Sleep Health最新文献

{"title":"Day-by-day changes in the indoor environment and sleep health among African American adults","authors":"Yinxian Chen MS ,&nbsp;Laura Ward MSPH ,&nbsp;Donald Bliwise PhD ,&nbsp;Dayna A. Johnson PhD","doi":"10.1016/j.sleh.2026.03.008","DOIUrl":"10.1016/j.sleh.2026.03.008","url":null,"abstract":"<div><h3>Objectives</h3><div>The study aimed to examine the association between day-by-day changes in the indoor physical environment and dimensions of sleep health among African American adults.</div></div><div><h3>Methods</h3><div>A total of 36 African American participants with at least five consecutive days of objective sleep measured by actigraphy (417 person-days) were included in the analysis. Indoor environmental factors, including temperature, carbon dioxide, noise, humidity, and barometric pressure, were objectively measured by an indoor environmental monitor. Linear mixed models were utilized to assess the within-person association between day-by-day changes in indoor environmental factors and sleep efficiency, wakefulness after sleep onset, sleep duration, and linear models for the association with sleep regularity, adjusting for sex, age, educational attainment, and the number of household members. The joint associations of all factors with sleep measures were assessed using quantile g-computation.</div></div><div><h3>Results</h3><div>The participants' mean (standard deviation) age was 55.3 (8.1), and most were female and had a bachelor's degree or higher. In complete case analysis (N = 29, 288 person-days), higher noise levels were associated with lower sleep efficiency (β = −1.81%, 95% CI: −2.00, −0.45), especially among those in the highest quartile vs. the lowest (β = −2.73%, 95% CI: −4.91, −0.54). Higher pressure levels were associated with longer sleep duration (β = 15.22 minutes, 95% CI: 1.11, 28.02) and more irregular sleep (β = 16.07 minutes, 95% CI: 1.40, 30.73). Having higher levels of all environmental factors was associated with lower sleep efficiency (β = −1.38%, 95% CI: −3.01, 0.26). Multiple-imputation analysis yielded consistent results.</div></div><div><h3>Conclusions</h3><div>Day-by-day changes in indoor physical environment were associated with sleep efficiency, duration, and irregularity among African American adults in the Atlanta Metro area.</div></div>","PeriodicalId":48545,"journal":{"name":"Sleep Health","volume":"12 3","pages":"Pages 425-432"},"PeriodicalIF":3.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147822328","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":"Objective prediction of siesta based on machine learning and association with obesity","authors":"María Rodríguez-Martín MSc ,&nbsp;Fernando Moreno Caballero BSc ,&nbsp;Hassan S. Dashti PhD, RD ,&nbsp;Richa Saxena PhD ,&nbsp;Frank A.J.L. Scheer PhD ,&nbsp;Jesualdo T. Fernández Breis PhD ,&nbsp;Marta Garaulet PhD","doi":"10.1016/j.sleh.2026.02.007","DOIUrl":"10.1016/j.sleh.2026.02.007","url":null,"abstract":"<div><h3>Objectives</h3><div>To predict siesta behavior using machine learning models trained on self-reported and objective data—temperature (T), activity (A), position (P), and the integrated TAP variable—and to explore its associations with obesity-related traits.</div></div><div><h3>Methods</h3><div>From ONTIME-MT, 889 adults wore wrist sensors for 7 days to continuously record temperature, activity, and position, and self-reported daily siesta. Machine learning models were developed to classify 30-second epoch siesta data, to reconstruct weekly siesta behavior. Anthropometric and metabolic parameters were assessed. Associations were analyzed using linear and logistic regression. Model generalizability was evaluated in an independent Mediterranean cohort (n = 70).</div></div><div><h3>Results</h3><div>The machine learning model allowed to obtain 83% of success in siesta patterns prediction. Among the input variables, activity was the most discriminative by the decision tree (threshold: 27 Δ°/min), followed by TAP (0.51 AU) and position (4.7°). In an independent external validation cohort, success in prediction reached 77%, indicating strong alignment between algorithm-based and self-reported siesta patterns detection. Predicted siesta—but not self-reported alone—was significantly associated with obesity-related traits. Later siesta timing was linked to increased waist circumference in women (β = 0.769 cm per hour; P = 0.026). Longer siesta duration was associated with increased obesity risk (OR=2.081; P=0.002), BMI (β=0.013 kg/m²/h; P = 0.034), and systolic blood pressure (β = 3.540<!--> <!-->mmHg/h; P = 0.049). Greater siesta frequency was associated with lower corrected insulin response (β = −0.037 AU/day; P = 0.012).