==== Front Front Public Health Front Public Health Front. Public Health Frontiers in Public Health 2296-2565 Frontiers Media S.A. 10.3389/fpubh.2023.1089587 Public Health Original Research Trends in the rate of regular exercise among adults: results from chronic disease and risk factor surveillance from 2010 to 2018 in Jiangsu, China Su Jian 1 † Yu Jinxin 2 † Qin Yu 1 Tao Ran 1 Yang Jie 1 Lu Shurong 1 Zhou Jinyi 1 Wu Ming 1 2 * 1Department of Non-communicable Chronic Disease Control, Provincial Center for Disease Control and Prevention, Nanjing, Jiangsu, China 2Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China Edited by: Yanan Zhao, Nanjing Normal University, China Reviewed by: Yaohua Yang, University of Virginia, United States; Christina Gjestvang, Norwegian School of Sport Sciences, Norway *Correspondence: Ming Wu, jswuming@vip.sina.com †These authors have contributed equally to this work 15 6 2023 2023 11 108958704 11 2022 25 5 2023 Copyright © 2023 Su, Yu, Qin, Tao, Yang, Lu, Zhou and Wu. 2023 Su, Yu, Qin, Tao, Yang, Lu, Zhou and Wu https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Objective The aims of this study were to estimate the rates of regular exercise and its trends among the adult population in Jiangsu, from 2010 to 2018, China, and to assess associations with sociodemographic factors. Methods Chronic disease and risk factor surveillance data from adults aged ≥18 years were gathered in Jiangsu Province from 2010 to 2018. Rates of regular exercise were calculated after post-stratification weighting, and time trends were compared among participants with different characteristics, including gender, age, urban–rural region, educational level, occupation, annual household income, body mass index (BMI), baseline self-reported chronic diseases, smoking status, alcohol consumption, and region. Multivariable logistic regression analyses were performed to assess the associations of sociodemographic characteristics with regular exercise. Results A total of 33,448 participants aged 54.05 ± 14.62 years and 55.4% female (8,374 in 2010, 8,302 in 2013, 8,372 in 2015, and 8,400 in 2018) were included in this study. The weighted rate of regular exercise was 12.28% (95% confidence interval [CI]: 9.11–15.45%) in 2010 and 21.47% (95% CI, 17.26–25.69%) in 2018, showing an overall increasing trend (P for trend = 0.009). Nevertheless, stratification analysis showed that the regular exercise rate decreased from 33.79% in 2010 to 29.78% in 2018 among retired adults. Significant associations were observed between regular exercise and age >45 years (45– < 60 years, odds ratio [OR]: 1.24, 95% CI: 1.14–1.34; ≥60 years, OR: 1.20, 95% CI: 1.08–1.34), urban residence (OR: 1.43, 95% CI: 1.32–1.54), higher education (primary, OR: 1.30, 95% CI: 1.16–1.46; secondary, OR: 2.00, 95% CI: 1.79–2.25; college or higher, OR: 3.21, 95% CI: 2.77–3.72), occupation (manual work, OR: 1.52, 95% CI: 1.33–1.73; non-manual work, OR: 1.69, 95% CI: 1.54–1.85; not working, OR: 1.22, 95% CI: 1.03–1.44; retired, OR: 2.94, 95% CI: 2.61–3.30), higher income (¥30,000– < ¥60,000, OR: 1.16, 95% CI: 1.06–1.28; ≥¥60,000, OR: 1.20, 95% CI: 1.10–1.32), higher BMI (overweight, OR: 1.12, 95% CI: 1.05–1.20), self-reported chronic disease at baseline (OR: 1.24, 95% CI:1.16–1.33), former smoking (OR: 1.15, 95% CI: 1.01–1.31) and ever (30 days ago) drinking (OR: 1.20, 95% CI: 1.11–1.29). Conclusion The rate of regular exercise among adults in Jiangsu Province was low, but this rate increased by 9.17% from 2010 to 2018, showing an upward trend. There were differences in the rate of regular exercise among different sociodemographic factors. exercise physical activity surveillance trends China section-at-acceptancePublic Health Education and Promotion ==== Body pmcIntroduction Physical inactivity is an established risk factor for non-communicable diseases such as hypertension, diabetes, cardiovascular diseases, cancer, and chronic obstructive pulmonary disease (COPD) (1–4). Eliminating physical inactivity would enhance global population life expectancy by 0.68 years (5). Physical inactivity also leads to higher health care costs (6). Despite such negative effects of physical inactivity, a large proportion of Chinese adults do not engage in leisure-time physical activity. According to the findings of the China Health and Nutrition Survey, the age-standardized leisure-time physical activity rate of Chinese adults increased from 7.13% in 2000 to 11.79% in 2011, but then dropped to 7.33% in 2015 (7). Other studies have found that leisure-time physical activity is associated with reduced risk of all-cause, cardiovascular, type 2 diabetes, and cancer mortality (8, 9), and regular exercise is essential for both physical and mental well-being (10). The World Health Organization 2020 guidelines on physical activity and sedentary behavior recommended regular aerobic exercise for all age groups (11). The most recent national fitness plan (2021–2025), which was announced in August 2021, highlighted the benefits of regular leisure-time physical activity in promoting population health and quality of life (12). Existing Chinese studies mostly described regular exercise rates in a single year, rather than analyzing changes in trends in regular exercise rates (13, 14). Previous studies have assessed trends in regular exercise rates in Western countries, but definitions of regular exercise rates are not uniform across countries (15, 16). The population in China differs from that in developed countries in terms of the importance of healthy lifestyles, the availability of infrastructure facilities, and the hosting of sports