Sleep Health最新文献

{"title":"ChatGPT's role in sleep health: Informative or misleading.","authors":"Mila Yunita, Palasara Brahmani Laras, Fiki Prayogi, Inas Afifah Zahra, Ilham Khairi Siregar","doi":"10.1016/j.sleh.2025.02.008","DOIUrl":"https://doi.org/10.1016/j.sleh.2025.02.008","url":null,"abstract":"","PeriodicalId":48545,"journal":{"name":"Sleep Health","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144043568","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":"Sustained impacts of neighborhood investments on sleep health over a 5-year period: Insights from a natural experiment in two urban neighborhoods in the United States.","authors":"Wendy M Troxel, Ann Haas, Bonnie Ghosh-Dastidar, Andrea L Rosso, Lauren Hale, Matthew P Buman, Tamara Dubowitz","doi":"10.1016/j.sleh.2025.03.009","DOIUrl":"https://doi.org/10.1016/j.sleh.2025.03.009","url":null,"abstract":"<p><strong>Study objectives: </strong>Neighborhood disinvestment is a downstream impact of structural racism, especially afflicting minoritized communities. Thus, neighborhood reinvestment may serve as a policy-level intervention to mitigate sleep and other health disparities. This study builds on previous work by leveraging a natural experimental design to evaluate the 5-year effects of neighborhood investments on residents' sleep.</p><p><strong>Methods: </strong>Data are from the PHRESH Zzz study, a natural experiment conducted in two low-income, predominantly Black American, urban neighborhoods in Pittsburgh, Pennsylvania, with a randomly selected cohort [n=567; mean age=54.8 (SD=14.8); 77.6% female; 96% Black]. Sleep duration, wakefulness after sleep onset, and sleep efficiency were assessed via actigraphy and sleep quality via survey in 2013, 2016, and 2018. All publicly funded neighborhood investments between 2013 and 2016 were recorded and geocoded to calculate the distance from each respondent's residence to the investment. The primary exposure variable was residents' proximity to neighborhood investments (<0.1 of a mile).</p><p><strong>Results: </strong>The overall pattern of results showed worsening sleep over time, regardless of exposure to investments. However, over the 5-year period, those who lived physically close to investments (<0.1 mile) experienced significantly smaller decreases in sleep efficiency and smaller increases in wakefulness after sleep onset, relative to those who lived farther away.</p><p><strong>Conclusions: </strong>Previously, we found that living near a neighborhood investment improved sleep outcomes over a short-term period of 3years. Current results indicate that improvements were partially sustained over 5years. Findings have implications for policy initiatives targeting upstream, structural determinants of sleep health disparities.</p>","PeriodicalId":48545,"journal":{"name":"Sleep Health","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144034770","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":"Association between household sleep environment and sleep health characteristics in middle-aged adults: The CARDIA sleep study.","authors":"Swaty Chapagai, Thanh-Huyen Vu, Shaina J Alexandria, Kathryn J Reid, Sabra Abbott, Katharine Harrington, S Justin Thomas, Cora E Lewis, Pamela J Schreiner, Mercedes R Carnethon, Kristen L Knutson","doi":"10.1016/j.sleh.2025.02.010","DOIUrl":"https://doi.org/10.1016/j.sleh.2025.02.010","url":null,"abstract":"<p><strong>Objectives: </strong>Household environmental factors and sleep hygiene may contribute to poor sleep health. We identified associations between household sleep environment (HHSE) and sleep health characteristics in White and Black adults.</p><p><strong>Methods: </strong>This study included cross-sectional data from the CARDIA sleep ancillary study at Year 35 (n=711). HHSE was assessed in two domains (sleep disruptors and sleep hygiene) using a questionnaire, and higher scores indicated more sleep disruptors or poorer sleep hygiene. Sleep outcomes included (1) self-reported sleep quality and daytime sleepiness and (2) actigraphy-measured sleep duration, sleep percentage, sleep timing (midpoint sleep time), and sleep regularity. We used robust regression to estimate differences in sleep outcomes corresponding to each 1-point increment in HHSE. Racial differences in associations of interest were examined by testing for interaction.