Abstract
Background
The health benefits of physical activity (PA) have been well recognized, while which types of PA are most beneficial are still unclear, especially for older adults. The study aimed to explore associations of different PAs (physical work, regular exercise, and leisure activities) with mortality among Chinese older adults, considering genetic risk.
Methods
A total of 9690 older adults from the Chinese Longitudinal Health Longevity Survey (CLHLS, 1998–2018) were included. Self-reported PAs information on physical work, regular exercise, and leisure activities were collected through face-to-face interviews. Leisure activities were interviewed about their engagement in 6 typical activities (i.e., housework tasks, personal outdoor activities, gardening, rearing domestic animals/pets, playing cards/mahjong, and attending in social activities). A weighted genetic risk score (GRS) was constructed based on 11 lifespan-related loci and divided into two groups according to the median scores (0.21). The Cox proportional risk model was used to assess the association between different types of PAs and genetic risk with all-cause mortality.
Results
During 63,832 person-years of follow-up, 5678 deaths were documented. The hazard ratios (HRs) for all-cause mortality between different PAs (lowest activity vs highest activity) were 0.85 (95% CI 0.79–0.92) for leisure activities, 0.93 (95% CI 0.87–0.99) for regular exercise, and 0.93 (95% CI 0.86–1.01) for physical work, respectively. Compared with low leisure activities, high leisure activities were associated with 16% reduction in all-cause mortality for individuals with low longevity GRS (HR 0.84, 95% CI 0.76–0.93), and 14% reduction in all-cause mortality for individuals with high longevity GRS (HR 0.86, 95% CI 0.78–0.96). Adherence to regular exercise was associated with 11% reduction in all-cause mortality for individuals with high longevity GRS (HR 0.89, 95% CI 0.81–0.97), while there was no statistically significance for those with low longevity GRS (HR 0.97, 95% CI 0.89–1.06) compared with those without regular exercise. There was no additive or multiplicative interaction between PAs and longevity genetics (Pinteraction > 0.05).
Conclusions
Leisure activities, as a low-risk, low-intensity, simple and inexpensive PA, rather than regular exercise, might bring the greatest health benefits, even for individuals with less longevity genes, highlighting the importance of providing individualized PA recommendations for older adults.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12916-025-04176-x.
Keywords: Older adults, Genetic risk, Physical activities, All-cause mortality
Background
Although global average life expectancy has increased, a significant difference between life expectancy (73.3 years) and healthy life expectancy (63.7 years) calls for an urgent requirement to promote healthy aging [1]. Physical activity (PA) is a pivotal modifiable factor for healthy aging [2–5], encompassing various domains such as leisure time, transportation, occupational, and household activities. Insufficient PA ranks as the fourth leading risk factor for global mortality [6]. Previous prospective cohort and randomized controlled studies have consistently associated higher PA with lower mortality risk [7]. The current PA guidelines recommend the same intensity (moderate-to-vigorous PA per week) and duration (150–300 min/week) for older adults as for general adults [8]. However, more than 60% of older adults fail to achieve the PA guidelines [9], as exacerbated by age-related declines in physical function. Notably, high-intensity strenuous exercise in older adults may increase the risk of adverse outcomes, including falls and sudden death [10–12]. Additionally, there is controversy regarding whether occupational PA increases mortality risk, decreases mortality risk, or has no effect on all-cause mortality [13, 14]. The current PA guidelines do not differentiate between occupational and non-occupational activities. Therefore, it is necessary to study which type of PA brings greater health benefits for older adults and provide further evidence for PA guidelines.
Longevity, a complex trait that can be influenced by both environmental and genetic factors [15], shows approximately 20–30% variability in lifespan due to genetic factors [16]. Gene-wide association studies (GWASs) have made it possible to identify multiple single nucleotide polymorphisms (SNPs) significantly associated with longevity [17, 18]. Generally, individuals who carry longevity-related variants genes or engage in PA are both expected to have longer lifespans, while few studies have considered genetic factors when investigating the association between PA and mortality. Our previous study observed that individuals with low genetic risk score (GRS) for longevity who adhere to healthy lifestyle of non-smoking, non-alcohol abuse, active PA, and healthy diet gain more years [19]. However, the effect of longevity genes on the relationship between different PAs and mortality remains unclear [20].
Given the limited available evidence regarding the most beneficial type of PA for older adults, this study examined the association between different forms of PA and the risk of all-cause mortality among Chinese older adults, taking into account longevity genes. This study aims to provide guidance for developing PA guidelines for older adults and promote personalized advice tailored to an individual’s genetic risk for optimal health benefits.