events, but the trends in the rate of regular exercise in the Chinese population may be useful to other developing countries in implementing physical activity programs. Chinese population is changing from a developing country to a developed country. The shift in physical activity is large. For example, the level of leisure-time physical activity showed an increasing trend and the consumption of physical exercise increased (17). As such, the study of trends in physical activity in Chinese population has implications for other developing countries. In addition, “The Health China 2030 Plan Outline” (18) and “Medium- and long-term planning for the prevention and treatment of chronic diseases in China (2017–2025)” (19) propose to increase the proportion of regular physical activity to reduce the burden of chronic diseases. Regular exercise rate and socioeconomic status interact with each other (20). Individuals with higher socioeconomic status are more likely to be exercise, and individuals with lower socioeconomic status are more often engaged in heavy workloads, longer work hours, and more night work, with less time and energy for physical activity (21). Regular exercise leads to more efficient learning, resulting in higher education level, higher income, and higher socioeconomic status (22). In addition, few previous studies have examined the association between regular exercise rates and socioeconomic factors. Investigating the association between regular exercise and socioeconomic status can help develop physical activity promotion interventions that target specific socioeconomic status groups. Methods Survey design and participants Chronic disease and risk factor surveillance (CDRFS) data were collected in Jiangsu Province. The CDRFS is a series of provincially representative cross-sectional surveys on chronic diseases and behavioral risk factors in the general population. In 2010, 2013, 2015, and 2018, the Department of Non-communicable Chronic Disease Control, Jiangsu Provincial Center for Disease Control and Prevention, carried out four surveillance programs on chronic diseases and their risk factors among permanent residents in Jiangsu Province. The CDRFS selected 14 disease surveillance points in Jiangsu Province, covering 13 prefecture-level cities. The subjects of the CDRFS were the general population from the community who had been living in their current residence for at least 6 months and were at least 18 years old. A multistage stratified cluster sampling method was used for the CDRFS. In the first stage, townships (rural) or subdistricts (urban) were randomly selected with Probability Proportionate to Size (PPS) sampling. In the surveys of 2010 and 2013, four townships or subdistricts were selected. In the 2015 and 2018 surveys, three townships or subdistricts were selected. In the second stage, villages (rural) or residential areas (urban) were randomly selected with PPS sampling. Three villages or residential areas were selected in 2010 and 2013, and two were selected in 2015 and 2018. In the third stage, each selected village or residential area was divided into groups of about 50 households, based on existing villager/resident groups in the village or residential area. One group was selected with simple random sampling. In the fourth stage, within each selected household, in the 2010 and 2013 surveys, the Kish method was used to select one permanent resident aged 18 years or older. In the surveys of 2015 and 2018, all permanent residents aged 18 years or older in the household were invited. The CDRFS system has been proven to be provincially representative and can adequately describe the epidemiological characteristics of population health indicators in Jiangsu Province (23, 24). Details of the design, objectives, and survey methods of the CDRFS have been described previously (25, 26). Each round of the 2010–2018 China Chronic Diseases and Risk Factors Surveillance Survey used independent sampling, with different participants for each survey. From 2010 to 2018, four surveys were conducted. A total of 34,065 community residents aged 18 years and older were surveyed, including 8,400 in 2010, 8,399 in 2013, 8,689 in 2015, and 8,577 in 2018. All participants provided informed consent. Data collection Data were collected through questionnaire interviews, anthropometric measurements, and laboratory tests. In each survey, questionnaire interviews included information on sociodemographic (sex, age, residence, region, education, occupation, annual household income, and baseline self-reported chronic diseases) and lifestyle risk factors (physical activity, smoking status, and alcohol consumption). Anthropometric measurements included standing height and weight. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. All were collected by trained and qualified personnel. The questionnaire was formulated and revised by the Chronic Disease Center of the Chinese Center for Disease Control and Prevention. All questionnaire data were required to be entered twice. The Jiangsu Provincial Center for Disease Control and Prevention conducted on-site supervision of surveillance points, randomly checked and reviewed the questionnaires, and audited the final reported questionnaires and database. Information on physical activity was self-reported by participants using the Global Physical Activity Questionnaire (GPAQ), which was developed by the World Health Organization for physical activity surveillance in countries and has good reliability and validity (27). GPAQ covered three components (intensity, duration, and frequency) and three domains (work-related, transportation-related, and