Taiyuan, the capital city of Shanxi Province in China, is the province’s political, economic, cultural, and international exchange hub (13). In terms of the degree of aging, the older adult’s population in Taiyuan City constituted 21.91% of the total registered population, which was comparable to moderately aging cities such as Beijing (24.0%), Tianjin (23.43%), Hangzhou (22.16%), Wuhan (20.95%), and Nanjing (20.85%).

This was a cross-sectional study conducted from July to August 2024 in Taiyuan, Shanxi Province. A total of 30 communities were selected across six administrative districts (Xiaodian, Yingze, Xinghualing, Jiancaoping, Wanbailin, and Jinyuan) using a multi-level stratified sampling method. Calculation of the sample size required a ratio of sample size to observed variables of 10:1–15:1. The study included 16 variables (eg, demographic variables). To ensure a 20% sample loss rate and the representativeness and accuracy of data, the sample size was calculated to be ≥196 cases (14, 15). Therefore, each community aimed to recruit 20 older adults aged 60 years or older who had resided in the community for at least 6 months and had no cognitive or communication impairments as interviewees, resulting in a planned sample size of 600 respondents. The study primarily collected data on individual characteristics, health status, and the built environment of residential communities. After excluding samples with missing information, a total of 586 valid research samples were obtained, including 291 males and 295 females (Figure 1).

The International Physical Activity Questionnaire-Short Form (IPAQ-SF) was used to measure PA of older adults. The reliability and validity of the questionnaire have been confirmed by previous studies (16). The metabolic equivalent (MET) values were set as follows: 8.0 for vigorous-intensity PA, 4.0 for moderate-intensity PA, and 3.3 for walking. The calculation formula was as follows: PA level of a certain intensity per week = MET value of this PA × frequency per week (d/w) × time per day (min/d). The total PA level was calculated as the sum of the three PA levels. Based on the grouping criteria for PA (Table 1), the level of PA was categorized into three tiers: high, medium, and low.

Drawing on existing research (17), this study collected individual characteristics including gender, age, income, education level, living arrangement, pension insurance, medical insurance, and household registration type via questionnaire survey. An evaluation index for the health status of the older adults was established through three factors: subjective self-rated health, the number of chronic diseases, and BMI, integrating both subjective and objective aspects and encompassing demand factors. Firstly, subjective health was evaluated using a subjective self-rated health scale. Subjective self-rated health demonstrated high consistency with “objective” health evaluations by doctors and was frequently employed as a basis for assessing the health status of the older adults in China (18). This measure was derived from the China Health and Retirement Longitudinal Study (CHARLS) questionnaire, specifically from the question: “How would you rate your health status?” Responses were coded as follows: “Very healthy” = 5 points, “Somewhat healthy” = 4 points, “Neutral “= 3 points, “Somewhat unhealthy” = 2 points, and “Very unhealthy” = 1 point. Scoring followed a positive direction, meaning that a higher score reflects a more positive subjective evaluation of one’s own health (19). Secondly, the number of chronic diseases (hypertension, diabetes, hyperlipidemia) among the older adults was collected via questionnaires. Finally, BMI was calculated by measuring the height and weight of the older adults, applied the formula: BMI = body mass (kg)/height (m)². According to the “The Guidelines for Prevention and Control of Overweight and Obesity of Chinese Adults,” the BMI categories were defined as follows: underweight: BMI < 18.5 kg/m², normal weight: BMI ranging from 18.5 to 24 kg/m², overweight: BMI ranging from 24 to 28 kg/m², and obesity: BMI ≥ 28 kg/m² (20).

For the Community built environment section, we employed the Neighborhood Environment Walkability Scale-Abbreviated (NEWS-A), which has been validated and widely adopted in China as an effective instrument for measuring urban residents’ perceptions of their built environment (21). The measurement content encompassed five dimensions: convenience of supporting facilities, destination accessibility, road connectivity, environmental beautification, and public security. The scale employed a 5-point Likert scale, ranging from strongly disagree (1 point) to strongly agree (5 points), with higher scores reflecting greater satisfaction with the built environment. Cronbach’s α was 0.932, indicating high internal consistency of the scale.

