Abstract
Objectives:
The aim of this study was to examine the dose–response associations of physical activity with blood pressure (BP) and hypertension risk among Chinese adults.
Methods:
Derived from the national community-based China Hypertension Survey database during 2012--2015, a total of 203 108 residents aged at least 18 years were included. Individual-level physical activity was evaluated using a standardized questionnaire, and minutes of metabolic equivalent tasks per week (MET-min/week) were calculated, integrating domain, intensity, frequency, and duration. Multivariable linear and logistic regressions were used to estimate associations of physical activity with BP and hypertension risk, and restricted cubic spline regressions were performed for their nonlinear dose–response relationships.
Results:
Overall, the median total physical activity (TPA) was 3213.0 MET-min/week and the prevalence of physical inactivity was 14.8%. TPA was negatively associated with BP. Increasing TPA levels was related to a steep decrease in systolic BP, up to approximately 2500 MET-min/week, with more modest benefits above that level of TPA. Higher levels of domain-specific and intensity-specific physical activity were found to be associated with lower BP levels and hypertension risk, except for the association between vigorous-intensity physical activity and systolic BP. We found that TPA within the range of 2000--4000 MET-min/week, a higher frequency and shorter duration were inversely associated with diastolic BP levels.
Conclusion:
Total, domain-specific, and intensity-specific physical activity were inversely related to BP levels, respectively, in a dose–response fashion. Of a given amount, higher-frequency, shorter-duration, and lower-intensity physical activity produced more beneficial effects.
Keywords: blood pressure, dose–response, hypertension risk, physical activity
INTRODUCTION
Insufficient physical activity has been the fourth leading risk factor for global mortality [1], contributing to over 5.3 million deaths [2]. However, monitoring physical activity levels in the population remains limited in the world [3]. Most surveys only focus on leisure time physical activity [4–7], ignoring other domains of physical activity, such as occupational, housework, and transportation physical activity, which are key domains in low- and middle-income countries [8–10]. In addition, some national surveillances of physical activity use different instruments and definitions, which limit international comparisons [3,11]. Thus, the comprehensive and internationally comparable surveillance data of physical activity are needed to quantify population levels of exposure, identify high-risk populations, and guide national action, especially in developing countries.
Although numerous studies have investigated the health effects of physical activity, it should be noted that most large-scale surveys focused on physical activity related mortality, cardiovascular disease (CVD), cancer, and diabetes, while studies on hypertension, or blood pressure (BP), are still limited [12–15]. Elevated BP, as the first leading risk factor for attributable deaths, accounted for 10.8 million deaths worldwide in 2019 [16], and it has been related to multiple CVDs, and chronic kidney diseases [17,18]. Thus, BP can serve as a biomarker to quantify the health impacts of physical activity, which is important for the early prevention and control of hypertension and CVDs.
Previous studies assessed the dose–response associations between physical activity and BP with the assumption of linearity, yet accumulating evidence has shown that the effects of physical activity are probably nonlinear [12,13,19]. Specially, some studies have suggested that excessive physical activity may damage people's health, but it is still inconclusive because the threshold level at which physical activity confers protective benefits remains unclear due to insufficient evidence, especially in large populations [19–21]. Accordingly, the nonlinear dose–response relationships between physical activity and BP are necessary to be examined in the real world to provide an overall picture of continuous estimated curves to further explore the optimal dose of physical activity.
In addition, current public health guidelines suggest the total recommended amount of physical activity in a week, whereas the recommendation could be achieved to one continuous bout or multiple short bouts dispersed throughout the week [6]. It remains unclear whether continuous and accumulated patterns of physical activity lead to different health effects, especially in large populations.
To address the knowledge gaps, based on data from a nationwide community-based survey of over 480 000 Chinese adults, our study aims to describe the continuous distribution of total, domain-specific, and intensity-specific physical activity; establish the nonlinear dose–response associations of physical activity with BP; and explore whether variations in frequency, duration, and intensity of physical activity influence the PA-BP associations with the same physical activity amount.
MATERIALS AND METHODS
Study design and participants
The study population was derived from the China Hypertension Survey (CHS) between 2012 and 2015. More details on the survey have been described previously [22–24]. Briefly, the CHS is a community-based hypertension surveillance survey involving a nationally representative sample of the general Chinese population aged at least 15 years in all 31 provinces of mainland China with a stratified multistage random sampling method. Overall, a total of 487 349 participants were recruited. The CHS included the sociodemographic factors, medical history, lifestyle behaviours, and other hypertension-related health risk factors for all the participants, measured by well trained staff. Written informed consent was obtained from each participant. Approval for the study was obtained from the ethics committees of Fuwai Hospital (Beijing, China). In this study, we excluded participants with missing information on BP (n = 3341) or physical activity (n = 268 702); those with the volume of total physical activity (TPA) more than 960 min/day (n = 1225); and those aged less than 18 years (n = 10 973). Finally, a total of 203 108 participants were included in the analyses of the distribution of physical activity and its association with BP (Supplemental Figure 1).