leisure-time physical activity). For leisure-time physical activity, participants were asked “Do you engage in vigorous-intensity activity that lasts at least 10 min and causes a significant increase in breathing and heart rate?.” If they answered yes, they were then asked how many days in a typical week and how many minutes per day they performed the activity. The same questions were asked for moderate-intensity activity. Regular exercise was defined as engaging in vigorous and/or moderate leisure-time physical activity at least 3 times a week for at least 10 min per session (28, 29). Occupations were classified into agriculture-related work (agriculture, forestry, animal husbandry, and fishery), manual work (production and transportation equipment operators and related personnel), non-manual work (business service personnel, persons in charge of institutions, officials and related personnel, professional and technical personnel, soldiers, other workers, students, and houseworkers), not working, and retired. BMI was classified into underweight (<18.5 kg/m2), normal (18.5 kg/m2–23.9 kg/m2), overweight (24.0 kg/m2–27.9 kg/m2), and obese (≥28.0 kg/m2) (30). Self-reported chronic diseases included hypertension, diabetes, dyslipidemia, myocardial infarction, stroke, COPD, and cancer. Self-reported chronic disease at baseline was defined as having at least one chronic disease. Statistical analysis Participants with missing data on physical activity (n = 301) and missing data on either body weight or height (n = 316) across all four surveys were excluded, leaving 33,448 participants included in this analysis. Post-stratification weighting was used to weight the data according to the 2010 Jiangsu provincial census data, to allow comparison of data across surveys from 2010 to 2018. The rate of regular exercise was analyzed by gender (female vs. male), age (18– < 45, 45– < 60, ≥60 years), residence (rural vs. urban), education (no formal education, primary, secondary, college or higher), occupation (agriculture-related, manual work, non-manual work, not working, retired), annual household income (<¥30,000, ¥30,000– < ¥60,000, ≥¥60,000, refusing to answer/unknown), BMI (underweight, normal, overweight, obesity), and self-reported chronic disease at baseline (no vs. yes). The proc. surveyfreq procedure was used to estimate the rate of regular exercise, with standard error and 95% confidence interval (CI) values. Trends in the rate of regular exercise over time were estimated using a univariate logistic regression model, which included the year of each survey as a continuous variable. A multivariable logistic regression model was used to examine associations between sociodemographic factors and regular exercise. To reduce the influence of differences in sociodemographic factors across years on the associations between regular exercise and sociodemographic factors, adjustment was made for the year of each survey in the multivariable logistic regression model combining the four surveys. A two-tailed p value <0.05 was considered statistically significant. All analyses were conducted using SAS version 9.4. Results A total of 33,448 individuals (mean age, 54.05 years [standard deviation, 14.62 years]; 18,544 [55.4%] female) were included in this analysis. A significant difference was found in population distribution across the four survey rounds. Participants were primarily urban residents (59.0%) and residents without chronic disease at baseline (64.2%). A total of 46.3% of the sample had a secondary school degree and 45.8% undertook non-manual work. The percentages of participants with higher household income (≥￥60,000) and obesity increased between 2010 and 2018 (Table 1). Table 1 Characteristics of participants, CDRFS 2010–2018.a Characteristic No. (%) of participants P value 2010–2018 (n = 33,448) 2010 (n = 8,374) 2013 (n = 8,302) 2015 (n = 8,372) 2018 (n = 8,400) Gender Female 18,544 (55.4) 4,586 (54.8) 4,737 (57.1) 4,542 (54.3) 4,679 (55.7) 0.002 Male 14,904 (44.6) 3,788 (45.2) 3,565 (42.9) 3,830 (45.7) 3,721 (44.3) Age, years 18- < 45 8,815 (26.4) 2,751 (32.9) 2,460 (29.6) 2,024 (24.2) 1,580 (18.8) <0.001 45- < 60 12,178 (36.4) 3,046 (36.4) 3,244 (39.1) 2,856 (34.1) 3,032 (36.1) ≥60 12,455 (37.2) 2,577 (30.8) 2,598 (31.3) 3,492 (41.7) 3,788 (45.1) Residence Rural 13,727 (41.0) 3,591 (42.9) 2,939 (35.4) 3,580 (42.8) 3,617 (43.1) <0.001 Urban 19,721 (59.0) 4,783 (57.1) 5,363 (64.6) 4,792 (57.2) 4,783 (56.9) Education No formal education 6,435 (19.2) 1,732 (20.7) 1,596 (19.2) 1,541 (18.4) 1,566 (18.6) <0.001 Primary 8,653 (25.9) 2,278 (27.2) 2,138 (25.8) 2,086 (24.9) 2,151 (25.6) Secondary 15,481 (46.3) 3,845 (45.9) 3,966 (47.8) 3,785 (45.2) 3,885 (46.2) College or higher 2,879 (8.6) 519 (6.2) 602 (7.3) 960 (11.5) 798 (9.5) Occupation Agriculture-related 9,047 (27.0) 2,699 (32.2) 2,875 (34.6) 1,863 (22.3) 1,610 (19.2) <0.001 Manual work 2,978 (8.9) 882 (10.5) 945 (11.4) 610 (7.3) 541 (6.4) Non-manual work 15,313 (45.8) 3,725 (44.5) 3,416 (41.1) 3,986 (47.6) 4,186 (49.8) Not working 1,783 (5.3) 207 (2.5) 321 (3.9) 541 (6.5) 714 (8.5) Retired 4,327 (12.9) 861 (10.3) 745 (9.0) 1,372 (16.4) 1,349 (16.1) Annual household income, ¥ <30,000 8,463 (25.3) 3,735 (44.6) 1,524 (18.4) 1,704 (20.4) 1,500 (17.9) <0.001 30,000- < 60,000 9,019 (27.0) 2,737 (32.7) 2,624 (31.6) 1,950 (23.3) 1,708 (20.3) ≥60,000 11,426 (34.2) 1,098 (13.1) 2,780 (33.5) 3,571 (42.7) 3,977 (47.3) Refuse to answer/Unknown 4,540 (13.6) 804 (9.60) 1,374 (16.6) 1,147 (13.7) 1,215 (14.5) BMI categoriesb Underweight 711 (2.1) 208 (2.5) 194 (2.3) 155 (1.9) 154 (1.8) <0.001 Normal 14,038 (42.0) 3,866 (46.2) 