The Role of Physical Activity in the Association Between Smoking Status and Cognitive Function : A Cross-Sectional Study Based on NHANES 2011-2014

The Role of Physical Activity in the Association Between Smoking Status and Cognitive Function : A Cross-Sectional Study Based on NHANES 2011-2014 | 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 The Role of Physical Activity in the Association Between Smoking Status and Cognitive Function : A Cross-Sectional Study Based on NHANES 2011-2014 Hone Chen, Yu ping Zhu, Ju xiang Yang, Yi Wang, Hong chu Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3884105/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 Objectives The aim of this study is to examine the relationship between physical activity, smoking status and cognitive function, and to test the potential moderating role of physical activity. Methods This cross-sectional study used data on smoking status, cognitive function and physical activity from 1735 participants aged 60 years and older in NHANES from 2011 to 2014. Linear and logistic regression models were used to assess the association between smoking status and cognitive function. Mediation and moderation analyses were conducted to examine the role of physical activity in this association. Results Former smokers scored on average 2.090 points higher on the Digit Symbol Substitution Test (DSST) compared to never smokers (95% CI 0.755, 3.472; P < 0.05), while logistic regression showed that current smokers had an odds ratio (OR) of 0.629 for cognitive impairment compared to never smokers (95% CI 0.421, 0.941). No significant associations were observed between smoking status and CERAD and AFT. Moderation analysis showed that leisure-time physical activity significantly attenuated the effect of smoking on DSST scores in current smokers compared to never smokers (β = -2.014, P < 0.05). Conclusion There was a significant correlation between smoking status, physical activity and DSST scores.Although the mediating effect of leisure-time physical activity in the association between smoking status and cognitive function is not significant, it attenuates the decline in DSST scores in current smokers. Cognition Exercise Smoking Moderation NHANES Figures Figure 1 Figure 2 Figure 3 1 Introduction With advancing age, various physiological functions and cognitive abilities gradually decline among the elderly [ 1 ] .The structure and function of the brain may undergo cognitive decline due to the influence of risk factors, ultimately progressing to cognitive impairment [ 2 ] .Cognitive impairment is a prevalent syndrome in the elderly population, characterized by declines in memory, executive function, and other cognitive domains, with Alzheimer's disease being the most common form of cognitive impairment [ 3 ] .Academic hypotheses regarding cognitive impairment include the amyloid cascade hypothesis, tau protein hypothesis, inflammation hypothesis, metal ion hypothesis, and oxidative stress hypothesis, with oxidative stress and its impact on neuronal damage being central and pivotal aspects of all mechanisms [ 4 ] .The interplay of glial cells, zinc homeostasis imbalance, and synaptic complexity also play crucial roles in aging and cognitive decline [ 5 ] . Smoking is a well-recognized risk factor for various health issues. Epidemiological studies reveal that short-term smoking may enhance cognitive function [ 6 ] , but long-term smoking increases the risk of cognitive decline [ 7 , 8 ] and raises the likelihood of developing neurodegenerative diseases such as Alzheimer's and Parkinson's [ 9–22 ] .Recent research indicates that quitting smoking and engaging in physical activity contribute to the deceleration of aging [ 23 ] ,and positively influence cognitive function in the elderly [ 24–26 ] . Regular physical activity is associated with improved cognitive function and reduced risk of cognitive impairment, suggesting a potential mediating role in the association between smoking and cognition. Studies demonstrate that cognitively impaired elderly individuals are eight times more likely to be physically frail than their cognitively normal counterparts [ 27 ] .Physical activity can enhance cognitive function in the elderly by increasing neural plasticity [ 28–30 ] .Smoking is associated with reduced levels of physical activity [ 31–34 ] ,and individuals who engage in regular exercise tend to exhibit lower rates of smoking and nicotine dependence [ 35 ] .Furthermore, physical activity mitigates the impact of negative self-perceived health (SPH) among smokers [ 36 ] .Loprinzi et al. (2015) proposed a conceptual model summarizing the relationships among physical activity, smoking status, and cognition, suggesting that smoking leads to decreased executive function, while physical activity may alleviate this relationship. However, empirical evidence supporting this model is lacking [ 37 ] . In summary, considering the interplay among smoking, physical activity, and cognitive function, this study conducts a cross-sectional analysis using data from the National Health and Nutrition Examination Survey (NHANES). The aim is to explore the relationship between smoking status and cognitive function and analyze the role of physical activity in this association. 2 Materials and Methods 2.1 Study Population NHANES, an acronym for the National Health and Nutrition Examination Survey, is a longitudinal, multi-stage, cluster-randomized health and nutrition survey in the United States. This comprehensive database includes diverse health and nutrition information for the U.S. population, encompassing data on physical examinations, laboratory tests, and various lifestyle factors. The NHANES protocol received approval from the NCHS Research Ethics Review Board, and all participants provided informed consent. Additional information is available on the official website of the center ( https://wwwn.cdc.gov/nchs/nhanes/tutorials/default.aspx ). For this study, data from NHANES conducted between 2011 and 2014 were utilized. The study population comprised individuals aged 60 and above (n = 3,632), as cognitive assessments were conducted within this age range. Participants with missing covariate and smoking status questionnaire data (n = 945), those with missing physical activity-related data (n = 945), and individuals with incomplete smoking status data (n = 3) were excluded. Ultimately, 1,735 participants were included in the study analysis (Fig. 1 ). 2.2 Cognitive Function Assessment In NHANES, cognitive function was assessed through the Vocabulary Learning and Recall module, developed by the Consortium to Establish a Registry for Alzheimer's Disease (CERAD), Animal Fluency Test (AFT), and Digit Symbol Substitution Test (DSST) [ 38 , 39 ] . The CERAD Word Learning Subtest (CERAD W-L) aims to assess immediate and delayed learning abilities for new language information, a component of the memory domain [ 40 ] .This test includes three consecutive learning trials (Immediate Word Recall Test, IWRT) and a delayed recall test (Delayed Word Recall Test, DWRT). Participants were required to verbally recite 10 unrelated words and subsequently recall as many words as possible immediately after each trial. The order of the ten words changed in each trial. The delayed word recall test was conducted after other cognitive tests (AFT and DSST). The Animal Fluency Test (AFT) examines explicit language fluency, a component of executive function. Scores can differentiate between individuals with normal cognitive function and those with mild or more severe forms of cognitive impairment, such as Alzheimer's disease. The Digit Symbol Substitution Test (DSST) is a performance module of the Wechsler Adult Intelligence Scale (WAIS III), relying on processing speed, sustained attention, and working memory. Participants were given two minutes to transcribe symbols corresponding to the numbers in 133 boxes [ 41 ] . 2.3 Smoking status Participants were categorized into never smokers (never smoked or smoked 100 cigarettes in their lifetime and quit), and current smokers (smoked > 100 cigarettes in their lifetime and currently smoke). 2.4 Physical Activity Data were collected using the Global Physical Activity Questionnaire. Created by the World Health Organization (WHO), this questionnaire assesses physical activity in various domains, including leisure-time physical activity (LTPA), occupational physical activity (OPA), and transportation-related physical activity (TPA). Typical weekly frequency (per week/time), duration (per session/minutes), and intensity (vigorous or moderate) were surveyed for OPA and LTPA. Physical activity was categorized based on the maximum intensity minutes per week for each type of activity, following the formula: PA = weekly frequency × duration of each PA. Classification was based on whether participants met the U.S. Physical Activity Guidelines (150 minutes of moderate-intensity PA per week [equivalent to 600 minutes/month] or 75 minutes of high-intensity PA per week) [ 42 ] . 