Methods
Study design and participants
The Chinese Longitudinal Healthy Longevity Survey (CLHLS) cohort study is an ongoing large-scale national cohort study covering 23 provinces and autonomous regions across China. This study was initially launched in 1998, along with 7 waves of follow-up, 6 waves of follow-up interviews conducted in 2000, 2002, 2005, 2008–2009, 2011–2012, 2014, and 2017–2018, with a detailed description of the study design in previously published literature [21]. The exclusion criteria were (1) age < 65 years (n = 600); (2) participants who were lost to follow-up during the initial follow-up (n = 6847); (3) individuals with missing information regarding PAs at baseline (n = 3100); and (4) participants with no available genetic data or whose genetic data did not pass quality control (n = 24,383). A total of 9690 study participants were finally included (Fig. 1). The study received approval from the Medical Ethics Committee of Peking University (IRB00001052-13074) and the Ethics Committee of the Institute of Environment and Health of the Chinese Center for Disease Control and Prevention (No. 201922). Each participant or their legal representatives signed written consent forms at the start of the surveys.
Fig. 1.
Flowchart of participants included in the study (n = 9960)
Assessment of physical activities
Baseline PAs were assessed by a self-reported questionnaire, which has been described and applied in the previous studies [22, 23]. The participants were asked if they were regularly engaged in physical work (yes/no). Physical work specifically refers to tasks related to agriculture or manual labor performed individually. Regular exercise was determined by asking participants if they are currently engaged in fitness-related exercise, e.g., walking, running, playing soccer, practicing qigong (a deep breathing exercise), or others. Participants who answered “yes” were classified as engaging in regular exercise, while those who answered “no” were considered not exercising.
Leisure activities were interviewed about their engagement in 6 typical activities, such as housework tasks, personal outdoor activities, gardening, rearing domestic animals/pets, playing cards/mahjong, and attending social activities. Assigning values to the responses enables quantification, with each is assigned specific scores (see detailed in Additional file 2: Table S1). The total leisure activity score was calculated as the sum of the scores assigned to the response categories for each leisure activity factor, ranging from 6 to 30 points, with a higher total score indicating greater adherence to leisure activities. The reliability and validity of self-reported leisure activities have been examined in previous studies [24, 25].
According to the WHO Guidelines on Physical Activity and Sedentary Behaviour [26], activities such as reading, watching TV/listening to the radio are classified as sedentary behavior. However, evidence from previous studies shows that reading and watching TV/listening to the radio are important for cognitive activity and promote healthy longevity among Chinese older adults [25, 27]. In this study, we then grouped the 8 activities into categories: individual activities (housework tasks, gardening, and personal outdoor activities), social activities (attending social activities, playing cards/mah-jongg, and rearing domestic animals), and sedentary behavior (reading and watching TV/listening to the radio).
Ascertainment of all-cause mortality
Survival status and death date were confirmed by close relatives or local physicians for deceased participants during follow-up. Follow-up duration was from recruitment to death or the last follow-up. At least three attempts to contact a participant had to be made before they were considered lost to follow-up.
Construction of genetic risk score
Process on variant selection and genotyping in the CLHLS study has been reported in detail elsewhere [17, 28]. The GRS for lifespan was calculated based on 11 previously reported genetic variants for longevity. For each participant, their GRS was calculated as the weighted sum of effect alleles at a given locus, with a higher score indicating greater longevity. Detailed information on each selected SNP and its respective weight coefficient is available from previous publications (Additional file 2: Table S2). Participants were then classified into low and high GRS groups on the median value of the longevity GRS (0.21).
Statistical analyses
At baseline, continuous variables were presented as mean ± standard deviation (SD) and were compared using one-way analysis of variance (ANOVA). Categorical variables at baseline were described as numbers (percentage), and group differences were evaluated using the chi-squared (χ2) test. Using Poisson regression models, age- and sex-adjusted mortality rates per 1000 person-years were calculated.
Cox proportional hazards regression models were used to estimate hazard ratios (HR) and 95% confidence intervals (CIs) for the associations between the longevity genes, different PAs (physical work, regular exercise, and leisure activities) and all-cause mortality. The duration of follow-up was calculated as the timescale for the analysis, with stratification based on the calendar year of recruitment. The proportional hazards assumption was evaluated using the Schoenfeld residual test, and no violations were observed (P > 0.05).
