Correlation of domain-specific physical activity with stroke: a population-based study

Correlation of domain-specific physical activity with stroke: a population-based study | 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 Correlation of domain-specific physical activity with stroke: a population-based study Xinyue Huang, Xutang Jiang, Qingxin Lin, Zhigang Pan, Weipeng Hu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7549863/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 The extent to which all forms of physical activity (PA), including leisure-time PA (LTPA), occupation-related PA (OPA), and transportation-related PA (TPA), exhibit equally advantageous correlations with stroke risk remains uncertain. Thus, this study aimed to assess the correlation between LTPA, OPA, and TPA and the incidence of stroke in adults. Methods This cross-sectional study included participants’ data from the National Health and Nutrition Examination Survey. Physical activity (PA) was assessed using self-report questionnaires and classified according to PA guidelines. Stroke was assessed using a health questionnaire. Multivariate logistic regression models adjusted for demographic data, behavioral factors, and health status were used to assess the relationship between PA patterns and stroke. Results Overall, 26,467 participants were included (mean age: 47 years; 13,791 female). Total PA (odds ratio [OR] = 0.75, 95% confidence interval [CI] 0.60–0.93) and LTPA (OR = 0.74, 95%CI 0.58–0.94) of participants who met the PA guidelines (150 min/week) were significantly correlated with stroke, with no significant correlations detected between OPA or TPA and stroke (p > 0.05). LTPA levels of 1–149, 150–299, and ≥ 300 min/week were significantly correlated with stroke (OR = 0.71, 95%CI: 0.42–0.88; OR = 0.63, 95%CI: 0.30–0.80; and OR = 0.73, 95%CI: 0.47–0.99), respectively. Conclusion There was a significant negative correlation between domain-specific PA and stroke risk. Specifically, negative correlation existed between LTPA and stroke risk. stroke physical activity domain-specific epidemiology NHANES Figures Figure 1 Figure 2 1 Introduction Stroke refers to neurological dysfunction caused by cerebral ischemia or hemorrhage caused by cerebrovascular obstruction or rupture and is a type of harmful cerebrovascular disease.[ 1 , 2 ] Stroke currently ranks as the second most prevalent cause of mortality globally and the third leading cause of disability, imposing a substantial burden on both families and society, thus emerging as a significant worldwide public health concern.[ 3 , 4 ] The risk factors for stroke include physical inactivity, obesity, smoking, alcohol consumption, unhealthy diet, hypertension, diabetes, and heart disease.[ 5 , 6 ] Managing these risk factors is crucial for the prevention and control of stroke. Physical activity (PA) as a healthy lifestyle serves as a fundamental strategy for stroke prevention,[ 7 , 8 ] encompassing distinct domains such as leisure-time PA (LTPA), occupation-related PA (OPA), and transportation-related PA (TPA).[ 9 , 10 ] Many epidemiological studies have investigated the relationship between overall PA or LTPA and stroke risk, consistently revealing inverse correlations; namely, higher PA levels are correlated with lower stroke risk.[ 11 , 12 ] However, these studies focused on PA in total PA or LTPA and ignored PA in other domains, such as OPA and TPA.[ 13 , 14 ] Therefore, the extent to which all PA domains (LTPA, OPA, and TPA) offer the same advantageous correlation for patients with stroke remains uncertain.[ 13 , 14 ] To bridge this research lacuna, this study examined the correlation between distinct PA domains (LTPA, OPA, and TPA) and stroke in US population, providing a foundation for future prospective research. Our hypothesis posited that all domain of PA has a beneficial relationship with stroke and explored the dose-response relationship between different domains of PA and stroke. 2 Methods 2.1 Study design The data used in the present study originated from the National Health and Nutrition Examination Survey (NHANES) cycles from 2007–2008 and 2017–2018. NHANES is a nationally representative population-based survey that was developed to evaluate the nutritional and physical statuses of Americans. This cross-sectional survey included demographic, examination, dietary, and questionnaire data.[ 15 ] The survey had a complex, multistage sampling design, with data collected at home and at a mobile examination center (MEC). This survey was conducted in accordance with the principles of the Declaration of Helsinki. The study protocol was approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board, and written informed consent was obtained from all participants. NHANES participants aged 20 years between 2007 and 2018 (n = 59842) were selected. In total, 41,443 participants were excluded for the following reasons: (1) age < 20 years (n = 25,072); (3) no PA data (n = 134); (3) no self-reported stroke status (n = 49); (4) no information on covariates (n = 8,120) (Fig. 1 ). 2.2 Outcome measurement The primary outcome examined in the study was stroke, which was self-reported and assessed using a Medical Condition Questionnaire. All those who answered “yes” or “no” to the following question were included: “Did your doctor or other health professional tell you that you had a stroke?” The outcomes were defined as participants who answered “yes” and the control group as those who answered “no.” 2.3 PA PA was evaluated utilizing a standardized questionnaire that inquired about the frequency and duration of vigorous and moderate exercise, fitness, and recreational activities lasting a minimum of 10 minutes within a typical week.[ 16 ] According to the Global Physical Activity Questionnaire, which assessed three PA domains: LTPA, OPA, and TPA (Supplementary Table S1 ), NHANES delineated exercise that caused a large increase in respiration or heart rate as high-intensity activity, whereas moderate-intensity activity was characterized by exercise resulting in a relatively minor increase in breathing or heart rate.[ 16 ] According to the PA guidelines,[ 17 ] one minute of high-intensity activity was considered equivalent to two minutes of moderate-intensity activity. PA was computed based on the amalgamation of the frequency and duration of moderate and vigorous PA within a standard week. Weekly PA (minutes) was calculated as moderate-intensity activity (minutes) in a week plus two high-intensity activities per week (minutes). Total PA comprised LTPA, OPA, and TPA. PA was classified in accordance with the 2018 PA guidelines,[ 17 ] which recommend that adults engage in at least 150–300 min of weekly moderate-intensity PA, 75–150 min/week of high-intensity PA, or equivalent combinations. Participants were divided into two categories: (1) participants meeting the 2018 PA guidelines (≥ 150 minutes in a standard week) and (2) those failing to meet the 2018 PA guidelines (< 150 minutes in a typical week). To explore the dose-response relationship between different domains of PA and stroke, the total PA minutes were further partitioned into four groups based on previous literature: inactive (0 min/week), insufficiently active (1–149 min/week), sufficiently active (150–299 min/week), or highly active (≥ 300 min/week).[ 18 ] 2.4 Covariates The covariates consisted of sociodemographic information, lifestyle behaviors, and health status. Sociodemographic information encompassed age, sex (male, female), race (Mexican Hispanic, other Hispanic, non-Hispanic white, non-Hispanic black, and other), education (below high school, high school, and college or higher), marital status (married/cohabiting, or single), and poverty income ratio ( 3.5). Lifestyle factors comprised smoking habits (never, former, and current), alcohol consumption (No, Yes), and obesity status. Obesity was classified according to the body mass index (BMI) criteria set by the World Health Organization: thin (< 18.5 kg/m 2 ), normal weight (18.5–24.9 kg/m 2 ), overweight (25.0–29.9 kg/m 2 ), and obesity (30.0 kg/m 2 ).[ 19 ] The clinical features included hypertension, diabetes mellitus, and heart disease. Hypertension was identified based on one or more of the following criteria: diagnosed by a healthcare professional, or a mean systolic blood pressure of 140 mmHg or diastolic blood pressure of 90 mmHg in three measurements.