JMIR Publications

ABSTRACT

Background:

Sleep problems tend to vary accordingly to the course of the disorder in individuals with mental health problems. Research in mental health has implicated sleep pathologies with depression. However, the gold standard for sleep assessment, polysomnography (PSG), 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.

Objective:

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 (PHQ-8).

Methods:

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 the participant’s 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.

Results:

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 (P < .05) correlated with the PHQ-8 score on the entire dataset, among them, awake proportion (z = 5.45, P < .001), awakening times (z = 5.53, P < .001), insomnia (z = 4.55, P < .001), mean sleep offset time (z = 6.19, P < .001) and hypersomnia (z = 5.30, P < .001) were the top 5 features ranked by z-test statistics. Associations between sleep features and the PHQ-8 score varied across different sites, possibly due to the difference in the populations. We observed that many of our findings were consistent with previous studies, which used other measurements to assess sleep, such as PSG and sleep questionnaires.

Conclusions:

Although consumer wearable devices may not be a substitute for PSG to assess sleep quality accurately, we demonstrate that some derived sleep features extracted from these wearable devices show potential for remote measurement of sleep as a biomarker of depression in real-world settings. These findings may provide the basis for the development of clinical tools to passively monitor disease state and trajectory, with minimal burden on the participant.