2.5 Statistical Analysis Baseline characteristics of participants were described using medians (interquartile range, IQR) for continuous variables and counts (percentages) for categorical variables. Given that most continuous variables (cognitive function scores, physical activity, age, etc.) did not conform to normality tests, non-parametric tests were used to compare inter-group differences for continuous variables. Chi-square tests were employed for categorical variables to compare demographic characteristics among participants with different smoking statuses. Cognitive function scores and minutes of physical activity were treated as continuous variables, and physical activity was categorized into four groups as a categorical variable. Linear regression analysis was used to explore the relationship between physical activity and smoking status and cognitive function. Logistic regression was employed to assess the odds ratio (OR) and corresponding 95% confidence interval (CI) for the association between smoking status and cognitive function. All analyses were adjusted for baseline age and gender (Model 1). Model 2 included additional adjustments for race/ethnicity (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, and Other), marital status (married, widowed, divorced, never married, cohabiting), household income-to-poverty ratio ( 3.5), and education level (less than high school, high school non-graduate, high school graduate/GED, some college or associate degree, college graduate and above). All analyses were performed using R 4.3.1(The University of Auckland,Auckland,New Zealand) and IBM SPSS Statistics software version 27.0 (IBM Corp, Armonk, NY, USA). Statistical significance was considered when two-tailed P < 0.05. 3 Result 3.1 Baseline Characteristics of Participants This study utilized data from 1,735 participants across two NHANES cycles (2011-2014). Participants were categorized into current smokers (n = 204), former smokers (n = 674), and never smokers (n = 857) based on smoking status. Smoking status showed statistically significant associations with demographic variables such as age, gender, race, education level, household income-to-poverty ratio, and marital status (P < 0.05). In general, never smokers were more likely to be female, non-Hispanic white, and have higher education levels. The relationship between smoking status and cognitive function in different domains indicated no association with scores for AFT and CERAD but a significant correlation with DSST scores. Regarding physical activity, the maximum minutes of leisure-time physical activity (LTPA) were significantly higher in never smokers compared to current smokers (Please refer to Table 1 at the end of the article for details). Table 1 Baseline characteristics of elderly subjects in the United States by smoking status Name Levels Current smoker Former smoker Never smoker P -value Age** Median (IQR) 65.00 (62.00 to 69.00) 68.00 (64.00 to 75.00) 68.00 (63.00 to 74.00) <0.001 Age.group** 60-69 years 147 (72.1%) 338 (50.1%) 448 (52.3%) <0.001 70-79 years 35 (17.2%) 200 (29.7%) 235 (27.4%) 80+ years 22 (10.8%) 136 (20.2%) 174 (20.3%) Sex** female 82 (40.2%) 232 (34.4%) 513 (59.9%) <0.001 male 122 (59.8%) 442 (65.6%) 344 (40.1%) Race** Mexican American 17 (8.3%) 51 (7.6%) 72 (8.4%) <0.001 Other Hispanic 19 (9.3%) 62 (9.2%) 93 (10.9%) Non-Hispanic White 80 (39.2%) 357 (53%) 397 (46.3%) Non-Hispanic Black 72 (35.3%) 147 (21.8%) 184 (21.5%) Other/multiracial 16 (7.8%) 57 (8.5%) 111 (13%) Education.attainment** Less Than 9th Grade 19 (9.3%) 60 (8.9%) 74 (8.6%) <0.001 9-11th Grade 37 (18.1%) 88 (13.1%) 87 (10.2%) High School Grad/GED 53 (26%) 165 (24.5%) 164 (19.1%) Some College or AA degree 77 (37.7%) 194 (28.8%) 263 (30.7%) College Graduate or above 18 (8.8%) 167 (24.8%) 269 (31.4%) PIR** Median (IQR) 1.71 (0.99 to 3.14) 2.61 (1.29 to 4.53) 2.53 (1.33 to 4.84) <0.001 n_pir** <1.3 81 (39.7%) 171 (25.4%) 208 (24.3%) 3.5 41 (20.1%) 255 (37.8%) 316 (36.9%) Marriage** Married 84 (41.2%) 402 (59.6%) 506 (59%) <0.001 Widowed 35 (17.2%) 98 (14.5%) 156 (18.2%) Divorced and Separated 52 (25.5%) 126 (18.7%) 136 (15.9%) Unmarried 18 (8.8%) 29 (4.3%) 43 (5%) Cohibitation 15 (7.4%) 19 (2.8%) 16 (1.9%) CERAD1 Median (IQR) 5.00 (4.00 to 6.00) 5.00 (4.00 to 6.00) 5.00 (4.00 to 6.00) 0.094 CERAD2 Median (IQR) 7.00 (6.00 to 8.00) 7.00 (6.00 to 8.00) 7.00 (6.00 to 8.00) 0.296 CERAD3 Median (IQR) 8.00 (7.00 to 9.00) 8.00 (7.00 to 9.00) 8.00 (7.00 to 9.00) 0.171 CERAD.total Median (IQR) 19.00 (16.50 to 22.00) 19.00 (16.00 to 22.00) 20.00 (16.00 to 23.00) 0.099 CERAD.delay.recall Median (IQR) 6.00 (5.00 to 8.00) 6.00 (5.00 to 8.00) 6.00 (5.00 to 8.00) 0.314 AFT Median (IQR) 16.00 (13.00 to 20.00) 17.00 (14.00 to 21.00) 17.00 (14.00 to 21.00) 0.056 DSST** Median (IQR) 43.00 (32.00 to 55.00) 48.00 (37.00 to 60.00) 50.00 (37.00 to 61.00) <0.001 TPA* Median (IQR) 0.00 (0.00 to 127.50) 0.00 (0.00 to 40.00) 0.00 (0.00 to 80.00) 0.003 OPA Median (IQR) 45.00 (0.00 to 390.00) 0.00 (0.00 to 360.00) 0.00 (0.00 to 300.00) 0.100 LTPA** Median (IQR) 0.00 (0.00 to 205.00) 120.00 (0.00 to 270.00) 120.00 (0.00 to 300.00) <0.001 Data presented are median (IQR), or n (%). Abbreviation:IQR=Interquartile Range;CERAD=Center for Epidemiology and Registration of Alzheimer's Disease,AFT=Animal Fluency Test; DSST=Digit Symbol Substitution Test;TPA= transportation-related physical activity,OPA=Occupationrelated physical activity,LTPA= leisure-time physical activity. *p < 0.05, ** p < 0.01. 3.2 Relationship Between Smoking Status and DSST Scores in Cognitive Function Smoking status exhibited a negative correlation with cognitive function DSST scores. After adjusting for age, gender, race, education level, household income-to-poverty ratio, and marital status through linear regression analysis, former smokers had DSST scores 2.090 higher than never smokers (95% CI 0.732, 3.448; P < 0.05). By comparing DSST scores with the mean, the population was divided into low cognitive and cognitive groups. In logistic regression, current smokers showed a significant association with lower DSST scores compared to never smokers (OR 0.629, 95% CI 0.421, 0.941; P < 0.05) (Table 2). Table 2 Regression analysis of correlation between smoking status and DSST score Smoke status Linear regression analysis Logistic regression analysis β(95%CI) P -value OR(95%CI) P -value Q1 (no smoker) Reference Reference Reference - Q2 2.090* 0.003 1.033 0.808 (former smoker) (0.732,3.448) (0.794,1.345) Q3 -0.799 0.448 0.629* 0.024 (current smoker) (-2.865,1.267) (0.421,0.941) *p < 0.05, ** p < 0.01. 3.3 Linear Regression of LTPA Maximum Activity Minutes Segments with DSST Scores The study found a positive correlation between LTPA and DSST scores. Linear regression based on segments of LTPA maximum activity minutes and DSST scores demonstrated a significant association when LTPA exceeded 150 min/week compared to LTPA = 0 min/week (Table 3). Table 3 Linear regression model of segmentation and DSST score according to the maximum activity minutes of LTPA. Maximum active minutes of LTPA Model 1 a Model 2 b β(95%CI) P -value c β(95%CI) P -value c Q1 0min/week Reference - Reference - Q2 1-150min/week 3.441(1.455,5.426)** <0.001 0.377(-1.272,2.027) 0.654 Q3 151-300min/week 6.383(4.202,8.565)** =300min/week 7.486(5.513,9.460)** <0.001 2.519(0.857,4.181)* 0.003 a Model 1 was adjusted for chronological age and sex; b Model 2 was further adjusted for ethnicity, education, family income-poverty ratio, marrital based on Model 1; *p < 0.05, ** p < 0.01. 3.4 Role of Physical Activity in the Association Between Smoking and Cognitive Function 3.4.1 Mediation Analysis of Physical Activity in the Association between Smoking Status and DSST The study indicated that, compared to never smokers, current smokers had a significantly direct effect (β = -4.8730, P < 0.001) in the association with DSST scores. However, LTPA did not mediate the relationship between different smoking statuses and DSST scores compared to never smokers. The relative total effect of former smokers (β = -4.9390, P < 0.001) was significant compared to never smokers. In summary, leisure-time physical activity did not exhibit a significant mediating effect in the association between smoking status and DSST scores (Table 4). Table 4 Multi-category mediation model: the mediation effect of LTPA and multidimensional smoking status on DSST score. Route Non-standardized coefficient( Standard error ) P -value 95%CI LTPA on SMQ1 0.3634(16.3508) 0.9823 ( -31.7059,32.4328) SMQ2 -34.2649(24.7471) 0.1663 (-82.7910,14.2612) DSST on SMQ1 -0.4119(1.0451) 0.6936 (-2.4616,1.6379) SMQ2 -3.3018(1.4366)* 0.0217 (-6.1195,-0.4841) LTPA 0.0094(0.0020)** <0.001 (0.0056,0.0133) LTPA*SMQ1 -0.0025(0.0029) 0.3810 (-0.0081,0.0031) LTPA*SMQ2 -0.0075(0.0033)* 0.0233 (-0.0140,-0.0010) Relative indirect effects a1b 0.0025(0.1127) - (-0.2520,0.2035) a2b -0.0660(0.1939) - (-0.6556,0.0841) Relative direct effects c1’ -0.9364(0.8562) 0.2743 (-2.6158,0.7430) c2’ -4.8730(1.2989)** <0.001 (-7.4205,-2.3255) Relative total effects c1 -0.9339(0.8641) 0.2799 (-2.6286,0.7609) c2 -4.9390(1.3075)** <0.001 (-7.5035,-2.3746) *p < 0.05, ** p < 0.01. 