Excessive Smart Device Use and Mental-Metabolic Health Risks in Middle-aged Adults: Evidence from the Korean National Health and Nutrition Examination Survey

Excessive Smart Device Use and Mental-Metabolic Health Risks in Middle-aged Adults: Evidence from the Korean National Health and Nutrition Examination Survey | 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 Excessive Smart Device Use and Mental-Metabolic Health Risks in Middle-aged Adults: Evidence from the Korean National Health and Nutrition Examination Survey Hee Sun Kim, Yun Mi Choi, Weon Young Lee, Bomi Park This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7253108/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 Excessive smart device use has been increasingly associated with adverse mental and metabolic health outcomes, but data on middle-aged adults are limited. This study examined the associations between heavy smart-device use and mental health, metabolic health, and their combined risk among Korean adults aged 40–59 years. Methods Data from 3,079 adults from the 2021 and 2023 Korea National Health and Nutrition Examination Surveys were analyzed. Excessive smart device use was defined as at least 4 h per day. Mental health outcomes included high stress and depressive symptoms; metabolic health outcomes covered metabolic syndrome and phenotypes (metabolically healthy normal weight, metabolically healthy obesity, metabolically unhealthy normal weight, and metabolically unhealthy obesity). Participants were grouped by risk: no-risk, single-domain risk (mental or metabolic), and dual-domain risk. Analyses were stratified by sex, sleep duration, muscle-strengthening activity, and sedentary behavior, adjusting for demographic (age, sex, education, household income, marital status, employment) and lifestyle factors (smoking and alcohol use) using complex survey sampling. Results Excessive smart device users demonstrated increased risk of experiencing high stress (adjusted odds ratio [aOR]: 1.39; 95% confidence interval [CI]: 1.16–1.67), depressive symptoms (aOR: 1.60; 95% CI: 1.21–2.11), metabolic syndrome (aOR: 1.23; 95% CI: 1.02–1.50), metabolically healthy obesity (aOR: 1.30; 95% CI: 1.01–1.67), and metabolically unhealthy obesity (aOR: 1.42; 95% CI: 1.13–1.77). Furthermore, excessive smart device use was associated with elevated odds of single-domain risk (aOR: 1.30; 95% CI: 1.08–1.57) and dual-domain risk (aOR: 1.76; 95% CI: 1.36–2.27). Stratified analyses indicated that several associations achieved statistical significance only among subgroups characterized by inadequate or excessive sleep, insufficient muscle-strengthening activity, or prolonged sedentary behavior. Conclusions Excessive smart device use is associated with mental health issues, adverse metabolic profiles, and their co-occurrence. Public health interventions should incorporate assessments and management of digital device use, particularly for individuals with poor sleep, low physical activity, and prolonged sedentary time. smart device use screen time stress depression metabolic syndrome metabolic phenotype combined risk sedentary behavior middle-aged adults Korea Introduction The rapid integration of smart devices, including smartphones, tablets, and personal computers, into daily life has profoundly reshaped how individuals communicate, work, and access information. Equipped with advanced sensors, processors, and communication technologies, these devices have become essential instruments for daily life [ 1 , 2 ]. Surveys indicate over 90% smartphone use in many high-income countries [ 3 ]. In South Korea, more than 90% of individuals aged 10–30 years, 88% of those in their 40s, and ~ 80% in their 50s consider smartphones indispensable [ 4 ]. While smart devices offer benefits in terms of connectivity and convenience, research has identified potential adverse health effects associated with their excessive use, particularly regarding mental and metabolic health. Among adolescents and young adults, excessive screen time has been consistently linked to increased stress, depressive symptoms, sleep disturbances, and suicidal ideation [ 5 , 6 ]. Prolonged use in these groups is also associated with lower physical activity, prolonged sedentary behavior, obesity, and an increased risk of cardiometabolic issues such as insulin resistance [ 7 , 8 ]. Research among university students further highlights the significant correlation between smartphone addiction and adverse physical symptoms (such as headaches and musculoskeletal pain) and psychological distress, including anxiety and depression [ 9 , 10 ]. Despite these growing concerns, few studies have investigated the impact of excessive smart device use on the health of middle-aged adults. This group (~ 40–59 years) faces unique challenges characterized by significant psychosocial burden alongside physiological aging, including navigating dual caregiving roles for children and aging parents, occupational demands, and financial pressures—all of which amplify stress and mental exhaustion [ 11 , 12 ]. Consequently, this stage of life may heighten vulnerability to mental health issues exacerbated by excessive smart device use. Concurrent with these psychological challenges, middle-aged adults experience increased risk of chronic metabolic conditions, including hypertension, diabetes, dyslipidemia, and obesity. In South Korea, 14.2% of people in their 40s and 12.6% in their 50s received clinical treatment for depression in 2022 [ 13 ]. Moreover, metabolic syndrome affects 29.6% of Korean adults aged ≥ 30 years, with prevalence increasing sharply with age [ 14 ]. Meanwhile, few studies have directly explored how digital behavior affects the unique dual vulnerability of middle-aged individuals to mental and metabolic health issues. Similarly, the impact of lifestyle-related factors, such as physical activity, sedentary behavior, and sleep duration, on the association between smart device use and health outcomes in middle-aged populations is relatively undefined. Understanding these modifiers is essential for developing targeted public health interventions for at-risk groups. We examined the association between excessive smart device use and mental and metabolic health in middle-aged Korean adults. Additionally, it investigates the co-occurrence of mental and metabolic health risks, as well as potential variations in these associations based on sex, physical activity, sedentary behavior, and sleep duration. The findings offer insights for developing targeted public health strategies that address digital behavior among at-risk middle-aged individuals. Methods Data Source and Study Population This study analyzed data from the 2021 and 2023 Korea National Health and Nutrition Examination Survey (KNHANES), a nationally representative cross-sectional survey conducted by the Korea Disease Control and Prevention Agency (KDCA) that uses stratified multistage probability sampling to collect health and nutritional data from noninstitutionalized Koreans. The detailed methodology, validity, and reliability of the KNHANES have been described elsewhere [ 15 ]. Smart device use among adults aged 40–59 was surveyed during the eighth survey cycle (2021–2023). Eligible participants were 40–59-years-old and completed both the health interview and examination; those missing data on smart device usage, outcome variables, or covariates were excluded. The final sample included 3079 participants. Measurement of Smart Device Use Smart device use