</div></div><div><h3>Conclusion</h3><div>Objective data from temperature, activity, position, and TAP, combined with ML models, accurately predict siesta behavior and its metabolic relevance. These findings support the use of machine learning approaches based on temperature, activity, position, and the integrated TAP, to assess siesta under free-living conditions.</div></div><div><h3>ClinicalTrials.gov identifier</h3><div>NCT03036592</div></div>","PeriodicalId":48545,"journal":{"name":"Sleep Health","volume":"12 3","pages":"Pages 504-512"},"PeriodicalIF":3.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147654683","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":"Unraveling factors associated with perceived difficulty in falling asleep in Italian adolescents: The role of personal, familial, and educational influences","authors":"Alberto Borraccino MD ,&nbsp;Gianmarco Giacomini MD ,&nbsp;Carolina Lombardi MD ,&nbsp;Andrea Pammolli MD, PhD ,&nbsp;Giacomo Lazzeri MD","doi":"10.1016/j.sleh.2025.11.007","DOIUrl":"10.1016/j.sleh.2025.11.007","url":null,"abstract":"<div><h3>Purpose</h3><div>This study assesses the role of personal, family-related, and school-related factors in adolescence in the perception of the presence of difficulties in falling asleep.</div></div><div><h3>Methods</h3><div>The study used data from the Italian 2022 Health Behavior in School-Aged Children (HBSC) study, focusing on 3201 participants (99.7% of response) from the Italian HBSC sample in Tuscany. Descriptive and hierarchical multivariable logistic regression models including personal, family- and school-related factors were used to assess perceived difficulties in falling asleep as reported by 11-, 13-, 15-, and 17-year-old adolescents.</div></div><div><h3>Results</h3><div>Around 49.7% of the sample was female. Nearly a quarter of the adolescents reported sleep difficulty daily or more than once a week. Females reported significantly higher risk of sleep difficulty (odds ratio (OR) of 1.38, 95% CI 1.1-1.7), as did those with higher levels of perceived school pressure (OR 1.47, 95% CI 1.3-1.6) and lack of student support (OR 1.14, 95% CI 1.0-1.3). Those in the middle level of the Family Affluence and older adolescents reported lower risk of sleep difficulty falling asleep (OR 0.74, 95% CI 0.6-0.9 and 0.46, 95% CI 0.3-0.6, respectively).</div></div><div><h3>Conclusion</h3><div>Since concurrent factors in different settings have shown to influence the expression of sleep problems, integrated intervention strategies should be applied to promote healthy sleep habits among adolescents. Therefore, it is necessary for invest in multidimensional intervention approaches, taking into account all the key stakeholders such as the individual, the family and the school into a more integrated perspective design.</div></div>","PeriodicalId":48545,"journal":{"name":"Sleep Health","volume":"12 3","pages":"Pages 393-399"},"PeriodicalIF":3.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145783397","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":"The effect of an experimental sleep extension manipulation on timing and duration of sedentary behavior and physical activity in habitually short-sleeping and inactive adolescents: Results from a randomized crossover trial","authors":"Jordan A. Davidson BS ,&nbsp;Seth A. Creasy PhD ,&nbsp;Emily Meneses MS ,&nbsp;John T. Brinton PhD ,&nbsp;Angel Bernard BS ,&nbsp;Anne E. Bowen MS ,&nbsp;Stephen M.M. Hawkins MD ,&nbsp;Melanie G. Cree MD, PhD ,&nbsp;Kristen J. Nadeau MD ,&nbsp;Edward L. Melanson PhD ,&nbsp;Kenneth P. Wright Jr PhD ,&nbsp;Stacey L. Simon PhD, DBSM","doi":"10.1016/j.sleh.2026.02.006","DOIUrl":"10.1016/j.sleh.2026.02.006","url":null,"abstract":"<div><h3>Background</h3><div>Short sleep duration, low physical activity (PA), and sedentary behavior (SB) are associated with negative health outcomes and highly prevalent in adolescents. This study examined changes in the amount and timing of PA and SB following a 1-week sleep extension manipulation in adolescents.</div></div><div><h3>Methods</h3><div>Forty-three habitually short-sleeping (≤7 h/night on school days), habitually inactive (&lt;3 hours of regular physical activity per week), and healthy-weight adolescents (16.0 ± 1.24 years, 69.8% female; 86% White) completed a randomized crossover procedure during the school year. Participants slept for 1 week on their typical school schedule (Typical Sleep, TS), and 1 week during which time in bed was extended by ≥1 hour each school night (Sleep Extension, EXT). Home-monitoring of sleep with wrist-worn actigraphy and activity with thigh-worn accelerometer was completed during both conditions. Relationships between sleep, SB, PA, and experimental manipulation were assessed with linear mixed models.