The data used in this study were obtained from the China Health and Retirement Longitudinal Study (CHARLS), which covers approximately 10,000 households and 17,000 individuals in 150 county- and 450 village-level units. CHARLS aimed to collect and analyze high-quality microdata from middle-aged and older adults aged 45 years and older, representing the aging population of China. This study used the cross-sectional data, released in 2018, focusing on individuals aged 60 years and older, to examine the impact of social participation on their life satisfaction. There were 15,941 valid samples in the original data for 2018. The data processing steps in this study are as follows: First, we combined household and individual databases. Second, to retain the sample of our research subjects, we excluded individuals under 60 years old (7,600). Finally, we removed samples with missing primary variables (229), resulting in 8,112 observations being included in the analysis.

We performed statistical analysis using Stata17. Based on the characteristics of the main research variables, this study used ordered probit (Oprobit) regression as the main regression to analyze the data. We conducted mediation analysis using stepwise regression and heterogeneity analysis using grouped regression. We have also employed OLS regression for the main analysis included the results in Supplementary Table 1. To verify the robustness of the analysis, we conducted a propensity score matching (PSM) analysis, with the results reported in Supplementary material A. Oprobit regression is widely used in the literature to deal with cases in which the explanatory variables are ordered, and based on the characteristics of the explanatory variables here, the model was set as follows:

In Equations (1, 2), the Yi* is the latent variable of the dependent variable “life satisfaction,” which is unobservable, and Y_i is a non-linear function. The relationship between them is as follows: when Yi* is lower than the threshold value μ₁ the older person is extremely satisfied with life (Y_i = 1); when Yi* is higher than μ₁ but < μ₂ then the older person is very satisfied with life (Y_i = 2). When Yi* is higher than μ₄ the older person is very dissatisfied with life (Y_i = 5). In Equation (1), S_i is the independent variable “social participation,” X_i includes all control variables at the individual level and household level, and ε_i is the random disturbance term. Assuming that ε_i follows a normal distribution across different observations, its mean and variance are standardized to 0 and 1. Z denotes all the explanatory variables, and the probability of Y_i can be obtained as shown in Equation (3).

All variables are presented in Table 1. The sample consists of slightly more males (51.5%) than females (48.5%). Most individuals are aged between 60 and 70 years. A larger proportion of older adults reside in rural areas (58.8%) compared to urban areas (41.2%). Regarding family connections, 18.5% of older adults do not have a partner, and 57.4% are not involved in caring for grandchildren. The number of older adults participating in social activities (50.7%) is comparable to those not participating (49.3%). Figure 1 illustrates that older adults who engage in social activities report lower scores and higher life satisfaction than those who do not.

Table 2 presents the results of the hierarchical Oprobit regression models that investigated the relationship of social activity participation and life satisfaction in older adults. In column (1–3), they respectively include individual-level control variables, individual and household-level control variables. The regression coefficients continue to show significant negative associations, while the R-squared value gradually increases (β = −0.0339, P < 0.01; β = −0.0472, P < 0.01; β = −0.0474, P < 0.01). This suggests that participating in social activities effectively associated the life satisfaction of older adults, and this positive association becomes more pronounced with greater richness of participation. In column (4–6), the measurement of the dependent variable was changed to whether they engage in social activities, and the correlation still exists (β = −0.0396, P < 0.05; β = −0.0590, P < 0.05;β = −0.0603, P < 0.05), demonstrating the robustness of the results. Furthermore, Table 2 also sheds light on other factors predicting older adults' life satisfaction, Age (β = −0.0186, P < 0.01), education (β = −0.0286, P < 0.01), work status (β = −0.138, P < 0.01), basic disease (β = 0.0785, P < 0.01), place of residence (β = 0.743, P < 0.01), and companionship (β = −0.114, P < 0.01). The results of the VIF analysis for each variable in the analytical model are presented in Supplementary Table 2.

The results of the mediation analyses are reported in Table 3. Based on the estimates presented in Table 3 (1–2), social participation predicted lower levels of loneliness among older adults (β = −0.0648, P < 0.01) and there was a significant positive relationship between social participation and life satisfaction (β = 0.294, P < 0.01). The statistical results shown in Table 3 (3–4) indicate that engaging in social activities enhances self-rated health (β = −0.0862, P < 0.01), and self-rated health is significantly positively correlated with life satisfaction (β = 0.293, P < 0.01). In both mediation analyses, the absolute value of the correlation coefficients between social participation and life satisfaction (β = −0.0356, P < 0.01; β = −0.0275, P < 0.05) were smaller than the direct correlation coefficient between social participation and life satisfaction (β = 0.0474, P < 0.01). The results of the mediation analysis are visualized in Figure 2.

