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Research in mental health has implicated sleep pathologies with depression.However, the gold standard for sleep assessment, polysomnography, is notsuitable for long-term, continuous, monitoring of daily sleep, and methods suchas sleep diaries rely on subjective recall, which is qualitative andinaccurate. Wearable devices, on the other hand, provide a low-cost andconvenient means to monitor sleep in home settings. The main aim of this studywas to devise and extract sleep features, from data collected using a wearabledevice, and analyse their correlation with depressive symptom severity andsleep quality, as measured by the self-assessed Patient Health Questionnaire8-item. Daily sleep data were collected passively by Fitbit wristband devices,and depressive symptom severity was self-reported every two weeks by the PHQ-8.The data used in this paper included 2,812 PHQ-8 records from 368 participantsrecruited from three study sites in the Netherlands, Spain, and the UK.Weextracted 21 sleep features from Fitbit data which describe sleep in thefollowing five aspects: sleep architecture, sleep stability, sleep quality,insomnia, and hypersomnia. Linear mixed regression models were used to exploreassociations between sleep features and depressive symptom severity. The z-testwas used to evaluate the significance of the coefficient of each feature. Wetested our models on the entire dataset and individually on the data of threedifferent study sites. We identified 16 sleep features that were significantlycorrelated with the PHQ-8 score on the entire dataset. Associations betweensleep features and the PHQ-8 score varied across different sites, possibly dueto the difference in the populations.

jmir mhealth and uhealth

The Relationship between Major Depression Symptom Severity and Sleep  Collected Using a Wristband Wearable Device: Multi-centre Longitudinal  Observational Study