Female sexual dysfunction and distress in premenopausal women with migraine followed in a tertiary headache center: A pilot study.

BACKGROUND: Female sexual dysfunction (FSD) is an underdiagnosed and undertreated problem. Few studies have addressed sexual distress in migraine. We aimed to perform a pilot study to determine if there is an association between migraine and sexual dysfunction/distress in premenopausal women and to identify their respective risk factors. METHODS: Retrospective, cross-sectional pilot study, including 71 premenopausal female patients with migraine, from the headache outpatient clinic of a tertiary hospital, and 34 age-matched-controls. Female Sexual Function Index-6 (FSFI-6), Female Sexual Distress Scale-Revised (FSDS-R), Migraine Disability Assessment (MIDAS) Scale, Brief Pain Inventory (BPI), Hospital Anxiety and Depression Scale (HADS) and Sleep Health Scale (RU-SATED) were applied. RESULTS: Of the 71 patients [40.0 (IQR = 11.00) years], only 12.7 % (n = 9) were not under migraine prophylactic treatment, and most (n = 33, 62.3 %) reported severe disability (MIDAS-IV). FSD and sexual distress were present in 50.7 % (36) patients with migraine [vs 20.6 % (7) controls]. Migraine patients showed lower FSFI-6 scores [19.0 (9.0) vs 24.0 (6.0), p = 0.005], with significantly lower levels of desire (p = 0.011), lubrication (p = 0.002), and satisfaction (p = 0.013), higher sexual distress [11.2 (25.6) vs 3.2 (9.6), p = 0.001], anxiety (p < 0.001), and depression (p < 0.001) levels, and lower sleep health scores (p = 0.005). Old age of onset, being under preventive medication, anxiety/depression, and dysfunctional sleep, were significantly associated with sexual distress. Certain domains of sexual function were associated with sociodemographic and migraine characteristics, anxiety, depression, and sleep health. CONCLUSIONS: This pilot study highlights the possible association between migraine and elevated sexual dysfunction/distress levels among premenopausal women. It underscores the importance of sexual health assessments in these individuals, particularly those with higher levels of anxiety, depression, or poor sleep quality. It is important to exercise caution when interpreting results, as they may not be applicable to a wider context. This research paves the way for a larger study that will include a broader population of women from the community and encompass patients followed in different levels of health care.

Sleep Capital: Linking Brain Health to Wellbeing and Economic Productivity Across the Lifespan.

INTRODUCTION AND FRAMEWORK: Sleep capital contributes to individual and societal wellbeing, productivity, and economic outcomes and involves a novel aspect of brain capital. It encompasses the quality and quantity of sleep as integral components that influence cognitive abilities, mental and brain health, and physical health, affecting workplace productivity, learning, decision-making, and overall economic performance. Here, we bring a framework to understand the complex relationship between sleep quality, health, wellbeing, and economic productivity. Then we outline the multilevel impact of sleep on cognitive abilities, mental/brain health, and economic indicators, providing evidence for the substantial returns on investment in sleep health initiatives. Moreover, sleep capital is a key factor when considering brain health across the lifespan, especially for the aging population. DISCUSSION: We propose specific elements and main variables to develop specific indexes of sleep capital to address its impacts on health, wellbeing and productivity. CONCLUSION: Finally, we suggest policy recommendations, workplace interventions, and individual strategies to promote sleep health and brain capital. Investing in sleep capital is essential for fostering a healthier, happier, fairer and more productive society.

EEGNet classification of sleep EEG for individual specialization based on data augmentation.

Sleep is an essential part of human life, and the quality of one's sleep is also an important indicator of one's health. Analyzing the Electroencephalogram (EEG) signals of a person during sleep makes it possible to understand the sleep status and give relevant rest or medical advice. In this paper, a decent amount of artificial data generated with a data augmentation method based on Discrete Cosine Transform from a small amount of real experimental data of a specific individual is introduced. A classification model with an accuracy of 92.85% has been obtained. By mixing the data augmentation with the public database and training with the EEGNet, we obtained a classification model with significantly higher accuracy for the specific individual. The experiments have demonstrated that we can circumvent the subject-independent problem in sleep EEG in this way and use only a small amount of labeled data to customize a dedicated classification model with high accuracy.

