ArticleSensors (Basel, Switzerland)2023
A Novel Approach to Clustering Accelerometer Data for Application in Passive Predictions of Changes in Depression Severity.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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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
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- 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
- Digital phenotyping with large language models to detect depressive state changes in patients.NPJ digital medicine · 2026Article
- Digital Biomarkers in Depression: Integrating DSM-5-TR Symptomatology and RDoC Psychological Systems.Current psychiatry reports · 2026Review
- Keying Into Cognition: Temporal Smoothing of Smartphone Typing Behaviors for Passive Assessment of Processing Speed and Executive Function in Individuals With Mood Disorders.Cognitive computation · 2026Article
- Digital phenotyping of affect and stress in emerging adults.Frontiers in digital health · 2026Article
- Revolutionizing Medicine: Chatbots as Catalysts for Improved Diagnosis, Treatment, and Patient Support.Cureus · 2025Review
- Towards Personalised Mood Prediction and Explanation for Depression from Biophysical Data.Sensors (Basel, Switzerland) · 2023Article
- Improving Depression Severity Prediction from Passive Sensing: Symptom-Profiling Approach.Sensors (Basel, Switzerland) · 2023Article
- Ensemble Approach to Combining Episode Prediction Models Using Sequential Circadian Rhythm Sensor Data from Mental Health Patients.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 4 institutions in 2 countries.
Funding
Abstract
The treatment of mood disorders, which can become a lifelong process, varies widely in efficacy between individuals. Most options to monitor mood rely on subjective self-reports and clinical visits, which can be burdensome and may not portray an accurate representation of what the individual is experiencing. A passive method to monitor mood could be a useful tool for those with these disorders. Some previously proposed models utilized sensors from smartphones and wearables, such as the accelerometer. This study examined a novel approach of processing accelerometer data collected from smartphones only while participants of the open-science branch of the BiAffect study were typing. The data were modeled by von Mises-Fisher distributions and weighted networks to identify clusters relating to different typing positions unique for each participant. Longitudinal features were derived from the clustered data and used in machine learning models to predict clinically relevant changes in depression from clinical and typing measures. Model accuracy was approximately 95%, with 97% area under the ROC curve (AUC). The accelerometer features outperformed the vast majority of clinical and typing features, which suggested that this new approach to analyzing accelerometer data could contribute towards unobtrusive detection of changes in depression severity without the need for clinical input.
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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.