Multi-Modal Sleep Measurement and Alignment Analysis in Outpatients with Major Depressive Episode
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Study Objectives
Sleep plays a crucial role for mental health. This study examines sleep tracking in naturalistic settings for patients with major depressive episodes (MDE) using actigraphy, smartphone data, bed sensors, and the ecological momentary assessment (EMA) and assesses discrepancies between these modalities.
Methods
We measured sleep onset, offset, and total sleep time (TST) over two weeks for 172 participants, including healthy controls and three MDE subgroups (borderline personality disorder, major depressive disorder, and bipolar disorder). Agreement between measurement modalities was assessed using Bland-Altman plots and Pearson correlation. Predictors of sleep alignment were analyzed using mixed-effects models, accounting for demographics, daylight length, and participant subgroup.
Results
Patients showed greater sleep variability than healthy controls. Actigraphy overestimated TST compared to bed sensors (0.48 min) and smartphones (0.99 min), while the smartphone underestimated TST compared to other modalities. Older age improved alignment between actigraphy and bed sensors, as well as smartphone and bed sensor sleep offset. TST alignment (smartphone vs. bed sensor) was worse in females and bipolar/borderline patients. Longer daylight duration improved TST and sleep offset alignment across modalities.
Conclusions
Our study highlights measurement biases, seasonal effects, and demographic factors associated with discrepancies in objective sleep measures. While these modalities show potential and offer several advantages in assessing sleep over longer periods, the discrepancies and factors associated with misalignment should be considered in future studies or clinical settings.
Statement of Significance
Tracking sleep in psychiatric patients is challenging due to frequent sleep disturbances, making accurate assessment crucial for diagnosis and care. Traditional methods are limited to lab settings, restricting long-term monitoring. This study evaluates the alignment of naturalistic sleep tracking using actigraphy, bed sensors, smartphone data, and self-reports in both healthy individuals and patients with depressive disorders. Our findings demonstrate the feasibility of using these non-invasive methods to monitor sleep for patients with major depressive episodes. We uncover systematic biases in sleep estimates across modalities, reveal demographic and environmental factors that influence measurement agreement, and show that psychiatric populations exhibit more variability in sleep patterns. This work addresses a critical gap in validating consumer-grade sleep tracking technologies for psychiatric populations in naturalistic contexts.
