ArticleFrontiers in digital health2022
Personalised depression forecasting using mobile sensor data and ecological momentary assessment.
Article in Frontiers in digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 3 of them syntheses that pooled it.
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The trial behind it
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Who cites it
23 citing papers in PubMed, 3 syntheses or guidelines pooled it, 38 citations in OpenAlex.
- Use of Mobile Sensing Data for Longitudinal Monitoring and Prediction of Depression Severity: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Harnessing digital health interventions to address the heterogeneity of depression: a systematic review.Frontiers in digital health · 2025Pooled it
- Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches.Sensors (Basel, Switzerland) · 2024Pooled it
- Did you miss me? Making the most of digital phenotyping data by imputing missingness with point process models: observational study.BMJ health & care informatics · 2026Observational
- Individual-Level Modeling of Depressive Symptom Severity Using Smartphone and Wearable Data: Time-Aware 1-Year Study With Feature-Group Contributions.JMIR formative research · 2026Article
- Variability in self-reported depression symptomology and associated behavioral markers in digital phenotyping.Scientific reports · 2026Article
- Predicting daily cognition and lifestyle behaviors for older adults using smart home data and ecological momentary assessment.The Clinical neuropsychologist · 2026Article
- Distinguishing Common Digital Phenotyping and Self-Report Parameters for Monitoring and Predicting Depression: Scoping Review.JMIR mHealth and uHealth · 2026Article
- Personalized vs. population-based speech models for multi-dimensional mental health prediction.Frontiers in digital health · 2026Article
- Comparing personalized and population-based models for predicting momentary negative affect in internalizing disorders: A digital phenotyping study.Neuroscience applied · 2026Article
- Cross-Platform Availability of Smartphone Sensors for Depression Indication Systems: Mixed-Methods Umbrella Review.Interactive journal of medical research · 2025Review
- 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
- Digital phenotyping in bipolar disorder: Using longitudinal Fitbit data and personalized machine learning to predict mood symptomatology.Acta psychiatrica Scandinavica · 2025Article
- The implementation of digital biomarkers in the diagnosis, treatment and monitoring of mood disorders: a narrative review.Frontiers in digital health · 2025Review
- A literature review of remote mental health screening: barriers, potential solutions, and tools.Frontiers in digital health · 2025Review
- Momentary Depression Severity Prediction in Patients With Acute Depression Who Undergo Sleep Deprivation Therapy: Speech-Based Machine Learning Approach.JMIR mental health · 2024Article
- Transatlantic transferability and replicability of machine-learning algorithms to predict mental health crises.NPJ digital medicine · 2024Article
- Towards Personalised Mood Prediction and Explanation for Depression from Biophysical Data.Sensors (Basel, Switzerland) · 2023Article
- Zero-shot personalization of speech foundation models for depressed mood monitoring.Patterns (New York, N.Y.) · 2023Article
- Human-centred artificial intelligence for mobile health sensing: challenges and opportunities.Royal Society open science · 2023Review
Corrections and comments
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Authors and funding
13 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Digital health interventions are an effective way to treat depression, but it is still largely unclear how patients' individual symptoms evolve dynamically during such treatments. Data-driven forecasts of depressive symptoms would allow to greatly improve the personalisation of treatments. In current forecasting approaches, models are often trained on an entire population, resulting in a general model that works overall, but does not translate well to each individual in clinically heterogeneous, real-world populations. Model fairness across patient subgroups is also frequently overlooked. Personalised models tailored to the individual patient may therefore be promising. Methods: We investigate different personalisation strategies using transfer learning, subgroup models, as well as subject-dependent standardisation on a newly-collected, longitudinal dataset of depression patients undergoing treatment with a digital intervention ( Results: In our experiments, we achieve a best mean-absolute-error (MAE) of Discussion: Our results suggest that personalisation using subject-dependent standardisation and transfer learning can improve predictions and forecasts, respectively, of depressive symptoms in participants of a digital depression intervention. We discuss technical and clinical limitations of this approach, avenues for future investigations, and how personalised machine learning architectures may be implemented to improve existing digital interventions for depression.
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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.