Abstract
The background of aging has increased the demand of adaptability on elderly sports. In order to response to the aging society and achieve sports’ health function for elderly participants, this research aim to clarify the elderly sport participants’ characters and their potential influencing factors. Caused by too many variables in CHARLS, its cross-sectional nature precludes causal inference. The approach is purely exploratory. We obtained cases of 4982 elderly sports participants from the China Health and Retirement Longitudinal Study (CHARLS). They were divided into 3 clusters by cluster analysis, based on intensity of participation, and then figured out their potential influence by multiple correspondence analysis. Cluster 1 (47%) includes low-intensity participants. They only participate in mild exercise with low frequency. Cluster 2(32%) is non-high-intensity participants. They are deeply involved in moderate and low-intensity exercises with moderate frequency. Cluster 3 (21%) consists of multi-intensity participants. They are engaged in low-intensity, moderate-intensity, and high-intensity sports with high frequency. The intensity indicates that sport is an effective lifestyle for elderly individuals, particularly those who are villagers and not affluent. The urban–rural fringe area is associated with non-high-intensity participants in elderly sports. Additionally, marital statuses “never married” and “separated” are more associated with multi-intensity participants (Cluster 3). Institutionalized older adults living with a spouse show a higher likelihood of participating in high-intensity physical activities.
Keywords: Cluster analysis, Age-appropriate sports service, Aging, Multi-intensity sports participants
Subject terms: Ageing, Health policy
Introduction
Although physical exercise is widely recognized as beneficial for older adults’ health1, uncertainty remains regarding the optimal intensity to maximize these benefits without compromising participant safety. In order to response to the aging society and achieve sports’ health function for elderly participants, this research aim to clarify the elderly sport participants’ characters and their potential influencing factors. These factors would illustrate patterns of sports participation intensity prevail among the elderly population in China. Moreover, it would clarify the relationship between demographic factors and different levels of intensity.
Caused by decline in physical function, elderly individuals can’t join high intensity sports like teenagers. The other side of the coin is that elderly individuals require exercise in certain level of intensity to reap health benefits. They often have to balance exercise intensity for health effects and physical abilities of elderly individuals. High-intensity exercise may raise injured risk for elderly individuals, while elderly individuals can’t benefit from too light exercise physically. This contrary poses a significant challenge for suppliers of elderly sports services2.
Based on these insights, elderly sports researches need to ensure the appropriate provision of sports services tailored to the demographic characteristics, health status, and active abilities of participating groups, thereby maximizing the health, economic, and social functions of elderly sports. This research is illustrated by two key aspects: intensity and category. By addressing these dimensions, the research could contribute to the development of more effective and inclusive sports services for the elderly.
Subgroup differences
An increasing number of researches underscored the importance of adapting exercise to the aging individuals needs of health benefit and participation safety3. The contrary (between exercise intensity for health effects and physical abilities of elderly individuals) mentioned above comes from aging. Aging is a multidimensional process involving biological, psychological, and social changes4. For elderly sports participants, regular physical activity mitigates age-related decline by improving cardiovascular health, muscle strength, and cognitive function5. Studies suggest that exercise delays cellular aging by enhancing telomerase activity6 and reduces frailty risk 7. However, individual variability in aging necessitates tailored exercise programs to improve quality of life8. Quality of life in elderly sports participants encompasses physical, psychological, and social well-being. WHO confirmed that regular physical activity enhances mobility, reduces chronic disease risks, and improves mental health by alleviating depression and anxiety9. Structured exercise programs, such as tai chi and walking groups, show significant quality of life improvements by promoting functional independence10. Thus, sports participation is a key determinant of successful aging. Strength training emerges as a key strategy for healthy aging, as it not only prevents frailty and falls, but also improves quality of life in older adults. Muscle strengthening, especially in the lower limbs, reduces short-term fall risk and contributes to balance and functional autonomy, ultimately enhancing quality of life in older adults11. Common influencing factors include demographic factors, social capital, motivation and demand factors and so on12–14. However, lots of researches15 focused on the general demands of elderly sports rather than addressing intensity-related topics and the associated factors with intensity, particularly in regions undergoing rapid changes in social background and urban–rural structure, such as China.
Intensity of elderly sports participation
In the context of the decline in physical function among the elderly, their adaptation to exercise intensity is pivotal to the effectiveness of sports participation. The perspective of aging sports researches focus on intensity, duration, and frequency. Some researches16,17 explored their interaction. Light intensity usually related to long duration. For example, light-intensity sports, such as walking for long duration, are the most popular among elderly individuals. Elderly individuals could reap more health benefit from that. However, other researches believe moderate intensity sports related more to reasonable duration and frequency. Scanlon18 found that elderly individuals who consistently engaged in moderate-intensity exercise for more than 20 min per session, more than three times a week, over a six-month period experienced significant improvements in their quality of life.