Racial differences in treatment adherence and response to acupuncture and cognitive behavioral therapy for insomnia among Black and White cancer survivors.

BACKGROUND: Racial disparities in sleep are well-documented. However, evidence-based options for addressing these disparities are lacking in cancer populations. To inform future research on sleep interventions, this study aims to understand racial differences in treatment responses to acupuncture and cognitive behavioral therapy for insomnia (CBT-I) among Black and White cancer survivors. METHODS: We conducted a secondary analysis of a comparative effectiveness trial evaluating acupuncture versus CBT-I for insomnia in cancer survivors. We compared insomnia severity, sleep characteristics, and co-morbid symptoms, as well as treatment attitudes, adherence, and responses among Black and White participants. RESULTS: Among 156 cancer survivors (28% Black), Black survivors reported poorer sleep quality, longer sleep onset latency, and higher pain at baseline, compared to White survivors (all p < 0.05). Black survivors demonstrated lower adherence to CBT-I than White survivors (61.5% vs. 88.5%, p = 0.006), but their treatment response to CBT-I was similar to white survivors. Black survivors had similar adherence to acupuncture as white survivors (82.3% vs. 93.4%, p = 0.16), but they had greater reduction in insomnia severity with acupuncture (-3.0 points, 95% CI -5.4 to 0.4, p = 0.02). CONCLUSION: This study identified racial differences in sleep characteristics, as well as treatment adherence and responses to CBT-I and acupuncture. To address racial disparities in sleep health, future research should focus on improving CBT-I adherence and confirming the effectiveness of acupuncture in Black cancer survivors.

Associations between indoor fine particulate matter (PM2.5) and sleep-disordered breathing in an urban sample of school-aged children.

OBJECTIVES: Environmental risk factors may contribute to sleep-disordered breathing. We investigated the association between indoor particulate matter ≤2.5µm in aerodynamic diameter (PM2.5) and sleep-disordered breathing in children in an urban US community. METHODS: The sample consisted of children aged 6-12years living in predominantly low-income neighborhoods in Boston, Massachusetts. Indoor PM2.5 was measured in participants' main living areas for 7days using the Environmental Multipollutant Monitoring Assembly device. High indoor PM2.5 exposure was defined as greater than the sample weekly average 80th percentile level (≥15.6 µg/m3). Sleep-disordered breathing was defined as an Apnea-Hypopnea-Index (AHI) or Oxygen-Desaturation-Index (ODI) (≥3% desaturation) of ≥5 events/hour. Habitual loud snoring was defined as caregiver-report of loud snoring (most or all the time each week) over the past 4weeks. We examined the associations of PM2.5 with sleep-disordered breathing or snoring using logistic regression adjusting for potential confounders. RESULTS: The sample included 260 children (mean age 9.6years; 41% female), with 32% (n = 76) classified as having sleep-disordered breathing. In a logistic regression model adjusted for socioeconomics and seasonality, children exposed to high indoor PM2.5 levels (n = 53) had a 3.53-fold increased odds for sleep-disordered breathing (95%CI: 1.57, 8.11, p = .002) compared to those with lower indoor PM2.5. This association persisted after additional adjustments for physical activity, outdoor PM2.5, environmental tobacco smoke, and health characteristics. Similar associations were observed for snoring and indoor PM2.5. CONCLUSIONS: Children with higher indoor PM2.5 exposure had greater odds of sleep-disordered breathing and habitual loud snoring, suggesting that indoor air quality contributes to sleep disparities.

Childhood sleep is prospectively associated with adolescent alcohol and marijuana use.