[ 20 ] Diabetes was identified as being informed by a physician or health provider that the patient had diabetes, with glycated hemoglobin (HbA1c) > 6.5% and fasting blood glucose > 126 mg/dL.[ 21 ] Heart disease included congestive heart failure, coronary heart disease, angina, and heart attack, and was diagnosed by a doctor or a health professional.[ 22 ] 2.5 Statistical analysis In accordance with the analysis guidelines issued by the National Center for Health Statistics, the strata (SDMVSTRA) and primary sampling units (SDMVPSU) were considered in the complex multistage probability sampling design. Since the NHANES lasted for six consecutive cycles, the 2-year MEC body weight was divided by six to ensure the sample's representativeness of the entire national populace. Continuous variables are presented as mean ± standard deviation (SD), and categorical variables are expressed as frequencies (%). The variables of participants across various PA patterns were compared using the t-test and Rao & Scott’s adjusted χ 2 test. Univariate and multivariate binary logistic regression models were employed to investigate the relationship between PA and stroke risk. Model 1 was unadjusted. Model 2 was controlled for age, sex, race, marital status, education level, and poverty rate. Besides the adjusted variables for Model 2, Model 3 also included adjustments for smoking, drinking, BMI, hypertension, diabetes, and heart disease. Statistical significance was considered at a two-tailed p-value of < 0.05 for all analyses. The statistical analyses were performed using R software (version 4.2.2; www.r-project.org ). 3 Results In total, 26,467 participants (12,676 male and 13,791 female) were pooled from NHANES with a mean age of 47 years. Overall, 1001 participants were diagnosed with stroke symptoms. Based on the 2018 PA guidelines, 15,897 participants (60%) met the recommendations for total PA (150 min/week). Furthermore, 8,752 (33.07%), 9,105 (34.4%), and 3,608 (13.63%) participants received LTPA, OPA, and TPA recommendations, respectively. Detailed characteristics of participants with and without stroke are presented in Table 1 . Table 1 Characteristics of the study population. Variables Non-stroke Stroke P-value Age, (mean ± SD, years) 58.8 ± 9.9 59.6 ± 9.8 58.3 ± 9.7 57.6 ± 10.0 < 0.0001 0.001 BMI (mean ± SD, years) 47 ± 17 64 ± 14 < 0.001 Gender, n(%) 0.13 Male 12,187 (47.68%) 489 (44.19%) Female 13,279 (52.32%) 512 (55.81%) Race < 0.001 Mexican American 3,726 (8.10%) 85 (4.12%) Other Hispanic 2,569 (5.53%) 64 (2.93%) Non-Hispanic White 10,925 (68.29%) 499 (69.90%) Non-Hispanic Black 5,309 (10.64%) 280 (15.09%) Other Race 2,937 (7.44%) 73 (7.96%) Education < 0.001 <high school 2,334 (4.62%) 142 (8.91%) Completed high school 3,452 (9.90%) 183 (15.64%) <High school 19,680 (85.48%) 676 (75.45%) Marital status, n(%) 0.004 co-habitant 15,314 (64.13%) 522 (58.01%) single 10,152 (35.87%) 479 (41.99%) Poverty income ratio, n(%) < 0.001 3.5 7,785 (43.23%) 178 (24.26%) Smoking, n(%) < 0.001 Non-smoker 14,401 (56.73%) 386 (40.09%) Former smoker 6,068 (24.46%) 368 (35.77%) Current smoker 4,997 (18.81%) 247 (24.15%) Alcohol intake 0.011 No 2,643 (13.04%) 70 (9.04%) Yes 22823 (86.96%) 931 (90.96%) BMI, kg/m2, n (%) 0.18 Underweight (< 18.5) 391 (1.50%) 14 (1.67%) Normal (18.5 to < 25) 6,822 (27.74%) 231 (23.35%) Overweight (25 to < 30) 8,288 (32.60%) 316 (29.69%) Obese (30 or greater) 9,965 (38.16%) 440 (45.29%) Hypertension 9,405 (32.85%) 789 (75.78%) < 0.001 Diabetes 4,341 (12.76%) 401 (37.22%) < 0.001 Congestive heart failure 666 (1.84%) 169 (17.27%) < 0.001 Coronary heart disease 897 (3%) 174 (18.18%) < 0.001 Angina 534 (1.85%) 105 (10.46%) < 0.001 Heart attack 879 (2.72%) 200 (19.09%) < 0.001 Total PA: achieved, n (%) 15,502 (65.55%) 395 (42.08%) < 0.001 LTPA: achieved, n (%) 8,590 (38.44%) 162 (19.21%) < 0.001 OPA: achieved, n (%) 8,859 (38.18%) 246 (26.27%) < 0.001 TPA: achieved, n (%) 3,529 (12.70%) 79 (7.42%) < 0.001 BMI, body mass index; PA, physical activity; LTPA, leisure-time PA; OPA, occupation-related PA; TPA, transportation-related PA. The outcomes of the multivariate logistic regression analysis between PA and stroke risk in different domains that met the PA guidelines are presented in Table 2 . In Model 1, total PA meeting the guidelines (odds ratio [OR] = 0.38, 95% CI 0.31–0.48) and LTPA (OR = 0.38, 95% CI 0.30–0.49) were inversely correlated with stroke. After adjusting for sociodemographic factors, the presence of total PA was significantly correlated with stroke (OR = 0.65, 95% CI 0.52–0.81), while LTPA was significantly correlated with stroke (OR = 0.62, 95% CI 0.49–0.79). Controlling for sociodemographic information, lifestyle behaviors, and health conditions, the correlation of total PA (OR = 0.75, 95% CI 0.60–0.93) and LTPA (OR = 0.66, 95% CI 0.47–0.94) remained significant with stroke, with no correlation detected between OPA or TPA and stroke in Models 1, 2, 3 (p > 0.05). Table 2 Multivariable OR for stroke based on the meeting PA guideline levels Model 1 Model 2 Model 3 OR (95% CI) p-value OR (95% CI) p-value OR (95% CI) p-value Total PA: achieved No 1.00(Reference) 1.00(Reference) 1.00(Reference) Yes 0.38 (0.31, 0.48) < 0.001 0.65 (0.52, 0.81) < 0.001 0.75 (0.60, 0.93) 0.011 LTPA: achieved No 1.00(Reference) 1.00(Reference) 1.00(Reference) Yes 0.38 (0.30, 0.49) < 0.001 0.62 (0.49, 0.79) < 0.001 0.74 (0.58, 0.94) 0.017 OPA: achieved No 1.00(Reference) 1.00(Reference) 1.00(Reference) Yes 0.58 (0.46, 0.72) < 0.001 0.84 (0.67, 1.05) 0.127 0.86 (0.69, 1.08) 0.191 TPA: achieved No 1.00(Reference) 1.00(Reference) 1.00(Reference) Yes 0.55 (0.39, 0.78) < 0.001 0.68 (0.48, 0.96) 0.029 0.8 (0.57, 1.14) 0.212 PA: physical activity; LTPA: leisure-time PA; OPA: occupation-related PA; TPA: transportation-related PA; OR: odds ratio; CI: confidence interval. Model 1 was the univariate model in which no covariates were adjusted. Model 2 was adjusted for demographic covariates, including sex, age group, race, education level, marital status, and poverty income ratio. Model 3 was additionally adjusted for smoking status, alcohol intake, BMI, hypertension, diabetes, congestive heart failure, coronary heart disease, angina, and heart attack. Furthermore, PA level was divided into four groups (0, 1–149, 150–299, and ≥ 300 min/week) to evaluate potential dose-response relationships between different PA domains and stroke risk and to assess the additional benefit of PA beyond or below the PA guidelines. A similar inverse relationship was observed between total PA and LTPA categories and stroke. In Model 3, adjusted for confounding factors compared with inactive adults, for total PA, insufficiently active, adequate, and extremely active adults was significantly correlated with stroke risk (OR = 0.61, 95%CI: 0.38–0.77; OR = 0.62, 95%CI: 0.23–0.70, and OR = 0.67, 95%CI: 0.45–0.80), respectively. In terms of LTPA, compared to inactive adults, insufficiently active, adequate, and extremely active adults was also significantly correlated with stroke risk (OR = 0.71, 95%CI: 0.42–0.88%; OR = 0.63, 95%CI: 0.30–0.80; and OR = 0.73, 95%CI: 0.47–0.99), respectively. However, TPA and OPA did not show significant correlation with stroke (p > 0.05) (Fig. 2 ). 4 Discussion In this cross-sectional survey of NHANES, it was discovered that engaging in LTPA at levels recommended by PA guidelines was linked to stroke after controlling for covariates, including age, sex, BMI, ethnicity, socioeconomic level, marital status, and smoking habits. Although no significant correlation was detected between OPA or TPA and stroke, total PA and LTPA were correlated with stroke. These findings underscore the critical importance of LTPA and represent a call to action for providers to focus on LTPA strategies in populations at risk of stroke. Investigation about the equally beneficial relationship between stroke and different PA domains (such as LTPA, OPA, and TPA) in the general population remain limited. Our findings suggest that LTPA is negatively correlated with risk of stroke, aligning with the results of previous studies.[ 23 , 24 ] Therefore, LTPA may be an effective and potentially modifiable lifestyle strategy for stroke prevention. Based on these results, public health policies should encourage and support people to increase LTPA in their daily lives to reduce stroke risk. Previous studies have reported a positive was correlation between OPA and stroke in women and demonstrated that higher-intensity OPA levels increase the risk of stroke.