</div></div><div><h3>Results</h3><div>SB and light PA (LPA) across the 24 days decreased significantly during EXT compared to TS by 72 minutes and 13.2 minutes, respectively (95% CI: −102, −42, <em>p</em> &lt; .001; 95% CI: −26.4, 0.00, <em>p</em> = .048). SB decreased predominantly between the hours of 18:00-00:00 (−39 minute 95% CI: −54.6, −24, <em>p</em> &lt; .001). There was no significant change in moderate-to-vigorous PA (MVPA) between conditions (<em>p</em> &gt; .05).</div></div><div><h3>Conclusion</h3><div>Increased sleep duration replaced time spent in SB primarily in the evening hours. While LPA decreased primarily in the morning hours, the amount of change was small and likely not clinically significant. Sleep extension did not impact MVPA.</div></div>","PeriodicalId":48545,"journal":{"name":"Sleep Health","volume":"12 3","pages":"Pages 400-406"},"PeriodicalIF":3.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147515727","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":"Methodological considerations and research contributions: A response to commentary on exercise and sleep in sedentary young women","authors":"Borui Zhang PhD,&nbsp;Chen Zheng PhD,&nbsp;Fenghua Sun PhD","doi":"10.1016/j.sleh.2026.02.009","DOIUrl":"10.1016/j.sleh.2026.02.009","url":null,"abstract":"","PeriodicalId":48545,"journal":{"name":"Sleep Health","volume":"12 3","pages":"Page 547"},"PeriodicalIF":3.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147515741","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":"Domains included in multidimensional sleep health composite scores: A scoping review","authors":"Catherine Siengsukon PT, PhD ,&nbsp;Jade Robichaud ,&nbsp;Lauren Hand PhD, RD ,&nbsp;Ashley Barry MPH, RRT ,&nbsp;Prasanna Vaduvathiriyan MLS ,&nbsp;Karen Bock PT, PhD","doi":"10.1016/j.sleh.2026.03.004","DOIUrl":"10.1016/j.sleh.2026.03.004","url":null,"abstract":"<div><div>While the SATED/Ru-SATED sleep health framework is well-recognized, there is no consensus for how many or which domains should be included in the construct of a multidimensional sleep health composite score. Therefore, the purpose of this scoping review is to determine what domains are included in multidimensional sleep health composite scores and how those domains are assessed. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) and the Joanna Briggs Institute’s (JBI) updated methodology for scoping reviews were used. Two authors independently reviewed titles/abstracts, full texts, and performed data extraction, and one author resolved discrepancies with discussion as needed. The search strategy generated 1879 references, 130 underwent a full-text screening, and 45 were eligible for inclusion. Five and six domains were the most common number of domains included in the multidimensional sleep health composite scores (n = 20 each). Articles mainly included only self-report measures (n = 21) or a mix of self-report and objective measures (n = 21). Twelve unique domains were identified with Duration being the most common (n = 45), followed by Alertness/Sleepiness (n = 42), Satisfaction/Quality (n = 39), and Timing (n = 37). In conclusion, while the domains most often included in the multidimensional sleep health composite scores followed the SATED/Ru-SATED framework, there was variability in the domains included as well as variability in how the domains were assessed. Consensus is needed on the definition of sleep health domains or, at a minimum, clear reporting on the definitions used. Further research is needed to determine which multidimensional sleep health domains are most associated with health outcomes.</div></div>","PeriodicalId":48545,"journal":{"name":"Sleep Health","volume":"12 3","pages":"Pages 537-544"},"PeriodicalIF":3.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147718446","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":"Technology use and sleep: An examination of population data from the National Sleep Foundation","authors":"Steven E. Carlson MS ,&nbsp;Christopher D. Sparks MPH ,&nbsp;Joseph M. Dzierzewski PhD ,&nbsp;Kelly G. Baron PhD, MPH, DBSM","doi":"10.1016/j.sleh.2026.03.006","DOIUrl":"10.1016/j.sleh.2026.03.006","url":null,"abstract":"<div><h3>Objectives</h3><div>Evening technology use is common and may be associated with poorer sleep health. We sought to understand pre-sleep technology use in a national sample of adults and its cross-sectional association with sleep health characteristics.