Abstract
As population aging increased globally, this study aimed to examine the relationship between leisure activity participation and cognitive function in the older Chinese population and the gender and age differences in this relationship. A total of 12526 individuals aged 60 years and older from the 2018 Chinese Longitudinal Healthy Longevity Survey were obtained for this study. Multiple linear regression analysis was used to explore the relationship. This study found productive, physical, intellectual, recreational and social activities were associated with cognitive function. Males enjoyed extra benefits from recreational activities compared with females. Females enjoyed extra benefits from productive activities and social activities compared with males. Older age enjoyed extra benefits from productive activities, physical activities, intellectual activities, recreational activities, social activities in comparison with younger age. Thus, Chinese older adults need to make efforts to increase the time and variety of leisure activities, especially for the oldest old. Compared with other types of leisure activities, it’s more important for old men to participate in recreational activities, and for old women to participate in productive activities and social activities.
Keywords: Leisure activity, Cognitive function, Gender difference, Age difference, Older adults
| Text box 1. Contributions to the literature |
|---|
| • There is limited evidence on the interaction between gender or age and different types of leisure activities on the cognitive function of the Chinese older adults. |
| • This study found productive, physical, intellectual, recreational and social activities were associated with cognitive function. |
| • Males enjoyed extra benefits from recreational activities. Females enjoyed extra benefits from productive activities and social activities. Older age enjoyed extra benefits from every type of activities in comparison with younger age. |
| • The findings could provide scientific evidence for the reasonable selection of leisure activities for the Chinese older adults. |
Background
As population aging increases globally, dementia is a growing public health concern, and the cognitive function in older adults has become a major public health issue. According to the World Alzheimer Report 2023, the number of people living with dementia worldwide is currently estimated at over 50 million and will almost triple by 2050 [1]. In China, more than 6.0% of older adults suffer from dementia, and more than 3.9% have AD (Alzheimer's disease) [2]. Low cognitive function has been considered the preclinical symptom of dementia [3]. China has the largest older population in the world, which indicates a high prevalence of dementia or mild cognitive impairment [4]. Thus, there is an urgent need to pay more attention to the cognitive function of Chinese old adults to promote health aging.
It is worth noting that leisure activity participation could be considered a protective factor for cognitive function [5]. After retirement, increased participation in leisure activities has emerged as the paramount manifestation of lifestyle transitions for older adults. Some studies have shown that leisure activities had a positive impact on cognitive function and the effect of leisure activity on the delay of cognitive function impairment varied depending on activity type, gender and the age of the elderly [6, 7]. According to previous studies and the current situation of older Chinese adults, leisure activities could be classified into five types: productive activities, physical activities, intellectual activities, recreational activities and social activities [8]. To our knowledge, most established studies only centered their focus on the effect of social, intellectual and physical activities and indicated that the social, intellectual and physical activities had significant positive effects on cognitive function in older adult [9, 10]. While few studies paid attention to productive activities like housework and recreational activities like watching TV in addition that the findings of these studies were inconsistent. One study found that high involvement in productive and recreational activities like housework and gardenwork was related to significantly steeper decline in spatial ability and memory [7]. Another study found there were no associations between recreational activities like watching TV and cognitive function in the elderly [11]. Confirming the effects of the types and amounts of leisure activities on the cognitive function of the older adults could provide scientific evidence for the reasonable selection of leisure activities for the Chinese older adults.
Previous relevant studies on gender and age differences only divided the older adults of different genders or ages into different groups and analyzed the associations of leisure activities and cognitive function separately [6, 7]. Exploring the interaction between gender or age and different types of leisure activities on the cognitive function of the older adults could help to identify the best leisure activities and make tailored suggestions for Chinese older adults of different genders and ages, achieving active and successful aging in China. However, there were rare studies verifying the interaction effects of gender or age and different types of leisure activities on cognitive function.
Hence, the objectives of our study were to investigate (1) the association between leisure activities and cognitive function among older Chinese adults; (2) the association between different types of leisure activities and cognitive function among older Chinese adults; and (3) the interaction effects of gender or age and different types of leisure activities on cognitive function in older Chinese adults.