The results, shown in Table 4 (1–2), indicate that there are differences in the positive correlation of social participation with the life satisfaction of rural and urban older adults. The absolute value of correlation coefficient with life satisfaction for rural older adults (β = −0.0603, P < 0.01) is greater than that for urban older adults (β = −0.0358, P < 0.05). The second examination of heterogeneity explored the association between social participation and life satisfaction among older adults with different levels of emotional support from their families. The findings reported in Table 4 (3–6) suggest a significant correlation between social participation and the life satisfaction of older adults without partners (β = −0.086, P < 0.05). In contrast, the relationship between social participation and the life satisfaction of older adults with partners appears to be weaker (β = −0.0356, P < 0.05). Among those not providing care for grandchildren, the coefficient of social engagement on life satisfaction was −0.0713, significant at the 1% level. However, for older adults providing care for grandchildren, the coefficient was only −0.034, significant at the 10% level.

Older adults are a vulnerable group in society, and their population will continue to increase for a long time in the future. Improving the life satisfaction of older adults is an inherent requirement and a key point in the development of a global health strategy, and it is of great significance to promote healthy aging.

The study found a significant positive association between participation in social activities and the life satisfaction of older adults. Additionally, the richness of participation in different types of social activities correlates with greater life satisfaction. This finding consistent with Hypothesis H1 and empirically contributes to the research on the relationship between social activities and life satisfaction (6).

The mediation analysis in our study showed that loneliness and self-rated health partially mediate the relationship between social participation and life satisfaction, with participation in social activities significantly associated with life satisfaction by reducing loneliness and improving self-rated health. We believe that different social activities contribute differently to the older adult population. For example, participating in community activities helps maintain neighborhood relationships among the older adults (9), while volunteering and interest activities broaden their social networks (15, 54), all of which can reduce loneliness and improve mental health. Physical exercise helps older adults improve their physical health (55). The health level of older adults is closely related to their life satisfaction, and good health can bring more life opportunities, a higher quality of life, and higher satisfaction to older adults (56). Both health and loneliness of older adults have been studied as important factors affecting their quality of life (57, 58), which was also verified in the analysis. Our findings support Hypothesis H2 that health and loneliness mediate the relationship between social participation and life satisfaction. This underscores the importance of encouraging active social engagement among older adults and provides useful references for policies on healthy aging, emphasizing social participation's role in alleviating loneliness and promoting health and life satisfaction.

Our study also provides extensive information on heterogeneity. First, social participation has a greater impact on rural older adults than on urban older adults. This may be due to the relative lack of leisure and entertainment facilities in rural areas compared with cities, and the higher quality of life expectations among older adults in urban areas (59). Simultaneously, it is easier and more convenient for urban older adults to meet their children (60). With the outflow of young and middle-aged people from rural areas in China, the number of empty-nest older adults in rural areas is increasing, making it difficult for them to obtain their sense of life satisfaction from their families (45). Older adults in rural areas are mostly left behind and have fewer face-to-face interactions with their children (37). Therefore, they are more inclined to seek emotional support through external social activities to enhance their life satisfaction. This result supports Hypothesis H3a.

Second, participation in social activities has a greater association with older adults who lack family companionship, suggesting that social participation may partially offset the absence of family companionship. On one hand, there is a marginal diminishing effect on the improvement of life satisfaction (61), and older adults with sufficient family companionship may have higher life satisfaction (60). On the other hand, the older adults lacking companionship are more likely to seek emotional support from outside (62), so subjective life satisfaction is more influenced by social activities. This further supports Hypothesis H3b, highlighting the need for more attention to be paid to vulnerable older adult populations with emotional deficits, thus aiding them in enjoying their later years.

This study argued that advocating for older adults to participate in social activities and enhance their sense of social inclusion is a key measure for further enhancing their life satisfaction and promoting healthy aging. Therefore, this paper puts forward the following recommendations: first, promote the development of public, cultural, and sports undertakings; improve and enrich relevant services at the community level, such as increasing sports equipment and providing activity venues for older adults; and second, encourage older adults to participate in social activities, such as community volunteer and interest activities, thus reducing the opportunity cost of their social participation. Finally, attention should be paid to older adults in rural areas and those who lack family companionship because their participation in social activities has a significant impact on their quality of life and should be encouraged. In summary, increasing opportunities and choices for older adults to participate in social activities is an important measure for creating an age-friendly society and is of great significance to the development of healthy aging.

First, despite the use of multiple methods to enhance the reliability of the results, this was a cross-sectional study owing to data limitations. The panel data allowed us to explore the dynamic changes after participating in social activities. Subsequent research employing panel data will bolster the robustness and reliability of causal inferences. Second, the data for this study were from China; however, the practical situation varies between countries, the utilization of data from diverse countries could enhance the generalizability of this study. Third, the measurement of life satisfaction is self-reported and may be influenced by factors such as social expectations. The adoption of a more objective measurement indicator, if feasible, would be advantageous.