INTRODUCTION: Prior studies have examined the cross-sectional relationship between adolescent sleep and substance use; however, fewer have explored the long-term connections between childhood sleep and adolescent substance use. METHODS: This study investigated both cross-sectional associations during adolescence and prospective associations between childhood weeknight sleep and later alcohol and marijuana use in the Future of Families and Child Wellbeing Study, a diverse national birth cohort of urban children from 20 cities with populations greater than 200,000. Parents reported their child's bedtime at ages 3, 5, and 9 and their child's sleep duration at ages 5 and 9. RESULTS: At age 15, adolescents self-reported their bedtime, sleep duration, and alcohol and marijuana use (n = 1514). Logistic regression analyses for each substance use outcome at age 15 were adjusted for sex, age at time of assessment, race/ethnicity, income-relative-to-poverty threshold, family structure, and caregiver education level. At age 15, later bedtime (AOR=1.39; 95 % CI=1.22, 1.57) and shorter sleep duration (AOR=1.28; 95 % CI=1.14, 1.43) were associated with greater odds of consuming a full drink of alcohol more than once, and later bedtime was associated with greater odds of trying marijuana (AOR=1.35; 95 % CI=1.20, 1.51). Unexpectedly, later bedtimes at age 3 were associated with lower odds of drinking alcohol by age 15 (AOR=0.74; 95 % CI=0.59, 0.92). In contrast, later bedtimes at age 9 were associated with greater odds of drinking alcohol (AOR=1.45; 95 % CI=1.11, 1.90). Additionally, later bedtime at age 5 (AOR=1.26; 95 % CI=1.01, 1.58) and shorter sleep duration at age 9 (AOR=1.19; 95 % CI=1.04, 1.36) were associated with greater odds of trying marijuana. CONCLUSION: Taken together, these associations support the importance of protecting childhood sleep habits to reduce the likelihood of substance use starting as early as mid-adolescence. IMPLICATIONS AND CONTRIBUTION: In this longitudinal cohort study, adolescents were more likely to have consumed alcohol or tried marijuana by age 15 if they had later bedtimes and shorter sleep duration during childhood and adolescence. Protecting sleep health throughout childhood may reduce the likelihood of substance use during early adolescence.

Associations of exposure to volatile organic compounds with sleep health and potential mediators: analysis of NHANES data.

Objective: The effect of environmental pollution on sleep has been widely studied, yet the relationship between exposure to volatile organic compounds (VOCs) and sleep health requires further exploration. We aimed to investigate the single and mixed effect of urinary VOC metabolites on sleep health and identify potential mediators. Methods: Data for this cross-sectional study was collected from the National Health and Nutrition Examination Surveys (NHANES) (2005-2006, 2011-2014). A weighted multivariate logistic regression was established to explore the associations of 16 VOCs with four sleep outcomes. Following the selection of important VOCs through the least absolute shrinkage and selection operator (LASSO) regression, principal component analyses (PCA), weight quantile sum (WQS), and Bayesian kernel machine regression (BKMR) analyses were conducted to explore the associations between exposure to single and mixed VOCs and sleep outcomes, as well as identify the most contributing components. A mediation analysis was performed to explore the potential effect of depression scores. Results: Of the 3,473 participants included in the study, a total of 618 were diagnosed with poor sleep patterns. In logistic regression analyses, 7, 10, 1, and 5 VOCs were significantly positively correlated with poor sleep patterns, abnormal sleep duration, trouble sleeping, and sleep disorders, respectively. The PCA analysis showed that PC1 was substantially linked to a higher risk of poor sleep patterns and its components. The WQS model revealed a positive association between VOC mixture of increased concentrations and poor sleep patterns [OR (95% CI): 1.285 (1.107, 1.493)], abnormal sleep duration [OR (95% CI): 1.154 (1.030, 1.295)], trouble sleeping [OR (95% CI): 1.236 (1.090, 1.403)] and sleep disorders [OR (95% CI): 1.378 (1.118, 1.705)]. The BKMR model found positive associations of the overall VOC exposure with poor sleep patterns, trouble sleeping, and sleep disorders. PCA, WQS, and BKMR models all confirmed the significant role of N-acetyl-S-(N-methylcarbamoyl)-l-cysteine (AMCC) in poor sleep patterns and its components. The depression score was a mediator between the positive VOC mixture index and the four sleep outcomes. Conclusion: Exposure to single and mixed VOCs negatively affected the sleep health of American population, with AMCC playing a significant role. The depression score was shown to mediate the associations of VOC mixtures with poor sleep patterns and its components.

Frequency and Amount of Caffeine Consumption and Its Effects on Sleep Quality Among the General Population in Makkah City, Saudi Arabia, in 2024.