To mitigate potential confounding, demographics and contextual covariates were selected based on a priori-developed acyclic graph (Additional file 2: Fig. S1). Model 1 was fitted with a primary adjustment for age (continuous variable) and sex (male, female). Model 2 was further adjusted for education (illiteracy, literacy), marital status (married, unmarried), source of income (dependent, independent), living arrangement (with family, alone or in an institution), residence (urban, rural), dietary diversity score (poor, well), smoking status (never, former, or current smoking), and drinking status (never, former, or current drinking) and was mutually adjusted for other PAs as appropriate. Model 3 was further adjusted for BMI (underweight, normal, or overweight/obesity), hypertension (yes, no), diabetes mellitus (yes, no), cardiovascular disease (CVD) (yes, no), respiratory disease (yes, no), activities of daily living (ADL) disability (yes, no), cognitive impairment (yes, no), and cancer (yes, no). Model 4 was further adjusted for longevity GRS. Supplemental covariates provide detailed definitions (see Additional file 1 [29–33]).
We further stratified the analyses by GRS for longevity to assess the impact of genetic susceptibility on the relationship between different PAs and mortality. In addition, multiplicative and additive models assessed the interaction between different PAs and longevity GRS on mortality [34]. Interaction terms of PA × longevity GRS were created to examine if the model included interaction effects, using the likelihood method. Statistically, P value < 0.05 indicated significant multiplicative interaction. The study assessed the additive interaction between different PAs and longevity GRS on all-cause mortality by calculating the relative excess risk due to interaction (RERI) and synergy index (S). The absence of additive interaction was indicated if RERI contained 0 and the S contained 1.
To confirm the robustness of the results, we conducted the following sensitivity analyses: (1) excluding participants who died within the first 2 years of follow-up, to reduce the potential for reverse causation; (2) excluding participants with baseline ADL disability, which reduces the impact of older adults with physical functional limitation who may experience difficulty with participating in PA; (3) treating leisure activities score as tertiles in the main analyses; (4) including reading and watching TV/listening to the radio in the total leisure activity score to examine the association between leisure activities (8 items) and all-cause mortality; (6) combining “leisure activities+ regular exercise” into “physical activity” group (9 items) to examine the association between PA and all-cause mortality; (7) examining the effects of different types of leisure activities on all-cause mortality; (8) assessing trends in PA over the duration of follow-up time.
All statistical analyses for this study were performed using R V.3.4.5 (R Development Core Team, Vienna, Austria) and PLINK 1.9. A significance level of P < 0.05 was applied, with statistical significance determined based on two-sided tests.
Results
Descriptive characteristics
Table 1 presents the baseline characteristics of the study population. The analysis included a cohort of 9690 adults with a baseline mean age of 83.11 ± 11.68 years. Among the participants, approximately 5171 (53.46%) were females, 5727 (59.18%) were illiterate, and 5653 (58.34%) were married. During 63,832 person-years of follow-up, 5678 deaths occurred. Approximately 85.36% (8271) of older adults were involved in physical work, while only 29.86% (2893) exercised regularly. Additionally, 51.30% (4971) participated in high leisure activities. There were no significant differences between the low genetic risk and high genetic risk groups (Additional file 2: Table S3). Baseline characteristics of the sample across physical work, regular exercise, and leisure activities categories are detailed in Additional file 2: Tables S3–S5, respectively.
Table 1.
Baseline characteristics of the study participant in CLHLS 1998–2018
| Characteristics | All participants (n = 9690) | No. of alive (n = 4012) | No. of deaths (n = 5678) | P value |
|---|---|---|---|---|
| Age, mean (SD), years | 83.11 ± 11.68 | 76.53 ± 10.16 | 87.76 ± 10.37 | |
| Sex, n (%) | 0.002 | |||
| Male | 4519 (46.64) | 1947 (48.53) | 2572 (45.30) | |
| Female | 5171 (53.46) | 2065 (51.47) | 3106 (54.70) | |
| Educational attainment, n (%) | < 0.001 | |||
| Illiteracy | 5727 (59.18) | 1967 (49.09) | 3760 (66.30) | |
| Literacy | 3951 (40.82) | 2040 (50.91) | 1911 (33.70) | |
| Marital status, n (%) | < 0.001 | |||