[ 25 , 26 ] However, in the present study, no significant correlation was detected between OPA and stroke risk, which is consistent with the findings of a study by Hu et al..[ 23 ] The relationship between TPA and stroke risk was also explored in our study, with no significant correlation detected, which differs from a previous study.[ 23 ] This may be because geographic and ethnic factors influence lifestyle patterns, including occupational activity and health outcomes. The biological mechanism between LTPA and the risk of stroke is not well understood but is considered to be multifactorial by altering various risk factors such as body weight, hypertension, and diabetes.[ 27 , 28 ] Moreover, LTPA may influence the occurrence of stroke by affecting inflammation, thereby reducing the level of C-reactive protein and mediating the effects of PA on stroke risk.[ 29 ] Higher levels of PA positively affect carotid artery distensibility, nitric oxide availability, and endothelial dysfunction. These physiological effects enhance cardiovascular health through an increase in cerebral blood flow and brain volume.[ 30 , 31 ] In conclusion, the mechanisms underlying the relationship between PA and stroke risk require further investigation. This study possesses several strengths, notably the examination of the relationship between total PA and PA domains (OPA, TPA, and LTPA) and stroke. Given the uncertainty regarding whether all PA domains share the same beneficial relationship, this research addressed a crucial knowledge gap. LTPA, in particular, showed a negative correlation with stroke risk in adults across various age groups, unlike OPA or TPA. The findings are derived from NHANES data, making it the first nationally representative study with a substantial sample size and extended duration to explore the relationship between the risk of stroke and PA in different domains. Therefore, it is possible that our results could be applicable to the population of the US. However, this study has some limitations. Our data are from self-report surveys with cross-sectional properties, which do not establish a temporal relationship. Future Mendelian randomization and prospective studies are necessary to investigate the impact of various PA domains on stroke risk and to confirm our findings. Second, self-report questionnaires were used to measure the PA domains instead of objective measures. The assessment of PA domains was evaluated during a typical week at a single time point. Future research should use continuous data from objective measurements to delve into the relationship between PA and stroke. Besides,stroke diagnosis, because is based only on self-reported doctor diagnosis — no objective confirmation (e.g., imaging) was used. Although it may be affected by participant recall bias, stroke is a major event that is hard to miss or forget. Finally, subtypes of stroke, such as hemorrhagic or ischemic, were not reported in the NHANES database., limiting the generalizability of the results. Further exploration is required to ascertain the universality of this study. 5 Conclusion This study provides new evidence of the correlation between domain-specific PA and stroke among adults in the US. Specifically, negative correlation existed between LTPA and stroke risk. Further studies, particularly prospective studies, are needed to better elucidate the relationship between domain-specific PA and stroke risk, including its potential role in stroke prediction and prevention. Abbreviations BMI Body mass index CI Confidence interval MEC Mobile examination center NCHS National Center for Health Statistics NHANES National Health and Nutrition Examination Survey OR Odds ratio PA Physical activity Declarations Acknowledgements The data and samples used for this research were sourced from NHANES. Our gratitude is extended to the U.S. Centers for Disease Control and Prevention as well as all participants who took part in this research endeavor. Authors’ contributions Xinyue Huang: Writing—original draft, Conceptualization, Methodology. Xutang Jiang: Writing—original draft, Visualization. Qingxin Lin: Writing—original draft, Data curation. Zhigang Pan: Writing—review and editing, Supervision. Weipeng Hu: Writing—review and editing, Supervision. Feng Zheng: Writing—review and editing, Supervision. All authors have approved the final version of this paper. Funding This work was supported by the Natural Science Foundation of Fujian Province (Grant Number 2023J01754), and the Health Technology Program Project of Fujian Province (Grant Number 2023GGA046). Data availability The datasets generated and/or analysed during the current study are available in the NHANES repository, https://www.cdc.gov/nchs/nhanes/. Ethics approval and consent to participate This survey was conducted in accordance with the principles of the Declaration of Helsinki. The NHANES protocol was approved by the NCHS ethics review committee (approval no. 98–12, 2005–06, 2011–17, 2018–01). All individuals who participated in the survey provided written informed consent. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Campbell BCV, Khatri P. Stroke. 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J Physiol. 2008;586:4005–10. https://doi.org/10.1113/jphysiol.2008.158279. Kramer AF, Hahn S, Cohen NJ, Banich MT, McAuley E, Harrison CR, et al. Ageing, fitness and neurocognitive function. Nature. 1999;400:418–9. https://doi.org/10.1038/22682. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7549863","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":531595005,"identity":"223c7c5c-576f-4c1f-822b-beff8d2dc6a3","order_by":0,"name":"Xinyue Huang","email":"","orcid":"","institution":"Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinyue","middleName":"","lastName":"Huang","suffix":""},{"id":531595009,"identity":"3b993bf3-6130-4763-a09d-59b48e9dc818","order_by":1,"name":"Xutang Jiang","email":"","orcid":"","institution":"Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xutang","middleName":"","lastName":"Jiang","suffix":""},{"id":531595012,"identity":"91419e24-3ce7-4031-98e3-6fbe2a18a5d2","order_by":2,"name":"Qingxin Lin","email":"","orcid":"","institution":"Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qingxin","middleName":"","lastName":"Lin","suffix":""},{"id":531595014,"identity":"a4338023-a1fc-4e78-9827-f3843f40ceda","order_by":3,"name":"Zhigang Pan","email":"","orcid":"","institution":"Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhigang","middleName":"","lastName":"Pan","suffix":""},{"id":531595016,"identity":"63e787fa-36c8-4adb-9175-f542d5540ec4","order_by":4,"name":"Weipeng Hu","email":"","orcid":"","institution":"Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Weipeng","middleName":"","lastName":"Hu","suffix":""},{"id":531595017,"identity":"53caf035-4597-4fce-a68a-6ceef13fc99b","order_by":5,"name":"Feng Zheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYDCCAwwMH8AMCRBRAcTMzA2EtDDOQGg5A9LCSIoWxjYQi4AWvtsHGBs+7qhlkJ/dfOzh13m10fztQC0/Krbh1CJ5LoGxceaZ4wyMc46lG8tuO5474zBjA2PPmds4tRicYWB/zNt2jIFZIsdMWnLbsdwGoBZmxja8Whib/wK1sEnkf5OWnHMsdz5RWhjbahh4JHLYJD821ORuIKRF8gxjY2Nv2wEeCYk0M2mGYwdyNwK1HMTnF74zzAcbfrbVycnPSH4m+aOmLnfe+cMHH/yowK0FGguHeUBMZh6Gw2CxA3jUw0AdRPcPKGMUjIJRMApGATIAANOBXNKd7ksQAAAAAElFTkSuQmCC","orcid":"","institution":"Fujian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Feng","middleName":"","lastName":"Zheng","suffix":""}],"badges":[],"createdAt":"2025-09-06 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23:14:35","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":37514,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7549863/v1/cebebc63c85d21e9de9cb613.pdf"},{"id":94047429,"identity":"7ecac4c7-bdc1-4fba-a2cb-d30e11306022","added_by":"auto","created_at":"2025-10-21 23:14:35","extension":"xml","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":102069,"visible":true,"origin":"","legend":"","description":"","filename":"3eca661440c34993aed90f39853434bb1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7549863/v1/5dc20dc6339b1ced2f825205.xml"},{"id":94047428,"identity":"6952f5cc-6e2b-4a17-9ae3-c724ffcb51e4","added_by":"auto","created_at":"2025-10-21 23:14:35","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":110549,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7549863/v1/2d230e4cb9cd6ed7322a9c4a.html"},{"id":94047419,"identity":"9c9b4afa-68a0-4e9c-bd28-b8f75b4f0eff","added_by":"auto","created_at":"2025-10-21 23:14:34","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":133578,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of study design and exclusion criteria.