BACKGROUND: Caffeine is a commonly consumed psychoactive substance with potential effects on sleep quality. However, few studies have examined this relationship in the general population of Makkah, Saudi Arabia. OBJECTIVE: To investigate the impact of caffeine consumption on sleep quality and identify demographic factors associated with caffeine intake and sleep quality in Makkah's general population. METHODS: This cross-sectional study used a convenient sampling approach to recruit 390 individuals residing in Makkah. Data were collected via a Google Forms questionnaire distributed through various social media platforms. Caffeine intake and sleep quality were assessed using a validated questionnaire adapted for Saudi Arabia and the Pittsburgh Sleep Quality Index (PSQI), respectively. RESULTS: The median total daily caffeine intake was 324.20 mg, with 43.1% of participants consuming more than the suggested cutoff of 400 mg daily. There were significant positive correlations between PSQI and total caffeine consumption in the morning, evening, and overall daily consumption. The results also indicated significant relationships between demographic factors, such as marital status, educational level, income, and BMI groups, and caffeine consumption. CONCLUSION: Caffeine intake was found to be high among the general population of Makkah, with a significant portion consuming more than the suggested daily limit. There was a significant positive correlation between caffeine consumption and poor sleep quality. Additionally, demographic factors such as marital status, educational level, income, and BMI groups were found to be associated with caffeine consumption.

Association between sleep duration and subjective memory complaints: A large-scale cross-sectional study based on NHANES.

Objective: When chatting, people often forget what they want to say, that is, they suffer from subjective memory complaints (SMCs). This research examines the Association between sleep duration and self-reported SMC in a sample representing the entire United States. Methods: We examined data from 5567 individuals (aged 20-80) who participated in the National Health and Nutrition Examination Survey (2015-2018) to evaluate the association between sleep duration and SMC. Odds ratios (ORs) and a restricted cubic spline (RCS) curve were calculated with multiple logistic regression, and subgroup analysis was performed. Results: Approximately 5.8 % (3 2 3) reported SMC, and most are older people (1 6 3). RCS analysis treating sleep duration as a continuous variable revealed a J-shaped curve association between sleep duration and SMC. Self-reported sleep duration was significantly linked to a 33 % elevated risk of SMC (OR, 1.33; 95 % confidence interval [CI], 1.23-1.43; P < 0.001). In the group analysis, individuals who slept more than 8 h per day had a greater association of experiencing SMC than those who slept for 6-8 h/day (OR, 1.75; 95 % CI, 1.36-2.23; P < 0.001). In the analysis of age groups, the stable association between sleep duration and SMC was observed only in the 60-80 age bracket (OR, 1.59; 95 % CI, 1.09-2.33; P < 0.001). Conclusions: We found that people with self-report sleep duration exceeding 8 h are more likely to experience SMC, especially older adults. Improving sleep health may be an effective strategy for preventing SMC and cognitive impairment.

An Examination of John Henryism in Adults Living with Sickle Cell Disease.

BACKGROUND: John Henryism (JH) is a behavioral predisposition for high-effort coping with adversity. JH has been associated with hypertension in Black Americans with low socioeconomic status (SES) and is also found to be associated with psychological well-being. Sickle cell disease (SCD), a rare genetic disease largely affecting Black Americans in the United States, presents as a chronic condition that may benefit from a deeper understanding of the impact of JH on overall health. PURPOSE: This study examined the association between high and low JH and diastolic blood pressure, systolic blood pressure, hypertension prevalence, and sleep function. We relied on the biopsychosocial transaction model to adjust for relevant clinical and sociodemographic variables. METHODS: This was a cross-sectional secondary analysis of 274 adults with SCD living in the United States and recruited between 2014 and 2020. Study visits consisted of physical examinations, medical history, demographic, and psychosocial questionnaires. Adjusted linear regressions estimated associations between high and low JH and diastolic and systolic blood pressure as well as self-reported sleep function. Multivariable logistic regression was used to examine associations with hypertension prevalence. RESULTS: High JH was significantly associated with lower diastolic blood pressure (ß = - 2.98; 95% confidence interval = - 5.92, - 0.04) but higher sleep dysfunction (ß = 2.76; 95% confidence interval = 1.45, 4.07). CONCLUSIONS: Overall, we found positive psychological coping resources associated with high JH, with the exception of sleep. CLINICALTRIALS: gov Identifier: NCT02156102.

Comparing patients treated with CBT for insomnia with healthy sleepers and sleepers with past insomnia on dimensions of sleep health.