| Married | 5653 (58.34) | 1667 (41.55) | 3986 (70.20) | |
| Unmarried | 4037 (41.66) | 2345 (58.45) | 1692 (29.80) | |
| Source of income, n (%) | < 0.001 | |||
| Dependent | 6800 (70.18) | 2219 (55.31) | 4581 (80.68) | |
| Independent | 2890 (29.82) | 1793 (44.69) | 1097 (19.32) | |
| Living arrangement, n (%) | 0.59 | |||
| With family | 8256 (85.32) | 3429 (85.58) | 4827 (85.15) | |
| Alone or in an institution | 1420 (14.68) | 578 (14.42) | 842 (14.85) | |
| Residence, n (%) | < 0.001 | |||
| Urban | 3184 (32.86) | 1523 (38.96) | 1661 (29.25) | |
| Rural | 6506 (67.14) | 2489 (62.04) | 4017 (70.75) | |
| Dietary diversity score, n (%) | < 0.001 | |||
| Well | 4140 (42.72) | 1482 (36.94) | 2658 (46.81) | |
| Poor | 5550 (57.28) | 2530 (63.06) | 3020 (53.19) | |
| Smoking status, n (%) | < 0.001 | |||
| Never | 6366 (65.70) | 2640 (65.80) | 3726 (65.62) | |
| Former | 2058 (21.24) | 921 (22.96) | 1137 (20.02) | |
| Current | 1266 (13.07) | 451 (11.24) | 815 (14.35) | |
| Drinking status, n (%) | 0.022 | |||
| Never | 6751 (69.67) | 2808 (69.99) | 3943 (69.44) | |
| Former | 2088 (21.55) | 887 (22.11) | 1201 (21.15) | |
| Current | 851 (8.78) | 317 (7.90) | 534 (9.40) | |
| BMI, n (%) | < 0.001 | |||
| Underweight (< 18.5 kg/m2) | 3006 (31.02) | 932 (23.23) | 2074 (36.53) | |
| Normal (18.5–23.9 kg/m2) | 5137 (53.01) | 2193 (54.66) | 2944 (51.85) | |
| Overweight or obesity (≥ 24 kg/m2) | 1547 (15.96) | 887 (22.11) | 660 (11.62) | |
| Disease, n (%) | ||||
| Hypertension, yes | 5317 (54.95) | 2185 (54.52) | 3132 (55.26) | 0.48 |
| Diabetes mellitus, yes | 199 (2.05) | 116 (2.89) | 83 (1.46) | < 0.001 |
| Cardiovascular disease, yes | 1142 (11.79) | 525 (13.09) | 617 (10.87) | 0.001 |
| Respiratory disease, yes | 1024 (10.57) | 371 (9.25) | 653 (11.50) | < 0.001 |
| Cancer, yes | 38 (0.39) | 17 (0.42) | 21 (0.37) | 0.80 |
| ADL disability, yes | 982 (10.13) | 113 (2.82) | 869 (15.30) | < 0.001 |
| Cognitive impairment, yes | 1831 (18.90) | 287 (7.15) | 1544 (27.19) | < 0.001 |
| Longevity genetic risk score, n (%) | 0.002 | |||
| Low longevity GRS | 4999 (51.59) | 1993 (49.68) | 3006 (52.94) | |
| High longevity GRS | 4691 (48.41) | 2019 (50.32) | 2672 (47.06) | |
| Physical activities, n (%) | ||||
| Physical work | 0.005 | |||
| Yes | 8271 (85.36) | 3376 (84.15) | 4895 (86.21) | |
| No | 1419 (14.64) | 636 (15.85) | 783 (13.79) | |
| Regular exercise | < 0.001 | |||
| Yes | 2893 (29.86) | 1404 (35.00) | 1489 (26.22) | |
| No | 6797 (70.14) | 2608 (65.00) | 4189 (73.78) | |
| Leisure activities | < 0.001 | |||
| Low leisure activities (≤ P50) | 4971 (51.30) | 1823 (45.44) | 3148 (55.44) | |
| High leisure activities (> P50) | 4719 (48.70) | 2189 (54.56) | 2530 (44.56) |
Data are presented using mean ± SD for continuous variables and numbers (percentages) for categorical variables. Of the 9690 older adults, the number of missing data ranged from 12 to 14 (12 for educational attainment, 14 for living arrangement, 14 for hypertension). Abbreviations: BMI, body mass index; ADL, activities of daily living; SD, standard deviation
Figure 2 shows the age- and sex-adjusted all-cause mortality rate per 1000 person-years for different PA groups. The death rate per 1000 person-years peaked in individuals with low leisure activities at 288.22 (95% CI 280.52 to 294.82), and was lowest among those with high leisure activities at 203.16 (95% CI 199.25 to 207.96).
Fig. 2.
Age- and sex-specific mortality rate per 1000 person-years according to different physical activities among older adults in CLHLS 1998–2018. The mortality rates were calculated using Poisson regression, with adjustment for age and sex. Physical work was categorized as either engaging in regular physical work or not engaging in physical work. Regular exercise was categorized as having regular exercise habits or not currently engaging in exercise. Leisure activities were divided based on the median and classified into low and high leisure activities groups
Physical activities associated with all-cause mortality
The associations between different PAs and all-cause mortality are presented in Table 2. Regular exercise or high leisure activities were related to lower risks of all-cause mortality. Compared with not regular exercise or low leisure activities, the fully adjusted HR (95% CI) of all-cause mortality was 0.93 (95% CI 0.87 to 0.99) for regular exercise and 0.85 (95% CI 0.79 to 0.92) for high leisure activities, respectively. No significant association was found between physical work and all-cause mortality (HR 0.93, 95% CI 0.86 to 1.01). Additional file 2: Table S7 shows the relationship between various PAs and all-cause mortality.
