Impact of diversified social interaction on elderly health in China: a longitudinal analysis based on interaction type and frequency

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This study examines the impact of different types and frequencies of social interaction on the health outcomes of elderly adults in China, adjusting for simultaneity and heterogeneity biases. Methods This study used data from the Chinese Health and Retirement Longitudinal Study, a five-wave panel survey conducted in 2011, 2013, 2015, 2018, and 2020, with 38,420 observations from 7,864 individuals aged 60 and older. We classified activities into three types: leisure-based individual interaction, community-based organisational interaction, and responsibility-driven caregiving interaction to capture the diversity of social interaction. Generalised estimating equation regression models were used to examine the associations between one- or two-wave-lagged social interaction and health outcomes (self-rated health, mental health, cognitive function, and diagnosed diseases). Random-effects estimation addressed individual-level heterogeneity. The 2SLS model was applied to examine the mutual causality relationship between interaction frequency and health, followed by a robustness test. Results Social interaction had a positive impact on elderly health, particularly in the medium- to long-term. One-wave-lagged interaction showed improved self-rated health (b=0.014, P <0.05), reduced mental distress (b=-0.232, P <0.01), and enhanced cognitive function (b=0.233, P <0.001) , with no effect on disease status. Leisure and community-based interactions significantly benefited physical and mental health, while responsibility-driven interactions improved cognition but increased mental distress. Interaction frequency was positively associated with health, with better access to facilities and public transport boosting interaction frequency. Living with children or a spouse, employment status and income level are also protective factors for health. Conclusion Active social interaction, regular participation in leisure activities, organized social activities, and informal social interactions have beneficial effects on health of older adults. Policies should prioritize supportive environments and age-friendly community renovations, while families and society should strengthen internal and external support systems to foster active and healthy aging. Social interaction Elderly health Interaction type Interaction frequency Figures Figure 1 Introduction China has entered a period of accelerated population aging, now home to the largest and fastest-growing aging population in the world [ 1 ]. Based on the findings of China’s seventh population census, the number of older adults aged 60 years and over reached 260 million, accounting for 18.7% of the total population [ 2 ]. This proportion is expected to rise to 40% by 2050, almost twice the global level [ 3 ]. Health is a critical determinant of successful aging [ 4 ]. The health of China’s elderly has been the subject of much concern. In 2022, nearly 180 million people aged 60 or older had at least one chronic disease [ 5 ] around 15 million were diagnosed with dementia [ 6 ]; over 42 million had a disability; and more than 30 million suffered from various mental diseases [ 7 ].Identifying the factors that influence elderly health is an essential first step in addressing these concerns. Among the social determinants of health, social interaction is an important modifiable factor [ 8 , 9 ] and an essential component of the global active ageing strategy [ 10 ]. It is a real-life activity that arises from association with one’s social ties, which are essential for strengthening social relationships, providing social support and fostering social integration [ 11 , 12 ]. For older adults, social interaction is particularly important in reducing risk factors and preventing illness. Adverse events such as bereavement, a drop in income or a loss of purpose following retirement increase older adults’ vulnerability to isolation and loneliness [ 13 , 14 ]. Without appropriate social interaction, these effects can accelerate physical and mental decline, leading to further social disengagement [ 15 , 16 ]. Global consensus has been reached on the importance of social interaction for active, healthy ageing [ 10 ]. Although health can also shape social interaction, studies accounting for multiple health factors have found that social interaction helps prevent the onset of diseases and functional disability [ 17 – 20 ], mental health disorder [ 21 , 22 ], cognitive impairment [ 20 , 23 , 24 ] and delayed mortality [ 25 , 26 ] among older adults. Research indicates social interaction may influence health outcomes by strengthening social identity [ 20 ]. Through social interaction, elderly individuals build social capital, which improves their health by providing emotional and material support [ 27 , 28 ], and promoting healthy behaviors through social influence [ 29 ]. These health-promoting effects may intensify over time [ 23 , 27 ], partly because people tend to evaluate their social relationships more positively and focus on nurturing more intimate ties (as opposed to weak or solely instrumental ties) as they grow older [ 30 ]. However, prior studies on the association between social interaction and health promotion have focused primarily on whether individuals engage in interaction, often overlooking the diversity of these interactions. Social interaction is a multifaceted concept, encompassing individuals, families, and communities, forming an integrated whole across these different levels [ 31 , 32 ]. The greater the diversity within these social networks, the more frequent the interactions tend to be [ 33 , 34 ]. However, current research tends to treat all forms of social interaction uniformly, neglecting the variations in type and frequency. Moreover, Chinese families demonstrate a feedback-based intergenerational relationship characterised by “nurturing” and “reciprocal nurturing,” with family-oriented social interactions playing a vital role in later life [ 35 , 36 ], shaping health outcomes in ways largely overlooked in existing research. Following these preceding studies, this study investigated the impacts of different types and frequencies of social interactions on the health of elderly adults using longitudinal survey data from China. The main contributions of this study are as follows. (1) It seeks to mitigate potential reverse causality and heterogeneity issues that may have influenced the mixed findings in prior research by employing the lagged variable method and random effects models. (2) The study explores the most efficient type and frequency of social interaction that can potentially promote health, considering the impact across different demographic groups to account for heterogeneity. (3) It reveals the net effect of these influences by addressing the possible endogenous issues. This study offers not only new directions for enhancing active ageing but also provides valuable insights into elderly health that can inform policy development in other Asian countries with similar family-oriented cultures. Method Sample This study utilises five waves of longitudinal data from the Chinese Health and Retirement Longitudinal Study (CHARLS) conducted by Peking University across representative regions of China in 2011, 2013, 2015, 2018 and 2020. The survey objects were individuals aged 45 and older. The baseline survey included about 10,000 households and 17,500 individuals, covering 150 county-level units and 450 village-level units [ 37 ]. CHARLS contained extensive data on demographics, family structure, household consumption, social interaction situation, subjective and objective health status and other related information. A total of 96,628 samples were collected across the five waves. This study focuses on individuals aged 60 and older from the baseline survey who remained in four follow-up waves, including 41,965 observations. After data cleaning and excluding those with missing key variables, 38,420 samples with complete repeated measurements were included. These samples represent 7,864 individuals, including 3,282 men and 4,402 women (Fig. 1). Variable Dependent variable Given that health is a multidimensional and broad concept, we examine four types of health outcomes: (1) self-rated health, (2) mental health, (3) cognitive ability and (4) diseases. Self-rated health Self-rated health (SRH) was measured using the question, “What do you think of your health? ” A five-point scale was used to categorise SRH as follows: excellent = 5, very good = 4, good = 3, poor = 2 and very poor = 1. For the regression model, SRH was recorded as a binary variable: 1 for excellent, very good and good; 0 for poor and very poor. Mental health Mental health was assessed using a 10-item version of the Centre for Epidemiologic Studies Depression Scale (CES-D), validated for use among elderly adults in China [ 38 ]. In CHARLS, respondents were asked, “How did you feel and behave last week?” Each item was rated on a four-point scale: rarely or none of the time, not much, sometimes and most of the time. The total score, ranging from 0 to 30, is the sum of all items, and the higher the score, the more serious the psychological distress of the individual and the poorer the mental health. Cognitive function In CHARLS, cognitive function was assessed using a simplified version of the Mini-Mental State Examination (MMSE) [ 39 ], which has been culturally adapted and validated for use among the Chinese elderly population, covering five domains: orientation, attention, calculation, memory and language [ 40 ]. The assessment included tasks such as serial subtraction, immediate and delayed word recall, orientation to time and place, sentence repetition and figure drawing [ 41 ]. Scores range from 0 to 21, with higher scores indicating better cognitive function. Diseases Disease status was determined based on the question, “Have you been diagnosed with any of the following conditions by a doctor?” The list includes 14 diseases: hypertension, dyslipidaemia, diabetes or high blood sugar, cancer or malignant tumours, lung disease, liver disease, heart disease, stroke, kidney disease, gastrointestinal disorders, mental or emotional conditions, memory-related diseases, arthritis/rheumatism and asthma. Each reported condition was coded as 1 and 0 otherwise. Independent variable Social interaction was the independent variable. Social interaction was measured with two dimensions, interaction type and interaction frequency [ 42 , 43 ], based on the theory of social capital. Interaction type In CHARLS, information on social interaction is obtained through the question, “Have you participated in the following social activity in the past month?” The listed activities include (a) interacting with a friend; (b) playing Mahjong, chess or playing cards; (c) helping family, friends or neighbours who do not live with you and did not pay for your help; (d) went to a sport, social or other kind of club; (e) participated in a community-related organisation; (f) done voluntary or charity work; (g) cared for a sick or disabled adult who does not live with you and who did not pay you for the help; (h) attended an educational or training course or (i) none of these. We drew upon social capital theory and categorised these activities into three types based on motivation, structure and function to capture the multidimensional nature of social interaction [ 9 , 44 , 45 ]. Type 1: Leisure-based individual interaction refers to self-initiated, interest-driven activities, which include (a), (b), (d) and (h). Type 2: Community-based organisational interaction involves structured participation within formal or semi-formal organisations, including (e) and (f). Type 3: Responsibility-driven caregiving interaction centres on caregiving, emotional exchange, and mutual support within kinship networks, including (c) and (g). In Asian cultures, particularly within the Chinese family structure, intergenerational caregiving is deeply embedded in familial norms and traditions. It is regarded as a crucial element of family continuity and the preservation of familial ties [ 36 , 46 , 47 ]. Previous studies have categorised grandchild care within broader constructs such as familial support roles or intergenerational interaction [ 47 , 48 ]. Accordingly, this study includes “caring for grandchildren” as an additional variable under responsibility-driven caregiving interaction. We constructed three binary variables corresponding to the three types of social interaction to facilitate the interpretation of regression results. A value of 1 was assigned if the respondent participated in at least one activity within a given category and 0 otherwise. Interaction frequency Interaction frequency was measured by the question, “How often in the last month have you done this?” Participation frequency for each activity was rated on a four-point scale: never (0), irregularly (1), almost every week (2) or almost daily (3). Covariates Key demographic and socioeconomic factors were controlled for in the analysis, including gender, age, education level, marital status, household income, employment status, insurance coverage, co-residence status (whether living with children or spouse), economic support (whether receiving financial support from children or grandchildren) and living area (whether residing in an urban area). In