No matter on which side, it had been admitted that most types of exercise offer health benefits, even at low intensities19. The core problem of elderly sports is to find proper intensity combined with suitable duration and frequency. In another word, public policy makers, suppliers of elderly sports service need to know different patterns of elderly sports participation. Based on this, they can provide age-appropriate sports service to realize the health function of sports. While researches indeed divided elderly sports participants into several categories by intensity, duration, frequency and other related factors20,21, they didn’t figure out the intensity patterns and their potential influencing factors. These are the objectives of this research.
Overall, these studies highlight the importance of considering exercise intensity, frequency, and duration in designing sports programs for the elderly. By doing so, we can better meet their unique needs and promote their overall well-being through tailored sports activities.
Categorization approaches
Clustering analysis, provides valuable insights into both the group and personalized demands of elderly sports participants. It is important to address the types of physical activity and sports, as they are key in dimensions of quality of life during aging. Specific examples such as aquatic activities, which support mental health, and general gymnastics, which enhance social integration, show that not only physical activity but also its specific modality can influence the well-being of older adults and foster active and healthy aging. This highlights the importance of incorporating psychosocial components into public health policies for older adults22. Based on this logic, some researches category elderly sports participants with health state, demographic variables, daily habits and social networks23–25. However, these researches usually category with certain aspect factors. Although these factors were regarded as vital influence on elderly sports participants, the illustration of each category are not enough. Additionally, sometimes the similar concept is vague, especially the difference between elderly sports participants influencing factors and elderly sports participants category influencing factors. The later one is usually ignored, causing unsystematic research on elderly sports.
Given that the elderly population is highly heterogeneous in terms of physical and social conditions, cluster analysis enables the identification of empirically-based sports participation profiles. These clustering analyses offer a comprehensive view of the diverse categories of elderly sports participation, based on demographic, social, and exercise-related factors. These insights are crucial for designing sports programs and services that cater to the unique needs and preferences of elderly individuals, fostering their active engagement and well-being.
The present study
Study justification
Based on a comprehensive literature review, the specific purpose of elderly sports is to enhance the adaptability of content, organizational form, and value concepts to the physical function of elderly participants. The whole purpose is to realize health function. On this perspective , each intensity class’s frequency and duration of exercise are the most crucial attributes for assessing adaptability. So they are standards of cluster analysis in this study. Specifically, this study aims to identify contradictions and differences of elderly sports participation. The primary contradiction lies on the logical discrepancy between the decline in elderly physical function and the vigorous intensity of traditional sports, particularly in the context of elderly health needs for disease prevention and chronic disease intervention. Based on this contradiction, this paper delves into the variations in domestic factors, physical activity ability factors and chronic disease factors as the potential associated factors of Multiple Correspondence Analysis (MCA).
Objectives and hypotheses
In order to response to the aging society and achieve sports’ health function for elderly participants, this research aim to clarify the elderly sport participants’ characters and their potential influencing factors. Based on these, we make the below hypotheses. Firstly, elderly sports participants could be classified with intensity of sports participation. Secondly, each cluster have several characteristics. Thirdly, these characteristics associated with aspects of demographic, physical activity ability and chronic disease.
Methods
Participants
The type of study is cross-sectional nature that precludes causal inference. This approach is purely exploratory, which means this study aims to clarify the elderly sport participants’ characters and their potential influencing factors. We obtained the data by registering and applying for data on the official website (https://charls.pku.edu.cn/en/) of 2018 China Health and Retirement Longitudinal Study (CHARLS). Once the application is accepted, users can download data on the official website. CHARLS was interviewer-administered, which conducted by National School of Development Peking University from July 1st to September 7th in 2018. CHARLS covered most area of China, including 30 provinces, encompassing 150 county-level units, 450 community (or village) units, and involves 19,000 households, with a subset of 12,400 households being specifically targeted 4982 participants with valid data on sports participation were selected from CHARLS 2018 dataset (19,816 cases), ensuring representativeness through stratification by age and physical activity motivation.
Age Utilizing the “year of birth of the individual” variable (BA002_1), respondents born prior to 1958 (considering their age at the 2018 survey) were designated as elderly. Age groups were further delineated as follows: “1 = 60–64 years,” “2 = 65–69 years,” “3 = 70–74 years”, “4 = 75–79 years” and “5 = 80 years and above.”