was assessed by asking participants, “On average, how many hours per day do you use smart devices (e.g., smartphones, tablets)?” Based on public health guidelines and prior research [ 6 , 16 ], excessive use was defined as 4 or more hours per day. Participants were grouped as normal users (< 4 h/day) and excessive users (≥ 4 h/day). Outcome Variables Primary outcome variables were classified into three domains: mental health, metabolic health, and combined risk. Mental health outcomes included high levels of perceived stress and depressive symptoms. High perceived stress was defined as self-reported frequent or severe daily stress. Depressive symptoms were identified through affirmative responses to experiencing feelings of sadness or hopelessness for at least two consecutive weeks within the past year that interfered with daily functioning. Metabolic health outcomes included metabolic syndrome and related phenotypes. Metabolic syndrome was defined according to the modified National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) criteria, incorporating Korean-specific waist circumference thresholds (≥ 90 cm for men and ≥ 85 cm for women) [ 17 ]. Metabolic phenotype classifications were established by integrating body mass index—with obesity defined as ≥ 25 kg/m² for Asian populations [ 18 ]—and metabolic syndrome status: metabolically healthy normal weight (MHNW), metabolically healthy obese (MHO), metabolically unhealthy normal weight (MUHNW), and metabolically unhealthy obese (MUHO) [ 19 ]. For the assessment of combined risk, participants were assigned to one of three groups: no risk (absence of mental or metabolic conditions), single risk (presence of either a mental or metabolic condition), and dual risk (presence of both conditions). Covariates Covariates, selected from prior research [ 6 , 8 ], included sociodemographic factors (age: 40–49 vs. 50–59); sex; education: high school or lower vs. college or higher; household income: low, middle, high; marital status: married vs. unmarried; employment status: employed vs. not employed) and behavioral factors (smoking: never, former, current; alcohol consumption: none, ≤ 4 times/month, ≥ 5 times/month). Statistical Analysis Descriptive statistics were used to characterize participants in the smart device use group. Differences in categorical variable distributions were assessed using chi-square tests. Multivariate logistic regression models were fitted to estimate adjusted odds ratios (aORs) and 95% confidence intervals (CIs) for binary outcomes, including high perceived stress, depressive symptoms, and metabolic syndrome. Additionally, multinomial logistic regression models were utilized to estimate associations between the four-category metabolic phenotype variable and the three-category combined risk variable. Potential effect modification was evaluated through stratified analyses by sex, sleep duration, physical activity, and sedentary behavior. Stratified analyses were conducted based on physical activity, sleep duration, and sedentary time. Physical activity was determined by self-reported engagement in muscle-strengthening exercises during the previous week and categorized as either any or none. Sleep duration was classified as adequate (7–8 h/night) or inadequate ( 9 h/night), following established sleep health guidelines [ 20 ]. Sedentary time was dichotomized as < 8 h versus ≥ 8 h per day, consistent with thresholds used in previous epidemiologic research [ 8 ]. All analyses incorporated the complex survey design features of KNHANES, including sampling weights, strata, and primary sampling units. Statistical analyses were performed with SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). Statistical significance was defined as a two-sided P -value less than 0.05. Ethical Statement The Institutional Review Board of Chung-Ang University approved the study (IRB No. 1041078-20250407-HR-113). Informed consent was waived as the analysis used publicly available, de-identified data. Results Participant Characteristics Among the 3,079 middle-aged adults included in the analysis, 41.3% (1,270) reported excessive smart device use (≥ 4 h/day; Table 1). Excessive users were more likely to be male (46.6% vs. 53.4%), in their 40s (52.6% vs. 47.4%), have a college education or higher (52.7% vs. 47.3%), higher income (45.2% vs. 54.8%), be unmarried (53.6% vs. 46.4%), economically active (44.8% vs. 55.2%), former (45.8% vs. 54.2%) or current smokers (42.1% vs. 57.9%), and to drink alcohol at least monthly (42.4% vs. 57.6%) compared to those with normal use (< 4 h/day). Table 1. Demographic, socioeconomic, and behavioral characteristics of study participants according to smart device use group Variable Daily smart device use P -value Excessive use (≥ 4 h/day) Normal use (< 4 h/day) Sex Male ( n = 1,332) 46.6% (1.6) 53.4% (1.6) < 0.0001 Female ( n = 1,747) 38.0% (1.3) 62.0% (1.3) Age 40s ( n = 1,485) 52.6% (1.5) 47.4% (1.5) < 0.0001 50s ( n = 1,594) 32.3% (1.4) 67.7% (1.4) Education level High school or less ( n = 1,490) 31.4% (1.5) 68.6% (1.5) < 0.0001 College or higher ( n = 1,589) 52.7% (1.5) 47.3% (1.5) Household income Low ( n = 505) 36.0% (2.4) 64.0% (2.4) 0.0001 Middle ( n = 681) 39.2% (2.2) 60.8% (2.2) High ( n = 1,893) 45.2% (1.3) 54.8% (1.3) Marital status Unmarried ( n = 272) 53.6% (3.5) 46.4% (3.5) 0.0001 Married ( n = 2,807) 41.2% (1.1) 58.8% (1.1) Employment status Not employed ( n = 754) 34.2% (1.9) 65.8% (1.9) < 0.0001 Employed ( n = 2,325) 44.8% (1.2) 55.2% (1.2) Smoking status Never ( n = 1,756) 40.8% (1.2) 59.2% (1.2) 0.121 Former ( n = 720) 45.8% (2.2) 54.2% (2.2) Current ( n = 603) 42.1% (2.3) 57.9% (2.3) Alcohol consumption frequency None ( n = 561) 39.9% (2.5) 60.1% (2.5) 0.522 ≤ 4 times/month ( n = 1,785) 43.1% (1.4) 56.9% (1.4) ≥ 5 times/month ( n = 733) 42.4% (2.0) 57.6% (2.0) Values are presented as % (standard error [SE]) Unweighted frequencies ( n ) are reported, and percentages (%) are weighted estimates that account for the complex sampling design of the KNHANES Associations with Mental Health and Metabolic Health Outcomes After adjusting for sociodemographic and behavioral covariates, excessive smart device use was significantly associated with higher odds of high perceived stress (aOR: 1.39; 95% CI: 1.16–1.67), depressive symptoms (aOR: 1.60; 95% CI: 1.21–2.11; Table 2), and developing MetS (aOR, 1.23; 95% CI: 1.02–1.50). Compared to the MHNW group, excessive users were significantly more likely to be classified as MHO (aOR: 1.30; 95% CI: 1.01–1.67) or MUHO (aOR: 1.42; 95% CI: 1.13–1.77). No significant association was observed for the MUHNW group. Multinomial logistic regression analysis of the combined mental–metabolic risk groups revealed that excessive smart device use was associated with increased odds for single-risk (aOR: 1.30; 95% CI: 1.08–1.57) and dual-risk (aOR: 1.76; 95% CI: 1.36–2.27) groups versus the no-risk group. Table 2. Associations between excessive smart device use and mental and metabolic health outcomes Unadjusted Adjusted a OR 95% CI aOR 95% CI Mental Health Outcomes High perceived stress Yes 1.40 1.18–1.66 1.39 1.16–1.67 No Ref Ref Depressive symptoms Yes 1.44 1.10–1.87 1.60 1.21–2.11 No Ref Ref Metabolic Health Outcomes Metabolic syndrome Yes 1.05 0.89–1.25 1.23 1.02–1.50 No Ref Ref Metabolic phenotypes b MHO 1.37 1.07–1.74 1.30 1.01–1.67 MUHO 1.28 1.05–1.56 1.42 1.13–1.78 MUHNW 0.85 0.64–1.12 1.11 0.82–1.49 MHNW Ref Ref Combined mental-metabolic Health Outcomes b Dual-risk group 1.47 1.14–1.87 1.76 