3.4.2 Moderation Analysis of Physical Activity in the Association between Smoking Status and DSST The study revealed that, compared to never smokers, LTPA did not have a moderating effect on the relationship between former smokers and DSST scores. However, LTPA mitigated the impact of current smoking on DSST scores (β = -2.014, P < 0.05), indicating a significant weakening or inhibitory effect of LTPA on the relationship between smoking and DSST scores (Table 5). Table 5 The moderating effect of LTPA and smoking status on DSST score Variables Model 1 Model 2 Model 3 Constant 49.238(0.573) 49.211(0.569) 49.206(0.568) Former smoker -0.934(0.864) -0.936(0.857) -0.937(0.856) Current smoker -4.939(1.307)** -4.704(1.298)** -4.848(1.298)** LTPA - 2.178(0.400)** 2.625(0.453)** Int1 - - - Int2 - - -2.014(0.961)* Abbreviation:Int1=represents the interaction between LTPA and FormSmoker,Int2 represents the interaction between LTPA and Current smoker. *p < 0.05, ** p < 0.01. To further analyze how LTPA modulates the relationship between smoking status and DSST scores, the study plotted the relationship between smoking status and DSST scores at different levels of LTPA maximum activity minutes intensity (Figure 2). It was evident that with an increase in LTPA activity minutes, DSST scores for current smokers increased, validating the moderating effect of LTPA. Figure 2 Slope plot of multiple classification regulation effect The relationship and impact mechanisms among physical activity, smoking status, and DSST scores are illustrated in Figure 3 based on the study results. Figure 3 The relationships of smoking status, LTPA, and DSST scores 4 Discussion The prevailing view in current research suggests that the potential mechanism by which smoking diminishes cognitive function involves inflammatory responses [ 43-47 ] .For instance, a preclinical study reported that smoking exacerbated cognitive impairment in a vascular dementia rat model through neuroinflammation [ 48 ] .Another potential mechanism may involve psychosocial processes such as sleep issues. A case-control study of mild cognitive impairment previously reported that sleep duration partially mediated the association between smoking and cognitive function [ 49 ] .Functional neuroimaging studies have also demonstrated some alterations in brain structure and function due to smoking. For example, a 24-month non-randomized intervention study found reduced gray matter density in regions crucial for cognitive function in current smokers compared to never smokers [ 26 ] .Contrary to some other studies indicating a greater risk of memory impairment associated with smoking [ 50 ] ,our study did not find a significant association between smoking status and memory. Additionally, DSST scores (processing speed) of current smokers were significantly negatively correlated with cognitive function compared to never smokers. These findings align with a study by Zhang et al. (2022) [ 46 ] .Concurrently, the study by Song et al. (2020) suggested that only former smokers exhibited better cognitive function compared to non-smokers [ 51 ] ,further supporting our findings. Therefore, besides strengthening effective smoking cessation measures, we need to identify other feasible intervention strategies to mitigate the adverse effects of smoking on cognitive function. Clinical trials have demonstrated that older adults participating in continuous 2 to 12-week exercise programs exhibit improvement in information processing speed [ 52 ] ,The potential mechanisms by which exercise improves cognitive function primarily involve increased neural activity and volume in the prefrontal and frontal cortices [ 53 , 54 ] 、enhanced hippocampal neurogenesis [ 55 ] 、changes in regions of the brain associated with information processing [ 56 ] and increased gene expression of brain-derived neurotrophic factors and other growth factors related to exercise. Low cognitive function and a history of smoking are associated with increased risk of mortality [ 57 ] .The more frequent the LTPA, the stronger the cognitive function, and individuals engaging in low-intensity physical activity regularly have a lower risk of mortality [ 58 ] .This study, combining the model summarized by Loprinzi et al. (2015)speculates that physical activity may be a crucial mediating factor in this association. The results indicate that leisure-time physical activity did not exhibit a significant mediating effect in the association between smoking status and DSST scores. LTPA had a significant moderating effect on the relationship between current smoking and DSST scores, suggesting that leisure-time physical activity can mitigate the decline in DSST scores caused by smoking. Loprinzi et al. (2014) demonstrated that physical activity could attenuate the association between nicotine dependence and depression [ 59 ] ,to some extent supporting our results regarding the moderating role of physical activity. This study suggests that smoking leads to cognitive decline in older adults, and leisure-time physical activity plays a moderating role in the association between smoking status and cognitive function. However, there are certain limitations to this study. Firstly, the data used are predominantly questionnaire-based, introducing a degree of subjectivity. Secondly, the study is cross-sectional, only allowing for the observation of correlations between variables without establishing causation. Therefore, future research efforts will focus on improving study methods and data accuracy to enhance the precision of results related to the interrelationships between smoking, physical activity, and cognitive function. The use of accelerometer-measured physical activity data could provide a more objective assessment of the results. Combining advanced neuroimaging methods or biomarker analysis could further explore potential neural mechanisms. To be cautious, exploring potential moderating factors, such as genetic influences, to identify specific subgroups that may exhibit different susceptibility and responsiveness to cognitive function in smokers. Longitudinal studies would provide valuable insights into the temporal dynamics of these associations. Additionally, incorporating relevant interventions, such as targeted exercise therapy and smoking cessation programs, to evaluate their effectiveness in alleviating smoking-related cognitive decline. 5 Conclusion Our study revealed a correlation between smoking status and cognitive function, with smoking status specifically showing a significant association with DSST scores (processing speed) in cognitive function. Current smokers exhibited a significant negative correlation with DSST scores. Leisure-time physical activity (LTPA) exceeding 150 min/week was significantly associated with improved DSST scores. While LTPA did not exhibit a significant mediating effect in the association between smoking status and DSST scores, it demonstrated a moderating effect in the relationship between current smoking and DSST scores. Our findings suggest that smoking cessation is a strategy for enhancing executive function among smokers, thereby improving cognitive function. Additionally, engaging in physical activity stands out as an effective strategy for enhancing cognitive function. Declarations Competing interests The authors declare that they have no competing interests. Author Contribution H.C. participated in the design of the study, contributed to data collection and data analysis; Y. Z. contributed to data reduction, directed the revision of the paper; J.Y. participated in data analysis; C.W. participated in the design of the study and directed the revision of the paper; Y.W. directed the revision of the paper; G. S. proposed the idea of topic selection and guided the dissertation revision. All authors contributed to the manuscript writing. All authors have read and approved the final version of the manuscript and agree with the order of presentation of the authors. Data availability The datasets generated during and/or analysed during the current study are available in the NHANES repository, [ https://wwwn.cdc.gov/nchs/nhanes/Default.aspx ] References Eisenstein T, Giladi N, Hendler T, Havakuk O, Lerner Y. Hippocampal and non-hippocampal correlates of physically active lifestyle and their relation to episodic memory in older adults. Neurobiol Aging. 2022;109:100–12. 'doi': 10.1016/j.neurobiolaging.2021.08.017['Copyright (c) 2021 Elsevier Inc. 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Smoking and Cognitive Function Among Middle-Aged Adults in China: Findings From the China Health and Retirement Longitudinal Study Baseline Survey. J Addict Nurs. 2020;31(3):E5–E12. 'doi': 10.1097/JAN.0000000000000352['2020-07-01]. Blomquist KB, Danner F. Effects of physical conditioning on information-processing efficiency. Percept Mot Skills. 1987;65(1):175–86. 'doi': 10.2466/pms.1987.65.1.175['1987-08-01]. Colcombe S, Kramer AF. Fitness effects on the cognitive function of older adults: a meta-analytic study. Psychol Sci. 2003;14(2):125–30. 'doi': 10.1111/1467–9280.t01-1-01430['2003-03-01]. Gordon BA, Rykhlevskaia EI, Brumback CR, Lee Y, et al. Neuroanatomical correlates of aging, cardiopulmonary fitness level, and education. Psychophysiology. 2008;45(5):825–38. 10.1111/j.1469-8986.2008.00676.x['2008-09-01] . 'doi':. van Praag H, Kempermann G, Gage FH. Running increases cell proliferation and neurogenesis in the adult mouse dentate gyrus. Nat Neurosci. 1999;2(3):266–70. 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08:29:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3884105/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3884105/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50116625,"identity":"e7bbb78e-1234-4f5a-a68b-126816d831cf","added_by":"auto","created_at":"2024-01-24 18:51:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":13423,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the enrolled participants.