Relationships between sleep efficiency and lifestyle evaluated by objective sleep assessment: SLeep Epidemiology Project at University of Tsukuba.

Objectively measured sleep efficiency has recently been shown to be associated with health problems. Although several factors have previously been reported to be associated with sleep efficiency, most of these studies were conducted on older or younger adults, and the factors associated with sleep efficiency in healthy workers remain unknown. The aim of this study was to investigate the relationship between sleep efficiency and lifestyle factors using sleep measurement data recorded by an activity meter worn by workers. In total, 693 workers (male, 43.6%; mean age, 42.7 ± 11.3 years) were recruited from five offices in 2017. Sleep was measured over the period of 1 week by actigraphy. Workers' attributes, lifestyle habits, and occupational stress were identified using a questionnaire, and the association of sleep efficiency with lifestyle, occupational stress, and attributes was explored by logistic regression analysis. A logistic regression analysis using attributes and occupational stress as adjustment variables revealed that "longer sleeping hours on weekends than on weekdays" [odds ratios (OR), 0.66; 95% confidence interval (CI), 0.47-0.94], "water ingestion at bedtime" [OR, 2.09; 95% CI, 1.28-3.41], and "smartphone use at bedtime" [OR, 1.90; 95% CI, 1.28-2.83] were associated with decreased sleep efficiency. This study found that lifestyle habits were associated with sleep efficiency among workers. It is necessary to verify whether intervention in these lifestyle habits would contribute to the improvement of sleep efficiency in future studies.

Prevalence, characteristics, and psychological outcomes of workplace cyberbullying during the COVID-19 pandemic in Japan: a cross-sectional online survey.

BACKGROUND: The rapid introduction of teleworking due to the coronavirus disease 2019 pandemic has led to concerns about increases in cyberbullying (CB) worldwide. However, little is known about workplace CB in non-Western countries. The first objective was to clarify the prevalence and characteristics regarding workplace CB victimization in Japan. The second objective was to demonstrate the psychological outcomes of CB victimization in combination with traditional bullying (TB). METHODS: We conducted an anonymous, cross-sectional, Internet-based survey targeting regular employees in Japan (N = 1200) in January 2021. We investigated CB victimization using the Inventory of Cyberbullying Acts at Work and TB victimization by using the Short Negative Act Questionnaire. Possible explanatory factors for TB/CB victimization were sociodemographic variables, personality trait, chronic occupational stress, organizational climate, and gratitude at work. We also measured psychological distress, insomnia, and loneliness to assess adverse effects of workplace bullying. Two-step cluster analysis was used in determining the patterns combined with TB and CB victimization. Hierarchical binomial logistic regression analysis was used. RESULTS: In total, 8.0% of employees reported experiencing CB on a weekly basis. CB victimization was associated with younger age, managerial position, higher qualitative workload, and active information dissemination via the Internet, and frequency of teleworking. Three clusters based on TB and CB victimization patterns were identified: those who belong to the first cluster suffered neither from TB and CB (81.0%), the second cluster suffered only from TB (14.3%), and the third cluster suffered from both TB and CB (4.8%). The third cluster exhibited higher odds ratios (ORs) and 95% confidence intervals (CIs) for psychological distress (OR = 12.63, 95% CI = 4.20-38.03), insomnia (OR = 6.26, 95% CI = 2.80-14.01), and loneliness (OR = 3.24, 95% CI = 1.74-6.04) compared to the first cluster. CONCLUSIONS: These findings firstly clarify the prevalence and correlated factors of CB victimization among employees in Japan. Further, we showed that psychological wellbeing can be impaired by the coexistence of TB and CB. Our research could be the first step to develop the effective countermeasures against workplace CB.

Ibaraki's Amabie-chan usage and its association with infection prevention behavior and fear of COVID-19: a cross-sectional preliminary survey of the Tsukuba Salutogenic Occupational Cohort Study.