addition, the survey year (2011, 2013, 2015, 2018 and 2020) and geographic region (Eastern, Central, Western and Northeastern, as defined by the National Bureau of Statistics of China) were adjusted by including a series of binary (dummy) variables. Instrumental variable Based on existing research, a bidirectional causal relationship may exist between interaction frequency and health status, where an individual’s health influences their frequency of social interaction and social interaction [ 33 ], which also affects their health outcomes. This study employs an instrumental variable (IV) approach to address the potential endogeneity of interaction frequency. Two instrumental variables are employed: (1) the availability of chess and card rooms or activity centres for the elderly in the village or community and (2) the number of bus lines serving the village or community. These variables are assumed to affect the frequency of social interaction among older adults while remaining exogenous to their health status. Moreover, an over-identification test is conducted to assess the validity of the instruments because the number of instruments exceeds the number of endogenous variables. Statistical analysis Descriptive statistical analysis Descriptive statistical analysis was used to describe the health outcomes, social interaction status and other covariates of the elderly population based on the overall sample and the male and female subsamples. Regression analysis In generalised estimating equations regression analysis, the following model was estimated: $$\:{H}_{it}=\alpha\:+\beta\:{SI}_{it}+\gamma\:{H}_{it-1}+{X}_{it}\delta\:+{u}_{i}+{\epsilon\:}_{it}$$ In this model [ 49 ], i and t denote an individual and time(survey wave). H and SI indicate health outcomes and social interactions, and X indicates a vector of covariates. u i represents a set of time-invariant individual attributes, and ε it is an error term. Lagged values of the key variables were introduced to address reverse causality between social interaction and health [ 50 ]. One-wave-lagged health outcomes (H it−1 ) were included to adjust for prior health status. In addition, to reduce bias from potential reverse causation (i.e., health affecting social interaction), one-wave-lagged values of social interaction (SP it−1 ) were used in place of contemporaneous values. This model is referred to as Model LV1. For longer-term effects, a two-wave-lagged model (Model LV2) was estimated by replacing SP it−1 and H it−1 with SP it−2 and H it−2 . A random effects (RE) model was applied to Model LV1 (denoted as Model LV1 + RE), incorporating time-invariant characteristics u i to further account for unobserved individual heterogeneity. Two methodological notes apply. First, the RE model was applied only to LV1 due to the constraints of using lagged terms across five survey waves. Second, fixed-effects (FE) models were not employed because the Hausman test did not reject the null hypothesis that the individual effects ui are uncorrelated with the explanatory variables. All regression models were stratified by gender, age group (60–64, 65–69 and 70 + years), and social interaction type to examine heterogeneity in social interaction effects. The focus was on the estimated coefficient of social interaction (β). A significantly positive β, after adjusting for lagged health and covariates, would suggest a beneficial effect of social interaction on health. Two-stage OLS model As noted earlier, the potential endogeneity of social interaction frequency may bias the baseline model’s estimation results [ 51 ]. We employ an instrumental variable approach using a two-stage least squares (2SLS) to address this issue. The regression equation was set as follows: Stage I: \(\:{E}_{i}={\beta\:}_{1}{Z}_{i}+\phi\:{X}_{i}+{\omega\:}_{i}\) Stage II: \(\:{H}_{i}={\beta\:}_{3}{E}_{i}+{\beta\:}_{4}{S}_{i}+{X}_{i}+{}_{i}\) In the stage I model, Z i is the instrumental variable, X i is the other control variables, ω i is the random disturbance term and β 1 and ϕ 1 are the regression coefficient estimates, which reflect the influence of instrumental variables and other control variables on the interaction frequency. In the stage II regression model, E i is the predicted value of the stage I regression results, S i is the interaction type, X i is the other control variables, ε i is the random disturbance term and β 3 and β 4 are the regression coefficients reflecting the effects of the predicted values and other explanatory variables on health outcomes for the elderly. Result Descriptive Statistics Characteristics of the elderly population Table 1 summarises the key characteristics of the respondents. Among the 7,684 older adults who participated in five follow-up surveys, the average age was 65.76 years; 47.0% were male, and 80.2% were married. In terms of education, 55.3% had less than a primary school education, while 23.9% had completed junior high school or higher. Regarding socioeconomic status, 61.4% were employed, and 95.3% had basic health insurance. The mean annual household income was 23,530 Yuan. Additionally, 49.1% lived with their children and spouses, and 74.9% received financial support from family. Gender differences were observed across several variables; men were more likely to be employed and better educated. Table 1 Descriptive statistics of the sample in five follow-up surveys(N = 7,684) Variable Definition All Male Female Individual characteristic variable Age 65.765(7.862) 65.780(7.772) 65.751(7.941) Gender Male = 1,Female = 0 0.470(0.499) 1(0) 0(0) Marital status Married = 1, other = 0 0.802(0.399) 0.870(0.336) 0.742(0.438) Illiterate Below primary school = 1, other = 0 0.553(0.497) 0.374(0.484) 0.711(0.453) Elementary Primary education = 1, other = 0 0.209(0.406) 0.279(0.449) 0.146(0.353) Secondary Junior high school or above = 1, other = 0 0.239(0.426) 0.347(0.476) 0.143(0.350) Socioeconomic characteristic variable Income Annual household income(10,000 Yuan) 2.353(5.351) 2.452(5.565) 2.266(5.153) Employment status Employed = 1, unemployed = 0 0.614(0.487) 0.687(0.464) 0.548(0.498) Insured status Attend = 1,not attend = 0 0.953(0.231) 0.960(0.197) 0.947(0.223) Co-residence Live with children or spouse = 1,other = 0 0.491(0.500) 0.482(0.500) 0.498(0.500) Economic-support Received economic support from family (yes = 1,no = 0) 0.749(0.433) 0.733(0.433) 0.764(0.425) Living area Urban = 1, other = 0 0.668(0.471) 0.673(0.469) 0.663(0.473) East Live in eastern area = 1, other = 0 0.459(0.498) 0.459(0.498) 0.458(0.498) Central Live in central area = 1, other = 0 0.318(0.466) 0.316(0.465) 0.319(0.466) West Live in western area = 1, other = 0 0.162(0.368) 0.164(0.371) 0.160(0.366) North-east Live in north-east area = 1, other = 0 0.062(0.241) 0.060(0.237) 0.063(0.244) Health characteristic variable Self-related health Self-rated: good = 1, bad = 0 0.722(0.448) 0.764(0.425) 0.685(0.465) Mental health 0 – 30, a higher value means the higher the degree of psychological distress 8.627(6.365) 7.410(5.770) 9.716(6.669) Cognitive function 0–21,a higher value means better cognition 9.853(4.787) 11.158(4.191) 8.648(4.984) Diseases Diagnosed with diseases (no = 0,yes = 1) 0.800(0.400) 0.775(0.418) 0.822(0.383) Notes: Standard deviations are in parentheses. Health status of the elderly population Overall, 72.2% of the respondents reported good SRH, with a higher proportion among men (76.4%) than women (68.5%). In terms of mental health, the average psychological distress score was 8.63, with women reporting significantly higher distress (9.71) than men (7.41). The average cognitive function score was 9.85, with men scoring 11.16 and women 8.65. Regarding physical health, 80.0% of the elderly had been diagnosed with at least one chronic disease, with a slightly higher prevalence among women (Table 1 ). Social interaction status of the elderly population Table 2 shows the participation patterns across different types and frequencies of social interaction. Overall, 44.2% of older adults engaged in at least one type of activity, and 22.5% participated in two or more types. Leisure-based interaction was the most common, with 43.0% participating: 21.5% almost daily, 9.2% weekly and 12.3% irregularly. Women were more likely than men to engage daily. Community-based interaction was the least common, with a participation rate of 6.2%, slightly higher among men. Responsibility-driven interaction was reported by 42.1% of the respondents, with women showing higher and more frequent involvement. Regarding community infrastructure, 40.5% of respondents lived in areas equipped with chess and card rooms or other activity centres, and the average number of bus lines serving their communities was two. Table 2 Proportion (%) of social interaction types (N = 7,684) Respondents = 7684 All Male Female Observations = 38420 Participating in at least one activity 44.2 44.2 44.2 Participating in two or more types of activities 22.5 10.9 11.6 Social interaction type and frequency Leisure-based interaction 43.0 42.8 43.2 freq1: never 57.0 57.2 56.8 ferq2: irregularly 12.3 13.3 11.4 freq3: almost every week 9.2 10.3 8.2 freq4: almost daily 21.5 19.2 23.6 Community-based interaction 6.2 7.4 5.1 freq1: never 95.4 95.9 94.9 ferq2: irregularly 3.4 3.8 3.0 freq3: almost every week 1.7 2.1 1.3 freq4: almost daily 1.1 1.4 0.8 Responsibility-driven interaction 42.1 41.0 43.2 freq1: never 57.9 59.0 56.8 ferq2: irregularly 15.0 18.6 11.4 freq3: almost every week 16.5 13.6 19.4 freq4: almost daily 10.6 8.8 12.4 Community activity facilities Chess and card room/elderly activity left 40.5 41.1 39.9 Number of bus lines to village/community 2.0 2.0 2.0 Regression analysis Effect of social interaction on elderly health Table 3 presents the key results of regression analysis for each health outcome across LV1, LV2 and LV1 + RE models. Social interaction (SI) improved SRH, with a stronger effect in the LV2, meaning its positive impact increased over time. A significant negative association with psychological distress was observed in LV1 and LV1 + RE models, indicating better mental health among socially active individuals. SI also had a robust positive impact on cognitive function, with the largest effect in the LV1. However, diseases were insensitive to SI. The similarity between the LV1 and LV1 + RE model suggests minimal bias from individual-level heterogeneity. Table 3 Estimated impacts of social interaction on health outcomes a Model LV1 LV2 LV1 + RE Coef. SE Coef. SE Coef. SE Self-rated health 0.014* 0.006 0.033** 0.006 0.014** 0.006 Mental health -0.232** 0.081 -0.013 0.079 -0.231** 0.081 Cognitive function 0.233*** 0.053 0.097* 0.048 0.233*** 0.051 Disease 0.007 0.003 0.009 0.004 0.008 0.004 Notes: a Controlled for one-wave-lagged (for LV1 and LV1 + RE) or two-lagged (for LV2) wave-lagged health outcome, as well as covariates,***p < 0.001, **p < 0.01, *p < 0.05 Tables 4 and 5 provided more detailed results for health outcomes based on LV1 models, focusing on self-rated health, mental health and cognitive function. Table 4 highlights the heterogeneity of the effects of social interaction by gender and age group. SI had a greater positive impact on SRH for men than women and individuals aged 70 and above compared to those in other age groups. It also significantly reduced psychological distress, especially for women and the oldest group. For cognitive function, men benefited more than women, with the strongest effect observed in the 60 – 64 age group. Table 4 Estimated impacts of social interaction on health outcomes by gender and age group b Model Gender Age group Male Female 60–64 65–69 ≥ 70 Coef. SE Coef. SE Coef. SE Coef. SE Coef. SE Self-rated health 0.016* 0.009 0.013* 0.006 0.008 0.008 0.014 0.011 0.026* 0.012 Mental health -0.200* 0.105 -0.268** 0.122 -0.276** 0.109 -0.066 0.144 -0.337** 0.152 Cognitive function 0.205** 0.071 0.125* 0.063 0.215*** 0.006 0.163 0.094 0.041 0.098 Notes: b Based on LV1 models, controlled for one-wave-lagged health outcome, as well as covariates, ***p < 0.001, **p < 0.01, *p < 0.05 Table 5 Estimated impacts of social interaction types on the health outcomes of the elderly b Variable Self-rated health Mental health Cognitive function Model 1 Model 2 Model 3 Coef. SE Coef. SE Coef. SE Leisure-based interaction 0.029*** 0.006 -0.626*** 0.089 0.674*** 0.058 Community-based interaction 0.015*** 0.012 -0.244** 0.172 0.436*** 0.112 Responsibility-driven interaction 0.006 0.004 0.269** 0.09 0.235*** 0.058 Gender 0.034*** 0.007 -1.73*** 0.096 1.082*** 0.062 Age 0.016*** 0.004 0.225*** 0.056 -0.472*** 0.037 Marital status -0.015* 0.008 -0.864*** 0.119 0.458*** 0.077 Education level 0.027*** 0.003 -0.642*** 0.047 1.728*** 0.031 Employment status 0.121*** 0.007 -0.387*** 0.1 0.066 0.065 Insured status 0.024*** 0.006 -0.084 0.09 0.006 0.058 Income 0.018*** 0.002 -0.407*** 0.035 0.17*** 0.023 Co-residence -0.001 0.006 -0.366*** 0.06 -0.081 0.058 Economic-support 0.002 0.009 -0.117 0.124 0.198* 0.08 Living area -0.093*** 0.007 1.265*** 1.181 -1.088*** 0.066 Regions -0.012*** 0.003 0.425*** 0.044 -0.128*** 0.032 Notes: b Based on LV1 models, controlled for one-wave-lagged health outcome, ***p < 0.001, **p < 0.01, *p < 0.05 Table 5 compares the effects of different types of social interaction on elderly health. Leisure-based interactions provided the greatest health benefits, improving SRH, cognitive function and reducing psychological distress. Community-based interactions also showed positive health effects. Responsibility-driven