Sleep Duration, but Not Screen-Based Sedentary Time, Predicts Glycemic Control: A Weighted NHANES 2017–2020 Analysis

Sleep Duration, but Not Screen-Based Sedentary Time, Predicts Glycemic Control: A Weighted NHANES 2017–2020 Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sleep Duration, but Not Screen-Based Sedentary Time, Predicts Glycemic Control: A Weighted NHANES 2017–2020 Analysis Sanjay Chalil Kundil This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9141383/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Both inadequate sleep and a sedentary lifestyle are associated with metabolic disorders. However, the effects of prolonged periods of screen-based sedentary behavior on long-term glycemic control in adults have not been fully studied. This study examined the independent associations of sleep duration and screen-based sedentary time with glycated hemoglobin (HbA1c) levels in U.S. adults. Methods We analyzed data from the NHANES 2017–March 2020 cycle ( N = 8,242). A multiple linear regression analysis examined relationships between sleep length, screen-based sedentary time, and hemoglobin A1c (HbA1c) while controlling for the effects of age and body mass index (BMI). Results The relationship between sleep duration and HbA1c was statistically significant, with more sleep correlating with lower HbA1c levels (𝛃 = -0.023, p < 0.001). Specifically, for every hour of sleep reported by participants, HbA1c decreased by 0.023 percentage points per additional hour of sleep. Conversely, there was no relationship between sedentary screen-based time and HbA1c levels (p = .998). Age and body mass index (BMI) were also statistically significantly related to HbA1c levels. Conclusion These findings suggest that glycemic control is more strongly associated with sleep duration and adiposity than with screen-based sedentary behavior. Therefore, it appears that the metabolic risk factors associated with digital sedentary time are primarily attributed to sleep disruption. Public health initiatives should prioritize sleep hygiene as an important strategy for improving metabolic health. Psychology NHANES HbA1c Sleep Duration Screen Time Metabolic Health Figures Figure 1 Introduction Daily use of digital devices has increased substantially over the past two decades. People living in industrialized nations can spend anywhere from a few to several hours using their tablets, computers, or televisions, which has led to increasing concern about the health risks attributed to excessive screen use [ 1 , 2 ]. Prolonged exposure to screens has been linked to numerous behavioral and physiological outcomes, such as decreases in total hours slept, increases in sedentary behaviors (i.e., TV and smartphone) and increases in cardiometabolic risk factors. Sleep duration is a significant contributor to metabolic health. An extensive number of epidemiological studies have shown that not getting enough sleep can negatively affect glucose metabolism, insulin sensitivity, and increase the likelihood of developing type 2 diabetes [ 3 , 4 ]. Experimental research has demonstrated that having your sleep time shortened can have an impact on the body’s endogenous control of glucose and appetite which can then lead to long-term metabolic dysregulation. One potential mechanism that links the use of digital devices and poor metabolic outcomes is through the disruption of sleep. Light emitted from screens during evening hours can inhibit the release of melatonin and delay the body's circadian rhythm, which may result in shorter and/or poorer quality of sleep [ 5 ]. Thus, screen time could indirectly affect metabolic health by its effect on total sleep time and circadian control. While much of the existing literature focuses on the pediatric population, the metabolic consequences for adults—who may face different occupational and social screen-use pressures—remain less clear. Furthermore, most studies utilize fasting glucose measures, which may not capture the long-term glycemic trends represented by Glycated Hemoglobin (HbA1c). Nevertheless, studies measuring the direct association between screen use and glycemic control show mixed results. A number of studies show a correlation between sedentary forms of digital behaviors and metabolic risk. However, some studies published indicate that many of these associations can be explained by behavioral patterns, such as physical inactivity, obesity, or sleep problems [ 2 ]. Thus, it is still undetermined if screen time independently predicts metabolic outcomes when controlling for important lifestyle and demographic factors. To address this research gap, this current study analysed the associations between daily screen usage, hours of sleep, and glycemic control in a nationally representative sample of US adults. Data for this study are publicly available from the Centers for Disease Control and Prevention (CDC) National Health and Nutrition Examination Survey (NHANES) website. Using these data, we examined whether sleep duration and screen-based sedentary time are independently associated with glycated haemoglobin (HbA1c) after adjusting for age and body mass index. Methods Data Source and Study Population The data for this study were obtained from the National Health and Nutrition Examination Survey (NHANES) cycle spanning 2017 to March 2020. NHANES is a cross-sectional, nationally representative survey conducted by the National Center for Health Statistics (NCHS) that utilizes a complex, multistage, probability sampling design to assess the health and nutritional status of the non-institutionalized U.S. population. For the current analysis, a sub-sample of adults aged 18 years and older was utilized. Participants were included if they completed the Mobile Examination Center (MEC) laboratory component and provided complete data for the primary variables of interest: Glycated Hemoglobin (HbA1c), sleep duration, and sedentary screen time. After merging the demographic, examination, and laboratory