</div></div><div><h3>Methods</h3><div>Participants reported their pre-sleep technology use in the hour before bedtime, as well as location of use. Self-reported sleep duration, sleep quality, sleep deficiency, and social jetlag were assessed from the Sleep Health Index. We used weighted analyses to evaluate the prevalence of technology use in the hour prior to bedtime, location of technology use, and sociodemographic differences in pre-bedtime technology use. Further, we examined associations between technology use and self-reported sleep characteristics.</div></div><div><h3>Results</h3><div>The sample included 1000 participants (<em>M</em>_<em>age</em> = 51.92, 51.1% women). The majority (89.9%) reported using electronic devices within 1 hour of bedtime, with 76.5% using technology in the bedroom. Hispanic participants and younger participants reported greater device use before bed and in the bedroom. Use of any device near bedtime was associated with poorer sleep quality and greater sleep deficits. Smartphone use, specifically, was associated with greater social jetlag.</div></div><div><h3>Conclusions</h3><div>Technology use before bed was reported in 9 out of 10 participants and was commonly used in the bedroom. Smartphone use in the hour before bed was associated with less consistent sleep patterns. Presence of racial and ethnic differences in pre-sleep technology use may signify a potential target for promoting health equity.</div></div>","PeriodicalId":48545,"journal":{"name":"Sleep Health","volume":"12 3","pages":"Pages 496-503"},"PeriodicalIF":3.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147700480","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":"Sleep health in sexual minority youth: An intersectional approach across ethnicity-race and sex—Findings from the Adolescent Brain Cognitive Development (ABCD) Study","authors":"Youchuan Zhang PhD ,&nbsp;Jinjin Yan PhD ,&nbsp;Zhenqiang Zhao PhD ,&nbsp;Yijie Wang PhD","doi":"10.1016/j.sleh.2026.03.007","DOIUrl":"10.1016/j.sleh.2026.03.007","url":null,"abstract":"<div><h3>Objectives</h3><div>Sexual minority (lesbian, gay, and bisexual) youth experience heightened risks for poor sleep health than heterosexual youth. However, they are often treated as a homogeneous group, obscuring critical within-group variations. This study examined sleep disparities by sexual orientation and, within sexual minority youth, how these disparities differ by ethnicity-race and sex—two critical social strata consistently linked to sleep disparities.</div></div><div><h3>Methods</h3><div>Data were drawn from 5359 youth (M_age = 11.52, 51% female) from the Adolescent Brain Cognitive Development Study. Using actigraphy-based, multidimensional sleep data, youth-reported sexual orientation, and caregiver-reported youth ethnicity-race and sex assigned at birth, we examined sleep differences (1) by sexual orientation within the full sample, and (2) within the sexual minority subsample by ethnicity-race, sex, and ethnicity-race × sex.</div></div><div><h3>Results</h3><div>Path analyses revealed poorer sleep among sexual minority youth across multiple dimensions of average sleep and variability than heterosexual youth. Within the sexual minority subsample, ethnically and racially minoritized youth exhibited poorer sleep than White youth; girls showed poorer sleep than boys; ethnically and racially minoritized girls—representing the group with the most marginalized social strata—consistently exhibited poorer sleep compared with groups with fewer marginalized strata. Supplementary analyses within heterosexual youth showed similar pattern, although sex differences were more mixed.</div></div><div><h3>Conclusions</h3><div>Sexual minority youth, especially those with more marginalized social strata (e.g., ethnically and racially minoritized youth, girls, and ethnically and racially minoritized girls), experience multidimensional sleep disparities. These disparities emerge as early as ages 10-13. Early, targeted intervention and policy efforts are needed to address and mitigate sleep health inequities in these vulnerable groups.</div></div>","PeriodicalId":48545,"journal":{"name":"Sleep Health","volume":"12 3","pages":"Pages 415-424"},"PeriodicalIF":3.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147822334","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":"Sleep quality, chronotype, and quality of life among Ukrainian students during war and armed conflict: A cluster analysis","authors":"Tetiana Vasylivna Mamontova ,&nbsp;Veronika Dmytrivna Mamontova ,&nbsp;Yevheniia Olehivna Zabazhan MMed ,&nbsp;Anatolii Anatoliiovich Miahkokhlib MMed","doi":"10.1016/j.sleh.2026.01.005","DOIUrl":"10.1016/j.sleh.2026.01.005","url":null,"abstract":"<div><h3>Objective</h3><div>War is a significant factor affecting human health, including sleep and quality of life, however, the characteristics of these differences among university students remain insufficiently studied. This study aimed to comprehensively assess sleep quality, chronotype, and quality of life indicators among students under wartime conditions, to analyze their inter-relationships, and to identify clusters based on their combined patterns.