Methods
Study design and sample
This study used data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS), an ongoing longitudinal study that began in 1998 with follow-up surveys every 2 to 3 years. The CLHLS is a nationwide Chinese survey conducted in randomly selected counties and cities in 22 out of 30 provinces. As a national representative sample, the CLHLS adopted a multistage disproportionate and targeted random sampling method. More details on the sampling procedures and data quality of this survey have been published elsewhere [12]. Ethics approval was granted by the Research Ethics Committees of Peking University. All participants or their legal representatives signed written consent forms at the baseline and follow-up surveys.
The current study was based on cross-sectional data from 2018, and included 12526 participants (60 years and older) as the study participants. In the latest survey conducted in 2018, CLHLS sampled 15,874 older adults and collected information on their cognitive function, mental status and leisure activities. Since there were a number of questionnaires lacked some information such as age, residence, leisure activity participation and cognitive function, we did not include this group of people in our study. In addition, our target population was the Chinese adults over 60 years old, and people under 60 years were excluded. Thus, our study sample size was 12526. The flow chart of the inclusion and exclusion criteria for the study sample is shown in Fig. 1.
Fig. 1.
Flow chart of sample selection
Assessment of cognitive function
Cognitive function was assessed by the Chinese version of the Mini-Mental State Examination through a home-based interview, which includes 24 items covering 7 subscales including (1) orientation (4 points for time orientation and 1 point for place orientation); (2) naming foods (naming as many kinds of food as possible in 1 minute, 7 points); (3) registration of 3 words (3 points); (4) attention and calculation (mentally subtracting 3 iteratively from 20, 5 points); (5) copying a figure (1 point); (6) recall (delayed recall of the 3 words mentioned above, 3 points); and (7) language (2 points for naming objectives, 1 point for repeating a sentence, and 3 points for listening and following directions). The score ranges from 0 to 30. Higher scores represent better cognitive function. The internal consistency coefficient Cronbach's α coefficients in this scale of study were 0.945 in 2018.
Measurements of leisure activity participation
According to previous related studies [8], leisure activities were divided into five types. Each leisure activity was clearly defined and independent of each other, with no overlapping: (1) Productive activities (“Housework” was selected from the questionnaire for measurement. Samples that reported no participation in the above activity indicated that they did not have productive activity participation.); (2) Physical activities (“Tai Chi”, “Square dancing”, “Other outdoor activities” were selected from the questionnaire for measurement. Samples that reported no participation in the above activities indicated that they did not participate in physical activity; samples that participated in at least one of the above activities indicated that they participated in physical activity.); (3) Intellectual activities (“Reading newspapers/books”, “Playing cards and/or mahjong” were selected from the questionnaire for measurement. Samples that reported no participation in the above activities indicated that they did not participate in intellectual activity; samples that participated in at least one of the above activities indicated that they participated in intellectual activity.); (4) Recreational activities (“Watching TV and/or listen to radio”, “Garden work”, “Raising livestock” were selected from the questionnaire for measurement. Samples that reported no participation in the above activities indicated that they did not participate in recreational activity; samples that participated in at least one of the above activities indicated that they participated in recreational activity.); (5) Social activities (“Visiting and interacting with friends”, and “Social activities (organized)” were selected from the questionnaire for measurement. Samples that reported no participation in the above activities indicated that they did not have social activity participation.). Scores on each activity were binary (e.g. 1 for participation and 0 for otherwise). The total score on leisure activity participation is 11.
Statistical analysis
Descriptive statistics were conducted to describe participants, including means and standard deviation of continuous variables and distributions of categorical variables. This study established a multiple linear regression model to examine the relationship between leisure activity participation and cognitive function in older adults. According to previous studies [8, 9, 13], this study included gender, age, education, residence, marital status, annual household income, occupation, activities of daily living (ADL), current smoking status, current alcohol consumption and chronic disease as confounding variables. In addition, the interaction effects of age or gender and each of the five types of leisure activities on cognitive function score were also examined. All data analyses were implemented using R version 4.1.2.
