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Prediction of good sleep with physical activity and light exposure: a preliminary study

Authors
 Kyung Mee Park  ;  Sang Eun Lee  ;  Changhee Lee  ;  Hyun Duck Hwang  ;  Do Hoon Yoon  ;  Eunchae Choi  ;  Eun Lee 
Citation
 JOURNAL OF CLINICAL SLEEP MEDICINE, Vol.18(5) : 1375-1383, 2022-05 
Journal Title
JOURNAL OF CLINICAL SLEEP MEDICINE
ISSN
 1550-9389 
Issue Date
2022-05
MeSH
Actigraphy ; Artificial Intelligence ; Exercise ; Humans ; Sleep ; Sleep Initiation and Maintenance Disorders* / therapy
Keywords
actigraphy ; deep learning ; insomnia ; machine learning ; sleep efficiency ; sleep prediction ; wearable device
Abstract
Study objectives: Cognitive behavioral treatment for insomnia is performed under the premise that feedback provided by evaluation of sleep diaries written by patients will result in good sleep. The sleep diary is essential for behavior therapy and sleep hygiene education. However, limitations include subjectivity and laborious input. We aimed to develop an artificial intelligence sleep prediction model and to find factors associated with good sleep using a wrist-worn actigraphy device.

Methods: We enrolled 109 participants who reported having no sleep disturbances. We developed a sleep prediction model using 733 days of actigraphy data of physical activity and light exposure. Twenty-four sleep prediction models were developed based on different data sources (actigraphy alone, sleep diary alone, or combined data), different durations of data (1 or 2 days), and different analysis methods (extreme gradient boosting, convolutional neural network, long short-term memory, logistic regression analysis). The outcome measure of "good sleep" was defined as ≥ 90% sleep efficiency.

Results: Actigraphy model performance was comparable to sleep diary model performance. Two-day models generally performed better than 1-day models. Among all models, the 2-day, combined (actigraphy and sleep diary), extreme gradient boosting model had the best performance for predicting good sleep (accuracy = 0.69, area under the curve = 0.70).

Conclusions: The findings suggested that it is possible to develop automated sleep models with good predictive performance. Further research including patients with insomnia is needed for clinical application.
Full Text
https://jcsm.aasm.org/doi/10.5664/jcsm.9872
DOI
10.5664/jcsm.9872
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Psychiatry (정신과학교실) > 1. Journal Papers
Yonsei Authors
Park, Kyung Mee(박경미) ORCID logo https://orcid.org/0000-0002-2416-2683
Lee, Eun(이은) ORCID logo https://orcid.org/0000-0002-7462-0144
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/188794
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