scholarly journals Internet-based Individualized Cognitive Behavioral Therapy for Shift Work Sleep Disorder (Empowered by Wellbeing Prediction): A Pilot Study Protocol (Preprint)

10.2196/24799 ◽  
2020 ◽  
Author(s):  
Asami Ito-Masui ◽  
Eiji Kawamoto ◽  
Ryota Sakamoto ◽  
Han Yu ◽  
Akane Sano ◽  
...  
Keyword(s):  
Pilot Study ◽  
Cognitive Behavioral Therapy ◽  
Sleep Disorder ◽  
Shift Work ◽  
Study Protocol ◽  
Behavioral Therapy ◽  
Cognitive Behavioral ◽  
Shift Work Sleep Disorder

scholarly journals Internet-based Individualized Cognitive Behavioral Therapy for Shift Work Sleep Disorder (Empowered by Wellbeing Prediction): A Pilot Study Protocol (Preprint)

2020 ◽  
Author(s):  
Asami Ito-Masui ◽  
Eiji Kawamoto ◽  
Ryota Sakamoto ◽  
Akane Sano ◽  
Eishi Motomura ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cognitive Behavioral Therapy ◽  
Sleep Disorder ◽  
Shift Work ◽  
Behavioral Therapy ◽  
Phase 1 ◽  
Cognitive Behavioral ◽  
Biometric Data ◽  
Shift Workers ◽  
Shift Work Sleep Disorder

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 healthcare workers with irregular sleeping and working habits by 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 shift work sleep disorder (iCBTS). This system includes current methods, such as medical sleep advice, as well as machine learning wellbeing prediction to improve sleep durations of shift workers and prevent declines in their wellbeing. METHODS This study consists of two phases: (1) preliminary data collection and machine learning for wellbeing prediction; (2) intervention and evaluation of iCBTS for shift work sleep disorder. Shift workers in the ICU at Mie University will wear a wearable sensor that collects biometric data and answer daily questionnaires regarding their wellbeing. Next, they will be provided with an iCBTS app for 4 weeks. Sleep and wellbeing measurements between baseline and the intervention period will then be compared. RESULTS Recruitment for phase 1 ended in October 2019. Recruitment for phase 2 is scheduled to start in October 2020. Preliminary results are expected to be available by summer 2021. CONCLUSIONS iCBTS empowered with wellbeing 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. CLINICALTRIAL UMIN clinical trials registry (phase 1: UMIN 000036122, phase 2: UMIN000040547)

A pilot study of functional Cognitive Behavioral Therapy (fCBT) for schizophrenia

2005 ◽  
Vol 74 (2-3) ◽  
pp. 201-209 ◽  
Author(s):  
Corinne Cather ◽  
David Penn ◽  
Michael W. Otto ◽  
Iftah Yovel ◽  
Kim T. Mueser ◽  
...  
Keyword(s):  
Pilot Study ◽  
Cognitive Behavioral Therapy ◽  
Behavioral Therapy ◽  
Cognitive Behavioral
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