3,592 (43.3) 3,297 (39.4) 3,283 (39.1) Overweight 13,264 (39.7) 3,159 (37.7) 3,246 (39.1) 3,445 (41.1) 3,414 (40.6) Obesity 5,435 (16.2) 1,141 (13.6) 1,270 (15.3) 1,475 (17.6) 1,549 (18.4) Self-reported chronic disease No 21,467 (64.2) 6,025 (71.9) 5,702 (68.7) 5,133 (61.3) 4,607 (54.8) <0.001 Yes 11,981 (35.8) 2,349 (28.1) 2,600 (31.3) 3,239 (38.7) 3,793 (45.2) Smoking Never 23,007 (68.8) 5,580 (66.6) 5,872 (70.7) 5,702 (68.1) 5,853 (69.7) <0.001 Former 2,160 (6.46) 523 (6.25) 411 (4.95) 625 (7.47) 601 (7.15) Current 8,281 (24.8) 2,271 (27.1) 2,019 (24.3) 2,045 (24.4) 1,946 (23.2) Drinking Never 21,161 (63.3) 5,484 (65.5) 5,626 (67.8) 4,992 (59.6) 5,059 (60.2) <0.001 Ever, 30 days ago 9,463 (28.3) 2,293 (27.4) 2,148 (25.9) 2,497 (29.8) 2,525 (30.1) Ever, within 30 days 2,819 (8.43) 597 (7.13) 528 (6.36) 881 (10.5) 813 (9.68) Region South 14,354 (42.9) 3,592 (42.9) 3,580 (43.1) 3,617 (43.2) 3,565 (42.4) 0.956 Central 7,176 (21.5) 1,798 (21.5) 1,776 (21.4) 1,800 (21.5) 1,802 (21.5) North 11,918 (35.6) 2,984 (35.6) 2,946 (35.5) 2,955 (35.3) 3,033 (36.1) BMI, body mass index; CDRFS, chronic disease and risk factor surveillance. a Data are expressed as unweighted number of participants and unweighted percentages. b Underweight (<18.5 kg/m2), normal (18.5 kg/m2–23.9 kg/m2), overweight (24.0 kg/m2–27.9 kg/m2), and obesity (≥28.0 kg/m2). From 2010 to 2018, the overall weighted rate of regular exercise increased by 9.17% from 12.28% (95% CI: 9.11–15.45%) to 21.47% (95% CI: 17.26–25.69%) (P for trend = 0.009). Trends in the overall group and for each gender, residence, and baseline self-reported chronic disease groups remained similar. Among the different age groups, the rate of regular exercise was highest among adults aged ≥60 years in 2010, while the rate among adults aged ≥60 years in 2018 was the lowest. The rate of regular exercise increased with higher education and household income. Regular exercise rates were highest among overweight adults and lowest among underweight adults. The rate of regular exercise among retirees decreased from 33.79% (95% CI: 28.61–38.97%) in 2010 to 29.78% (95% CI: 24.21–35.36%) in 2018. In each year, however, retirees still had the highest rate of regular exercise among all occupations (Figure 1; Supplementary Table S1). Figure 1 Trends in weighted rate of regular exercise, CDRFS 2010-2018a. BMI, body mass index; CDRFS, chronic disease and risk factor surveillance. aData were weighted to be provincially representative in each category and error bars indicate 95% CIs. bUnderweight (<18.5 kg/m2), normal (18.5 kg/m2-23.9 kg/m2), overweight (24.0 kg/m2-27.9 kg/m2), and obesity (≥28.0 kg/m2). Various sociodemographic factors were positively associated with the rate of regular exercise. After multivariable adjustment, a significantly higher estimated regular exercise rate was observed among participants aged 45– < 60 years, those residing in urban areas, individuals with a college degree or higher, participants who were retired, individuals with annual household income ≥￥60,000, overweight participants, those with self-reported chronic diseases at baseline, ever drinkers, and residents of south Jiangsu Province. Compared with participants in the 18– < 45 years old age group, those who were 45– < 60 years old had a 24% higher regular exercise rate (OR: 1.24, 95% CI: 1.14–1.34). Compared with rural residents, the OR for the regular exercise rate for urban residents was 1.43 (95% CI: 1.32–1.54). The rate of regular exercise was higher in participants who were retired than in participants with agriculture-related jobs (OR: 2.94, 95% CI: 2.61–3.30). Relative to participants with a household income <¥30,000, the OR for those in the ¥30,000– < ¥60,000 was 1.20 (95% CI: 1.10–1.32). Further, participants with higher education and higher BMI tended to exercise more regularly (P for trend <0.001). Self-reported chronic disease at baseline was associated with a 24% higher regular exercise rate (OR: 1.24, 95% CI: 1.16–1.33) (Table 2). The rate of regular exercise was 20% higher among those who had consumed alcohol in the previous 30 days relative to those who had never consumed alcohol (OR = 1.20, 95% CI: 1.11–1.29). The association between sociodemographic factors and regular exercise according to each survey was similar to that of the overall survey (Supplementary Table S2). We observed that rural participants with college degrees or higher had higher rates of regular exercise than rural participants without formal education (OR = 4.90, 95% CI: 3.79–6.30; Pinteraction = 0.001). We did not observe an interaction between smoking and BMI (Supplementary Figures S1, S2). Table 2 Association between regular exercise (yes vs. no) and sociodemographic factors using pooled data of CDRFS 2010–2018a (OR and 95% CI). Characteristic Counts Rate of regular exercise (%, 95 CI) OR (95% CI) P value Gender Female 2944/18544 15.46 (12.56–18.37) ref Male 2736/14904 18.46 (15.57–21.36) 0.96 (0.88–1.05) 0.414 Age, years 18- < 45 1428/8815 16.46 (13.45–19.47) ref 45- < 60 2105/12178 17.45 (14.47–20.44) 1.24 (1.14–1.34) <0.001 ≥60 2147/12455 17.36 (14.52–20.20) 1.20 (1.08–1.34) <0.001 P for trendb <0.001 Residence Rural 1462/13727 11.24 (7.47–15.02) ref Urban 4218/19721 20.83 (16.97–24.69) 1.43 (1.32–1.54) <0.001 Education No formal education 542/6435 7.82 (5.23–10.40) ref Primary 1077/8653 11.47 (9.31–13.63) 1.30 (1.16–1.46) <0.001 Secondary 3190/15481 18.78 (16.35–21.20) 2.00 (1.79–2.25) <0.001 College or higher 871/2879 28.58 (24.54–32.61) 3.21 (2.77–3.72) <0.001 P for trendc <0.001 Occupation Agriculture-related 730/9047 7.85 (5.85–9.85) ref Manual work 472/2978 15.16 (12.43–17.88) 1.52 (1.33–1.73) <0.001 Non-manual work 2793/15313 19.44 (16.89–21.99) 1.69 (1.54–1.85) <0.001 Not working 220/1783 12.54 (9.95–15.14) 1.22 (1.03–1.44) 0.017 Retired 1465/4327 34.04 (30.90–37.19) 2.94 (2.61–3.30) <0.001 Annual household income, ¥ <30,000 938/8463 11.25 (8.49–14.01) ref 30,000- < 60,000 1500/9019 15.68 (12.90–18.47) 1.16 (1.06–1.28) 0.001 ≥60,000 2539/11426 21.99 (18.39–25.60) 1.20 (1.10–1.32) <0.001 Refuse to answer/Unknown 703/4540 15.82 (12.87–18.77) 1.04 (0.93–1.17) 0.449 P for trendd 0.170 BMI categoriese Underweight 83/711 11.52 (8.73–14.30) 0.69 (0.54–0.87) 0.003 Normal 2244/14038 15.91 (13.16–18.65) ref Overweight 2425/13264 18.34 (15.21–21.48) 1.12 (1.05–1.20) 0.001 Obesity 928/5435 17.37 (13.87–20.86) 1.05 (0.96–1.15) 0.250 P for trendf 0.010 Self-reported chronic disease No 3224/21467 15.36 (12.61–18.10) ref Yes 2456/11981 21.08 (18.12–24.05) 1.24 (1.16–1.33) <0.001 Smoking Never 3835/23007 16.61 (13.62–19.61) ref Former 463/2160 22.21 (18.98–25.43) 1.15 (1.01–1.31) 0.039 Current 1382/8281 16.81 (14.21–19.41) 0.88 (0.81–0.97) 0.011 Drinking Never 3367/21161 15.56 (12.67–18.45) ref Ever, 30 days ago 1755/9463 18.80 (15.59–22.02) 1.20 (1.11–1.29) <0.001 Ever, within 30 days 558/2819 20.04 (17.03–23.04) 1.16 (1.04–1.29) 0.008 Region South 3317/14354 22.66 (18.22–27.10) ref Central 791/7176 11.40 (6.64–16.17) 0.62 (0.57–0.68) <0.001 North 1572/11918 13.39 (6.71–20.07) 0.94 (0.87–1.02) 0.143 BMI, body mass index; CDRFS, chronic disease and risk factor surveillance. a Unweighted estimates and 95%CIs were estimated for the overall survey. b P for trend over age was calculated using the median value of each category as a continuous variable. c P for trend over education was calculated using education level as a continuous variable. d P for trend over annual household income was calculated using the median value of each category as a continuous variable. e Underweight (<18.5 kg/m2), normal (18.5 kg/m2–23.9 kg/m2), overweight (24.0 kg/m2–27.9 kg/m2), and obesity (≥28.0 kg/m2). f P for trend over BMI categories was calculated using the median value of each category as a continuous variable. Discussion Using provincially representative data from the CDRFS in Jiangsu, China, from 2010 to 2018, we analyzed the weighted rate of regular exercise, estimated its trend over time, and examined associations of regular exercise with demographic factors. Our analysis showed that the regular exercise rate of adults in Jiangsu Province was 16.02% in 2013, which was higher than the national average level (15.0%) (29). Provinces with similar GDP to Jiangsu Province, e.g., Shandong Province (19.8%) (14), had a higher rate of regular exercise than Jiangsu Province. Provinces with lower GDP than Jiangsu Province, e.g., Gansu Province (13.95%) (13), had a lower rate of regular exercise than Jiangsu Province. Therefore, we speculate that the economy is one of the factors affecting the regular exercise rate. This part needs to be further explored. These findings demonstrate that health policies relevant to Jiangsu Province need to be developed and targeted measures implemented to increase the rate of regular exercise. Additionally, our research demonstrates that the regular exercise rate in Jiangsu Province increased by 9.17% from 2010 to 2018. A study in Beijing found that the regular exercise rate in the city increased by 8.14% from 2008 to 2017 (31, 32). Nevertheless, data reported from Iran and Madrid indicated that the trends in leisure-time physical activity declined, possibly due to increased sedentary time and obesity rates (33, 34). The rate of regular exercise among retirees generally showed a downward trend over time; however, the rate of regular exercise among retirees was still relatively high compared to that of participants in the other occupation groups, and retirement was positively associated with regular exercise. The decline in the regular exercise rate among retirees could be attributed to the fact that China is transforming into an aging nation and the older adult population is growing rapidly (35). The aging of society makes it more likely that retirees continue working, resulting in a lack of time for leisure-time physical activity (36). Additionally, the increase in older adults taking care of their grandchildren in China has led to a decrease in the time they spend on exercise (37). The average retirement age is 60. Compared with non-retirees under 45 years old, retired people have less stress from study, work, and life and take more exercise (38). We observed a much higher rate of regular exercise in urban than in rural areas, as well as significant increases in regular exercise rates in both areas, consistent with a previous study (39). The disparity between areas may primarily be attributable to the fact that rural areas are less likely to have facilities associated with leisure-time physical activity, such as parks and green spaces (40). Additionally, residents in urban areas have higher incomes and are increasingly concerned about quality of life, and thus exercise more frequently (41). Positive associations of regular exercise with educational and income levels were observed in our study, consistent with other studies showing that education and income are positive influences on leisure-time physical activity (20, 42). Some previous studies (43, 44) have reported an inverse relationship between BMI and leisure-time physical activity, which contradicts our findings. In our study, overweight adults had the highest regular exercise rate. The possible reasons for these different findings might be that Jiangsu Province implemented “The