OBJECTIVES: To compare patients treated with cognitive behavioral therapy for insomnia (CBT-I) with healthy sleepers and individuals with past but not current insomnia on multidimensional sleep health. METHODS: The study evaluates CBT-I on six dimensions of sleep health (regularity, satisfaction, alertness, timing, efficiency, duration) in a sample of individuals with insomnia compared to two other unique sleep samples. Participants were in one of three groups: insomnia (CUR, n = 299), healthy sleeper (HS, n = 122), or past insomnia (PAST, n = 35). Daily diaries and validated measures were employed to capture six dimensions of sleep health. The CUR group received four 60-minute sessions of CBT-I every 2weeks, and sleep health indices were measured at baseline and post-treatment. The HS and PAST groups were measured only at baseline. RESULTS: Results of the pairwise t tests indicated improvements in sleep satisfaction, alertness (fatigue but not sleepiness), timing, efficiency, and duration (Cohen's d=0.22 to 1.55). ANCOVA models revealed significant differences in sleep health scores between treated insomnia patients and the other two sleep groups. Treated patients demonstrated less bedtime and risetime variability, in addition to lower napping duration. Overall, the study observed significant changes in various domains of sleep health after four sessions of cognitive behavioral therapy for insomnia; however, differences remain when compared to the other groups in the study. CONCLUSION: There may be ongoing sleep vulnerability in patients treated with cognitive behavioral therapy for insomnia though future inclusion of a control group would increase internal validity. Borrowing from transdiagnostic sleep modules may be helpful to support remaining deficits after cognitive behavioral therapy for insomnia.

An empirical comparison of sleep-specific versus generic quality of life instruments among Australians with sleep disorders.

PURPOSE: In Australian adults diagnosed with a sleep disorder(s), this cross-sectional study compares the empirical relationships between two generic QoL instruments, the EuroQoL 5-dimension 5-level (EQ-5D-5L) and ICEpop CAPability measure for Adults (ICECAP-A), and three sleep-specific metrics, the Epworth Sleepiness Scale (ESS), 10-item Functional Outcomes of Sleep Questionnaire (FOSQ-10), and Pittsburgh Sleep Quality Index (PSQI). METHODS: Convergent and divergent validity between item/dimension scores was examined using Kendall's Tau-B correlation, with correlations below 0.30 considered weak, between 0.30 and 0.50 moderate and those above 0.50 strong (indicating that instruments were measuring similar constructs). Exploratory factor analysis (EFA) was conducted to identify shared underlying constructs. RESULTS: A total of 1509 participants (aged 18-86 years) were included in the analysis. Convergent validity between dimensions/items of different instruments was weak to moderate. A 5-factor EFA solution, representing 'daytime dysfunction', 'fatigue', 'wellbeing', 'physical health', and 'perceived sleep quality', was simplest with close fit and fewest cross-loadings. Each instrument's dimensions/items primarily loaded onto their own factor, except for the EQ-5D-5L and PSQI. Nearly two-thirds of salient loadings were of excellent magnitude (0.72 to 0.91). CONCLUSION: Moderate overlap between the constructs assessed by generic and sleep-specific instruments indicates that neither can fully capture the complexity of QoL alone in general disordered sleep populations. Therefore, both are required within economic evaluations. A combination of the EQ-5D-5L and, depending on context, ESS or PSQI offers the broadest measurement of QoL in evaluating sleep health interventions.

Bedtime Regularity and Sleep Sufficiency in Children With Tourette Syndrome.

BACKGROUND: Despite research demonstrating sleep disturbance in children with Tourette syndrome (TS), few studies have examined bedtime regularity and sleep sufficiency, two important sleep health dimensions. Therefore, this study examined bedtime regularity and sleep sufficiency in children with TS relative to matched healthy control subjects, and its associated demographic, clinical, and behavioral factors. METHODS: Participants were 384 parents or caregivers of children aged three to 17 years, including 192 with current TS and 192 matched healthy control subjects drawn from the 2020-2021 cycle of the National Survey of Children's Health. Parents completed questions assessing demographic (i.e., age, race, sex), clinical (i.e., attention-deficit/hyperactivity disorder [ADHD], autism spectrum disorder, anxiety, depression, tic severity, behavioral or conduct problems, ADHD medication, health condition-related impairment), and behavioral (i.e., screen time) characteristics. Mann-Whitney U test and chi-square test of independence were performed to compare groups on bedtime regularity and sleep sufficiency, respectively. Ordinal regression and binary logistic regression without and with backward elimination were performed to evaluate indicators of bedtime regularity and sleep sufficiency, respectively, in children with TS. RESULTS: Children with current TS had significantly poorer bedtime regularity, but not sleep sufficiency, relative to matched healthy control subjects. In children with TS, anxiety and two or more hours of daily screen time were associated with higher likelihood of poor bedtime regularity. Autism was associated with lower likelihood of insufficient sleep, and depression was associated with increased likelihood of insufficient sleep. CONCLUSIONS: Findings put forth screen time, anxiety, and depression as intervention targets to optimize sleep health in children with TS.