Motivation of physical activity Sports participation serves as entertainment by providing enjoyment and social interaction26, while as exercise, it enhances physical health through structured activity. Both aspects improve well-being. Based on these, in variable “Purpose of Physical Activity” (DA051_1), participants who indicated “2 = exercise” and “3 = entertainment” as their reasons for engaging in physical activity were identified as elderly sport participants. It is worth noting that within the CHARLS survey, “physical activity” refers to physiological exercise. The options for the purpose of physical activity encompassed “work needs,” “entertainment,” “exercise,” and “other.” Consequently, elderly individuals who engaged in physical activity for the purposes of “entertainment” and “exercise” were selected. This encompasses not only active participants motivated by disease prevention, sporting interest, and social interaction, but also those participating passively with the aim of chronic disease management (e.g., reducing blood sugar levels).
Measures
Drawing upon the existing research contrasting the decline in physical function among the elderly with the intensity of sports participation27,28, the dimensions of sports participation among older adults are delineated as intensity, frequency, and duration. Specifically, intensity is interpreted through subjective physical sensation29. Utilizing the questionnaire and interpretive framework of China Health and Retirement Longitudinal Study, exercise intensity is categorized into three levels: high-intensity, moderate-intensity, and light. High-intensity activities entail substantial physical exertion and cause participants to breathe heavily, examples include fast cycling and aerobic exercises. Moderate-intensity activities may result in participants breathing faster than usual, such as cycling, practicing Tai Chi, or walking at a steady pace. Light activities encompass walking and leisure exercises, among others. Considering the aforementioned contradiction and the defined concept of participation, the relevant variables are organized into four sections (Table 1
Table 1.
Relevant variables of cluster analysis.
| Variables | Categorization | |
|---|---|---|
| Elderly sports participation | Time with intensive physical activity (DA053_1) |
0–30 min as time intensity 1 30–120 min as time intensity 2 120–240 min as time intensity 3 over 240 min as time intensity4 |
| Time with moderate physical activity (DA053_2) | ||
| Time with intensive physical activity (DA053_3) | ||
| Days with light physical activity (DA052_1) | ||
| Days with moderate physical activity(DA052_2) | ||
| Days with light physical activity (DA052_3) | ||
| Demographic characteristics of the elderly | Gender (BA000_w2_3) | |
| Actual year of birth (BA002_1) |
1 = 60–64 years 2 = 65–69 years 3 = 70–74 years 4 = 75–79 years 5 = over 80 years |
|
| Income = how much receive (GA002) + pension amount (GA003_W4_1) |
1 = 0 RMB-10000 RMB 2 = 10,000 RMB-30000 RMB 3 = 30,000 RMB-60000 RMB 4 = 60,000 RMB-120000 RMB 5 = over 120,000 RMB |
|
| Location of residential address (BB001_W3_2) | ||
| Type of residential address (BB001_W3_1) | ||
| Marital Status (BE001) | ||
| highest education level (BB001_W2_4) | ||
| Chronic diseases and self-assessed health status of the elderly | Diagnosed with hypertension by a doctor (DA007_1) | |
| Diagnosed with dyslipidemia by a doctor (DA007_2) | ||
| Diagnosed with diabetes by a doctor (DA007_3) | ||
| Diagnosed with cancer by a doctor (DA007_4) | ||
| Diagnosed with chronic lung diseases by a doctor (DA007_5) | ||
| Diagnosed with liver disease by a doctor (DA007_6) | ||
| Diagnosed with heart attack by a doctor (DA007_7) | ||
| Diagnosed with stroke by a doctor (DA007_8) | ||
| Diagnosed with kidney disease by a doctor (DA007_9) | ||
| Diagnosed with stomach disease by a doctor (DA007_10) | ||
| Diagnosed with emotional problems by a doctor (DA007_11) | ||
| Diagnosed with memory-related disease by a doctor (DA007_12) | ||
| Diagnosed with arthritis by a doctor (DA007_13) | ||
| Diagnosed with asthma by a doctor (DA007_14) | ||
| Mobility status of the elderly | Difficulty with running or jogging about 1 km (DB001) | |
| Difficulty with walking 1 km (DB002) | ||
| Difficulty with stooping, kneeling, or crouching (DB006) | ||
| Difficulty with lifting or carrying weights over 10 Jin (DB008) | ||
| Difficulty with household chores (DB0016) |
Data analysis
Cluster analysis
We utilize SPSS 26.0 software’s “Analyze—Classify—Two Step Cluster” function to conduct a cluster analysis on elderly sports participants in China. Clustering methods generally encompass K-means, hierarchical, and two-step clustering. The rationale for selecting two-step clustering is as follows: (1) Compared to hierarchical clustering, two-step clustering is more suited to handling larger datasets (typically, hierarchical clustering is applicable to fewer than 3000 cases). (2) In contrast to K-means clustering, two-step clustering has the capability to process both categorical and continuous variables simultaneously (K-means clustering can only accommodate continuous variables)30. Furthermore, this method automatically determines the optimal number of clusters.