</div></div><div><h3>Methods</h3><div>A cross-sectional study included 130 students aged 18-25 years. The Pittsburgh Sleep Quality Index (PSQI), the Morningness-Eveningness Questionnaire (MEQ), and the Short Form-36 Health Survey (SF-36) were used to assess sleep quality, chronotype, and quality of life.</div></div><div><h3>Results</h3><div>Poor sleep quality was identified in 67.7% of participants (PSQI: 5.92 ± 2.96). Students with poor sleep quality were more likely to live separately from family members (<em>p</em> &lt; .05) and to consume alcohol (<em>p</em> &lt; .05). According to chronotype, participants were classified as intermediate (74.6%), morning (9.2%), or evening (16.1%). The evening chronotype was associated with lower vitality scores (<em>p</em> &lt; .05) and more frequent alcohol consumption (<em>p</em> &lt; .05). Poor sleep quality was associated with lower scores in the physical and mental health components; the mental component of quality of life emerged as an independent predictor of poor sleep quality (OR = 0.41; 95% CI: 0.20-0.86). Cluster analysis identified four distinct profiles of sleep quality, chronotype, and quality of life, ranging from optimal to less favorable.</div></div><div><h3>Conclusions</h3><div>The findings indicate that, under wartime conditions, the interaction between sleep quality, chronotype, and quality of life among students forms four distinct profiles, reflecting the multidimensional nature of the impact of the war context on sleep and related aspects of students’ quality of life.</div></div>","PeriodicalId":48545,"journal":{"name":"Sleep Health","volume":"12 3","pages":"Pages 469-478"},"PeriodicalIF":3.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147460659","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":"Cardiovascular disease risk in former National Football League players: Impact of obstructive sleep apnea","authors":"Ian M. Greenlund PhD ,&nbsp;Marc A. Forgione MD ,&nbsp;Scott A. Perryman BA ,&nbsp;Jonathan R. Perryman MD ,&nbsp;David Gergen CDT ,&nbsp;Rodolfo Martínez-Ferraté MD ,&nbsp;Joshua M. Bock PhD ,&nbsp;Naima Covassin PhD ,&nbsp;Jan Bukartyk MS ,&nbsp;Ben W. Eidem MD ,&nbsp;Arthur J. Roberts MD ,&nbsp;Virend K. Somers MD, PhD","doi":"10.1016/j.sleh.2026.01.009","DOIUrl":"10.1016/j.sleh.2026.01.009","url":null,"abstract":"<div><h3>Objectives</h3><div>Obstructive sleep apnea (OSA) and cardiovascular disease (CVD) are common among former National Football League (NFL) athletes. The Living Heart Foundation Heart-Obesity-Prevention-Education Group aimed to provide an in-depth health assessment for CVD risk factors among former NFL players to examine the role of OSA on blood pressure (BP) and echocardiographic measurements. We hypothesized that OSA, maladaptive echocardiographic measurements, and elevated BP would be highly prevalent in former NFL athletes, and that OSA presence would detrimentally impact echocardiographic parameters.</div></div><div><h3>Methods</h3><div>One hundred fifty-three former NFL athletes were enrolled in the present study by the Living Heart Foundation. Participants (n = 86) completed a cardiovascular health assessment consisting of clinical BP readings and transthoracic echocardiography (TTE), which included measurements of ejection fraction, left atrial size, left ventricle diastolic diameter, intraventricular septal thickness, and ascending aortic diameter. Participants (n = 110) were offered an at-home overnight sleep test to estimate sleep apnea presence and severity.</div></div><div><h3>Results</h3><div>Eighty percent (n = 80) of our sample exhibited at least mild OSA. Further, 34% (n = 29) displayed elevated intraventricular septal thickness and subclinical aortic dilation was present in 47% (n = 40) of retired NFL athletes. In a subanalysis of former players with at-home sleep apnea testing results and TTE measures (n = 43), NFL retirees with OSA demonstrated greater ascending aortic diameter compared to former NFL athletes without OSA (<em>p</em> = .048).</div></div><div><h3>Conclusions</h3><div>There is a high prevalence of suspected OSA, in addition to maladaptive cardiac remodeling, among retired NFL athletes. Aortic abnormalities may be related to untreated OSA in this population.</div></div>","PeriodicalId":48545,"journal":{"name":"Sleep Health","volume":"12 3","pages":"Pages 520-525"},"PeriodicalIF":3.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147482002","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}