datasets and removing records with missing values, the final analytical sample consisted of N=8,242 participants. Measures The primary outcome variable was glycemic control, which is measured by the percentage of glycated hemoglobin (HbA1c) from whole blood samples. HbA1c reflects average blood glucose levels over the prior two to three-month period. Sleep duration was collected via self-reported data on the number of hours typically slept on weekdays/nights as a continuous variable measured as hours per night. Sedentary screen time refers to time spent watching television or using computers or handheld devices outside of work or school. These were also self-reported, with participants estimating the typical amount of time spent in sedentary screen time per day. For potential confounding factors, model adjustments included age measured in years and body mass index (BMI; kg/m 2 ), which was calculated using height/weight recorded at the physical examination. Statistical Analysis Statistical analyses were conducted using JASP (version 0.18.3) and R . To ensure the findings were representative of the U.S. population, all analyses incorporated the NHANES-provided Full Sample MEC Exam Weights (WTMECPRP). These weights account for the complex survey design, including oversampling of certain demographic groups and non-response bias. A weighted multiple linear regression was employed to examine the independent associations of sleep duration and screen time with HbA1c levels while adjusting for Age and BMI. The model's assumptions—including linearity, homoscedasticity, and normality of residuals—were verified. Statistical significance was defined as p < .05. Ethical Approval NHANES protocols were approved by the NCHS Ethics Review Board, and all participants provided informed consent. This secondary analysis of de-identified public data was exempt from further institutional review. Results Sample Characteristics The final analytical sample consisted of 8,242 adults representative of the U.S. population. The demographic and clinical characteristics of the study population, including weighted means and proportions for all primary variables, are summarized in Table 1. The weighted mean age of the sample was 49.46 years (SD = 18.33), and the mean body mass index (BMI) was 29.87 kg/m 2 (SD = 7.507). Participants reported an average habitual sleep duration of 7.59 hours per night (SD = 1.668) and a mean daily screen-based sedentary time of 384.5 minutes (SD = 730.9). The mean HbA1c for the population was 5.83% (SD = 1.10). Multiple Regression Analysis A weighted multiple linear regression was conducted to evaluate the independent associations of sleep duration and screen time with HbA1c levels, while adjusting for Age and BMI. The overall model was statistically significant, F (4, 8237) = 287.3, p < .001 , and explained 12.2% of the variance in HbA1c (R 2 = 0.122, Adjusted R 2 = 0.122). Predictors of HbA1c A weighted multiple linear regression was conducted to evaluate the independent associations of sleep duration and screen time with HbA1c levels, the results of which are presented in Table 2. Sleep duration was shown to be the most strongly associated independent predictor of HbA1c levels (𝛃 = -0.023, SE = 0.007, t = -3.33, p < .001 ). For every additional hour of sleep, HbA1c levels were 0.023 points lower, controlling for both adiposity and age. In contrast, time spent on a screen each day was not significantly associated with HbA1c levels (𝛃 = 4.32 ✕ 10 -8 , p = .998). The coefficient being effectively zero shows that screentime exposure itself did not add much to predicting blood glucose under this model. Regarding the covariates, both BMI (𝛃 = 0.025, p < .001) and age (𝛃 = 0.018, p < .001) maintained strong, statistically significant associations with HbA1c levels. Table 2: Weighted Multiple Linear Regression Predicting HbA1c Levels Predictor 𝛃 SE t p (Intercept) 4.380 0.077 56.632 < .001 Sleep Duration (hours) -0.023 0.007 -3.332 < .001 Screen Time (minutes) 0.000* 0.000 0.003 .998 BMI (kg/m 2 ) 0.025 0.002 16.166 < .001 Age (years) 0.018 0.001 28.892 < .001 Note: R 2 = .122; F (4, 8237) = 287.3, p < .001. *The unstandardized coefficient for Screen Time was 4.32 x 10 -8 . Graphical Analysis of Predictors Partial regression plots were generated to visualize the independent effects of each variable while holding others constant, as illustrated in Figure 1. The plot for sleep duration in Figure 2 demonstrates a clear negative slope, confirming that increased sleep is associated with lower HbA1c. In contrast, the plot for screen time in Figure 3 displays a null slope, reinforcing the regression finding that screen-based sedentary behavior does not independently predict glycemic status in this model. The final study population was determined after excluding participants with missing glycemic data or incomplete sedentary behavior surveys, resulting in a weighted sample representing the non-institutionalized U.S. adult population (Figure 4). Figure 1A: Partial regression plot showing the significant inverse relationship between sleep duration and HbA1c, maintaining Age and BMI as constants. Figure 1B: Partial regression plot for screen-based sedentary time, illustrating the lack of independent association with glycemic control. Figures 1C and 1D: Partial regression plots confirming the positive associations between BMI, Age, and HbA1c levels. Discussion The present study utilized a nationally representative sample of U.S. adults to examine the relative contributions of sleep duration and screen-based sedentary time to glycemic control. Our primary finding was that sleep duration is a highly significant independent predictor of HbA1c levels ( p < .001) , whereas screen time showed no significant association ( p = .998) once adjusting for Age and BMI. These results suggest that while screen time is a pervasive modern behavior, its metabolic impact may be secondary to the physiological toll of insufficient sleep. This research