ArticleNpj mental health research2026
A systematic exploration of digital biomarkers for the detection of depressive episodes in bipolar disorder.
Article in Npj mental health research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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Who cites it
2 citing papers in PubMed.
- Wearable-Based Assessment for Relapse Prediction Following Repetitive Transcranial Magnetic Stimulation for Depression: Protocol for a Feasibility Study (WARN-D Study).JMIR research protocols · 2026Article
- Digital tools for assessing bipolar disorder: A scoping review of the current landscape.Neuroscience applied · 2026Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Digital phenotyping promises to transform psychiatry by using multimodal, densely sampled data. However, its potential is hindered by the lack of focus on identifying and validating digital biomarkers that accurately reflect mental states before evaluating their impact on outcomes. This longitudinal study used explainable machine learning to analyze multivariate, densely sampled data from 133 bipolar disorder (BD) participants over a median of 251 days, identifying robust digital biomarkers defining depressive episodes. The analysis included features from email-based daily self-reported mood, energy, and anxiety, as well as passively collected activity and sleep data using an Oura ring. The most robust descriptors of depressive episodes were lower daily mood variability, lower daily activity variability, and higher daily sleep onset latency variability. Self-reported daily mood features achieved the highest performance (AU-ROC: 0.82 ± 0.03). Our results establish the value of multimodal data and represent a critical first step toward automated detection and prediction of illness episodes in BD.
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Registered trials
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