Unveiling Coronasomnia: Pandemic Stress and Sleep Problems During the COVID-19 Outbreak.

The COVID-19 pandemic posed an unprecedented challenge to public well-being, necessitating an examination of its health impact. This review discusses the relationship between pandemic-induced stressors and individual sleep patterns and quality. The pandemic stressors include lockdown or physical distancing measures, direct virus exposure, and the dissemination of misinformation and disinformation. The pandemic led to delayed sleep-wake cycles, except for healthcare professionals, and worsened sleep quality. The prevalence of insomnia was higher for women due to pre-existing conditions and susceptibility stressors such as lockdown stress and family responsibilities. Healthcare professionals, who experienced worsened work conditions during the pandemic, reported higher rates of insomnia and sleep difficulties due to infection anxiety and post-traumatic stress from direct virus exposure. For the general population, stress stemmed from social isolation under lockdown and overwhelming false information available online, resulting in sleep problems. Taken together, the findings highlight the importance of promoting social interactions, providing psychological support services, and caution in navigating health information. In summary, this review underscores the need for individual- and group-centered approaches in ongoing research and interventions to address pandemic-related stress and sleep issues during COVID-19.

Socioeconomic factors, sleep timing and duration, and amygdala resting-state functional connectivity in children.

Introduction: Reduced sleep health has been consistently linked with increased negative emotion in children. While sleep characteristics have been associated with neural function in adults and adolescents, much less is known about these associations in children while considering socioeconomic context. In this study, we examined the associations among socioeconomic factors, sleep duration and timing, and resting-state functional connectivity (rsFC) of the amygdala in children. Methods: Participants were typically-developing 5- to 9-year-olds from socioeconomically diverse families (61% female; N = 94). Parents reported on children's weekday and weekend bedtimes and wake-up times, which were used to compute sleep duration and midpoint. Analyses focused on amygdala-anterior cingulate cortex (ACC) connectivity followed by amygdala-whole brain connectivity. Results: Lower family income-to-needs ratio and parental education were significantly associated with later weekday and weekend sleep timing and shorter weekday sleep duration. Shorter weekday sleep duration was associated with decreased amygdala-ACC and amygdala-insula connectivity. Later weekend sleep midpoint was associated with decreased amygdala-paracingulate cortex and amygdala-postcentral gyrus connectivity. Socioeconomic factors were indirectly associated with connectivity in these circuits via sleep duration and timing. Discussion: These results suggest that socioeconomic disadvantage may interfere with both sleep duration and timing, in turn possibly altering amygdala connectivity in emotion processing and regulation circuits in children. Effective strategies supporting family economic conditions may have benefits for sleep health and brain development in children.

Sleep Health Ambassadors in Greater Detroit: A Model for Religio-Culturally Conscious Care in Places of Worship From Dearborn to Hamtramck.

An innovative healthcare delivery model in Greater Detroit is proposed to integrate religious and cultural identities with health strategies to address specific disparities, such as higher rates of diabetes and cardiovascular diseases linked to poor sleep, among minority communities, particularly among its diverse Muslim population. This model advocates for culturally conscious care, deeply appreciating the sociocultural determinants of health. It proposes utilizing mosques as community hubs to deploy sleep health ambassadors trained in sleep science and cultural sensitivity. These ambassadors would engage the community through trusted platforms, offering tailored health interventions aligned with religious practices and cultural norms. This approach not only promises improved health outcomes, such as enhancements in sleep quality, reductions in sleep-related health issues, and increased community health awareness, but also empowers the community by incorporating local religious leaders and stakeholders in program planning and implementation, for example, through the introduction of tailored sleep hygiene workshops that align with the timing of religious practices, such as Ramadan, and culturally sensitive screening for sleep apnea. Success will be measured by improvements in self-reported sleep quality, a reduction in daytime sleepiness, and community surveys assessing awareness and engagement. By demonstrating efficacy in managing sleep health, this model could scale to address broader health issues, ensuring interventions are culturally appropriate and effectively managed within community-specific contexts. This model holds the promise of significantly reducing health disparities by adapting health interventions to the cultural and religious contexts of communities, potentially transforming the landscape of community health management.