</p><p><strong>Results: </strong>Participants' mean age was 61.5 (SD=3.6) years, 63% were women, and 36.7% were Black. After multivariable adjustment (β [95% CI]), more sleep disruptors (0.145 [0.04, 0.24]) and poor sleep hygiene (0.170 [0.10, 0.23]) were associated with self-reported poor sleep quality. Poor sleep hygiene was associated with actigraphy-measured shorter sleep duration (-1.397 [-2.73, -0.01]) and sleep irregularity (0.017 [0.01, 0.02]). In stratified analysis, more sleep disruptors were associated with poor sleep quality (0.320 [0.10, 0.53]) and greater daytime sleepiness (0.330 [0.11, 0.54]) only in Black participants.</p><p><strong>Conclusions: </strong>Poor HHSE was related to self-reported poor sleep quality and to objective shorter sleep duration and sleep irregularity. Targeted interventions to mitigate sleep disruptors and promote good sleep hygiene may help to improve sleep health.</p>","PeriodicalId":48545,"journal":{"name":"Sleep Health","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144057477","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 a multidimensional polygenic sleep health score and a sleep lifestyle index with disease outcomes and their interaction in a clinical biobank.","authors":"Valentina Paz, Hannah Wilcox, Matthew Goodman, Heming Wang, Victoria Garfield, Richa Saxena, Hassan S Dashti","doi":"10.1016/j.sleh.2025.02.009","DOIUrl":"https://doi.org/10.1016/j.sleh.2025.02.009","url":null,"abstract":"<p><strong>Objectives: </strong>Sleep is a complex behavior regulated by genetic and environmental factors impacting disease outcomes. However, the effect of multidimensional sleep encompassing several sleep dimensions on common diseases, specifically mental health disorders, has yet to be fully elucidated. Using the Mass General Brigham Biobank, we examined the association of multidimensional sleep with disease outcomes and investigated whether sleep behaviors modulate genetic predisposition to unfavorable sleep on mental health diseases.</p><p><strong>Methods: </strong>We generated a Polygenic Sleep Health Score using previously identified single nucleotide polymorphisms and constructed a Sleep Lifestyle Index based on self-reported questions and electronic health records; tested their association; performed phenome-wide association analyses between these indexes and clinical phenotypes; and analyzed their interaction on prevalent mental health diseases. A total of 15,884 participants were included in the analysis (mean age 54.4; 58.6% female).</p><p><strong>Results: </strong>The Polygenic Sleep Health Score was associated with the Sleep Lifestyle Index (β=0.050, 95% CI=0.032, 0.068) and with 114 disease outcomes spanning 12 disease groups, including obesity, sleep, and substance use disease outcomes (p<3.3×10^-5). The Sleep Lifestyle Index was associated with 458 disease outcomes spanning 17 groups, including sleep, mood, and anxiety disease outcomes (p<5.1×10^-5). A total of 108 disease outcomes were associated with both indexes, spanning 12 disease groups. No interactions were found between the indexes on mental health diseases.</p><p><strong>Conclusions: </strong>Favorable sleep behaviors and genetic predisposition to healthy sleep may independently protect against disease, underscoring the impact of multidimensional sleep on population health and the need for prevention strategies focused on healthy sleep habits.</p>","PeriodicalId":48545,"journal":{"name":"Sleep Health","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144042636","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":"Temporal changes in sleep parameters and body mass index after using a sleep-tracking app with gamification.","authors":"Masao Iwagami, Jaehoon Seol, Masashi Yanagisawa","doi":"10.1016/j.sleh.2025.03.001","DOIUrl":"https://doi.org/10.1016/j.sleh.2025.03.001","url":null,"abstract":"<p><strong>Objectives: </strong>We examined whether sleep parameters would improve with a sleep-tracking game app and whether the participants' improvement was associated with changes in their body mass index.</p><p><strong>Methods: </strong>We analyzed data from 2063 volunteer Japanese participants (mean age 38.3±10.7years, 82.1% female) who used both the Pokémon Sleep and Asken apps for ≥90days.