Assessment and management of sleep disorders in shift workers: Challenges and considerations for general practice.

BACKGROUND: Shift work is characterised by displaced sleep opportunities and associated sleep disturbance. Shift workers often report sleepiness and other wake time symptoms associated with poor sleep. However, clinical sleep disorders are also prevalent in shift workers. Although prevalence rates are similar or higher in shift workers compared with the general population, help seeking in shift workers with sleep disorders is low. OBJECTIVE: This article aims to provide general practitioners with a contemporary overview of the prevalence rates for sleep disorders in shift workers, to clarify the existing evidence relating to mental and physical health consequences of sleep disorders in shift workers and to highlight the need to consider undiagnosed sleep disorders before attributing sleep-related symptoms solely to work schedules. DISCUSSION: Symptoms of sleep loss associated with shift work overlap with symptoms experienced by individuals living with sleep disorders. Although >40% of middle-aged Australians live with a sleep disorder that requires investigation and management, symptoms in shift workers are often attributed to the work schedule and, as a result, might not be investigated for appropriate diagnosis and treatment. We argue that screening for sleep disorders in shift workers with sleep complaints should be a priority.

Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality.

Sleep is essential to life. Accurate measurement and classification of sleep/wake and sleep stages is important in clinical studies for sleep disorder diagnoses and in the interpretation of data from consumer devices for monitoring physical and mental well-being. Existing non-polysomnography sleep classification techniques mainly rely on heuristic methods developed in relatively small cohorts. Thus, we aimed to establish the accuracy of wrist-worn accelerometers for sleep stage classification and subsequently describe the association between sleep duration and efficiency (proportion of total time asleep when in bed) with mortality outcomes. We developed a self-supervised deep neural network for sleep stage classification using concurrent laboratory-based polysomnography and accelerometry. After exclusion, 1448 participant nights of data were used for training. The difference between polysomnography and the model classifications on the external validation was 34.7 min (95% limits of agreement (LoA): -37.8-107.2 min) for total sleep duration, 2.6 min for REM duration (95% LoA: -68.4-73.4 min) and 32.1 min (95% LoA: -54.4-118.5 min) for NREM duration. The sleep classifier was deployed in the UK Biobank with 100,000 participants to study the association of sleep duration and sleep efficiency with all-cause mortality. Among 66,214 UK Biobank participants, 1642 mortality events were observed. Short sleepers (<6 h) had a higher risk of mortality compared to participants with normal sleep duration of 6-7.9 h, regardless of whether they had low sleep efficiency (Hazard ratios (HRs): 1.58; 95% confidence intervals (CIs): 1.19-2.11) or high sleep efficiency (HRs: 1.45; 95% CIs: 1.16-1.81). Deep-learning-based sleep classification using accelerometers has a fair to moderate agreement with polysomnography. Our findings suggest that having short overnight sleep confers mortality risk irrespective of sleep continuity.

Sleep disorder risk, perceived control over sleep, and mental health symptoms in paramedicine students.

Paramedics commonly experience both poor sleep and mental health symptoms. Clarifying whether sleep or mental health symptoms are a challenge prior to commencement of employment is important, as early prevention and intervention initiatives during training could support these workers. Paramedicine students (n=53) were included, with sleep disorder screening (obstructive sleep apnea, insomnia and restless legs syndrome), and mental health outcomes (depressive symptoms: Patient Health Questionnaire-9, and anxiety symptoms: General Anxiety Disorder-7). Data were analysed using robust regression models, adjusted for age, sex, and shift work status. Meeting criteria for a sleep disorder (n=21) was associated with higher scores for anxiety (8.2 [95% CI: 5.9-10.5] v 4.6, [3.4-5.8]) and depressive symptoms (11.1 [8.6-13.6] v 4.4 [3.1-5.7)] compared to those who did not meet the criteria for a sleep disorder (n=32). Depressive symptoms were lower in those with perceived control over sleep (5.2 [3.2-7.2] v 9.8 [7.7-11.8]). There was no interaction between sleep disorder risk and perceived control over sleep on mental health symptoms. Investigation and management of factors contributing to low perceived control over sleep, together with early screening and management of sleep disorders, are likely to be important priorities to support paramedic student wellbeing prior to commencing shift work.

Reconsidering screening thresholds in health assessments for obstructive sleep apnea using operational and safety incident data.