Usability of an eHealth sleep education intervention for university students.

Background: It has been estimated that more than one-third of university students suffer from insomnia. Few accessible eHealth sleep education programmes exist for university students and of the ones that do exist, fewer were developed using a user-centred approach, which allows for student input to be systematically collected and utilized to provide students with a programme that they consider to be easy to use and implement and to be effective. Better Nights, Better Days-Youth (BNBD-Youth) is a four-session eHealth sleep education programme designed for youth but previously only evaluated in younger adolescents (ages 14-18 years). Aims: The purpose of this study is to evaluate the usability of the BNBD-Youth programme with university students using Morville's User Experience Honeycomb framework to determine if this programme would meet the needs of university students and if so what modifications would be needed. Methods: Canadian undergraduate students (n = 46) completed the BNBD-Youth programme. Students completed online usability questionnaires based on the seven dimensions of Morville's User Experience Honeycomb (i.e. useful, usable, valuable, credible, desirable, accessible and findable) after each session and after completion of the programme. Open- and closed-ended questions were used to obtain both quantitative and qualitative responses. Results: Average quantitative ratings were positive across user experience dimensions, ranging from 3.43 to 4.46 (out of 5). Qualitative responses indicated overall positive experiences with the programme. The only constructive feedback that met the criteria for revising the programme was to include more interactive features in Session 4. Conclusions: This study demonstrates that university students found BNBD-Youth to be a usable programme for older youth. Demonstrating usability is an essential step in developing a programme with a user-centred design that university students will want to use in the future. Once the BNBD-Youth programme is revised to create the BNBD-University (BNBD-Uni) programme, additional usability and effectiveness testing will be conducted.

SleepSync: Early Testing of a Personalised Sleep-Wake Management Smartphone Application for Improving Sleep and Cognitive Fitness in Defence Shift Workers.

Shift work, long work hours, and operational tasks contribute to sleep and circadian disruption in defence personnel, with profound impacts on cognition. To address this, a digital technology, the SleepSync app, was designed for use in defence. A pre-post design study was undertaken to examine whether four weeks app use improved sleep and cognitive fitness (high performance neurocognition) in a cohort of shift workers from the Royal Australian Air Force. In total, 13 of approximately 20 shift-working personnel from one base volunteered for the study. Sleep outcomes were assessed using the Insomnia Severity Index (ISI), the Patient-Reported Outcomes Measurement Information System (PROMIS), Sleep Disturbance and Sleep-Related Impairment Scales, the Glasgow Sleep Effort Scale, the Sleep Hygiene Index, and mental health was assessed using the Depression, Anxiety, and Stress Scale-21. Sustained attention was measured using the 3-min Psychomotor Vigilance Task (PVT) and controlled response using the NBack. Results showed significant improvements in insomnia (ISI scores 10.31 at baseline and 7.50 after app use), sleep-related impairments (SRI T-scores 53.03 at baseline to 46.75 post-app use), and healthy sleep practices (SHI scores 21.61 at baseline to 18.83 post-app use; all p < 0.001). Trends for improvement were recorded for depression. NBack incorrect responses reduced significantly (9.36 at baseline; reduced by -3.87 at last week of app use, p < 0.001), but no other objective measures improved. These findings suggest that SleepSync may improve sleep and positively enhance cognitive fitness but warrants further investigation in large samples. Randomised control trials with other cohorts of defence personnel are needed to confirm the utility of this intervention in defence settings.

Sleep Health Analysis Through Sleep Symptoms in 35,808 Individuals Across Age and Sex Differences: Comparative Symptom Network Study.