</p><p><strong>Results: </strong>During the 90days after starting the Pokémon Sleep app, on average, total sleep time increased by approximately 0.8 hour from 5.5 to 6.3 hours. Calculating the slopes for individuals, improvements were noted for total sleep time, sleep latency, percentage of wakefulness after sleep onset, and bedtime among 45.3%, 18.1%, 24.4%, and 21.3% of the participants, respectively. Body mass index tended to decrease faster in participants with improved total sleep time, sleep latency, and bedtime.</p><p><strong>Conclusions: </strong>A sleep-tracking game app improved sleep parameters of some users, and this improvement was associated with a decrease in body mass index.</p>","PeriodicalId":48545,"journal":{"name":"Sleep Health","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144041858","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":"Housing well-being and sleep in Australia.","authors":"Shannon L Edmed, M Mamun Huda, Md Ashraful Alam, Cassandra L Pattinson, Kalina R Rossa, Shamsi Shekari Soleimanloo, Simon S Smith","doi":"10.1016/j.sleh.2025.02.001","DOIUrl":"https://doi.org/10.1016/j.sleh.2025.02.001","url":null,"abstract":"<p><strong>Objectives: </strong>The built and social aspects of housing influence a broad range of health and social outcomes and may be important enablers of barriers to occupants accessing sufficient and good-quality sleep. The aim of this study was to examine the relationships between housing well-being (a conceptualization of housing-related [dis]advantage), with self-reported sleep outcomes in Australians.</p><p><strong>Methods: </strong>We conducted a cross-sectional analysis of Wave 21 (2021) of the Household, Income and Labour Dynamics in Australia (HILDA) Survey, a longitudinal, population-based study of Australian households. Multilevel logistic regression models were used to examine the association between \"housing well-being\" and sleep outcomes. The sample analyzed consisted of 13,516 respondents.</p><p><strong>Results: </strong>Greater housing insecurity (a composite score comprising tenure type, housing affordability stress, and housing mobility) was associated with higher odds of insufficient sleep (AOR: 1.28; 95% CI: 1.15, 1.41) and sleep disturbance (AOR: 1.13; 95% CI: 1.04, 1.22) after adjustment. Unsuitable housing (a composite score comprising dwelling type, occupant density, and housing quality) was associated with increased odds of insufficient sleep (AOR: 1.36; 95% CI: 1.19, 1.55), napping (AOR: 1.13; 95% CI: 1.02, 1.25), and sleep disturbance (AOR: 1.14; 95% CI: 1.03, 1.26) after adjustment.</p><p><strong>Conclusions: </strong>Improving housing condition and circumstances may support the sleep health of Australians. Future research should explore the ways in which sleep may play a mediating role through which built environments affect other health and well-being outcomes.</p>","PeriodicalId":48545,"journal":{"name":"Sleep Health","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144019612","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":"An ecological analysis of sleep health across five African-origin populations spanning the epidemiologic transition.","authors":"Candice Choo-Kang, Stephanie J Crowley, Sirimon Reutrakul, Dale E Rae, Estelle V Lambert, Nandipha Sinyanya, Pascal Bovet, Bharathi Viswanathan, Kweku Bedu-Addo, Jacob Plange-Rhule, Prince Oti-Boateng, Oscar Akunor Dei, Kingsley Apusiga, Terrence E Forrester, Marie Williams, Michaela Deglon, Jack A Gilbert, Brian T Layden, Cara Joyce, Amy Luke, Lara R Dugas","doi":"10.1016/j.sleh.2025.02.002","DOIUrl":"https://doi.org/10.1016/j.sleh.2025.02.002","url":null,"abstract":"<p><strong>Background: </strong>Sleep health is emerging as a public health priority due to its strong associations with several key domains of health. However, most of the existing literature are from studies located in high income settings and may not be representative of low-middle income settings. Leveraging the Modeling the Epidemiologic Transition Study, a study of cardiometabolic disease risk in five diverse African-origin populations, we explored differences in objectively measured sleep behavior across cohorts from Ghana, South Africa, Jamaica, Seychelles, and the United States.