interactions improved cognition but were associated with increased mental distress. Several individual characteristics revealed heterogeneity. Education and employment were linked to better health outcomes. However, urban residence was correlated with lower cognitive function and higher psychological distress, likely due to urban stressors [ 52 – 54 ]. Co-residence with children or a spouse helped reduce distress, highlighting the protective role of family support in promoting emotional well-being. Table 6 indicates that the frequency of SI has a significant positive effect on health. Specifically, for each 1-unit increase in leisure-based interaction frequency, a 6.1% higher likelihood of better SRH, a 27.5% reduction in mental distress, and a 24.7% improvement in cognitive function were observed. Community-based interaction showed similar effects, with a one-unit increase leading to a 5.4% higher likelihood of better SRH, a 20.7% reduction in mental distress, and a 20.4% improvement in cognition. However, a one-unit increase in responsibility-driven interaction frequency resulted in a 9.3% increase in mental distress. Table 6 Estimated impacts of social interaction frequency on the health outcomes of the elderly Variable Self-rated health Mental health Cognitive function Model 4 Model 5 Model 6 Coef. SE Coef. SE Coef. SE Leisure *frequency 0.061*** 0.009 -0.275*** 0.023 0.247*** 0.015 Community *frequency 0.054** 0.027 -0.204** 0.064 0.207*** 0.041 Responsibility *frequency 0.045 0.015 0.093** 0.034 0.018 0.025 Robustness test There may exist an endogenous relationship between social interaction frequency and health. To address this, we used instrumental variables to resolve the endogeneity issue. Table 7 presents the 2SRI estimation results. The instrumental variables had a significant positive effect on interaction frequency. In the stage I regression, the presence of chess and card rooms or elderly activity centres increased social interaction frequency by 3.5%, and more bus lines in villages or communities also raised participation in social interaction. In the stage II regression, after adding control variables, interaction frequency continued to have a positive effect on SRH, with a coefficient of 0.064, significantly higher than the basic regression result (Model 4). Addressing the endogeneity of interaction frequency strengthened its positive impact on SRH. Table 7 Instrumental variable estimation results c Variable Stage I Stage II Coef. SE Coef. SE Social Interaction frequency 0.064** 0.019 Acti-card 0.035** 0.017 Bus line 0.029** 0.001 Region characteristics controlled F 10.19 P 0.113 Pseudo R 2 0.0481 Notes: c Based on the two-stage OLS model, ***p < 0.001, **p < 0.01, *p < 0.05 Discussion Promoting elderly health is essential for advancing healthy ageing. Using longitudinal data from CHARLS conducted from 2011 to 2020, this study investigates the impact of diverse social interactions on the health of older adults, focusing on the type and frequency of interactions. The findings show social interaction had a positive impact on health outcomes, particularly in the medium to long term. Our research aligns with existing literature on the relationship between social interaction and health, highlighting the critical role of social interaction in promoting elderly well-being. Our study presents four noteworthy findings. Impact of social interaction on health varies across different types The most positive effects were observed for leisure-based interactions, such as interacting with friends, playing Mahjong, chess, or cards, performing square dancing, or attending social clubs. Playing Mahjong and cards, which represent transitional fun in China, can foster friendships and positively impact health [ 55 , 56 ]. A Chinese study also shows these activities protect against the negative effects of stressful life events [ 57 ]. People may be willing to talk about problems they encounter and try to seek emotional support while playing with friends or relatives [ 58 ]. Existing research suggests higher engagement in such intellectually stimulating activities exercises reaction ability and memory, delaying deterioration [ 59 – 61 ]. Square dancing, another popular activity among older adults, combines movement with music in a group setting, delivering physical and psychological benefits [ 62 , 63 ]. Its inherently social nature, with frequent interactions before and after dancing, fosters a strong sense of community and belonging, further enhancing emotional well-being [ 64 ]. Community-based interaction had the second greatest impact. Active community engagement is linked to higher life satisfaction and lower depression [ 65 , 66 ], helping older adults feel connected and alleviating isolation often caused by retirement or living alone [ 67 ]. Altruistic activities, such as voluntary or charity work, also enhance well-being and improve health [ 68 ]. Participation in such activities is uncommon among older adults in China despite the beneficial effects of organised social activities on health. In this study, community-based interaction had the lowest participation rate (only 6.4%), highlighting that civil society in China is still developing. Most community-organised activities for older adults are cultural or voluntary events managed by residential committees. Limited activity types, lack of effective promotion and the narrow scope of activities may restrict participation. A Chinese qualitative study has reported that while many older adults are willing to engage in community life, barriers such as physical limitations, social exclusion, age discrimination, limited social connections, lack of information and low socioeconomic status hinder participation [ 69 ]. Consequently, the elite group of older adults tends to be the most socially active ones, while those with lower status have limited participation in organised activities. Notably, responsibility-driven interactions, such as caregiving and informal support, improved health and cognition but also increased mental distress. This dual effect can be explained by the cultural and social contexts in which these interactions occur. First, Chinese traditional values promote “family culture”, where the family is viewed as a crucial social unit [ 36 , 46 ]. The multi-generational family structure, with close intergenerational ties, includes grandparental care as a vital component [ 7 , 35 ]. Older adults often aspire to the ideal of “children and grandchildren under one roof” and take on significant responsibilities in caring for their grandchildren and other family members. While this step enhances emotional connections and well-being [ 70 ], long-term caregiving can also impose physical and mental strain on elderly individuals. Furthermore, with demographic changes and urbanisation, the outflow of the younger population and increasing work pressures have caused more elderly individuals to assume the role of “multiple caregivers” responsible for grandchildren and adult children [ 71 ]. This shift in roles leads to greater time and energy demands, resulting in fatigue, emotional distress and psychological pressure. This finding breaks through the current research that only discussed the impact of traditional social interaction and further discovers the impact of responsibility-driven social interaction on elderly health. This innovative perspective not only enriches the theoretical framework of social interaction but also provides empirical support for understanding the comprehensive social interaction system of the elderly within the individual–family–community framework. Endogeneity of social interaction effect on health status Social interaction had a greater positive effect on self-rated health and cognitive function for men, while women experienced stronger improvements in mental health. The difference may reflect the gender gap because women often bear heavier household responsibilities and routine domestic tasks [ 72 ], making their mental health more sensitive to social interaction outside the home. Additionally, social interaction had a more pronounced preventive impact on health for individuals aged 70 and above, given their greater vulnerability to isolation, mental distress and overall health decline associated with ageing [ 13 , 67 ]. Social interaction also plays a crucial role in providing emotional support, reducing loneliness and depression and stimulating cognitive function. Interaction frequency played a positive role in the health of the elderly population Frequent social interaction positively affects the health of the elderly. Increased participation in leisure- and community-based interaction is associated with improved health. Additionally, this study confirms the causal relationship between interaction frequency and health. Better access to facilities like chess rooms, activity centres, and public transport significantly increased elderly interaction, indicating that social interaction is not purely a personal choice but also shaped by external conditions. Previous research has demonstrated that the availability and accessibility of community spaces, public parks and organised recreational activities support active and socially engaged lifestyles [ 73 , 74 ]. Access to these facilities, along with convenient transportation, directly determines whether and how much older adults can engage in social interactions. Accessible community spaces and convenient transport create “opportunity windows” for the elderly to step out of their homes, providing essential support for their physical and mental health. Role of family support Another finding of this study is the protective role of family support in reducing mental distress. Living with children or a spouse was associated with lower distress levels, consistent with previous research highlighting family support as a crucial buffer against the mental health challenges of ageing [ 75 ]. In contrast, elderly individuals in Western countries often derive greater mental health benefits from friendships than from family support [ 30 , 76 ]. However, in Asian countries, where family-oriented cultures are emphasised, older adults living with family members receive daily support and companionship, which significantly enhances their practical and emotional well-being [ 77 ]. Therefore, fostering intergenerational communication and reinforcing family support can help alleviate emotional stress, promote “ageing in company” with children and improve overall health outcomes. This paper explores the relationship between social interaction and elderly health, providing practical insights for promoting healthy and active ageing. First, the Chinese government should prioritise the development of community support systems by improving infrastructure, such as activity centres cultural and sports facilities, while also enhancing age-friendly renovations and supporting elderly associations. Second, a collaborative support system involving families and communities is essential. For instance, community leisure centres could integrate children’s activity rooms to better support elderly individuals with intergenerational caregiving roles. Furthermore, enhancing community-based elderly care services and fostering an environment conducive to social interaction can help older adults realise their values, strengthen their sense of belonging and improve their health and social engagement. Conclusion Active social interaction, regular participation in leisure activities, and organised and informal social networks provide substantial health benefits for older adults in China. Clarifying the effect of different types of social interaction on physical and mental health may contribute to insights into the development of policies and interventions to promote elderly interaction. Policies should prioritise creating supportive environments and enhancing age-friendly renovations in communities. Families and society should emphasise elderly social interaction and strengthen internal and external support systems. This collaborative approach will effectively integrate resources to achieve active and healthy ageing. Limitation This study has some limitations. First, it examines how lagged social interaction (by one or two periods) affected health outcomes in follow-up years while overlooking the impact of changes in social interaction over time on health. Second, not all dimensions of social interaction were considered due to the study’s scope and data constraints. Instead, the study focused solely on the type and frequency of interactions, emphasising their density and scale while neglecting the potential effects of interaction depth. Third, the study centres on the causal link between social interaction and elderly health without exploring the underlying mechanisms or pathways, which calls for further investigation in future research. Abbreviations CHARLS Chinese Health and Retirement Longitudinal Study SRH Self-rated health CES-D Center for Epidemiologic Studies Depression Scale MMSE Mini-Mental State Examination SI Social interaction 2SLS Two-stage least squares LV Lagged variable RE Random effects Declarations Ethics approval and consent to participate The CHARLS dataset is publicly available, and the study protocol was approved by Peking University’s Ethical Review Committee. Informed consent was obtained from all participants by the original CHARLS research team. The analysis of publicly available data was exempted by Research Ethics Committee of Wuhan University, as this study involved secondary analysis of anonymized data, no additional ethical clearance was required. This study was conducted in accordance with the ethical principles of the Declaration of Helsinki. Consent for publication Not applicable. Availability of data and materials The data used in this study are publicly available and can be accessed from the China Health and Retirement Longitudinal Study (CHARLS) dataset, which is available at https://charls.pku.edu.cn/. Users are required to apply for access under the name of an institution. Author Contributions LL designed this study. LL, YX supervised the data screening and quality control. LL analysed data and drafted the manuscript. YX, ZS,WR, LZ offered supervision and revised the manuscript. All authors read and approved the final manuscript. Competing interests The authors declare that they have no competing interests. Funding This study was funded by the National Natural Science Foundation of China (Grant No: 72474162). The information, conclusions, and opinions expressed in this article are those of the authors and no endorsement by the National Natural Science Foundation of China is intended or should be inferred. References Lobanov RS, He Q, Chen Y, Liu Y, Wu Y, Liu Y, Venkatraman T, French E, Curry N, Hemmings N, Bandosz P, Chan WK, Liao J, Brunner EJ. Growing old in China in socioeconomic and epidemiological context: Systematic review of social care policy for older people. BMC Public Health. 