BACKGROUND Sleep is essential for overall health and plays a critical role in the diagnosis of psychiatric disorders. Although polysomnography (PSG) remains the gold standard for measuring sleep, its reliance on laboratory settings limits its feasibility for long-term, naturalistic monitoring, particularly for patients with mental disorders. OBJECTIVE This study assesses sleep tracking reliability and alignment in healthy individuals and mood disorder patients using wearables, nearables, and Ecological Momentary Assessment (EMA), while examining measurement biases and the impact of seasonal and demographic factors on discrepancies across methods. METHODS A 14-day study conducted in Finland enrolled a total 201 participants, comprising of patients with a major depressive episode and healthy controls. 172 participants with sufficient observations were retained for further analyses. Participants’ sleep patterns (onset, offset, and total sleep time (TST)) were gathered daily from an actigraph (Actiwatch 2), a bed sensor (Murata SCA11H), mobile screen events, and a daily survey. The alignment between sleep measurement methods was evaluated using Bland-Altman plots and Pearson correlation. Linear mixed models were used to assess the effects of demographics, season, and disorder type on the sleep measures alignment. RESULTS Patients exhibited greater variability in sleep measures than healthy controls. For sleep onset, mean biases between devices were small and not statistically significant in either group, with moderate to strong correlations. In contrast, sleep offset showed significantly larger biases in patients: actigraph vs bed (+34.9 min, P=.013), phone vs bed (–45.3 min, P=.0037), and actigraph vs phone (+78.7 min, P CONCLUSIONS This study demonstrates the feasibility of using actigraphy, smartphone data, and bed sensors for sleep tracking in naturalistic settings with patients. It highlights measurement biases across devices, the impact of seasonal variations on sleep research in unique geographical regions like Finland, and key demographic factors influencing sleep measurement discrepancies.
Mahir et al. (Fri,) studied this question.