\u003c/p\u003e","description":"","filename":"Figure1Flowchartoftheenrolledparticipants..png","url":"https://assets-eu.researchsquare.com/files/rs-3884105/v1/79b5e34265c0cf1a427ea9c1.png"},{"id":50116624,"identity":"7d3b6ac1-0700-4347-afef-be22697e0291","added_by":"auto","created_at":"2024-01-24 18:51:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":25418,"visible":true,"origin":"","legend":"\u003cp\u003eSlope plot of multiple classification regulation effect\u003c/p\u003e","description":"","filename":"Figure2Slopeplotofmultipleclassificationregulationeffect.png","url":"https://assets-eu.researchsquare.com/files/rs-3884105/v1/c70d91cfff230935bbeb11ff.png"},{"id":50116626,"identity":"d32d24ab-3963-4e34-b966-dfc28749b6a4","added_by":"auto","created_at":"2024-01-24 18:51:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":8764,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationships of smoking status, LTPA, and DSST scores\u003c/p\u003e","description":"","filename":"Figure3TherelationshipsofsmokingstatusLTPAandDSSTscores.png","url":"https://assets-eu.researchsquare.com/files/rs-3884105/v1/62be2efa3c76b5d602b1079a.png"},{"id":50912121,"identity":"a20f1938-0eb0-427f-abef-e309ffe620e9","added_by":"auto","created_at":"2024-02-09 12:52:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":414186,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3884105/v1/5242ef5d-f433-4793-ab4f-62b05d52c97c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Role of Physical Activity in the Association Between Smoking Status and Cognitive Function : A Cross-Sectional Study Based on NHANES 2011-2014","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eWith advancing age, various physiological functions and cognitive abilities gradually decline among the elderly\u003csup\u003e[\u003c/sup\u003e1\u003csup\u003e]\u003c/sup\u003e.The structure and function of the brain may undergo cognitive decline due to the influence of risk factors, ultimately progressing to cognitive impairment\u003csup\u003e[\u003c/sup\u003e2\u003csup\u003e]\u003c/sup\u003e.Cognitive impairment is a prevalent syndrome in the elderly population, characterized by declines in memory, executive function, and other cognitive domains, with Alzheimer's disease being the most common form of cognitive impairment\u003csup\u003e[\u003c/sup\u003e3\u003csup\u003e]\u003c/sup\u003e.Academic hypotheses regarding cognitive impairment include the amyloid cascade hypothesis, tau protein hypothesis, inflammation hypothesis, metal ion hypothesis, and oxidative stress hypothesis, with oxidative stress and its impact on neuronal damage being central and pivotal aspects of all mechanisms \u003csup\u003e[\u003c/sup\u003e4\u003csup\u003e]\u003c/sup\u003e.The interplay of glial cells, zinc homeostasis imbalance, and synaptic complexity also play crucial roles in aging and cognitive decline\u003csup\u003e[\u003c/sup\u003e5\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSmoking is a well-recognized risk factor for various health issues. Epidemiological studies reveal that short-term smoking may enhance cognitive function \u003csup\u003e[\u003c/sup\u003e6\u003csup\u003e]\u003c/sup\u003e, but long-term smoking increases the risk of cognitive decline\u003csup\u003e[\u003c/sup\u003e7\u003csup\u003e,\u003c/sup\u003e 8\u003csup\u003e]\u003c/sup\u003eand raises the likelihood of developing neurodegenerative diseases such as Alzheimer's and Parkinson's \u003csup\u003e[\u003c/sup\u003e9\u0026ndash;22\u003csup\u003e]\u003c/sup\u003e.Recent research indicates that quitting smoking and engaging in physical activity contribute to the deceleration of aging\u003csup\u003e[\u003c/sup\u003e23\u003csup\u003e]\u003c/sup\u003e,and positively influence cognitive function in the elderly\u003csup\u003e[\u003c/sup\u003e24\u0026ndash;26\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRegular physical activity is associated with improved cognitive function and reduced risk of cognitive impairment, suggesting a potential mediating role in the association between smoking and cognition. Studies demonstrate that cognitively impaired elderly individuals are eight times more likely to be physically frail than their cognitively normal counterparts \u003csup\u003e[\u003c/sup\u003e27\u003csup\u003e]\u003c/sup\u003e.Physical activity can enhance cognitive function in the elderly by increasing neural plasticity\u003csup\u003e[\u003c/sup\u003e28\u0026ndash;30\u003csup\u003e]\u003c/sup\u003e.Smoking is associated with reduced levels of physical activity \u003csup\u003e[\u003c/sup\u003e31\u0026ndash;34\u003csup\u003e]\u003c/sup\u003e,and individuals who engage in regular exercise tend to exhibit lower rates of smoking and nicotine dependence\u003csup\u003e[\u003c/sup\u003e35\u003csup\u003e]\u003c/sup\u003e.Furthermore, physical activity mitigates the impact of negative self-perceived health (SPH) among smokers \u003csup\u003e[\u003c/sup\u003e36\u003csup\u003e]\u003c/sup\u003e.Loprinzi et al. (2015) proposed a conceptual model summarizing the relationships among physical activity, smoking status, and cognition, suggesting that smoking leads to decreased executive function, while physical activity may alleviate this relationship. However, empirical evidence supporting this model is lacking\u003csup\u003e[\u003c/sup\u003e37\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn summary, considering the interplay among smoking, physical activity, and cognitive function, this study conducts a cross-sectional analysis using data from the National Health and Nutrition Examination Survey (NHANES). The aim is to explore the relationship between smoking status and cognitive function and analyze the role of physical activity in this association.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.1 Study Population\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eNHANES, an acronym for the National Health and Nutrition Examination Survey, is a longitudinal, multi-stage, cluster-randomized health and nutrition survey in the United States. This comprehensive database includes diverse health and nutrition information for the U.S. population, encompassing data on physical examinations, laboratory tests, and various lifestyle factors. The NHANES protocol received approval from the NCHS Research Ethics Review Board, and all participants provided informed consent. Additional information is available on the official website of the center (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wwwn.cdc.gov/nchs/nhanes/tutorials/default.aspx\u003c/span\u003e\u003cspan address=\"https://wwwn.cdc.gov/nchs/nhanes/tutorials/default.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor this study, data from NHANES conducted between 2011 and 2014 were utilized. The study population comprised individuals aged 60 and above (n\u0026thinsp;=\u0026thinsp;3,632), as cognitive assessments were conducted within this age range. Participants with missing covariate and smoking status questionnaire data (n\u0026thinsp;=\u0026thinsp;945), those with missing physical activity-related data (n\u0026thinsp;=\u0026thinsp;945), and individuals with incomplete smoking status data (n\u0026thinsp;=\u0026thinsp;3) were excluded. Ultimately, 1,735 participants were included in the study analysis (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 Cognitive Function Assessment\u003c/h2\u003e \u003cp\u003eIn NHANES, cognitive function was assessed through the Vocabulary Learning and Recall module, developed by the Consortium to Establish a Registry for Alzheimer's Disease (CERAD), Animal Fluency Test (AFT), and Digit Symbol Substitution Test (DSST)\u003csup\u003e[\u003c/sup\u003e38\u003csup\u003e,\u003c/sup\u003e 39\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe CERAD Word Learning Subtest (CERAD W-L) aims to assess immediate and delayed learning abilities for new language information, a component of the memory domain\u003csup\u003e[\u003c/sup\u003e40\u003csup\u003e]\u003c/sup\u003e.This test includes three consecutive learning trials (Immediate Word Recall Test, IWRT) and a delayed recall test (Delayed Word Recall Test, DWRT). Participants were required to verbally recite 10 unrelated words and subsequently recall as many words as possible immediately after each trial. The order of the ten words changed in each trial. The delayed word recall test was conducted after other cognitive tests (AFT and DSST).\u003c/p\u003e \u003cp\u003eThe Animal Fluency Test (AFT) examines explicit language fluency, a component of executive function. Scores can differentiate between individuals with normal cognitive function and those with mild or more severe forms of cognitive impairment, such as Alzheimer's disease.\u003c/p\u003e \u003cp\u003eThe Digit Symbol Substitution Test (DSST) is a performance module of the Wechsler Adult Intelligence Scale (WAIS III), relying on processing speed, sustained attention, and working memory. Participants were given two minutes to transcribe symbols corresponding to the numbers in 133 boxes\u003csup\u003e[\u003c/sup\u003e41\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Smoking status\u003c/h2\u003e \u003cp\u003eParticipants were categorized into never smokers (never smoked or smoked\u0026thinsp;\u0026lt;\u0026thinsp;100 cigarettes in their lifetime), former smokers (smoked\u0026thinsp;\u0026gt;\u0026thinsp;100 cigarettes in their lifetime and quit), and current smokers (smoked\u0026thinsp;\u0026gt;\u0026thinsp;100 cigarettes in their lifetime and currently smoke).