Table 2.
Hazard ratio (95% confidence interval) for the association of physical activities and all-cause mortality
| Groups | No. of deaths/no. of participants | Person-years | Model 1 | Model 2 | Model 3 | Model 4 |
|---|---|---|---|---|---|---|
| HR (95% CI) | HR (95% CI) | HR (95% CI) | HR (95% CI) | |||
| Physical work | ||||||
| No | 783/1419 | 9044 | Reference | Reference | Reference | Reference |
| Yes | 4895/8271 | 54,788 | 1.07 (0.99–1.16) | 0.94 (0.87–1.02) | 0.93 (0.86–1.01) | 0.93 (0.86–1.01) |
| Regular exercise | ||||||
| No | 4189/6797 | 42,500 | Reference | Reference | Reference | Reference |
| Yes | 1489/2893 | 21,332 | 0.78 (0.73–0.83) *** | 0.88 (0.83–0.94) ** | 0.92 (0.87–0.99) * | 0.93 (0.87–0.99) * |
| Leisure activities | ||||||
| Low leisure activities | 3148/4971 | 28,219 | Reference | Reference | Reference | Reference |
| High leisure activities | 2530/4719 | 35,612 | 0.84 (0.80–0.89) *** | 0.81 (0.75–0.87) *** | 0.85 (0.79–0.92) *** | 0.85 (0.79–0.92) *** |
Model 1: adjusted for age and sex. Model 2: Model 1 further adjusted for educational attainment, marital status, source of income, living arrangement, residence, dietary diversity score, smoking status, drinking status, and mutually adjusted for regular exercise, leisure activities, or physical work as appropriate. Model 3: Model 2 further adjusted for body mass index, hypertension, diabetes mellitus, cardiovascular disease, respiratory disease, activities of daily living disability, cognitive impairment, and cancer. Model 4: Model 3 further adjusted for longevity genetic risk score. Abbreviations: HR, hazard ratios; CI, confidence interval. *P <.05, ** P <.01, *** P <.001
Longevity genetic risk associated with all-cause mortality
The GRS for longevity in this study was approximately normally distributed (Additional file 2: Fig. S2). Longevity GRS for different PAs were shown in Additional file 2: Fig. S3. Older adults in the longevity GRS group exhibited a 7.0% decrease (HR 0.93, 95% CI 0.88 to 0.98) in all-cause mortality compared to those in the low longevity GRS group (Additional file 2: Table S8).
Physical activities associated with all-cause mortality stratified by longevity genetic risk score
Figure 3 illustrates the associations between different PAs and all-cause mortality stratified by longevity GRS. High leisure activities were associated with 16% mortality reduction in low longevity GRS individuals (HR 0.84, 95% CI 0.76 to 0.93) and 14% reduction in high longevity GRS individuals (HR 0.86, 95% CI 0.78 to 0.96), compared to low leisure activities. Regular exercise was linked to an 11% decrease in all-cause mortality for individuals with high longevity GRS (HR 0.89, 95% CI 0.81 to 0.97), while there was no statistical significance for those with low longevity GRS (HR 0.97, 95% CI 0.89 to 1.06), compared without regular exercise. Physical work was not significantly associated with all-cause mortality among older adults across all longevity GRS categories. However, no additive or multiplicative interaction between PAs and GRS on all-cause mortality was observed (Additional file 2: Table S9). When leisure activity levels were categorized into tertiles, we found that moderate and high levels of leisure activity significantly decreased the risk of death. Specifically, individuals with low GRS for longevity experienced 14% reduction in death risk for moderate leisure activities and 28% reduction for high leisure activities. For those with high GRS for longevity, engaging in high levels of leisure activity resulted in 17% reduction in the risk of death (Additional file 2: Table S10).
Fig. 3.
