The Impact of Moderate-to-Low Intensity Socially-Engaged Physical Activity on Cognitive Functioning in Older Adults: An Empirical Analysis Based on CHARLS 2020 Baseline Data

The Impact of Moderate-to-Low Intensity Socially-Engaged Physical Activity on Cognitive Functioning in Older Adults: An Empirical Analysis Based on CHARLS 2020 Baseline Data | 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 Impact of Moderate-to-Low Intensity Socially-Engaged Physical Activity on Cognitive Functioning in Older Adults: An Empirical Analysis Based on CHARLS 2020 Baseline Data Hao-Jie Tang, Quan-Hong Xiao, Han-Yu Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6425624/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background Amidst the accelerating global aging process, cognitive decline in older adults has emerged as a critical public health challenge. While existing studies predominantly focus on examining the isolated effects of physical activity or social engagement on cognitive function, systematic investigations into moderate-to-low intensity physical activities with integrated social attributes (e.g., dancing, mahjong) remain inadequate. Methods Utilizing baseline data from the China Health and Retirement Longitudinal Study (CHARLS 2020; n =7,141), this study employs multiple linear regression models to comprehensively assess the impact of socially-engaged moderate-to-low intensity physical activities on cognitive performance among adults aged 60 years and older. Results Key findings demonstrate that in older adults, moderate-to-low intensity physical activity ( β =0.068–0.075, P <0.001) and social engagement (e.g., community participation, educational training; β =0.036–0.046, P <0.01) are significantly positively associated with cognitive performance. In contrast, high-intensity physical activity showed a significant negative correlation with cognitive function ( β =−0.087, P <0.001) in this population. Hierarchical regression analysis further reveals that integrated models combining social and physical activities exhibit superior explanatory power ( R² =2.5%) compared to single-activity models ( R² =1.3–1.5%, P <0.001) among older adults, indicating statistically significant interaction effects. Notably, Socially-Engaged Physical activities(SEPA) (e.g., dancing: β =0.069–0.100; mahjong: β =0.069–0.100, P <0.001) demonstrate enhanced cognitive benefits for older adults, with standardized coefficients ( β =0.082) significantly exceeding those of non-social physical activities ( β =0.071) and purely social interactions ( β =0.038). Conclusion These findings indicate that specific-intensity activities combining motor coordination and social cognition confer greater cognitive benefits in older adults. This study provides robust epidemiological evidence for optimizing cognitive interventions in aging populations, advocating for prioritized implementation of socially-embedded moderate-intensity activities over isolated exercise regimens in public health strategies. Cognitive Function Moderate-to-Low Intensity Socially-Engaged Physical Activity CHARLS Older Adults Figures Figure 1 Introduction The intensification of global aging trends has rendered cognitive decline in older adults an increasingly severe public health issue. Cognitive deterioration not only significantly reduces the quality of life for older adults but also escalates caregiving burdens on families and society. Existing research has made significant progress in uncovering the cognitive protective effects of physical activity and social engagement: physical activity enhances neuroplasticity by promoting the secretion of brain-derived neurotrophic factor (BDNF) and cerebrovascular remodeling [ 1 , 2 ], while social interaction mitigates cognitive decline by boosting cognitive reserve and buffering psychological stress [ 3 , 4 ]. However, current studies exhibit two critical limitations: first, they predominantly adopt a fragmented approach, isolating investigations into physical activity (focusing on metabolic equivalents of exercise intensity) or social participation (emphasizing social network size), thereby neglecting mechanistic exploration of their synergistic effects; second, classifications of activity intensity remain overly generalized, particularly lacking systematic stratification of moderate-to-low intensity activities (3–6 METs), leading to misjudgments of potential risks associated with high-intensity exercise [ 5 , 6 , 7 ]. This study first put forward a "Socially-Engaged Physical activities( SEPA )"composite analytical framework [ 8 , 9 ], the central hypothesis is that activities integrating both physical exertion and social interaction (e.g., dancing, mahjong) may generate cognitive protective effects surpassing those of unimodal interventions through synergistic physiological and psychological mechanisms. The theoretical foundation includes the dual-pathway model [ 10 , 11 ]: physiologically, moderate-intensity activities promote hippocampal neurogenesis via enhanced cerebral blood flow perfusion while avoiding oxidative stress damage from high-intensity exercise [ 12 , 13 , 14 ]; psychologically, structured social interactions activate the default mode network, improving episodic memory encoding efficiency [ 15 ]. Although international studies have identified cognitive benefits of composite activities (e.g., a 23% reduction in dementia risk through group exercise in European cohorts), two critical gaps persist: 1. The failure to quantitatively isolate synergistic effects through model-based analyses to assess incremental explanatory power; 2. The absence of direct comparisons between SEPA and physical activities of equivalent intensity. Despite emerging frameworks like "composite health behaviors" [ 16 ], large-scale longitudinal empirical analyses—particularly those with refined intensity stratification—remain scarce globally. Leveraging baseline data from the China Health and Retirement Longitudinal Study (CHARLS 2020; n = 7,141), this study introduces two methodological innovations: 1.Paradigm shift in effect comparison: A three-tier nested regression model with counterfactual analysis isolates the independent effects of social-physical activities, quantifying incremental contributions of synergistic mechanisms [ 17 , 18 , 19 ]; 2.Precision intensity thresholding: Stratifying physical activities into low (1.0–2.9 METs), moderate (3.0–5.9 METs) and high (≥ 6.0 METs) intensity tiers to compare cognitive impacts between SEPA and equivalent-intensity physical activities. This research aims to address gaps in the synergistic mechanisms of composite activities and intensity stratification, providing a theoretical foundation for developing safe and universally applicable cognitive intervention strategies in developing nations. Method Data Sources This study is based on the China Health and Retirement Longitudinal Study (CHARLS) database. CHARLS is a large-scale national longitudinal survey project initiated by the National School of Development at Peking University and led by the Institute of Social Science Survey (ISSS) at Peking University, focusing on individuals aged 45 and above. It aims to construct a public micro-database covering socioeconomic status, health behaviors, healthcare, and other multidimensional indicators to provide high-quality data support for aging research. The survey was collaboratively conducted by the Institute of Population and Labor Economics at the Chinese Academy of Social Sciences and the Center for Aging and Health Research at Peking University. Since its baseline survey in 2011, CHARLS has conducted multiple rounds of tracking in 150 county-level administrative districts and 450 village-level units across China, accumulating a valid sample size of 17,000 individuals. The questionnaire design strictly adheres to international epidemiological survey protocols and underwent three rounds of international expert reviews to ensure data quality. Reliability and validity assessments of related indicators rank it among the top cohort studies in developing Asian countries [ 20 , 21 ]. This study utilizes CHARLS 2020 baseline data for secondary analysis, employing Stata 18.0 for data cleaning and quality control, ultimately including 7,141 valid samples of older adults aged 60 and above who met the inclusion criteria. Variable Definition Dependent Variable Cognitive level was assessed using the standardized cognitive assessment scale developed by the CHARLS team at Peking University. This scale includes six cognitive domains: orientation, memory, attention, and others, with a total score range of 0–21 points. The raw data were recoded using STATA 18.0 to generate a continuous variable named "Cognitive Ability Index (0–21 points)". This variable not only retains the reliability and validity characteristics of the original scale (Cronbach's α = 0.82, ICC = 0.79) but also enables cross-group comparability through standardized processing [ 22 ]. Independent Variables The independent variables included in this study were physical activity and social activity. According to the International Physical Activity Guidelines, respondents' daily physical activity duration was categorized into five levels: <10 min (0 points), 10–29 min (1 point), 30–119 min (2 points), 120–239 min (3 points), and ≥ 240 min (4 points). Activity intensity was calculated using the median method [ 23 ], with specific thresholds defined as: low intensity (≤ 600 MET-min/week), moderate intensity (601–3000 MET-min/week), and high intensity (≥ 3001 MET-min/week) [ 24 , 25 ]. Metabolic equivalent (MET) values were calculated based on the International Physical Activity Questionnaire Short Form (IPAQ-SF) standards [ 26 ], using the formula: MET-min/week = Σ (MET coefficient of activity type × daily activity minutes × activity days) [ 24 ]. Social activity variables encompassed 11 specific behaviors from the original questionnaire: visiting neighbors, playing mahjong, providing help, dancing, participating in community organizations, volunteering, attending school/training, and other social activities. Among these, dancing and playing mahjong were categorized as moderate-to-low intensity physical activities, aligning with the definition of SEPA:"A category of temporally synchronized multidimensional integrative activities characterized by the coupling of interpersonal interaction and metabolic expenditure, achieving sociobehavioral integration and neurophysiological stimulation synergies”. Descriptive statistics for the variables are presented in Table 1 . The cognitive ability assessment scores of the sample population exhibited significant heterogeneity (M = 12.01, SD = 3.34), with scores covering the full theoretical range of 21 points. This dispersion (coefficient of variation = 27.81%) provided an ideal variance foundation for subsequent regression modeling. The average value for "visiting neighbors" was 