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7549863/v1/11c510fbf24ecc5bf35a21af.jpg"},{"id":94047422,"identity":"792e6453-2430-48f2-ae0d-1ed5215ae12c","added_by":"auto","created_at":"2025-10-21 23:14:34","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":477166,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariable OR for stroke based on the amount of PA.\u003c/p\u003e\n\u003cp\u003ePA, physical activity; OR, odds ratio; CI, confidence interval. All ORs were adjusted for age, sex, body mass index, race, education level, marital status, smoking status, poverty income ratio, smoking status, alcohol intake, BMI status, hypertension, diabetes, congestive heart failure, coronary heart disease, angina, and heart attack.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7549863/v1/4dce927857e27ba5e4dfa5c9.jpg"},{"id":97856067,"identity":"d49b0b72-618f-463a-9e42-9e109610f99b","added_by":"auto","created_at":"2025-12-10 07:55:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1354232,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7549863/v1/510ea4e1-fa60-43ca-9847-0364123127b9.pdf"},{"id":94047420,"identity":"6fbf0ecf-5442-4c63-beda-d34260f3b02c","added_by":"auto","created_at":"2025-10-21 23:14:34","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":26530,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7549863/v1/accc23f5f479a4b0299f1435.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correlation of domain-specific physical activity with stroke: a population-based study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eStroke refers to neurological dysfunction caused by cerebral ischemia or hemorrhage caused by cerebrovascular obstruction or rupture and is a type of harmful cerebrovascular disease.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Stroke currently ranks as the second most prevalent cause of mortality globally and the third leading cause of disability, imposing a substantial burden on both families and society, thus emerging as a significant worldwide public health concern.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] The risk factors for stroke include physical inactivity, obesity, smoking, alcohol consumption, unhealthy diet, hypertension, diabetes, and heart disease.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] Managing these risk factors is crucial for the prevention and control of stroke.\u003c/p\u003e\u003cp\u003ePhysical activity (PA) as a healthy lifestyle serves as a fundamental strategy for stroke prevention,[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] encompassing distinct domains such as leisure-time PA (LTPA), occupation-related PA (OPA), and transportation-related PA (TPA).[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] Many epidemiological studies have investigated the relationship between overall PA or LTPA and stroke risk, consistently revealing inverse correlations; namely, higher PA levels are correlated with lower stroke risk.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] However, these studies focused on PA in total PA or LTPA and ignored PA in other domains, such as OPA and TPA.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] Therefore, the extent to which all PA domains (LTPA, OPA, and TPA) offer the same advantageous correlation for patients with stroke remains uncertain.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eTo bridge this research lacuna, this study examined the correlation between distinct PA domains (LTPA, OPA, and TPA) and stroke in US population, providing a foundation\u003c/p\u003e\u003cp\u003efor future prospective research. Our hypothesis posited that all domain of PA has a beneficial relationship with stroke and explored the dose-response relationship between different domains of PA and stroke.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study design\u003c/h2\u003e\u003cp\u003eThe data used in the present study originated from the National Health and Nutrition Examination Survey (NHANES) cycles from 2007\u0026ndash;2008 and 2017\u0026ndash;2018. NHANES is a nationally representative population-based survey that was developed to evaluate the nutritional and physical statuses of Americans. This cross-sectional survey included demographic, examination, dietary, and questionnaire data.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] The survey had a complex, multistage sampling design, with data collected at home and at a mobile examination center (MEC). This survey was conducted in accordance with the principles of the Declaration of Helsinki. The study protocol was approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board, and written informed consent was obtained from all participants.\u003c/p\u003e\u003cp\u003eNHANES participants aged 20 years between 2007 and 2018 (n\u0026thinsp;=\u0026thinsp;59842) were selected. In total, 41,443 participants were excluded for the following reasons: (1) age\u0026thinsp;\u0026lt;\u0026thinsp;20 years (n\u0026thinsp;=\u0026thinsp;25,072); (3) no PA data (n\u0026thinsp;=\u0026thinsp;134); (3) no self-reported stroke status (n\u0026thinsp;=\u0026thinsp;49); (4) no information on covariates (n\u0026thinsp;=\u0026thinsp;8,120) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Outcome measurement\u003c/h2\u003e\u003cp\u003eThe primary outcome examined in the study was stroke, which was self-reported and assessed using a Medical Condition Questionnaire. All those who answered \u0026ldquo;yes\u0026rdquo; or \u0026ldquo;no\u0026rdquo; to the following question were included: \u0026ldquo;Did your doctor or other health professional tell you that you had a stroke?\u0026rdquo; The outcomes were defined as participants who answered \u0026ldquo;yes\u0026rdquo; and the control group as those who answered \u0026ldquo;no.\u0026rdquo;\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 PA\u003c/h2\u003e\u003cp\u003ePA was evaluated utilizing a standardized questionnaire that inquired about the frequency and duration of vigorous and moderate exercise, fitness, and recreational activities lasting a minimum of 10 minutes within a typical week.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] According to the Global Physical Activity Questionnaire, which assessed three PA domains: LTPA, OPA, and TPA (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), NHANES delineated exercise that caused a large increase in respiration or heart rate as high-intensity activity, whereas moderate-intensity activity was characterized by exercise resulting in a relatively minor increase in breathing or heart rate.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] According to the PA guidelines,[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] one minute of high-intensity activity was considered equivalent to two minutes of moderate-intensity activity. PA was computed based on the amalgamation of the frequency and duration of moderate and vigorous PA within a standard week. Weekly PA (minutes) was calculated as moderate-intensity activity (minutes) in a week plus two high-intensity activities per week (minutes). Total PA comprised LTPA, OPA, and TPA. PA was classified in accordance with the 2018 PA guidelines,[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] which recommend that adults engage in at least 150\u0026ndash;300 min of weekly moderate-intensity PA, 75\u0026ndash;150 min/week of high-intensity PA, or equivalent combinations. Participants were divided into two categories: (1) participants meeting the 2018 PA guidelines (\u0026ge;\u0026thinsp;150 minutes in a standard week) and (2) those failing to meet the 2018 PA guidelines (\u0026lt;\u0026thinsp;150 minutes in a typical week). To explore the dose-response relationship between different domains of PA and stroke, the total PA minutes were further partitioned into four groups based on previous literature: inactive (0 min/week), insufficiently active (1\u0026ndash;149 min/week), sufficiently active (150\u0026ndash;299 min/week), or highly active (\u0026ge;\u0026thinsp;300 min/week).