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Summary Gender and age are well‐established determinants of health and sleep health that influence overall health, which also often varies by gender and age. Sleep architecture is an important component of sleep health. The goal of this analysis was to examine whether associations between age and sleep stages differ by gender in the absence of moderate–severe obstructive sleep apnea (OSA) in a rural setting in Brazil. This study conducted polysomnography recordings in the Baependi Heart Study, a cohort of Brazilian adults. Our sample included 584 women and 309 men whose apnea–hypopnea index was ≤15 events/h. We used splines to distinguish non‐linear associations between age, total sleep time, wake after sleep onset (WASO), N2, N3, and rapid‐eye‐movement sleep. The mean (standard deviation; range) age was 47 (14; 18–89) years. All sleep outcomes were associated with age. Compared to men, women had more N3 sleep and less WASO after adjusting for age. Model‐based comparisons between genders at specific ages showed statistically higher mean WASO for men at ages 60 (+13.6 min) and 70 years (+19.5 min) and less N3 for men at ages 50 (−13.2 min), 60 (−19.0 min), and 70 years (−19.5 min) but no differences at 20, 30, 40 or 80 years. The other sleep measures did not differ by gender at any age. Thus, even in the absence of moderate–severe OSA, sleep architecture was associated with age across adulthood, and there were gender differences in WASO and N3 at older ages in this rural community. [ABSTRACT FROM AUTHOR]

Study Objectives: Stroke can result in or exacerbate various sleep disorders. The presence of behaviors such as daytime sleepiness poststroke can indicate underlying sleep disorders which can significantly impact functional recovery and thus require prompt detection and monitoring for improved care. Actigraphy, a quantitative measurement technology, has been primarily validated for nighttime sleep in healthy adults; however, its validity for daytime sleep monitoring is currently unknown. Therefore this study aims to identify the best-performing actigraphy sensor and algorithm for detecting daytime sleep in poststroke individuals., Methods: Participants wore Actiwatch Spectrum and ActiGraph wGT3X-BT on their less-affected wrist, while trained observers recorded daytime sleep occurrences and activity levels (active, sedentary, and asleep) during non-therapy times. Algorithms, Actiwatch (Autoscore AMRI) and ActiGraph (Cole-Kripke, Sadeh), were compared with on-site observations and assessed using F2 scores, emphasizing sensitivity to detect daytime sleep., Results: Twenty-seven participants from an inpatient stroke rehabilitation unit contributed 173.5 hours of data. The ActiGraph Cole-Kripke algorithm (minute sleep time = 15 minutes, bedtime = 10 minutes, and wake time = 10 minutes) achieved the highest F2 score (0.59). Notably, when participants were in bed, the ActiGraph Cole-Kripke algorithm continued to outperform Sadeh and Actiwatch AMRI, with an F2 score of 0.69., Conclusions: The study demonstrates both Actiwatch and ActiGraph's ability to detect daytime sleep, particularly during bed rest. ActiGraph (Cole-Kripke) algorithm exhibited a more balanced sleep detection profile and higher F2 scores compared to Actiwatch, offering valuable insights for optimizing daytime sleep monitoring with actigraphy in stroke patients., (© The Author(s) 2024. Published by Oxford University Press on behalf of Sleep Research Society.)

We aim to examine the association of sleep duration, sleep quality, late chronotype, and circadian misalignment with glycemic control and risk of complications in young adults with youth-onset type 2 diabetes followed in the Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) study. Self-reported sleep duration, quality, timing, and circadian misalignment were assessed via a modified Pittsburgh Sleep Quality Index (PSQI) questionnaire, and chronotype was assessed via the Morningness-Eveningness Questionnaire (MEQ). We examined diabetes complications including loss of glycemic control (defined as hemoglobin A1c ≥8%), hypertension, dyslipidemia, albuminuria, and diabetic peripheral neuropathy. Multivariable logistic regression models were constructed to assess associations between sleep and circadian measures with outcomes of interest, such as loss of glycemic control and diabetes complications. A total of 421 participants (34.2% male), mean age 23.6 ± 2.5 yr, mean body mass index (BMI) of 36.1 ± 8.3 kg/m 2 , and mean diabetes duration of 10.0 ± 1.5 yr were evaluated. Self-reported short sleep duration, daytime sleepiness, and sleep quality were not associated with loss of glycemic control or diabetes complications. Late self-reported bedtime (after midnight) on work/school nights, rather than self-expressed chronotype or circadian misalignment, was independently associated with loss of glycemic control. An association was seen between late bedtimes and albuminuria but was attenuated after adjusting for depression. In conclusion, late bedtime on work/school days, rather than short sleep duration, daytime sleepiness, or poor sleep quality, was independently associated with loss of glycemic control in this longitudinal cohort of young adults with youth-onset type 2 diabetes. NEW & NOTEWORTHY The prevalence of type 2 diabetes in youth is increasing at an alarming rate. Identifying potentially modifiable factors modulating glycemic control is critically important to reduce micro and macrovascular complications. In a large cohort of youth-onset type 2 diabetes, self-reported late bedtime on work/school days was independently associated with loss of glycemic control in this longitudinal cohort of young adults with youth-onset type 2 diabetes.