</p><p><strong>Methods: </strong>Data from 809 participants (35-55 years old, 63% women) from the 5 Modeling the Epidemiologic Transition Study research sites were included. Objectively-measured sleep, using actigraphy, was scored according to the criteria of Patel and colleagues. For those with at least 5 nights of valid data, ecological mean sleep onset time, wake-up time, sleep duration, wake after sleep onset, and sleep efficiency were examined.</p><p><strong>Findings: </strong>Adjusted models indicate that sleep onset was earlier in all sites when compared to US (p<.005). Sleep efficiency varied by locations, being lower in participants from Ghana, South Africa, and Jamaica when compared to United States (Ghana β: -3.7, South Africa: -5.8, Jamaica: -1.3, p<.05 for all) and higher in Seychelles when compared to United States (Seychelles β: 1.6; p=.02). Women presented with shorter sleep duration but with higher sleep efficiency.</p><p><strong>Interpretation: </strong>Sleep duration, timing (wake time, midsleep time and sleep onset), and efficiency differ by country and sex, likely driven by socio-economic settings. Understanding sleep patterns in different contexts is needed to make informed and culturally appropriate health recommendations.</p>","PeriodicalId":48545,"journal":{"name":"Sleep Health","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143781541","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":"Characterizing the sleep location, patterns, and maternally perceived sleep problems of the infants of Māori and non-Māori mothers in Aotearoa New Zealand","authors":"Mikaela L. Carter ALM, BSc ,&nbsp;Sarah-Jane Paine PhD ,&nbsp;Bronwyn M. Sweeney PhD ,&nbsp;Joanne E. Taylor PhD ,&nbsp;T. Leigh Signal PhD","doi":"10.1016/j.sleh.2024.10.010","DOIUrl":"10.1016/j.sleh.2024.10.010","url":null,"abstract":"<div><h3>Objectives</h3><div>To investigate potential sleep inequities between the infants of Māori and non-Māori mothers in Aotearoa New Zealand, identify socio-ecological factors associated with infant sleep, and determine features of infant sleep that contribute to a mother-perceived infant sleep problem.</div></div><div><h3>Design</h3><div>Secondary analysis of longitudinal data from the Moe Kura: Mother and Child, Sleep and Well-being in Aotearoa New Zealand study when infants were approximately 12 weeks old.</div></div><div><h3>Participants</h3><div>383 Māori and 702 non-Māori mother-infant dyads.</div></div><div><h3>Methods</h3><div>Chi-square and independent t-tests measured bivariate associations between maternal ethnicity and infant sleep characteristics. Multivariable and ordinal logistic regression models assessed the relative impact of different socio-ecological factors on infant sleep outcome variables.</div></div><div><h3>Results</h3><div>Key developmental markers of infant sleep did not differ by maternal ethnicity. There were some ethnicity-based differences in sleep location. Maternal ethnicity, maternal age, parity, maternal depression, maternal relationship status, life stress, breastfeeding, work status, and bedsharing were related to different dimensions of infant sleep, and to maternal perceptions of a sleep problem.</div></div><div><h3>Conclusion</h3><div>Sleep at 12<!--> <!-->weeks is highly variable between infants and is associated with numerous socio-ecological factors. Findings support a social determinants explanation for sleep health inequities seen later in childhood.</div></div>","PeriodicalId":48545,"journal":{"name":"Sleep Health","volume":"11 2","pages":"Pages 158-165"},"PeriodicalIF":3.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142927619","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":"Performance evaluation of a machine learning-based methodology using dynamical features to detect nonwear intervals in actigraphy data in a free-living setting","authors":"Jyotirmoy Nirupam Das ,&nbsp;Linying Ji PhD ,&nbsp;Yuqi Shen ,&nbsp;Soundar Kumara PhD ,&nbsp;Orfeu M. Buxton PhD ,&nbsp;Sy-Miin Chow PhD","doi":"10.1016/j.sleh.2024.10.003","DOIUrl":"10.1016/j.sleh.2024.10.003","url":null,"abstract":"<div><h3>Goal and aims</h3><div>One challenge using wearable sensors is nonwear time. Without a nonwear (e.g., capacitive) sensor, actigraphy data quality can be biased by subjective determinations confounding sleep/wake classification. We developed and evaluated a machine learning algorithm supplemented by dynamic features to discern wear/nonwear episodes.