BACKGROUND: Sleep health is a multidimensional construct that includes objective and subjective parameters and is influenced by individual sleep-related behaviors and sleep disorders. Symptom network analysis allows modeling of the interactions between variables, enabling both the visualization of relationships between different factors and the identification of the strength of those relationships. Given the known influence of sex and age on sleep health, network analysis can help explore sets of mutually interacting symptoms relative to these demographic variables. OBJECTIVE: This study aimed to study the centrality of symptoms and compare age and sex differences regarding sleep health using a symptom network approach in a large French population that feels concerned about their sleep. METHODS: Data were extracted from a questionnaire provided by the Réseau Morphée health network. A network analysis was conducted on 39 clinical variables related to sleep disorders and sleep health. After network estimation, statistical analyses consisted of calculating inferences of centrality, robustness (ie, testifying to a sufficient effect size), predictability, and network comparison. Sleep clinical variable centralities within the networks were analyzed by both sex and age using 4 age groups (18-30, 31-45, 46-55, and >55 years), and local symptom-by-symptom correlations determined. RESULTS: Data of 35,808 participants were obtained. The mean age was 42.7 (SD 15.7) years, and 24,964 (69.7%) were women. Overall, there were no significant differences in the structure of the symptom networks between sexes or age groups. The most central symptoms across all groups were nonrestorative sleep and excessive daytime sleepiness. In the youngest group, additional central symptoms were chronic circadian misalignment and chronic sleep deprivation (related to sleep behaviors), particularly among women. In the oldest group, leg sensory discomfort and breath abnormality complaint were among the top 4 central symptoms. Symptoms of sleep disorders thus became more central with age than sleep behaviors. The high predictability of central nodes in one of the networks underlined its importance in influencing other nodes. CONCLUSIONS: The absence of structural difference between networks is an important finding, given the known differences in sleep between sexes and across age groups. These similarities suggest comparable interactions between clinical sleep variables across sexes and age groups and highlight the implication of common sleep and wake neural circuits and circadian rhythms in understanding sleep health. More precisely, nonrestorative sleep and excessive daytime sleepiness are central symptoms in all groups. The behavioral component is particularly central in young people and women. Sleep-related respiratory and motor symptoms are prominent in older people. These results underscore the importance of comprehensive sleep promotion and screening strategies tailored to sex and age to impact sleep health.

Revolutionizing Sleep Health: The Emergence and Impact of Personalized Sleep Medicine.

Personalized sleep medicine represents a transformative shift in healthcare, emphasizing individualized approaches to optimizing sleep health, considering the bidirectional relationship between sleep and health. This field moves beyond conventional methods, tailoring care to the unique physiological and psychological needs of individuals to improve sleep quality and manage disorders. Key to this approach is the consideration of diverse factors like genetic predispositions, lifestyle habits, environmental factors, and underlying health conditions. This enables more accurate diagnoses, targeted treatments, and proactive management. Technological advancements play a pivotal role in this field: wearable devices, mobile health applications, and advanced diagnostic tools collect detailed sleep data for continuous monitoring and analysis. The integration of machine learning and artificial intelligence enhances data interpretation, offering personalized treatment plans based on individual sleep profiles. Moreover, research on circadian rhythms and sleep physiology is advancing our understanding of sleep's impact on overall health. The next generation of wearable technology will integrate more seamlessly with IoT and smart home systems, facilitating holistic sleep environment management. Telemedicine and virtual healthcare platforms will increase accessibility to specialized care, especially in remote areas. Advancements will also focus on integrating various data sources for comprehensive assessments and treatments. Genomic and molecular research could lead to breakthroughs in understanding individual sleep disorders, informing highly personalized treatment plans. Sophisticated methods for sleep stage estimation, including machine learning techniques, are improving diagnostic precision. Computational models, particularly for conditions like obstructive sleep apnea, are enabling patient-specific treatment strategies. The future of personalized sleep medicine will likely involve cross-disciplinary collaborations, integrating cognitive behavioral therapy and mental health interventions. Public awareness and education about personalized sleep approaches, alongside updated regulatory frameworks for data security and privacy, are essential. Longitudinal studies will provide insights into evolving sleep patterns, further refining treatment approaches. In conclusion, personalized sleep medicine is revolutionizing sleep disorder treatment, leveraging individual characteristics and advanced technologies for improved diagnosis, treatment, and management. This shift towards individualized care marks a significant advancement in healthcare, enhancing life quality for those with sleep disorders.