1.36–2.27 Single-risk group 1.22 1.03–1.45 1.30 1.08–1.57 No-risk group Ref Ref OR, odds ratio; aOR, adjusted odds ratio; CI, confidence interval All analyses accounted for the complex sampling design of KNHANES a Models adjusted for age, sex, education level, household income, marital status, employment status, smoking status, and alcohol consumption frequency b Multinomial logistic regression analysis of metabolic phenotypes and combined mental and metabolic health outcomes Stratified Analyses Stratified analyses identified demographic and behavioral effect modifiers (Table 3). In sex-stratified analyses, excessive smart device use was significantly associated with high perceived stress and depressive symptoms in males and females. Associations involving metabolic syndrome, MUHO, and dual-risk classification were observed only in males, whereas MHO was significant only in females (aOR: 1.56; 95% CI: 1.13–2.15). When analyses were stratified by sleep duration, excessive smart device use was significantly associated with high perceived stress, depressive symptoms, metabolic syndrome, MUHO, and dual-risk classification among individuals with insufficient or excessive sleep ( 9 h/day). In contrast, MHO was significant only in the adequate sleep group. Regarding muscle-strengthening activities, excessive smart device use was significantly associated with high perceived stress, depressive symptoms, and dual-risk classification in both activity groups. However, associations with metabolic syndrome, MHO, and MUHO were observed only among participants who did not engage in muscle-strengthening activities. In analyses stratified by sedentary time, excessive smart device use was significantly associated with dual-risk classification, regardless of sedentary duration. Statistically significant associations with high perceived stress and depressive symptoms were observed only among individuals reporting ≥8 h per day of sedentary behavior. Discussion This study found that middle-aged Korean adults using smart devices for ≥4 daily had significantly greater odds of high perceived stress, depressive symptoms, metabolic syndrome, and MHO and MUHO phenotypes. Excessive users were also 1.8 times more likely to have combined mental and metabolic risks. Stratified analyses showed that certain associations were statistically significant only among participants with behavioral vulnerabilities, such as inadequate or excessive sleep, lack of muscle-strengthening activity, or prolonged sedentary time. Most previous research on the effects of excessive screen exposure on health outcomes has focused on adolescents and young adults, showing a dose-dependent increase in depressive symptoms, anxiety, obesity, metabolic syndrome, and, to a lesser extent, cardiovascular events [5-10]. In contrast, population-based studies specifically focusing on midlife remain scarce. Most previous research has predominantly targeted adolescents or university students, or examined general adult populations, thereby limiting the ability to identify age-specific associations relevant to middle-aged individuals. The current study contributes to the literature in four key ways. First, it shows that excessive use of smart devices in middle-aged adults is associated with a 1.8-fold increase in combined mental and metabolic risks. Second, using a four-category metabolic phenotype framework (MHNW, MHO, MUHNW, and MUHO), the study highlights a particularly strong association with the high-risk and understudied MUHO group. Third, it demonstrates that sleep duration, muscle-strengthening activity, and sedentary behavior modify these effects, identifying groups most likely to benefit from targeted interventions. Fourth, by incorporating the KNHANES complex sampling weights, the findings offer nationally representative estimates that surpass those of earlier, less generalizable studies. Several complementary mechanisms may explain the associations between excessive screen exposure and adverse health outcomes observed in this study. Evening exposure to light-emitting devices disrupts sleep quality, circadian timing, and melatonin production [21], leading to hormonal dysregulation that affects glucose metabolism and stress responses [22]. Prolonged screen use involves extended sedentary behavior, which directly contributes to obesity, metabolic syndrome, and type 2 diabetes through reduced energy expenditure and impaired metabolic function [23,24]. Problematic screen use may also affect neurobiological pathways, with excessive social media engagement linked to altered brain reward and stress systems [25]. Chronic low-grade inflammation represents a potential unifying pathway connecting these behaviors to both mental and metabolic health problems, as sleep disruption elevates inflammatory markers [26] and inflammation plays a key role in depression pathophysiology [27]. Translating these findings into practice may require interventions that address digital device use and modifiable lifestyle factors. Potential targeted strategies include pairing digital counseling with sleep hygiene education, encouraging resistance exercise, and scheduling breaks to reduce prolonged periods of sitting. These can be incorporated into routine midlife health screenings and supported by workplace or community initiatives, such as digital detox programs. Collectively, these findings support integrated behavior-focused public health strategies targeting at-risk middle-aged adults. This study has several limitations that warrant consideration. First, its cross-sectional design precludes causal inference and raises the possibility of reverse causality. Second, self-reported measures of exposure and lifestyle behaviors are prone to recall and social-desirability bias, potentially leading to misclassification. Third, although adjustments were made for various covariates, unmeasured confounders cannot be ruled out. Fourth, the mental health outcomes relied on single-item screens, and list-wise deletion may have introduced a selection bias. Finally, while the results are representative of Korean adults, cultural differences may limit their generalizability. Future research should employ prospective designs, incorporate more objective devices, gather more granular psychosocial and lifestyle data, and utilize validated assessments to clarify causality and refine intervention targets. Conclusion This study highlights that excessive smart device use is associated with poorer mental and metabolic health in middle-aged adults—an often under-researched population in digital health research. By analyzing nationally representative data and considering mental and physical outcomes, the research underscores that mHealth interventions, such as on-device screen-time alerts or daily activity prompts, may help high-use adults monitor and potentially reduce their digital exposure. Targeted public health strategies are needed to address these digital behaviors and the associated lifestyle factors. Meanwhile, future studies should assess the effectiveness of these mHealth tools in improving health among this vulnerable age group. Abbreviations aOR adjusted odds ratio BMI body mass index CI confidence interval KDCA Korea Disease Control and Prevention Agency MHNW metabolically healthy normal weight MHO metabolically healthy obese MUHNW metabolically unhealthy normal weight MUHO metabolically unhealthy obese NCEP-ATP III National Cholesterol Education Program Adult Treatment Panel III OR odds ratio Declarations