Shift work disorder and the prevalence of help seeking behaviors for sleep concerns in Australia: A descriptive study

Shift work disorder (SWD) is a circadian rhythm sleep-wake disorder, defined by symptoms of insomnia and excessive levels of sleepiness resulting from work that occurs during non-standard hours. Sleep problems are common in shift workers, yet our understanding of help seeking behaviours for sleep in shift workers is limited. The primary aim of this study was to examine the help seeking behaviours of Australian workers who meet criteria for SWD. Of the 448 (46% of sample, n = 964 total) Australian workers reporting non-standard work hours, 10.5% (n = 41) met the criteria for probable shift work disorder (pSWD). Non-standard workers with pSWD did not seek help for sleep problems at higher rates than workers without SWD. Of the small proportion of workers with pSWD who sought help, general practitioners were the most common healthcare professionals for sleep problems. Self-management was common in workers with pSWD, with a high self-reported prevalence of alcohol use (31.7%) as a sleep management strategy, and caffeine consumption (76.9%) as a sleepiness management strategy. The majority of individuals with pSWD reported the mentality of 'accept it and keep going' as a sleepiness management strategy, highlighting a potential barrier to help seeking behaviour in workers with pSWD. These findings provide novel insight into the help seeking behaviours of those with pSWD. There is a need for further research to understand why individuals at risk for SWD are not actively seeking help, and to develop health promotion and intervention strategies to improve help seeking when needed.

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

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