ArticleScientific reports2021
Depression predictions from GPS-based mobility do not generalize well to large demographically heterogeneous samples.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 49 citations in OpenAlex.
- How do machine learning models perform in the detection of depression, anxiety, and stress among undergraduate students? A systematic review.Cadernos de saude publica · 2024Pooled it
- A Systematic Review of Location Data for Depression Prediction.International journal of environmental research and public health · 2023Pooled it
- A multilevel meta-analysis of passive smartphone sensing of adolescent mental health.NPJ digital medicine · 2026Article
- Differential associations of passively sensed behaviors with in-vivo depression symptoms.Journal of psychopathology and clinical science · 2025Article
- Integration of Multi-Modal Biosensing Approaches for Depression: Current Status, Challenges, and Future Perspectives.Sensors (Basel, Switzerland) · 2025Review
- 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
- App-Based Ecological Momentary Assessment of Problematic Smartphone Use During Examination Weeks in University Students: 6-Week Observational Study.Journal of medical Internet research · 2025Observational
- Dynamic Bidirectional Associations Between Global Positioning System Mobility and Ecological Momentary Assessment of Mood Symptoms in Mood Disorders: Prospective Cohort Study.Journal of medical Internet research · 2024Article
- Beyond Detection: Towards Actionable Sensing Research in Clinical Mental Healthcare.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2024Article
- Mobile sensing-based depression severity assessment in participants with heterogeneous mental health conditions.Scientific reports · 2024Article
- Article
- Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data.Npj mental health research · 2024Article
- Differential temporal utility of passively sensed smartphone features for depression and anxiety symptom prediction: a longitudinal cohort study.Npj mental health research · 2024Article
- Towards Personalised Mood Prediction and Explanation for Depression from Biophysical Data.Sensors (Basel, Switzerland) · 2023Article
- Specific associations of passively sensed smartphone data with future symptoms of avoidance, fear, and physiological distress in social anxiety.Internet interventions · 2023Article
- Day-to-day intrapersonal variability in mobility patterns and association with perceived stress: A cross-sectional study using GPS from 122 individuals in three European cities.SSM - population health · 2022Article
- Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review.Sensors (Basel, Switzerland) · 2022Review
- A call for open data to develop mental health digital biomarkers.BJPsych open · 2022Article
- Personalised depression forecasting using mobile sensor data and ecological momentary assessment.Frontiers in digital health · 2022Article
- Machine learning for passive mental health symptom prediction: Generalization across different longitudinal mobile sensing studies.PloS one · 2022Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Depression is one of the most common mental health issues in the United States, affecting the lives of millions of people suffering from it as well as those close to them. Recent advances in research on mobile sensing technologies and machine learning have suggested that a person's depression can be passively measured by observing patterns in people's mobility behaviors. However, the majority of work in this area has relied on highly homogeneous samples, most frequently college students. In this study, we analyse over 57 million GPS data points to show that the same procedure that leads to high prediction accuracy in a homogeneous student sample (N = 57; AUC = 0.82), leads to accuracies only slightly higher than chance in a U.S.-wide sample that is heterogeneous in its socio-demographic composition as well as mobility patterns (N = 5,262; AUC = 0.57). This pattern holds across three different modelling approaches which consider both linear and non-linear relationships. Further analyses suggest that the prediction accuracy is low across different socio-demographic groups, and that training the models on more homogeneous subsamples does not substantially improve prediction accuracy. Overall, the findings highlight the challenge of applying mobility-based predictions of depression at scale.
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Registered trials
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