Ethical approval and consent to participate The Institutional Review Board of Chung-Ang University approved the study (IRB No. 1041078-20250407-HR-113). Informed consent was waived as the analysis used publicly available, de-identified data. Acknowledgments: We thank the Korea Disease Control and Prevention Agency for granting access to the KNHANES dataset and the participants who contributed to this national survey. Funding: This research received no specific grants from any funding agency in the public, commercial, or not-for-profit sectors. Conflicts of Interest: The authors declare no competing interests. Data Availability : Data supporting the findings of this study are available from the Korea Disease Control and Prevention Agency (https://knhanes.kdca.go.kr). Author Contributions Hee Sun Kim: Formal analysis, Visualization, Writing - Original Draft Yun Mi Choi: Investigation, Writing- Reviewing and Editing, Weon Young Lee: Validation, Writing- Reviewing and Editing, Bomi Park: Conceptualization, Supervision, Writing- Reviewing and Editing, References Al-Fuqaha A, Guizani M, Mohammadi M, Aledhari M, Ayyash M. Internet of things: A survey on enabling technologies, protocols, and applications. 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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-7253108","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":497795697,"identity":"3be2819f-cb1a-4b56-9748-3ea186088556","order_by":0,"name":"Hee Sun Kim","email":"","orcid":"","institution":"Chung-Ang University","correspondingAuthor":false,"prefix":"","firstName":"Hee","middleName":"Sun","lastName":"Kim","suffix":""},{"id":497795698,"identity":"0fc5f179-0941-447e-bee1-30b73c9efaac","order_by":1,"name":"Yun Mi Choi","email":"","orcid":"","institution":"Hallym University Dongtan Sacred Heart Hospital, Hallym University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yun","middleName":"Mi","lastName":"Choi","suffix":""},{"id":497795699,"identity":"cdc25740-0ebe-408b-a440-278e157d4f7f","order_by":2,"name":"Weon Young Lee","email":"","orcid":"","institution":"Chung-Ang University","correspondingAuthor":false,"prefix":"","firstName":"Weon","middleName":"Young","lastName":"Lee","suffix":""},{"id":497795700,"identity":"56c54ce4-55be-4d5a-a792-09324db41b7c","order_by":3,"name":"Bomi Park","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYBACAyBmbKiAcQ8QreUMyVoa20jRYi7d/OzjzHl28gYHmB9+YDhzj7AWyznHjGdu3JZsuOEAm7EEw41iIhx2I8GY8eG2A4wbDjCYMTB8SCBGS/pnxodzDthvOMD+jVgtOcaMGxsOJG44wAO05QYRWiznnClmnHEsOXnmYZ5iiYQzRGgxl27fzNhTY2fbd7x944cPx4jQwiABYzADMTEakLSMglEwCkbBKMAFAHVvPZP0ZZWWAAAAAElFTkSuQmCC","orcid":"","institution":"Chung-Ang University","correspondingAuthor":true,"prefix":"","firstName":"Bomi","middleName":"","lastName":"Park","suffix":""}],"badges":[],"createdAt":"2025-07-30 12:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7253108/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7253108/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100685172,"identity":"7791bd2b-763d-4453-9064-39d53fc3e397","added_by":"auto","created_at":"2026-01-20 12:50:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":784300,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7253108/v1/32b67910-bcb6-4df7-89b7-e5c79ba0e43c.pdf"},{"id":89009191,"identity":"53f6ac2b-7d4a-4498-89e6-b9f52002f9be","added_by":"auto","created_at":"2025-08-13 16:58:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19018,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7253108/v1/5441312bbe89aab3becbf229.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Excessive Smart Device Use and Mental-Metabolic Health Risks in Middle-aged Adults: Evidence from the Korean National Health and Nutrition Examination Survey","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe rapid integration of smart devices, including smartphones, tablets, and personal computers, into daily life has profoundly reshaped how individuals communicate, work, and access information. Equipped with advanced sensors, processors, and communication technologies, these devices have become essential instruments for daily life [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Surveys indicate over 90% smartphone use in many high-income countries [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In South Korea, more than 90% of individuals aged 10–30 years, 88% of those in their 40s, and ~ 80% in their 50s consider smartphones indispensable [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile smart devices offer benefits in terms of connectivity and convenience, research has identified potential adverse health effects associated with their excessive use, particularly regarding mental and metabolic health. Among adolescents and young adults, excessive screen time has been consistently linked to increased stress, depressive symptoms, sleep disturbances, and suicidal ideation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Prolonged use in these groups is also associated with lower physical activity, prolonged sedentary behavior, obesity, and an increased risk of cardiometabolic issues such as insulin resistance [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Research among university students further highlights the significant correlation between smartphone addiction and adverse physical symptoms (such as headaches and musculoskeletal pain) and psychological distress, including anxiety and depression [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite these growing concerns, few studies have investigated the impact of excessive smart device use on the health of middle-aged adults. This group (~ 40–59 years) faces unique challenges characterized by significant psychosocial burden alongside physiological aging, including navigating dual caregiving roles for children and aging parents, occupational demands, and financial pressures—all of which amplify stress and mental exhaustion [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Consequently, this stage of life may heighten vulnerability to mental health issues exacerbated by excessive smart device use.\u003c/p\u003e\u003cp\u003eConcurrent with these psychological challenges, middle-aged adults experience increased risk of chronic metabolic conditions, including hypertension, diabetes, dyslipidemia, and obesity. In South Korea, 14.2% of people in their 40s and 12.6% in their 50s received clinical treatment for depression in 2022 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Moreover, metabolic syndrome affects 29.6% of Korean adults aged ≥ 30 years, with prevalence increasing sharply with age [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Meanwhile, few studies have directly explored how digital behavior affects the unique dual vulnerability of middle-aged individuals to mental and metabolic health issues. Similarly, the impact of lifestyle-related factors, such as physical activity, sedentary behavior, and sleep duration, on the association between smart device use and health outcomes in middle-aged populations is relatively undefined. Understanding these modifiers is essential for developing targeted public health interventions for at-risk groups.\u003c/p\u003e\u003cp\u003eWe examined the association between excessive smart device use and mental and metabolic health in middle-aged Korean adults. Additionally, it investigates the co-occurrence of mental and metabolic health risks, as well as potential variations in these associations based on sex, physical activity, sedentary behavior, and sleep duration. The findings offer insights for developing targeted public health strategies that address digital behavior among at-risk middle-aged individuals.