Outline of the National Fitness Program 2001–2010” to improve fitness publicity for people with overweight and obesity (45). The number of people who consciously participate in exercise increased, with running, square dancing, climbing mountains, and fitness becoming fashionable. Further, individuals with overweight and obesity have greater motivation to take part in physical activity with the aim of weight management and improving their appearance (46, 47). In addition, being overweight may not mean obesity, but may be an increase in muscle mass from exercise (48). Exercise increases muscle mass, and muscle is denser than fat, so people with more muscle mass at the same weight may have a higher BMI (49). Follow-up studies should focus on indicators of both waist circumference and visceral fat. Multivariable logistic regression analyses suggested that lack of regular exercise was more severe among individuals who were younger, lived in a rural area, had a lower education level, participated in agriculture-related work, and had a lower income. In summary, people in socially/economically disadvantaged positions were more likely to have a low regular exercise rate. In a recent Swedish study, economic stress was associated with low leisure-time physical activity and the strength of the association increased sharply with higher levels of economic stress (50). Therefore, targeted interventions for the abovementioned groups should be created, to encourage them to take more exercise (11). This study provides data support for the government to develop physical activity policies. The government can launch educational campaigns and awareness programs to promote the benefits of regular exercise and encourage citizens to adopt a more active lifestyle. The government can also improve infrastructure and recreational exercise facilities to encourage citizens to be physically active. Society can support community sports organizations by providing funding, resources, and infrastructure to enable citizens to participate in organized sports activities. Individuals can develop regular exercise habits by raising their awareness of exercise. The strengths of our study include the large sample size, with a representative population and the use of a validated measure of physical activity (51). Our study has some potential limitations. First, the surveys were not conducted annually, which could have allowed for a more detailed and quantitative analysis of the trend. Second, all sociodemographic characteristics and physical activity data were based on self-reported information, which is likely to have introduced bias. Third, the study had a cross-sectional design; hence the causality of relationships between sociodemographic factors and regular exercise could not be determined. However, this study has four different time points, and the time interval can reflect the trend of change. Conclusion Our study showed a significant increase in the overall rate of regular exercise in adults in Jiangsu Province over the 8 year period from 2010 to 2018; however, the rate decreased among retired participants. Being older, living in urban areas, having a higher education level, engaging in non-agriculture-related work, having a higher income, having a higher BMI, having a chronic disease at baseline, former smoking, and ever drinking were all positively associated with regular exercise from 2010 to 2018. Interventions targeted at population subgroups with low regular exercise rates, or those showing decreasing trends in regular exercise, are warranted. Data availability statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. Ethics statement The protocol for the 2015 chronic disease and risk factor surveillance survey was approved by the ethics review committee of the Chinese Center for Disease Control and Prevention (CDC), and all other surveys were approved by the ethical committee of National Center for Chronic and Noncommunicable Disease Control and Prevention (NCNCD), China CDC. The patients/participants provided their written informed consent to participate in this study. Author contributions JS and JY designed the research. JY performed the data analyses and drafted the manuscript. JS revised the data analysis. JS, YQ, RT, JY, and SL conducted field investigations and data collection. SL, JZ, and MW revised the manuscript critically for important intellectual content. All authors contributed to the article and approved the submitted version. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. We acknowledge the contributions of the investigation staff and laboratory testing personnel at each of the 14 disease surveillance points. Supplementary material The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2023.1089587/full#supplementary-material Click here for additional data file. ==== Refs References 1. Williamson W Foster C Reid H Kelly P Lewandowski AJ Boardman H . Will exercise advice be sufficient for treatment of young adults with prehypertension and hypertension? A systematic review and meta-analysis. Hypertension. (2016) 68 :78–87. doi: 10.1161/HYPERTENSIONAHA.116.07431, PMID: 27217408 2. Kyu HH Bachman VF Alexander LT Mumford JE Afshin A Estep K . Physical activity and risk of breast cancer, colon cancer, diabetes, ischemic heart disease, and ischemic stroke events: systematic review and dose-response meta-analysis for the global burden of disease study 2013. BMJ. (2016) 354 :i3857. doi: 10.1136/bmj.i3857, PMID: 27510511 3. Zhao M Veeranki SP Magnussen CG Xi B . Recommended physical activity and all cause and cause specific mortality in US adults: prospective cohort study. BMJ. (2020) 370 :m2031. doi: 10.1136/bmj.m2031, PMID: 32611588 4. Ruegsegger GN Booth FW . Health benefits of exercise. Cold Spring Harb Perspect Med. (2018) 8 :a029694. doi: 10.1101/cshperspect.a029694, PMID: 28507196 5. Lee IM Shiroma EJ Lobelo F Puska P Blair SN Katzmarzyk PT . Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. (2012) 380 :219–29. doi: 10.1016/S0140-6736(12)61031-9, PMID: 22818936 6. Ding D Lawson KD Kolbe-Alexander TL Finkelstein EA Katzmarzyk PT van Mechelen W . The economic burden of physical inactivity: a global analysis of major non-communicable diseases. Lancet. (2016) 388 :1311–24. doi: 10.1016/S0140-6736(16)30383-X, PMID: 27475266 7. Zou Q Wang H Du W Su C Ouyang Y Wang Z . Trends in leisure-time physical activity among Chinese adults – China, 2000-2015. China CDC Wkly. (2020) 2 :135–9. doi: 10.46234/ccdcw2020.037, PMID: 34594841 8. Arem H Moore SC Patel A Hartge P Berrington de Gonzalez A Visvanathan K . Leisure time physical activity and mortality: a detailed pooled analysis of the dose-response relationship. JAMA Intern Med. (2015) 175 :959–67. doi: 10.1001/jamainternmed.2015.0533, PMID: 25844730 9. Raza W Krachler B Forsberg B Sommar JN . Health benefits of leisure time and commuting physical activity: a meta-analysis of effects on morbidity. J Transp Health. (2020) 18 :100873. doi: 10.1016/j.jth.2020.100873 10. Garber CE Blissmer B Deschenes MR Franklin BA Lamonte MJ Lee IM . American College of Sports Medicine position stand. Quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults: guidance for prescribing exercise. Med Sci Sports Exerc. (2011) 43 :1334–59. doi: 10.1249/MSS.0b013e318213fefb, PMID: 21694556 11. Bull FC Al-Ansari SS Biddle S Borodulin K Buman MP Cardon G . World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. (2020) 54 :1451–62. doi: 10.1136/bjsports-2020-102955, PMID: 33239350 12. The State Council. The National Fitness Plan (2021–2025). Available at: http://www.gov.cn/zhengce/content/2021-08/03/content_5629218.htm?ivk_sa (Accessed September 4, 2022). 13. Zhang J Zhang Z Dong C He H Xi J Ren X . Prevalence of leisure time physical exercises and influencing factors in adults in Gansu. Dis Surveill. (2020) 35 :723–8. doi: 10.3784/j.issn.1003-9961.2020.08.011 14. Zhang Y Cong G Chen X Zhang J Lu Z Zhang G . Leisure-time exercise, sedentary behavior and sleeping time and their relationships with hypertension and diabetes among adult residents in Shandong province. Chin J Public Health. (2018) 34 :803–7. doi: 10.11847/zgggws1118463 15. López-Bueno R Smith L Tully MA Shin JI Calatayud J López-Sánchez GF . Increase in regular leisure-time physical activity in Spanish adults between 1987 and 2017. Am J Prev Med. (2021) 61 :e73–9. doi: 10.1016/j.amepre.2021.02.001, PMID: 33785277 16. Centers for Disease Control and Prevention (CDC). Prevalence of regular physical activity among adults – United States, 2001 and 2005. MMWR Morb Mortal Wkly Rep. (2007) 56 :1209–12. PMID: 18030281 17. China Sports News. The characteristics of the times and development trend of leisure sports in China. (2020). Available at: https://www.sport.gov.cn/n20001280/n20745751/n20767279/c21256817/content.html (Accessed May 9, 2022). 18. Xinhua News Agency. The health China 2030 plan outline. (2016). Available at: http://www.gov.cn/zhengce/2016-10/25/content_5124174.htm (Accessed May 9, 2023). 19. State Council. Medium- and long-term planning for the prevention and treatment of chronic diseases in China (2017–2025). Available at: http://www.gov.cn/gongbao/content/2017/content_5174509.htm (Accessed May 9, 2023). 20. Huikari S Junttila H Ala-Mursula L Jämsä T Korpelainen R Miettunen J . Leisure-time physical activity is associated with socio-economic status beyond income - cross-sectional survey of the northern Finland birth cohort 1966 study. Econ Hum Biol. (2021) 41 :100969. doi: 10.1016/j.ehb.2020.100969, PMID: 33429255 21. Stalsberg R Pedersen AV . Are differences in physical activity across socioeconomic groups associated with choice of physical activity variables to report? Int J Environ Res Public Health. (2018) 15 :922. doi: 10.3390/ijerph15050922, PMID: 29734745 22. Boost your thinking skills with exercise. Moderate-intensity exercise can help improve your thinking and memory in just 6 months. Harv Health Lett. (2014) 39 :3. 23. Zhao Z Wang L Li Y Jiang Y Zhang M Huang Z . Provincial representativeness assessment of China non-communicable and chronic disease risk factor surveillance system in 2013. Zhonghua Yu Fang Yi Xue Za Zhi. (2018) 52 :165–9. doi: 10.3760/cma.j.issn.0253-9624.2018.02.009, PMID: 29429271 24. Wang L Zhang M Zhou M Jiang Y Li Y Huang Z . Study on construction and application of technology system of chronic diseases and risk factor surveillance in China. Chin J Epidemiol. (2021) 42 :1154–9. doi: 10.3760/cma.j.cn112338-20210104-00002, PMID: 34814524 25. Wang L Zhang M Li Y Huang Z Deng Q Zhao Z . Scheme of the Chinese chronic non-communicable disease and risk factor surveillance. Zhonghua Yu Fang Yi Xue Za Zhi. (2018) 52 :191–4. doi: 10.3760/cma.j.issn.0253-9624.2018.02.015, PMID: 29429277 26. Zhang X Zhang M Zhao Z Huang Z Deng Q Li Y . Geographic variation in prevalence of adult obesity in China: results from the 2013–2014 national chronic disease and risk factor surveillance. Ann Intern Med. (2020) 172 :291–3. doi: 10.7326/M19-0477, PMID: 31658469 27. Cleland CL Hunter RF Kee F Cupples ME Sallis JF Tully MA . Validity of the global physical activity questionnaire (GPAQ) in assessing levels and change in moderate-vigorous physical activity and sedentary behaviour. BMC Public Health. (2014) 14 :1255. doi: 10.1186/1471-2458-14-1255, PMID: 25492375 28. Wu M Zhou J . Surveillance report on chronic diseases and their risk factors in Jiangsu, China. Nanjing: Nanjing Normal University Press (2013). 