0.32, indicating that 32% of the sample engaged in this behavior during the study period. Table 1 Descriptive Statistics of Cognitive Activity Variables in Older Adults. Variables M SD Cases Dependent Variable Cognitive Ability (0–21 points, higher = better) 12.01 3.345 7141 Independent Variables Visiting Neighbors 0.32 0.466 7141 Providing Help 0.13 0.338 7141 Community Organization Participation 0.03 0.171 7141 Volunteering 0.03 0.160 7141 Attending School/Training 0.01 0.096 7141 Other Social Activities 0.02 0.142 7141 Light Exercise 0.81 0.396 7141 Moderate Exercise 0.56 0.496 7141 Vigorous Exercise 0.33 0.471 7141 Dancing 0.07 0.256 7141 Playing Mahjong 0.19 0.391 7141 Statistical Analysis This study employed SPSS 27.0 statistical software to construct a three-stage hierarchical regression model. Using a nested model comparison strategy, we systematically evaluated the joint impact mechanisms of social activities and physical activities on cognitive function in older adults [ 27 , 28 ]. The specific modeling workflow was as follows: first, social interaction variables were incorporated into the baseline model (Block 1); subsequently, traditional physical activity metrics were integrated into Block 2; finally, variable substitution in Block 3 established a counterfactual model to isolate the unique effects of socially embedded physical activities. Model validation adopted a progressive hypothesis testing framework: ΔR² values quantified explanatory power increments, while changes in F-statistics ( ΔF ) verified the significance levels of newly added variable sets. By integrating synergistic mechanisms between social and physical activities, combined with empirical data from typical socially embedded physical activities (e.g., dancing and mahjong), this study systematically revealed their influence effects on cognitive function in older populations. Results Impact of Physical Activity and Social Activities on Cognition The effects of each variable on the cognitive level of older adults are shown in Table 2 . The regression analysis revealed different impacts of key variables. In terms of physical activity, light and moderate intensity showed significant positive effects on cognitive level (light: β = 0.070, p < 0.001; moderate: β = 0.068, p < 0.001), while vigorous activity demonstrated a significant negative effect ( β =-0.087, p < 0.001). Among social activities, social interactions such as providing help ( β = 0.026, p = 0.030), educational participation ( β = 0.036, p = 0.002), and community organization involvement ( β = 0.046, p < 0.001) showed significant cognitive benefits. Core variables of SEPA—dancing ( β = 0.055, p < 0.001) and mahjong ( β = 0.090, p < 0.001)—exhibited standardized effect sizes that surpassed those of single-dimension physical or social activities, highlighting their unique synergistic advantages. Model validation confirmed data reliability ( VIF < 1.2), indicating no multicollinearity interference among variables. Table 2 . Multivariate Linear Regression Analysis of Cognitive Level. Variables Unstandardized Coefficients Beta t P Collinearity Statistics B Std.Error Tolerance VIF (Constant) 11.151 0.098 113.953 0 Visiting Neighbors 0.121 0.088 0.017 1.377 0.169 0.897 1.115 Providing Help 0.261 0.121 0.026 2.164 0.03 0.899 1.112 Community Organization Participation 0.901 0.238 0.046 3.787 < 0.001 0.903 1.107 Volunteering 0.256 0.252 0.012 1.017 0.309 0.921 1.085 Attending School/Training 1.271 0.41 0.036 3.101 0.002 0.976 1.025 Other Social Activities 1.014 0.272 0.043 3.724 < 0.001 0.997 1.003 Light Exercise 0.593 0.1 0.07 5.948 < 0.001 0.964 1.038 Moderate Exercise 0.456 0.082 0.068 5.579 < 0.001 0.913 1.096 Vigorous Exercise -0.621 0.084 -0.087 -7.364 < 0.001 0.952 1.05 Dancing 0.713 0.156 0.055 4.584 < 0.001 0.948 1.055 Playing Mahjong 0.766 0.101 0.09 7.565 < 0.001 0.957 1.045 Dependent Variable: Cognitive Ability (0–21 points, higher = better) Isolated Model Analysis of SEPA The regression analysis results revealed a significant negative association between vigorous physical activity and cognitive function level in older adults ( β =-0.087, p < 0.001). Existing literature confirms that high-intensity exercise is physiologically suitable only for healthy older adults subpopulations [ 29 , 30 , 31 ]. Therefore, this study excluded vigorous physical activity from the hierarchical regression model discussion. Dancing and mahjong activities, which combine both moderate-to-low intensity physical activity (MET ≤ 4.0) and structured social interaction attributes, were excluded from the social activity model to avoid overlapping variable characteristics interfering with effect estimation. The theoretical modeling adopted the following control strategies: excluding physical activity variables when testing the isolated social activity model; controlling for social variables in the physical activity model; systematically analyzing synergistic mechanisms through three-stage nested model comparisons, as shown in Table 3 . ANOVA tests (Table 4 ) confirmed that introducing interaction terms produced statistically significant improvements in model explanatory power. Table 3 Nested Model Comparison of Social Activities and Physical Activities. Dependent Variable: Cognitive Level Model 1 Model 2 Model 3 Visiting Neighbors (3.855) (3.039) Providing Help (2.797) (2.235) Community Organization Participation (4.791) (4.541) Volunteering (1.427) (1.144) Attending School/Training (2.858) (2.776) Other Social Activities (3.714) (3.617) Light Exercise (6.446) (7.150) Moderate Exercise (4.601) (5.459) R² 0.015 0.025 0.013 df1 6 2 6 df2 7134 7132 7132 ΔF 17.6 36.3 13.8 Sig. F Change P < 0.001 P < 0.001 P < 0.001 Note: Values in parentheses are t-values. Table 4 ANOVA a Test. Model SS df MS F P 1 Regression 1165.45 6 194.24 17.61 < 0.001 b Residual 78709.13 7134 11.03 Total 79874.57 7140 2 Regression 1958.80 8 244.85 22.41 < 0.001 c Residual 77915.77 7132 10.925 Total 79874.57 7140 3 Regression 1051.67 2 525.84 47.62 < 0.001 d Residual 78822.90 7138 11.04 Total 79874.57 7140 a:Dependent Variable: Cognitive Ability (0–21 points, higher = better) b:Predictors: (Constant), Other Social Activities, Volunteering, Attending School/Training, Visiting Neighbors, Community Organization Participation, Providing Help c:Predictors: (Constant), Other Social Activities, Volunteering, Attending School/Training, Visiting Neighbors, Community Organization Participation, Providing Help, Light Exercise, Moderate Exercise d:Predictors: (Constant), Light Exercise, Moderate Exercise Hierarchical regression model analysis revealed that the baseline model (Model 1) incorporating social activity variables explained a variance of R² =0.015 [adjusted R² =0.014, F (6,7134) = 17.6]. When introducing moderate-to-low intensity physical activity to construct the main effects model (Model 2), the explanatory power significantly increased by ΔR² =0.010 ( F-change (2,7132) = 36.3, p < 0.001), reaching R² =0.025 (adjusted R² =0.023). The reverse control model (Model 3) demonstrated that the absolute value of model changes maintained significant explanatory power after excluding interference from socially-embedded activities ( ΔR² =0.013, F-change (6,7132) = 13.8, p < 0.001), confirming the statistical robustness of joint effects. Effect size analysis showed that the dual-intervention model achieved a 66.7% gain in variance explanation compared to single-dimension models, with its standardized effect strength ( Cohen's f² =0.026) exceeding the small effect threshold ( f² ≥0.02), meeting substantive criteria for synergistic effects. To further analyze the specific impacts of socially-embedded physical activities, this study compared beta values between typical SEPA (dancing, mahjong) and their corresponding intensity-matched physical activity variables, see Fig. 1 . This study classified activity intensity based on metabolic equivalent (MET) standards: mahjong playing was categorized as light physical activity (MET = 2.8), while dancing activities categorized as moderate physical activity (MET = 4.5) [ 32 ]. Data analysis revealed that all four activity variables exerted significant main effects on cognitive level (p < 0.001). Comparison of standardized coefficients showed that light physical activity ( β = 0.075, t = 6.356) demonstrated stronger cognitive enhancement effects compared to moderate physical activity ( β = 0.056, t = 4.700). Further analysis identified intensity-specific advantage effects in typical socially-embedded physical activities: moderate-intensity dancing activities ( β = 0.069, t = 5.857) exhibited a 22% increase in effect size compared to regular physical activities at equivalent intensity ( Δβ = 0.013), while light-intensity mahjong activities ( β = 0.1, t = 8.510) showed a 34% greater effect size enhancement compared to regular light-intensity physical activities ( Δβ = 0.025). Discussion 1.Moderate-to-Low Intensity Physical Activities Significantly Enhance older adult cognition This study empirically analyzed the effects of SEPA on older adult cognition using CHARLS 2020 baseline data, aligning with cutting-edge research in cognitive aging. Multiple linear regression data revealed cognitive benefits of moderate-to-low intensity physical activities ( β =0.056–0.081, p <0.001), consistent with animal model studies demonstrating neuroplasticity modulation through prefrontal-hippocampal circuit activation induced by such activities [33, 34, 35, 36]. Descriptive statistics showed 33% of participants (n=7,141) engaged in high-intensity activities. While some studies suggest greater cognitive benefits from high-intensity activities [37, 38], our results revealed significant negative associations between high-intensity activities and cognition ( β =−0.087, p <0.001). This discrepancy may stem from non-adaptive high-intensity exercise potentially exacerbating neuroinflammation through oxidative stress pathways, particularly in older adults with vascular risk factors [29, 30, 31]. In China, 71.9% of adults aged 60+ have ≥1 chronic condition, with 44.5% experiencing multimorbidity [39, 40]. Although high-intensity activities might benefit cognitively healthy elders, only 27% of Chinese older adults meet health criteria, indicating limited applicability and potential cognitive risks of high-intensity interventions for general older populations [41]. 