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eData Source and Study Population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study analyzed data from the 2021 and 2023 Korea National Health and Nutrition Examination Survey (KNHANES), a nationally representative cross-sectional survey conducted by the Korea Disease Control and Prevention Agency (KDCA) that uses stratified multistage probability sampling to collect health and nutritional data from noninstitutionalized Koreans. The detailed methodology, validity, and reliability of the KNHANES have been described elsewhere [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Smart device use among adults aged 40–59 was surveyed during the eighth survey cycle (2021–2023). Eligible participants were 40–59-years-old and completed both the health interview and examination; those missing data on smart device usage, outcome variables, or covariates were excluded. The final sample included 3079 participants.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMeasurement of Smart Device Use\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSmart device use was assessed by asking participants, “On average, how many hours per day do you use smart devices (e.g., smartphones, tablets)?” Based on public health guidelines and prior research [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], excessive use was defined as 4 or more hours per day. Participants were grouped as normal users (\u0026lt; 4 h/day) and excessive users (≥ 4 h/day).\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutcome Variables\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePrimary outcome variables were classified into three domains: mental health, metabolic health, and combined risk. Mental health outcomes included high levels of perceived stress and depressive symptoms. High perceived stress was defined as self-reported frequent or severe daily stress. Depressive symptoms were identified through affirmative responses to experiencing feelings of sadness or hopelessness for at least two consecutive weeks within the past year that interfered with daily functioning.\u003c/p\u003e\u003cp\u003eMetabolic health outcomes included metabolic syndrome and related phenotypes. Metabolic syndrome was defined according to the modified National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) criteria, incorporating Korean-specific waist circumference thresholds (≥ 90 cm for men and ≥ 85 cm for women) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Metabolic phenotype classifications were established by integrating body mass index—with obesity defined as ≥ 25 kg/m² for Asian populations [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]—and metabolic syndrome status: metabolically healthy normal weight (MHNW), metabolically healthy obese (MHO), metabolically unhealthy normal weight (MUHNW), and metabolically unhealthy obese (MUHO) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFor the assessment of combined risk, participants were assigned to one of three groups: no risk (absence of mental or metabolic conditions), single risk (presence of either a mental or metabolic condition), and dual risk (presence of both conditions).\u003c/p\u003e\u003cp\u003e\u003cb\u003eCovariates\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCovariates, selected from prior research [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], included sociodemographic factors (age: 40–49 vs. 50–59); sex; education: high school or lower vs. college or higher; household income: low, middle, high; marital status: married vs. unmarried; employment status: employed vs. not employed) and behavioral factors (smoking: never, former, current; alcohol consumption: none, ≤ 4 times/month, ≥ 5 times/month).\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistics were used to characterize participants in the smart device use group. Differences in categorical variable distributions were assessed using chi-square tests. Multivariate logistic regression models were fitted to estimate adjusted odds ratios (aORs) and 95% confidence intervals (CIs) for binary outcomes, including high perceived stress, depressive symptoms, and metabolic syndrome. Additionally, multinomial logistic regression models were utilized to estimate associations between the four-category metabolic phenotype variable and the three-category combined risk variable. Potential effect modification was evaluated through stratified analyses by sex, sleep duration, physical activity, and sedentary behavior.\u003c/p\u003e\u003cp\u003eStratified analyses were conducted based on physical activity, sleep duration, and sedentary time. Physical activity was determined by self-reported engagement in muscle-strengthening exercises during the previous week and categorized as either any or none. Sleep duration was classified as adequate (7–8 h/night) or inadequate (\u0026lt; 6 h or \u0026gt; 9 h/night), following established sleep health guidelines [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Sedentary time was dichotomized as \u0026lt; 8 h versus ≥ 8 h per day, consistent with thresholds used in previous epidemiologic research [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAll analyses incorporated the complex survey design features of KNHANES, including sampling weights, strata, and primary sampling units. Statistical analyses were performed with SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). Statistical significance was defined as a two-sided \u003cem\u003eP\u003c/em\u003e-value less than 0.05.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthical Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Institutional Review Board of Chung-Ang University approved the study (IRB No. 1041078-20250407-HR-113). Informed consent was waived as the analysis used publicly available, de-identified data.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eParticipant Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 3,079 middle-aged adults included in the analysis, 41.3% (1,270) reported excessive smart device use (\u0026ge; 4 h/day; Table 1). Excessive users were more likely to be male (46.6% vs. 53.4%), in their 40s (52.6% vs. 47.4%), have a college education or higher (52.7% vs. 47.3%), higher income (45.2% vs. 54.8%), be unmarried (53.6% vs. 46.4%), economically active (44.8% vs. 55.2%), former (45.8% vs. 54.2%) or current smokers (42.1% vs. 57.9%), and to drink alcohol at least monthly (42.4% vs. 57.6%) compared to those with normal use (\u0026lt; 4 h/day).