Blood pressure measurement
The CHS obtained BP measurements from individuals, and hypertension was defined as a systolic BP (SBP) at least 140 mmHg and/or diastolic BP (DBP) at least 90 mmHg or current treatment [18]. BP was measured three times on the right arm positioned at heart level, and there were 30 s between each measurement. Measurements were conducted using the OMRON HBP-1300 professional portable BP monitor (OMRON, Kyoto, Japan), the accuracy of which was verified in our prior study [25]. The average of the three measurements was used for analysis.
Physical activity assessment
In the CHS, individual-level physical activity was evaluated by a standardized questionnaire, which was adapted from the long-form International Physical Activity Questionnaire (IPAQ). The IPAQ was developed to measure self-reported physical activity in large populations [26–28], and it has been widely used in many international studies as a validated instrument. In the CHS, the physical activity questions comprised the intensity, frequency, and duration of physical activity at least 10 min in a week in occupation and housework, transportation and leisure time domains, respectively [29]. The amount of TPA was quantified as the metabolic equivalent of task minutes per week (MET-min/week) based on the domain, intensity, frequency, and duration of physical activity. More details about the physical activity assessment were in Supplemental Materials. According to the WHO guidelines on physical activity [30], physical inactivity was defined as a failure to accumulate 600 MET-min, or 150 min of moderate physical activity, or 75 min of vigorous physical activity, or the combination of both intensities per week. To provide international comparable data, based on WHO and IPAQ guidelines [28,30], we categorized the amount of TPA into five levels: 0, 1–599, 600–1199 (current 2020 WHO guideline recommendations), 1200–2999, and at least 3000 MET-min/week.
Covariate assessment
Information on sociodemographic characteristics (urban/rural residence, age, sex, education, marital, employment), lifestyle factors (smoking, alcohol consumption), biomedical factors (CVD), medication (antihypertensive medicine intake), and family history of hypertension were obtained from each participant using a standardized questionnaire. Body weight and height were measured by well trained staff using calibrated instruments, and the BMI was calculated by weight (kg)/height squared (m2). Given that the survey was implemented throughout the year, we also included the survey season for adjustment. More details have been described in our prior studies [23,24,31] and are illustrated in the Supplemental Materials.
Statistical analyses
Descriptive statistics were used to summarize the characteristics of our study population. The age and sex-standardized prevalence estimates of physical inactivity were calculated using the 2010 China population. We used linear regressions to quantify the associations between physical activity and BP, and logistic regressions to quantify the associations between physical activity and hypertension risk. The adjusted covariates included geographic region, urban/rural residence, survey season, sex, age, education, employment, marital status, smoking, drinking, BMI, CVD, family history of hypertension, and antihypertensive medication (for the association with BP). We then conducted subgroup analyses to explore variations in TPA-BP associations across normotensive and hypertensive population. The nonlinear dose–response associations of physical activity with SBP, DBP, and hypertension risk were further assessed respectively using restricted cubic spline regression with four knots, with likelihood ratio tests to evaluate nonlinearity. Several sensitivity analyses were conducted to test the robustness of our primary results. We further calculated the population attributable fraction (PAF) associated with physical inactivity for hypertension to estimate the impact of physical inactivity on hypertension prevalence in our study population [2]. More details are provided in the Supplemental Materials.
All analyses were conducted using SAS version 9.4 (SAS Institute, Cary, North Carolina, USA) and R software version 4.1.2 (R Development Core Team, Vienna, Austria). A two-tailed P value less than 0.05 was considered statistically significant.
RESULTS
Participant characteristics
The characteristics of the 203 108 study participants are summarized in Table 1. Our study population, with a mean age of 48.3 years, comprised 52.6% women and 45.2% urban residents. The median TPA was 3213.0 (interquartile range [IQR]: 4545.0) in MET-min/week, and the median levels of occupation and housework, transportation, and leisure-time physical activity were 1680.0, 396.0, and 0 MET-min/week, respectively. Of the study population, 79.7% participated in occupation and housework PA, 64.1% participated in transportation physical activity, and 24.9% participated in leisure-time physical activity. On the contrary, 79.8% of the participants reported engagement in moderate-intensity physical activity, while only 22.5% reported engagement in vigorous-intensity physical activity (Supplemental Table 1). Overall, the crude and standardized prevalence rates of physical inactivity were 15.2% and 14.8%, respectively. See additional results of the distribution of physical activity in Supplemental Tables 1–3.