[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Covariates\u003c/h2\u003e\u003cp\u003eThe covariates consisted of sociodemographic information, lifestyle behaviors, and health status. Sociodemographic information encompassed age, sex (male, female), race (Mexican Hispanic, other Hispanic, non-Hispanic white, non-Hispanic black, and other), education (below high school, high school, and college or higher), marital status (married/cohabiting, or single), and poverty income ratio (\u0026lt;\u0026thinsp;1.3, 1.3\u0026ndash;3.5 and \u0026gt;\u0026thinsp;3.5). Lifestyle factors comprised smoking habits (never, former, and current), alcohol consumption (No, Yes), and obesity status. Obesity was classified according to the body mass index (BMI) criteria set by the World Health Organization: thin (\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e), normal weight (18.5\u0026ndash;24.9 kg/m\u003csup\u003e2\u003c/sup\u003e), overweight (25.0\u0026ndash;29.9 kg/m\u003csup\u003e2\u003c/sup\u003e), and obesity (30.0 kg/m\u003csup\u003e2\u003c/sup\u003e).[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] The clinical features included hypertension, diabetes mellitus, and heart disease. Hypertension was identified based on one or more of the following criteria: diagnosed by a healthcare professional, or a mean systolic blood pressure of 140 mmHg or diastolic blood pressure of 90 mmHg in three measurements.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] Diabetes was identified as being informed by a physician or health provider that the patient had diabetes, with glycated hemoglobin (HbA1c)\u0026thinsp;\u0026gt;\u0026thinsp;6.5% and fasting blood glucose\u0026thinsp;\u0026gt;\u0026thinsp;126 mg/dL.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] Heart disease included congestive heart failure, coronary heart disease, angina, and heart attack, and was diagnosed by a doctor or a health professional.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e\u003cp\u003e In accordance with the analysis guidelines issued by the National Center for Health Statistics, the strata (SDMVSTRA) and primary sampling units (SDMVPSU) were considered in the complex multistage probability sampling design. Since the NHANES lasted for six consecutive cycles, the 2-year MEC body weight was divided by six to ensure the sample's representativeness of the entire national populace.\u003c/p\u003e\u003cp\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and categorical variables are expressed as frequencies (%). The variables of participants across various PA patterns were compared using the t-test and Rao \u0026amp; Scott\u0026rsquo;s adjusted χ\u003csup\u003e2\u003c/sup\u003e test. Univariate and multivariate binary logistic regression models were employed to investigate the relationship between PA and stroke risk. Model 1 was unadjusted. Model 2 was controlled for age, sex, race, marital status, education level, and poverty rate. Besides the adjusted variables for Model 2, Model 3 also included adjustments for smoking, drinking, BMI, hypertension, diabetes, and heart disease. Statistical significance was considered at a two-tailed p-value of \u0026lt;\u0026thinsp;0.05 for all analyses. The statistical analyses were performed using R software (version 4.2.2; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.r-project.org\u003c/span\u003e\u003cspan address=\"http://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003eIn total, 26,467 participants (12,676 male and 13,791 female) were pooled from NHANES with a mean age of 47 years. Overall, 1001 participants were diagnosed with stroke symptoms. Based on the 2018 PA guidelines, 15,897 participants (60%) met the recommendations for total PA (150 min/week). Furthermore, 8,752 (33.07%), 9,105 (34.4%), and 3,608 (13.63%) participants received LTPA, OPA, and TPA recommendations, respectively. Detailed characteristics of participants with and without stroke are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of the study population.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-stroke\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStroke\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, years) 58.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9 59.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8 58.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7 57.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 0.001\u003c/p\u003e\u003cp\u003eBMI (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47\u0026thinsp;\u0026plusmn;\u0026thinsp;17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12,187 (47.68%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e489 (44.19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13,279 (52.32%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e512 (55.81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMexican American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,726 (8.10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85 (4.12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Hispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,569 (5.53%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64 (2.93%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10,925 (68.29%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e499 (69.90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,309 (10.64%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e280 (15.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,937 (7.44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73 (7.96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;high school\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,334 (4.62%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e142 (8.91%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCompleted high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,452 (9.90%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e183 (15.64%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;High school\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19,680 (85.48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e676 (75.45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eco-habitant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15,314 (64.13%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e522 (58.01%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10,152 (35.87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e479 (41.99%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoverty income ratio, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8,054 (21.12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e404 (32.17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.3\u0026ndash;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9,627 (35.65%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e419 (43.57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,785 (43.23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e178 (24.26%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking, n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14,401 (56.73%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e386 (40.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6,068 (24.46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e368 (35.77%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,997 (18.81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e247 (24.15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol intake\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,643 (13.04%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70 (9.04%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22823 (86.96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e931 (90.96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI, kg/m2, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight (\u0026lt;\u0026thinsp;18.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e391 (1.50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (1.67%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal (18.5 to \u0026lt;\u0026thinsp;25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6,822 (27.74%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e231 (23.35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverweight (25 to \u0026lt;\u0026thinsp;30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8,288 (32.60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e316 (29.69%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObese (30 or greater)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9,965 (38.