The rail industry in Australia screens workers for probable obstructive sleep apnea (OSA) due to known safety risks. However, existing criteria to trigger screening only identify a small proportion of workers with OSA. The current study sought to examine the relationship between OSA risk and rail incidents in real-world data from Australian train drivers, and conducted a proof of concept analysis to determine whether more conservative screening criteria are justified. Health assessment (2016-2018) and subsequent rail incident data (2016-2020) were collected from two passenger rail service providers. Predictors included OSA status (confirmed no OSA with a sleep study, controlled OSA, unknown OSA [no recorded sleep assessment data] and confirmed OSA with no indication of treatment); OSA risk according to the current Standard, and OSA risk according to more conservative clinical markers (BMI threshold and cardiometabolic burden). Coded rail safety incidents involving the train driver were included. Data were analysed using zero-inflated negative binomial models to account for over-dispersion with high 0 counts, and rail safety incidents are reported using Incidence Risk Ratios (IRRs). A total of 751 train drivers, typically middle-aged, overweight to obese and mostly men, were included in analyses. There were 43 (5.7%) drivers with confirmed OSA, 62 (8.2%) with controlled OSA, 13 (1.7%) with confirmed no OSA and 633 (84.4%) drivers with unknown OSA. Of the 633 train drivers with unknown OSA status, 21 (3.3%) met 'at risk' criteria for OSA according to the Standard, and incidents were 61% greater (IRR: 1.61, 95% Confidence Interval (CI) 1.02-2.56) in the years following their health assessment compared to drivers who did not meet 'at risk' criteria. A more conservative OSA risk status using lower BMI threshold and cardiometabolic burden identified an additional 30 'at risk' train drivers who had 46% greater incidents compared to drivers who did not meet risk criteria (IRR (95% CI) 1.46 (1.00-2.13)). Our more conservative OSA risk criteria identified more workers, with greater prospective incidents. These findings suggest that existing validated tools could be considered in future iterations of the Standard in order to more sensitively screen for OSA.

Sex differences in response to cognitive behavioural therapy for insomnia: A chart review of 455 patients with chronic insomnia.

BACKGROUND: Insomnia is more prevalent in females, however studies examining sex differences in response to insomnia treatment are scarce. This study assessed sex-specific differences in cognitive behavioural therapy for insomnia (CBT-I)-related changes in insomnia symptoms in a large clinical cohort. METHODS: A chart review was conducted of a clinical cohort (females n = 305, males n = 150) referred to a sleep clinic. Participants had a registered psychologist confirm diagnosis of chronic insomnia according to DSM-IV/V criteria and a Level 1 or 2 sleep study. Daily sleep diaries and questionnaires including the Insomnia Severity Index (ISI), Flinders Fatigue Scale (FFS), the Daytime Feelings and Functioning Scale (DFFS), and the Depression, Anxiety and Stress Scale-21 items (DASS), were administered at baseline, post-treatment, and three-month follow-up. Linear mixed models determined interactions between sex and timepoint on symptoms. RESULTS: Mean (SD) age was 51.7 yrs (15.7, range = 18-90 yrs), and mean BMI was 26.3 kg/m2 (4.9), neither of which differed by sex. At pre-treatment, females demonstrated higher objective total sleep time (min) [343.5 (97.6) vs 323.8 min (92.1), p = 0.044], ISI [19.7 (4.2) vs 18.6 (4.4), p = 0.033], and FFS scores [19.2 (6.0) vs 16.9 (7.2), p = 0.003]. Compared to males, females experienced a greater reduction in FFS and DFFS scores and DASS depressive symptoms (p for interaction: 0.017, 0.043, 0.016 respectively) from baseline to follow-up. The greater reduction in depressive symptoms did not persist after controlling for age, BMI, and sleep apnea severity. Subjective total sleep time similarly increased across treatment for both males [baseline: 335.7 (15.1), post: 357.9 (15.5)] and females [baseline: 318.3 (10.4), post: 354.4 (10.7)], p for interaction: 0.22. CONCLUSION: Females and males experience similar, substantial benefits from CBT-I after accounting for comorbidities, suggesting the same treatment can resolve insomnia in both sexes.

Content Comparison of Quality-of-Life Instruments Used in Economic Evaluations of Sleep Disorder Interventions: A Systematic Review.