Results
Descriptive analyses
Table 1 presented the sample characteristics of this study by gender. The average cognition score was 24.62, which was higher in males (26.11) than in females (23.38). 86.93% had participated in leisure activities, with a higher percentage of males (91.77%) than females (82.89%). Leisure activity score (the number of leisure activities participated in) was 3.34, which was higher in males (3.70) than in females (3.03). Overall, 58.20% had participated in productive activities, 37.09% had participated in physical activities, 34.23% in intellectual activities, 78.28% in recreational activities, and 63.24% in social activities. The participation of all other types of leisure activities was higher in males than in females except for productive activities.
Table 1.
Sociodemographic, leisure activity participation and cognitive function characteristics among older Chinese adults (n = 12526) by gender
| Variables | N(%)/mean (SD) | Male (n = 5699) | Female (n = 6827) |
|---|---|---|---|
| Total/Count(%) | Total/Count (%) | ||
| Age, years, mean (SD) | 83.83(11.22) | 82.13(10.41) | 85.24(11.67) |
| Aged (60 ~ 79) | 4829(38.55) | 2449(42.97) | 2380(34.86) |
| Aged, 80 and over | 7697(61.45) | 3250(57.03) | 4447(65.14) |
| Educationa, years | |||
| None (0) | 5857(46.76) | 1465(25.71) | 4392(64.33) |
| Primary school (1 ~ 6) | 4156(33.18) | 2528(44.36) | 1628(23.85) |
| Middle school or higher (> 6) | 2421(19.33) | 1659(29.11) | 762(11.16) |
| Residence | |||
| Rural | 10583(84.49) | 4779(83.86) | 5804(85.02) |
| Urban | 1943(15.51) | 920(16.14) | 1023(14.98) |
| Marital statusa | |||
| Married and living with spouse | 5383(42.97) | 3351(58.80) | 2032(29.76) |
| Others | 7031(56.13) | 2294(40.25) | 4737(69.39) |
| Family annual incomea, RMB, mean | 43764.43(37162.33) | 45095.90(37346.93) | 42646.78(36972.47) |
| < 30,000 | 5366(42.84) | 2369(41.57) | 2997(43.90) |
| ≥ 30,000 | 6844(54.64) | 3203(56.20) | 3641(53.33) |
| Occupationa | |||
| Manual worker | 11091(88.54) | 4727(82.94) | 6364(93.22) |
| Nonmanual worker | 1297(10.35) | 899(15.77) | 398(5.83) |
| Smokinga | |||
| Noncurrent | 10424(83.22) | 3953(69.36) | 6471(94.79) |
| Current | 1986(15.86) | 1706(29.94) | 280(4.10) |
| Alcohol drinkinga | |||
| Noncurrent | 10474(83.62) | 4152(72.85) | 6322(92.60) |
| Current | 1876(14.98) | 1486(26.07) | 390(5.71) |
| ADL condition | |||
| Not impaired | 9699(77.43) | 4684(82.19) | 5015(73.46) |
| Impaired | 2827(22.57) | 1015(17.81) | 1812(26.54) |
| Chronic disease | |||
| Yes | 10361(82.72) | 4683(82.17) | 5678(83.17) |
| No | 2165(17.28) | 1016(17.83) | 1149(16.83) |
| Leisure activity | |||
| Leisure activity participation | 10889(86.93) | 5230(91.77) | 5659(82.89) |
| Leisure activity score | 3.34(2.18) | 3.70(2.13) | 3.03(2.17) |
| Productive activity | 7290(58.20) | 3184(55.87) | 4106(60.14) |
| Physical activity | 4646(37.09) | 2361(41.43) | 2285(33.47) |
| Intellectual activity | 4288(34.23) | 2692(47.24) | 1596(23.38) |
| Recreational activity | 9805(78.28) | 4864(85.35) | 4941(72.37) |
| Social activity | 7922(63.24) | 3831(67.22) | 4091(59.92) |
| Cognition scores, mean | 24.62(7.10) | 26.11(6.07) | 23.38(7.64) |
SD Standard deviation, ADL Activities of daily living
Prefixing ain the variable names means that this variable has missing values
With regard to the demographic variables, 45.50% of the study samples were male. 61.45% were 80 years old and above. The average age was 83.83 years. Regarding education level, 46.76% were illiterate, 33.18% were educated for 1 to 6 years, and 19.33% were educated for 6 years and above. The majority of the surveyed older adults were rural, while 15.51% were urban. Most of the surveyed older adults were manual workers (including “staff/service worker/industrial worker”, “self-employer”, “agriculture, forestry, animal husbandry, fishery”, “housewife”, “military personnel”, “unemployed”), while only 10.35% were nonmanual workers (including “professional or technical personnel/doctors/teachers”, “governmental, institutional or managerial personnel”). In terms of marital status, 42.97% overall were married and living with their spouse. The majority of the surveyed older adults were noncurrent smokers, while only 15.86% were current smokers. Most of the surveyed older adults were noncurrent alcohol drinkers, while only 14.98% were current alcohol drinkers. A total of 77.43% did not have any ADL disabilities. 82.72% of the surveyed older adults suffered from chronic diseases.
