IntroductionInsomnia in shift workers has distinctive features due to circadian rhythm disruption caused by reversed or unstable sleep-wake cycle work schedules. While previous studies have primarily focused on a limited number of predictors for insomnia severity in shift workers, there is a need to further explore key predictors, and develop a data-driven prediction model for insomnia in shift workers. This study aims to identify potential predictors of insomnia severity in shift workers using a machine learning (ML) approach and evaluate the accuracy of the resulting prediction model.MethodsWe assessed the predictors of insomnia severity in large samples of individuals (4,572 shift workers and 2,093 non-shift workers). The general linear model with the least absolute shrinkage and selection operator (LASSO) was used to determine an ML-based prediction model. Additional analyses were conducted to assess the interaction effects depending on the shift work schedule.ResultsThe ML algorithms identified 41 key predictors from 281 variables: 1 demographic, 7 physical health, 13 job characteristics, and 20 mental health factors. Compared to the non-shift workers, the shift workers showed a stronger association between insomnia severity and five predicting variables: passiveness at work, authoritarian work atmosphere, easiness to wake up, family and interpersonal stress, and medication. The prediction model demonstrated good performance with high accuracy and specificity overall despite a limited F1 score (classification effectiveness) and recall (sensitivity). Specifically, a prediction model for shift workers showed better balance in F1 scores and recall compared to that for non-shift workers.DiscussionThis ML algorithm provides an effective method for identifying key factors that predict insomnia severity in shift workers. Our findings align with the traditional insomnia model while also reflecting the distinctive features of shift work such as workplace conditions. Although the potential for immediate clinical application is limited, this study can serve as guidance for future research in improving a prediction model for shift workers. Constructing comprehensive ML-based prediction models that include our key predictors could be a crucial approach for clinical purposes.

ObjectWe explored the circadian preferences of non-shift workers (non-SWs) and various types of shift workers (SWs), and the associations of these preferences with sleep and mood.MethodsIn total, 4,561 SWs (2,419 women and 2,142 men aged 37.00 ± 9.80 years) and 2,093 non-SWs (1,094 women and 999 men aged 37.80 ± 9.73 years) completed an online survey. Of all SWs, 2,415 (1,079 women and 1,336 men aged 37.77 ± 9.96 years) reported regularly rotating or fixed schedules (“regular SWs”), and 2,146 (1,340 women and 806 men aged 36.12 ± 9.64 years) had irregular schedules (“irregular SWs”). Of the regular SWs, 2,040 had regularly rotating schedules, 212 had fixed evening schedules, and 163 had fixed night schedules. All participants completed the Morningness-Eveningness Questionnaire (MEQ) exploring circadian preferences, the short form of the Center for Epidemiological Studies-Depression Scale (CES-D) evaluating depression, the Insomnia Severity Index (ISI), and the Epworth Sleepiness Scale (ESS).ResultsCompared to non-SWs, SWs had lower MEQ scores, i.e., more eveningness, after controlling for age, gender, income, occupation, and weekly work hours (F = 87.97, p < 0.001). Irregular SWs had lower MEQ scores than regular SWs (F = 50.89, p < 0.001). Among regular SWs, the MEQ scores of fixed evening and fixed night SWs were lower than those of regularly rotating SWs (F = 22.42, p < 0.001). An association between the MEQ and ESS scores was apparent in non-SWs (r = −0.85, p < 0.001) but not in SWs (r = 0.001, p = 0.92).ConclusionSWs exhibited more eveningness than non-SWs; eveningness was particularly prominent in SWs with irregular or fixed evening/night shifts. Eveningness was associated with sleepiness only in non-SWs, but not in SWs.