2.Synergistic Effects of Moderate-to-Low Intensity Physical Activities and Social Activities on Cognitive Function in Older Adults In terms of the explanatory power of nested models, single-activity models (physical/social) each explained only 1.3%-1.5% of cognitive variance ( R² ). However, the combined model (Model 2) increased explanatory power to 2.5%, with statistically significant F-change ( p <0.001), indicating that the combined implementation of physical and social activities may yield more significant intervention effects on older adult cognition compared to isolated single-activity interventions [42]. Older adults engaging in both moderate-to-low intensity physical activities and social interactions demonstrated higher cognitive function levels than those participating in single-activity interventions. Notably, although Model 3 showed slightly lower overall explanatory power than Model 1 ( R² =1.3% vs. 1.5%), its parsimony advantage became evident—achieving more efficient prediction with only two variables (significantly improved F-value). These variables (moderate and low-intensity physical activities) exhibit characteristics of core protective factors. This finding aligns with the research objective of identifying universal activity factors rather than pursuing complete explanation of cognitive decline. 3.Moderate-to-Low Intensity SEPA May Confer Greater Cognitive Benefits for Older Adults Further analysis revealed significantly higher participation rates in moderate-to-low intensity activities compared to other variables, indicating their feasibility for global older populations. When comparing typical socially-embedded activities (e.g., dancing, mahjong) with generic moderate-to-low intensity physical activities, the standardized coefficients ( β =0.069, 0.1) and effect sizes ( t =5.85, 8.51) of socially-embedded activities surpassed those of intensity-matched physical activities [43]. This suggests that SEPA, as a distinct category, may exert superior cognitive protective effects through dual-pathway mechanisms (combining physical stimulation and social engagement) compared to generic moderate-to-low intensity activities. These findings emphasize the importance of prioritizing SEPA in exercise regimens for older adults to more effectively mitigate cognitive decline. This study has limitations in exploring the association between SEPA and older adult cognition function: Restricted by database variables, only two observational indicators (dancing and mahjong) met the criteria for socially-embedded physical activities. This methodological constraint necessitates future experimental expansion of variable systems. Notably, the predictive models constructed from current data demonstrate limited overall explanatory power for cognitive function (R² <3%), consistent with theoretical models positing that older adult cognition is jointly influenced by genetic predisposition, metabolic status, and comorbidity profiles. Nevertheless, the core contribution lies in longitudinally demonstrating, for the first time, the superior efficacy of moderate-to-low intensity SEPA compared to other physical activity types in delaying age-related cognitive decline. This discovery provides a critical evidence-based foundation for the formulation of national guidelines on physical activity for older adults and dementia prevention strategies. Conclusion This study derived the following core conclusions through longitudinal analysis of CHARLS data: SEPA demonstrate superior preventive efficacy against cognitive decline in older adults: Among Chinese older populations, engagement in activities combining physical exercise and social interaction (e.g., dancing, mahjong) exhibits significantly greater efficacy in delaying cognitive decline compared to isolated physical training or purely social behaviors. Physical Activity intensity exhibits differential effects: High-intensity physical activity may mask potential benefits through negative cognitive associations ( β =-0.087, p <0.001), emphasizing the necessity of matching activity intensity with individual health status. Concurrently, moderate-to-low intensity physical activities show the broadest applicability and statistically significant positive correlations with cognitive performance in Chinese older populations. Research limitations and future directions: The limited variable scope constrains generalizability, necessitating expanded experimental validation. With modest model explanatory power ( R² <4%), future investigations should integrate longitudinal tracking data to deepen mechanistic exploration, incorporating psychosocial factors (e.g., social support, self-efficacy) and biomarkers (e.g., inflammatory markers, brain-derived neurotrophic factor) to refine theoretical frameworks. This research contributes empirical evidence from the Chinese context to global geriatric cognition studies, highlighting the unique value of SEPA while advocating interdisciplinary collaboration to elucidate their biological and psychosocial protective mechanisms. Declarations Acknowledgements We thank the CHARLS research and field teams and all respondents for their contribution. Author contributions HJ: literature review, study design, data analysis, preparation of the initial draft and writing. QH:study design,supervision, revision of the paper and editing of the manuscript. HY: literature search and data analysis . All authors read and approved the final manuscript. Funding This research received no external funding. Data availability The datasets analyzed during the current study are available in the CHARLS repository:http:// charls. pku. edu. cn/. Ethics approval and consent to participate CHARLS study is an open dataset. The CHARLS was approved by the Ethics Review Committee of Peking University(IRB00001052-11015)and all participants signed an informed consent. This research followed the guidance of the Declaration of Helsinki. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Meo ED, Portaccio E, Prestipino E, Nacmias B, Bagnoli S, Razzolini L, et al. Effect of BDNF Val66Met polymorphism on hippocampal subfields in multiple sclerosis patients. Mol Psychiatry. 2022;27(2):1010–9. Hosang GM, Shiles C, Tansey KE, Mcguffin P, Uher R. Interaction between stress and the BDNF Val66Met polymorphism in depression: A systematic review and meta-analysis - ScienceDirect. Compr Psychiatr. 2014;12(8):7–7. Yuqing H, Huixiu H, Yajie Z, Jie G, Huanhuan L, Chao S. Association Between Social Activities and Cognitive Function in Older Adults: A Scoping Review and Nursing Implications. Chin J Nurs. 2025;60(2):251–6. Kuiper JS, Zuidersma M, Zuidema SU, Burgerhof JGM, Stolk RP, Oude Voshaar RC et al. Social relationships and cognitive decline: a systematic review and meta-analysis of longitudinal cohort studies. Int J Epidemiol 2016:dyw089. Yanzhang C, Jing J, Chenglin Z. Physical Activity and Cognitive Flexibility in Older Adults: Brain Activation Patterns and the Mediating Role of Working Memory. J Xi'an Phys Educ Univ 2023(4). Msn XX, Msn LM, Msn YW, Msn YL, Msn MD, Phd BM et al. Pathway linking nutritional status to cognitive function in older adults with chronic diseases: Exploring the mediating role of sleep quality. Geriatr Nurs 2025; 62(PartB). Cui Y, Duan Y, Du J, Yang L, Tian X, Liu H. Relationship between leisure activity and depression in Chinese older adults: chain mediating effect of diet and cognition. BMC Geriatr 2025; 25(1). Stephan Y, Sutin AR, Luchetti M, Aschwanden D, Terracciano A. Physical, cognitive, and social activities as mediators between personality and cognition: evidence from four prospective samples. Aging Mental Health. 2024;28(9):11. Matz-Costa, Christina, Carr, Dawn C, McNamara, Tay K et al. Physical, Cognitive, Social, and Emotional Mediators of Activity Involvement and Health in Later Life. Res Aging 2016. Vaartio-Rajalin H, Snellman F, Gustafsson Y, Rauhala A, Viklund E. Understanding Health, Subjective Aging, and Participation in Social Activities in Later Life: A Regional Finnish Survey. J Appl Gerontol. 2024;43(6):12. Newsom JT, Shaw BA, August KJ, Strath SJ. Physical activity-related social control and social support in older adults: Cognitive and emotional pathways to physical activity. J Health Psychol 2016:1359105316656768. Snyder JS, Kee N, Wojtowicz JM, Snyder JS, Kee N, Wojtowicz JM. Effects of adult neurogenesis on synaptic plasticity in the rat dentate gyrus. J Neurophys 85: 2423–2431. Journal of Neurophysiology. 2001; 85(6):2423–2431. Fabel K. Additive effects of physical exercise and environmental enrichment on adult hippocampal neurogenesis in mice. Front NeuroSci. 2009;3(50):50. Erickson KI, Leckie RL, Weinstein AM. Physical activity, fitness, and gray matter volume. Neurobiol Aging. 2014;35(Suppl 2):S20–8. Mousavi-Nasab SMH, Kormi-Nouri R, Nilsson L-G. Examination of the bidirectional influences of leisure activity and memory in old people: a dissociative effect on episodic memory. Br J Psychol 2014; 105(3). Hsu H, Liu CC, Yang SF, Chen HC. A health promotion program for older adults (KABAN!): effects on health literacy, quality of life, and emotions. Educ Gerontol. 2023;49(8):639–56. Song Y, Yuan Q, Liu H, Gu KN, Liu Y. Machine learning algorithms to predict mild cognitive impairment in older adults in China: A cross-sectional study. J Affect Disord 2025; 368(000). Holger D, Robert K. A comparison of sequential and non-sequential designs for discrimination between nested regression models. Biometrika 2004(1):165–76. Debrand T, Sirven N. Promoting Social Participation for Healthy Ageing - A Counterfactual Analysis from the Survey of Health, Ageing, and Retirement in Europe (SHARE). N icolas Sirven. 2008. Kailin G, Shiqiang W, Dan L, Yijie W, Shaokun W, Zhihan X. ​Longitudinal Changes in Physical Activity and Its Influencing Factors Among Chinese Older Adults: An Analysis Based on CHARLS 2011 and 2018 Data. J Wuhan Sports Univ. 2022;56(7):68–75. China Health and Retirement Longitudinal Study. Peking University. 2023. http://charls.pku.edu.cn/pages/about/111/zh-cn.htm . Accessed January 20th, 2025. Meng Q, Wang H, Strauss J, Langa KM, Zhao Y. Validation of neuropsychological tests for the China Health and Retirement Longitudinal Study Harmonized Cognitive Assessment Protocol. Int Psychogeriatr. 2019;31(12):1–11. Zeng Z, Bian Y, Cui Y, Yang D, Wang Y, Yu C. Physical Activity Dimensions and Its Association with Risk of Diabetes in Middle and Older Aged Chinese People. Int J Environ Res Public Health.2020;(21). Jiayu W, Detao M, Xiaoxiao L. Association of Physical Activity Levels with Parkinson’s Disease Risk: A Cross-Sectional Analysis Using CHARLS Data. Chin J Rehabilitation Theory Pract. 2023;29(10):1135–9. Mengyu F, Jun L, Pingping H. Scoring Methods for Physical Activity Levels in the International Physical Activity Questionnaire. Chin J Epidemiol. 2014;35(8):4. Bai A, Tao L, Huang J, Tao J, Liu J. Effects of physical activity on cognitive function among patients with diabetes in China: a nationally longitudinal study. BMC Public Health 2021; 21(1). Nagamatsu LS, Weinstein AM, Erickson KI, Fanning J, Awick EA, Kramer AF, et al. Exercise Mode Moderates the Relationship Between Mobility and Basal Ganglia Volume in Healthy Older Adults. J Am Geriatr Soc. 2016;64(1):102–8. David P, Buckley TR. Model Selection and Model Averaging in Phylogenetics: Advantages of Akaike Information Criterion and Bayesian Approaches Over Likelihood Ratio Tests. Syst Biol 2004(5):793–808. Michael DST, Miller AA. Cerebral Small Vessel Disease: Targeting Oxidative Stress as a Novel Therapeutic Strategy? Front Pharmacol. 