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Physical Activity\u003c/h2\u003e \u003cp\u003eData were collected using the Global Physical Activity Questionnaire. Created by the World Health Organization (WHO), this questionnaire assesses physical activity in various domains, including leisure-time physical activity (LTPA), occupational physical activity (OPA), and transportation-related physical activity (TPA). Typical weekly frequency (per week/time), duration (per session/minutes), and intensity (vigorous or moderate) were surveyed for OPA and LTPA. Physical activity was categorized based on the maximum intensity minutes per week for each type of activity, following the formula: PA\u0026thinsp;=\u0026thinsp;weekly frequency \u0026times; duration of each PA. Classification was based on whether participants met the U.S. Physical Activity Guidelines (150 minutes of moderate-intensity PA per week [equivalent to 600 minutes/month] or 75 minutes of high-intensity PA per week)\u003csup\u003e[\u003c/sup\u003e42\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e \u003cp\u003eBaseline characteristics of participants were described using medians (interquartile range, IQR) for continuous variables and counts (percentages) for categorical variables. Given that most continuous variables (cognitive function scores, physical activity, age, etc.) did not conform to normality tests, non-parametric tests were used to compare inter-group differences for continuous variables. Chi-square tests were employed for categorical variables to compare demographic characteristics among participants with different smoking statuses. Cognitive function scores and minutes of physical activity were treated as continuous variables, and physical activity was categorized into four groups as a categorical variable. Linear regression analysis was used to explore the relationship between physical activity and smoking status and cognitive function. Logistic regression was employed to assess the odds ratio (OR) and corresponding 95% confidence interval (CI) for the association between smoking status and cognitive function. All analyses were adjusted for baseline age and gender (Model 1). Model 2 included additional adjustments for race/ethnicity (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, and Other), marital status (married, widowed, divorced, never married, cohabiting), household income-to-poverty ratio (\u0026lt;\u0026thinsp;1.3, 1.3\u0026ndash;3.5, \u0026gt;\u0026thinsp;3.5), and education level (less than high school, high school non-graduate, high school graduate/GED, some college or associate degree, college graduate and above).\u003c/p\u003e \u003cp\u003eAll analyses were performed using R 4.3.1(The University of Auckland,Auckland,New Zealand) and IBM SPSS Statistics software version 27.0 (IBM Corp, Armonk, NY, USA). Statistical significance was considered when two-tailed P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Result","content":"\u003cp\u003e\u003cstrong\u003e3.1 Baseline Characteristics of Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized data from 1,735 participants across two NHANES cycles (2011-2014). Participants were categorized into current smokers (n = 204), former smokers (n = 674), and never smokers (n = 857) based on smoking status. Smoking status showed statistically significant associations with demographic variables such as age, gender, race, education level, household income-to-poverty ratio, and marital status (P \u0026lt; 0.05). In general, never smokers were more likely to be female, non-Hispanic white, and have higher education levels. The relationship between smoking status and cognitive function in different domains indicated no association with scores for AFT and CERAD but a significant correlation with DSST scores. Regarding physical activity, the maximum minutes of leisure-time physical activity (LTPA) were significantly higher in never smokers compared to current smokers (Please refer to Table 1 at the end of the article for details).\u003c/p\u003e\n\u003cp\u003eTable 1 Baseline characteristics of elderly subjects in the United States by smoking status\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003eName\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003eLevels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eCurrent smoker\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eFormer smoker\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eNever smoker\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003eAge**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e65.00 (62.00 to 69.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e68.00 (64.00 to 75.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e68.00 (63.00 to 74.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003eAge.group**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e60-69 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e147 (72.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e338 (50.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e448 (52.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e70-79 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e35 (17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e200 (29.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e235 (27.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e80+ years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e22 (10.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e136 (20.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e174 (20.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003eSex**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e82 (40.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e232 (34.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e513 (59.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e122 (59.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e442 (65.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e344 (40.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003eRace**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e17 (8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e51 (7.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e72 (8.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e19 (9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e62 (9.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e93 (10.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e80 (39.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e357 (53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e397 (46.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e72 (35.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e147 (21.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e184 (21.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003eOther/multiracial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e16 (7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e57 (8.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e111 (13%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003eEducation.attainment**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003eLess Than 9th Grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e19 (9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e60 (8.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e74 (8.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e9-11th Grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e37 (18.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e88 (13.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e87 (10.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003eHigh School Grad/GED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e53 (26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e165 (24.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e164 (19.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003eSome College or AA degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e77 (37.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e194 (28.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e263 (30.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003eCollege Graduate or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e18 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e167 (24.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e269 (31.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003ePIR**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e1.71 (0.99 to 3.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e2.61 (1.29 to 4.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e2.53 (1.33 to 4.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003en_pir**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e\u0026lt;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e81 (39.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e171 (25.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e208 (24.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e1.3-3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e82 (40.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e248 (36.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e333 (38.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003e\u0026gt;3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e41 (20.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e255 (37.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e316 (36.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003eMarriage**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e84 (41.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e402 (59.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e506 (59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e35 (17.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e98 (14.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e156 (18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003eDivorced and Separated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e52 (25.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e126 (18.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e136 (15.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003eUnmarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e18 (8.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e29 (4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e43 (5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003eCohibitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e15 (7.