BACKGROUND: Ibaraki's Amabie-chan is a COVID-19 infection control system unique to Ibaraki prefecture, Japan. It requires residents to register each time they visit events, commercial facilities, and restaurants. The number of registrations has been limited, and its function alerting about people positive for COVID-19 infection seems not to be working. Nevertheless, registration with the system might have some impact on the user's behavior. In the current preliminary survey, the possible impact of Ibaraki's Amabie-chan on infection prevention behavior and fear of COVID-19 was investigated. METHODS: A cross-sectional, web-based, anonymous, and self-administered survey was conducted at two workplaces in Tsukuba Science City, Ibaraki, Japan. The first survey was conducted at one of the workplaces in November 2020, and the second survey, at the other workplace in February 2021. Variables of interest were sex, age group, marital status, employment status, Ibaraki's Amabie-chan use, COVID-19 Contact-Confirming Application use, ten items of infection prevention behaviors, and fear of COVID-19. Hierarchical linear regression analysis was performed. RESULTS: In both surveys, use of Ibaraki's Amabie-chan was significantly associated with COCOA use and with "physical condition management such as body temperature measurement." No association was found with other infection prevention behaviors or with fear of COVID-19. CONCLUSIONS: Our findings did not provide sufficient evidence for the effectiveness of Ibaraki's Amabie-chan in regard to users' infection control behavior. Further detailed study is needed to investigate the effectiveness in terms of infection prevention and the cost-effectiveness of Ibaraki's Amabie-chan.

Morning preference is associated with subjective happiness among Japanese female workers: A moderation analysis by sleep characteristics from the SLEPT study.

Research on mental health in working populations has predominantly focussed on negative outcomes, while studies on the positive aspects of life remain very limited, especially in Japan. Although morningness has recently been considered a factor for being happy, the role of sleep characteristics as it relates to the association between morningness and happiness has not been substantiated. The aim of this study is to investigate the associations between diurnal preference and level of subjective happiness in healthy, full-time, daytime workers in Japan. We also tested the moderation effect of sleep parameters on the relationship between diurnal preference and subjective happiness. This is an exploratory analysis from the cross-sectional data of the SLeep Epidemiology Project at the University of Tsukuba (SLEPT Study). Subjective happiness was evaluated using a single-item question. Diurnal preference was assessed using the Morningness-Eveningness Questionnaire (MEQ), in which higher scores indicate greater tendency to have morning preference. The participants underwent assessment of sleep parameters for 7 consecutive days using a waist-worn actigraphy device and kept a sleep diary. Sleep parameters investigated were subjective sleep quality, sleep disturbance, daytime sleepiness, weekend oversleep, total sleep time, sleep onset time, wake time, and sleep efficiency. A total of 205 males (average 42.6 ± 10.4 years) and 272 females (41.1 ± 9.8 years) were eligible for analysis. Hierarchical liner regression analysis was used to show the relationships of subjective happiness with MEQ score, and the sleep parameters. Further, moderation analysis was conducted by adding the interaction between MEQ score and the sleep parameters. After adjusting for age, psychological distress, self-rated health, and occupational stress, we found that subjective happiness was significantly associated with higher MEQ score and sleep efficiency but only in female. The moderating role of sleep parameters was not found. We discussed the implications of the obtained results and a possible strategy to maintain and improve subjective happiness of female workers who have evening preference.

Comparison of predicted psychological distress among workers between artificial intelligence and psychiatrists: a cross-sectional study in Tsukuba Science City, Japan.

OBJECTIVES: Psychological distress is a worldwide problem and a serious problem that needs to be addressed in the field of occupational health. This study aimed to use artificial intelligence (AI) to predict psychological distress among workers using sociodemographic, lifestyle and sleep factors, not subjective information such as mood and emotion, and to examine the performance of the AI models through a comparison with psychiatrists. DESIGN: Cross-sectional study. SETTING: We conducted a survey on psychological distress and living conditions among workers. An AI model for predicting psychological distress was created and then the results were compared in terms of accuracy with predictions made by psychiatrists. PARTICIPANTS: An AI model of the neural network and six psychiatrists. PRIMARY OUTCOME: The accuracies of the AI model and psychiatrists for predicting psychological distress. METHODS: In total, data from 7251 workers were analysed to predict moderate and severe psychological distress. An AI model of the neural network was created and accuracy, sensitivity and specificity were calculated. Six psychiatrists used the same data as the AI model to predict psychological distress and conduct a comparison with the AI model. RESULTS: The accuracies of the AI model and psychiatrists for predicting moderate psychological distress were 65.2% and 64.4%, respectively, showing no significant difference. The accuracies of the AI model and psychiatrists for predicting severe psychological distress were 89.9% and 85.5%, respectively, indicating that the AI model had significantly higher accuracy. CONCLUSIONS: A machine learning model was successfully developed to screen workers with depressed mood. The explanatory variables used for the predictions did not directly ask about mood. Therefore, this newly developed model appears to be able to predict psychological distress among workers easily, regardless of their subjective views.