demonstrates that the findings regarding the relationship between amount of sleep and sedentary habits are consistent with previous research and also contribute to our understanding of how adults are inactive. For example, using previous NHANES datasets, Smiley et al. (2019) demonstrated a strong link between the amount of sleep and the severity of metabolic syndrome and therefore confirmed our results that sleeping a greater number of hours at night is a more reliable indicator for glycemic control than any modern sedentary behaviour [6]. Hancox and Landhuis (2012) similarly confirm that sleeping fewer hours is an important predictor of higher HbA1c levels and increased risk for prediabetes, and therefore, sleep is clearly a fundamental component of the regulation of glucose level within the body [7]. Interestingly, while studies of paediatric populations often find a strong connection between screen time and cardiometabolic risk factors, these findings indicate that the connection between screen time and cardiometabolic risk factors for adult populations is somewhat less robust. These analyses were conducted using the National Health and Nutrition Examination Survey (NHANES) 2017-2020 pre-pandemic data files provided by the Centers for Disease Control and Prevention [8]. Comparing these findings indicates that clinical programming should provide an emphasis on the importance of sleep duration when developing strategies to improve the metabolism of patients. Sleep Duration as a Key Behavioral Predictor Our findings align with experimental research suggesting that sleep restriction directly impairs glucose metabolism through increased sympathetic nervous system activity and altered neuroendocrine control of appetite [4]. Interestingly, the "null" finding for screen time ( p = .998) challenges the assumption that digital sedentary behavior is inherently toxic to blood sugar. Instead, it supports a "displacement hypothesis": screen time may be harmful primarily when it replaces sleep. When sleep duration is held constant, as it was in our model, the independent effect of the screen itself disappears. Clinical and Public Health Implications From a clinical perspective, these results highlight that sleep hygiene may be a more potent target for diabetes prevention than "digital detox" alone. While reducing sedentary time is generally beneficial for health, our data suggest that an extra hour of sleep may provide a more measurable benefit for HbA1c (𝛃 = -0.023) than an equivalent reduction in screen time. For public health interventions, focusing on the quality and duration of the "rest period" may be more effective for glycemic regulation than focusing solely on the "active period" of the day. Strengths and Limitations A major strength of this study is the use of NHANES data , which allows for high generalizability to the U.S. adult population and utilizes gold-standard laboratory measures for HbA1c. However, several limitations must be noted. First, the cross-sectional nature of the data precludes any causal inferences; we cannot definitively state that increasing sleep will lower HbA1c. Second, both sleep and screen time were self-reported, which may be subject to recall bias. Finally, while we controlled for BMI and Age, other factors such as nutritional intake and genetic predisposition were not included in this model. Conclusion In conclusion, sleep duration emerged as a significant and independent predictor of glycemic control in a nationally representative sample of U.S. adults. After adjusting for age and body mass index, individuals reporting longer habitual sleep demonstrated modest but measurable reductions in glycated hemoglobin (HbA1c), whereas daily screen-based sedentary time showed no independent association with glycemic status. These findings suggest that the metabolic consequences often attributed to digital sedentary behavior may be more closely linked to sleep disruption than to screen exposure itself. From a clinical and public health perspective, interventions aimed at improving sleep hygiene and protecting adequate sleep duration may provide greater benefits for glycemic regulation than strategies focused solely on reducing recreational screen time. While the cross-sectional design of this analysis precludes causal inference, the results highlight the importance of considering sleep as a central behavioral determinant of metabolic health in modern digital environments. Future longitudinal and experimental research should further examine the mechanisms linking sleep patterns, digital media use, and metabolic outcomes to better inform prevention strategies for diabetes and related metabolic disorders. Declarations Ethics Statement: "This study is a secondary analysis of de-identified, publicly available data from the National Health and Nutrition Examination Survey (NHANES). The original NHANES data collection protocols were approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board, and all participants provided written informed consent. As this study involves only de-identified secondary data, it was exempt from further Institutional Review Board (IRB) approval." Author Contributions: Sanjay Chalil Kundil was responsible for the study conception and design, data acquisition (NHANES 2017-2020), statistical analysis in JASP and R, interpretation of the results, and drafting and revising the manuscript. Conflict of Interest: The author declares that there are no financial or personal relationships that could be perceived as influencing the research described in this manuscript. Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Data Availability Statement: The datasets analyzed during the current study are available in the CDC NHANES repository:https://www.cdc.gov/nchs/nhanes/index.htm. Disclosures: Human subjects: Consent was obtained or waived by all participants in this study. The NHANES study protocol was approved by the NCHS Research Ethics Review Board. This is a secondary analysis of de-identified public data and is exempt from further IRB review. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work. References Twenge JM, Campbell WK (2018) Associations between screen time and lower psychological well-being among children and adolescents: evidence from a population-based study. Prev Med Rep 12:271–283. 10.1016/j.pmedr.2018.10.003 Stiglic N, Viner RM (2019) Effects of screentime on the health and well-being of children and adolescents: a systematic review of reviews. BMJ Open 9:e023191. 10.1136/bmjopen-2018-023191 Buxton OM, Marcelli E (2010) Short and long sleep are positively associated with obesity, diabetes, hypertension, and cardiovascular disease among adults in the United States. Soc Sci Med 71:1027–1036. 10.1016/j.socscimed.2010.05.041 Spiegel K, Knutson K, Leproult R, Tasali E, Cauter EV (2005) Sleep loss: a novel risk factor for insulin resistance and Type 2 diabetes. J Appl Physiol 99(5):2008–2019. https://doi.org/10.1152/japplphysiol.00660.2005 Chang AM, Aeschbach D, Duffy JF, Czeisler CA (2015) Evening use of light-emitting eReaders negatively affects sleep, circadian timing, and next-morning alertness. Proc Natl Acad Sci U S A 112:1232–1237. 10.1073/pnas.1418490112 Smiley A, King D, Bidulescu A (2019) The Association between Sleep Duration and Metabolic Syndrome: The NHANES 2013/2014. Nutrients 11(11):2582. https://doi.org/10.3390/nu11112582 Hancox RJ, Landhuis CE (2011) Association between sleep duration and haemoglobin A1cin young adults. J Epidemiol Commun Health 66(10):957–961. https://doi.org/10.1136/jech-2011-200217 Centers for Disease Control and Prevention (CDC): National Health and Nutrition Examination Survey (NHANES) 2017-March 2020 pre-pandemic data files (2021) Accessed: March 14, 2026: https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?cycle=2017-2020 Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9141383","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":607151625,"identity":"21c49a05-377e-4938-bd12-6d9640483885","order_by":0,"name":"Sanjay Chalil Kundil","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYJACAxBmY2Y+AKQlZIjXwsfOlgDSwkO8TXL8PCCtDIS1yLefPVDws83OmI2Z5/OrGzUWPAzsh49uwGv6mbwEw962ZDM2Zt5t1jnHgA7jSUu7gd8fOQYGvG0HbEBajHPYgFokeMzwapHvf2Ng+BesheeZcc4/IrQw3MgxMAbaAnQYD/Pj3DYitBjceGNgLHMuGeh9NjPm3D4JHjZCfpHvzzEzfFNmZzi///Djzznf6uT42Q8fw+8wBgY2A0Y2CEMCTBJQDgLMDxj+QBgfiFA9CkbBKBgFIxAAAEktPviM5GyNAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0003-5995-3762","institution":"Independent Freelance Researcher","correspondingAuthor":true,"prefix":"","firstName":"Sanjay","middleName":"Chalil","lastName":"Kundil","suffix":""}],"badges":[],"createdAt":"2026-03-16 19:32:45","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":true,"humanSubjectCaseReport":true,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9141383/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9141383/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104888021,"identity":"fc83f289-80b9-45b2-95f4-5de68ae9f358","added_by":"auto","created_at":"2026-03-18 10:13:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":794122,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA: Partial regression plot showing the significant inverse relationship between sleep duration and HbA1c, maintaining Age and BMI as constants.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB: Partial regression plot for screen-based sedentary time, illustrating the lack of independent association with glycemic control.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC and D: Partial regression plots confirming the positive associations between BMI, Age, and HbA1c levels.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9141383/v1/fbedfd3f28cc7db0a99b930f.png"},{"id":104888094,"identity":"5fde1aa2-1497-40ed-9bd3-dafb295fb820","added_by":"auto","created_at":"2026-03-18 10:13:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1688909,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9141383/v1/eb94060d-0d09-42d2-877c-c8b21715b7bb.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eSleep Duration, but Not Screen-Based Sedentary Time, Predicts Glycemic Control: A Weighted NHANES 2017–2020 Analysis\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eDaily use of digital devices has increased substantially over the past two decades. People living in industrialized nations can spend anywhere from a few to several hours using their tablets, computers, or televisions, which has led to increasing concern about the health risks attributed to excessive screen use [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Prolonged exposure to screens has been linked to numerous behavioral and physiological outcomes, such as decreases in total hours slept, increases in sedentary behaviors (i.e., TV and smartphone) and increases in cardiometabolic risk factors.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eSleep duration is a significant contributor to metabolic health. An extensive number of epidemiological studies have shown that not getting enough sleep can negatively affect glucose metabolism, insulin sensitivity, and increase the likelihood of developing type 2 diabetes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Experimental research has demonstrated that having your sleep time shortened can have an impact on the body\u0026rsquo;s endogenous control of glucose and appetite which can then lead to long-term metabolic dysregulation.\u003c/p\u003e \u003cp\u003eOne potential mechanism that links the use of digital devices and poor metabolic outcomes is through the disruption of sleep. Light emitted from screens during evening hours can inhibit the release of melatonin and delay the body's circadian rhythm, which may result in shorter and/or poorer quality of sleep [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Thus, screen time could indirectly affect metabolic health by its effect on total sleep time and circadian control. While much of the existing literature focuses on the pediatric population, the metabolic consequences for adults\u0026mdash;who may face different occupational and social screen-use pressures\u0026mdash;remain less clear. Furthermore, most studies utilize fasting glucose measures, which may not capture the long-term glycemic trends represented by Glycated Hemoglobin (HbA1c).