The two-step cluster analysis is grounded on six variables: the number of days per week and duration per day for participation in high-intensity exercise, the number of days per week and duration per day for engagement in moderate-intensity activities, and the number of days per week and duration per day for involvement in low-intensity activities. Notably, the daily durations for high-intensity, moderate-intensity, and low-intensity activities are categorical variables, rather than continuous, as their value ranges span from 1 to 4, rather than 1 to 5.
Multiple correspondence analysis
Multiple correspondence analysis (MCA) is a well-established tool for examining the associations between categorical variables. It serves to illuminate the differences among categories within the same variable and the correlations between multiple categorical variables. Multiple correspondence analysis (MCA) allows exploration of latent associations between sociodemographic profiles and patterns of sports participation, making it suitable for multiple nominal variables (Table 1).
Additionally, it offers graphical representation, providing a more concise and intuitive understanding. Based on the cluster analysis categorization and the screened variables, multiple correspondence analyses are conducted on the following: the chronic disease status of the elderly and their sports participation, the mobility of the elderly and their sports participation, the demographic characteristics of the elderly and their sports participation, and the daily activity content of the elderly and their sports participation.
Results
The general characteristics of the aged sports participants
Among the elderly population, a significant portion continues to engage in walking, leisure exercises, and other forms of light physical activity to maintain muscle and bone health. Specifically, 83.3% participate in light exercise for an average of 30 min to 2 h per session, with 2814 individuals (56.5%) reporting such activity levels.
Moderate-intensity sports participation is less prevalent. Notably, 53.8% of elderly individuals do not engage in moderate-intensity sports, while 29.8% participate seven days a week, illustrating a polarized trend. The number of individuals participating between one and six days per week is relatively low. Regarding the duration of each moderate-intensity exercise session, excluding non-participants, 23.1% engage for 30 min to 2 h.
High-intensity vigorous sports are less favored among the elderly, with 80.6% not participating. However, among those who do engage, 9.4% participate seven days a week, making it the highest frequency for this intensity level. Additionally, 6.6% participate in high-intensity sports for more than four hours per session, the highest proportion for any duration category, followed by 2 to 4 h (5.9%) and 30 min to 2 h (6.1%).
Cluster analysis of aged sports participants
Utilizing a two-step clustering based on sports intensity, this study analyzed elderly sports participants. According to the findings, elderly sports participants can be categorized into three groups based on exercise intensity (Table 2): low-intensity participants (2342 participants, 47%), moderate-to-low-intensity participants (1593 participants, 32%), and full-depth participants (1047 participants, 21%).
Table 2.
Cluster analysis of elder sports participants.
| Variable | Cluster 1 | Cluster 2 | Cluster 3 | Overall |
|---|---|---|---|---|
| N = 2342 (47%) | N = 1593 (32%) | N = 1047 (21%) | N = 4982 | |
| Duration of light exercise each time | ||||
| 0 min | 11.5% (120) | 2.4% (120) | ||
| 0–30 min | 14.7% (345) | 10.5% (168) | 9.6% (101) | 12.3% (614) |
| 30 min–2 h | 57.6% (1349) | 60.8% (968) | 47.5% (497) | 56.5% (2814) |
| 2–4 h | 21.4% (501) | 22.3% (356) | 23.1% (242) | 22.1% (1099) |
| More than 4 h | 6.3% (147) | 6.3% (101) | 8.3% (87) | 6.7% (335) |
| Duration of moderate-intensity exercise each time | ||||
| 0 min | 100% (2342) | 32.5% (340) | 53.8% (2682) | |
| 0–30 min | 16.1% (257) | 8.1% (85) | 6.9% (342) | |
| 30 min–2 h | 51% (813) | 32.4% (339) | 23.1% (1152) | |
| 2–4 h | 22.4% (357) | 17.8% (186) | 10.9% (543) | |
| More than 4 h | 10.4% (166) | 9.3% (97) | 5.3% (263) | |
| Duration of high-intensity exercise each time | ||||
| 0 min | 100% (2342) | 100% (1593) | 7.7% (81) | 80.6% (4016) |
| 10–30 min | 3.7% (39) | 0.8% (39) | ||
| 30 min–2 h | 29% (304) | 6.1% (304) | ||
| 2–4 h | 28% (293) | 5.9% (293) | ||