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e19 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e16 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003eCERAD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e5.00 (4.00 to 6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e5.00 (4.00 to 6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e5.00 (4.00 to 6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003eCERAD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e7.00 (6.00 to 8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e7.00 (6.00 to 8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e7.00 (6.00 to 8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003eCERAD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e8.00 (7.00 to 9.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e8.00 (7.00 to 9.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e8.00 (7.00 to 9.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003eCERAD.total\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e19.00 (16.50 to 22.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e19.00 (16.00 to 22.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e20.00 (16.00 to 23.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003eCERAD.delay.recall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e6.00 (5.00 to 8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e6.00 (5.00 to 8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e6.00 (5.00 to 8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003eAFT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e16.00 (13.00 to 20.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e17.00 (14.00 to 21.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e17.00 (14.00 to 21.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003eDSST**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e43.00 (32.00 to 55.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e48.00 (37.00 to 60.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e50.00 (37.00 to 61.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003eTPA*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e0.00 (0.00 to 127.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.00 (0.00 to 40.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.00 (0.00 to 80.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003eOPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e45.00 (0.00 to 390.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.00 (0.00 to 360.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e0.00 (0.00 to 300.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.510204081632654%\"\u003e\n \u003cp\u003eLTPA**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e0.00 (0.00 to 205.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e120.00 (0.00 to 270.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e120.00 (0.00 to 300.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eData presented are median (IQR), or n (%).\u003c/p\u003e\n\u003cp\u003eAbbreviation:IQR=Interquartile Range;CERAD=Center for Epidemiology and Registration of Alzheimer\u0026apos;s Disease,AFT=Animal Fluency Test; DSST=Digit Symbol Substitution Test;TPA= transportation-related physical activity,OPA=Occupationrelated \u0026nbsp;physical activity,LTPA= leisure-time physical activity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;*p \u0026lt; 0.05, ** p \u0026lt; 0.01.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Relationship Between Smoking Status and DSST Scores in Cognitive Function\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSmoking status exhibited a negative correlation with cognitive function DSST scores. After adjusting for age, gender, race, education level, household income-to-poverty ratio, and marital status through linear regression analysis, former smokers had DSST scores 2.090 higher than never smokers (95% CI 0.732, 3.448; P \u0026lt; 0.05). By comparing DSST scores with the mean, the population was divided into low cognitive and cognitive groups. In logistic regression, current smokers showed a significant association with lower DSST scores compared to never smokers (OR 0.629, 95% CI 0.421, 0.941; P \u0026lt; 0.05) (Table 2).\u003c/p\u003e\n\u003cp\u003eTable 2 Regression analysis of correlation between smoking status and DSST score\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003eSmoke status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.775510204081634%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eLinear regression analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.69387755102041%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eLogistic regression analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.804123711340207%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026beta;(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.804123711340207%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003cp\u003e(no smoker)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.394366197183096%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.718309859154928%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.394366197183096%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.492957746478874%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.804123711340207%\" valign=\"top\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e2.090*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e1.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e0.808\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.804123711340207%\" valign=\"top\"\u003e\n \u003cp\u003e(former smoker)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e(0.732,3.448)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e(0.794,1.345)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.804123711340207%\" valign=\"top\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e-0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"top\"\u003e\n \u003cp\u003e0.448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e0.629*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.804123711340207%\" valign=\"top\"\u003e\n \u003cp\u003e(current smoker)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e(-2.865,1.267)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.711340206185568%\" valign=\"top\"\u003e\n \u003cp\u003e(0.421,0.941)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;*p \u0026lt; 0.05, ** p \u0026lt; 0.01.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Linear Regression of LTPA Maximum Activity Minutes Segments with DSST Scores\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study found a positive correlation between LTPA and DSST scores. Linear regression based on segments of LTPA maximum activity minutes and DSST scores demonstrated a significant association when LTPA exceeded 150 min/week compared to LTPA = 0 min/week (Table 3).\u003c/p\u003e\n\u003cp\u003eTable 3 Linear regression model of segmentation and DSST score according to the maximum activity minutes of LTPA.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.22222222222222%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMaximum active minutes of LTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.37373737373738%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.4040404040404%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026beta;(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.666666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026beta;(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.666666666666666%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.68041237113402%\" valign=\"top\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003cp\u003e0min/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.77319587628866%\" valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.8659793814433%\" valign=\"top\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.68041237113402%\" valign=\"top\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003cp\u003e1-150min/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.77319587628866%\" valign=\"top\"\u003e\n \u003cp\u003e3.441(1.455,5.426)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.8659793814433%\" valign=\"top\"\u003e\n \u003cp\u003e0.377(-1.272,2.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.68041237113402%\" valign=\"top\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003cp\u003e151-300min/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.77319587628866%\" valign=\"top\"\u003e\n \u003cp\u003e6.383(4.202,8.565)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.8659793814433%\" valign=\"top\"\u003e\n \u003cp\u003e2.189(0.369,4.009)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.68041237113402%\" valign=\"top\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003cp\u003e\u0026gt;=300min/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.77319587628866%\" valign=\"top\"\u003e\n \u003cp\u003e7.486(5.513,9.460)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.8659793814433%\" valign=\"top\"\u003e\n \u003cp\u003e2.519(0.857,4.181)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eModel 1 was adjusted for chronological age and sex;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Model 2 was further adjusted for ethnicity, education, family income-poverty ratio, marrital based on Model 1;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e*p \u0026lt; 0.05, ** p \u0026lt; 0.01.