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Study objectivesSleep duration has been associated with risk of dementia and stroke, but few studies have investigated the relationship between sleep duration and brain MRI measures, particularly in middle age.MethodsIn a prospective cohort of 613 black and white adults (mean age = 45.4 years) enrolled in the Coronary Artery Risk Development in Young Adults (CARDIA) study, participants reported typical sleep duration, dichotomized into moderate sleep duration (> 6 to ≤ 8 h) and short sleep duration (≤ 6 h) at baseline (2005-2006). Five years later, we obtained brain MRI markers of white matter including fractional anisotropy, mean diffusivity, and white matter hyperintensities.ResultsCompared to moderate sleepers, short sleepers had an elevated ratio of white matter hyperintensities to normal tissue in the parietal region (OR = 2.31, 95% CI: 1.47, 3.61) adjusted for age, race/sex, education, hypertension, stroke/TIA, depression, smoking status, and physical activity. White matter diffusivity was also higher, approximately a 0.2 standard deviation difference, in frontal, parietal, and temporal white matter regions, among those reporting shorter sleep duration in (P < 0.05 for all).ConclusionsShort sleep duration was associated with worse markers of white matter integrity in midlife. These mid-life differences in white matter may underlie the link between poor sleep and risk of dementia and stroke.

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.)