基于互联网的个性化认知行为疗法对轮班工作睡眠障碍的福祉预测赋能：一项试点研究方案

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

作者信息

Ito-Masui Asami, Kawamoto Eiji, Sakamoto Ryota, Yu Han, Sano Akane, Motomura Eishi, Tanii Hisashi, Sakano Shoko, Esumi Ryo, Imai Hiroshi, Shimaoka Motomu

机构信息

Departments of Molecular and Pathobiology and Cell Adhesion Biology, Mie University Graduate School of Medicine, Tsu City, Mie, Japan.

Departments of Emergency and Disaster Medicine, Mie University Graduate School of Medicine, Tsu City, Mie, Japan.

出版信息

JMIR Res Protoc. 2021 Mar 18;10(3):e24799. doi: 10.2196/24799.

DOI:10.2196/24799

PMID:33626497

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8088862/

Abstract

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.

摘要

背景

轮班工作睡眠障碍（SWSDs）与护士的高离职率相关，被视为一个重大的医疗安全问题。然而，由于认识不足，初始管理可能会受到阻碍。近年来，使用可穿戴传感器在现实生活环境中持续监测生物特征数据，从而实现对睡眠和工作习惯不规律的医护人员的工作生活平衡进行可视化、收集和分析成为可能。此外，基于互联网的精神疾病认知行为疗法已被证明是有效的。可穿戴传感器和机器学习的应用可能会增强基于互联网的认知行为疗法的有益效果。

目的

在本研究中，我们旨在开发并评估一种新的基于互联网的轮班工作睡眠障碍认知行为疗法（iCBTS）的效果。该系统包括医疗睡眠建议等现有方法，以及用于改善轮班工人睡眠时间和防止其幸福感下降的机器学习幸福感预测。

方法

本研究包括两个阶段：（1）初步数据收集和用于幸福感预测的机器学习；（2）对iCBTS治疗SWSD的干预和评估。三重大学医院重症监护病房的轮班工人将佩戴收集生物特征数据的可穿戴传感器，并回答有关其幸福感的每日问卷。随后，他们将获得iCBTS应用程序4周。将比较基线期和干预期之间的睡眠和幸福感测量结果。