29. Wang L Wang L Wang L Deng Q Li C Li Y . Surveillance report on chronic diseases and their risk factors in China. Beijing: Military Medical Press (2013). 30. Chen C Lu FC Department of Disease Control Ministry of health, PR China. The guidelines for prevention and control of overweight and obesity in Chinese adults. Biomed Environ Sci. (2004) 17 :1–36. 31. Ma A Dong Z Dong J . Current status and influencing factors of leisure-time physical exercise among adults from 2008 to 2014 in Beijing. Prevent Control Chronic Diseases China. (2018) 3 :193–7. doi: 10.16386/j.cjpccd.issn.1004-6194.2018.03.008 32. Xie C Ma A Xie J Jiang B Fang K Wei Y . Current status of regular exercise and influencing factors in 18–64 year old labor force population in Beijing. Zhonghua Liu Xing Bing Xue Za Zhi. (2022) 43 :373–9. doi: 10.3760/cma.j.cn112338-20210727-00589, PMID: 35345293 33. Afghan M Ghasemi A Azizi F . Seven-year changes of leisure-time and occupational physical activity among Iranian adults (Tehran lipid and glucose study). Iran J Public Health. (2016) 45 :41–7. PMID: 27057520 34. Meseguer CM Galán I Herruzo R Rodríguez-Artalejo F . Trends in leisure time and occupational physical activity in the Madrid region, 1995–2008. Rev Esp Cardiol. (2011) 64 :21–7. doi: 10.1016/j.recesp.2010.07.007, PMID: 21194824 35. Fang EF Xie C Schenkel JA Wu C Long Q Cui H . A research agenda for ageing in China in the 21st century (2nd edition): focusing on basic and translational research, long-term care, policy and social networks. Ageing Res Rev. (2020) 64 :101174. doi: 10.1016/j.arr.2020.101174, PMID: 32971255 36. Fong JH . Leisure-time physical activity and its correlates in a multi-ethnic sample of adults over age 50 in Singapore. BMC Public Health. (2021) 21 :379. doi: 10.1186/s12889-021-10431-6, PMID: 33602165 37. Choi SE Zhang Z . Caring as curing: grandparenting and depressive symptoms in China. Soc Sci Med. (2021) 289 :114452. doi: 10.1016/j.socscimed.2021.114452, PMID: 34624620 38. Huang Y Ng OL Ha ASC . A qualitative exploration of facilitators and barriers to physical activity participation among Chinese retired adults in Hong Kong. Int J Environ Res Public Health. (2022) 19 :3495. doi: 10.3390/ijerph19063495, PMID: 35329182 39. Robertson MC Song J Taylor WC Durand CP Basen-Engquist KM . Urban-rural differences in aerobic physical activity, muscle strengthening exercise, and screen-time sedentary behavior. J Rural Health. (2018) 34 :401–10. doi: 10.1111/jrh.12295, PMID: 29451333 40. Hansen AY Umstattd Meyer MR Lenardson JD Hartley D . Built environments and active living in rural and remote areas: a review of the literature. Curr Obes Rep. (2015) 4 :484–93. doi: 10.1007/s13679-015-0180-9, PMID: 26364307 41. Gomes CS Vieira CS Rocha FL Temponi HR Vieira MAS Mendes MS . Spatial analysis of leisure-time physical activity in an urban area. Rev Bras Epidemiol. (2021) 24 :e210012. doi: 10.1590/1980-549720210012.supl.1, PMID: 33886885 42. Trost SG Owen N Bauman AE Sallis JF Brown W . Correlates of adults’ participation in physical activity: review and update. Med Sci Sports Exerc. (2002) 34 :1996–2001. doi: 10.1097/00005768-200212000-00020, PMID: 12471307 43. Santos IKSD Conde WL . BMI variation, dietary patterns and physical activity among adults aged 21–44 years. Cien Saude Colet. (2021) 26 :3853–63. doi: 10.1590/1413-81232021269.2.23562019, PMID: 34468678 44. Palacios-Ceña D Alonso-Blanco C Jiménez-Garcia R Hernández-Barrera V Carrasco-Garrido P Pileño-Martinez E . Time trends in leisure time physical activity and physical fitness in elderly people: 20 year follow-up of the Spanish population national health survey (1987–2006). BMC Public Health. (2011) 11 :799. doi: 10.1186/1471-2458-11-799, PMID: 21995560 45. General Administration of Sports of China. The outline of the National Fitness Program 2001–2010. Available at: https://www.sport.gov.cn/n315/n331/n403/n1955/c573921/content.html (Accessed March 5, 2023). 46. Knittle K Nurmi J Crutzen R Hankonen N Beattie M Dombrowski SU . How can interventions increase motivation for physical activity? A systematic review and meta-analysis. Health Psychol Rev. (2018) 12 :211–30. doi: 10.1080/17437199.2018.1435299 29385950 47. Galan-Lopez P Lopez-Cobo I García-Lázaro I Ries F . Associations between motives for physical exercise, body composition and cardiorespiratory fitness: a cross-sectional study. Int J Environ Res Public Health. (2022) 19 :14128. doi: 10.3390/ijerph192114128, PMID: 36361006 48. Centers for Disease Control and Prevention. Healthy Weight, Nutrition, and Physical Activity. (2022). Available at: https://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/index.html#Other%20Ways (Accessed March 8, 2023). 49. Bedosky Lauren . Does muscle weigh more than fat? It's complicated. (2023). Available at: https://www.health.com/fitness/does-muscle-weigh-more-than-fat#:~:text=Muscle%20can%20weigh%20more%20than%20fat%20because%20it%E2%80%99s,more%20of%20the%20compact%20tissue%20in%20your%20hand. (Accessed May 9, 2023). 50. Lindström M Rosvall M . Economic stress and low leisure-time physical activity: two life course hypotheses. SSM Popul Health. (2018) 4 :358–64. doi: 10.1016/j.ssmph.2018.04.005, PMID: 29854921 51. Keating XD Zhou K Liu X Hodges M Liu J Guan J . Reliability and concurrent validity of global physical activity questionnaire (GPAQ): a systematic review. Int J Environ Res Public Health. (2019) 16 :4128. doi: 10.3390/ijerph16214128, PMID: 31717742