Bibliographic Details
| Title: |
Relationship Between Major Depression Symptom Severity and Sleep Collected Using a Wristband Wearable Device: Multicenter Longitudinal Observational Study |
| Authors: |
Zhang, Yuezhou, Folarin, Amos A, Sun, Shaoxiong, Cummins, Nicholas, Bendayan, Rebecca, Ranjan, Yatharth, Rashid, Zulqarnain, Conde, Pauline, Stewart, Callum, Laiou, Petroula, Matcham, Faith, White, Katie M, Lamers, Femke, Siddi, Sara, Simblett, Sara, Myin-Germeys, Inez, Rintala, Aki, Wykes, Til, Haro, Josep Maria, Penninx, Brenda WJH, Narayan, Vaibhav A, Hotopf, Matthew, Dobson, Richard JB |
| Source: |
JMIR mHealth and uHealth, Vol 9, Iss 4, p e24604 (2021) |
| Publisher Information: |
JMIR Publications, 2021. |
| Publication Year: |
2021 |
| Collection: |
LCC:Information technology
LCC:Public aspects of medicine |
| Subject Terms: |
Information technology, T58.5-58.64, Public aspects of medicine, RA1-1270 |
| More Details: |
BackgroundSleep problems tend to vary according to the course of the disorder in individuals with mental health problems. Research in mental health has associated 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. ObjectiveThe main aim of this study was to devise and extract sleep features from data collected using a wearable device and analyze their associations with depressive symptom severity and sleep quality as measured by the self-assessed Patient Health Questionnaire 8-item (PHQ-8). MethodsDaily sleep data were collected passively by Fitbit wristband devices, and depressive symptom severity was self-reported every 2 weeks by the PHQ-8. The data used in this paper included 2812 PHQ-8 records from 368 participants recruited from 3 study sites in the Netherlands, Spain, and the United Kingdom. We extracted 18 sleep features from Fitbit data that describe participant sleep in the following 5 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 score was used to evaluate the significance of the coefficient of each feature. ResultsWe tested our models on the entire dataset and separately on the data of 3 different study sites. We identified 14 sleep features that were significantly (P |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2291-5222 |
| Relation: |
https://mhealth.jmir.org/2021/4/e24604; https://doaj.org/toc/2291-5222 |
| DOI: |
10.2196/24604 |
| Access URL: |
https://doaj.org/article/59a951a328f245ffab3440386c7da5dd |
| Accession Number: |
edsdoj.59a951a328f245ffab3440386c7da5dd |
| Database: |
Directory of Open Access Journals |