Sleep quality often deteriorates with age, and insomnia among the elderly increases the risks of both physical and psychiatric disorders. To elucidate the mechanisms and identify useful diagnostic biomarkers for insomnia in the elderly, the current study investigated the associations of waking brain activity patterns with susceptibility to stress-induced insomnia (sleep reactivity) and dysfunctional beliefs about sleep, major factors precipitating and maintaining insomnia, respectively. Forty-five participants aged 60 years or older with insomnia completed selfreported measures assessing depression, anxiety, sleep quality, dysfunctional beliefs about sleep, and sleep reactivity. Participants were then examined by quantitative electroencephalography (qEEG) during wakefulness, and spectral analysis was conducted to examine associations of regional frequency band power with these insomnia-precipitating and -maintaining factors. Dysfunctional beliefs about sleep were significantly correlated with higher beta/high-beta frequency band powers, while sleep reactivity was correlated with higher theta and delta frequency band powers. These findings suggest that sleep reactivity of older adults is associated with widespread cortical deactivation leading to poor stress coping, while their dysfunctional beliefs about sleep are associated with hyperactivation which is related to cognitive processes. These associations suggest that cognitive inflexibility and maladaptive stress-coping contribute to insomnia among the elderly.

Background: Social Zeitgeber Theory posits that disruptions in social rhythms can increase susceptibility to bipolar disorder (BD). Shift work (SW) is one of the external factors that cause instability in social rhythms and the sleep -wake cycle. This study evaluated the moderating influences of SW on the risks of BD and sleep -related parameters and depressive symptoms. Furthermore, we evaluated the specific work schedules including daytime, nighttime, and regular and irregular rotating SW. Methods: An online survey was administered to 6665 participants, with 3379 (50.7 %) classified as individuals with high scores of Mood Disorder Questionnaire (MDQ). The survey included the Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Scale (ISI), Epworth Sleepiness Scale (ESS), and Center for Epidemiologic Studies Depression Scale (CES-D). Results: A multivariate regression model revealed significant interactive effects of MDQ positivity and SW on PSQI, ISI, and CES-D scores, but not ESS scores. In a secondary analysis of the high MDQ screen group, daytime workers had lower scores in sleep disturbances and depressive symptoms compared to those engaged in other forms of SW. Limitations: Online surveys were accessible voluntarily, leading to potential selection bias. Cross-sectional data identified associations, not causal relationships. Only a self -reported questionnaire was used. Conclusions: Our findings emphasize the advantages of a daytime work schedule for individuals at high risk of BD. In accordance with the principles of social rhythm interpersonal therapy, sleep interventions for individuals at high risk of BD should include the maintenance of a consistent daytime schedule.

Objective We investigated the difference in sleep inertia between shift workers (SWs) and non-shift workers (NSWs) and the effects of depressive symptoms, sleepiness, insomnia, and chronotype on sleep inertia in SWs and NSWs. Methods Altogether, 4,561 SWs (2,142 men and 2,419 women, aged 36.99 +/- 9.84 years) and 2,093 NSWs (999 men and 1,094 women, aged 37.80 +/- 9.73 years) participated in the current study. All participants completed the Sleep Inertia Questionnaire (SIQ), EpidemiologQuestionnaire (MEQ) through an online survey. Results SWs reported higher SIQ scores than NSWs after controlling for CESD, ESS, ISI, or MEQ. The CESD, ESS, ISI, and MEQ predicted SIQ in both SWs and NSWs. The effects of CESD and MEQ on SIQ were stronger in SWs and NSWs, respectively. The indirect effects of CESD and MEQ on SIQ via ESS were significantly moderated by work schedules. Conclusion SWs showed higher sleep inertia than NSWs. The effect of chronotypes on sleep inertia was more prominent in NSWs than in SWs. However, the effect of depressive symptoms on sleep inertia was more prominent in SWs than in NSWs. Moreover, the effect of depressive symptoms or chronotypes on sleep inertia was mediated by sleepiness, and such indirect effects were significantly different among the participants stratified by work schedules. Psychiatry Investig