2023;23(1):1272. 10.1186/s12889-023-15583-1 . China National Bureau of Statistics. 2021. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6637688","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":475353516,"identity":"85370d9c-f985-4253-95cd-8e9e9b8c9755","order_by":0,"name":"Linbin Luo","email":"","orcid":"","institution":"School of Political Science and Public Administration, Wuhan University, Wuhan Hubei, China","correspondingAuthor":false,"prefix":"","firstName":"Linbin","middleName":"","lastName":"Luo","suffix":""},{"id":475353519,"identity":"49889aa7-159d-4e05-8172-7ae97e054057","order_by":1,"name":"Yiqing Xing","email":"","orcid":"","institution":"School of Political Science and Public Administration, Wuhan University, Wuhan Hubei, China","correspondingAuthor":false,"prefix":"","firstName":"Yiqing","middleName":"","lastName":"Xing","suffix":""},{"id":475353520,"identity":"4da93ce0-c04d-421a-89a3-72bd3aff910a","order_by":2,"name":"Zhao Shang","email":"","orcid":"","institution":"School of Political Science and Public Administration, Wuhan University, Wuhan Hubei, China","correspondingAuthor":false,"prefix":"","firstName":"Zhao","middleName":"","lastName":"Shang","suffix":""},{"id":475353522,"identity":"db240631-509f-4b1e-9335-37154eb3cd44","order_by":3,"name":"Weicun Ren","email":"","orcid":"","institution":"School of Political Science and Public Administration, Wuhan University, Wuhan Hubei, China","correspondingAuthor":false,"prefix":"","firstName":"Weicun","middleName":"","lastName":"Ren","suffix":""},{"id":475353524,"identity":"cf8c96d8-916e-46b0-9fe5-a7814b7ba89f","order_by":4,"name":"Liang Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIiWNgGAWjYBAC+xlg6gADAztjA5Bhw2AA4vPg0cII18IM1pJGkhYw4zBhLczSzc8efqm5k7idmbnxc8Gv84nbJRIYH7xtY5A3x6GFTeaYubHMsWeJO5sZm6Vn9t1O3DkjgdlwbhuD4c4G7Fp4JBLMpCUbDiduOMzYIM3bczt3w40ENmneNoYEgwPYtUhIpH+DaWn+zdtzDqSF/Tc+LQYSOWaSHyFa2qR5fhwA28JMQEuZNMOxw8YgLda8Dcn1G848bJacc07CcAMOLfYz0rdJ/qg5LLvhePvj2zx/7IwNjicf/PCmzEYely0gwAyPBcY2MNkA8iRu9SAlP+DMP3gVjoJRMApGwQgFAHhdY6Gh04MuAAAAAElFTkSuQmCC","orcid":"","institution":"School of Political Science and Public Administration, Wuhan University, Wuhan Hubei, China","correspondingAuthor":true,"prefix":"","firstName":"Liang","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-05-11 05:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6637688/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6637688/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12877-025-06408-4","type":"published","date":"2025-09-26T15:57:37+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85724526,"identity":"b2f08229-7d1f-42d5-9837-c3c3f516e9d5","added_by":"auto","created_at":"2025-07-01 06:23:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":148603,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram for participants included in the study\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6637688/v1/8e8a8131fd0b4e09c8167055.png"},{"id":92430482,"identity":"828d9fc7-01d1-4734-83f6-d504b10e19d1","added_by":"auto","created_at":"2025-09-29 16:05:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1425723,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6637688/v1/1c7f0953-9f39-479d-a704-9a6f45bcd4f1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of diversified social interaction on elderly health in China: a longitudinal analysis based on interaction type and frequency","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChina has entered a period of accelerated population aging, now home to the largest and fastest-growing aging population in the world [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Based on the findings of China\u0026rsquo;s seventh population census, the number of older adults aged 60 years and over reached 260\u0026nbsp;million, accounting for 18.7% of the total population [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This proportion is expected to rise to 40% by 2050, almost twice the global level [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Health is a critical determinant of successful aging [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The health of China\u0026rsquo;s elderly has been the subject of much concern. In 2022, nearly 180\u0026nbsp;million people aged 60 or older had at least one chronic disease [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] around 15\u0026nbsp;million were diagnosed with dementia [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]; over 42\u0026nbsp;million had a disability; and more than 30\u0026nbsp;million suffered from various mental diseases [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].Identifying the factors that influence elderly health is an essential first step in addressing these concerns.\u003c/p\u003e \u003cp\u003eAmong the social determinants of health, social interaction is an important modifiable factor [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and an essential component of the global active ageing strategy [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. It is a real-life activity that arises from association with one\u0026rsquo;s social ties, which are essential for strengthening social relationships, providing social support and fostering social integration [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. For older adults, social interaction is particularly important in reducing risk factors and preventing illness. Adverse events such as bereavement, a drop in income or a loss of purpose following retirement increase older adults\u0026rsquo; vulnerability to isolation and loneliness [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Without appropriate social interaction, these effects can accelerate physical and mental decline, leading to further social disengagement [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGlobal consensus has been reached on the importance of social interaction for active, healthy ageing [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Although health can also shape social interaction, studies accounting for multiple health factors have found that social interaction helps prevent the onset of diseases and functional disability [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], mental health disorder [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], cognitive impairment [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] and delayed mortality [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] among older adults. Research indicates social interaction may influence health outcomes by strengthening social identity [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Through social interaction, elderly individuals build social capital, which improves their health by providing emotional and material support [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and promoting healthy behaviors through social influence [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These health-promoting effects may intensify over time [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], partly because people tend to evaluate their social relationships more positively and focus on nurturing more intimate ties (as opposed to weak or solely instrumental ties) as they grow older [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, prior studies on the association between social interaction and health promotion have focused primarily on whether individuals engage in interaction, often overlooking the diversity of these interactions. Social interaction is a multifaceted concept, encompassing individuals, families, and communities, forming an integrated whole across these different levels [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The greater the diversity within these social networks, the more frequent the interactions tend to be [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, current research tends to treat all forms of social interaction uniformly, neglecting the variations in type and frequency. Moreover, Chinese families demonstrate a feedback-based intergenerational relationship characterised by \u0026ldquo;nurturing\u0026rdquo; and \u0026ldquo;reciprocal nurturing,\u0026rdquo; with family-oriented social interactions playing a vital role in later life [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], shaping health outcomes in ways largely overlooked in existing research.\u003c/p\u003e \u003cp\u003eFollowing these preceding studies, this study investigated the impacts of different types and frequencies of social interactions on the health of elderly adults using longitudinal survey data from China. The main contributions of this study are as follows. (1) It seeks to mitigate potential reverse causality and heterogeneity issues that may have influenced the mixed findings in prior research by employing the lagged variable method and random effects models. (2) The study explores the most efficient type and frequency of social interaction that can potentially promote health, considering the impact across different demographic groups to account for heterogeneity. (3) It reveals the net effect of these influences by addressing the possible endogenous issues. This study offers not only new directions for enhancing active ageing but also provides valuable insights into elderly health that can inform policy development in other Asian countries with similar family-oriented cultures.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample\u003c/h2\u003e \u003cp\u003eThis study utilises five waves of longitudinal data from the Chinese Health and Retirement Longitudinal Study (CHARLS) conducted by Peking University across representative regions of China in 2011, 2013, 2015, 2018 and 2020. The survey objects were individuals aged 45 and older. The baseline survey included about 10,000 households and 17,500 individuals, covering 150 county-level units and 450 village-level units [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. CHARLS contained extensive data on demographics, family structure, household consumption, social interaction situation, subjective and objective health status and other related information.\u003c/p\u003e \u003cp\u003eA total of 96,628 samples were collected across the five waves. This study focuses on individuals aged 60 and older from the baseline survey who remained in four follow-up waves, including 41,965 observations. After data cleaning and excluding those with missing key variables, 38,420 samples with complete repeated measurements were included. These samples represent 7,864 individuals, including 3,282 men and 4,402 women (Fig.\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVariable\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDependent variable\u003c/h2\u003e \u003cp\u003eGiven that health is a multidimensional and broad concept, we examine four types of health outcomes: (1) self-rated health, (2) mental health, (3) cognitive ability and (4) diseases.\u003c/p\u003e \u003cp\u003eSelf-rated health\u003c/p\u003e \u003cp\u003eSelf-rated health (SRH) was measured using the question, \u0026ldquo;What do you think of your health? \u0026rdquo; A five-point scale was used to categorise SRH as follows: excellent\u0026thinsp;=\u0026thinsp;5, very good\u0026thinsp;=\u0026thinsp;4, good\u0026thinsp;=\u0026thinsp;3, poor\u0026thinsp;=\u0026thinsp;2 and very poor\u0026thinsp;=\u0026thinsp;1. For the regression model, SRH was recorded as a binary variable: 1 for excellent, very good and good; 0 for poor and very poor.\u003c/p\u003e \u003cp\u003eMental health\u003c/p\u003e \u003cp\u003eMental health was assessed using a 10-item version of the Centre for Epidemiologic Studies Depression Scale (CES-D), validated for use among elderly adults in China [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In CHARLS, respondents were asked, \u0026ldquo;How did you feel and behave last week?