: a longitudinal cohort study.Npj mental health research · 2024Article
- Passive sensing data predicts stress in university students: a supervised machine learning method for digital phenotyping.Frontiers in psychiatry · 2024Article
- Integrated Digital Mental Health Care: A Vision for Addressing Population Mental Health Needs.International journal of general medicine · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSmartphones can facilitate patients completing surveys and collecting sensor data to gain insight into their mental health conditions. However, the utility of sensor data is still being explored. Prior studies have reported a wide range of correlations between passive data and survey scores.
aimsTo explore correlations in a large data-set collected with the mindLAMP app. Additionally, we explored whether passive data features could be used in models to predict survey results.
methodParticipants were asked to complete daily and weekly mental health surveys. After screening for data quality, our sample included 147 college student participants and 270 weeks of data. We examined correlations between six weekly surveys and 13 metrics derived from passive data features. Finally, we trained logistic regression models to predict survey scores from passive data with and without daily surveys.
resultsSimilar to other large studies, our correlations were lower than prior reports from smaller studies. We found that the most useful features came from GPS, call, and sleep duration data. Logistic regression models performed poorly with only passive data, but when daily survey scores were included, performance greatly increased.
conclusionsAlthough passive data alone may not provide enough information to predict survey scores, augmenting this data with short daily surveys can improve performance. Therefore, it may be that passive data can be used to refine survey score predictions and clinical utility may be derived from the combination of active and passive data.
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