| More than 4 h | 31.5% (330) | 6.6% (330) | ||
| Days of light exercise per week | ||||
| 0 day | 11.5% (120) | 2.4% (120) | ||
| 1 days | 1.2% (27) | 0.9% (14) | 1.9% (20) | 1.2% (61) |
| 2 days | 2.4% (56) | 3.3% (53) | 3.5% (37) | 2.9% (146) |
| 3 days | 5% (117) | 4.8% (76) | 3.9% (41) | 4.7% (234) |
| 4 days | 1.9% (44) | 2.5% (40) | 2% (21) | 2.1% (105) |
| 5 days | 2.6% (60) | 2.9% (46) | 3.2% (33) | 2.8% (139) |
| 6 days | 0.5% (11) | 0.4% (7) | 0.8% (8) | 0.5% (26) |
| 7 days | 86.5% (2027) | 85.2% (1357) | 73.3% (767) | 83.3% (4151) |
| Days of moderate-intensity exercise per week | ||||
| 0 day | 100% (2342) | 32.5% (340) | 53.8% (2682) | |
| 1 days | 5% (80) | 4.1% (43) | 2.5% (123) | |
| 2 days | 10% (160) | 6.2% (65) | 4.5% (225) | |
| 3 days | 11.7% (187) | 7.7% (81) | 5.4% (268) | |
| 4 days | 4.6% (73) | 3.7% (39) | 2.2% (112) | |
| 5 days | 2.5% (40) | 2.9% (30) | 1.4% (70) | |
| 6 days | 0.8% (12) | 0.3% (3) | 0.3% (15) | |
| 7 days | 65.3% (1041) | 42.6% (446) | 29.8% (1487) | |
| Days of high-intensive exercise per week | ||||
| 0 day | 100% (2342) | 100% (1593) | 7.7% (81) | 80.6% (4016) |
| 1 days | 8.6% (90) | 1.8% (90) | ||
| 2 days | 13.8% (145) | 2.9% (145) | ||
| 3 days | 13.6% (142) | 2.9% (142) | ||
| 4 days | 5.7% (60) | 1.2% (60) | ||
| 5 days | 5.1% (53) | 1.1% (53) | ||
| 6 days | 0.8% (8) | 0.2% (8) | ||
| 7 days | 44.7% (468) | 9.4% (468) | ||
To measure the health effects of different exercise intensities, this study combined low, moderate, and high-intensity sports based on established theoretical frameworks, such as those proposed31. Specifically, the study examined the improvement in cardiovascular disease with each intensity level alone and in combination. Traditional classifications of exercise intensity rely on measured values of heart rate, heart strength, or self-perception, exhibiting a sequential nature. However, this study’s classification is based on individual participation behavior, incorporating multiple intensity levels, frequency, and duration of participation, allowing for superposition cases. As a result, elderly sports participants were divided into 3clusters (Table 2) in this method. Additionally, the automatic selection of the number of clusters was validated by silhouette ratio (Fig.1). The was above 0.5, which indicated the reasonable structure of cluster result32.
Fig. 1.
Clustering quality.
Cluster 1 comprises low-intensity participants who engage exclusively in light exercise. This group has a high participation frequency, with 86.5% engaging seven days a week. Most sessions last between 30 min and 2 h (57.6%), followed by 2 to 4 h (21.4%), indicating a moderate and manageable level of activity compared to the other two groups.
Cluster 2 includes non-high-intensity participants, who are deeply involved in moderate-to-low-intensity exercises. For moderate-intensity exercise, the highest participation is observed among those engaging seven days a week (65.3%), followed by three days a week (11.7%). As exercise intensity decreases, more elderly individuals participate in low-intensity exercise seven days a week (85.2%). Regardless of intensity, most participants prefer sessions lasting 30 min to 2 h (60.8% for moderate and 51% for low intensity). As intensity increases from low to moderate, the proportion of sessions lasting 30 min to 2 h decreases, while the proportion of shorter (0 to 30 min) and longer (> 4 h) sessions increase (Table 2).
Cluster 3 consists of multi-intensity participants engaging in low, moderate, and high-intensity sports, demonstrating resilience with aging. For all intensity levels, the average participation rate is highest at seven days per week for low-intensity (73.3%), followed by moderate (42.6%) and high (44.7%) intensity. In low-intensity exercise, the proportion of participants engaging seven days a week decreases, while the proportion of non-participants increases to 32.5%. For high-intensity exercise, the increase is more evident on two and three days per week (13.8% and 13.6%, respectively). Notably, 32.5% do not participate in moderate-intensity exercise weekly, the highest proportion among the three intensity levels, excluding high-intensity. In terms of duration, 30 min to 2 h is most preferred for low- and moderate-intensity exercise (47.5% and 32.4%, respectively), while high-intensity exercise has the highest proportion of sessions lasting more than four hours (31.5%), with 30 min to 2 h and 2 to 4 h accounting for 29% and 28%, respectively.