Study Objectives: To evaluate wearable devices and machine learning for detecting sleep apnea in patients with stroke at an acute inpatient rehabilitation facility (IRF)., Methods: A total of 76 individuals with stroke wore a standard home sleep apnea test (ApneaLink Air), a multimodal, wireless wearable sensor system (ANNE), and a research-grade actigraphy device (ActiWatch) for at least one night during their first week after IRF admission as part of a larger clinical trial. Logistic regression algorithms were trained to detect sleep apnea using biometric features obtained from the ANNE sensors and ground truth apnea rating from the ApneaLink Air. Multiple algorithms were evaluated using different sensor combinations and different apnea detection criteria based on the Apnea-Hypopnea Index (AHI≥5, AHI≥15)., Results: Seventy-one (96%) participants wore the ANNE sensors for multiple nights. In contrast, only forty-eight participants (63%) could be successfully assessed for OSA by ApneaLink; 28 (37%) refused testing. The best-performing model utilized photoplethysmography (PPG) and finger temperature features to detect moderate-severe sleep apnea (AHI≥15), with 88% sensitivity and a positive likelihood ratio (LR+) of 44.00. This model was tested on additional nights of ANNE data achieving 71% sensitivity (10.14 LR+) when considering each night independently and 86% accuracy when averaging multi-night predictions., Conclusions: This research demonstrates the feasibility of accurately detecting moderate-severe sleep apnea early in the stroke recovery process using wearable sensors and machine learning techniques. These findings can inform future efforts to improve early detection for post-stroke sleep disorders, thereby enhancing patient recovery and long-term outcomes., (© The Author(s) 2024. Published by Oxford University Press on behalf of Sleep Research Society. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Study Objective: The objective of this study was to examine the association between the timing of dietary macronutrients and sodium intake and sleep quantity and quality., Methods: This was a cross-sectional study that included 34 adults between 21 and 50 years of age. The main outcome measures were objective sleep measures assessed from three nights of wrist actigraphy including sleep duration, fragmentation, and wake after sleep onset (WASO), and one night of polysomnography (PSG), including rapid eye movement (REM) sleep, non-REM stage 2 (N2), stage 3 (N3), and WASO. Multiple linear regression models and linear mixed models were used to estimate the associations between sleep measures and dietary measures (carbohydrates, fats, saturated fats, proteins, and sodium). Dietary timing was examined in two ways: (1) the average amount of each nutrient consumed within 3 hours of sleep start, and (2) the interval between the final intake of each nutrient and sleep., Results: Average fat intake within 3 hours of sleep was associated with greater WASO from PSG ( β  = 4.48, p  = 0.01). No other associations were found between the macronutrients or sodium intake ( p  > 0.05) within 3 hours of sleep and the sleep parameters from PSG or actigraphy. Similarly, no associations were found between any of the PSG or actigraphy sleep measures and the interval between final nutrient intakes and sleep with sleep duration., Conclusions: The study suggests that greater fat but not carbohydrate, protein, saturated fat, or sodium intake close to sleep may be associated with greater sleep disruption; however, no other associations were observed., (© The Author(s) 2024. Published by Oxford University Press on behalf of Sleep Research Society.)

Introduction: Fatigue, brain fog, and sleep disturbance are among the most common symptoms of postacute sequelae of SARS-CoV-2 infection (PASC). We sought to determine the impact of sleep disruption on cognition and quality of life in patients with neurologic manifestations of PASC (Neuro-PASC)., Methods: Thirty-nine patients were recruited from Neuro-COVID-19 clinic. Mean age was 48.1 years, 71.8% were female, and 82% were never hospitalized for COVID-19. Patients were evaluated via clinical assessment, quality-of-life measures in domains of cognitive function, fatigue, sleep disturbance, anxiety, and depression, NIH Toolbox cognitive tests, and 7 days of wrist actigraphy., Results: The median number of neurologic symptoms attributed to PASC was 6, with brain fog being the most common in 89.7%. Regarding non-neurologic symptoms, 94.9% complained of fatigue and 74.4% of insomnia. Patients reported significant impairment in all quality-of-life domains and performed worse in a task of attention compared to a normative US population. Actigraphy showed Neuro-PASC patients had lower sleep efficiency, longer sleep latency (both p  < 0.001), and later sleep midpoint ( p  = 0.039) compared to 71 age-matched healthy controls with no PASC history. Self-reported cognitive symptoms correlated with the severity of fatigue ( p  < 0.001), anxiety ( p  = 0.05), and depression ( p  < 0.01). Objective evidence of sleep disruption measured by wakefulness after sleep onset, sleep efficiency, and latency were associated with decreased performance in attention and processing speed., Conclusion: Prospective studies including larger populations of patients are needed to fully determine the interplay of sleep disruption on the cognitive function and quality of life of patients with PASC., (© The Author(s) 2024. Published by Oxford University Press on behalf of Sleep Research Society.)