</div></div><div><h3>Focus technology</h3><div>Actigraphy data from wrist actigraph (Spectrum, Philips-Respironics).</div></div><div><h3>Reference technology</h3><div>The built-in nonwear sensor as “ground truth” to classify nonwear periods using other data, mimicking features of Actiwatch 2.</div></div><div><h3>Sample</h3><div>Data were collected over 1<!--> <!-->week from employed adults (n = 853).</div></div><div><h3>Design</h3><div>Extreme gradient boosting (XGBoost), a tree-based classifier algorithm, was used to classify wear/nonwear, supplemented by dynamic features calculated over various time windows.</div></div><div><h3>Core analytics</h3><div>The performance of the proposed algorithm was tested over 30-second epochs.</div><div>Additional analytics and exploratory analyses: Evaluation of the SHapley Additive exPlanations (SHAP) values to find the effectiveness of the dynamic features.</div></div><div><h3>Core outcomes</h3><div>The XGBoost classifier yielded substantial improvements in balanced accuracy, sensitivity, and specificity, including dynamic features and comparison to default actiwatch classification algorithms.</div></div><div><h3>Important supplemental outcomes</h3><div>The proposed classifier effectively distinguished between valid and invalid days, and the duration of contiguous periods of nonwear correctly identified.</div></div><div><h3>Core conclusion</h3><div>Our findings highlight the potential of XGBoost using dynamic features of varying activity levels across the time series to provide insights on wear/nonwear classification using a large dataset. The methodology provides an alternative to laborious manual benchmarking of the data for similar devices that do not have a nonwear sensor.</div></div>","PeriodicalId":48545,"journal":{"name":"Sleep Health","volume":"11 2","pages":"Pages 166-173"},"PeriodicalIF":3.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142956756","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":"Deportation entanglement and US Latino sleep health: A nationally representative multiwave study","authors":"Eileen Díaz McConnell PhD ,&nbsp;Connor M. Sheehan PhD","doi":"10.1016/j.sleh.2025.01.004","DOIUrl":"10.1016/j.sleh.2025.01.004","url":null,"abstract":"<div><h3>Objectives</h3><div>This study uses nationally representative multiwave data to analyze the relationship between the US mass deportation system and the sleep health of Latino/Hispanic adults. Deportation-related concerns can be a unique source of stress and rumination relevant for the sleeping patterns of the US Latino population.</div></div><div><h3>Methods</h3><div>We analyze two waves of Pew Research Center survey data about Latinos collected in 2019 and 2020 (N<!--> <!-->=<!--> <!-->1591). We fit logistic regression models to investigate whether emotional and tangible entanglements with the deportation system in December 2019 were linked with self-reported sleep troubles among Latinos in March 2020, net of other covariates.</div></div><div><h3>Results</h3><div>Nearly half of Latinos worry “some” or a “lot” about deportation and nearly half know someone who has been recently deported or detained. A third of Latino adults reported moderate to a lot of trouble sleeping three or more nights per week. The logistic regression results reveal that controlling for a comprehensive set of covariates, Latinos who were “a lot” worried about deportation or who know people who have been deported/detained were significantly more likely to report that they had trouble sleeping.</div></div><div><h3>Conclusions</h3><div>The results suggest that the US deportation system presents a unique source of stress and rumination that negatively impacts Latino sleep. These findings advance social scientific knowledge and the socio-ecological framework regarding how US policy priorities of deporting and detaining immigrants can potentially alter the sleeping patterns of a large segment of Americans, even among those not directly targeted for removal from the country.</div></div>","PeriodicalId":48545,"journal":{"name":"Sleep Health","volume":"11 2","pages":"Pages 133-139"},"PeriodicalIF":3.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143504457","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}