ReviewNPJ digital medicine2022
Digital health tools for the passive monitoring of depression: a systematic review of methods.
Review in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 129 papers, 10 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
129 citing papers in PubMed, 10 syntheses or guidelines pooled it, 203 citations in OpenAlex.
- Recommendations for Research and Clinical Implementation of Ambulatory Assessment, Mood Monitoring, Digital Phenotyping, and Remote Measurement Technology in Mood Disorders: Synthesis of Systematic Review Findings.JMIR mental health · 2026Pooled it
- The Role of Digital Biomarkers in Physiological Signal-Based Depression Assessment: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Mood Monitoring, Mood Tracking, and Ambulatory Assessment Interventions in Depression and Bipolar Disorder: Systematic Review and Meta-Analysis of Randomized Controlled Trials.JMIR mental health · 2026Pooled it
- Use of Mobile Sensing Data for Longitudinal Monitoring and Prediction of Depression Severity: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Applying AI in the Context of the Association Between Device-Based Assessment of Physical Activity and Mental Health: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- Beyond the current state of just-in-time adaptive interventions in mental health: a qualitative systematic review.Frontiers in digital health · 2025Pooled it
- Non-verbal behaviours as predictors for treatment response in patients with depression or schizophrenia: a systematic review.Frontiers in psychiatry · 2025Pooled it
- The Relation Between Passively Collected GPS Mobility Metrics and Depressive Symptoms: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Depressive symptom screening in elderly by passive sensing data of smartphones or smartwatches: A systematic review.PloS one · 2024Pooled it
- The Use of Mobile Assessments for Monitoring Mental Health in Youth: Umbrella Review.Journal of medical Internet research · 2023Pooled it
- Ecological Momentary Assessment versus Weekly Questionnaire Assessment of Change in Depression.Depression and anxiety · 2024Trial
- Smartphone-Based Passive Sensing of Activity Levels and Behavioral Activation During Psychosocial Interventions for Older Adults With Depression: Longitudinal Observational Study.Journal of medical Internet research · 2026Observational
- Investigating mechanisms and monitoring outcomes in functional neurological disorder: the unrealized potential of digital remote monitoring technologies.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2026Review
- Validating objective and scalable speech markers of depression across two independent psychiatric cohorts.Annals of general psychiatry · 2026Article
- Fine-tuning large language models for detecting negative self-referential language in smartphone text among youth with depression.Journal of psychopathology and clinical science · 2026Article
- Lower Daily Walking Volume and Less Walking at Intermediate and Higher Cadences in Older Women with Clinician-Diagnosed Major Depressive Disorder: An Exploratory Cross-Sectional Study.Healthcare (Basel, Switzerland) · 2026Article
- Predicting weekly instability in depressive symptoms among individuals diagnosed with Major Depressive Disorder using deep learning and passively-collected movement data.Journal of affective disorders reports · 2026Article
- Feasibility and Acceptability of a Smartphone and Wearable Assessment Protocol for Adolescents with Depression.Research on child and adolescent psychopathology · 2026Article
- Sleep and Activity Patterns in Depression From Wearable Data: Unsupervised Clustering Study.Journal of medical Internet research · 2026Article
- Autonomic Non-Responsiveness in HRV Biofeedback: A Narrative Conceptual Review and Future Directions for AI-Guided Closed-Loop Adaptive Systems.Medicina (Kaunas, Lithuania) · 2026Review
69 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 3 institutions in 2 countries.
Funding
Abstract
The use of digital tools to measure physiological and behavioural variables of potential relevance to mental health is a growing field sitting at the intersection between computer science, engineering, and clinical science. We summarised the literature on remote measuring technologies, mapping methodological challenges and threats to reproducibility, and identified leading digital signals for depression. Medical and computer science databases were searched between January 2007 and November 2019. Published studies linking depression and objective behavioural data obtained from smartphone and wearable device sensors in adults with unipolar depression and healthy subjects were included. A descriptive approach was taken to synthesise study methodologies. We included 51 studies and found threats to reproducibility and transparency arising from failure to provide comprehensive descriptions of recruitment strategies, sample information, feature construction and the determination and handling of missing data. The literature is characterised by small sample sizes, short follow-up duration and great variability in the quality of reporting, limiting the interpretability of pooled results. Bivariate analyses show consistency in statistically significant associations between depression and digital features from sleep, physical activity, location, and phone use data. Machine learning models found the predictive value of aggregated features. Given the pitfalls in the combined literature, these results should be taken purely as a starting point for hypothesis generation. Since this research is ultimately aimed at informing clinical practice, we recommend improvements in reporting standards including consideration of generalisability and reproducibility, such as wider diversity of samples, thorough reporting methodology and the reporting of potential bias in studies with numerous features.
Identifiers
What OpenQuestion holds
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.