ArticlePsychiatry research2024
Detecting major depressive disorder presence using passively-collected wearable movement data in a nationally-representative sample.
Article in Psychiatry research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis 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
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 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
- Severity of depression and anxiety symptoms is reflected in physiological and behavioral metrics collected from a consumer-grade wearable ring.BMC medicine · 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
- Passive Screening for Depressive Symptoms Using Daily Wrist Actigraphy and Deep Learning: Model Development and Validation Study.JMIR mHealth and uHealth · 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
- Article
- Revolutionizing Sleep Medicine: The Impact of Machine Learning on Diagnosis, Treatment, and Personalized Care.Health science reports · 2026Article
- Using wearable data to detect depression severity across clinical and non-clinical samples.Scientific reports · 2026Article
- Sensor-Equipped Digital Technologies for Monitoring and Detecting Depressive Disorders: A Systematic Review.Health science reports · 2026Review
- Digital Biomarkers in Depression: Integrating DSM-5-TR Symptomatology and RDoC Psychological Systems.Current psychiatry reports · 2026Review
- Passive Sensing for Mental Health Monitoring Using Machine Learning With Wearables and Smartphones: Scoping Review.Journal of medical Internet research · 2025Article
- A bibliometric analysis of wearable device research in sleep science: Trends and hotspots.Medicine · 2025Review
- A comparison of objective and subjective measures of physical activity, sedentary and sleep behaviors between persons with and without depressive symptoms.Journal of affective disorders · 2025Article
- OBF-Psychiatric, a motor activity dataset of patients diagnosed with major depression, schizophrenia, and ADHD.Scientific data · 2025Article
- Beyond Detection: Towards Actionable Sensing Research in Clinical Mental Healthcare.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2024Article
- Association of sleep fragmentation with general and abdominal obesity: a population-based longitudinal study.International journal of obesity (2005) · 2024Article
- Recent Progress in Biosensors for Depression Monitoring-Advancing Personalized Treatment.Biosensors · 2024Review
- Using digital phenotyping to capture depression symptom variability: detecting naturalistic variability in depression symptoms across one year using passively collected wearable movement and sleep data.Translational psychiatry · 2023Article
- Datasets of Smartphone Modalities for Depression Assessment: A Scoping Review.IEEE transactions on affective computingArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Major Depressive Disorder (MDD) is a heterogeneous disorder, resulting in challenges with early detection. However, changes in sleep and movement patterns may help improve detection. Thus, this study aimed to explore the utility of wrist-worn actigraphy data in combination with machine learning (ML) and deep learning techniques to detect MDD using a commonly used screening method: Patient Health Questionnaire-9 (PHQ-9). Participants (N = 8,378; MDD Screening = 766 participants) completed the and wore Actigraph GT3X+ for one week as part of the National Health and Nutrition Examination Survey (NHANES). Leveraging minute-level, actigraphy data, we evaluated the efficacy of two commonly used ML approaches and identified actigraphy-derived biomarkers indicative of MDD. We employed two ML modeling strategies: (1) a traditional ML approach with theory-driven feature derivation, and (2) a deep learning Convolutional Neural Network (CNN) approach, coupled with gramian angular field transformation. Findings revealed movement-related features to be the most influential in the traditional ML approach and nighttime movement to be the most influential in the CNN approach for detecting MDD. Using a large, nationally-representative sample, this study highlights the potential of using passively-collected, actigraphy data for understanding MDD to better improve diagnosing and treating MDD.
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.