\u0026rdquo; Each item was rated on a four-point scale: rarely or none of the time, not much, sometimes and most of the time. The total score, ranging from 0 to 30, is the sum of all items, and the higher the score, the more serious the psychological distress of the individual and the poorer the mental health.\u003c/p\u003e \u003cp\u003eCognitive function\u003c/p\u003e \u003cp\u003eIn CHARLS, cognitive function was assessed using a simplified version of the Mini-Mental State Examination (MMSE) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], which has been culturally adapted and validated for use among the Chinese elderly population, covering five domains: orientation, attention, calculation, memory and language [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The assessment included tasks such as serial subtraction, immediate and delayed word recall, orientation to time and place, sentence repetition and figure drawing [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Scores range from 0 to 21, with higher scores indicating better cognitive function.\u003c/p\u003e \u003cp\u003eDiseases\u003c/p\u003e \u003cp\u003eDisease status was determined based on the question, \u0026ldquo;Have you been diagnosed with any of the following conditions by a doctor?\u0026rdquo; The list includes 14 diseases: hypertension, dyslipidaemia, diabetes or high blood sugar, cancer or malignant tumours, lung disease, liver disease, heart disease, stroke, kidney disease, gastrointestinal disorders, mental or emotional conditions, memory-related diseases, arthritis/rheumatism and asthma. Each reported condition was coded as 1 and 0 otherwise.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIndependent variable\u003c/h3\u003e\n\u003cp\u003eSocial interaction was the independent variable. Social interaction was measured with two dimensions, interaction type and interaction frequency [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], based on the theory of social capital.\u003c/p\u003e \u003cp\u003eInteraction type\u003c/p\u003e \u003cp\u003eIn CHARLS, information on social interaction is obtained through the question, \u0026ldquo;Have you participated in the following social activity in the past month?\u0026rdquo; The listed activities include (a) interacting with a friend; (b) playing Mahjong, chess or playing cards; (c) helping family, friends or neighbours who do not live with you and did not pay for your help; (d) went to a sport, social or other kind of club; (e) participated in a community-related organisation; (f) done voluntary or charity work; (g) cared for a sick or disabled adult who does not live with you and who did not pay you for the help; (h) attended an educational or training course or (i) none of these.\u003c/p\u003e \u003cp\u003eWe drew upon social capital theory and categorised these activities into three types based on motivation, structure and function to capture the multidimensional nature of social interaction [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. \u003cb\u003eType 1: Leisure-based individual interaction\u003c/b\u003e refers to self-initiated, interest-driven activities, which include (a), (b), (d) and (h). \u003cb\u003eType 2: Community-based organisational interaction\u003c/b\u003e involves structured participation within formal or semi-formal organisations, including (e) and (f). \u003cb\u003eType 3: Responsibility-driven caregiving interaction\u003c/b\u003e centres on caregiving, emotional exchange, and mutual support within kinship networks, including (c) and (g). In Asian cultures, particularly within the Chinese family structure, intergenerational caregiving is deeply embedded in familial norms and traditions. It is regarded as a crucial element of family continuity and the preservation of familial ties [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Previous studies have categorised grandchild care within broader constructs such as familial support roles or intergenerational interaction [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Accordingly, this study includes \u0026ldquo;caring for grandchildren\u0026rdquo; as an additional variable under responsibility-driven caregiving interaction.\u003c/p\u003e \u003cp\u003eWe constructed three binary variables corresponding to the three types of social interaction to facilitate the interpretation of regression results. A value of 1 was assigned if the respondent participated in at least one activity within a given category and 0 otherwise.\u003c/p\u003e \u003cp\u003eInteraction frequency\u003c/p\u003e \u003cp\u003eInteraction frequency was measured by the question, \u0026ldquo;How often in the last month have you done this?\u0026rdquo; Participation frequency for each activity was rated on a four-point scale: never (0), irregularly (1), almost every week (2) or almost daily (3).\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eKey demographic and socioeconomic factors were controlled for in the analysis, including gender, age, education level, marital status, household income, employment status, insurance coverage, co-residence status (whether living with children or spouse), economic support (whether receiving financial support from children or grandchildren) and living area (whether residing in an urban area). In addition, the survey year (2011, 2013, 2015, 2018 and 2020) and geographic region (Eastern, Central, Western and Northeastern, as defined by the National Bureau of Statistics of China) were adjusted by including a series of binary (dummy) variables.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eInstrumental variable\u003c/h2\u003e \u003cp\u003eBased on existing research, a bidirectional causal relationship may exist between interaction frequency and health status, where an individual\u0026rsquo;s health influences their frequency of social interaction and social interaction [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], which also affects their health outcomes. This study employs an instrumental variable (IV) approach to address the potential endogeneity of interaction frequency.\u003c/p\u003e \u003cp\u003eTwo instrumental variables are employed: (1) the availability of chess and card rooms or activity centres for the elderly in the village or community and (2) the number of bus lines serving the village or community. These variables are assumed to affect the frequency of social interaction among older adults while remaining exogenous to their health status. Moreover, an over-identification test is conducted to assess the validity of the instruments because the number of instruments exceeds the number of endogenous variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistical analysis\u003c/p\u003e \u003cp\u003eDescriptive statistical analysis was used to describe the health outcomes, social interaction status and other covariates of the elderly population based on the overall sample and the male and female subsamples.\u003c/p\u003e \u003cp\u003eRegression analysis\u003c/p\u003e \u003cp\u003eIn generalised estimating equations regression analysis, the following model was estimated:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{H}_{it}=\\alpha\\:+\\beta\\:{SI}_{it}+\\gamma\\:{H}_{it-1}+{X}_{it}\\delta\\:+{u}_{i}+{\\epsilon\\:}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn this model [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], i and t denote an individual and time(survey wave). H and SI indicate health outcomes and social interactions, and X indicates a vector of covariates. u\u003csub\u003ei\u003c/sub\u003e represents a set of time-invariant individual attributes, and ε\u003csub\u003eit\u003c/sub\u003e is an error term.\u003c/p\u003e \u003cp\u003eLagged values of the key variables were introduced to address reverse causality between social interaction and health [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. One-wave-lagged health outcomes (H\u003csub\u003eit\u0026minus;1\u003c/sub\u003e) were included to adjust for prior health status. In addition, to reduce bias from potential reverse causation (i.e., health affecting social interaction), one-wave-lagged values of social interaction (SP\u003csub\u003eit\u0026minus;1\u003c/sub\u003e) were used in place of contemporaneous values. This model is referred to as Model LV1.\u003c/p\u003e \u003cp\u003eFor longer-term effects, a two-wave-lagged model (Model LV2) was estimated by replacing SP\u003csub\u003eit\u0026minus;1\u003c/sub\u003e and H\u003csub\u003eit\u0026minus;1\u003c/sub\u003e with SP\u003csub\u003eit\u0026minus;2\u003c/sub\u003e and H\u003csub\u003eit\u0026minus;2\u003c/sub\u003e. A random effects (RE) model was applied to Model LV1 (denoted as Model LV1\u0026thinsp;+\u0026thinsp;RE), incorporating time-invariant characteristics u\u003csub\u003ei\u003c/sub\u003e to further account for unobserved individual heterogeneity. Two methodological notes apply. First, the RE model was applied only to LV1 due to the constraints of using lagged terms across five survey waves. Second, fixed-effects (FE) models were not employed because the Hausman test did not reject the null hypothesis that the individual effects ui are uncorrelated with the explanatory variables.\u003c/p\u003e \u003cp\u003eAll regression models were stratified by gender, age group (60\u0026ndash;64, 65\u0026ndash;69 and 70\u0026thinsp;+\u0026thinsp;years), and social interaction type to examine heterogeneity in social interaction effects. The focus was on the estimated coefficient of social interaction (β). A significantly positive β, after adjusting for lagged health and covariates, would suggest a beneficial effect of social interaction on health.\u003c/p\u003e \u003cp\u003eTwo-stage OLS model\u003c/p\u003e \u003cp\u003eAs noted earlier, the potential endogeneity of social interaction frequency may bias the baseline model\u0026rsquo;s estimation results [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. We employ an instrumental variable approach using a two-stage least squares (2SLS) to address this issue. The regression equation was set as follows:\u003c/p\u003e \u003cp\u003eStage I: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{E}_{i}={\\beta\\:}_{1}{Z}_{i}+\\phi\\:{X}_{i}+{\\omega\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eStage II: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{H}_{i}={\\beta\\:}_{3}{E}_{i}+{\\beta\\:}_{4}{S}_{i}+{X}_{i}+{}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eIn the stage I model, Z\u003csub\u003ei\u003c/sub\u003e is the instrumental variable, X\u003csub\u003ei\u003c/sub\u003e is the other control variables, ω\u003csub\u003ei\u003c/sub\u003e is the random disturbance term and β\u003csub\u003e1\u003c/sub\u003e and ϕ\u003csub\u003e1\u003c/sub\u003e are the regression coefficient estimates, which reflect the influence of instrumental variables and other control variables on the interaction frequency. In the stage II regression model, E\u003csub\u003ei\u003c/sub\u003e is the predicted value of the stage I regression results, S\u003csub\u003ei\u003c/sub\u003e is the interaction type, X\u003csub\u003ei\u003c/sub\u003e is the other control variables, ε\u003csub\u003ei\u003c/sub\u003e is the random disturbance term and β\u003csub\u003e3\u003c/sub\u003e and β\u003csub\u003e4\u003c/sub\u003e are the regression coefficients reflecting the effects of the predicted values and other explanatory variables on health outcomes for the elderly.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Statistics\u003c/h2\u003e \u003cp\u003eCharacteristics of the elderly population\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarises the key characteristics of the respondents. Among the 7,684 older adults who participated in five follow-up surveys, the average age was 65.76 years; 47.0% were male, and 80.2% were married. In terms of education, 55.3% had less than a primary school education, while 23.9% had completed junior high school or higher. Regarding socioeconomic status, 61.4% were employed, and 95.3% had basic health insurance. The mean annual household income was 23,530 Yuan. Additionally, 49.1% lived with their children and spouses, and 74.9% received financial support from family. Gender differences were observed across several variables; men were more likely to be employed and better educated.