\u003c/p\u003e \u003cp\u003eNevertheless, studies measuring the direct association between screen use and glycemic control show mixed results. A number of studies show a correlation between sedentary forms of digital behaviors and metabolic risk. However, some studies published indicate that many of these associations can be explained by behavioral patterns, such as physical inactivity, obesity, or sleep problems\u003c/p\u003e \u003cp\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Thus, it is still undetermined if screen time independently predicts metabolic outcomes when controlling for important lifestyle and demographic factors.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTo address this research gap, this current study analysed the associations between daily screen usage, hours of sleep, and glycemic control in a nationally representative sample of US adults. Data for this study are publicly available from the Centers for Disease Control and Prevention (CDC) National Health and Nutrition Examination Survey (NHANES) website. Using these data, we examined whether sleep duration and screen-based sedentary time are independently associated with glycated haemoglobin (HbA1c) after adjusting for age and body mass index.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData Source and Study Population\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data for this study were obtained from the National Health and Nutrition Examination Survey (NHANES) cycle spanning 2017 to March 2020. NHANES is a cross-sectional, nationally representative survey conducted by the National Center for Health Statistics (NCHS) that utilizes a complex, multistage, probability sampling design to assess the health and nutritional status of the non-institutionalized U.S. population. For the current analysis, a sub-sample of adults aged 18 years and older was utilized. Participants were included if they completed the Mobile Examination Center (MEC) laboratory component and provided complete data for the primary variables of interest: Glycated Hemoglobin (HbA1c), sleep duration, and sedentary screen time. After merging the demographic, examination, and laboratory datasets and removing records with missing values, the final analytical sample consisted of N=8,242 participants.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003eMeasures\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe primary outcome variable was glycemic control, which is measured by the percentage of glycated hemoglobin (HbA1c) from whole blood samples. HbA1c reflects average blood glucose levels over the prior two to three-month period. Sleep duration was collected via self-reported data on the number of hours typically slept on weekdays/nights as a continuous variable measured as hours per night. Sedentary screen time refers to time spent watching television or using computers or handheld devices outside of work or school. These were also self-reported, with participants estimating the typical amount of time spent in sedentary screen time per day. For potential confounding factors, model adjustments included age measured in years and body mass index (BMI; kg/m\u003csup\u003e2\u003c/sup\u003e), which was calculated using height/weight recorded at the physical examination.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003eStatistical Analysis\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eStatistical analyses were conducted using \u003cstrong\u003eJASP (version 0.18.3)\u003c/strong\u003e and \u003cstrong\u003eR\u003c/strong\u003e. To ensure the findings were representative of the U.S. population, all analyses incorporated the NHANES-provided Full Sample MEC Exam Weights (WTMECPRP). These weights account for the complex survey design, including oversampling of certain demographic groups and non-response bias.\u003c/p\u003e\n\u003cp\u003eA \u003cstrong\u003eweighted multiple linear regression\u003c/strong\u003e was employed to examine the independent associations of sleep duration and screen time with HbA1c levels while adjusting for Age and BMI. The model's assumptions—including linearity, homoscedasticity, and normality of residuals—were verified. Statistical significance was defined as p \u0026lt; .05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthical Approval\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNHANES protocols were approved by the NCHS Ethics Review Board, and all participants provided informed consent. This secondary analysis of de-identified public data was exempt from further institutional review.\u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003e\u003cstrong\u003e\u003cem\u003eSample Characteristics\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe final analytical sample consisted of 8,242 adults representative of the U.S. population. The demographic and clinical characteristics of the study population, including weighted means and proportions for all primary variables, are summarized in Table 1. The weighted mean age of the sample was 49.46 years (SD = 18.33), and the mean body mass index (BMI) was 29.87 kg/m\u003csup\u003e2\u003c/sup\u003e (SD = 7.507). Participants reported an average habitual sleep duration of 7.59 hours per night (SD = 1.668) and a mean daily screen-based sedentary time of 384.5 minutes (SD = 730.9). The mean HbA1c for the population was 5.83% (SD = 1.10).