  Research in mental health has implicated sleep pathologies with depression.
However, the gold standard for sleep assessment, polysomnography, is not
suitable for long-term, continuous, monitoring of daily sleep, and methods such
as sleep diaries rely on subjective recall, which is qualitative and
inaccurate. Wearable devices, on the other hand, provide a low-cost and
convenient means to monitor sleep in home settings. The main aim of this study
was to devise and extract sleep features, from data collected using a wearable
device, and analyse their correlation with depressive symptom severity and
sleep quality, as measured by the self-assessed Patient Health Questionnaire
8-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 participants
recruited from three study sites in the Netherlands, Spain, and the UK.We
extracted 21 sleep features from Fitbit data which describe sleep in the
following five aspects: sleep architecture, sleep stability, sleep quality,
insomnia, and hypersomnia. Linear mixed regression models were used to explore
associations between sleep features and depressive symptom severity. The z-test
was used to evaluate the significance of the coefficient of each feature. We
tested our models on the entire dataset and individually on the data of three
different study sites. We identified 16 sleep features that were significantly
correlated with the PHQ-8 score on the entire dataset. Associations between
sleep features and the PHQ-8 score varied across different sites, possibly due
to the difference in the populations.