Multiple linear analyses
Table 2 showed the results of the multiple linear analysis. After adjusting for potentially confounding variables, leisure activity participation and leisure activity scores were both significantly associated with cognitive function among older adults. Compared to those who had not participated in leisure activities, older adults who had participated in leisure activities had much higher cognition scores and much better cognitive function. In addition, after adjusting for potential confounding variables, among the older adults, cognitive function was significantly associated with productive activity participation, physical activity participation, intellectual activity participation, recreational activity participation and social activity participation.
Table 2.
Association between participation in different types of leisure activities and cognitive function among older Chinese adults
| β | P value | β | P value | β | P value | |
|---|---|---|---|---|---|---|
| Leisure activity participation (ref: No) | ||||||
| Yes | 5.76 | <0.001 | ||||
| Leisure activity score | 0.76 | <0.001 | ||||
| Productive activities (ref: No) | ||||||
| Yes | 1.31 | <0.001 | ||||
| Physical activities (ref: No) | ||||||
| Yes | 0.46 | <0.001 | ||||
| Intellectual activities (ref: No) | ||||||
| Yes | 0.96 | <0.001 | ||||
| Recreational activities (ref: No) | ||||||
| Yes | 3.20 | <0.001 | ||||
| Social activities (ref: No) | ||||||
| Yes | 1.15 | <0.001 | ||||
| Age | -0.19 | <0.001 | -0.18 | <0.001 | -0.16 | <0.001 |
| Sex (ref: Female) | ||||||
| Male | 0.03 | <0.001 | 0.76 | <0.001 | 0.70 | <0.001 |
| Education (ref: Middle school or higher) | ||||||
| None | -1.60 | <0.001 | -0.97 | <0.001 | -1.03 | <0.001 |
| Primary school | 0.15 | 0.244 | 0.67 | <0.001 | 0.48 | 0.003 |
| Residence (ref: Rural) | ||||||
| Urban | 0.35 | 0.017 | 0.25 | 0.101 | 0.23 | 0.124 |
| Marital status (ref: Married and living with spouse) | ||||||
| Others | -0.18 | 0.133 | -0.09 | 0.490 | -0.05 | 0.670 |
| Family annual income | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 |
| Occupation (ref: Manual worker) | ||||||
| Nonmanual worker | 0.83 | <0.001 | 0.79 | <0.001 | 0.75 | <0.001 |
| Smoking (ref: Noncurrent) | ||||||
| Current | -0.22 | 0.134 | -0.18 | 0.243 | -0.20 | 0.169 |
| Alcohol drinking (ref: Noncurrent) | ||||||
| Current | 0.20 | 0.164 | 0.11 | 0.480 | 0.10 | 0.485 |
| ADL condition (ref: Not impaired) | ||||||
| Impaired | -3.30 | <0.001 | -3.56 | <0.001 | -3.08 | <0.001 |
| Chronic disease (ref: No) | ||||||
| Yes | 0.39 | 0.003 | 0.44 | 0.001 | 0.39 | 0.003 |
Table 3 presented the results of the interaction effects of gender and five types of leisure activities on cognitive function. Males enhanced the relationship between recreational activities and better cognitive function. Females enhanced the relationship between productive activities, social activities and better cognitive function. In other words, males enjoyed extra benefits from recreational activities compared with females. Females enjoyed extra benefits from productive activities and social activities compared with males.
Table 3.
