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

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.