Correspondence analysis
Multiple Correspondence Analysis of Demographic Characteristics and Exercise Participation Types in the Elderly. Figure 2 presents a nuanced perspective on the relationship between demographic characteristics and exercise participation types among the elderly:
Low-Intensity Participants in Nursing Facilities: Older women with annual incomes ranging from 60,000 to 120,000 are more inclined to engage in low-intensity sports activities within nursing facilities. This demographic group exhibits a specific preference for such settings and intensity levels.
Non-High Intensity Participants in Urban–Rural Fringe Areas: Elderly men residing in urban–rural fringe areas and earning between 30,000 and 60,000 annually are more likely to participate in sports at non-high intensity levels. Notably, labels closely associated with this group include “primary school graduation” and “living with spouse,” suggesting potential influences of educational background and family structure on exercise habits.
multi-intensity participants associated with Lower-Income, Solo-Dwellers: Elderly individuals who live alone (due to separation or never being married) and have annual incomes below 10,000 yuan tend to engage in a wide range of sports intensities. The education level most closely related to this group is “not completed primary school,” highlighting the potential impact of socioeconomic status on exercise participation patterns.
Association with Age and Rural Residence: The scatter points representing “60–64 years old,” “65–69 years old,” and “rural area” are notably closer and equidistant from the scatter points of the three types of elderly sports participation. This indicates that older sports participants are more strongly associated with being “younger old people” and residing in “non-town centers,” suggesting potential regional and age-related influences on exercise behaviors.
Fig. 2.
Multiple correspondence analysis of demographic characteristics and sports participation types of the elderly.
Overall, the analysis reveals that elderly sports participants generally have low levels of education, are concentrated in the 60–69 age group, and live in non-urban center areas. As the type of sports participation varies, those engaging in high-intensity, long-duration, and frequent exercise are more strongly associated with low-income levels and living alone. Gender-specific patterns also emerge, with males more strongly linked to non-intensity sports participation and females more closely associated with low-intensity activities. These findings contribute to a deeper understanding of the demographic factors influencing elderly sports participation and provide valuable insights for policymakers and practitioners in the field of sports and social issues.
Multiple correspondence analysis of physical activity ability and sports participation in the elderly. The results of the multiple correspondence analysis, as depicted in Fig. 3, offer intriguing insights into the relationship between physical activity ability and sports participation among the elderly. Specifically, elderly individuals who reported difficulty jogging for 1 km were more likely to engage in low-intensity sports compared to those who had difficulties with bending, stooping, and squatting. This finding underscores a unique association between low-intensity sports participation and jogging ability among the elderly, truly reflecting the principle of “walking according to one’s ability.”
Fig. 3.
Multiple correspondence analysis of physical activity ability and sports participation in the elderly.
Notably, the correlation appears to be confined to low-intensity sports participants, indicating that activity ability plays a more significant role in determining participation levels within this category. Furthermore, the analysis reveals no discernible relationship between performing household chores and the ability to walk 1 km, suggesting that these two activities may involve different sets of physical skills and requirements.
Multiple correspondence analysis of chronic disease and sports participation in the elderly. Figure 4 presents a compelling analysis of the relationship between chronic disease status and sports participation among the elderly. The results indicate a strong correlation between the three types of sports participation observed in this demographic and the absence of chronic diseases. This finding suggests that elderly individuals who engage in various forms of sports are more likely to be free from chronic health conditions, highlighting the potential benefits of regular physical activity in maintaining good health and well-being as one ages.
Fig. 4.
Multiple correspondence analysis of chronic disease status and types of sports participation in the elderly.
Discussion
In order to response to aging society and achieve sports’ health function for elderly participants, this research aim to clarify the elderly sport participants’ characters and their potential influencing factors. Based on cluster analysis, this research divided elderly sports participants into 3 clusters (Table 2). Multiple correspondence analysis aimed to find relationship between elderly sports participators cluster and potential influencing factors. As for Demographic Characteristics aspect (Fig. 2), it indicated that elderly sports participants generally have low levels of education, are concentrated in the 60 to 69 age group, and live in non-urban center areas. As the type of sports participation varies, those engaging in high-intensity, long-duration, and frequent exercise are more strongly associated with low-income levels and living alone. As for physical activity ability aspect (Fig. 3), elderly individuals who reported difficulty jogging for 1 km were more likely to engage in low-intensity (cluster 1) sports compared to those who had difficulties with bending, stooping, and squatting. Activity ability plays a more significant role in determining participation levels within this category. As for chronic disease aspect (Fig. 4), elderly individuals who engage in various forms of sports are more likely to be free from chronic health conditions, highlighting the potential benefits of regular physical activity in maintaining good health and well-being as one ages. Thus, the findings confirm that psychosocial motivations and regional context significantly influence sports participation type, as outlined in the study’s objectives.