BACKGROUND: Assessment of quality of life (QoL) in people living with sleep disorders using questionnaires is necessary to compare intervention benefits. Knowledge of the content and concepts covered by specific QoL instruments is essential to determine which instruments are best suited for conducting economic evaluations of sleep-related interventions. OBJECTIVES: This review aims to identify the QoL instruments that have been applied in economic evaluations of sleep disorder interventions and compare their conceptual overlap and content coverage using the framework of the International Classification of Functioning, Disability and Health (ICF). METHODS: A systematic review of full economic evaluations in sleep published in peer-reviewed journals from conception to 30 May, 2023 was conducted. MEDLINE, PsychInfo, ProQuest, Cochrane, Scopus, CINAHL, Web of Science and Emcare were searched for eligible studies. Studies incorporating either generic or sleep-specific QoL instruments as the primary or secondary measures of effectiveness within a full economic evaluation were included. Quality appraisal against the JBI Critical Appraisal Checklist for Economic Evaluations and EURONHEED checklists and mapping of QoL items to ICF categories were performed by two reviewers, with a third helping settle any potential differences. RESULTS: Sixteen instruments were identified as having been used in sleep health economic evaluations. The EQ-5D-3L, Epworth Sleepiness Scale, and Insomnia Severity Index were the most widely used, but the latter two are predominantly diagnostic tools and not specifically designed to guide economic evaluations. Other instruments with broader ICF content coverage have been least used, and these include the Sleep Apnea Quality of Life Index, Functional Outcomes of Sleep Questionnaire, 15 Dimensions, Short-Form 6 Dimensions, 12-item Short Form Survey, 36-item Short Form Survey and the GRID Hamilton Rating Scale for Depression. CONCLUSIONS: This study provides an overview of current QoL instruments used in economic evaluations of sleep with respect to their content coverage. A combination of generic and sleep-specific instruments with broader ICF content coverage is recommended for such evaluations.

Are we getting enough sleep? Frequent irregular sleep found in an analysis of over 11 million nights of objective in-home sleep data.

OBJECTIVES: Evidence-based guidelines recommend that adults should sleep 7-9 h/night for optimal health and function. This study used noninvasive, multinight, objective sleep monitoring to determine average sleep duration and sleep duration variability in a large global community sample, and how often participants met the recommended sleep duration range. METHODS: Data were analyzed from registered users of the Withings under-mattress Sleep Analyzer (predominantly located in Europe and North America) who had ≥28 nights of sleep recordings, averaging ≥4 per week. Sleep durations (the average and standard deviation) were assessed across a ∼9-month period. Associations between age groups, sex, and sleep duration were assessed using linear and logistic regressions, and proportions of participants within (7-9 hours) or outside (<7 hours or >9 hours) the recommended sleep duration range were calculated. RESULTS: The sample consisted of 67,254 adults (52,523 males, 14,731 females; aged mean ± SD 50 ± 12 years). About 30% of adults demonstrated an average sleep duration outside the recommended 7-9 h/night. Even in participants with an average sleep duration within 7-9 hours, about 40% of nights were outside this range. Only 15% of participants slept between 7 and 9 hours for at least 5 nights per week. Female participants had significantly longer sleep durations than male participants, and middle-aged participants had shorter sleep durations than younger or older participants. CONCLUSIONS: These findings indicate that a considerable proportion of adults are not regularly sleeping the recommended 7-9 h/night. Even among those who do, irregular sleep is prevalent. These novel data raise several important questions regarding sleep requirements and the need for improved sleep health policy and advocacy.

Diagnosis and management of sleep disorders in shift workers, with patient informed solutions to improve health services research and practice.

BACKGROUND: The combination of shift work and an unmanaged sleep disorder carries health and safety risks. Yet, diagnosis rates for sleep disorders are low in shift workers. The aim of this study was to understand the experience of sleep disorder diagnosis and treatment in shift workers, and consider patient informed solutions to improve access to health services. METHODS: Semi-structured interviews were conducted with 16 Australian shift workers with a diagnosed sleep disorder. Patient journey mapping and reflexive thematic analysis were used to understand diagnosis and management experiences. RESULTS: There were highly variable experiences with diagnosis and management, often taking >5 years to seek help from a health care provider (HCP) after noticing symptoms of a sleep disorder. Three themes were constructed, including 'the cause of the problem', 'prioritising work', and '(dis)satisfaction and (dis)connection'. Extent of patient and HCP awareness of sleep disorders, and a prevailing attitude of shift work being 'the problem' impacted diagnosis, as did organisational needs (including rostering, which had both positive and negative influences on help seeking). Relationships with HCPs were important, and living on non-standard time was both a barrier and an enabler to sleep disorder care. Participants identified the need for education and awareness, prompts and easy access to screening and referral pathways, and tailored models of care. CONCLUSION: Education and awareness initiatives for shift workers, their employers and HCPs, together with development of a model of care for shift workers with sleep disorders may address some of the unique barriers to diagnosis and management.

