ArticleSensors (Basel, Switzerland)2020
Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers, 8 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
79 citing papers in PubMed, 8 syntheses or guidelines pooled it, 142 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 Monitoring Mental Disorders: Systematic Review.Journal of medical Internet research · 2023Pooled it
- A Systematic Review of Location Data for Depression Prediction.International journal of environmental research and public health · 2023Pooled it
- Association between Daily Pattern of Physical Activity and Depression: A Systematic Review.International journal of environmental research and public health · 2022Pooled it
- Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review.Journal of medical Internet research · 2022Pooled it
- Digital phenotype of mood disorders: A conceptual and critical review.Frontiers in psychiatry · 2022Pooled it
- Just-in-Time Adaptive Mechanisms of Popular Mobile Apps for Individuals With Depression: Systematic App Search and Literature Review.Journal of medical Internet research · 2021Pooled it
- Incremental Value of Smartphone Sensing for Monitoring Momentary Affect Intensity in Adults Using Transformer-Based Models: Observational Study.JMIR mHealth and uHealth · 2026Observational
- A bibliometric analysis of research on the application of just-in-time adaptive interventions in mental health.Medicine · 2026Review
- 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
- Detection of Posttraumatic Stress Disorder With Rest-Activity Data: Machine Learning Approach Using Wearable and Self-Report Data.JMIR formative research · 2026Article
- Comparing training window selection methods for prediction in non-stationary time series.The British journal of mathematical and statistical psychology · 2026Article
- Predicting Momentary Suicidal Ideation From Smartphone Screenshots Using Vision-Language Models: Prospective Machine Learning Study.JMIR mental health · 2026Article
- Predicting daily cognition and lifestyle behaviors for older adults using smart home data and ecological momentary assessment.The Clinical neuropsychologist · 2026Article
- Smartphone-Based Digital Phenotyping Across Health Conditions: Scoping Review.Journal of medical Internet research · 2026Article
- Low-Burden Detection of Clinical Worsening in Body Dysmorphic Disorder Using Smartphone Sensor and Demographic Data.Behavior therapy · 2026Observational
- Digital Biomarkers in Depression: Integrating DSM-5-TR Symptomatology and RDoC Psychological Systems.Current psychiatry reports · 2026Review
- 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
- Article
19 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
2 authors at 1 institution in 1 country.
Funding
Abstract
Prior research has recently shown that passively collected sensor data collected within the contexts of persons daily lives via smartphones and wearable sensors can distinguish those with major depressive disorder (MDD) from controls, predict MDD severity, and predict changes in MDD severity across days and weeks. Nevertheless, very little research has examined predicting depressed mood within a day, which is essential given the large amount of variation occurring within days. The current study utilized passively collected sensor data collected from a smartphone application to future depressed mood from hour-to-hour in an ecological momentary assessment study in a sample reporting clinical levels of depression (
Indexed as
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.