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Demographic, socioeconomic, and behavioral characteristics of study participants according to smart device use group\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eDaily smart device use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExcessive use\u003c/p\u003e\n \u003cp\u003e(\u0026ge;\u0026nbsp;4 h/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNormal use\u003c/p\u003e\n \u003cp\u003e(\u0026lt; 4 h/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 1,332)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46.6% (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.4% (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 1,747)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38.0% (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62.0% (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40s (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 1,485)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52.6% (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47.4% (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50s (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 1,594)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32.3% (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e67.7% (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh school or less (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 1,490)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31.4% (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e68.6% (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCollege or higher (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 1,589)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52.7% (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47.3% (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLow (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 505)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36.0% (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e64.0% (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMiddle (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 681)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e39.2% (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60.8% (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 1,893)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45.2% (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54.8% (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnmarried (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 272)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.6% (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46.4% (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMarried (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 2,807)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41.2% (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58.8% (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNot employed (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 754)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34.2% (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65.8% (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEmployed (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 2,325)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e44.8% (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e55.2% (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNever (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 1,756)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40.8% (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59.2% (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFormer (\u003cem\u003en\u003c/em\u003e = 720)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45.8% (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54.2% (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCurrent (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 603)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42.1% (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57.9% (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol consumption frequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNone (\u003cem\u003en\u003c/em\u003e = 561)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39.9% (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60.1% (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026le;\u0026nbsp;4 times/month (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 1,785)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43.1% (1.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e56.9% (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;\u0026nbsp;5 times/month (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 733)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42.4% (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e57.6% (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are presented as % (standard error [SE])\u003c/p\u003e\n\u003cp\u003eUnweighted frequencies (\u003cem\u003en\u003c/em\u003e) are reported, and percentages (%) are weighted estimates that account for the complex sampling design of the KNHANES\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociations with Mental Health and Metabolic Health Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter adjusting for sociodemographic and behavioral covariates, excessive smart device use was significantly associated with higher odds of high perceived stress (aOR: 1.39; 95% CI: 1.16\u0026ndash;1.67), depressive symptoms (aOR: 1.60; 95% CI: 1.21\u0026ndash;2.11; Table 2), and developing MetS (aOR, 1.23; 95% CI: 1.02\u0026ndash;1.50).\u003c/p\u003e\n\u003cp\u003eCompared to the MHNW group, excessive users were significantly more likely to be classified as MHO (aOR: 1.30; 95% CI: 1.01\u0026ndash;1.67) or MUHO (aOR: 1.42; 95% CI: 1.13\u0026ndash;1.77). No significant association was observed for the MUHNW group.\u003c/p\u003e\n\u003cp\u003eMultinomial logistic regression analysis of the combined mental\u0026ndash;metabolic risk groups revealed that excessive smart device use was associated with increased odds for single-risk (aOR: 1.30; 95% CI: 1.08\u0026ndash;1.57) and dual-risk (aOR: 1.76; 95% CI: 1.36\u0026ndash;2.27) groups versus the no-risk group.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Associations between excessive smart device use and mental and metabolic health outcomes\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eUnadjusted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eAdjusted\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMental Health Outcomes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh perceived stress\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.18\u0026ndash;1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.16\u0026ndash;1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepressive symptoms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.10\u0026ndash;1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.21\u0026ndash;2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetabolic Health Outcomes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetabolic syndrome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.89\u0026ndash;1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.02\u0026ndash;1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetabolic phenotypes\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.07\u0026ndash;1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.01\u0026ndash;1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMUHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.05\u0026ndash;1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.13\u0026ndash;1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMUHNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.64\u0026ndash;1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.82\u0026ndash;1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMHNW\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCombined mental-metabolic Health Outcomes\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDual-risk group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.14\u0026ndash;1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.36\u0026ndash;2.