Healthy lifestyle is associated with reduced cardiovascular disease, depression and mortality in people at elevated risk of sleep apnea.

We assessed: (1) the independent and joint association of obstructive sleep apnea risk and healthy lifestyle with common consequences (excessive daytime sleepiness, depression, cardiovascular disease and stroke) of obstructive sleep apnea; and (2) the effect of healthy lifestyle on survival in people with increased obstructive sleep apnea risk. Data from 13,694 adults (median age 46 years; 50% men) were used for cross-sectional and survival analyses (mortality over 15 years). A healthy lifestyle score with values from 0 (most unhealthy) to 5 (most healthy) was determined based on diet, alcohol intake, physical activity, smoking and body mass index. In the cross-sectional analysis, obstructive sleep apnea risk was positively associated with all chronic conditions and excessive daytime sleepiness in a dose-response manner (p for trend < 0.001). The healthy lifestyle was inversely associated with all chronic conditions (p for trend < 0.001) but not with excessive daytime sleepiness (p for trend = 0.379). Higher healthy lifestyle score was also associated with reduced odds of depression and cardiovascular disease. We found an inverse relationship between healthy lifestyle score with depression (p for trend < 0.001), cardiovascular disease (p for trend = 0.003) and stroke (p for trend = 0.025) among those who had high obstructive sleep apnea risk. In the survival analysis, we found an inverse association between healthy lifestyle and all-cause mortality for all categories of obstructive sleep apnea risk (moderate/high- and high-risk groups [p for trend < 0.001]). This study emphasises the crucial role of a healthy lifestyle in mitigating the effects of obstructive sleep apnea risk in individuals with an elevated obstructive sleep apnea risk.

The Development and Validation of the Health Belief Model for Shift Workers (HBM-SW) Scale.

OBJECTIVE: Shift work is associated with circadian misalignment, sleep loss, and suboptimal health behaviors, which may contribute to longer term negative health outcomes. To inform future interventions for shift workers, the present study aimed to develop and evaluate the Health Belief Model for Shift Workers (HBM-SW) scale. METHODS: The HBM-SW development involved a seven-step process, including a literature review, expert panel analysis, cognitive interviews with shift workers, and a trial with a pilot sample of shift workers (n = 153). Utilizing exploratory factor analysis for factor identification and item reduction, the developed scale loaded on seven factors in line with the theoretical framework of the Health Belief Model: Perceived Threat, Perceived Severity, Perceived Benefits, Perceived Barriers, Cues to Action, Self-efficacy, and Health Motivation. Validation of the scale was conducted utilizing Pittsburgh Sleep Quality Index, International Physical Activity Questionnaire, and Food Frequency Questionnaire. RESULTS: The pilot sample had an average age of 34.0 (18.0) years, was majority female (54.2%), with an average of 8.0 (11.0) years shift work experience. The HBM-SW showed good - excellent (α = 0.74-0.93) internal consistency and moderate - good (ICC = 0.64-0.89) test re-test reliability. Using health behavior outcome measures, the HBM-SW scale showed meaningful correlations with sleep quality, sleep duration, diet quality and leisure time physical activity, and acceptable validity and reliability. Further testing should be conducted in a larger sample to facilitate confirmatory factor analysis. CONCLUSIONS: The developed Health Belief Model for Shift Workers scale is likely beneficial for use in future studies of interventions for shift workers.

Multi-night measurement for diagnosis and simplified monitoring of obstructive sleep apnoea.

