Internet-Based Individualized Cognitive Behavioral Therapy for Shift Work Sleep Disorder Empowered by Well-Being Prediction: Protocol for a Pilot Study

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dc.citation.articleNumbere24799
dc.citation.issueNumber3
dc.citation.journalTitleJMIR Research Protocols
dc.citation.volumeNumber10
dc.contributor.authorIto-Masui, Asami
dc.contributor.authorKawamoto, Eiji
dc.contributor.authorSakamoto, Ryota
dc.contributor.authorYu, Han
dc.contributor.authorSano, Akane
dc.contributor.authorMotomura, Eishi
dc.contributor.authorTanii, Hisashi
dc.contributor.authorSakano, Shoko
dc.contributor.authorEsumi, Ryo
dc.contributor.authorImai, Hiroshi
dc.contributor.authorShimaoka, Motomu
dc.date.accessioned2021-04-21T15:46:14Z
dc.date.available2021-04-21T15:46:14Z
dc.date.issued2021
dc.description.abstractBackground: 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. Clinical Trial: 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
dc.identifier.citationIto-Masui, Asami, Kawamoto, Eiji, Sakamoto, Ryota, et al.. "Internet-Based Individualized Cognitive Behavioral Therapy for Shift Work Sleep Disorder Empowered by Well-Being Prediction: Protocol for a Pilot Study." <i>JMIR Research Protocols,</i> 10, no. 3 (2021) JMIR: https://doi.org/10.2196/24799.
dc.identifier.digitalInternet-BasedIndividualizedCognitiveBehavioralTherapy
dc.identifier.doihttps://doi.org/10.2196/24799
dc.identifier.urihttps://hdl.handle.net/1911/110292
dc.language.isoeng
dc.publisherJMIR
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleInternet-Based Individualized Cognitive Behavioral Therapy for Shift Work Sleep Disorder Empowered by Well-Being Prediction: Protocol for a Pilot Study
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
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