Internet-Based Individualized Cognitive Behavioral Therapy for Shift Work Sleep Disorder Empowered by Well-Being...

Area of Science:

• Occupational Health
• Sleep Medicine
• Digital Health

Background:

• Shift Work Sleep Disorders (SWSDs) are a significant safety concern in healthcare, linked to high nurse turnover.
• Traditional management of SWSDs is often hindered by a lack of awareness and effective interventions.
• Wearable sensors offer real-time monitoring of biometric data for individuals with irregular sleep patterns.

Purpose of the Study:

• To develop and evaluate a novel internet-based cognitive behavioral therapy for SWSD (iCBTS).
• To integrate machine learning for well-being prediction to improve sleep duration and prevent well-being decline in shift workers.
• To enhance the efficacy of digital interventions for SWSD through combined technological approaches.

Main Methods:

• Phase 1: Preliminary data collection and machine learning model development for well-being prediction.
• Phase 2: Intervention study involving shift workers in an intensive care unit using wearable sensors and an iCBTS app for 4 weeks.
• Comparison of sleep and well-being measurements from baseline to the intervention period.

Main Results:

• Recruitment for Phase 1 concluded in October 2019; Phase 2 commenced in October 2020.
• Preliminary results are anticipated by summer 2021.
• The study is registered under UMIN Clinical Trials Registry numbers UMIN000036122 (Phase 1) and UMIN000040547 (Phase 2).

Conclusions:

• The iCBTS system, enhanced with well-being prediction, is expected to improve sleep duration and overall well-being in shift workers.
• This innovative approach holds potential for significantly improving the management of sleep disorders among shift workers.
• Findings will elucidate the effectiveness of integrating wearable technology and machine learning in digital therapeutics for SWSD.