Substantial night-to-night variability in obstructive sleep apnoea (OSA) severity has raised misdiagnosis and misdirected treatment concerns with the current prevailing single-night diagnostic approach. In-home, multi-night sleep monitoring technology may provide a feasible complimentary diagnostic pathway to improve both the speed and accuracy of OSA diagnosis and monitor treatment efficacy. This review describes the latest evidence on night-to-night variability in OSA severity, and its impact on OSA diagnostic misclassification. Emerging evidence for the potential impact of night-to-night variability in OSA severity to influence important health risk outcomes associated with OSA is considered. This review also characterises emerging diagnostic applications of wearable and non-wearable technologies that may provide an alternative, or complimentary, approach to traditional OSA diagnostic pathways. The required evidence to translate these devices into clinical care is also discussed. Appropriately sized randomised controlled trials are needed to determine the most appropriate and effective technologies for OSA diagnosis, as well as the optimal number of nights needed for accurate diagnosis and management. Potential risks versus benefits, patient perspectives, and cost-effectiveness of these novel approaches should be carefully considered in future trials.

Use of Insomnia Treatments and Discussions About Sleep with Health Professionals Among Australian Adults with Mental Health Conditions.

Background: Insomnia is a common issue among individuals with mental health conditions, yet the frequency of insomnia treatment remains unclear. The purpose of this study was to investigate the prevalence of probable insomnia, discussions regarding sleep with health professionals, and the utilisation of commonly delivered insomnia treatments in Australian adults diagnosed with mental health conditions. Methods: This study represents a secondary analysis of data collected through a cross-sectional, national online survey conducted in 2019. A subset included participants (n = 624, age 18-85y) who self-reported a diagnosis of depression, bipolar disorder, anxiety, panic disorder, or post-traumatic stress disorder. Participants were classed as having probable insomnia based on self-reported symptoms and a minimum availability of 7.5 hours in bed. Results: Among individuals with probable insomnia (n = 296, 47.4%), 64.5% (n = 191) reported discussing sleep with one or more health professionals, predominantly with general practitioners (n = 160, 83.8%). However, 35.4% (n = 105) of people with probable insomnia had not discussed their sleep with a health professional. Additionally, 35.1% (n = 104) used prescribed medication for sleep, while only 15.9% (n = 47) had used the first line recommended treatment of cognitive-behavioral therapy for insomnia in the last 12 months. Conclusion: Although most participants who met the criteria for probable insomnia had engaged in discussions about sleep with health professionals, utilisation of first line recommended treatment was low. Interventions that promote routine assessment of sleep and first line treatment for insomnia by health professionals would likely benefit people with mental health conditions.

Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality.

Background: Sleep is essential to life. Accurate measurement and classification of sleep/wake and sleep stages is important in clinical studies for sleep disorder diagnoses and in the interpretation of data from consumer devices for monitoring physical and mental well-being. Existing non-polysomnography sleep classification techniques mainly rely on heuristic methods developed in relatively small cohorts. Thus, we aimed to establish the accuracy of wrist-worn accelerometers for sleep stage classification and subsequently describe the association between sleep duration and efficiency (proportion of total time asleep when in bed) with mortality outcomes. Methods: We developed and validated a self-supervised deep neural network for sleep stage classification using concurrent laboratory-based polysomnography and accelerometry data from three countries (Australia, the UK, and the USA). The model was validated within-cohort using subject-wise five-fold cross-validation for sleep-wake classification and in a three-class setting for sleep stage classification wake, rapid-eye-movement sleep (REM), non-rapid-eye-movement sleep (NREM) and by external validation. We assessed the face validity of our model for population inference by applying the model to the UK Biobank with 100,000 participants, each of whom wore a wristband for up to seven days. The derived sleep parameters were used in a Cox regression model to study the association of sleep duration and sleep efficiency with all-cause mortality. Findings: After exclusion, 1,448 participant nights of data were used to train the sleep classifier. The difference between polysomnography and the model classifications on the external validation was 34.7 minutes (95% limits of agreement (LoA): -37.8 to 107.2 minutes) for total sleep duration, 2.6 minutes for REM duration (95% LoA: -68.4 to 73.4 minutes) and 32.1 minutes (95% LoA: -54.4 to 118.5 minutes) for NREM duration. The derived sleep architecture estimate in the UK Biobank sample showed good face validity. Among 66,214 UK Biobank participants, 1,642 mortality events were observed. Short sleepers (<6 hours) had a higher risk of mortality compared to participants with normal sleep duration (6 to 7.9 hours), regardless of whether they had low sleep efficiency (Hazard ratios (HRs): 1.69; 95% confidence intervals (CIs): 1.28 to 2.24 ) or high sleep efficiency (HRs: 1.42; 95% CIs: 1.14 to 1.77). Interpretation: Deep-learning-based sleep classification using accelerometers has a fair to moderate agreement with polysomnography. Our findings suggest that having short overnight sleep confers mortality risk irrespective of sleep continuity.