2016;7:e8045. Manojlovicm NT, R B-M, Juskovicp M, Vasiljevic J. Effects Of Combination Of L-Ascorbic Acid And Alpha-Tocopherol On Biochemical Parameters Of Swimming-Induced Oxidative Stress In Guinea Pigs. Oxid Commun 2017; 40(2). Knez WL, Coombes JS, Jenkins DG. Ultra-Endurance Exercise and Oxidative Damage. Sports Med. 2006;36(5):429–41. Ainsworth B. 2011 Compendium of Physical Activities: a second update of codes and MET values. Medicine Science in Sports Exercise. 2011; 43. Voss MW, Vivar C, Kramer AF, Van Praag H. Bridging animal and human models of exercise-induced brain plasticity. Trends Cogn Sci. 2013;17(10):525–44. Jiang H, Kimura Y, Inoue S, Li C, Hatakeyama J, Wakayama M et al. Effects of different exercise modes and intensities on cognitive performance, adult hippocampal neurogenesis, and synaptic plasticity in mice. Exp Brain Res 2024; 242(7). Greenwood PM, Raja P. Neuronal and Cognitive Plasticity: A Neurocognitive Framework for Ameliorating Cognitive Aging. Front Aging Neurosci. 2010;2(150):150. Erickson KI. Aerobic exercise effects on cognitive and neural plasticity in older adults. Br J Sports Med. 2009;43(1):22. Ramos JS, Dalleck LC, Tjonna AE, Beetham KS, Coombes JS. The Impact of High-Intensity Interval Training Versus Moderate-Intensity Continuous Training on Vascular Function: a Systematic Review and Meta-Analysis. Sports Med. 2015;45(5):679–92. Lapierre-Nguyen S, Kim JS, Soonhuae C. Acute and Chronic Endothelial Responses to Home-based High Intensity Interval Training in Healthy Older Adults. Physiology. 2024;39(S1):3. Wei P, Qingqing J, Jing S, Youde L. Multimorbidity Patterns Among Chinese Older Adults: A Population-Based Analysis Using CHARLS Data. Mod Prev Med 2024(16). Junxia W, Lingzhi D, Jinyang J, Shuzhen C. Prevalence and Influencing Factors of Multimorbidity Among Chinese Older Adults: A Cross-Sectional Analysis Based on the CHARLS Database. Appl Prev Med. 2023;29(3):151–4. Kaleth AS, Saha CK, Jensen MP, Slaven JE, Ang DC. Effect of Moderate to Vigorous Physical Activity on Long-Term Clinical Outcomes and Pain Severity in Fibromyalgia. Arthritis Care Res. 2013;65(8):1211–8. Cohn-Schwartz E. Pathways From Social Activities to Cognitive Functioning: The Role of Physical Activity and Mental Health. Oxf Open 2020; 4(3). Zhao Y, Huo X, Du H, Lai X, Li Z, Zhang Z, et al. Moderating effect of instrumental activities of daily living on the relationship between loneliness and depression in people with cognitive frailty. BMC Geriatr. 2025;25(1):1–12. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 21 Jul, 2025 Reviews received at journal 10 Jul, 2025 Reviews received at journal 21 Jun, 2025 Reviewers agreed at journal 15 Jun, 2025 Reviewers agreed at journal 11 Jun, 2025 Reviewers agreed at journal 10 Jun, 2025 Reviewers agreed at journal 04 Jun, 2025 Reviewers invited by journal 28 May, 2025 Editor assigned by journal 20 May, 2025 Editor invited by journal 06 May, 2025 Submission checks completed at journal 03 May, 2025 First submitted to journal 03 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6425624","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":463272011,"identity":"d7221961-1af1-4ff6-a313-227293d0fe28","order_by":0,"name":"Hao-Jie Tang","email":"","orcid":"","institution":"Central South University of Forestry and Technology (CSUFT)","correspondingAuthor":false,"prefix":"","firstName":"Hao-Jie","middleName":"","lastName":"Tang","suffix":""},{"id":463272012,"identity":"0be5a6e2-0547-4c6a-8464-ee538cec03e2","order_by":1,"name":"Quan-Hong Xiao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYDACZhBhAMQSzAcOfPhBmha2xIMze0iyToLH+DAHGxEKDY4zP3zMU2Anx3e758NhBh4GeX6xA/i1SDazGRvOMEg2lrxzdsPhAgsGw5mzE/Br4WdmMJP4YMCcuOFG7obDM3gYEgxuE9DCxsz+TSLBoB6oJefBYR42IrTwM/OAbDkM0sJAnBbJZp5ioF+OG0veSDMABrIEYb8YnD++8THPn2o5vhvJjz98+GEjzy9NQAsCHACTEsQqR2gZBaNgFIyCUYAJAPqSQyCTJn5PAAAAAElFTkSuQmCC","orcid":"","institution":"Central South University of Forestry and Technology (CSUFT)","correspondingAuthor":true,"prefix":"","firstName":"Quan-Hong","middleName":"","lastName":"Xiao","suffix":""},{"id":463272013,"identity":"5edc781e-fb29-4861-87cd-edd5c4e91b26","order_by":2,"name":"Han-Yu Huang","email":"","orcid":"","institution":"Central South University of Forestry and Technology (CSUFT)","correspondingAuthor":false,"prefix":"","firstName":"Han-Yu","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2025-04-11 07:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6425624/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6425624/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83617770,"identity":"374edad7-7245-4ca1-a06b-e77923938704","added_by":"auto","created_at":"2025-05-29 14:11:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":26019,"visible":true,"origin":"","legend":"\u003cp\u003eComparative analysis of typical SEPA.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6425624/v1/49a02b72535336c74343ec83.png"},{"id":83617869,"identity":"00588d8d-422c-4b5e-a8ad-8339529df1e0","added_by":"auto","created_at":"2025-05-29 14:19:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1076690,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6425624/v1/a5c46369-b4d5-4fee-8767-b35a29787370.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Impact of Moderate-to-Low Intensity Socially-Engaged Physical Activity on Cognitive Functioning in Older Adults: An Empirical Analysis Based on CHARLS 2020 Baseline Data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe intensification of global aging trends has rendered cognitive decline in older adults an increasingly severe public health issue. Cognitive deterioration not only significantly reduces the quality of life for older adults but also escalates caregiving burdens on families and society. Existing research has made significant progress in uncovering the cognitive protective effects of physical activity and social engagement: physical activity enhances neuroplasticity by promoting the secretion of brain-derived neurotrophic factor (BDNF) and cerebrovascular remodeling [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], while social interaction mitigates cognitive decline by boosting cognitive reserve and buffering psychological stress [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, current studies exhibit two critical limitations: first, they predominantly adopt a fragmented approach, isolating investigations into physical activity (focusing on metabolic equivalents of exercise intensity) or social participation (emphasizing social network size), thereby neglecting mechanistic exploration of their synergistic effects; second, classifications of activity intensity remain overly generalized, particularly lacking systematic stratification of moderate-to-low intensity activities (3\u0026ndash;6 METs), leading to misjudgments of potential risks associated with high-intensity exercise [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This study first put forward a \"Socially-Engaged Physical activities(\u003cb\u003eSEPA\u003c/b\u003e)\"composite analytical framework [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], the central hypothesis is that activities integrating both physical exertion and social interaction (e.g., dancing, mahjong) may generate cognitive protective effects surpassing those of unimodal interventions through synergistic physiological and psychological mechanisms. The theoretical foundation includes the dual-pathway model [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]: physiologically, moderate-intensity activities promote hippocampal neurogenesis via enhanced cerebral blood flow perfusion while avoiding oxidative stress damage from high-intensity exercise [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]; psychologically, structured social interactions activate the default mode network, improving episodic memory encoding efficiency [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Although international studies have identified cognitive benefits of composite activities (e.g., a 23% reduction in dementia risk through group exercise in European cohorts), two critical gaps persist: 1. The failure to quantitatively isolate synergistic effects through model-based analyses to assess incremental explanatory power; 2. The absence of direct comparisons between SEPA and physical activities of equivalent intensity. Despite emerging frameworks like \"composite health behaviors\" [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], large-scale longitudinal empirical analyses\u0026mdash;particularly those with refined intensity stratification\u0026mdash;remain scarce globally. Leveraging baseline data from the China Health and Retirement Longitudinal Study (CHARLS 2020; n\u0026thinsp;=\u0026thinsp;7,141), this study introduces two methodological innovations: 1.Paradigm shift in effect comparison: A three-tier nested regression model with counterfactual analysis isolates the independent effects of social-physical activities, quantifying incremental contributions of synergistic mechanisms [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]; 2.Precision intensity thresholding: Stratifying physical activities into low (1.0\u0026ndash;2.9 METs), moderate (3.0\u0026ndash;5.9 METs) and high (\u0026ge;\u0026thinsp;6.0 METs) intensity tiers to compare cognitive impacts between SEPA and equivalent-intensity physical activities. This research aims to address gaps in the synergistic mechanisms of composite activities and intensity stratification, providing a theoretical foundation for developing safe and universally applicable cognitive intervention strategies in developing nations.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Sources\u003c/h2\u003e \u003cp\u003eThis study is based on the China Health and Retirement Longitudinal Study (CHARLS) database. CHARLS is a large-scale national longitudinal survey project initiated by the National School of Development at Peking University and led by the Institute of Social Science Survey (ISSS) at Peking University, focusing on individuals aged 45 and above. It aims to construct a public micro-database covering socioeconomic status, health behaviors, healthcare, and other multidimensional indicators to provide high-quality data support for aging research. The survey was collaboratively conducted by the Institute of Population and Labor Economics at the Chinese Academy of Social Sciences and the Center for Aging and Health Research at Peking University. Since its baseline survey in 2011, CHARLS has conducted multiple rounds of tracking in 150 county-level administrative districts and 450 village-level units across China, accumulating a valid sample size of 17,000 individuals. The questionnaire design strictly adheres to international epidemiological survey protocols and underwent three rounds of international expert reviews to ensure data quality. Reliability and validity assessments of related indicators rank it among the top cohort studies in developing Asian countries [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This study utilizes CHARLS 2020 baseline data for secondary analysis, employing Stata 18.0 for data cleaning and quality control, ultimately including 7,141 valid samples of older adults aged 60 and above who met the inclusion criteria.