Based on Andersen’s behavioral model (Figure 2) and drawing upon prior research findings, this study examined the influencing factors of PA among the older adults in Taiyuan from three perspectives: predisposing factors, enabling factors, and demand factors. Detailed variable operationalization and statistical descriptions were presented in Table 2.

SPSS 26.0 was utilized for data analysis, with frequency and proportion employed to describe the basic characteristics of the older adults population. Based on the Andersen’s behavioral model, ordinal Logistic regression was conducted to explore the influencing factors of PA levels among the older adults, while binary Logistic regression was applied to examine the determinants of insufficient PA in this group. The significance level for all statistical analyses was set at α = 0.05.

Table 3 showed a total of 586 older adults individuals aged 60 years and older were included in this survey, composed 291 males and 295 females, with the majority ranged from 60 to 79 years old. Specifically, 72% of the participants held urban household registration, and their educational attainment primarily ranged from primary school to senior high school (or vocational college). Notably, 51.2% of the respondents reported having at least one chronic disease, 41.3% were classified as overweight or obese, and 48.5% rated their health status as “relatively healthy” or better.

The PA level of older adults was associated with multi-dimensional factors. An ordered logistic regression model was constructed for a comprehensive analysis. Specifically, four models were developed: Model 1 included only predisposing factors as control variables; Model 2 added enabling factors to Model 1; Model 3 incorporated need factors into Model 1; and Model 4 integrated all three types of factors—predisposing, enabling, and need factors. The significance level for hypothesis testing was set at α = 0.05. The results were presented as follows:

Based on the goodness-of-fit test, Model 4 demonstrated the largest R² value (Cox and Snell R² = 0.146, Nagelkerke R² = 0.168), indicating that it provided the strongest explanatory power for the PA level of the older adults. Therefore, Model 4 was selected as the final model to elucidate the influencing factors of PA levels among the older adults population (Table 4).

A further analysis of factors associated with insufficient PA among older adults was conducted. Adequate PA was defined as no less than 75 min of high-intensity exercise or 150 min of moderate-intensity exercise per week, or an equivalent combination of moderate- and vigorous-intensity exercise (with high-intensity exercise time multiplied by 2 equating to moderate-intensity exercise time). Conversely, insufficient PA was defined as failing to meet these criteria (22). Binary Logistic regression analysis was conducted with adequate PA as the reference group. Model 5 was constructed using predisposing factors as control variables, Model 6 incorporated enabling factors based on Model 5, Model 7 included need factors based on Model 5, and finally, Model 8 integrated all three types of factors (predisposing, enabling, and need factors). The significance level was set at α = 0.05.

According to the goodness-of-fit test, Model 8 had the largest R² value (Cox and Snell R² = 0.172, Nagelkerke R² = 0.236), indicating that it provided the highest explanatory power for insufficient PA among the older adults. Therefore, Model 8 was selected as the final model to analyze the influencing factors of insufficient PA in the older adults (Table 5).

In terms of Predisposing factors, demographic characteristics (gender and age) were significantly associated with PA. Specifically, the odds of having a higher PA level were 89.3% higher for women than for men, while their odds of insufficient PA were only 0.47 times those of men. This disparity may have stemmed from differences in physical attributes and traditional gender roles. Chinese women often assumed responsibilities such as caring for the next generation and managing household chores, which facilitated regular low-to-moderate intensity physical activities but left little time for high-intensity exercises (23). In contrast, men generally possessed greater physical fitness, which enabled them to participate more readily in high-intensity activities like lifting heavy objects or engaging in fitness routines, thus increasing their likelihood of engaging in moderate-to-high-intensity physical activities (24). Additionally, PA levels among older adults were closely correlated to age. As age increases, PA levels tended to decline, while the likelihood of insufficient PA rose. For instance, the probability of engaging in PA was 73.9% higher among adults aged 70–79 compared to those aged 80 and above, whereas the probability of insufficient PA among individuals aged 60–69 was only 45.8% that of those aged 80 and above. These findings align with prior research, which attributed the trend to physiological changes associated with aging, including organ degeneration, reduced muscle mass and density, and diminished mobility. Furthermore, concerns about injury and other factors contributed to a gradual reduction in both the duration and intensity of PA as people grew older (25, 26).

