Sleep Health最新文献

{"title":"Sociodemographic correlates of daytime napping in Canadian preschoolers.","authors":"Esther Acheampong, Justin J Lang, Graham J Reid, Jean-Philippe Chaput","doi":"10.1016/j.sleh.2026.09.010","DOIUrl":"https://doi.org/10.1016/j.sleh.2026.09.010","url":null,"abstract":"<p><strong>Objectives: </strong>To examine the sociodemographic and sleep-related characteristics of Canadian children aged 3 to 4 years who napped compared with those who did not.</p><p><strong>Methods: </strong>This cross-sectional study used data from the 2023 Canadian Health Survey on Children and Youth (CHSCY), a nationally representative survey. Parents of children aged 3 to 4 years (n=6291) reported napping status, nap duration, nighttime sleep characteristics, and sociodemographic factors. Weighted proportions and chi-square tests compared nappers and non-nappers. Multivariable logistic regression identified sociodemographic characteristics independently associated with daytime napping.</p><p><strong>Results: </strong>Napping prevalence was lower among 4-year-olds than 3-year-olds (27.6% vs. 48.7%, P<.001). Children who napped were more likely to be from non-White ethnoracial backgrounds, live in lower-income households, and have landed immigrant status, whereas non-nappers were more often White, from higher-income households, and Canadian-born. After multivariable adjustment, Black and Arab/West Asian children had higher odds of daytime napping, whereas Indigenous children had lower odds than White children. Immigration status was no longer independently associated with daytime napping. No significant differences were observed by gender, parental education, or perceived general or mental health. Non-nappers were more likely to obtain at least 10 hours of nighttime sleep (3 years: 95.4% vs. 79.4%, P<.001; 4 years: 90.1% vs. 76.7%, P<.001) and, at age 3, to fall asleep within 10 minutes at night (55.3% vs. 39.7%, P<.001). Among 4-year-olds, nappers were more likely to report no or almost never experiencing nighttime awakenings (57.3% vs. 44.3%, P<.001). Mean nap duration remained stable at approximately 2.4 to 2.5 hours.</p><p><strong>Conclusions: </strong>Daytime napping in Canadian preschoolers varies by developmental stage and sociodemographic characteristics, supporting previous evidence that sleep patterns during the preschool years reflect both developmental variability and contextual influences.</p>","PeriodicalId":48545,"journal":{"name":"Sleep Health","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149225311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Considering counterfactual prediction models in sleep research: A demonstration of incident stroke prediction in an observational cohort study.","authors":"Lucas Mentch, Alexander Dukart, Ryan Cecil, Bradley Wheeler, Daniel J Buysse, Meredith L Wallace","doi":"10.1016/j.sleh.2026.09.014","DOIUrl":"https://doi.org/10.1016/j.sleh.2026.09.014","url":null,"abstract":"<p><strong>Objective: </strong>Data-driven risk prediction algorithms are increasingly common. However, studies suggesting that a single set of variables are most \"important\" within a given risk prediction model ignore the reality that other equally useful models containing entirely different features may exist. We aim to use a novel approach (Model Path Selection) to demonstrate that, for any given health outcome, there are likely many comparable counterfactual models including different sets of variables. We focus on incident stroke as a scientifically meaningful outcome to motivate this demonstration.</p><p><strong>Methods: </strong>Model Path Selection uses an iterative model-building procedure to illustrate a set of counterfactual prediction models (\"paths\"). We applied Model Path Selection to Sleep Heart Health Study data (N = 4,512 adults) to predict 5-year incident stroke, considering 30 features comprising demographics, self-reported and measured medical risk factors, polysomnography, and self-reported sleep. Area Under the Curve was estimated for each resulting path based on 10-fold cross-validation.</p><p><strong>Results: </strong>Model Path Selection identified 61 unique counterfactual models. 13 of these achieved Area Under the Curves between 0.81 and 0.82. Area Under the Curves of the remaining models ranged from 0.53 to 0.68 (median 0.61). All 13 top models included age plus various combinations of other features (polysomnography indices, body mass index, and/or the Short Form 36 mental and physical component scores).