Interaction effects of gender and different types of leisure activities on cognitive function
| Variables | β | P value |
|---|---|---|
| Male(ref: female) | 1.52* | < 0.001 |
| Male*Productive activities | -0.06* | < 0.001 |
| Male*Physical activities | -0.22 | 0.318 |
| Male*Intellectual activities | -0.44 | 0.055 |
| Male*Recreational activities | 0.58* | 0.041 |
| Male*Social activities | -0.58* | 0.013 |
Model adjusted for age, education, residence, marital status, family annual income, occupation, smoking, alcohol drinking, activity of daily living (ADL) condition, and chronic disease. Symbol[*] meant the effect was statistically significant (P<0.05)
The results of the interaction effects of age and the five types of leisure activities on cognitive function were shown in Table 4. Older age enhanced the relationship between productive activities, physical activities, intellectual activities, recreational activities, social activities and better cognitive function. In other words, older age enjoyed extra benefits from productive activities, physical activities, intellectual activities, recreational activities, social activities in comparison with younger age.
Table 4.
Interaction effects of age and different types of leisure activities on cognitive function
| Variables | β | P value |
|---|---|---|
| Age | -0.37* | < 0.001 |
| Age* Productive activities | 0.06* | < 0.001 |
| Age* Physical activities | 0.04* | < 0.001 |
| Age*Intellectual activities | 0.10* | < 0.001 |
| Age*Recreational activities | 0.11* | < 0.001 |
| Age*Social activities | 0.07* | < 0.001 |
Model adjusted for gender, education, residence, marital status, family annual income, occupation, smoking, alcohol drinking, activity of daily living (ADL) condition, and chronic disease. Symbol[*] meant the effect was statistically significant (P<0.05)
Discussion
After adjusting for potential confounding variables, our study found that older adults who participated in leisure activities were significantly associated with better cognitive function. Productive activities in this study were defined as doing housework, which included meal preparation, medication management, shopping and handling finances. All of these things could improve memory and thinking abilities, which contribute to improving older adults’ cognitive function [14]. Participation in physical activities was positively associated with older adults’ quality of life, life satisfaction, and self-efficacy, contributing to positive moods and cognitive function [15]. When reading newspapers and books, older adults think positively about comprehension, which stimulates and improves cognitive function effectively [16]. Watching TV or listening to the radio could act as a positive cognitive stimulus for older adults’ brains [17]. There are several possible explanations for the relationship between social activities and cognitive function. Social activity participation maintains or expands the social network of older adults. Studies have indicated that extensive social networks seem to protect against dementia [18]. Individuals with an expanded social network have more chances to access various forms of material resources or health-related information, which may have a positive impact on cognitive function [19]. Additionally, participation in social activities will open a door for older adults to acquire new learning as an effective intellectual stimulation [20].
Influenced by Chinese specific cultural practices or norms, some Chinese older adults always engaged in playing Tai Chi, kungfu and majiang, which might have specific beneficial effects on cognition at the behavioral and neuroelectric levels [21]. Regarding the differences across gender, our study found that males enjoyed extra benefits from recreational activities compared with females. Females enjoyed extra benefits from productive activities and social activities compared with males. Previous studies found that there were significant gender differences in leisure activity participation. One study found that women were more active than men within the domain of domestic activities, while men were more active than women in self-improvement [7]. In traditional Chinese culture, men often seek significance and identity through being esteemed in their work unit, whereas women tend to pay more attention to the internal family. This finding was also consistent with the Chinese cultural context and our study findings.
Regarding the differences across age groups, this study found that older age enjoyed extra benefits from productive, physical, intellectual, recreational and social activities in comparison with younger age. Compared to younger older adults, the oldest old adults were more likely to sustain multiple types of neuropathology, and this increased burden was associated with decreased cognitive function. Factors such as a higher prevalence of sensory deficits, frequent comorbidities leading to polypharmacy, and a higher prevalence of social isolation make the oldest old population a unique group, differing from elderly individuals under 80 years of age (“young older adults”) [22]. This also highlighted the importance of participation in leisure activities among the older adults in their senior years.