The goodness-of-fit test results from the regression analysis indicated that enabling factors showed the strongest association with the PA levels of older adults. Specifically, purchasing pension insurance and subjective evaluations of community support facilities were identified as the primary influencing factors. The findings demonstrated that purchasing pension insurance significantly enhanced PA levels among older adults while reducing the likelihood of physical inactivity. Compared to those who purchased pension insurance, older adults without such insurance exhibited a 68.8% higher probability of lower PA levels and a 294.4% greater likelihood of insufficient PA. This suggested that the financial security provided by pension insurance encourages older adults to engage more actively in outdoor activities and social interactions, thereby improving their PA levels (27). Additionally, this financial security not only enhanced the quality of life for older adults but also fosters a positive attitude and health awareness, motivating them to maintain their wellbeing through active lifestyles (28). However, further analysis revealed no significant association between increased income and PA. Although higher income might increase the frequency of PA, the broader range of recreational options available to high-income older adults could lead to relatively shorter durations and lower intensities of PA (29). In summary, there may be a “bottleneck effect” in the promotion of PA by economic status among older adults. In addition, the findings indicated that greater convenience of community supporting facilities was associated with higher PA levels and a lower probability of physical inactivity among older adults. Specifically, for each point increase in the convenience score of community facilities, the odds of the PA level rising by 36.3% and the odds of insufficient PA decreasing by 28.2%. Previous research corroborated this conclusion, and the convenience of facilities surrounding the residential area could significantly boost the PA of the older adults (17). This was because the fitness service facilities and sports fields accessible in the community offered convenient PA venues and equipment for residents, thereby effectively stimulated the PA behavior of the residents. This convenience encompassed not only the physical accessibility of facilities but also the diversity and quality of facilities, which collectively promoted the PA participation of the older adults (30).

Regarding demand factors, only self-rated health was significantly associated with PA. This study found that decreases in self-rated health scores among older adults corresponded to reduced PA levels and an increased likelihood of insufficient PA. While many scholars have extensively explored and confirmed the positive effects of PA on health (31), they often overlooked the critical premise that good physical health serves as the foundation for engaging in PA. Older adults with lower self-rated health scores tend to reduce the time and intensity of PA due to their health conditions. This may stem from fears that excessive PA could exacerbate existing health issues or a lack of motivation caused by physical discomfort (32). A study further corroborated this perspective, indicating that older adults were more likely to participate in physical exercise when their physical condition permits, whereas those with poorer health may limit their participation due to safety concerns and family considerations (33). Furthermore, the results showed no statistically significant association between the incidence of chronic diseases and PA among older adults. However, some scholars argued that older adults with chronic diseases are more inclined to engage in physical exercise to improve their health status and manage disease conditions (34). Conversely, some studies suggest that older adults with chronic diseases exhibit lower PA levels and may opt for lighter physical labor, reducing the duration and intensity of exercise to some extent (35). In conclusion, the relationship between the prevalence of chronic diseases and PA among the older adults remains unclear, and future studies should further investigate this association. Additionally, prior research had indicated that overweight and obese individuals are more likely to require PA to reduce their BMI (36). However, in the present study, a large proportion of participants had normal BMI (42.8%) or were overweight (41.3%), while fewer were underweighted (4.3%) or obese (11.6%). This suggests that the BMI distribution was concentrated in the normal and overweight categories, which may account for the lack of a statistically significant effect of BMI on PA levels.

Despite its contributions, this study had certain limitations. First, the cross-sectional design restricted our ability to establish causal relationships between variables, as longitudinal data would have been necessary to explore temporal associations. Second, reliance on subjective measures for assessing PA and perceptions of the built environment might have introduced potential biases. Future studies should incorporate objective measures, such as accelerometers for PA assessment and geographic information systems (GIS) for evaluating the built environment, to enhance result reliability.