Regional disparities in sports participation: a preference for high intensity in rural elderly
These findings are consistent with studies showing greater physical activity propensity in rural areas due to less sedentary lifestyles33, but contrast with others assuming greater urban access to sports services34. The findings reveal that non-metropolitan areas serve as the primary locations for elderly sports participants. The results of a multiple correspondence analysis further demonstrate a close association between the urban–rural fringe area and non-high-intensity participants in elderly sports, although direct causality cannot be established in this study. Notably, elderly individuals from rural and urban–rural fringe areas constitute 72.1% of the elderly sports participants, with 62.9% hailing from rural regions.
Two facts may explain this phenomenon. Firstly, the demand for elderly sports differs across regions. Urban areas offer more options of leisure and entertainment than non-metropolitan areas. More options lead to more competition when individuals chose their lifestyle during leisure time. Conversely, in non-metropolitan areas of China, as wealth levels rise, more elderly individuals are liberated from physical work. However, recreational options had not kept pace with economic growth, leading to limited choices for leisure. Additionally, the experience of physical work has cultivated habits of physical activity among elderly in non-metropolitan areas. Although they are no longer compelled to engage in such activities for their livelihood, 84.5% of Cluster 3 participants live in non-metropolitan areas, supporting the hypothesis that rural area cultivates more elderly individuals chose the sport as recreational alternative.
Secondly, the accessibility of urban sports facilities for the elderly is lacking. Elderly individuals prefer mild sports, such as Tai chi and square dancing. Individuals usually participate in these sports at open space where is commonly available in rural areas. Furthermore, in rural areas, villagers usually exercise free. This habit promotes more frequent and intense participation in sports. 92.3% of Cluster 3 participate intensive sports, and 44.7% of Cluster 3 participates intensive sports 7 days per week, supporting the hypothesis that elderly sports participants in rural area prefer intensive sports. These individuals constitute the primary group of multi-intensity participants (Cluster 3). However, in metropolitan areas, high population density in urban centers makes it challenging to ensure venue accessibility. This situation usually leads more cost participating in sports at urban area than rural areas.
Psychosocial drivers: loneliness and marital status as predictors of sports engagement
The association between loneliness and intense participation may reflect a coping mechanism, although direct causality cannot be established in this study. Specifically, elderly sports participants who experience greater loneliness prefer more intense sports activities. According to multiple correspondence analysis (Fig. 2), marital statuses such as “never married” and “separated” are more strongly associated with high-intensity and multi-intensity participants (Cluster 3).
Elderly individuals living alone shows more psychological demands. Sports could response to the demands. As cognitive ability and social adaptability decline, the elderly often experience a reduced sense of self-efficacy35. This is particularly evident among empty-nest elderly individuals who face unforeseen events, such as the death of friends, lack of companionship from relatives, and diminishing energy over time. These individuals are more inclined to deeply engage in sports. They release stress and express themselves by intense physical activity. 92.3% of Cluster 3 participate intensive sports, and 44.7 Cluster 3 participates intensive sports 7 days per week. Overall, multi-intensity participants (Cluster 3) prefer intense sports (92.3%) for longtime and high frequency, although they are aging.
Health needs versus participation gaps: the Unmet potential of sports for chronic disease management
As for low-intensity (Cluster 1) and non-intensity participation (Cluster 2), these patterns may be associated with traditional values of Chinese filial piety. Many elderly people believe that living in elderly service institutions indicates the lack of offspring or an unhappy family. The indication shames them. Consequently, most elderly individuals will not reside in institutions, unless they are unable to take care of themselves and their families can’t offer help. Based on these beliefs, a majority of elderly individuals living in nursing home are not in good health (76.2%). These individuals show high demands for Chronic Disease Management. So, their participations of sports are not intense, and they belong to categories of low-intensity or non-intensity participation (Cluster 1 or Cluster 2). Additionally, elderly sports participants are weakly associated with chronic disease.