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVariable Definition\u003c/h3\u003e\n\u003cp\u003eDependent Variable\u003c/p\u003e \u003cp\u003eCognitive level was assessed using the standardized cognitive assessment scale developed by the CHARLS team at Peking University. This scale includes six cognitive domains: orientation, memory, attention, and others, with a total score range of 0\u0026ndash;21 points. The raw data were recoded using STATA 18.0 to generate a continuous variable named \"Cognitive Ability Index (0\u0026ndash;21 points)\". This variable not only retains the reliability and validity characteristics of the original scale (Cronbach's α\u0026thinsp;=\u0026thinsp;0.82, ICC\u0026thinsp;=\u0026thinsp;0.79) but also enables cross-group comparability through standardized processing [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIndependent Variables\u003c/p\u003e \u003cp\u003eThe independent variables included in this study were physical activity and social activity. According to the International Physical Activity Guidelines, respondents' daily physical activity duration was categorized into five levels: \u0026lt;10 min (0 points), 10\u0026ndash;29 min (1 point), 30\u0026ndash;119 min (2 points), 120\u0026ndash;239 min (3 points), and \u0026ge;\u0026thinsp;240 min (4 points). Activity intensity was calculated using the median method [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], with specific thresholds defined as: low intensity (\u0026le;\u0026thinsp;600 MET-min/week), moderate intensity (601\u0026ndash;3000 MET-min/week), and high intensity (\u0026ge;\u0026thinsp;3001 MET-min/week) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Metabolic equivalent (MET) values were calculated based on the International Physical Activity Questionnaire Short Form (IPAQ-SF) standards [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], using the formula: MET-min/week\u0026thinsp;=\u0026thinsp;Σ (MET coefficient of activity type \u0026times; daily activity minutes \u0026times; activity days) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Social activity variables encompassed 11 specific behaviors from the original questionnaire: visiting neighbors, playing mahjong, providing help, dancing, participating in community organizations, volunteering, attending school/training, and other social activities. Among these, dancing and playing mahjong were categorized as moderate-to-low intensity physical activities, aligning with the definition of SEPA:\"A category of temporally synchronized multidimensional integrative activities characterized by the coupling of interpersonal interaction and metabolic expenditure, achieving sociobehavioral integration and neurophysiological stimulation synergies\u0026rdquo;. Descriptive statistics for the variables are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The cognitive ability assessment scores of the sample population exhibited significant heterogeneity (M\u0026thinsp;=\u0026thinsp;12.01, SD\u0026thinsp;=\u0026thinsp;3.34), with scores covering the full theoretical range of 21 points. This dispersion (coefficient of variation\u0026thinsp;=\u0026thinsp;27.81%) provided an ideal variance foundation for subsequent regression modeling. The average value for \"visiting neighbors\" was 0.32, indicating that 32% of the sample engaged in this behavior during the study period.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics of Cognitive Activity Variables in Older Adults.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCases\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive Ability (0\u0026ndash;21 points, higher\u0026thinsp;=\u0026thinsp;better)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndependent Variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisiting Neighbors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProviding Help\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity Organization Participation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolunteering\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttending School/Training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Social Activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight Exercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate Exercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVigorous Exercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDancing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlaying Mahjong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThis study employed SPSS 27.0 statistical software to construct a three-stage hierarchical regression model. Using a nested model comparison strategy, we systematically evaluated the joint impact mechanisms of social activities and physical activities on cognitive function in older adults [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The specific modeling workflow was as follows: first, social interaction variables were incorporated into the baseline model (Block 1); subsequently, traditional physical activity metrics were integrated into Block 2; finally, variable substitution in Block 3 established a counterfactual model to isolate the unique effects of socially embedded physical activities. Model validation adopted a progressive hypothesis testing framework: \u003cb\u003eΔR\u0026sup2;\u003c/b\u003e values quantified explanatory power increments, while changes in F-statistics (\u003cb\u003eΔF\u003c/b\u003e) verified the significance levels of newly added variable sets. By integrating synergistic mechanisms between social and physical activities, combined with empirical data from typical socially embedded physical activities (e.g., dancing and mahjong), this study systematically revealed their influence effects on cognitive function in older populations.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eImpact of Physical Activity and Social Activities on Cognition\u003c/h2\u003e \u003cp\u003eThe effects of each variable on the cognitive level of older adults are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The regression analysis revealed different impacts of key variables. In terms of physical activity, light and moderate intensity showed significant positive effects on cognitive level (light: \u003cb\u003eβ\u003c/b\u003e\u0026thinsp;=\u0026thinsp;0.070, \u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; moderate: \u003cb\u003eβ\u003c/b\u003e\u0026thinsp;=\u0026thinsp;0.068, \u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while vigorous activity demonstrated a significant negative effect (\u003cb\u003eβ\u003c/b\u003e=-0.087, \u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among social activities, social interactions such as providing help (\u003cb\u003eβ\u003c/b\u003e\u0026thinsp;=\u0026thinsp;0.026, \u003cb\u003ep\u003c/b\u003e\u0026thinsp;=\u0026thinsp;0.030), educational participation (\u003cb\u003eβ\u003c/b\u003e\u0026thinsp;=\u0026thinsp;0.036, \u003cb\u003ep\u003c/b\u003e\u0026thinsp;=\u0026thinsp;0.002), and community organization involvement (\u003cb\u003eβ\u003c/b\u003e\u0026thinsp;=\u0026thinsp;0.046, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) showed significant cognitive benefits. Core variables of SEPA\u0026mdash;dancing (\u003cb\u003eβ\u003c/b\u003e\u0026thinsp;=\u0026thinsp;0.055, \u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and mahjong (\u003cb\u003eβ\u003c/b\u003e\u0026thinsp;=\u0026thinsp;0.090, \u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u0026mdash;exhibited standardized effect sizes that surpassed those of single-dimension physical or social activities, highlighting their unique synergistic advantages. Model validation confirmed data reliability (\u003cb\u003eVIF\u003c/b\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1.2), indicating no multicollinearity interference among variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003e.\u003c/b\u003eMultivariate Linear Regression Analysis of Cognitive Level.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnstandardized Coefficients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eCollinearity Statistics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd.Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTolerance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eVIF\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e113.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisiting Neighbors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProviding Help\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity Organization Participation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolunteering\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttending School/Training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Social Activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight Exercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate Exercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVigorous Exercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-7.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDancing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlaying Mahjong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eDependent Variable: Cognitive Ability (0\u0026ndash;21 points, higher\u0026thinsp;=\u0026thinsp;better)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIsolated Model Analysis of SEPA\u003c/h2\u003e \u003cp\u003eThe regression analysis results revealed a significant negative association between vigorous physical activity and cognitive function level in older adults (\u003cb\u003eβ\u003c/b\u003e=-0.087, \u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Existing literature confirms that high-intensity exercise is physiologically suitable only for healthy older adults subpopulations [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Therefore, this study excluded vigorous physical activity from the hierarchical regression model discussion. Dancing and mahjong activities, which combine both moderate-to-low intensity physical activity (MET\u0026thinsp;\u0026le;\u0026thinsp;4.0) and structured social interaction attributes, were excluded from the social activity model to avoid overlapping variable characteristics interfering with effect estimation. The theoretical modeling adopted the following control strategies: excluding physical activity variables when testing the isolated social activity model; controlling for social variables in the physical activity model; systematically analyzing synergistic mechanisms through three-stage nested model comparisons, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. ANOVA tests (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) confirmed that introducing interaction terms produced statistically significant improvements in model explanatory power.