Effect of an Internet-Delivered Cognitive Behavioral Therapy-Based Sleep Improvement App for Shift Workers at High Risk of Sleep Disorder: Single-Arm, Nonrandomized Trial

• Published in: Journal of medical Internet research Vol. 25; no. 1; p. e45834
• Main Authors: Ito-Masui, Asami, Sakamoto, Ryota, Matsuo, Eri, Kawamoto, Eiji, Motomura, Eishi, Tanii, Hisashi, Yu, Han, Sano, Akane, Imai, Hiroshi, Shimaoka, Motomu
• Format: Journal Article
• Language: English
• Published: Canada Journal of Medical Internet Research 22.08.2023; Gunther Eysenbach MD MPH, Associate Professor; JMIR Publications
• Subjects: Activities of daily living; Adult; Algorithms; Behavior modification; Behavioral health care; Burnout; Chronic insomnia; Circadian rhythm; Cognitive Behavioral Therapy; Cognitive therapy; Cognitive-behavioral factors; Consumer electronics industry; Emergency services; Emotions; Heart rate; High risk; Humans; Hygiene; Insomnia; Intensive care; Internet; Intervention; Machine learning; Medical research; Medicine, Experimental; Mental health; Mobile Applications; Neural networks; Nurses; Original Paper; Physicians; Prediction models; Questionnaires; Risk factors; Self awareness; Sensors; Shift work; Shiftwork; Sleep; Sleep deprivation; Sleep disorders; Sleep Duration; Sleep Initiation and Maintenance Disorders; Time series; Tracking; Well being; Workers; United States; Japan; mobile apps; fitness tracker; internet-based cognitive behavioral therapy; mobile phone; subjective well-being; shift worker sleep disorder; machine learning
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/45834

Shift workers are at high risk of developing sleep disorders such as shift worker sleep disorder or chronic insomnia. Cognitive behavioral therapy (CBT) is the first-line treatment for insomnia, and emerging evidence shows that internet-based CBT is highly effective with additional features such as continuous tracking and personalization. However, there are limited studies on internet-based CBT for shift workers with sleep disorders. This study aimed to evaluate the impact of a 4-week, physician-assisted, internet-delivered CBT program incorporating machine learning-based well-being prediction on the sleep duration of shift workers at high risk of sleep disorders. We evaluated these outcomes using an internet-delivered CBT app and fitness trackers in the intensive care unit. A convenience sample of 61 shift workers (mean age 32.9, SD 8.3 years) from the intensive care unit or emergency department participated in the study. Eligible participants were on a 3-shift schedule and had a Pittsburgh Sleep Quality Index score ≥5. The study comprised a 1-week baseline period, followed by a 4-week intervention period. Before the study, the participants completed questionnaires regarding the subjective evaluation of sleep, burnout syndrome, and mental health. Participants were asked to wear a commercial fitness tracker to track their daily activities, heart rate, and sleep for 5 weeks. The internet-delivered CBT program included well-being prediction, activity and sleep chart, and sleep advice. A job-based multitask and multilabel convolutional neural network-based model was used for well-being prediction. Participant-specific sleep advice was provided by sleep physicians based on daily surveys and fitness tracker data. The primary end point of this study was sleep duration. For continuous measurements (sleep duration, steps, etc), the mean baseline and week-4 intervention data were compared. The 2-tailed paired t test or Wilcoxon signed rank test was performed depending on the distribution of the data. In the fourth week of intervention, the mean daily sleep duration for 7 days (6.06, SD 1.30 hours) showed a statistically significant increase compared with the baseline (5.54, SD 1.36 hours; P=.02). Subjective sleep quality, as measured by the Pittsburgh Sleep Quality Index, also showed statistically significant improvement from baseline (9.10) to after the intervention (7.84; P=.001). However, no significant improvement was found in the subjective well-being scores (all P>.05). Feature importance analysis for all 45 variables in the prediction model showed that sleep duration had the highest importance. The physician-assisted internet-delivered CBT program targeting shift workers with a high risk of sleep disorders showed a statistically significant increase in sleep duration as measured by wearable sensors along with subjective sleep quality. This study shows that sleep improvement programs using an app and wearable sensors are feasible and may play an important role in preventing shift work-related sleep disorders. RR2-10.2196/24799.