Insomnia and workplace productivity loss among young working adults: a prospective observational study of clinical sleep disorders in a community cohort.

OBJECTIVE: To examine associations between three clinically significant sleep disorders (chronic insomnia, obstructive sleep apnoea, restless legs syndrome) and workplace productivity losses among young Australian adults. DESIGN, SETTING: Prospective, observational study; 22-year follow-up of participants in the longitudinal birth cohort Raine Study (Perth, Western Australia). PARTICIPANTS: Currently employed 22-year-old Raine Study participants who underwent in-laboratory sleep disorder screening for moderate to severe obstructive sleep apnoea (apnoea-hypopnea index of more than fifteen events/hour or obstructive sleep apnoea syndrome) and were assessed for insomnia and restless legs syndrome using validated measures. MAIN OUTCOME MEASURES: Total workplace productivity loss over twelve months, assessed with the World Health Organization Health and Work Performance Questionnaire. RESULTS: Of 1235 contactable 22-year-old Raine Study cohort members, 554 people (44.9%; 294 women [53%]) underwent overnight polysomnography, completed the baseline sleep questionnaire, and completed at least three quarterly workplace productivity assessments. One or more clinically significant sleep disorders were identified in 120 participants (21.7%); 90 participants had insomnia (17%), thirty clinically significant obstructive sleep apnoea (5.4%), and two restless legs syndrome (0.4%). Seventeen people (14% of those with sleep disorders) had previously been diagnosed with a sleep disturbance by a health professional, including fourteen with insomnia. Median total workplace productivity loss was greater for participants with sleep disorders (164 hours/year; interquartile range [IQR], 0-411 hours/year) than for those without sleep disorders (30 hours/year; IQR, 0-202 hours/year); total workplace productivity loss was 40% greater for participants with sleep disorders (adjusted incidence rate ratio, 1.40; bias-corrected and accelerated 95% confidence interval, 1.10-1.76). The estimated population total productivity loss (weighted for disorder prevalence) was 28 644 hours per 1000 young workers per year, primarily attributable to insomnia (28 730 hours/1000 workers/year). CONCLUSION: Insomnia is a risk factor for workplace productivity loss in young workers. Tailored interventions are needed to identify and manage sleep disorders, particularly as most of the sleep disorders detected in the Raine Study had not previously been diagnosed.

High night-to-night variability in sleep apnea severity is associated with uncontrolled hypertension.

Obstructive sleep apnea (OSA) severity can vary markedly from night-to-night. However, the impact of night-to-night variability in OSA severity on key cardiovascular outcomes such as hypertension is unknown. Thus, the primary aim of this study is to determine the effects of night-to-night variability in OSA severity on hypertension likelihood. This study uses in-home monitoring of 15,526 adults with ~180 nights per participant with an under-mattress sleep sensor device, plus ~30 repeat blood pressure measures. OSA severity is defined from the mean estimated apnea-hypopnoea index (AHI) over the ~6-month recording period for each participant. Night-to-night variability in severity is determined from the standard deviation of the estimated AHI across recording nights. Uncontrolled hypertension is defined as mean systolic blood pressure ≥140 mmHg and/or mean diastolic blood pressure ≥90 mmHg. Regression analyses are performed adjusted for age, sex, and body mass index. A total of 12,287 participants (12% female) are included in the analyses. Participants in the highest night-to-night variability quartile within each OSA severity category, have a 50-70% increase in uncontrolled hypertension likelihood versus the lowest variability quartile, independent of OSA severity. This study demonstrates that high night-to-night variability in OSA severity is a predictor of uncontrolled hypertension, independent of OSA severity. These findings have important implications for the identification of which OSA patients are most at risk of cardiovascular harm.

Sleep Irregularity Is Associated With Hypertension: Findings From Over 2 Million Nights With a Large Global Population Sample.