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNested Model Comparison of Social Activities and Physical Activities.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eDependent Variable: Cognitive Level\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisiting Neighbors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3.855)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.039)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProviding Help\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.797)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.235)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity Organization Participation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(4.791)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.541)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolunteering\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.427)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.144)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttending School/Training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.858)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.776)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Social Activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3.714)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(3.617)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight Exercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(6.446)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(7.150)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate Exercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.601)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(5.459)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u0026sup2;\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edf1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edf2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eΔF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSig. F Change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: Values in parentheses are t-values.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANOVA\u003csup\u003ea\u003c/sup\u003e Test.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSS\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eMS\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1165.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e194.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78709.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79874.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1958.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e244.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77915.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79874.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1051.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e525.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78822.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79874.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003ea:Dependent Variable: Cognitive Ability (0\u0026ndash;21 points, higher\u0026thinsp;=\u0026thinsp;better)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eb:Predictors: (Constant), Other Social Activities, Volunteering, Attending School/Training, Visiting Neighbors, Community Organization Participation, Providing Help\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003ec:Predictors: (Constant), Other Social Activities, Volunteering, Attending School/Training, Visiting Neighbors, Community Organization Participation, Providing Help, Light Exercise, Moderate Exercise\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003ed:Predictors: (Constant), Light Exercise, Moderate Exercise\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHierarchical regression model analysis revealed that the baseline model (Model 1) incorporating social activity variables explained a variance of \u003cb\u003eR\u0026sup2;\u003c/b\u003e=0.015 [adjusted \u003cb\u003eR\u0026sup2;\u003c/b\u003e=0.014, \u003cb\u003eF\u003c/b\u003e(6,7134)\u0026thinsp;=\u0026thinsp;17.6]. When introducing moderate-to-low intensity physical activity to construct the main effects model (Model 2), the explanatory power significantly increased by \u003cb\u003eΔR\u0026sup2;\u003c/b\u003e=0.010 (\u003cb\u003eF-change\u003c/b\u003e(2,7132)\u0026thinsp;=\u0026thinsp;36.3, \u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), reaching \u003cb\u003eR\u0026sup2;\u003c/b\u003e=0.025 (adjusted \u003cb\u003eR\u0026sup2;\u003c/b\u003e=0.023). The reverse control model (Model 3) demonstrated that the absolute value of model changes maintained significant explanatory power after excluding interference from socially-embedded activities (\u003cb\u003eΔR\u0026sup2;\u003c/b\u003e=0.013, \u003cb\u003eF-change\u003c/b\u003e(6,7132)\u0026thinsp;=\u0026thinsp;13.8, \u003cb\u003ep\u003c/b\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming the statistical robustness of joint effects. Effect size analysis showed that the dual-intervention model achieved a 66.7% gain in variance explanation compared to single-dimension models, with its standardized effect strength (\u003cb\u003eCohen's f\u0026sup2;\u003c/b\u003e=0.026) exceeding the small effect threshold (\u003cb\u003ef\u0026sup2;\u003c/b\u003e\u0026ge;0.02), meeting substantive criteria for synergistic effects. To further analyze the specific impacts of socially-embedded physical activities, this study compared beta values between typical SEPA (dancing, mahjong) and their corresponding intensity-matched physical activity variables, see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis study classified activity intensity based on metabolic equivalent (MET) standards: mahjong playing was categorized as light physical activity (MET\u0026thinsp;=\u0026thinsp;2.8), while dancing activities categorized as moderate physical activity (MET\u0026thinsp;=\u0026thinsp;4.5) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Data analysis revealed that all four activity variables exerted significant main effects on cognitive level \u003cb\u003e(p\u003c/b\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Comparison of standardized coefficients showed that light physical activity (\u003cb\u003eβ\u003c/b\u003e\u0026thinsp;=\u0026thinsp;0.075, \u003cb\u003et\u003c/b\u003e\u0026thinsp;=\u0026thinsp;6.356) demonstrated stronger cognitive enhancement effects compared to moderate physical activity (\u003cb\u003eβ\u003c/b\u003e\u0026thinsp;=\u0026thinsp;0.056, \u003cb\u003et\u003c/b\u003e\u0026thinsp;=\u0026thinsp;4.700). Further analysis identified intensity-specific advantage effects in typical socially-embedded physical activities: moderate-intensity dancing activities (\u003cb\u003eβ\u003c/b\u003e\u0026thinsp;=\u0026thinsp;0.069, \u003cb\u003et\u003c/b\u003e\u0026thinsp;=\u0026thinsp;5.857) exhibited a 22% increase in effect size compared to regular physical activities at equivalent intensity (\u003cb\u003eΔβ\u003c/b\u003e\u0026thinsp;=\u0026thinsp;0.013), while light-intensity mahjong activities (\u003cb\u003eβ\u003c/b\u003e\u0026thinsp;=\u0026thinsp;0.1, \u003cb\u003et\u003c/b\u003e\u0026thinsp;=\u0026thinsp;8.510) showed a 34% greater effect size enhancement compared to regular light-intensity physical activities (\u003cb\u003eΔβ\u003c/b\u003e\u0026thinsp;=\u0026thinsp;0.025).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003e1.Moderate-to-Low Intensity Physical Activities Significantly Enhance older adult cognition\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study empirically analyzed the effects of SEPA on older adult cognition using CHARLS 2020 baseline data, aligning with cutting-edge research in cognitive aging. Multiple linear regression data revealed cognitive benefits of moderate-to-low intensity physical activities (\u003cstrong\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/strong\u003e=0.056\u0026ndash;0.081, \u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u0026lt;0.001), consistent with animal model studies demonstrating neuroplasticity modulation through prefrontal-hippocampal circuit activation induced by such activities [33, 34, 35, 36]. Descriptive statistics showed 33% of participants (n=7,141) engaged in high-intensity activities. While some studies suggest greater cognitive benefits from high-intensity activities [37, 38], our results revealed significant negative associations between high-intensity activities and cognition (\u003cstrong\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/strong\u003e=\u0026minus;0.087, \u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u0026lt;0.001). This discrepancy may stem from non-adaptive high-intensity exercise potentially exacerbating neuroinflammation through oxidative stress pathways, particularly in older adults with vascular risk factors [29, 30, 31]. In China, 71.9% of adults aged 60+ have \u0026ge;1 chronic condition, with 44.5% experiencing multimorbidity [39, 40]. Although high-intensity activities might benefit cognitively healthy elders, only 27% of Chinese older adults \u0026nbsp;meet health criteria, indicating limited applicability and potential cognitive risks of high-intensity interventions for general\u0026nbsp;older\u0026nbsp;populations [41].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.Synergistic Effects of Moderate-to-Low Intensity Physical Activities and Social Activities on Cognitive Function in Older Adults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn terms of the explanatory power of nested models, single-activity models (physical/social) each explained only 1.3%-1.5% of cognitive variance (\u003cstrong\u003e\u003cem\u003eR\u0026sup2;\u003c/em\u003e\u003c/strong\u003e). However, the combined model (Model 2) increased explanatory power to 2.5%, with statistically significant F-change (\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u0026lt;0.001), indicating that the combined implementation of physical and social activities may yield more significant intervention effects on older adult cognition compared to isolated single-activity interventions [42]. Older adults engaging in both moderate-to-low intensity physical activities and social interactions demonstrated higher cognitive function levels than those participating in single-activity interventions. Notably, although Model 3 showed slightly lower overall explanatory power than Model 1 (\u003cstrong\u003e\u003cem\u003eR\u0026sup2;\u003c/em\u003e\u003c/strong\u003e=1.3% vs. 1.5%), its parsimony advantage became evident\u0026mdash;achieving more efficient prediction with only two variables (significantly improved F-value). These variables (moderate and low-intensity physical activities) exhibit characteristics of core protective factors. This finding aligns with the research objective of identifying universal activity factors rather than pursuing complete explanation of cognitive decline.