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Full-day sleep pattern analysis in common mental disorders: Leveraging highly discrepant recordings from two consumer
Óscar Jiménez Rama1,2, Antonio Artés1,2, Enrique Baca-García3,4,5,6,7,8,9
1Department of Signal Theory and Communications, Carlos III University, Madrid, Spain.
Plos One
|April 9, 2026
Summary
Discrepancies between sleep trackers revealed six abnormal sleep behavior patterns in Common Mental Disorders (CMD) patients. This passive monitoring approach aids early detection of mental health changes and treatment side effects.
Area of Science:
- Digital Health
- Sleep Science
- Mental Health Research
Background:
- Consumer sleep-tracking devices are increasingly used to study sleep and circadian rhythms in real-world settings.
- Sleep disturbances are strongly linked to Common Mental Disorders (CMD), making sleep tracking a valuable tool for mental health assessment.
Purpose of the Study:
- To identify and characterize abnormal sleep behaviors by analyzing discrepancies between two complementary sleep-tracking devices.
- To interpret inter-device disagreement as a potential behavioral signal rather than measurement noise.
- To uncover clinically relevant sleep health patterns using a holistic, 24-hour behavioral view.
Main Methods:
- Analysis of sleep data from 149 patients with non-severe CMD over three months, using a wristband tracker (W) and a sleep mat (M).
- Application of k-means clustering on high-discrepancy sleep recordings (>5 hours) to identify robust sleep behavior patterns.
- Integration of additional behavioral metrics (e.g., daily steps, smartphone usage) to validate identified sleep clusters.
Main Results:
- Identification of six statistically robust, clinically interpretable outlier patterns in sleep health.
- These patterns reflect a full 24-hour window of sleep-related behavior, including nocturnal, diurnal, and peri-sleep activities.
- Discrepancy analysis revealed patterns indicative of oversleeping, unintended sleep onset outside the bed, and atypical sleep-wake cycles.
Conclusions:
- Leveraging discrepancies in passive sleep monitoring can reveal clinically relevant sleep patterns in patients with Common Mental Disorders.
- This approach offers a holistic view of sleep behavior, aiding in the early detection of pathological changes like depressive episodes.
- Findings support the use of passive sleep monitoring for informing clinical decisions and identifying treatment-related behavioral side effects.
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