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e440 (45.29%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9,405 (32.85%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e789 (75.78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,341 (12.76%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e401 (37.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCongestive heart failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e666 (1.84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e169 (17.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoronary heart disease\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e897 (3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e174 (18.18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAngina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e534 (1.85%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e105 (10.46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart attack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e879 (2.72%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e200 (19.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal PA: achieved, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15,502 (65.55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e395 (42.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLTPA: achieved, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8,590 (38.44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e162 (19.21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOPA: achieved, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8,859 (38.18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e246 (26.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTPA: achieved, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,529 (12.70%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79 (7.42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eBMI, body mass index; PA, physical activity; LTPA, leisure-time PA; OPA, occupation-related PA; TPA, transportation-related PA.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe outcomes of the multivariate logistic regression analysis between PA and stroke risk in different domains that met the PA guidelines are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In Model 1, total PA meeting the guidelines (odds ratio [OR]\u0026thinsp;=\u0026thinsp;0.38, 95% CI 0.31\u0026ndash;0.48) and LTPA (OR\u0026thinsp;=\u0026thinsp;0.38, 95% CI 0.30\u0026ndash;0.49) were inversely correlated with stroke. After adjusting for sociodemographic factors, the presence of total PA was significantly correlated with stroke (OR\u0026thinsp;=\u0026thinsp;0.65, 95% CI 0.52\u0026ndash;0.81), while LTPA was significantly correlated with stroke (OR\u0026thinsp;=\u0026thinsp;0.62, 95% CI 0.49\u0026ndash;0.79). Controlling for sociodemographic information, lifestyle behaviors, and health conditions, the correlation of total PA (OR\u0026thinsp;=\u0026thinsp;0.75, 95% CI 0.60\u0026ndash;0.93) and LTPA (OR\u0026thinsp;=\u0026thinsp;0.66, 95% CI 0.47\u0026ndash;0.94) remained significant with stroke, with no correlation detected between OPA or TPA and stroke in Models 1, 2, 3 (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariable OR for stroke based on the meeting PA guideline levels\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal PA: achieved\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.38 (0.31, 0.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.65 (0.52, 0.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.75 (0.60, 0.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLTPA: achieved\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.38 (0.30, 0.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.62 (0.49, 0.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.74 (0.58, 0.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOPA: achieved\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.58 (0.46, 0.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.84 (0.67, 1.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.86 (0.69, 1.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.191\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTPA: achieved\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.00(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.55 (0.39, 0.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.68 (0.48, 0.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.8 (0.57, 1.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.212\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003ePA: physical activity; LTPA: leisure-time PA; OPA: occupation-related PA; TPA: transportation-related PA; OR: odds ratio; CI: confidence interval.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eModel 1 was the univariate model in which no covariates were adjusted.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eModel 2 was adjusted for demographic covariates, including sex, age group, race, education level, marital status, and poverty income ratio.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eModel 3 was additionally adjusted for smoking status, alcohol intake, BMI, hypertension, diabetes, congestive heart failure, coronary heart disease, angina, and heart attack.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e Furthermore, PA level was divided into four groups (0, 1\u0026ndash;149, 150\u0026ndash;299, and \u0026ge;\u0026thinsp;300 min/week) to evaluate potential dose-response relationships between different PA domains and stroke risk and to assess the additional benefit of PA beyond or below the PA guidelines. A similar inverse relationship was observed between total PA and LTPA categories and stroke. In Model 3, adjusted for confounding factors compared with inactive adults, for total PA, insufficiently active, adequate, and extremely active adults was significantly correlated with stroke risk (OR\u0026thinsp;=\u0026thinsp;0.61, 95%CI: 0.38\u0026ndash;0.77; OR\u0026thinsp;=\u0026thinsp;0.62, 95%CI: 0.23\u0026ndash;0.70, and OR\u0026thinsp;=\u0026thinsp;0.67, 95%CI: 0.45\u0026ndash;0.80), respectively. In terms of LTPA, compared to inactive adults, insufficiently active, adequate, and extremely active adults was also significantly correlated with stroke risk (OR\u0026thinsp;=\u0026thinsp;0.71, 95%CI: 0.42\u0026ndash;0.88%; OR\u0026thinsp;=\u0026thinsp;0.63, 95%CI: 0.30\u0026ndash;0.80; and OR\u0026thinsp;=\u0026thinsp;0.73, 95%CI: 0.47\u0026ndash;0.99), respectively. However, TPA and OPA did not show significant correlation with stroke (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003e In this cross-sectional survey of NHANES, it was discovered that engaging in LTPA at levels recommended by PA guidelines was linked to stroke after controlling for covariates, including age, sex, BMI, ethnicity, socioeconomic level, marital status, and smoking habits. Although no significant correlation was detected between OPA or TPA and stroke, total PA and LTPA were correlated with stroke. These findings underscore the critical importance of LTPA and represent a call to action for providers to focus on LTPA strategies in populations at risk of stroke.\u003c/p\u003e\u003cp\u003eInvestigation about the equally beneficial relationship between stroke and different PA domains (such as LTPA, OPA, and TPA) in the general population remain limited. Our findings suggest that LTPA is negatively correlated with risk of stroke, aligning with the results of previous studies.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] Therefore, LTPA may be an effective and potentially modifiable lifestyle strategy for stroke prevention. Based on these results, public health policies should encourage and support people to increase LTPA in their daily lives to reduce stroke risk.\u003c/p\u003e\u003cp\u003ePrevious studies have reported a positive was correlation between OPA and stroke in women and demonstrated that higher-intensity OPA levels increase the risk of stroke.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] However, in the present study, no significant correlation was detected between OPA and stroke risk, which is consistent with the findings of a study by Hu et al..