\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 the sample in five follow-up surveys(N\u0026thinsp;=\u0026thinsp;7,684)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eIndividual characteristic variable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.765(7.862)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.780(7.772)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65.751(7.941)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u0026thinsp;=\u0026thinsp;1,Female\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.470(0.499)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u0026thinsp;=\u0026thinsp;1, other\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.802(0.399)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.870(0.336)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.742(0.438)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBelow primary school\u0026thinsp;=\u0026thinsp;1, other\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.553(0.497)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.374(0.484)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.711(0.453)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElementary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary education\u0026thinsp;=\u0026thinsp;1, other\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.209(0.406)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.279(0.449)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.146(0.353)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJunior high school or above =\u0026thinsp;1, other\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.239(0.426)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.347(0.476)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.143(0.350)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eSocioeconomic characteristic variable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual household income(10,000 Yuan)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.353(5.351)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.452(5.565)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.266(5.153)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployed\u0026thinsp;=\u0026thinsp;1, unemployed\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.614(0.487)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.687(0.464)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.548(0.498)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsured status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAttend\u0026thinsp;=\u0026thinsp;1,not attend\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.953(0.231)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.960(0.197)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.947(0.223)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo-residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLive with children or spouse\u0026thinsp;=\u0026thinsp;1,other\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.491(0.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.482(0.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.498(0.500)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic-support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReceived economic support from family (yes\u0026thinsp;=\u0026thinsp;1,no\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.749(0.433)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.733(0.433)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.764(0.425)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u0026thinsp;=\u0026thinsp;1, other\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.668(0.471)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.673(0.469)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.663(0.473)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLive in eastern area\u0026thinsp;=\u0026thinsp;1, other\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.459(0.498)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.459(0.498)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.458(0.498)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLive in central area\u0026thinsp;=\u0026thinsp;1, other\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.318(0.466)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.316(0.465)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.319(0.466)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLive in western area\u0026thinsp;=\u0026thinsp;1, other\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.162(0.368)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.164(0.371)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.160(0.366)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth-east\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLive in north-east area\u0026thinsp;=\u0026thinsp;1, other\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.062(0.241)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060(0.237)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.063(0.244)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eHealth characteristic variable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-related health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-rated: good\u0026thinsp;=\u0026thinsp;1, bad\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.722(0.448)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.764(0.425)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.685(0.465)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 \u003cspan fontcategory=\"NonProportional\" class=\"\" name=\"Emphasis\"\u003e\u0026ndash;\u003c/span\u003e 30, a higher value means the higher the degree of psychological distress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.627(6.365)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.410(5.770)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.716(6.669)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;21,a higher value means better cognition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.853(4.787)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.158(4.191)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.648(4.984)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiagnosed with diseases (no\u0026thinsp;=\u0026thinsp;0,yes\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.800(0.400)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.775(0.418)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.822(0.383)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: Standard deviations are in parentheses.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHealth status of the elderly population\u003c/p\u003e \u003cp\u003eOverall, 72.2% of the respondents reported good SRH, with a higher proportion among men (76.4%) than women (68.5%). In terms of mental health, the average psychological distress score was 8.63, with women reporting significantly higher distress (9.71) than men (7.41). The average cognitive function score was 9.85, with men scoring 11.16 and women 8.65. Regarding physical health, 80.0% of the elderly had been diagnosed with at least one chronic disease, with a slightly higher prevalence among women (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSocial interaction status of the elderly population\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the participation patterns across different types and frequencies of social interaction. Overall, 44.2% of older adults engaged in at least one type of activity, and 22.5% participated in two or more types. Leisure-based interaction was the most common, with 43.0% participating: 21.5% almost daily, 9.2% weekly and 12.3% irregularly. Women were more likely than men to engage daily. Community-based interaction was the least common, with a participation rate of 6.2%, slightly higher among men. Responsibility-driven interaction was reported by 42.1% of the respondents, with women showing higher and more frequent involvement. Regarding community infrastructure, 40.5% of respondents lived in areas equipped with chess and card rooms or other activity centres, and the average number of bus lines serving their communities was two.\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\u003eProportion (%) of social interaction types (N\u0026thinsp;=\u0026thinsp;7,684)\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\" colname=\"c1\"\u003e \u003cp\u003eRespondents\u0026thinsp;=\u0026thinsp;7684\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u0026thinsp;=\u0026thinsp;38420\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipating in at least one activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipating in two or more types of activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eSocial interaction type and frequency\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeisure-based interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efreq1: never\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eferq2: irregularly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efreq3: almost every week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efreq4: almost daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity-based interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efreq1: never\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eferq2: irregularly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efreq3: almost every week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efreq4: almost daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponsibility-driven interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efreq1: never\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eferq2: irregularly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efreq3: almost every week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efreq4: almost daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eCommunity activity facilities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChess and card room/elderly activity left\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of bus lines to village/community\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegression analysis\u003c/p\u003e \u003cp\u003eEffect of social interaction on elderly health\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the key results of regression analysis for each health outcome across LV1, LV2 and LV1\u0026thinsp;+\u0026thinsp;RE models. Social interaction (SI) improved SRH, with a stronger effect in the LV2, meaning its positive impact increased over time. A significant negative association with psychological distress was observed in LV1 and LV1\u0026thinsp;+\u0026thinsp;RE models, indicating better mental health among socially active individuals. SI also had a robust positive impact on cognitive function, with the largest effect in the LV1. However, diseases were insensitive to SI. The similarity between the LV1 and LV1\u0026thinsp;+\u0026thinsp;RE model suggests minimal bias from individual-level heterogeneity.\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\u003eEstimated impacts of social interaction on health outcomes \u003csup\u003ea\u003c/sup\u003e\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eLV1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eLV2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eLV1\u0026thinsp;+\u0026thinsp;RE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-rated health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.014*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.033**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.014**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.232**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.231**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.233***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.097*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.233***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNotes: \u003csup\u003ea\u003c/sup\u003e Controlled for one-wave-lagged (for LV1 and LV1\u0026thinsp;+\u0026thinsp;RE) or two-lagged (for LV2) wave-lagged health outcome, as well as covariates,***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e provided more detailed results for health outcomes based on LV1 models, focusing on self-rated health, mental health and cognitive function. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e highlights the heterogeneity of the effects of social interaction by gender and age group. SI had a greater positive impact on SRH for men than women and individuals aged 70 and above compared to those in other age groups. It also significantly reduced psychological distress, especially for women and the oldest group. For cognitive function, men benefited more than women, with the strongest effect observed in the 60 \u003cspan fontcategory=\"NonProportional\" class=\"\" name=\"Emphasis\"\u003e\u0026ndash;\u003c/span\u003e 64 age group.