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSingle-risk group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.03\u0026ndash;1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.08\u0026ndash;1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo-risk group\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOR, odds ratio; aOR, adjusted odds ratio; CI, confidence interval\u003c/p\u003e\n\u003cp\u003eAll analyses accounted for the complex sampling design of KNHANES\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Models adjusted for age, sex, education level, household income, marital status, employment status, smoking status, and alcohol consumption frequency\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003csup\u003eb\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003eMultinomial logistic regression analysis of metabolic phenotypes and combined mental and metabolic health outcomes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStratified Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStratified analyses identified demographic and behavioral effect modifiers (Table 3). In sex-stratified analyses, excessive smart device use was significantly associated with high perceived stress and depressive symptoms in males and females. Associations involving metabolic syndrome, MUHO, and dual-risk classification were observed only in males, whereas MHO was significant only in females (aOR: 1.56; 95% CI: 1.13\u0026ndash;2.15).\u003c/p\u003e\n\u003cp\u003eWhen analyses were stratified by sleep duration, excessive smart device use was significantly associated with high perceived stress, depressive symptoms, metabolic syndrome, MUHO, and dual-risk classification among individuals with insufficient or excessive sleep (\u0026lt; 6 or \u0026gt; 9 h/day). In contrast, MHO was significant only in the adequate sleep group.\u003c/p\u003e\n\u003cp\u003eRegarding muscle-strengthening activities, excessive smart device use was significantly associated with high perceived stress, depressive symptoms, and dual-risk classification in both activity groups. However, associations with metabolic syndrome, MHO, and MUHO were observed only among participants who did not engage in muscle-strengthening activities.\u003c/p\u003e\n\u003cp\u003eIn analyses stratified by sedentary time, excessive smart device use was significantly associated with dual-risk classification, regardless of sedentary duration. Statistically significant associations with high perceived stress and depressive symptoms were observed only among individuals reporting \u0026ge;8 h per day of sedentary behavior.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study found that middle-aged Korean adults using smart devices for \u0026ge;4 daily had significantly greater odds of high perceived stress, depressive symptoms, metabolic syndrome, and MHO and MUHO phenotypes. Excessive users were also 1.8 times more likely to have combined mental and metabolic risks. Stratified analyses showed that certain associations were statistically significant only among participants with behavioral vulnerabilities, such as inadequate or excessive sleep, lack of muscle-strengthening activity, or prolonged sedentary time.\u003c/p\u003e\n\u003cp\u003eMost previous research on the effects of excessive screen exposure on health outcomes has focused on adolescents and young adults, showing a dose-dependent increase in depressive symptoms, anxiety, obesity, metabolic syndrome, and, to a lesser extent, cardiovascular events [5-10]. In contrast, population-based studies specifically focusing on midlife remain scarce. Most previous research has predominantly targeted adolescents or university students, or examined general adult populations, thereby limiting the ability to identify age-specific associations relevant to middle-aged individuals.\u003c/p\u003e\n\u003cp\u003eThe current study contributes to the literature in four key ways. First, it shows that excessive use of smart devices in middle-aged adults is associated with a 1.8-fold increase in combined mental and metabolic risks. Second, using a four-category metabolic phenotype framework (MHNW, MHO, MUHNW, and MUHO), the study highlights a particularly strong association with the high-risk and understudied MUHO group. Third, it demonstrates that sleep duration, muscle-strengthening activity, and sedentary behavior modify these effects, identifying groups most likely to benefit from targeted interventions. Fourth, by incorporating the KNHANES complex sampling weights, the findings offer nationally representative estimates that surpass those of earlier, less generalizable studies.\u003c/p\u003e\n\u003cp\u003eSeveral complementary mechanisms may explain the associations between excessive screen exposure and adverse health outcomes observed in this study. Evening exposure to light-emitting devices disrupts sleep quality, circadian timing, and melatonin production [21], leading to hormonal dysregulation that affects glucose metabolism and stress responses [22]. Prolonged screen use involves extended sedentary behavior, which directly contributes to obesity, metabolic syndrome, and type 2 diabetes through reduced energy expenditure and impaired metabolic function [23,24]. Problematic screen use may also affect neurobiological pathways, with excessive social media engagement linked to altered brain reward and stress systems [25]. Chronic low-grade inflammation represents a potential unifying pathway connecting these behaviors to both mental and metabolic health problems, as sleep disruption elevates inflammatory markers [26] and inflammation plays a key role in depression pathophysiology [27].\u003c/p\u003e\n\u003cp\u003eTranslating these findings into practice may require interventions that address digital device use and modifiable lifestyle factors. Potential targeted strategies include pairing digital counseling with sleep hygiene education, encouraging resistance exercise, and scheduling breaks to reduce prolonged periods of sitting. These can be incorporated into routine midlife health screenings and supported by workplace or community initiatives, such as digital detox programs. Collectively, these findings support integrated behavior-focused public health strategies targeting at-risk middle-aged adults.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study has several limitations that warrant consideration. First, its cross-sectional design precludes causal inference and raises the possibility of reverse causality. Second, self-reported measures of exposure and lifestyle behaviors are prone to recall and social-desirability bias, potentially leading to misclassification. Third, although adjustments were made for various covariates, unmeasured confounders cannot be ruled out. Fourth, the mental health outcomes relied on single-item screens, and list-wise deletion may have introduced a selection bias. Finally, while the results are representative of Korean adults, cultural differences may limit their generalizability. Future research should employ prospective designs, incorporate more objective devices, gather more granular psychosocial and lifestyle data, and utilize validated assessments to clarify causality and refine intervention targets.