Adjusted hazard ratio (95% confidence interval) for the association between physical activities and all-cause mortality by different longevity genetic risk score groups. Models adjusted for age, sex, educational attainment, marital status, source of income, living arrangement, residence, dietary diversity score, smoking status, drinking status, body mass index, hypertension, diabetes mellitus, cardiovascular disease, respiratory disease, cancer, activities of daily living disability, cognitive impairment, mutually adjusted for regular exercise, physical work or leisure activities, as appropriate. Additive interactions for physical work and longevity genetic risk score are RERI = − 0.10 (− 0.27 to 0.07), AP = − 0.09 (− 0.25 to 0.07), S = 0.54 (0.18 to 1.6); Pmultiplicative =0.229. Additive interactions for regular exercise and longevity genetic risk score are RERI = − 0.02 (− 0.14 to 0.11), AP = − 0.01 (0.12 to 0.10), S = 0.90 (0.45 to 1.83); Pmultiplicative = 0.729. Additive interactions for leisure activities and longevity genetic risk score are RERI = 0.01 (− 0.10 to 0.13), AP = 0.01 (0.08 to 0.11), S = 1.06 (0.65 to 1.72); Pmultiplicative = 0.967. Abbreviations: AP, attributable proportion due to interaction; CI, confidence interval; HR, hazard ratio; RERI, relative excess risk due to interaction; SI, synergy index
Sensitivity analyses
A series of sensitivity analyses showed the results were stable, regardless of longevity GRS, and high leisure activities significantly attenuated the risk of death in older adults (Additional file 2: Table S11–13). The nine-activity model demonstrated marginally stronger protective effects than the eight-activity construct, reinforcing robustness (Additional file 2: Table S14). Upon further exploration of the types of leisure activities, we found that engaging in individual activities reduces the risk of all-cause mortality, regardless of genetic longevity risk (Additional file 2: Table S15). Although some variations in PA levels were noted during the follow-up period, these changes were of a relatively minor magnitude (Additional file 2: Fig. S4).
Discussion
This study, based on 9690 older adults who participated in the CLHLS from 1998 to 2018, comprehensively examined the association of different forms of PAs, longevity genes, and all-cause mortality. We found that leisure activities and regular exercise were associated with a reduced risk of all-cause mortality. Notably, participation in leisure activities showed health benefits regardless of longevity GRS. The benefits of regular exercise were only observed with low longevity GRS, and there was no statistical association between physical work and all-cause mortality among older adults across all longevity GRS categories.
Our study suggests that participation in leisure activities and regular exercise may reduce all-cause mortality risk, which aligns with other studies that various PAs decrease mortality risk to differing extents [22, 35–37]. For example, a previous study using 1998–2014 CLHLS data on adults ≥ 80 years (n = 30,070) indicated an 11–18% decreased mortality risk associated with leisure activities participation [22]. Similarly, a US prospective cohort study with 272,550 adults aged 50–69 years found that engaging in weekly leisure-time PA was associated with 3% to 16% reduction in all-cause mortality risk [37]. While some studies have shown that higher occupational PA increases mortality risk among general adults [13, 38], limited attention has been given to older adults regarding the association between physical work and mortality risk. A cohort study conducted in Taiwan, which included 2133 older adults over 8 years, found no significant relationship between occupational PA and all-cause mortality [39], consistent with our findings. This might be because the majority of Chinese older adults have lower levels of education attainment, live in rural areas, and engage in physical work related to agriculture (85.36%), and our study failed to accurately quantify the metabolic equivalents of their occupational PA. The biological mechanisms by which PAs contribute to longevity are unclear. The main mechanisms are as follows: (1) High PA levels may enhance metabolic health and stress resilience, promoting healthy aging [40]. (2) High PA levels could boost metabolic health and hormesis, influencing age-related weight and body composition changes, thus preventing sarcopenia and dementia [41]. (3) Regular exercise might improve endothelial function, potentially affecting mortality [42].
Our study shows that participation in leisure activities reduces mortality risk by 15%, which is higher than the 7% risk reduction associated with regular exercise. A systematic review and meta-analysis indicated that occupational PA, exercise and sports, leisure time for routine daily living activities, and PA in daily living contributed to mortality risk reductions of 17%, 34%, 34%, and 36%, respectively [43]. In contrast, our definition of leisure activities (including social activities and housework tasks) more closely aligns with activities of daily living and offers comparable or even greater health benefits than regular exercise. Leisure activities boost social self-identity and humoral immunity [44] and are inversely associated with stress, anxiety, and depression [45, 46]. Regular exercise, characterized as planned, structured, and repetitive aerobic activity, may yield comparatively modest health benefits, potentially due to adverse structural adaptations of the cumulative hemodynamic strain associated with prolonged endurance training [47].