\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\u003eEstimated impacts of social interaction on health outcomes by gender and age group \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c12\" namest=\"c6\"\u003e \u003cp\u003eAge group\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e60\u0026ndash;64\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e65\u0026ndash;69\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;70\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-rated health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.016*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.013*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.026*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.200*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.268**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.276**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.337**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.205**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.125*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.215***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eNotes: \u003csup\u003eb\u003c/sup\u003e Based on LV1 models, controlled for one-wave-lagged health outcome, as well as covariates, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\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=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated impacts of social interaction types on the health outcomes of the elderly \u003csup\u003eb\u003c/sup\u003e\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSelf-rated health\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMental health\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eCognitive function\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeisure-based interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.029***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.626***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.674***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity-based interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.015***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.244**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.436***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponsibility-driven interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.269**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.235***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.034***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.73***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.082***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.016***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.225***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.472***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.015*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.864***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.458***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.027***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.642***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.728***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment status\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\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.387***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsured status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.024***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.018***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.407***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.17***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo-residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.366***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic-support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.198*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.093***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.265***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.088***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.012***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.425***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.128***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNotes: \u003csup\u003eb\u003c/sup\u003e Based on LV1 models, controlled for one-wave-lagged health outcome, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e compares the effects of different types of social interaction on elderly health. Leisure-based interactions provided the greatest health benefits, improving SRH, cognitive function and reducing psychological distress. Community-based interactions also showed positive health effects. Responsibility-driven interactions improved cognition but were associated with increased mental distress. Several individual characteristics revealed heterogeneity. Education and employment were linked to better health outcomes. However, urban residence was correlated with lower cognitive function and higher psychological distress, likely due to urban stressors [\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Co-residence with children or a spouse helped reduce distress, highlighting the protective role of family support in promoting emotional well-being.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e indicates that the frequency of SI has a significant positive effect on health. Specifically, for each 1-unit increase in leisure-based interaction frequency, a 6.1% higher likelihood of better SRH, a 27.5% reduction in mental distress, and a 24.7% improvement in cognitive function were observed. Community-based interaction showed similar effects, with a one-unit increase leading to a 5.4% higher likelihood of better SRH, a 20.7% reduction in mental distress, and a 20.4% improvement in cognition. However, a one-unit increase in responsibility-driven interaction frequency resulted in a 9.3% increase in mental distress.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated impacts of social interaction frequency on the health outcomes of the elderly\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSelf-rated health\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMental health\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eCognitive function\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeisure *frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.061***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.275***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.247***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunity *frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.054**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.204**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.207***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponsibility *frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.093**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cp\u003eRobustness test\u003c/p\u003e \u003cp\u003eThere may exist an endogenous relationship between social interaction frequency and health. To address this, we used instrumental variables to resolve the endogeneity issue. Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents the 2SRI estimation results. The instrumental variables had a significant positive effect on interaction frequency. In the stage I regression, the presence of chess and card rooms or elderly activity centres increased social interaction frequency by 3.5%, and more bus lines in villages or communities also raised participation in social interaction. In the stage II regression, after adding control variables, interaction frequency continued to have a positive effect on SRH, with a coefficient of 0.064, significantly higher than the basic regression result (Model 4). Addressing the endogeneity of interaction frequency strengthened its positive impact on SRH.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInstrumental variable estimation results \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eStage I\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eStage II\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Interaction frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.064**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActi-card\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.035**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBus line\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.029**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003econtrolled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudo R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: \u003csup\u003ec\u003c/sup\u003e Based on the two-stage OLS model, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePromoting elderly health is essential for advancing healthy ageing. Using longitudinal data from CHARLS conducted from 2011 to 2020, this study investigates the impact of diverse social interactions on the health of older adults, focusing on the type and frequency of interactions. The findings show social interaction had a positive impact on health outcomes, particularly in the medium to long term. Our research aligns with existing literature on the relationship between social interaction and health, highlighting the critical role of social interaction in promoting elderly well-being. Our study presents four noteworthy findings.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eImpact of social interaction on health varies across different types\u003c/h2\u003e \u003cp\u003eThe most positive effects were observed for leisure-based interactions, such as interacting with friends, playing Mahjong, chess, or cards, performing square dancing, or attending social clubs. Playing Mahjong and cards, which represent transitional fun in China, can foster friendships and positively impact health [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. A Chinese study also shows these activities protect against the negative effects of stressful life events [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. People may be willing to talk about problems they encounter and try to seek emotional support while playing with friends or relatives [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Existing research suggests higher engagement in such intellectually stimulating activities exercises reaction ability and memory, delaying deterioration [\u003cspan additionalcitationids=\"CR60\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Square dancing, another popular activity among older adults, combines movement with music in a group setting, delivering physical and psychological benefits [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Its inherently social nature, with frequent interactions before and after dancing, fosters a strong sense of community and belonging, further enhancing emotional well-being [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCommunity-based interaction had the second greatest impact. Active community engagement is linked to higher life satisfaction and lower depression [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], helping older adults feel connected and alleviating isolation often caused by retirement or living alone [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Altruistic activities, such as voluntary or charity work, also enhance well-being and improve health [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Participation in such activities is uncommon among older adults in China despite the beneficial effects of organised social activities on health. In this study, community-based interaction had the lowest participation rate (only 6.4%), highlighting that civil society in China is still developing. Most community-organised activities for older adults are cultural or voluntary events managed by residential committees. Limited activity types, lack of effective promotion and the narrow scope of activities may restrict participation. A Chinese qualitative study has reported that while many older adults are willing to engage in community life, barriers such as physical limitations, social exclusion, age discrimination, limited social connections, lack of information and low socioeconomic status hinder participation [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Consequently, the elite group of older adults tends to be the most socially active ones, while those with lower status have limited participation in organised activities.\u003c/p\u003e \u003cp\u003eNotably, responsibility-driven interactions, such as caregiving and informal support, improved health and cognition but also increased mental distress. This dual effect can be explained by the cultural and social contexts in which these interactions occur. First, Chinese traditional values promote \u0026ldquo;family culture\u0026rdquo;, where the family is viewed as a crucial social unit [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The multi-generational family structure, with close intergenerational ties, includes grandparental care as a vital component [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Older adults often aspire to the ideal of \u0026ldquo;children and grandchildren under one roof\u0026rdquo; and take on significant responsibilities in caring for their grandchildren and other family members. While this step enhances emotional connections and well-being [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e], long-term caregiving can also impose physical and mental strain on elderly individuals.\u003c/p\u003e \u003cp\u003eFurthermore, with demographic changes and urbanisation, the outflow of the younger population and increasing work pressures have caused more elderly individuals to assume the role of \u0026ldquo;multiple caregivers\u0026rdquo; responsible for grandchildren and adult children [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. This shift in roles leads to greater time and energy demands, resulting in fatigue, emotional distress and psychological pressure. This finding breaks through the current research that only discussed the impact of traditional social interaction and further discovers the impact of responsibility-driven social interaction on elderly health. This innovative perspective not only enriches the theoretical framework of social interaction but also provides empirical support for understanding the comprehensive social interaction system of the elderly within the individual\u0026ndash;family\u0026ndash;community framework.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEndogeneity of social interaction effect on health status\u003c/h2\u003e \u003cp\u003eSocial interaction had a greater positive effect on self-rated health and cognitive function for men, while women experienced stronger improvements in mental health. The difference may reflect the gender gap because women often bear heavier household responsibilities and routine domestic tasks [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e], making their mental health more sensitive to social interaction outside the home. Additionally, social interaction had a more pronounced preventive impact on health for individuals aged 70 and above, given their greater vulnerability to isolation, mental distress and overall health decline associated with ageing [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Social interaction also plays a crucial role in providing emotional support, reducing loneliness and depression and stimulating cognitive function.