</p><p><strong>Conclusions: </strong>Model Path Selection can illuminate counterfactual prediction models, differentiate features that may be uniquely predictive from those that can be swapped out with other features, enhance reproducibility, inform the design of clinically practical predictive tools, and suggest sleep-related risk factors to be targeted for health promotion.</p>","PeriodicalId":48545,"journal":{"name":"Sleep Health","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149225173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Divergent associations of weekly and seasonal sleep variability with 1-year glycemic change in Chinese adults.","authors":"Xinyuan Wang, Yuanyuan Cao, Zisuo Sun, Shaoxian Liang, Chenzhe Sun, Jing Gao, Zhuang Zhang, Wanshui Yang","doi":"10.1016/j.sleh.2026.09.008","DOIUrl":"https://doi.org/10.1016/j.sleh.2026.09.008","url":null,"abstract":"<p><strong>Objectives: </strong>Sleep variability is often examined as a unitary construct, yet its metabolic relevance may differ by temporal scale. Although social jetlag, reflecting weekly misalignment between sleep and social schedules, is associated with worse glycemic control, the longitudinal relationship of naturally occurring seasonal sleep variability with glucose metabolism remains unclear. We aimed to evaluate and contrast the associations of annualized social jetlag and seasonal sleep variability with 1-year changes in fasting blood glucose.</p><p><strong>Methods: </strong>We analyzed data from 357 Chinese adults in a longitudinal design. Sleep was recorded via diary over three consecutive days in each season, enabling computation of annualized social jetlag and intra-individual standard deviations of sleep duration and timing across seasons. Fasting blood glucose was measured at baseline and 1 year later.</p><p><strong>Results: </strong>Compared with minimal social jetlag (<0.25 hour), high social jetlag (≥0.5 hour) was associated with a 6.1% increase in fasting glucose (95% CI: 1.3, 11.2). In contrast, greater seasonal variability in sleep duration (per 1-hour increase in standard deviations) was associated with a 6.5% decrease in glucose (95% CI: -11.1, -1.7). An interaction was observed for MTNR1B rs1387153 (P_interaction = .04), with an inverse association between seasonal variability in wake-up timing and glucose present in C-allele carriers but absent in TT homozygotes.</p><p><strong>Conclusions: </strong>Weekly social jetlag and seasonal sleep variability show opposing longitudinal associations with glycemic change. Maintaining a natural degree of seasonal sleep plasticity is associated with better glucose regulation, and this relationship appears to be modified by genetic variations in MTNR1B.</p>","PeriodicalId":48545,"journal":{"name":"Sleep Health","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149169444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-reported sleep health is more than a combination of multiple objective sleep measures.","authors":"Amy N Costa, Margaret Kuzemchak, Lan Yu, Daniel J Buysse, Rachel P Kolko Conlon, Meredith L Wallace","doi":"10.1016/j.sleh.2026.09.007","DOIUrl":"https://doi.org/10.1016/j.sleh.2026.09.007","url":null,"abstract":"<p><strong>Objectives: </strong>The self-reported Ru-SATED questionnaire is a psychometrically validated measure of multidimensional sleep health. Ru-SATED is increasingly examined in relation to health outcomes; however, its relationship to behavioral sleep is unknown. This study describes the combinations of bed-measured sleep health features that are associated with the Ru-SATED sleep health score.</p><p><strong>Methods: </strong>Sleep Number bed users (N = 2674, Mage = 52.4 ± 16.2, 55.2% women) completed the Ru-SATED 4.0 multidimensional sleep health scale and contributed past-month smart bed data (7-28 days). Associations between Ru-SATED and bed features (duration, efficiency, regularity, timing) were modeled using: (1) Spearman correlations; (2) multivariable regression with bed features included additively; (3) polynomial regression with complex interactions among bed features combined with novel graphical techniques; and (4) non-parametric tree-structured regression.</p><p><strong>Results: </strong>Individual bed measures had weak correlations with Ru-SATED (r = 0.04-0.21; R²-adjusted = 0.002-0.040). Models incorporating multiple bed features explained larger proportions of variance in Ru-SATED (Additive regression R²-adjusted = 0.078; polynomial regression R²-adjusted = 0.084; tree-structured regression R²-adjusted = 0.090). Across models, bed-measured duration (ΔR² = 0.015-0.018), regularity (ΔR² = 0.011-0.019), and efficiency (ΔR² = 0.010-0.015) made the largest contributions to Ru-SATED, followed by timing (ΔR² = 0.001-0.003). Individuals with the best Ru-SATED score had good bed-measured regularity, efficiency, duration, and timing, while those with the worst Ru-SATED had some combination of poor regularity, duration, efficiency, and/or timing.