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights that excessive smart device use is associated with poorer mental and metabolic health in middle-aged adults\u0026mdash;an often under-researched population in digital health research. By analyzing nationally representative data and considering mental and physical outcomes, the research underscores that mHealth interventions, such as on-device screen-time alerts or daily activity prompts, may help high-use adults monitor and potentially reduce their digital exposure. Targeted public health strategies are needed to address these digital behaviors and the associated lifestyle factors. Meanwhile, future studies should assess the effectiveness of these mHealth tools in improving health among this vulnerable age group.\u003c/p\u003e\n"},{"header":"Abbreviations","content":"\u003cp\u003eaOR adjusted odds ratio\u003c/p\u003e\n\u003cp\u003eBMI body mass index\u003c/p\u003e\n\u003cp\u003eCI confidence interval\u003c/p\u003e\n\u003cp\u003eKDCA Korea Disease Control and Prevention Agency\u003c/p\u003e\n\u003cp\u003eMHNW metabolically healthy normal weight\u003c/p\u003e\n\u003cp\u003eMHO metabolically healthy obese\u003c/p\u003e\n\u003cp\u003eMUHNW metabolically unhealthy normal weight\u003c/p\u003e\n\u003cp\u003eMUHO metabolically unhealthy obese\u003c/p\u003e\n\u003cp\u003eNCEP-ATP III National Cholesterol Education Program Adult Treatment Panel III\u003c/p\u003e\n\u003cp\u003eOR odds ratio\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Institutional Review Board of Chung-Ang University approved the study (IRB No.\u0026nbsp;1041078-20250407-HR-113). Informed consent was waived as the analysis used publicly available, de-identified data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e We thank the Korea Disease Control and Prevention Agency for granting access to the KNHANES dataset and the participants who contributed to this national survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research received no specific grants from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e: Data supporting the findings of this study are available from the Korea Disease Control and Prevention Agency (https://knhanes.kdca.go.kr).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHee Sun Kim: Formal analysis, Visualization, Writing - Original Draft\u003c/p\u003e\n\u003cp\u003eYun Mi Choi: Investigation, Writing- Reviewing and Editing,\u003c/p\u003e\n\u003cp\u003eWeon Young Lee: Validation, Writing- Reviewing and Editing,\u003c/p\u003e\n\u003cp\u003eBomi Park: Conceptualization, Supervision, Writing- Reviewing and Editing,\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAl-Fuqaha A, Guizani M, Mohammadi M, Aledhari M, Ayyash M. 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Role of sleep and sleep loss in hormonal release and metabolism. Endocr Rev 2010 Dec;31(6):887-898. doi: 10.1210/er.2009-0029.\u003c/li\u003e\n\u003cli\u003eHamilton MT, Hamilton DG, Zderic TW. Role of low energy expenditure and sitting in obesity, metabolic syndrome, type 2 diabetes, and cardiovascular disease. Diabetes 2007 Nov;56(11):2655-2667. doi: 10.2337/db07-0882.\u003c/li\u003e\n\u003cli\u003eDunstan DW, Howard B, Healy GN, Owen N. Too much sitting \u0026ndash; A health hazard. Exerc Sport Sci Rev 2012 Apr;40(2):65-75. doi: 10.1097/JES.0b013e3182450d82.\u003c/li\u003e\n\u003cli\u003eMeshi D, Tamir DI, Heekeren HR. The emerging neuroscience of social media. Trends Cogn Sci 2015 Dec;19(12):771-782. doi: 10.1016/j.tics.2015.09.004.\u003c/li\u003e\n\u003cli\u003ePrather AA, Vogelzangs N, Penninx BWJH. Sleep duration, sleep quality, and markers of inflammation: results from a large, nationally representative study. Sleep 2015 Feb 1;38(2):199-204. doi: 10.5665/sleep.4394.\u003c/li\u003e\n\u003cli\u003eMiller AH, Raison CL. The role of inflammation in depression: from evolutionary imperative to modern treatment target. Nat Rev Immunol 2016 Jan;16(1):22-34. doi: 10.1038/nri.2015.5.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 3","content":"\u003cp\u003eTable 3 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","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":"smart device use, screen time, stress, depression, metabolic syndrome, metabolic phenotype, combined risk, sedentary behavior, middle-aged adults, Korea","lastPublishedDoi":"10.21203/rs.3.rs-7253108/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7253108/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eExcessive smart device use has been increasingly associated with adverse mental and metabolic health outcomes, but data on middle-aged adults are limited. This study examined the associations between heavy smart-device use and mental health, metabolic health, and their combined risk among Korean adults aged 40\u0026ndash;59 years.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eData from 3,079 adults from the 2021 and 2023 Korea National Health and Nutrition Examination Surveys were analyzed. Excessive smart device use was defined as at least 4 h per day. Mental health outcomes included high stress and depressive symptoms; metabolic health outcomes covered metabolic syndrome and phenotypes (metabolically healthy normal weight, metabolically healthy obesity, metabolically unhealthy normal weight, and metabolically unhealthy obesity). Participants were grouped by risk: no-risk, single-domain risk (mental or metabolic), and dual-domain risk. Analyses were stratified by sex, sleep duration, muscle-strengthening activity, and sedentary behavior, adjusting for demographic (age, sex, education, household income, marital status, employment) and lifestyle factors (smoking and alcohol use) using complex survey sampling.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eExcessive smart device users demonstrated increased risk of experiencing high stress (adjusted odds ratio [aOR]: 1.39; 95% confidence interval [CI]: 1.16\u0026ndash;1.67), depressive symptoms (aOR: 1.60; 95% CI: 1.21\u0026ndash;2.11), metabolic syndrome (aOR: 1.23; 95% CI: 1.02\u0026ndash;1.50), metabolically healthy obesity (aOR: 1.30; 95% CI: 1.01\u0026ndash;1.67), and metabolically unhealthy obesity (aOR: 1.42; 95% CI: 1.13\u0026ndash;1.77). Furthermore, excessive smart device use was associated with elevated odds of single-domain risk (aOR: 1.30; 95% CI: 1.08\u0026ndash;1.57) and dual-domain risk (aOR: 1.76; 95% CI: 1.36\u0026ndash;2.27). Stratified analyses indicated that several associations achieved statistical significance only among subgroups characterized by inadequate or excessive sleep, insufficient muscle-strengthening activity, or prolonged sedentary behavior.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eExcessive smart device use is associated with mental health issues, adverse metabolic profiles, and their co-occurrence. Public health interventions should incorporate assessments and management of digital device use, particularly for individuals with poor sleep, low physical activity, and prolonged sedentary time.\u003c/p\u003e","manuscriptTitle":"Excessive Smart Device Use and Mental-Metabolic Health Risks in Middle-aged Adults: Evidence from the Korean National Health and Nutrition Examination Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-13 16:58:41","doi":"10.21203/rs.3.rs-7253108/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":"532f2d56-c394-4350-9d7f-3612adabcd66","owner":[],"postedDate":"August 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-20T10:51:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-13 16:58:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7253108","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7253108","identity":"rs-7253108","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}