BACKGROUND: Irregularities in sleep duration and sleep timing have emerged as potential risk factors for hypertension. This study examined associations between irregularity in sleep duration and timing with hypertension in a large, global sample over multiple months. METHODS: Data from 12 287 adults, who used an under-mattress device to monitor sleep duration and timing and also provided blood pressure recordings on ≥5 separate occasions, were analyzed. Sleep duration irregularity was assessed as the SD in total sleep time across the ≈9-month recording period. Sleep timing irregularity was assessed as SDs in sleep onset time, sleep midpoint, and sleep offset time. Logistic regressions were conducted to investigate associations between sleep irregularity and hypertension, defined as median systolic blood pressure ≥140 mm Hg or median diastolic blood pressure ≥90 mm Hg. RESULTS: Participants were middle-aged (mean±SD, 50±12 years), mostly men (88%) and overweight (body mass index, 28±6 kg/m-2). Sleep duration irregularity was consistently associated with an ≈9% to 17% increase in hypertension independently of the total sleep time. A ≈34-minute increase in sleep onset time irregularity was associated with a 32% increase in hypertension (1.32 [1.20-1.45]). A 32-minute increase in sleep midpoint irregularity was associated with an 18% increase in hypertension (1.18 [1.09-1.29]), while a 43-minute increase in sleep offset time irregularity was associated with an 8.9% increase in hypertension (1.09 [1.001-1.18]). CONCLUSIONS: These findings support that sleep irregularity, both in duration and timing, is a risk marker for poor cardiovascular health. Further mechanistic investigations of temporal relationships between day-to-day fluctuations in sleep duration and timing, next-day blood pressure, and other cardiovascular outcomes are warranted.

Childhood sleep health and epigenetic age acceleration in late adolescence: Cross-sectional and longitudinal analyses.

AIM: Investigate if childhood measures of sleep health are associated with epigenetic age acceleration in late adolescence. METHODS: Parent-reported sleep trajectories from age 5 to 17, self-reported sleep problems at age 17, and six measures of epigenetic age acceleration at age 17 were studied in 1192 young Australians from the Raine Study Gen2. RESULTS: There was no evidence for a relationship between the parent-reported sleep trajectories and epigenetic age acceleration (p ≥ 0.17). There was a positive cross-sectional relationship between self-reported sleep problem score and intrinsic epigenetic age acceleration at age 17 (b = 0.14, p = 0.04), which was attenuated after controlling for depressive symptom score at the same age (b = 0.08, p = 0.34). Follow-up analyses suggested this finding may represent greater overtiredness and intrinsic epigenetic age acceleration in adolescents with higher depressive symptoms. CONCLUSION: There was no evidence for a relationship between self- or parent-reported sleep health and epigenetic age acceleration in late adolescence after adjusting for depressive symptoms. Mental health should be considered as a potential confounding variable in future research on sleep and epigenetic age acceleration, particularly if subjective measures of sleep are used.

"I need to be alert at night to provide care": Factors associated with problematic sleep among young Australian caregivers.

OBJECTIVES: There is increased recognition that young people (<25 years) may occupy a carer role for family or others with health conditions or disability. This is often in addition to study and social activities. This means competing demands on time, and insufficient sleep. Our aim was to determine the contribution of caring duties to problematic sleep in young carers. METHODS: A survey of Australian carers was conducted, including questions on demographics, characteristics of the carer and care recipient, and sleep quality and quantity. Participants were eligible if they reported sleep time <7 hr or dissatisfaction with their sleep, and were aged 15-24 years. RESULTS: A total of 110 participants (71.8%_female = 79, 15-17 years = 62, 18-24 years = 48) were included in analysis; 55.5% (n= 61) reporting dissatisfaction with their sleep and 62.7% (n= 69) reporting typically less than 7 hr sleep per night. Sleep duration was significantly shorter for those who reported 1-2 or ≥3 awakenings to provide care, compared with no awakenings (p_< .05). Sleep quality, as described by scores on the Pittsburgh Sleep Quality Index (PSQI) was also significantly worse for those who were frequently awoken by their care recipient (p < .05). Worrying about the care recipient, being woken by the care recipient, and listening out for the care recipient were the most frequently identified factors impacting on sleep. CONCLUSION: Young carers experience reduced sleep duration and poor sleep quality. Strategies to alleviate the burden of care work on young carer's sleep would benefit the health and safety of this group.