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eInteraction frequency played a positive role in the health of the elderly population\u003c/h2\u003e \u003cp\u003eFrequent social interaction positively affects the health of the elderly. Increased participation in leisure- and community-based interaction is associated with improved health. Additionally, this study confirms the causal relationship between interaction frequency and health. Better access to facilities like chess rooms, activity centres, and public transport significantly increased elderly interaction, indicating that social interaction is not purely a personal choice but also shaped by external conditions. Previous research has demonstrated that the availability and accessibility of community spaces, public parks and organised recreational activities support active and socially engaged lifestyles [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Access to these facilities, along with convenient transportation, directly determines whether and how much older adults can engage in social interactions. Accessible community spaces and convenient transport create \u0026ldquo;opportunity windows\u0026rdquo; for the elderly to step out of their homes, providing essential support for their physical and mental health.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eRole of family support\u003c/h2\u003e \u003cp\u003eAnother finding of this study is the protective role of family support in reducing mental distress. Living with children or a spouse was associated with lower distress levels, consistent with previous research highlighting family support as a crucial buffer against the mental health challenges of ageing [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. In contrast, elderly individuals in Western countries often derive greater mental health benefits from friendships than from family support [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. However, in Asian countries, where family-oriented cultures are emphasised, older adults living with family members receive daily support and companionship, which significantly enhances their practical and emotional well-being [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Therefore, fostering intergenerational communication and reinforcing family support can help alleviate emotional stress, promote \u0026ldquo;ageing in company\u0026rdquo; with children and improve overall health outcomes.\u003c/p\u003e \u003cp\u003eThis paper explores the relationship between social interaction and elderly health, providing practical insights for promoting healthy and active ageing. First, the Chinese government should prioritise the development of community support systems by improving infrastructure, such as activity centres cultural and sports facilities, while also enhancing age-friendly renovations and supporting elderly associations. Second, a collaborative support system involving families and communities is essential. For instance, community leisure centres could integrate children\u0026rsquo;s activity rooms to better support elderly individuals with intergenerational caregiving roles. Furthermore, enhancing community-based elderly care services and fostering an environment conducive to social interaction can help older adults realise their values, strengthen their sense of belonging and improve their health and social engagement.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eActive social interaction, regular participation in leisure activities, and organised and informal social networks provide substantial health benefits for older adults in China. Clarifying the effect of different types of social interaction on physical and mental health may contribute to insights into the development of policies and interventions to promote elderly interaction. Policies should prioritise creating supportive environments and enhancing age-friendly renovations in communities. Families and society should emphasise elderly social interaction and strengthen internal and external support systems. This collaborative approach will effectively integrate resources to achieve active and healthy ageing.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eLimitation\u003c/h2\u003e \u003cp\u003eThis study has some limitations. First, it examines how lagged social interaction (by one or two periods) affected health outcomes in follow-up years while overlooking the impact of changes in social interaction over time on health. Second, not all dimensions of social interaction were considered due to the study\u0026rsquo;s scope and data constraints. Instead, the study focused solely on the type and frequency of interactions, emphasising their density and scale while neglecting the potential effects of interaction depth. Third, the study centres on the causal link between social interaction and elderly health without exploring the underlying mechanisms or pathways, which calls for further investigation in future research.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCHARLS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChinese Health and Retirement Longitudinal Study\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSRH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSelf-rated health\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCES-D\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCenter for Epidemiologic Studies Depression Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMMSE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMini-Mental State Examination\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSocial interaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e2SLS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTwo-stage least squares\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLagged variable\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRandom effects\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CHARLS dataset is publicly available, and the study protocol was approved by Peking University\u0026rsquo;s Ethical Review Committee. Informed consent was obtained from all participants by the original CHARLS research team. The analysis of publicly available data was exempted by Research Ethics Committee of Wuhan University, as this study involved secondary analysis of anonymized data, no additional ethical clearance was required. This study was conducted in accordance with the ethical principles 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\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are publicly available and can be accessed from the China Health and Retirement Longitudinal Study (CHARLS) dataset, which is available at https://charls.pku.edu.cn/. Users are required to apply for access under the name of an institution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLL designed this study. LL, YX supervised the data screening and quality control. LL analysed data and drafted the manuscript. YX, ZS,WR, LZ offered supervision and revised the manuscript. All authors read and approved the final manuscript.\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\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the National Natural Science Foundation of China (Grant No: 72474162). The information, conclusions, and opinions expressed in this article are those of the authors and no endorsement by the National Natural Science Foundation of China is intended or should be inferred.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLobanov RS, He Q, Chen Y, Liu Y, Wu Y, Liu Y, Venkatraman T, French E, Curry N, Hemmings N, Bandosz P, Chan WK, Liao J, Brunner EJ. Growing old in China in socioeconomic and epidemiological context: Systematic review of social care policy for older people. BMC Public Health. 2023;23(1):1272. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12889-023-15583-1\u003c/span\u003e\u003cspan address=\"10.1186/s12889-023-15583-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChina National Bureau of Statistics. 2021. 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Soc Sci Med. 2015;136\u0026ndash;137:106\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.socscimed.2015.05.010\u003c/span\u003e\u003cspan address=\"10.1016/j.socscimed.2015.05.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Social interaction, Elderly health, Interaction type, Interaction frequency","lastPublishedDoi":"10.21203/rs.3.rs-6637688/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6637688/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the social determinants of health, social interaction is an important modifiable factor and an essential component of the global active ageing strategy. This study examines the impact of different types and frequencies of social interaction on the health outcomes of elderly adults in China, adjusting for simultaneity and heterogeneity biases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used data from the Chinese Health and Retirement Longitudinal Study, a five-wave panel survey conducted in 2011, 2013, 2015, 2018, and 2020, with 38,420 observations from 7,864 individuals aged 60 and older. We classified activities into three types: leisure-based individual interaction, community-based organisational interaction, and responsibility-driven caregiving interaction to capture the diversity of social interaction. Generalised estimating equation regression models were used to examine the associations between one- or two-wave-lagged social interaction and health outcomes (self-rated health, mental health, cognitive function, and diagnosed diseases). Random-effects estimation addressed individual-level heterogeneity. The 2SLS model was applied to examine the mutual causality relationship between interaction frequency and health, followed by a robustness test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSocial interaction had a positive impact on elderly health, particularly in the medium- to long-term. One-wave-lagged interaction showed improved self-rated health (b=0.014, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05), reduced mental distress (b=-0.232, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01), and enhanced cognitive function (b=0.233, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) , with no effect on disease status. Leisure and community-based interactions significantly benefited physical and mental health, while responsibility-driven interactions improved cognition but increased mental distress. Interaction frequency was positively associated with health, with better access to facilities and public transport boosting interaction frequency. Living with children or a spouse, employment status and income level are also protective factors for health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eActive social interaction, regular participation in leisure activities, organized social activities, and informal social interactions have beneficial effects on health of older adults. Policies should prioritize supportive environments and age-friendly community renovations, while families and society should strengthen internal and external support systems to foster active and healthy aging.\u003c/p\u003e","manuscriptTitle":"Impact of diversified social interaction on elderly health in China: a longitudinal analysis based on interaction type and frequency","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-01 06:23:10","doi":"10.21203/rs.3.rs-6637688/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-31T07:55:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-17T04:14:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-14T05:59:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-08T19:24:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"299276076699386067174065286682385814072","date":"2025-07-08T18:29:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"18988038330372940686351701751758338085","date":"2025-07-08T03:13:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325329073224783604463210249310217028629","date":"2025-07-07T22:33:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238830199531922108430135387630543151604","date":"2025-07-07T22:02:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-23T11:58:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-18T06:08:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-28T05:06:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-28T03:40:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2025-05-28T03:39:05+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":"3c239820-4e30-4107-9fd5-8f5590260ff8","owner":[],"postedDate":"July 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-29T16:00:46+00:00","versionOfRecord":{"articleIdentity":"rs-6637688","link":"https://doi.org/10.1186/s12877-025-06408-4","journal":{"identity":"bmc-geriatrics","isVorOnly":false,"title":"BMC Geriatrics"},"publishedOn":"2025-09-26 15:57:37","publishedOnDateReadable":"September 26th, 2025"},"versionCreatedAt":"2025-07-01 06:23:10","video":"","vorDoi":"10.1186/s12877-025-06408-4","vorDoiUrl":"https://doi.org/10.1186/s12877-025-06408-4","workflowStages":[]},"version":"v1","identity":"rs-6637688","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6637688","identity":"rs-6637688","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}