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Role of Physical Activity in the Association Between Smoking and Cognitive Function\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.1 Mediation Analysis of Physical Activity in the Association between Smoking Status and DSST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study indicated that, compared to never smokers, current smokers had a significantly direct effect (\u0026beta; = -4.8730, P \u0026lt; 0.001) in the association with DSST scores. However, LTPA did not mediate the relationship between different smoking statuses and DSST scores compared to never smokers. The relative total effect of former smokers (\u0026beta; = -4.9390, P \u0026lt; 0.001) was significant compared to never smokers. In summary, leisure-time physical activity did not exhibit a significant mediating effect in the association between smoking status and DSST scores (Table 4).\u003c/p\u003e\n\u003cp\u003eTable 4 Multi-category mediation model: the mediation effect of LTPA and multidimensional smoking status on DSST score.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003eRoute\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eNon-standardized coefficient( Standard error )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003eLTPA on\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003eSMQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e0.3634(16.3508)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e0.9823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003e( -31.7059,32.4328)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003eSMQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e-34.2649(24.7471)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e0.1663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003e(-82.7910,14.2612)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003eDSST on\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003eSMQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e-0.4119(1.0451)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e0.6936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003e(-2.4616,1.6379)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003eSMQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e-3.3018(1.4366)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e0.0217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003e(-6.1195,-0.4841)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e0.0094(0.0020)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003e(0.0056,0.0133)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003eLTPA*SMQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e-0.0025(0.0029)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e0.3810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003e(-0.0081,0.0031)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003eLTPA*SMQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e-0.0075(0.0033)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e0.0233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003e(-0.0140,-0.0010)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003eRelative indirect effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003ea1b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e0.0025(0.1127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003e(-0.2520,0.2035)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003ea2b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e-0.0660(0.1939)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003e(-0.6556,0.0841)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.687830687830687%\" valign=\"top\"\u003e\n \u003cp\u003eRelative direct effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.09700176366843%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.99294532627866%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.687830687830687%\" valign=\"top\"\u003e\n \u003cp\u003ec1\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.09700176366843%\" valign=\"top\"\u003e\n \u003cp\u003e-0.9364(0.8562)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.99294532627866%\" valign=\"top\"\u003e\n \u003cp\u003e0.2743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e(-2.6158,0.7430)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.687830687830687%\" valign=\"top\"\u003e\n \u003cp\u003ec2\u0026rsquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.09700176366843%\" valign=\"top\"\u003e\n \u003cp\u003e-4.8730(1.2989)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.99294532627866%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"top\"\u003e\n \u003cp\u003e(-7.4205,-2.3255)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003eRelative total effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003ec1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e-0.9339(0.8641)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e0.2799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003e(-2.6286,0.7609)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.612244897959183%\" valign=\"top\"\u003e\n \u003cp\u003ec2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e-4.9390(1.3075)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.448979591836736%\" valign=\"top\"\u003e\n \u003cp\u003e(-7.5035,-2.3746)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*p \u0026lt; 0.05, ** p \u0026lt; 0.01.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.2 Moderation Analysis of Physical Activity in the Association between Smoking Status and DSST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study revealed that, compared to never smokers, LTPA did not have a moderating effect on the relationship between former smokers and DSST scores. However, LTPA mitigated the impact of current smoking on DSST scores (\u0026beta; = -2.014, P \u0026lt; 0.05), indicating a significant weakening or inhibitory effect of LTPA on the relationship between smoking and DSST scores (Table 5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5 The moderating effect of LTPA and smoking status on DSST score\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.232323232323232%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.232323232323232%\" valign=\"top\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e49.238(0.573)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e49.211(0.569)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003e49.206(0.568)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.232323232323232%\" valign=\"top\"\u003e\n \u003cp\u003eFormer smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e-0.934(0.864)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e-0.936(0.857)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003e-0.937(0.856)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.232323232323232%\" valign=\"top\"\u003e\n \u003cp\u003eCurrent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e-4.939(1.307)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e-4.704(1.298)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003e-4.848(1.298)**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.232323232323232%\" valign=\"top\"\u003e\n \u003cp\u003eLTPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e2.178(0.400)**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003e2.625(0.453)**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.232323232323232%\" valign=\"top\"\u003e\n \u003cp\u003eInt1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.232323232323232%\" valign=\"top\"\u003e\n \u003cp\u003eInt2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.252525252525253%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.262626262626263%\" valign=\"top\"\u003e\n \u003cp\u003e-2.014(0.961)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviation:Int1=represents the interaction between LTPA and FormSmoker,Int2 represents the interaction between LTPA and Current smoker.\u003c/p\u003e\n\u003cp\u003e*p \u0026lt; 0.05, ** p \u0026lt; 0.01.\u003c/p\u003e\n\u003cp\u003eTo further analyze how LTPA modulates the relationship between smoking status and DSST scores, the study plotted the relationship between smoking status and DSST scores at different levels of LTPA maximum activity minutes intensity (Figure 2). It was evident that with an increase in LTPA activity minutes, DSST scores for current smokers increased, validating the moderating effect of LTPA.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Figure 2 Slope plot of multiple classification regulation effect\u003c/p\u003e\n\u003cp\u003eThe relationship and impact mechanisms among physical activity, smoking status, and DSST scores are illustrated in Figure 3 based on the study results.\u003c/p\u003e\n\u003cp\u003eFigure 3 The relationships of smoking status, LTPA, and DSST scores\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe prevailing view in current research suggests that the potential mechanism by which smoking diminishes cognitive function involves inflammatory responses\u003csup\u003e[\u003c/sup\u003e43-47\u003csup\u003e]\u003c/sup\u003e.For instance, a preclinical study reported that smoking exacerbated cognitive impairment in a vascular dementia rat model through neuroinflammation\u0026nbsp;\u003csup\u003e[\u003c/sup\u003e48\u003csup\u003e]\u003c/sup\u003e.Another potential mechanism may involve psychosocial processes such as sleep issues. A case-control study of mild cognitive impairment previously reported that sleep duration partially mediated the association between smoking and cognitive function\u003csup\u003e[\u003c/sup\u003e49\u003csup\u003e]\u003c/sup\u003e.Functional neuroimaging studies have also demonstrated some alterations in brain structure and function due to smoking. For example, a 24-month non-randomized intervention study found reduced gray matter density in regions crucial for cognitive function in current smokers compared to never smokers\u0026nbsp;\u003csup\u003e[\u003c/sup\u003e26\u003csup\u003e]\u003c/sup\u003e.Contrary to some other studies indicating a greater risk of memory impairment associated with smoking\u003csup\u003e[\u003c/sup\u003e50\u003csup\u003e]\u003c/sup\u003e,our study did not find a significant association between smoking status and memory. Additionally, DSST scores (processing speed) of current smokers were significantly negatively correlated with cognitive function compared to never smokers. These findings align with a study by Zhang et al. (2022)\u003csup\u003e[\u003c/sup\u003e46\u003csup\u003e]\u003c/sup\u003e.Concurrently, the study by Song et al. (2020) suggested that only former smokers exhibited better cognitive function compared to non-smokers\u0026nbsp;\u003csup\u003e[\u003c/sup\u003e51\u003csup\u003e]\u003c/sup\u003e,further supporting our findings. Therefore, besides strengthening effective smoking cessation measures, we need to identify other feasible intervention strategies to mitigate the adverse effects of smoking on cognitive function.