</p><p><strong>Conclusions: </strong>Ru-SATED was associated with combinations of behavioral bed measures, above any single bed measure. Bed-measured duration, regularity, and efficiency made the strongest contributions. Findings further validate the multidimensional sleep health framework and may assist with presenting clinically relevant information regarding patient sleep health patterns and goals.</p>","PeriodicalId":48545,"journal":{"name":"Sleep Health","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149170880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smartphones, digital media use, and sleep in young adolescents.","authors":"Chighaf Bakour, Jonathan Andreadakis, Chloe Flanigan, Wendy M Rote, Stephen W Song, Taylor Warren, Samya Muftah, Justin D Martin","doi":"10.1016/j.sleh.2026.09.012","DOIUrl":"https://doi.org/10.1016/j.sleh.2026.09.012","url":null,"abstract":"<p><strong>Objectives: </strong>The purpose of this study was to examine the association between smartphone ownership and usage behaviors and sleep duration and quality among adolescents aged 11 to 13 years, and whether these associations differed by sex.</p><p><strong>Methods: </strong>We analyzed data from 1510 participants in the 2024 Life in Media Survey (LIMS), an online cross-sectional survey of 11- to 13-year-olds in Florida. Logistic regression models adjusted for appropriate confounders assessed associations between smartphone ownership and 6 usage behaviors, including device location at night; in-bed usage; evening usage time; online posting; notifications; and exposure cyberbullying, and the outcomes of sleep duration and trouble sleeping. Sex-stratified analyses were conducted to assess for effect modification.</p><p><strong>Results: </strong>Smartphone ownership was associated with lower odds of insufficient sleep and trouble sleeping; however, several usage behaviors were strongly linked to poorer sleep. Keeping a smartphone in hand/in bed, using it in bed, posting publicly online, spending more than 2 hours on the device in the evening, and experiencing cyberbullying were each associated with increased odds of insufficient sleep duration and trouble sleeping. Using the phone in bed showed the strongest associations (aORs up to 13 for trouble sleeping). Cyberbullying consistently predicted all adverse sleep outcomes. Sex-stratified results showed variation in some associations.</p><p><strong>Conclusions: </strong>In early adolescence, how smartphones are used, rather than merely owning one, has substantial implications for sleep. Behaviors such as nighttime proximity, in-bed use, late‑evening screen time, and exposure to online stressors were most detrimental. Promoting healthier bedtime practices and reducing negative online experiences may improve sleep in this age group.</p>","PeriodicalId":48545,"journal":{"name":"Sleep Health","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149170917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bedtime technology activities and multidimensional sleep health in rural Appalachian adults: Findings from the REST-KY study.","authors":"Maliha Mehnaz Mitu, Joon Chung, Emily Slade, Daniela C Moga, Michael A Grandner, Mairead E Moloney","doi":"10.1016/j.sleh.2026.09.019","DOIUrl":"https://doi.org/10.1016/j.sleh.2026.09.019","url":null,"abstract":"<p><strong>Objectives: </strong>Bedtime technology use is frequently associated with poor sleep outcomes, yet evidence examining the impacts of specific technologies on multidimensional sleep health remains limited, particularly in health disparity populations. We examined associations between bedtime technology activities and sleep health in rural Appalachian adults.</p><p><strong>Methods: </strong>The sample comprised 375 adults from 12 economically distressed counties in Eastern Kentucky. Participants completed online surveys assessing bedtime technology use in the hour before sleep and sleep health across 8 dimensions (satisfaction, alertness, timing, efficiency, duration, duration irregularity, social jetlag, and wake after sleep onset), operationalized as an extended sleep health score (range: 0-8). Linear regression models examined associations between technology use frequency (frequent vs. infrequent) and overall sleep health, adjusting for sociodemographic factors, mental health symptoms, chronotype, and sleep medication use. Logistic regression explored dimension-specific associations for various technology types.