Multimodal Sleep Measurement and Alignment Analysis in Outpatients With Major Depressive Episode: Observational Study

BACKGROUND: Sleep is essential for overall health and plays a critical role in the diagnosis of psychiatric disorders. Although polysomnography 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 patients with mood disorders using wearables, nearables, and ecological momentary assessment, while examining measurement biases and the impact of seasonal and demographic factors on discrepancies across methods. METHODS: We conducted a 14-day study in Finland and enrolled a total of 201 participants, comprising patients with a major depressive episode and healthy controls. Of these, 169 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 versus bed (+34.9 minutes; P=.01), smartphone versus bed (-45.3 minutes; P=.004), and actigraph versus smartphone (+78.7 minutes; P<.001), while controls exhibited minimal and nonsignificant differences. For TST, smartphone underestimates sleep compared to both bed sensors (-0.71 minutes; P<.001) and actigraphs (-1.35 minutes; P<.001). Across devices, TST correlations remained low, spanning r=0.12 (P=.58) to r=0.55 (P=.10) in controls and r=0.17 (P=.19) to r=0.43 (P=.002) in patients. Mixed models showed that older age was linked to better sleep offset alignment between actigraphy and bed sensors (β=-0.02, 95% CI -0.04 to 0.00; P=.048), as well as smartphone and bed sensor (β=-0.03, 95% CI -0.06 to 0.00; P=.03). Patients with bipolar/borderline personality disorder showed lower TST alignment, and alignment between smartphone and bed sensor was worse in females (β=-1.03, 95% CI -1.74 to -0.33, P=.004). Longer daylight duration was also associated with improved alignment in sleep offset and TST. 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.

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