\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003eMultiple Regression Analysis\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eA weighted multiple linear regression was conducted to evaluate the independent associations of sleep duration and screen time with HbA1c levels, while adjusting for Age and BMI. The overall model was statistically significant, \u003cstrong\u003e\u003cem\u003eF\u003c/em\u003e(4, 8237) = 287.3, p \u0026lt; .001\u003c/strong\u003e, and explained \u003cstrong\u003e12.2%\u003c/strong\u003e of the variance in HbA1c (R\u003csup\u003e2\u003c/sup\u003e = 0.122, Adjusted R\u003csup\u003e2\u003c/sup\u003e = 0.122).\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003ePredictors of HbA1c\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eA weighted multiple linear regression was conducted to evaluate the independent associations of sleep duration and screen time with HbA1c levels, the results of which are presented in Table 2. Sleep duration was shown to be the most strongly associated independent predictor of HbA1c levels (𝛃\u003cstrong\u003e\u0026nbsp;= -0.023, \u003cem\u003eSE\u003c/em\u003e = 0.007, \u003cem\u003et\u003c/em\u003e = -3.33, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001\u003c/strong\u003e). For every additional hour of sleep, HbA1c levels were 0.023 points lower, controlling for both adiposity and age. In contrast, time spent on a screen each day was not significantly associated with HbA1c levels (𝛃 = 4.32 ✕ 10\u003csup\u003e-8\u003c/sup\u003e, p = .998). The coefficient being effectively zero shows that screentime exposure itself did not add much to predicting blood glucose under this model. Regarding the covariates, both BMI (𝛃 = 0.025, p \u0026lt; .001) and age (𝛃 = 0.018, p \u0026lt; .001) maintained strong, statistically significant associations with HbA1c levels.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eTable 2: Weighted Multiple Linear Regression Predicting HbA1c Levels\u003c/strong\u003e\u003c/h3\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"661\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e𝛃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e(Intercept)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e4.380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e56.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleep Duration (hours)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-3.332\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; .001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScreen Time (minutes)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e0.000*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e.998\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e16.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 186px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e28.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 109px;\"\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u0026nbsp;\u003c/em\u003e= .122; \u003cem\u003eF\u003c/em\u003e(4, 8237) = 287.3,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e \u0026lt; .001.\u003c/p\u003e\n\u003cp\u003e*The unstandardized coefficient for Screen Time was 4.32 x 10\u003csup\u003e-8\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGraphical Analysis of Predictors\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePartial regression plots were generated to visualize the independent effects of each variable while holding others constant, as illustrated in Figure 1. The plot for sleep duration in Figure 2 demonstrates a clear negative slope, confirming that increased sleep is associated with lower HbA1c. In contrast, the plot for screen time in Figure 3 displays a null slope, reinforcing the regression finding that screen-based sedentary behavior does not independently predict glycemic status in this model. The final study population was determined after excluding participants with missing glycemic data or incomplete sedentary behavior surveys, resulting in a weighted sample representing the non-institutionalized U.S. adult population (Figure 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1A: Partial regression plot showing the significant inverse relationship between sleep duration and HbA1c, maintaining Age and BMI as constants.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003eFigure 1B: Partial regression plot for screen-based sedentary time, illustrating the lack of independent association with glycemic control.\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003e\u003cstrong\u003eFigures 1C and 1D: Partial regression plots confirming the positive associations between BMI, Age, and HbA1c levels.\u003c/strong\u003e\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study utilized a nationally representative sample of U.S. adults to examine the relative contributions of sleep duration and screen-based sedentary time to glycemic control. Our primary finding was that \u003cstrong\u003esleep duration is a highly significant independent predictor of HbA1c levels (\u003cem\u003ep\u003c/em\u003e \u0026lt; .001)\u003c/strong\u003e, whereas screen time showed no significant association (\u003cem\u003ep\u003c/em\u003e = .998) once adjusting for Age and BMI. These results suggest that while screen time is a pervasive modern behavior, its metabolic impact may be secondary to the physiological toll of insufficient sleep.\u003c/p\u003e\n\u003cp\u003eThis research demonstrates that the findings regarding the relationship between amount of sleep and sedentary habits are consistent with previous research and also contribute to our understanding of how adults are inactive. For example, using previous NHANES datasets, Smiley et al. (2019) demonstrated a strong link between the amount of sleep and the severity of metabolic syndrome and therefore confirmed our results that sleeping a greater number of hours at night is a more reliable indicator for glycemic control than any modern sedentary behaviour [6]. Hancox and Landhuis (2012) similarly confirm that sleeping fewer hours is an important predictor of higher HbA1c levels and increased risk for prediabetes, and therefore, sleep is clearly a fundamental component of the regulation of glucose level within the body [7]. Interestingly, while studies of paediatric populations often find a strong connection between screen time and cardiometabolic risk factors, these findings indicate that the connection between screen time and cardiometabolic risk factors for adult populations is somewhat less robust. These analyses were conducted using the National Health and Nutrition Examination Survey (NHANES) 2017-2020 pre-pandemic data files provided by the Centers for Disease Control and Prevention [8]. Comparing these findings indicates that clinical programming should provide an emphasis on the importance of sleep duration when developing strategies to improve the metabolism of patients.