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.Moderate-to-Low Intensity SEPA May Confer Greater Cognitive Benefits for Older Adults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurther analysis revealed significantly higher participation rates in moderate-to-low intensity activities compared to other variables, indicating their feasibility for global older populations. When comparing typical socially-embedded activities (e.g., dancing, mahjong) with generic moderate-to-low intensity physical activities, the standardized coefficients (\u003cstrong\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/strong\u003e=0.069, 0.1) and effect sizes (\u003cstrong\u003e\u003cem\u003et\u003c/em\u003e\u003c/strong\u003e=5.85, 8.51) of socially-embedded activities surpassed those of intensity-matched physical activities [43]. This suggests that SEPA, as a distinct category, may exert superior cognitive protective effects through dual-pathway mechanisms (combining physical stimulation and social engagement) compared to generic moderate-to-low intensity activities. These findings emphasize the importance of prioritizing SEPA in exercise regimens for older adults to more effectively mitigate cognitive decline.\u003c/p\u003e\n\u003cp\u003eThis study has limitations in exploring the association between SEPA and older adult \u0026nbsp;cognition function: Restricted by database variables, only two observational indicators (dancing and mahjong) met the criteria for socially-embedded physical activities. This methodological constraint necessitates future experimental expansion of variable systems. Notably, the predictive models constructed from current data demonstrate limited overall explanatory power for cognitive function \u003cstrong\u003e\u003cem\u003e(R\u0026sup2;\u003c/em\u003e\u003c/strong\u003e\u0026lt;3%), consistent with theoretical models positing that older adult cognition is jointly influenced by genetic predisposition, metabolic status, and comorbidity profiles. Nevertheless, the core contribution lies in longitudinally demonstrating, for the first time, the superior efficacy of moderate-to-low intensity SEPA compared to other physical activity types in delaying age-related cognitive decline. This discovery provides a critical evidence-based foundation for the formulation of national guidelines on physical activity for older adults and dementia prevention strategies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study derived the following core conclusions through longitudinal analysis of CHARLS data:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSEPA demonstrate superior preventive efficacy against cognitive decline in older adults:\u0026nbsp;\u003c/strong\u003eAmong Chinese older populations, engagement in activities combining physical exercise and social interaction (e.g., dancing, mahjong) exhibits significantly greater efficacy in delaying cognitive decline compared to isolated physical training or purely social behaviors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhysical Activity intensity exhibits differential effects:\u003c/strong\u003e High-intensity physical activity may mask potential benefits through negative cognitive associations (\u003cstrong\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/strong\u003e=-0.087, \u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u0026lt;0.001), emphasizing the necessity of matching activity intensity with individual health status. Concurrently, moderate-to-low intensity physical activities show the broadest applicability and statistically significant positive correlations with cognitive performance in Chinese older populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch limitations and future directions:\u003c/strong\u003e The limited variable scope constrains generalizability, necessitating expanded experimental validation. With modest model explanatory power (\u003cstrong\u003e\u003cem\u003eR²\u003c/em\u003e\u003c/strong\u003e\u0026lt;4%), future investigations should integrate longitudinal tracking data to deepen mechanistic exploration, incorporating psychosocial factors (e.g., social support, self-efficacy) and biomarkers (e.g., inflammatory markers, brain-derived neurotrophic factor) to refine theoretical frameworks.\u003c/p\u003e\n\u003cp\u003eThis research contributes empirical evidence from the Chinese context to global geriatric cognition studies, highlighting the unique value of SEPA while advocating interdisciplinary collaboration to elucidate their biological and psychosocial protective mechanisms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the CHARLS research and field teams and all respondents for their contribution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHJ: literature review, study design, data analysis, preparation of the initial draft and writing. QH:study design,supervision, revision of the paper and editing of the manuscript. HY: literature search and data analysis . All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available in the CHARLS repository:http:// charls. pku. edu. cn/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCHARLS study is an open dataset. The CHARLS was approved by the Ethics Review Committee of Peking University(IRB00001052-11015)and all participants signed an informed consent. This research followed the guidance of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMeo ED, Portaccio E, Prestipino E, Nacmias B, Bagnoli S, Razzolini L, et al. Effect of BDNF Val66Met polymorphism on hippocampal subfields in multiple sclerosis patients. Mol Psychiatry. 2022;27(2):1010\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHosang GM, Shiles C, Tansey KE, Mcguffin P, Uher R. Interaction between stress and the BDNF Val66Met polymorphism in depression: A systematic review and meta-analysis - ScienceDirect. Compr Psychiatr. 2014;12(8):7\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuqing H, Huixiu H, Yajie Z, Jie G, Huanhuan L, Chao S. Association Between Social Activities and Cognitive Function in Older Adults: A Scoping Review and Nursing Implications. Chin J Nurs. 2025;60(2):251\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuiper JS, Zuidersma M, Zuidema SU, Burgerhof JGM, Stolk RP, Oude Voshaar RC et al. Social relationships and cognitive decline: a systematic review and meta-analysis of longitudinal cohort studies. Int J Epidemiol 2016:dyw089.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYanzhang C, Jing J, Chenglin Z. Physical Activity and Cognitive Flexibility in Older Adults: Brain Activation Patterns and the Mediating Role of Working Memory. J Xi'an Phys Educ Univ 2023(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMsn XX, Msn LM, Msn YW, Msn YL, Msn MD, Phd BM et al. Pathway linking nutritional status to cognitive function in older adults with chronic diseases: Exploring the mediating role of sleep quality. Geriatr Nurs 2025; 62(PartB).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCui Y, Duan Y, Du J, Yang L, Tian X, Liu H. Relationship between leisure activity and depression in Chinese older adults: chain mediating effect of diet and cognition. BMC Geriatr 2025; 25(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStephan Y, Sutin AR, Luchetti M, Aschwanden D, Terracciano A. Physical, cognitive, and social activities as mediators between personality and cognition: evidence from four prospective samples. Aging Mental Health. 2024;28(9):11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatz-Costa, Christina, Carr, Dawn C, McNamara, Tay K et al. Physical, Cognitive, Social, and Emotional Mediators of Activity Involvement and Health in Later Life. Res Aging 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVaartio-Rajalin H, Snellman F, Gustafsson Y, Rauhala A, Viklund E. Understanding Health, Subjective Aging, and Participation in Social Activities in Later Life: A Regional Finnish Survey. J Appl Gerontol. 2024;43(6):12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNewsom JT, Shaw BA, August KJ, Strath SJ. Physical activity-related social control and social support in older adults: Cognitive and emotional pathways to physical activity. J Health Psychol 2016:1359105316656768.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSnyder JS, Kee N, Wojtowicz JM, Snyder JS, Kee N, Wojtowicz JM. Effects of adult neurogenesis on synaptic plasticity in the rat dentate gyrus. J Neurophys 85: 2423\u0026ndash;2431. \u003cem\u003eJournal of Neurophysiology.\u003c/em\u003e 2001; 85(6):2423\u0026ndash;2431.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFabel K. Additive effects of physical exercise and environmental enrichment on adult hippocampal neurogenesis in mice. Front NeuroSci. 2009;3(50):50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErickson KI, Leckie RL, Weinstein AM. Physical activity, fitness, and gray matter volume. Neurobiol Aging. 2014;35(Suppl 2):S20\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMousavi-Nasab SMH, Kormi-Nouri R, Nilsson L-G. Examination of the bidirectional influences of leisure activity and memory in old people: a dissociative effect on episodic memory. Br J Psychol 2014; 105(3).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHsu H, Liu CC, Yang SF, Chen HC. A health promotion program for older adults (KABAN!): effects on health literacy, quality of life, and emotions. Educ Gerontol. 2023;49(8):639\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong Y, Yuan Q, Liu H, Gu KN, Liu Y. Machine learning algorithms to predict mild cognitive impairment in older adults in China: A cross-sectional study. J Affect Disord 2025; 368(000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolger D, Robert K. A comparison of sequential and non-sequential designs for discrimination between nested regression models. Biometrika 2004(1):165\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDebrand T, Sirven N. Promoting Social Participation for Healthy Ageing - A Counterfactual Analysis from the Survey of Health, Ageing, and Retirement in Europe (SHARE). N\u003cem\u003eicolas Sirven.\u003c/em\u003e 2008.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKailin G, Shiqiang W, Dan L, Yijie W, Shaokun W, Zhihan X. ​Longitudinal Changes in Physical Activity and Its Influencing Factors Among Chinese Older Adults: An Analysis Based on CHARLS 2011 and 2018 Data. J Wuhan Sports Univ. 2022;56(7):68\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChina Health and Retirement Longitudinal Study. Peking University. 