Search

There is a growing consensus regarding the significant individual differences in responses to sleep inertia. However, little is known about the longitudinal trajectories of sleep inertia. Therefore, we aimed to investigate the incidence rates of sleep inertia, longitudinal trajectories, influencing factors and outcomes among intern nurses with shift work. A total of 1079 intern nurses with shift work completed online surveys at 2 weeks (T1) and 16 weeks (T2) after their clinical internship, respectively. The demographics and willingness to continue working as nurses, circadian type, sleep problems and emotional problems were measured. Hierarchical logistic regressions were used to examine predictors for the distinct longitudinal trajectory of sleep inertia, and binary logistic regressions were used to examine the predictive effects of trajectory memberships of sleep inertia on mental health problems (daytime sleepiness, anxiety, depression) at follow-up. Overall, the incidence rates of severe sleep inertia increased significantly from 20.4% at T1 to 25.3% at T2 among intern nurses with shift work. All participants were classified into four trajectory memberships. Multiple factors were significant predictors of distinct trajectories of sleep inertia. Additionally, individuals with deteriorated and persistent-high sleep inertia were more likely to develop mental health problems at the subsequent point. Sleep inertia is prevalent among intern nurses with shift work, and its distinct change patterns were associated with multiple risk factors and various adverse outcomes. Therefore, these factors should be considered when designing individualized and appropriate interventions for sleep inertia among medical staff., (© 2024 European Sleep Research Society.)

Summary: Insomnia is associated with fatigue, but it is unclear whether response to cognitive behaviour therapy for insomnia is altered in individuals with co‐occurring symptoms of insomnia and chronic fatigue. This is a secondary analysis using data from 1717 participants with self‐reported insomnia in a community‐based randomized controlled trial of digital cognitive behaviour therapy for insomnia compared with patient education. We employed baseline ratings of the Chalder Fatigue Questionnaire to identify participants with more or fewer symptoms of self‐reported chronic fatigue (chronic fatigue, n = 592; no chronic fatigue, n = 1125). We used linear mixed models with Insomnia Severity Index, Short Form‐12 mental health, Short Form‐12 physical health, and the Hospital Anxiety and Depression Scale separately as outcome variables. The main covariates were main effects and interactions for time (baseline versus 9‐week follow‐up), intervention, and chronic fatigue. Participants with chronic fatigue reported significantly greater improvements following digital cognitive behaviour therapy for insomnia compared with patient education on the Insomnia Severity Index (Cohen's d = 1.36, p < 0.001), Short Form‐12 mental health (Cohen's d = 0.19, p = 0.029), and Hospital Anxiety and Depression Scale (Cohen's d = 0.18, p = 0.010). There were no significant differences in the effectiveness of digital cognitive behaviour therapy for insomnia between chronic fatigue and no chronic fatigue participants on any outcome. We conclude that in a large community‐based sample of adults with insomnia, co‐occurring chronic fatigue did not moderate the effectiveness of digital cognitive behaviour therapy for insomnia on any of the tested outcomes. This may further establish digital cognitive behaviour therapy for insomnia as an adjunctive intervention in individuals with physical and mental disorders. [ABSTRACT FROM AUTHOR]

Summary: Cognitive behavioural therapy for insomnia is efficacious and recommended for insomnia, but availability is scarce. Cognitive behavioural therapy for insomnia self‐help interventions could increase availability, especially if unguided. Optimizing cognitive behavioural therapy for insomnia methods and system user‐friendliness, we developed a short, digital, self‐help programme—FastAsleep—based on the behavioural components of sleep restriction and stimulus control. This study investigated its feasibility and preliminary effects. Thirty media‐recruited participants with moderate to severe insomnia were assessed via telephone before using FastAsleep for 4 weeks, and were interviewed afterwards. Self‐ratings with web questionnaires were conducted at screening, pre‐, mid‐ and post‐treatment, and at 3‐month follow‐up. Primary outcomes were feasibility (credibility, adherence, system user‐friendliness and adverse effects), and secondary outcomes were changes in symptom severity (insomnia, depression and anxiety). Adherence was generally high, participants' feasibility ratings were favourable, and adverse effects matched previously reported levels for cognitive behavioural therapy for insomnia. Symptoms of insomnia decreased after the treatment period (Hedge's g = 1.79, 95% confidence interval = 1.20–2.39), as did symptoms of depression and anxiety. FastAsleep can be considered feasible and promising for alleviating insomnia symptoms among patients fit for self‐care. Future controlled trials are needed to establish the efficacy of FastAsleep and its suitability in a stepped care model. [ABSTRACT FROM AUTHOR]