BackgroundStudies on the brain structures of shift workers are limited; thus, this cross-sectional study aimed to compare the brain structures and the brain structural correlates of subjective sleepiness and insomnia symptoms between shift workers and non-shift workers.MethodsShift workers (n = 63) and non-shift workers (n = 58) completed questionnaires assessing subjective sleepiness and insomnia symptoms. Cortical thickness, cortical surface area, and subcortical volumes were measured by magnetic resonance imaging. The brain morphometric measures were compared between the groups, and interaction analyses using the brain morphometric measures as the dependent variable were performed to test the interactions between the study group and measures of sleep disturbance (i.e., subjective sleepiness and insomnia symptoms).ResultsNo differences in cortical thickness, cortical surface area, or subcortical volumes were detected between shift workers and non-shift workers. A single cluster in the left motor cortex showed a significant interaction between the study group and subjective sleepiness in the cortical surface area. The correlation between the left motor cortex surface area and the subjective sleepiness level was negative in shift workers and positive in non-shift workers. Significant interaction between the study group and insomnia symptoms was present for the left/right putamen volumes. The correlation between the left/right putamen volumes and insomnia symptom levels was positive in shift workers and negative in non-shift workers.ConclusionLeft motor cortex surface area and bilateral putamen volumes were unique structural correlates of subjective sleepiness and insomnia symptoms in shift workers, respectively.

Introduction:Shift work is known to reduce productivity and safety at work. Previous studies have suggested that a variety of interrelated factors, such as mood, cognition, and sleep, can affect the performance of shift workers. This study aimed to identify potential pathways from depression, sleep, and cognition to work performance in shift and non-shift workers.Material and methods:Online survey including the Center for Epidemiologic Studies Depression Scale (CES-D), Cognitive Failure Questionnaire (CFQ), and Pittsburgh Sleep Quality Index (PSQI), as well as two items representing work mistakes were administered to 4,561 shift workers and 2,093 non-shift workers. A multi-group structural equation model (SEM) was used to explore differences in the paths to work mistakes between shift and non-shift workers.Results:Shift workers had higher PSQI, CES-D, and CFQ scores, and made more mistakes at work than non-shift workers. The SEM revealed that PSQI, CES-D, and CFQ scores were significantly related to mistakes at work, with the CFQ being a mediating variable. There were significant differences in the path coefficients of the PSQI and CES-D between shift and non-shift workers. The direct effects of sleep disturbances on mistakes at work were greater in shift workers, while direct effects of depressive symptoms were found only in non-shift workers.Discussion:The present study found that shift workers made more mistakes at work than non-shift workers, probably because of depressed mood, poor sleep quality, and cognitive inefficiency. Sleep influences work performance in shift workers more directly compared to non-shift workers.

Objectives This meta-analysis was designed to assess the effects of social-cognitive training (SCT) and whether study quality, treatment approach, treatment context, and sample characteristics influence these effects. Methods Electronic databases were searched up to 5 August 2020 using variants of keywords: 'social cognition', 'training', 'rehabilitation', 'remediation', and 'schizophrenia'. Methodological moderators were extracted through the Clinical Trials Assessment Measure and verified by authors. This study was pre-registered on PROSPERO (CRD42020154026). Results Forty-two controlled trials with 1,868 participants were identified. The meta-analysis revealed moderate effects on emotion recognition, mental state attribution, and social perception. No significant effects were evident on psychiatric symptoms or social functioning. A small signal was evident for the generalization of treatment gains to executive function. Moderator analyses revealed that studies of lower methodological quality reported larger effects, and samples with lower mean years of education were associated with larger effects of SCT on mental state attribution. Treatment effects did not differ by other moderator variables such as treatment context and intervention types. Conclusions SCT benefits people with schizophrenia on a variety of social-cognitive outcomes. Differences in baseline symptoms, gender distribution, antipsychotic medication dose, IQ, and other sample features did not create barriers to treatment benefits. Future studies should aim to enhance the generalization of training effects on broader clinical outcomes.