2023.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://charls.pku.edu.cn/pages/about/111/zh-cn.htm\u003c/span\u003e\u003cspan address=\"http://charls.pku.edu.cn/pages/about/111/zh-cn.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed January 20th, 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeng Q, Wang H, Strauss J, Langa KM, Zhao Y. Validation of neuropsychological tests for the China Health and Retirement Longitudinal Study Harmonized Cognitive Assessment Protocol. Int Psychogeriatr. 2019;31(12):1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng Z, Bian Y, Cui Y, Yang D, Wang Y, Yu C. Physical Activity Dimensions and Its Association with Risk of Diabetes in Middle and Older Aged Chinese People. Int J Environ Res Public Health.2020;(21).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiayu W, Detao M, Xiaoxiao L. Association of Physical Activity Levels with Parkinson\u0026rsquo;s Disease Risk: A Cross-Sectional Analysis Using CHARLS Data. Chin J Rehabilitation Theory Pract. 2023;29(10):1135\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMengyu F, Jun L, Pingping H. Scoring Methods for Physical Activity Levels in the International Physical Activity Questionnaire. Chin J Epidemiol. 2014;35(8):4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBai A, Tao L, Huang J, Tao J, Liu J. Effects of physical activity on cognitive function among patients with diabetes in China: a nationally longitudinal study. BMC Public Health 2021; 21(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNagamatsu LS, Weinstein AM, Erickson KI, Fanning J, Awick EA, Kramer AF, et al. Exercise Mode Moderates the Relationship Between Mobility and Basal Ganglia Volume in Healthy Older Adults. J Am Geriatr Soc. 2016;64(1):102\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavid P, Buckley TR. Model Selection and Model Averaging in Phylogenetics: Advantages of Akaike Information Criterion and Bayesian Approaches Over Likelihood Ratio Tests. Syst Biol 2004(5):793\u0026ndash;808.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMichael DST, Miller AA. Cerebral Small Vessel Disease: Targeting Oxidative Stress as a Novel Therapeutic Strategy? Front Pharmacol. 2016;7:e8045.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eManojlovicm NT, R B-M, Juskovicp M, Vasiljevic J. Effects Of Combination Of L-Ascorbic Acid And Alpha-Tocopherol On Biochemical Parameters Of Swimming-Induced Oxidative Stress In Guinea Pigs. Oxid Commun 2017; 40(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnez WL, Coombes JS, Jenkins DG. Ultra-Endurance Exercise and Oxidative Damage. Sports Med. 2006;36(5):429\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAinsworth B. 2011 Compendium of Physical Activities: a second update of codes and MET values. \u003cem\u003eMedicine Science in Sports Exercise.\u003c/em\u003e 2011; 43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVoss MW, Vivar C, Kramer AF, Van Praag H. Bridging animal and human models of exercise-induced brain plasticity. Trends Cogn Sci. 2013;17(10):525\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang H, Kimura Y, Inoue S, Li C, Hatakeyama J, Wakayama M et al. Effects of different exercise modes and intensities on cognitive performance, adult hippocampal neurogenesis, and synaptic plasticity in mice. Exp Brain Res 2024; 242(7).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreenwood PM, Raja P. Neuronal and Cognitive Plasticity: A Neurocognitive Framework for Ameliorating Cognitive Aging. Front Aging Neurosci. 2010;2(150):150.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErickson KI. Aerobic exercise effects on cognitive and neural plasticity in older adults. Br J Sports Med. 2009;43(1):22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamos JS, Dalleck LC, Tjonna AE, Beetham KS, Coombes JS. The Impact of High-Intensity Interval Training Versus Moderate-Intensity Continuous Training on Vascular Function: a Systematic Review and Meta-Analysis. Sports Med. 2015;45(5):679\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLapierre-Nguyen S, Kim JS, Soonhuae C. Acute and Chronic Endothelial Responses to Home-based High Intensity Interval Training in Healthy Older Adults. Physiology. 2024;39(S1):3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei P, Qingqing J, Jing S, Youde L. Multimorbidity Patterns Among Chinese Older Adults: A Population-Based Analysis Using CHARLS Data. Mod Prev Med 2024(16).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJunxia W, Lingzhi D, Jinyang J, Shuzhen C. Prevalence and Influencing Factors of Multimorbidity Among Chinese Older Adults: A Cross-Sectional Analysis Based on the CHARLS Database. Appl Prev Med. 2023;29(3):151\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaleth AS, Saha CK, Jensen MP, Slaven JE, Ang DC. Effect of Moderate to Vigorous Physical Activity on Long-Term Clinical Outcomes and Pain Severity in Fibromyalgia. Arthritis Care Res. 2013;65(8):1211\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohn-Schwartz E. Pathways From Social Activities to Cognitive Functioning: The Role of Physical Activity and Mental Health. Oxf Open 2020; 4(3).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Y, Huo X, Du H, Lai X, Li Z, Zhang Z, et al. Moderating effect of instrumental activities of daily living on the relationship between loneliness and depression in people with cognitive frailty. BMC Geriatr. 2025;25(1):1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cognitive Function, Moderate-to-Low Intensity, Socially-Engaged Physical Activity, CHARLS, Older Adults","lastPublishedDoi":"10.21203/rs.3.rs-6425624/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6425624/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eAmidst the accelerating global aging process, cognitive decline in older adults has emerged as a critical public health challenge. While existing studies predominantly focus on examining the isolated effects of physical activity or social engagement on cognitive function, systematic investigations into moderate-to-low intensity physical activities with integrated social attributes (e.g., dancing, mahjong) remain inadequate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eUtilizing baseline data from the China Health and Retirement Longitudinal Study (CHARLS 2020; n =7,141), this study employs multiple linear regression models to comprehensively assess the impact of socially-engaged moderate-to-low intensity physical activities on cognitive performance among adults aged 60 years and older.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eKey findings demonstrate that in older adults, moderate-to-low intensity physical activity (\u003cem\u003e\u003cstrong\u003eβ\u003c/strong\u003e\u003c/em\u003e=0.068–0.075, \u003cem\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/em\u003e\u0026lt;0.001) and social engagement (e.g., community participation, educational training; \u003cem\u003e\u003cstrong\u003eβ\u003c/strong\u003e\u003c/em\u003e=0.036–0.046, \u003cem\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/em\u003e\u0026lt;0.01) are significantly positively associated with cognitive performance. In contrast, high-intensity physical activity showed a significant negative correlation with cognitive function (\u003cem\u003e\u003cstrong\u003eβ\u003c/strong\u003e\u003c/em\u003e=−0.087, \u003cem\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/em\u003e\u0026lt;0.001) in this population. Hierarchical regression analysis further reveals that integrated models combining social and physical activities exhibit superior explanatory power (\u003cem\u003e\u003cstrong\u003eR²\u003c/strong\u003e\u003c/em\u003e=2.5%) compared to single-activity models (\u003cem\u003e\u003cstrong\u003eR²\u003c/strong\u003e\u003c/em\u003e=1.3–1.5%, \u003cem\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/em\u003e\u0026lt;0.001) among older adults, indicating statistically significant interaction effects. Notably, Socially-Engaged Physical activities(SEPA) (e.g., dancing: \u003cem\u003e\u003cstrong\u003eβ\u003c/strong\u003e\u003c/em\u003e=0.069–0.100; mahjong: \u003cem\u003e\u003cstrong\u003eβ\u003c/strong\u003e\u003c/em\u003e=0.069–0.100, \u003cem\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/em\u003e\u0026lt;0.001) demonstrate enhanced cognitive benefits for older adults, with standardized coefficients (\u003cem\u003e\u003cstrong\u003eβ\u003c/strong\u003e\u003c/em\u003e=0.082) significantly exceeding those of non-social physical activities (\u003cem\u003e\u003cstrong\u003eβ\u003c/strong\u003e\u003c/em\u003e=0.071) and purely social interactions (\u003cem\u003e\u003cstrong\u003eβ\u003c/strong\u003e\u003c/em\u003e=0.038).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eThese findings indicate that specific-intensity activities combining motor coordination and social cognition confer greater cognitive benefits in older adults. This study provides robust epidemiological evidence for optimizing cognitive interventions in aging populations, advocating for prioritized implementation of socially-embedded moderate-intensity activities over isolated exercise regimens in public health strategies.\u003c/p\u003e","manuscriptTitle":"The Impact of Moderate-to-Low Intensity Socially-Engaged Physical Activity on Cognitive Functioning in Older Adults: An Empirical Analysis Based on CHARLS 2020 Baseline Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-29 14:03:46","doi":"10.21203/rs.3.rs-6425624/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-21T10:04:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-10T09:40:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-21T20:39:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"105276226154326331963372045556877566743","date":"2025-06-15T23:47:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"197750994866907981479859844431571435415","date":"2025-06-11T22:38:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52306614595329901950874555414589981404","date":"2025-06-10T13:31:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"122142544884310383636698885171311221994","date":"2025-06-04T15:57:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-28T07:22:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-20T08:58:35+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-06T08:23:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-03T13:42:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2025-05-03T13:41:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1d8d5478-03bd-4470-8f56-5bf7ce058391","owner":[],"postedDate":"May 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-18T17:23:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-29 14:03:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6425624","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6425624","identity":"rs-6425624","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}