Summary: Using data from 1721 participants in a community‐based randomized control trial of digital cognitive behavioural therapy for insomnia compared with patient education, we employed linear mixed modelling analyses to examine whether chronotype moderated the benefits of digital cognitive behavioural therapy for insomnia on self‐reported levels of insomnia severity, fatigue and psychological distress. Baseline self‐ratings on the reduced version of the Horne–Östberg Morningness–Eveningness Questionnaire were used to categorize the sample into three chronotypes: morning type (n = 345; 20%); intermediate type (n = 843; 49%); and evening type (n = 524; 30%). Insomnia Severity Index, Chalder Fatigue Questionnaire, and Hospital Anxiety and Depression Scale were assessed pre‐ and post‐intervention (9 weeks). For individuals with self‐reported morning or intermediate chronotypes, digital cognitive behavioural therapy for insomnia was superior to patient education on all ratings (Insomnia Severity Index, Chalder Fatigue Questionnaire, and Hospital Anxiety and Depression Scale) at follow‐up (p‐values ≤ 0.05). For individuals with self‐reported evening chronotype, digital cognitive behavioural therapy for insomnia was superior to patient education for Insomnia Severity Index and Chalder Fatigue Questionnaire, but not on the Hospital Anxiety and Depression Scale (p = 0.139). There were significant differences in the treatment effects between the three chronotypes on the Insomnia Severity Index (p = 0.023) estimated difference between evening and morning type of −1.70, 95% confidence interval: −2.96 to −0.45, p = 0.008, and estimated difference between evening and intermediate type −1.53, 95% confidence interval: −3.04 to −0.03, p = 0.046. There were no significant differences in the treatment effects between the three chronotypes on the Chalder Fatigue Questionnaire (p = 0.488) or the Hospital Anxiety and Depression Scale (p = 0.536). We conclude that self‐reported chronotype moderates the effects of digital cognitive behavioural therapy for insomnia on insomnia severity, but not on psychological distress or fatigue. [ABSTRACT FROM AUTHOR]

Sleep disturbances and circadian disruption play a central role in adverse health, safety, and performance outcomes in shift workers. While biomathematical models of sleep and alertness can be used to personalise interventions for shift workers, their practical implementation is undertested. This study tested the feasibility of implementing two biomathematical models-the Phillips-Robinson Model and the Model for Arousal Dynamics-in 28 shift-working nurses, 14 in each group. The study examined the overlap and adherence between model recommendations and sleep behaviours, and changes in sleep following the implementation of recommendations. For both groups combined, the mean (SD) percentage overlap between when a model recommended an individual to sleep and when sleep was obtained was 73.62% (10.24%). Adherence between model recommendations and sleep onset and offset times was significantly higher with the Model of Arousal Dynamics compared to the Phillips-Robinson Model. For the Phillips-Robinson model, 27% of sleep onset and 35% of sleep offset times were within ± 30 min of model recommendations. For the Model of Arousal Dynamics, 49% of sleep onset, and 35% of sleep offset times were within ± 30 min of model recommendations. Compared to pre-study, significant improvements were observed post-study for sleep disturbance (Phillips-Robinson Model), and insomnia severity and sleep-related impairments (Model of Arousal Dynamics). Participants reported that using a digital, automated format for the delivery of sleep recommendations would enable greater uptake. These findings provide a positive proof-of-concept for using biomathematical models to recommend sleep in operational contexts., (© 2023 The Authors. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society.)

Shift work directly causes circadian disruption and reduces sleep quality. Physical activity is also associated with sleep quality. However, no study has reported the relationship between a specific level of physical activity and sleep quality. This study aimed to investigate the relationship between sleep quality and the amount of physical activity by stratifying subjects into gender and shift‐work subgroups. Among those who participated in the Kangbuk Samsung Health Study in 2016–2017, data from 185,958 full‐time workers were analysed. We evaluated their physical activity by metabolic equivalents (METs‐min/week), sleep quality and shift work. A chi‐squared test, a t test and logistic regression analysis were performed. An increase in sleep quality was found for the group with physical activity of 600–9,000 METs‐min/week compared to that in the sedentary group among all subjects. In female day workers, the sleep quality of the group with 600–6,000 METs‐min/week was significantly higher (odds ratio [OR], 0.760; 95% confidence interval [CI], 0.673–857) than that in the sedentary group. In male day workers, sleep quality increased when physical activity was increased up to 6,000–9,000 METs‐min/week (OR, 0.760; 95% CI, 0.673–857). In female shift workers, there was no significant difference in sleep quality according to physical activity level. In male shift workers, sleep quality was better in the group with physical activity of 1,800–3,000 METs‐min/week (OR, 0.826; 95% CI, 0.692–0.986) or 3,000–6,000 METs‐min/week (OR, 0.771; 95% CI, 0.642–0.926). Optimal physical activity is good for sleep quality. The sleep quality of females is significantly worse than that of males in both day and shift workers. [ABSTRACT FROM AUTHOR]