\u003c/p\u003e\n\u003cp\u003eClinical trials have demonstrated that older adults participating in continuous 2 to 12-week exercise programs exhibit improvement in information processing speed\u003csup\u003e[\u003c/sup\u003e52\u003csup\u003e]\u003c/sup\u003e,The potential mechanisms by which exercise improves cognitive function primarily involve increased neural activity and volume in the prefrontal and frontal cortices\u0026nbsp;\u003csup\u003e[\u003c/sup\u003e53\u003csup\u003e,\u0026nbsp;\u003c/sup\u003e54\u003csup\u003e]\u003c/sup\u003e、enhanced hippocampal neurogenesis\u0026nbsp;\u003csup\u003e[\u003c/sup\u003e55\u003csup\u003e]\u003c/sup\u003e、changes in regions of the brain associated with information processing\u0026nbsp;\u003csup\u003e[\u003c/sup\u003e56\u003csup\u003e]\u003c/sup\u003eand increased gene expression of brain-derived neurotrophic factors and other growth factors related to exercise. Low cognitive function and a history of smoking are associated with increased risk of mortality\u003csup\u003e[\u003c/sup\u003e57\u003csup\u003e]\u003c/sup\u003e.The more frequent the LTPA, the stronger the cognitive function, and individuals engaging in low-intensity physical activity regularly have a lower risk of mortality\u003csup\u003e[\u003c/sup\u003e58\u003csup\u003e]\u003c/sup\u003e.This study, combining the model summarized by Loprinzi et al. (2015)speculates that physical activity may be a crucial mediating factor in this association. The results indicate that leisure-time physical activity did not exhibit a significant mediating effect in the association between smoking status and DSST scores. LTPA had a significant moderating effect on the relationship between current smoking and DSST scores, suggesting that leisure-time physical activity can mitigate the decline in DSST scores caused by smoking. Loprinzi et al. (2014) demonstrated that physical activity could attenuate the association between nicotine dependence and depression\u003csup\u003e[\u003c/sup\u003e59\u003csup\u003e]\u003c/sup\u003e,to some extent supporting our results regarding the moderating role of physical activity.\u003c/p\u003e\n\u003cp\u003eThis study suggests that smoking leads to cognitive decline in older adults, and leisure-time physical activity plays a moderating role in the association between smoking status and cognitive function. However, there are certain limitations to this study. Firstly, the data used are predominantly questionnaire-based, introducing a degree of subjectivity. Secondly, the study is cross-sectional, only allowing for the observation of correlations between variables without establishing causation. Therefore, future research efforts will focus on improving study methods and data accuracy to enhance the precision of results related to the interrelationships between smoking, physical activity, and cognitive function. The use of accelerometer-measured physical activity data could provide a more objective assessment of the results. Combining advanced neuroimaging methods or biomarker analysis could further explore potential neural mechanisms. To be cautious, exploring potential moderating factors, such as genetic influences, to identify specific subgroups that may exhibit different susceptibility and responsiveness to cognitive function in smokers. Longitudinal studies would provide valuable insights into the temporal dynamics of these associations. Additionally, incorporating relevant interventions, such as targeted exercise therapy and smoking cessation programs, to evaluate their effectiveness in alleviating smoking-related cognitive decline.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eOur study revealed a correlation between smoking status and cognitive function, with smoking status specifically showing a significant association with DSST scores (processing speed) in cognitive function. Current smokers exhibited a significant negative correlation with DSST scores. Leisure-time physical activity (LTPA) exceeding 150 min/week was significantly associated with improved DSST scores. While LTPA did not exhibit a significant mediating effect in the association between smoking status and DSST scores, it demonstrated a moderating effect in the relationship between current smoking and DSST scores. Our findings suggest that smoking cessation is a strategy for enhancing executive function among smokers, thereby improving cognitive function. Additionally, engaging in physical activity stands out as an effective strategy for enhancing cognitive function.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eH.C. participated in the design of the study, contributed to data collection and data analysis; Y. Z. contributed to data reduction, directed the revision of the paper; J.Y. participated in data analysis; C.W. participated in the design of the study and directed the revision of the paper; Y.W. directed the revision of the paper; G. S. proposed the idea of topic selection and guided the dissertation revision. All authors contributed to the manuscript writing. All authors have read and approved the final version of the manuscript and agree with the order of presentation of the authors.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe datasets generated during and/or analysed during the current study are available in the NHANES repository, [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wwwn.cdc.gov/nchs/nhanes/Default.aspx\u003c/span\u003e\u003cspan address=\"https://wwwn.cdc.gov/nchs/nhanes/Default.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEisenstein T, Giladi N, Hendler T, Havakuk O, Lerner Y. Hippocampal and non-hippocampal correlates of physically active lifestyle and their relation to episodic memory in older adults. Neurobiol Aging. 2022;109:100\u0026ndash;12. 'doi': 10.1016/j.neurobiolaging.2021.08.017['Copyright (c) 2021 Elsevier Inc. 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Am J Health Promot. 2014;29(1):37\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4278/ajhp.130301-QUAN-92['2014-09-01]\u003c/span\u003e\u003cspan address=\"10.4278/ajhp.130301-QUAN-92['2014-09-01]\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 'doi':.\u003c/span\u003e\u003c/li\u003e\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":"Cognition, Exercise, Smoking, Moderation, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-3884105/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3884105/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThe aim of this study is to examine the relationship between physical activity, smoking status and cognitive function, and to test the potential moderating role of physical activity.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional study used data on smoking status, cognitive function and physical activity from 1735 participants aged 60 years and older in NHANES from 2011 to 2014. Linear and logistic regression models were used to assess the association between smoking status and cognitive function. Mediation and moderation analyses were conducted to examine the role of physical activity in this association.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFormer smokers scored on average 2.090 points higher on the Digit Symbol Substitution Test (DSST) compared to never smokers (95% CI 0.755, 3.472; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while logistic regression showed that current smokers had an odds ratio (OR) of 0.629 for cognitive impairment compared to never smokers (95% CI 0.421, 0.941). No significant associations were observed between smoking status and CERAD and AFT. Moderation analysis showed that leisure-time physical activity significantly attenuated the effect of smoking on DSST scores in current smokers compared to never smokers (β = -2.014, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThere was a significant correlation between smoking status, physical activity and DSST scores.Although the mediating effect of leisure-time physical activity in the association between smoking status and cognitive function is not significant, it attenuates the decline in DSST scores in current smokers.\u003c/p\u003e","manuscriptTitle":"The Role of Physical Activity in the Association Between Smoking Status and Cognitive Function : A Cross-Sectional Study Based on NHANES 2011-2014","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-24 18:51:53","doi":"10.21203/rs.3.rs-3884105/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":"a0537227-21a9-4c2f-8c76-530e213bc641","owner":[],"postedDate":"January 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-09T12:44:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-24 18:51:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3884105","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3884105","identity":"rs-3884105","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}