Internet-Based Individualized Cognitive Behavioral Therapy for Shift Work Sleep Disorder Empowered by Well-Being Prediction: Protocol for a Pilot Study
| Autor: | Akane Sano, Asami Ito-Masui, Eishi Motomura, Han Yu, Motomu Shimaoka, Ryota Sakamoto, Eiji Kawamoto, Ryo Esumi, Shoko Sakano, Hiroshi Imai, Hisashi Tanii |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
safety
medicine.medical_specialty medicine.medical_treatment Computer applications to medicine. Medical informatics R858-859.7 shift workers CBT medical safety health care workers Shift work sleep disorder Shift work 03 medical and health sciences 0302 clinical medicine Physical medicine and rehabilitation well-being Health care Protocol medicine online intervention 030212 general & internal medicine sleep sleep disorder Sleep disorder business.industry wearable sensors pilot study safety issue General Medicine medicine.disease cognitive behavioral therapy Clinical trial Cognitive behavioral therapy shift work machine learning shift work sleep disorders Medicine The Internet Sleep (system call) business 030217 neurology & neurosurgery |
| Zdroj: | JMIR Research Protocols, Vol 10, Iss 3, p e24799 (2021) JMIR Research Protocols |
| ISSN: | 1929-0748 |
| Popis: | Background Shift work sleep disorders (SWSDs) are associated with the high turnover rates of nurses, and are considered a major medical safety issue. However, initial management can be hampered by insufficient awareness. In recent years, it has become possible to visualize, collect, and analyze the work-life balance of health care workers with irregular sleeping and working habits using wearable sensors that can continuously monitor biometric data under real-life settings. In addition, internet-based cognitive behavioral therapy for psychiatric disorders has been shown to be effective. Application of wearable sensors and machine learning may potentially enhance the beneficial effects of internet-based cognitive behavioral therapy. Objective In this study, we aim to develop and evaluate the effect of a new internet-based cognitive behavioral therapy for SWSD (iCBTS). This system includes current methods such as medical sleep advice, as well as machine learning well-being prediction to improve the sleep durations of shift workers and prevent declines in their well-being. Methods This study consists of two phases: (1) preliminary data collection and machine learning for well-being prediction; (2) intervention and evaluation of iCBTS for SWSD. Shift workers in the intensive care unit at Mie University Hospital will wear a wearable sensor that collects biometric data and answer daily questionnaires regarding their well-being. They will subsequently be provided with an iCBTS app for 4 weeks. Sleep and well-being measurements between baseline and the intervention period will be compared. Results Recruitment for phase 1 ended in October 2019. Recruitment for phase 2 has started in October 2020. Preliminary results are expected to be available by summer 2021. Conclusions iCBTS empowered with well-being prediction is expected to improve the sleep durations of shift workers, thereby enhancing their overall well-being. Findings of this study will reveal the potential of this system for improving sleep disorders among shift workers. Trial Registration UMIN Clinical Trials Registry UMIN000036122 (phase 1), UMIN000040547 (phase 2); https://tinyurl.com/dkfmmmje, https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000046284 International Registered Report Identifier (IRRID) DERR1-10.2196/24799 |
| Databáze: | OpenAIRE |
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