</p><p><strong>Results: </strong>Most participants (73.3%) were female and White (97.3%) (mAge 45.6 [range 18-81: SD = 13.1]); mean extended sleep health score was 5.1 (SD = 1.7). In adjusted models, less frequent work-related computer use was associated with higher extended sleep health scores (mean difference = 0.70, 95% CI: 0.26-1.14). Dimension-specific analyses linked infrequent work-related computer use to favorable alertness (OR = 2.33, 95% CI: 1.21-4.49) and wake after sleep onset (OR = 3.15, 95% CI: 1.41-6.98).</p><p><strong>Conclusions: </strong>Differential associations across technology types and sleep dimensions suggest that cognitively engaging, work-related technology use (e.g., work-related computer use) may be particularly detrimental to sleep, highlighting the importance of examining specific technology use patterns in sleep health interventions for rural, health disparate populations.</p>","PeriodicalId":48545,"journal":{"name":"Sleep Health","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149149018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rest-activity rhythms, sleep dimensions, and mortality in adults with cardiovascular-kidney-metabolic syndrome: A survey-weighted NHANES cohort study.","authors":"Miao Huang, Renjie Huang, Dongyan Wang","doi":"10.1016/j.sleh.2026.09.009","DOIUrl":"https://doi.org/10.1016/j.sleh.2026.09.009","url":null,"abstract":"<p><strong>Objectives: </strong>To examine rest-activity rhythms and objective sleep dimensions with all-cause mortality, with heart-disease mortality as a supportive secondary outcome, among adults with cardiovascular-kidney-metabolic syndrome.</p><p><strong>Methods: </strong>We analyzed National Health and Nutrition Examination Survey 2011-2014 data linked to mortality through 2019 among adults with archived cardiovascular-kidney-metabolic stages 1 to 4. Rest-activity rhythm metrics (interdaily stability, intradaily variability, relative amplitude) and sleep parameters (duration, efficiency, timing variability) were derived from wrist-worn actigraphy. Survey-weighted Cox models incorporated mobile examination center weights, strata, and primary sampling units.</p><p><strong>Results: </strong>Among 3061 participants, 274 all-cause and 79 heart-disease deaths occurred. Higher intradaily variability was associated with higher all-cause mortality (hazard ratio [HR] per 0.1 unit, 1.15; 95% confidence interval [CI], 1.08-1.23), whereas higher relative amplitude was associated with lower mortality (HR per 0.1 unit, 0.74; 95% CI, 0.66-0.82). Longer mean sleep duration (HR per hour, 0.82; 95% CI, 0.74-0.91) and higher sleep efficiency (HR per 0.1-unit increase, 0.70; 95% CI, 0.57-0.86) were associated with lower all-cause mortality, whereas greater sleep-midpoint variability was associated with higher all-cause mortality (HR per hour, 1.26; 95% CI, 1.04-1.52). Associations of relative amplitude and mean sleep duration with all-cause mortality were nonlinear. Heart-disease mortality findings were supportive.</p><p><strong>Conclusions: </strong>Greater rest-activity rhythm fragmentation, lower rest-activity rhythm amplitude, and several sleep dimensions were associated with all-cause mortality. These findings do not establish causality or clinical thresholds.</p>","PeriodicalId":48545,"journal":{"name":"Sleep Health","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149149032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of rest-activity rhythm metrics derived from Oura ring and actiwatch devices: A device validation and analytical pipeline comparison study.","authors":"Shuo Qin, Aurore Jouvencel, Chun Siong Soon, Noof Shaif, Ju Lynn Ong, Michael W L Chee","doi":"10.1016/j.sleh.2026.09.011","DOIUrl":"https://doi.org/10.1016/j.sleh.2026.09.011","url":null,"abstract":"<p><strong>Objectives: </strong>Non-parametric rest-activity rhythm (RAR) metrics are increasingly used as 24-hour behavioral markers of circadian and general health. Although traditionally derived from research-grade actigraphy, consumer wearables may provide scalable alternatives for long-term monitoring. This study evaluated the performance of RAR metrics derived from Oura Ring versus Actiwatch (AW2) during concurrent wear and examined the influence of analytic pipeline.