Excessive daytime sleepiness (EDS) is a common feature among shift workers as well as in obstructive sleep apnea (OSA) patients. There are several important accidents related to sleep disturbances causing EDS. The aim of this study was to evaluate EDS in a group of shift workers (regular rotating) from civil aviation and to compare them with OSA patients (n = 300) and with a group of regular workers (RW) (n = 140). Our sample was composed of 730 working‐age individuals (aged 18–67 years). The regular rotating shift workers (SW) sample was composed of 290 aeronautical mechanics. EDS was evaluated with the Epworth Sleepiness Scale (ESS) and defined as a score ≥ 11. The prevalence value obtained for the EDS of RW was 37.1%, for SW it was 60.7% and for OSA patients it was 40.7%. A logistic regression model for EDS in a subsample composed of men and matched for age and BMI, controlling for self‐reported sleep duration, showed an increased risk of EDS for SW (OR = 3.91, p =.001), with the RW group as reference. OSA patients did not differ from RW on EDS levels. This study emphasizes the presence of EDS in a shift work group of civil aviation professionals, which exceeded the EDS level of a positive control group of OSA patients. Sleep hygiene education for companies' workers and management is important and mitigation strategies should be implemented to reduce excessive sleepiness among workers. [ABSTRACT FROM AUTHOR]

The prevalence of shift work disorder (SWD) has been studied using self‐reported data and the International Classification of Sleep Disorders, Second Edition (ICSD‐2) criteria. We examined the prevalence in relation to ICSD‐2 and ICSD‐3 criteria, work schedules and the number of non‐day shifts (work outside 06:00–18:00 hours) using objective working‐hours data. Secondly, we explored a minimum cut‐off for the occurrence of SWD symptoms. Hospital shift workers without (n = 1,813) and with night shifts (n = 2,917) and permanent night workers (n = 84) answered a survey (response rate 69%) on SWD and fatigue on days off. The prevalence of SWD was calculated for groups with ≥1, ≥3, ≥5 and ≥7 monthly non‐day shifts utilizing the working hours registry. ICSD‐3‐based SWD prevalence was 2.5%–3.7% (shift workers without nights), 2.6%–9.5% (shift workers with nights) and 6.0% (permanent night workers), depending on the cut‐off of non‐day shifts (≥7–1/month, respectively). The ICSD‐2‐based prevalence was higher: 7.1%–9.2%, 5.6%–33.5% and 16.7%, respectively. The prevalence was significantly higher among shift workers with than those without nights (p‐values <.001) when using the cut‐offs of ≥1–3 non‐day shifts. Shift workers with nights who had ≥3 days with ICSD‐3‐based SWD symptoms/month more commonly had fatigue on days off (49.3%) than those below the cut‐off (35.8%, p <.05). The ICSD‐3 criteria provided lower estimates for SWD prevalence than ISCD‐2 criteria, similarly to exclusion of employees with the fewest non‐day shifts. The results suggest that a plausible cut‐off for days with ICSD‐3‐based SWD symptoms is ≥3/month, resulting in 3%–6% prevalence of SWD. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the association between shift work disorder and mental health in hospital‐based nurses. Staff completed an online survey comprising demographic questions, the Shift Work Disorder Questionnaire, Patient Health‐9 and the General Anxiety Disorder‐7 scale. Sick leave data were collected from archival records from the Human Resources Department. Two hundred and two nurses (95% female; age M = 35.28 years ± SD = 12) participated (42% of eligible staff). Those at high risk of shift work disorder had higher depression (M = 7.54 ± SD = 4.28 vs. M = 3.78 ± SD = 3.24; p < 0.001) and anxiety (M = 5.66 ± SD = 3.82 vs. M = 2.83 ± SD = 3.33, p < 0.001) compared to those at low risk. Linear regression models showed that being at high risk of shift work disorder was the most significant predictor of depression, explaining 18.8% of the variance in depression (R2 = 0.188, adjusted R2 = 0.184, F(1, 200) = 46.20, p < 0.001). Shift work disorder combined with the number of night shifts and alcoholic drinks on non‐work days accounted for 49.7% of the variance in anxiety scores (R2 = 0.497, adjusted R2 = 0.453, F(3, 35) = 11.51, p < 0.001). Mean sick leave in those with high risk of shift work disorder was 136.17 hr (SD = 113.11) versus 103.98 hr (SD = 94.46) in others (p = 0.057). Depression and years of shift work accounted for 18.9% of the variance in sick leave taken (R2 = 0.189, adjusted R2 = 0.180, F(2, 175) = 20.36, p < 0.001). Shift work disorder is strongly associated with depression and anxiety, providing a potential target to improve mental health in shift workers. Depression, in turn, is a significant contributing factor to sick leave. [ABSTRACT FROM AUTHOR]