[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] The relationship between TPA and stroke risk was also explored in our study, with no significant correlation detected, which differs from a previous study.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] This may be because geographic and ethnic factors influence lifestyle patterns, including occupational activity and health outcomes.\u003c/p\u003e\u003cp\u003eThe biological mechanism between LTPA and the risk of stroke is not well understood but is considered to be multifactorial by altering various risk factors such as body weight, hypertension, and diabetes.[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] Moreover, LTPA may influence the occurrence of stroke by affecting inflammation, thereby reducing the level of C-reactive protein and mediating the effects of PA on stroke risk.[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] Higher levels of PA positively affect carotid artery distensibility, nitric oxide availability, and endothelial dysfunction. These physiological effects enhance cardiovascular health through an increase in cerebral blood flow and brain volume.[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] In conclusion, the mechanisms underlying the relationship between PA and stroke risk require further investigation.\u003c/p\u003e\u003cp\u003eThis study possesses several strengths, notably the examination of the relationship between total PA and PA domains (OPA, TPA, and LTPA) and stroke. Given the uncertainty regarding whether all PA domains share the same beneficial relationship, this research addressed a crucial knowledge gap. LTPA, in particular, showed a negative correlation with stroke risk in adults across various age groups, unlike OPA or TPA. The findings are derived from NHANES data, making it the first nationally representative study with a substantial sample size and extended duration to explore the relationship between the risk of stroke and PA in different domains. Therefore, it is possible that our results could be applicable to the population of the US.\u003c/p\u003e\u003cp\u003eHowever, this study has some limitations. Our data are from self-report surveys with cross-sectional properties, which do not establish a temporal relationship. Future Mendelian randomization and prospective studies are necessary to investigate the impact of various PA domains on stroke risk and to confirm our findings. Second, self-report questionnaires were used to measure the PA domains instead of objective measures. The assessment of PA domains was evaluated during a typical week at a single time point. Future research should use continuous data from objective measurements to delve into the relationship between PA and stroke. Besides,stroke diagnosis, because is based only on self-reported doctor diagnosis \u0026mdash; no objective confirmation (e.g., imaging) was used. Although it may be affected by participant recall bias, stroke is a major event that is hard to miss or forget.\u003c/p\u003e\u003cp\u003eFinally, subtypes of stroke, such as hemorrhagic or ischemic, were not reported in the NHANES database., limiting the generalizability of the results. Further exploration is required to ascertain the universality of this study.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study provides new evidence of the correlation between domain-specific PA and stroke among adults in the US. Specifically, negative correlation existed between LTPA and stroke risk. Further studies, particularly prospective studies, are needed to better elucidate the relationship between domain-specific PA and stroke risk, including its potential role in stroke prediction and prevention.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e Body mass index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e Confidence interval\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMEC\u003c/strong\u003e Mobile examination center\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNCHS\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;National Center for Health Statistics\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNHANES\u003c/strong\u003e National Health and Nutrition Examination Survey\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e Odds ratio\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePA\u003c/strong\u003e Physical activity\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data and samples used for this research were sourced from NHANES. Our gratitude is extended to the U.S. Centers for Disease Control and Prevention as well as all participants who took part in this research endeavor.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXinyue Huang: Writing\u0026mdash;original draft, Conceptualization, Methodology. Xutang Jiang: Writing\u0026mdash;original draft, Visualization. Qingxin Lin: Writing\u0026mdash;original draft, Data curation. Zhigang Pan: Writing\u0026mdash;review and editing, Supervision. Weipeng Hu: Writing\u0026mdash;review and editing, Supervision. Feng Zheng: Writing\u0026mdash;review and editing, Supervision. All authors have approved the final version of this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Natural Science Foundation of Fujian Province (Grant Number 2023J01754), and the Health Technology Program Project of Fujian Province (Grant Number 2023GGA046).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available in the NHANES repository, https://www.cdc.gov/nchs/nhanes/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis survey was conducted in accordance with the principles of the Declaration of Helsinki. The NHANES protocol was approved by the NCHS ethics review committee (approval no. 98\u0026ndash;12, 2005\u0026ndash;06, 2011\u0026ndash;17, 2018\u0026ndash;01). All individuals who participated in the survey provided written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCampbell BCV, Khatri P. Stroke. Lancet. 2020;396:129\u0026ndash;42. https://doi.org/10.1016/S0140-6736(20)31179-X.\u003c/li\u003e\n\u003cli\u003eSpence JD. Nutrition and stroke prevention. Stroke. 2006;37:2430\u0026ndash;5. https://doi.org/10.1161/01.STR.0000236633.40160.ee.\u003c/li\u003e\n\u003cli\u003eGBD 2016 Lifetime Risk of Stroke Collaborators, Feigin VL, Nguyen G, Cercy K, Johnson CO, Alam T, et al. Global, Regional, and Country-Specific Lifetime Risks of Stroke, 1990 and 2016. N Engl J Med. 2018;379:2429\u0026ndash;37. https://doi.org/10.1056/NEJMoa1804492.\u003c/li\u003e\n\u003cli\u003eGBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396:1204\u0026ndash;22. https://doi.org/10.1016/S0140-6736(20)30925-9.\u003c/li\u003e\n\u003cli\u003eBoehme AK, Esenwa C, Elkind MSV. Stroke Risk Factors, Genetics, and Prevention. Circ Res. 2017;120:472\u0026ndash;95. https://doi.org/10.1161/CIRCRESAHA.116.308398.\u003c/li\u003e\n\u003cli\u003eTsao CW, Aday AW, Almarzooq ZI, Alonso A, Beaton AZ, Bittencourt MS, et al. Heart Disease and Stroke Statistics-2022 Update: A Report From the American Heart Association. Circulation. 2022;145:e153\u0026ndash;639. https://doi.org/10.1161/CIR.0000000000001052.\u003c/li\u003e\n\u003cli\u003eGhozy S, Zayan AH, El-Qushayri AE, Parker KE, Varney J, Kallmes KM, et al. Physical activity level and stroke risk in US population: A matched case-control study of 102,578 individuals. Ann Clin Transl Neurol. 2022;9:264\u0026ndash;75. https://doi.org/10.1002/acn3.51511.\u003c/li\u003e\n\u003cli\u003eHooker SP, Diaz KM, Blair SN, Colabianchi N, Hutto B, McDonnell MN, et al. Association of Accelerometer-Measured Sedentary Time and Physical Activity With Risk of Stroke Among US Adults. JAMA Netw Open. 2022;5:e2215385. https://doi.org/10.1001/jamanetworkopen.2022.15385.\u003c/li\u003e\n\u003cli\u003eDivney AA, Murillo R, Rodriguez F, Mirzayi CA, Tsui EK, Echeverria SE. Diabetes Prevalence by Leisure-, Transportation-, and Occupation-Based Physical Activity Among Racially/Ethnically Diverse U.S. Adults. Diabetes Care. 2019;42:1241\u0026ndash;7. https://doi.org/10.2337/dc18-2432.\u003c/li\u003e\n\u003cli\u003eHe F, Li Y, Hu Z, Zhang H. Association of domain-specific physical activity with depressive symptoms: A population-based study. Eur Psychiatry. 2022;66:e5. https://doi.org/10.1192/j.eurpsy.2022.2350.\u003c/li\u003e\n\u003cli\u003eLear SA, Hu W, Rangarajan S, Gasevic D, Leong D, Iqbal R, et al. The effect of physical activity on mortality and cardiovascular disease in 130 000 people from 17 high-income, middle-income, and low-income countries: the PURE study. Lancet. 