</p><p><strong>Methods: </strong>Forty-one healthy adults (mean age 26.3 years; 27 female) wore both devices for 14 days. Interdaily stability (IS), intradaily variability (IV), relative amplitude (RA), and L5/M10 timing were calculated using nparACT, MotionWare, and a custom pipeline. Performance was evaluated using ICC(A,1), ICC(C,1), Spearman's ρ, and Bland-Altman analysis. Oura MET was zero-referenced from its resting floor of 0.9 for RA analysis.</p><p><strong>Results: </strong>Participants contributed a mean of 10.6 valid concurrent wear days. Oura Ring had slightly higher IS and lower IV than AW2. IS, IV, L5/M10 timing, and RA all showed moderate-to-excellent absolute agreement (ICC[A,1] = 0.67-0.98) and strong rank-order associations (Spearman's ρ = 0.71-0.98). Estimates were similar across the 3 analytical pipelines, although differences in M10/L5 implementation influenced timing estimates in one outlier.</p><p><strong>Conclusion: </strong>Oura-derived RAR metrics showed acceptable overall agreement with AW2 when the known difference in measurement origin was accounted for in the RA calculation. Results were generally consistent across analytic pipelines, supporting analytic reproducibility. The M10 outlier showed that unusual activity values on individual days can affect M10 estimates across pipelines.</p>","PeriodicalId":48545,"journal":{"name":"Sleep Health","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149149085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Associations of workplace violence and emotional labor with insomnia symptoms: Directional pathways and the mediating role of presleep arousal in a 3-wave panel study of Korean workers.","authors":"Seo-Young Kim, Seong-Sik Cho, Hye-Eun Lee, Mo-Yeol Kang","doi":"10.1016/j.sleh.2026.09.001","DOIUrl":"https://doi.org/10.1016/j.sleh.2026.09.001","url":null,"abstract":"<p><strong>Objectives: </strong>Workplace violence and emotional labor are prevalent occupational stressors linked to insomnia. However, the longitudinal mechanisms and directionality remain unclear. This study examines the reciprocal associations among workplace violence, emotional labor, and insomnia symptoms, focusing on the mediating roles of cognitive and somatic presleep arousal.</p><p><strong>Methods: </strong>Three-wave longitudinal panel data (1-year intervals) from the Korean Work, Sleep, and Health Study (KWSHS) were analyzed: 1492 workers for workplace violence and 710 customer-facing workers for emotional labor. Cross-lagged panel models were used to test directional relationships, and mediation effects were evaluated using the product-of-coefficients approach with delta-method standard errors. Linear mixed models assessed whether the adverse impacts of these stressors on sleep outcomes intensified over time.</p><p><strong>Results: </strong>Cross-lagged panel models found that workplace violence and presleep arousal reciprocally predicted one another, leading to subsequent insomnia symptoms. Conversely, emotional labor exhibited a unidirectional pathway, predicting later arousal and insomnia symptoms without significant reverse effects. Cognitive and somatic arousal showed significant indirect associations between workplace violence and insomnia; for emotional labor, only the cognitive arousal pathway remained significant after false-discovery-rate correction. Linear mixed models indicated significant Time × workplace violence interactions, suggesting that adverse impacts of workplace violence on somatic arousal and insomnia symptoms intensify over time, whereas emotional labor showed relatively stable effects over time.</p><p><strong>Conclusions: </strong>Both workplace violence and emotional labor were prospectively associated with insomnia symptoms, with presleep arousal as a potential intermediate pathway. Workplace violence showed reciprocal associations with insomnia, whereas emotional labor was primarily associated with subsequent insomnia symptoms.</p>","PeriodicalId":48545,"journal":{"name":"Sleep Health","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149128520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What does \"Sleep Duration\" mean? Clarifying public guidelines in the era of wearables to prevent harm.","authors":"Michelle Jonelis","doi":"10.1016/j.sleh.2026.09.003","DOIUrl":"https://doi.org/10.1016/j.sleh.2026.09.003","url":null,"abstract":"","PeriodicalId":48545,"journal":{"name":"Sleep Health","volume":" ","pages":""},"PeriodicalIF":4.8,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149116892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}