Overall, these findings contribute to a deeper understanding of the regional variations in elderly sports participation and highlight the need for tailored strategies to promote sports engagement across different areas. Moreover, the findings of psychosocial aspects underscore the complex interplay between loneliness, marital status, cultural values, and sports participation among elderly individuals. They highlight the need for tailored sports programs and interventions that address the unique psychological and physical needs of this demographic. Based on these finding and discussion, mobile community programs combining low-impact exercises with psychosocial support could be an effective solution for rural areas. Additionally, in order to reduce injury risks, the community facilities need age-appropriate design and fitness guidance. As for public policy perspective, sports facilities in rural area need more financial support, especially for age-appropriate designed facilities. In urban area, public policy should focus on fusion of sports and medicine for elderly sports participants. Community sports club combined with community clinics and elderly care home could be effective. It responses to demands of Chronic Disease Management.
Research limitations and prospects
Although this research aims to clarify the elderly sport participants’ characters and their potential influencing factors based on data from CHARLS, its limitations should be properly examined. This study relies on self-reported data, which may introduce recall or social desirability bias. The intensity measures are based on self-assessment, consistent with previous studies (Rhodes et al., 1999), though not formally validated in the Chinese context. Additionally, caused by too many variables in CHARLS, its cross-sectional nature precludes causal inference. The approach is purely exploratory. Moreover, insufficient examples at discussion part may lead to conclusions that only apply to certain situation.
Despite the limitations, this research focuses on elderly sport intensity characters and their potential influencing factors. Future research could set these potential factors as hypotheses and prove it by other type research, such as observational, experimental, retrospective, randomized controlled trial and so on. Future research could also validate elderly sports intensity scale in different area context, rather than being original to CHARLS. Future more, research should distinguish the characters of elderly sports intensity and influential factors from different area. This kind of research will provide more practical recommendation and more precise conclusion.
Conclusion
The intensity of participation in elderly sports doesn’t necessarily reflect an increase in physical strength with aging. On the contrary, it indicates that sports serve as an effective lifestyle choice for a significant portion of elderly individuals, particularly those who are villagers and not economically well-off. Multi-intensity participants (Cluster 3) are more closely associated with the following attributes: living alone or never married, earning less than 10,000 yuan per year, not completing primary school, being relatively young, and residing in non-town centers. 73.3% of Cluster 3 participants earn less than 10,000 yuan annually and 78.2% of Cluster 3 live in rural area, supporting the hypothesis that sports offer an affordable recreational alternative in low-resource settings. Additionally, the absence of a spouse provides more leisure time. These individuals show a strong need for accessible and fulfilling recreational options. Based on these, sport becomes a suitable, low-cost, and high-yield lifestyle.
The majority of elderly people (79%) engage in mild to non-vigorous intensity sports participation, categorized as Compatible participants (Cluster 1 and Cluster 2). These participants exhibit greater demands on the health functions of elderly sports. However, multiple correspondence analysis revealed a weak association between elderly sports participants and those with chronic diseases. Although the health motivation of individuals with chronic diseases is apparent, it does not translate into sports participation. This suggests that sports services for the elderly have not yet aligned with their health motivations, which may hinder the expansion of low-intensity or non-intensity participants (Cluster 1 or Cluster 2). The lack of age-friendly fitness facilities and guidance not only results in unclear health benefits of sports, such as prevention and treatment of chronic diseases, but may also increase the risk of sports injuries.
Research posed more importance on the need for tailored sports programs and services that cater to the unique needs and motivations of elderly individuals, particularly those with limited economic and educational resources and those with chronic diseases. By addressing these needs, we can promote broader participation in sports among the elderly and harness the full potential of sports as a tool for health and well-being.
Author contributions
All authors contributed to and refined the article. Lanfang Zhu, Chenyang Zhao and Yu Feng conceived of the study. Jian Kang, Chenyang Zhao and Suqi Jiang collected data collected data and refined the analysis plan. Lanfang Zhu, Jian Kang and Chenyang Zhao conducted the initial data analysis, Lanfang Zhu wrote the first draft of the study. All authors participated in editing the manuscript until completion.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study is supported by Zhejiang Provincial Philosophy and Social Science Planning Project (project number: 25NDJC030YB); Shanghai Philosophy and Social Sciences Planning Project (project number: 2024BTY002).
Data availability
This sample is grounded in the most recent data from the 2018China Health and Retirement Longitudinal Study (CHARLS), conducted by Peking University. (https://charls.charlsdata.com/pages/Data/2018-charls-wave4/zh-cn.html).
Declarations
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
This sample is grounded in the most recent data from the 2018China Health and Retirement Longitudinal Study (CHARLS), conducted by Peking University. (https://charls.charlsdata.com/pages/Data/2018-charls-wave4/zh-cn.html).