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003eSleep Duration as a Key Behavioral Predictor\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eOur findings align with experimental research suggesting that sleep restriction directly impairs glucose metabolism through increased sympathetic nervous system activity and altered neuroendocrine control of appetite [4]. Interestingly, the \"null\" finding for screen time (\u003cem\u003ep\u003c/em\u003e = .998) challenges the assumption that digital sedentary behavior is inherently toxic to blood sugar. Instead, it supports a \"displacement hypothesis\": screen time may be harmful primarily when it replaces sleep. When sleep duration is held constant, as it was in our model, the independent effect of the screen itself disappears.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003eClinical and Public Health Implications\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eFrom a clinical perspective, these results highlight that \u003cstrong\u003esleep hygiene may be a more potent target for diabetes prevention than \"digital detox\" alone.\u003c/strong\u003e While reducing sedentary time is generally beneficial for health, our data suggest that an extra hour of sleep may provide a more measurable benefit for HbA1c (𝛃 = -0.023) than an equivalent reduction in screen time. For public health interventions, focusing on the quality and duration of the \"rest period\" may be more effective for glycemic regulation than focusing solely on the \"active period\" of the day.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003e\u003cem\u003eStrengths and Limitations\u003c/em\u003e\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eA major strength of this study is the use of \u003cstrong\u003eNHANES data\u003c/strong\u003e, which allows for high generalizability to the U.S. adult population and utilizes gold-standard laboratory measures for HbA1c. However, several limitations must be noted. First, the cross-sectional nature of the data precludes any causal inferences; we cannot definitively state that increasing sleep will lower HbA1c. Second, both sleep and screen time were self-reported, which may be subject to recall bias. Finally, while we controlled for BMI and Age, other factors such as nutritional intake and genetic predisposition were not included in this model.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, sleep duration emerged as a significant and independent predictor of glycemic control in a nationally representative sample of U.S. adults. After adjusting for age and body mass index, individuals reporting longer habitual sleep demonstrated modest but measurable reductions in glycated hemoglobin (HbA1c), whereas daily screen-based sedentary time showed no independent association with glycemic status. These findings suggest that the metabolic consequences often attributed to digital sedentary behavior may be more closely linked to sleep disruption than to screen exposure itself. From a clinical and public health perspective, interventions aimed at improving sleep hygiene and protecting adequate sleep duration may provide greater benefits for glycemic regulation than strategies focused solely on reducing recreational screen time. While the cross-sectional design of this analysis precludes causal inference, the results highlight the importance of considering sleep as a central behavioral determinant of metabolic health in modern digital environments. Future longitudinal and experimental research should further examine the mechanisms linking sleep patterns, digital media use, and metabolic outcomes to better inform prevention strategies for diabetes and related metabolic disorders.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics Statement:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026quot;This study is a secondary analysis of de-identified, publicly available data from the National Health and Nutrition Examination Survey (NHANES). The original NHANES data collection protocols were approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board, and all participants provided written informed consent. As this study involves only de-identified secondary data, it was exempt from further Institutional Review Board (IRB) approval.\u0026quot;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor Contributions:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSanjay Chalil Kundil was responsible for the study conception and design, data acquisition (NHANES 2017-2020), statistical analysis in JASP and R, interpretation of the results, and drafting and revising the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConflict of Interest:\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares that there are no financial or personal relationships that could be perceived as influencing the research described in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData Availability Statement:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available in the CDC NHANES repository:https://www.cdc.gov/nchs/nhanes/index.htm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDisclosures:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHuman subjects: Consent was obtained or waived by all participants in this study. The NHANES study protocol was approved by the NCHS Research Ethics Review Board. This is a secondary analysis of de-identified public data and is exempt from further IRB review. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTwenge JM, Campbell WK (2018) Associations between screen time and lower psychological well-being among children and adolescents: evidence from a population-based study. Prev Med Rep 12:271\u0026ndash;283. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.pmedr.2018.10.003\u003c/span\u003e\u003cspan address=\"10.1016/j.pmedr.2018.10.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStiglic N, Viner RM (2019) Effects of screentime on the health and well-being of children and adolescents: a systematic review of reviews. BMJ Open 9:e023191. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjopen-2018-023191\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2018-023191\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuxton OM, Marcelli E (2010) Short and long sleep are positively associated with obesity, diabetes, hypertension, and cardiovascular disease among adults in the United States. Soc Sci Med 71:1027\u0026ndash;1036. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.socscimed.2010.05.041\u003c/span\u003e\u003cspan address=\"10.1016/j.socscimed.2010.05.041\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpiegel K, Knutson K, Leproult R, Tasali E, Cauter EV (2005) Sleep loss: a novel risk factor for insulin resistance and Type 2 diabetes. J Appl Physiol 99(5):2008\u0026ndash;2019. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1152/japplphysiol.00660.2005\u003c/span\u003e\u003cspan address=\"10.1152/japplphysiol.00660.2005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang AM, Aeschbach D, Duffy JF, Czeisler CA (2015) Evening use of light-emitting eReaders negatively affects sleep, circadian timing, and next-morning alertness. Proc Natl Acad Sci U S A 112:1232\u0026ndash;1237. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.1418490112\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1418490112\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmiley A, King D, Bidulescu A (2019) The Association between Sleep Duration and Metabolic Syndrome: The NHANES 2013/2014. Nutrients 11(11):2582. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/nu11112582\u003c/span\u003e\u003cspan address=\"10.3390/nu11112582\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHancox RJ, Landhuis CE (2011) Association between sleep duration and haemoglobin A1cin young adults. J Epidemiol Commun Health 66(10):957\u0026ndash;961. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/jech-2011-200217\u003c/span\u003e\u003cspan address=\"10.1136/jech-2011-200217\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCenters for Disease Control and Prevention (CDC): National Health and Nutrition Examination Survey (NHANES) 2017-March 2020 pre-pandemic data files (2021) Accessed: March 14, 2026: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?cycle=2017-2020\u003c/span\u003e\u003cspan address=\"https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?cycle=2017-2020\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Independent Researcher","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"NHANES, HbA1c, Sleep Duration, Screen Time, Metabolic Health","lastPublishedDoi":"10.21203/rs.3.rs-9141383/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9141383/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBoth inadequate sleep and a sedentary lifestyle are associated with metabolic disorders. However, the effects of prolonged periods of screen-based sedentary behavior on long-term glycemic control in adults have not been fully studied. This study examined the independent associations of sleep duration and screen-based sedentary time with glycated hemoglobin (HbA1c) levels in U.S. adults.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed data from the NHANES 2017\u0026ndash;March 2020 cycle (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8,242). A multiple linear regression analysis examined relationships between sleep length, screen-based sedentary time, and hemoglobin A1c (HbA1c) while controlling for the effects of age and body mass index (BMI).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe relationship between sleep duration and HbA1c was statistically significant, with more sleep correlating with lower HbA1c levels (\u0026#120515; = -0.023, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Specifically, for every hour of sleep reported by participants, HbA1c decreased by 0.023 percentage points per additional hour of sleep. Conversely, there was no relationship between sedentary screen-based time and HbA1c levels (p = .998). Age and body mass index (BMI) were also statistically significantly related to HbA1c levels.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThese findings suggest that glycemic control is more strongly associated with sleep duration and adiposity than with screen-based sedentary behavior. Therefore, it appears that the metabolic risk factors associated with digital sedentary time are primarily attributed to sleep disruption. Public health initiatives should prioritize sleep hygiene as an important strategy for improving metabolic health.\u003c/p\u003e","manuscriptTitle":"Sleep Duration, but Not Screen-Based Sedentary Time, Predicts Glycemic Control: A Weighted NHANES 2017–2020 Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-18 10:11:17","doi":"10.21203/rs.3.rs-9141383/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bf9e2682-57e3-4dd8-9693-f9507b717734","owner":[],"postedDate":"March 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":64605997,"name":"Psychology"}],"tags":[],"updatedAt":"2026-03-18T10:11:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-18 10:11:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9141383","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9141383","identity":"rs-9141383","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}