Age-related declines in physical function and increased susceptibility to chronic conditions often constrain engagement in moderate-to-vigorous PA. Our findings indicate that leisure activities offer health benefits to older adults irrespective of genetic predispositions for longevity, whereas adherence to regular exercise only reduces mortality risk in individuals with high longevity GRS. This disparity may arise from the higher participation rates in leisure activities compared to regular exercise among older adults. Furthermore, leisure activities may overcome the initial barriers associated with regular exercise initiation, thereby enhancing overall PA levels. Few studies have quantified the potential interaction between genetic predisposition for longevity and PAs on the risk of all-cause mortality. No interactions between different PAs and GRS for longevity were observed in this study. Similarly, some previous studies have not observed the potential interactions between PAs and candidate genes for age-related diseases [48–50]. For instance, a US prospective study involving 5446 women and using three SNPs to construct GRS for longevity demonstrated an inverse relationship between PA and mortality across all GRS categories [48]. A UK-based prospective study with 10 years of follow-up found that PA, encompassing exercise, housework, and visiting family and friends, was linked to reduced dementia risk regardless of genetic predispositions [49]. Similarly, Zhou et al. observed that participation in leisure activities could decrease the risk of frailty across all levels of gene risk among Chinese adults aged 80 years or older [50]. However, some studies have found interactions between PAs and cognitive functioning [51, 52]. The absence of detected interactions in our study may stem from limited statistical power or insufficient accounting for rare genetic variants influencing longevity.
Our study, combined with longevity genes, provides further evidence that higher leisure activity in older adults reduce the risk of mortality. For individuals with fewer longevity genes, engaging in leisure activities may be a more effective alternative to regular exercise for maintaining and increasing overall PA levels, thereby reducing the risk of death. For individuals with more longevity genes, we recommend combining regular exercise with leisure activities for health benefits. Encouraging older adults to participate in these leisure activities could be a valuable component of PA guidelines, promoting a more inclusive and diverse approach to enhance their well-being. These findings paved the way for personalized medicine, where interventions could be tailored based on both genetic predisposition and lifestyle factors. Future studies should further quantify the health benefits of achieving minimum PA levels and validate these findings in other ethnic populations.
One of the major strengths of our study is its comprehensive examination of the relationship between physical work, regular exercise, and leisure activities on all-cause mortality among Chinese older adults, considering longevity genes. Moreover, the study is based on a large-scale, multicenter prospective cohort study with 20 years of follow-up and considers multiple potential confounding variables, which enhances the validation and reliability. However, this study has several limitations. Firstly, the PA assessments were conducted through face-to-face interviews that relied on self-reported data, which may introduce misclassification bias. Further research should evaluate PA using accelerometers or wearable devices to obtain more accurate and objective measurements. Secondly, the assessment of physical work in this study was basic and may not accurately reflect the true level of occupational PA among older adults. Thirdly, the evaluation of regular exercise in the study was limited, primarily concentrating on whether older adults had exercise habits, without adequately capturing the actual intensity and quantitative levels of exercise. Future studies should collect detailed information on the intensity, duration, and frequency of exercise. Fourthly, the potential impact of changes in PA patterns during the follow-up period on all-cause mortality was not analyzed in this study. Although this study examined changes in PA levels during each follow-up, the PA changes were minimal and consistent with findings from other studies [53]. Fifthly, the GRS based on 11 significant SNPs does not fully account for the inheritance of longevity in this study. Future research should incorporate a broader range of genomic variants to create a comprehensive lifetime GRS for Chinese older adults. Finally, this study was conducted only for Chinese older adults aged 65 and above, so the results may not be generalized to other ethnic populations.
Conclusions
In conclusion, this study suggests that leisure activities, characterized as low-risk, low-intensity, simple, and cost-effective forms of PA, rather than regular exercise, may provide greater health benefits for Chinese older adults, taking into account the longevity gene. These findings highlight the importance of promoting individualized PA recommendations for Chinese older adults and interventions for healthy aging.
Supplementary Information
Additional file 1. Supplementary methods. Assessment of physical activities. Assessment of covariates.
Additional file 2. Supplementary results. Table S1. The specific assignment of physical activities. Table S2. List of SNPs included in the longevity genetic risk score. Table S3. Baseline characteristics of the study participants by longevity genetic risk score. Table S4. Baseline characteristics of the study participants by categories of physical work. Table S5. Baseline characteristics of the study participants by categories of regular exercise. Table S6. Baseline characteristics of the study participants by categories of leisure activities. Table S7. Associations between leisure activities and all-cause mortality among Chinese older adults. Table S8. Hazard ratios (95% CIs) for longevity genetic risk score with all-cause mortality risk among Chinese older adults. Table S9. Analysis of the interaction of longevity genetic risk score and different physical activities with all-cause mortality among Chinese older adults. Table S10. Hazard ratios (95% CIs) for leisure activities with all-cause mortality risk by different longevity genetic risk score groups. Table S11. Associations of physical activities and genetic risk scores with all-cause mortality excluding participants died within the first 2 years after baseline. Table S12. Associations of physical activities and longevity genetic risk score with all-cause mortality excluding participants ADL. Table S13. Hazard ratios (95% CIs) for leisure activities with all-cause mortality risk by different longevity genetic risk score groups. Table S14. Hazard ratios (95% CIs) for physical activities with all-cause mortality risk by different longevity genetic risk score groups. Table S15. Associations between different leisure activity types and all-cause mortality among Chinese older adults. Fig.S1 Directed acyclic graph for the causal relationship between physical activities and all-cause mortality. Fig. S2 Distribution of genetic risk score of longevity. Fig.S3 Longevity genetic risk scores for different physical activities. Fig.S4 Trends in physical activity score over follow-up time.