This study indicated that participation in leisure activities is important for improving cognitive function in the older adults. This study also found a significant dose-response association between participation in increased leisure activities and reduced risk of dementia. Thus, in order to improve cognitive function, the older adults should increase not only the amount of time they participate in leisure activities, but also the types of leisure activities they participate in. Compared with other types of leisure activities, it’s more important for male to participate in recreational activities, and for female to participate in productive activities and social activities. The community could tailor different leisure activities for elderly of different genders and ages, for instance, organized social activities for older women, low to moderate intensity leisure activities for the oldest old adults. The government could provide more resources and venues for the elderly to participate in different types of leisure activities, strengthening the supportive environment across the whole society to improve cognitive function of the older adults effectively.
The limitations of this study are as follows. Firstly, all items were self-reported, which may lead to biased results. There may also be some other confounding factors that were not considered in this study, such as living status (alone or not) and depression. Future studies could take these potential confounding variables into consideration. Secondly, cross-sectional studies could not examine causal relationship, in other words, the present cross-sectional design could not exclude the possibility that older adults with better cognitive function tend to participate in more leisure activities. However, the data used is the most recent CLHLS data available, which could best reflect the current status of leisure activity participation and cognitive function of the Chinese older adults. Additionally, using cross-sectional data from 2018 was sufficient for this study to detect statistically significant associations between leisure activities and cognitive function, to explore the interaction effects of gender or age and different types of leisure activities on cognitive function in Chinese older adults. If using multi-stage panel data, there may be changes in survey methodology, question wording, or response options over time that could introduce bias or make it difficult to compare results across years. Thirdly, data during or after the COVID-19 pandemic were not available for this study. According to previous studies, COVID-19 could affect the participation in leisure activities, especially reducing the participation in physical and social activities while increasing the participation in indoor productive activities, recreational activities and intellectual activities [23]. China has optimized and adjusted the COVID-19 response measures for nearly two years, the lifestyle and leisure activity participation patterns of the elderly in China have been more similar to those before the COVID-19 pandemic. Based on this, the data of 2018 could reflect the current situation to a large extent. Due to no updated data available after the COVID-19 pandemic, we could not compare the differences before and after the COVID-19 pandemic in this study. Future studies can further explore the impact of the COVID-19 pandemic on the basis of this study.
The database used in this study, the Chinese Longitudinal Healthy Longevity Survey (CLHLS), is the earliest and longest-lasting social science survey in China, which makes the findings in this study more convincing. Although there are some limitations, our study is a large representativeness sampling study to decipher the relationship between leisure activity participation and cognitive function, which varies across activity types, gender and the age of the older Chinese population. This study found the significant positive association between five different types of leisure activities and cognitive function among older Chinese adults. The interaction between gender or age and different types of leisure activities on cognitive function in Chinese older adults was statistically significant. Future studies could expand the study population to the older adults in other countries, further explore the interaction effects of cultural backgrounds and different types of leisure activities on cognitive function in older adults across the world on the basis of this study.
Conclusions
This study found that participation in productive, physical, intellectual, recreational and social activities was positively associated with cognitive function. Efforts to increase the time and variety of leisure activities are needed to improve cognitive function in older Chinese adults, especially for the oldest old adults. Compared with other types of leisure activities, it’s more important for male to participate in recreational activities, and for female to participate in productive activities and social activities.
Acknowledgements
Not applicable.
Abbreviations
- AD
Alzheimer’s disease
- CLHLS
Chinese Longitudinal Healthy Longevity Survey
- ADL
Activities of daily living
- B
Non-standardized coefficients
- SE
Standard error
- Beta
Standardized coefficients
Authors’ contributions
XRG analyzed and interpreted the data, and wrote the first draft of the manuscript. ZTC commented and revised the manuscript. JG conceived the study design, supervised the work and revised the manuscript. All authors read and approved the final manuscript.
Funding
This study was supported by the National Key Research and Development Plan Project (2022YFC3600904), Beijing Natural Science Foundation(L242145), and the International Institute of Population Health, Peking University Health Science Center (Number: JKGL202302).
Data availability
All data from the CLHLS are publicly available at https://opendata.pku.edu.cn. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
This survey was granted by the Research Ethics Committees of Peking University and Duke University for the Protection of Human Subjects (IRB00001052-13074). All participants gave written consent after being informed to the aim of the survey and their rights to refuse to participate.
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.
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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
All data from the CLHLS are publicly available at https://opendata.pku.edu.cn. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.