2017;390:2643\u0026ndash;54. https://doi.org/10.1016/S0140-6736(17)31634-3.\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Donnell MJ, Chin SL, Rangarajan S, Xavier D, Liu L, Zhang H, et al. Global and regional effects of potentially modifiable risk factors associated with acute stroke in 32 countries (INTERSTROKE): a case-control study. Lancet. 2016;388:761\u0026ndash;75. https://doi.org/10.1016/S0140-6736(16)30506-2.\u003c/li\u003e\n\u003cli\u003eWilley JZ, Moon YP, Paik MC, Boden-Albala B, Sacco RL, Elkind MSV. Physical activity and risk of ischemic stroke in the Northern Manhattan Study. Neurology. 2009;73:1774\u0026ndash;9. https://doi.org/10.1212/WNL.0b013e3181c34b58.\u003c/li\u003e\n\u003cli\u003eLi X, Liu S, Mu X, Gao H, Zi Y, Yang H, et al. Association Between Change in Leisure-Time Physical Activity During the Postretirement Period and Incident Stroke. Neurology. 2022;:10.1212/WNL.0000000000200555. https://doi.org/10.1212/WNL.0000000000200555.\u003c/li\u003e\n\u003cli\u003eNHANES - National Health and Nutrition Examination Survey Homepage. 2023. https://www.cdc.gov/nchs/nhanes/index.htm. Accessed 16 Sep 2023.\u003c/li\u003e\n\u003cli\u003eChen R, Wang K, Chen Q, Zhang M, Yang H, Zhang M, et al. Weekend warrior physical activity pattern is associated with lower depression risk: Findings from NHANES 2007-2018. Gen Hosp Psychiatry. 2023;84:165\u0026ndash;71. https://doi.org/10.1016/j.genhosppsych.2023.07.006.\u003c/li\u003e\n\u003cli\u003ePiercy KL, Troiano RP, Ballard RM, Carlson SA, Fulton JE, Galuska DA, et al. The Physical Activity Guidelines for Americans. JAMA. 2018;320:2020\u0026ndash;8. https://doi.org/10.1001/jama.2018.14854.\u003c/li\u003e\n\u003cli\u003eAlmohamad M, Krall Kaye E, Mofleh D, Spartano NL. The association of sedentary behaviour and physical activity with periodontal disease in NHANES 2011-2012. J Clin Periodontol. 2022;49:758\u0026ndash;67. https://doi.org/10.1111/jcpe.13669.\u003c/li\u003e\n\u003cli\u003eChristensen K, Gleason CE, Mares JA. Dietary carotenoids and cognitive function among US adults, NHANES 2011-2014. Nutr Neurosci. 2020;23:554\u0026ndash;62. https://doi.org/10.1080/1028415X.2018.1533199.\u003c/li\u003e\n\u003cli\u003eHe F, Li Y, Hu Z, Zhang H. Association of domain-specific physical activity with depressive symptoms: A population-based study. Eur Psychiatr. 2023;66:e5. https://doi.org/10.1192/j.eurpsy.2022.2350.\u003c/li\u003e\n\u003cli\u003eAmerican Diabetes Association. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2020. Diabetes Care. 2020;43 Suppl 1:S14\u0026ndash;31. https://doi.org/10.2337/dc20-S002.\u003c/li\u003e\n\u003cli\u003eGhozy S, Zayan AH, El-Qushayri AE, Parker KE, Varney J, Kallmes KM, et al. Physical activity level and stroke risk in US population: A matched case-control study of 102,578 individuals. Ann Clin Transl Neurol. 2022;9:264\u0026ndash;75. https://doi.org/10.1002/acn3.51511.\u003c/li\u003e\n\u003cli\u003eHu G, Sarti C, Jousilahti P, Silventoinen K, Barengo NC, Tuomilehto J. Leisure time, occupational, and commuting physical activity and the risk of stroke. Stroke. 2005;36:1994\u0026ndash;9. https://doi.org/10.1161/01.STR.0000177868.89946.0c.\u003c/li\u003e\n\u003cli\u003eKhurshid S, Al-Alusi MA, Churchill TW, Guseh JS, Ellinor PT. Accelerometer-Derived \u0026ldquo;Weekend Warrior\u0026rdquo; Physical Activity and Incident Cardiovascular Disease. JAMA. 2023;330:247\u0026ndash;52. https://doi.org/10.1001/jama.2023.10875.\u003c/li\u003e\n\u003cli\u003eEvenson KR, Rosamond WD, Cai J, Toole JF, Hutchinson RG, Shahar E, et al. Physical activity and ischemic stroke risk. The atherosclerosis risk in communities study. Stroke. 1999;30:1333\u0026ndash;9. https://doi.org/10.1161/01.str.30.7.1333.\u003c/li\u003e\n\u003cli\u003eHall C, Heck JE, Sandler DP, Ritz B, Chen H, Krause N. Occupational and leisure-time physical activity differentially predict 6-year incidence of stroke and transient ischemic attack in women. Scand J Work Environ Health. 2019;45:267\u0026ndash;79. https://doi.org/10.5271/sjweh.3787.\u003c/li\u003e\n\u003cli\u003eWilley JZ, Voutsinas J, Sherzai A, Ma H, Bernstein L, Elkind MSV, et al. Trajectories in Leisure-Time Physical Activity and Risk of Stroke in Women in the California Teachers Study. Stroke. 2017;48:2346\u0026ndash;52. https://doi.org/10.1161/STROKEAHA.117.017465.\u003c/li\u003e\n\u003cli\u003eHoward VJ, McDonnell MN. Physical activity in primary stroke prevention: just do it! Stroke. 2015;46:1735\u0026ndash;9. https://doi.org/10.1161/STROKEAHA.115.006317.\u003c/li\u003e\n\u003cli\u003eLin X, Zhang X, Guo J, Roberts CK, McKenzie S, Wu W-C, et al. Effects of Exercise Training on Cardiorespiratory Fitness and Biomarkers of Cardiometabolic Health: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. J Am Heart Assoc. 2015;4:e002014. https://doi.org/10.1161/JAHA.115.002014.\u003c/li\u003e\n\u003cli\u003eAinslie PN, Cotter JD, George KP, Lucas S, Murrell C, Shave R, et al. Elevation in cerebral blood flow velocity with aerobic fitness throughout healthy human ageing. J Physiol. 2008;586:4005\u0026ndash;10. https://doi.org/10.1113/jphysiol.2008.158279.\u003c/li\u003e\n\u003cli\u003eKramer AF, Hahn S, Cohen NJ, Banich MT, McAuley E, Harrison CR, et al. Ageing, fitness and neurocognitive function. Nature. 1999;400:418\u0026ndash;9. https://doi.org/10.1038/22682.\u003c/li\u003e\n\u003c/ol\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":"stroke, physical activity, domain-specific, epidemiology, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-7549863/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7549863/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe extent to which all forms of physical activity (PA), including leisure-time PA (LTPA), occupation-related PA (OPA), and transportation-related PA (TPA), exhibit equally advantageous correlations with stroke risk remains uncertain. Thus, this study aimed to assess the correlation between LTPA, OPA, and TPA and the incidence of stroke in adults.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis cross-sectional study included participants\u0026rsquo; data from the National Health and Nutrition Examination Survey. Physical activity (PA) was assessed using self-report questionnaires and classified according to PA guidelines. Stroke was assessed using a health questionnaire. Multivariate logistic regression models adjusted for demographic data, behavioral factors, and health status were used to assess the relationship between PA patterns and stroke.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOverall, 26,467 participants were included (mean age: 47 years; 13,791 female). Total PA (odds ratio [OR]\u0026thinsp;=\u0026thinsp;0.75, 95% confidence interval [CI] 0.60\u0026ndash;0.93) and LTPA (OR\u0026thinsp;=\u0026thinsp;0.74, 95%CI 0.58\u0026ndash;0.94) of participants who met the PA guidelines (150 min/week) were significantly correlated with stroke, with no significant correlations detected between OPA or TPA and stroke (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). LTPA levels of 1\u0026ndash;149, 150\u0026ndash;299, and \u0026ge;\u0026thinsp;300 min/week were significantly correlated with stroke (OR\u0026thinsp;=\u0026thinsp;0.71, 95%CI: 0.42\u0026ndash;0.88; OR\u0026thinsp;=\u0026thinsp;0.63, 95%CI: 0.30\u0026ndash;0.80; and OR\u0026thinsp;=\u0026thinsp;0.73, 95%CI: 0.47\u0026ndash;0.99), respectively.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThere was a significant negative correlation between domain-specific PA and stroke risk. Specifically, negative correlation existed between LTPA and stroke risk.\u003c/p\u003e","manuscriptTitle":"Correlation of domain-specific physical activity with stroke: a population-based study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-21 23:14:30","doi":"10.21203/rs.3.rs-7549863/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":"b08692f4-3690-4b68-9890-03cac45af95b","owner":[],"postedDate":"October 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-10T07:53:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-21 23:14:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7549863","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7549863","identity":"rs-7549863","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}