Acknowledgements
This work was supported by the National Natural Sciences Foundation of China (grants No.82222063, YL; 82025030 XS; 82388102, HS). We thank all investigators and participants who conducted and participated in the survey.
Abbreviations
- PA
Physical activity
- GWASs
Genome-wide association studies
- SNPs
Single nucleotide polymorphisms
- CLHLS
Chinese Longitudinal Healthy Longevity Survey
- GRS
Genetic risk score
- BMI
Body mass index
- ADL
Activities of daily living
- CVD
Cardiovascular disease
- RERI
Relative excess risk due to interaction
Authors’ contributions
XS and YL contributed to the study funding, concept, and design. LX and JW drafted the manuscript. LX and performed the statistical analysis. All authors contributed to the manuscript revision and approval. YL, JW, XL, YL, CC, ZX, JZ, YW and GC contributed to critical review of the manuscript. YX, MZ, ZL, BW, ZZ, FL, and YL provided administrative, technical, or material support. All authors were involved in the interpretation of results, helped refine the manuscript, and approved its final version. YL and XS had full access to all data in the study and took responsibility for data integrity, verification, and analysis.
All authors were involved in the interpretation of results, helped refine the manuscript, and approved its final version. YL and XS had full access to all data in the study and took responsibility for data integrity, verification, and analysis.
Funding
This study was supported by funding and grants for XS and YL from the National Natural Science Foundation of China (8222063,82025030 and 82388102).
Data availability
No datasets were generated or analysed during the current study.
Declarations
Ethics approval and consent to participate
The study involves human participants, and the Medical Ethics Committee of Peking University (IRB00001052-13074) and the Ethics Committee of the Institute of Environment and Health of the Chinese Center for Disease Control and Prevention (No. 201922).
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Lanjing Xu and Jun Wang contributed equally to this work.
Contributor Information
Yuebin Lv, Email: lvyuebin@nieh.chinacdc.cn.
Xiaoming Shi, Email: shixm@chinacdc.cn.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Additional file 1. Supplementary methods. Assessment of physical activities. Assessment of covariates.
Additional file 2. Supplementary results. Table S1. The specific assignment of physical activities. Table S2. List of SNPs included in the longevity genetic risk score. Table S3. Baseline characteristics of the study participants by longevity genetic risk score. Table S4. Baseline characteristics of the study participants by categories of physical work. Table S5. Baseline characteristics of the study participants by categories of regular exercise. Table S6. Baseline characteristics of the study participants by categories of leisure activities. Table S7. Associations between leisure activities and all-cause mortality among Chinese older adults. Table S8. Hazard ratios (95% CIs) for longevity genetic risk score with all-cause mortality risk among Chinese older adults. Table S9. Analysis of the interaction of longevity genetic risk score and different physical activities with all-cause mortality among Chinese older adults. Table S10. Hazard ratios (95% CIs) for leisure activities with all-cause mortality risk by different longevity genetic risk score groups. Table S11. Associations of physical activities and genetic risk scores with all-cause mortality excluding participants died within the first 2 years after baseline. Table S12. Associations of physical activities and longevity genetic risk score with all-cause mortality excluding participants ADL. Table S13. Hazard ratios (95% CIs) for leisure activities with all-cause mortality risk by different longevity genetic risk score groups. Table S14. Hazard ratios (95% CIs) for physical activities with all-cause mortality risk by different longevity genetic risk score groups. Table S15. Associations between different leisure activity types and all-cause mortality among Chinese older adults. Fig.S1 Directed acyclic graph for the causal relationship between physical activities and all-cause mortality. Fig. S2 Distribution of genetic risk score of longevity. Fig.S3 Longevity genetic risk scores for different physical activities. Fig.S4 Trends in physical activity score over follow-up time.
Data Availability Statement
No datasets were generated or analysed during the current study.