TABLE 1.
Characteristics of participants by levels of total physical activity
| Total physical activity (MET-min/week) | ||||||
| Variables | 0 | 1–599 | 600–1199 | 1200–2999 | ≥3000 | Overall |
| Categorical variables | ||||||
| Number of participants | 16 985 (8.4) | 13 888 (6.8) | 20 266 (10.0) | 45 801 (22.6) | 106 168 (52.3) | 203 108 (100.0) |
| Region, n (%) | ||||||
| East | 6880 (40.5) | 5791 (41.7) | 8104 (40.0) | 16 797 (36.7) | 33 422 (31.5) | 70 994 (35.0) |
| Center | 6782 (39.9) | 5049 (36.4) | 7723 (38.1) | 18 179 (39.7) | 44 133 (41.6) | 81 866 (40.3) |
| West | 3323 (19.6) | 3048 (22.0) | 4439 (21.9) | 10 825 (23.6) | 28 613 (27.0) | 50 248 (24.7) |
| Residence, n (%) | ||||||
| Urban | 8242 (48.5) | 7199 (51.8) | 10 758 (53.1) | 24 876 (54.3) | 40 662 (38.3) | 91 737 (45.2) |
| Rural | 8743 (51.5) | 6689 (48.2) | 9508 (46.9) | 20 925 (45.7) | 65 506 (61.7) | 111 371 (54.8) |
| Sex, n (%) | ||||||
| Male | 9744 (57.4) | 7366 (53.0) | 9493 (46.8) | 20 735 (45.3) | 48 947 (46.1) | 96 285 (47.4) |
| Female | 7241 (42.6) | 6522 (47.0) | 10 773 (53.2) | 25 066 (54.7) | 57 221 (53.9) | 106 823 (52.6) |
| Age group, years, n (%) | ||||||
| 18–24 | 1788 (10.5) | 1882 (13.6) | 2756 (13.6) | 5857 (12.8) | 7838 (7.4) | 20 121 (9.9) |
| 25–34 | 3355 (19.8) | 3201 (23.1) | 4461 (22.0) | 9090 (19.9) | 17 622 (16.6) | 37 729 (18.6) |
| 35–44 | 2277 (13.4) | 2051 (14.8) | 3130 (15.4) | 7223 (15.8) | 19 913 (18.8) | 34 594 (17.0) |
| 45–54 | 1990 (11.7) | 1477 (10.6) | 2553 (12.6) | 6164 (13.5) | 20 981 (19.8) | 33 165 (16.3) |
| 55–64 | 1732 (10.2) | 1255 (9.0) | 2233 (11.0) | 6182 (13.5) | 19 388 (18.3) | 30 790 (15.2) |
| ≥65 | 5843 (34.4) | 4022 (29.0) | 5133 (25.3) | 11 285 (24.6) | 20 426 (19.2) | 46 709 (23.0) |
| Education attainment, n (%) | ||||||
| Unknown | 45 (0.3) | 15 (0.1) | 19 (0.1) | 59 (0.1) | 130 (0.1) | 268 (0.1) |
| Elementary school | 7487 (44.1) | 4895 (35.3) | 6957 (34.3) | 15 836 (34.6) | 46 370 (43.7) | 81 545 (40.2) |
| Middle school | 4524 (26.6) | 3455 (24.9) | 5250 (25.9) | 12 813 (28.0) | 36 868 (34.7) | 62 910 (31.0) |
| High school or above | 4929 (29.0) | 5523 (39.8) | 8040 (39.7) | 17 093 (37.3) | 22 800 (21.5) | 58 385 (28.8) |
| Marital status, n (%) | ||||||
| Unknown | 63 (0.4) | 30 (0.2) | 41 (0.2) | 97 (0.2) | 292 (0.3) | 523 (0.3) |
| Unmarried | 2264 (13.3) | 2407 (17.3) | 3395 (16.8) | 7017 (15.3) | 8927 (8.4) | 24 010 (11.8) |
| Married or partnered | 12 303 (72.4) | 9891 (71.2) | 14 829 (73.2) | 34 338 (75.0) | 88 737 (83.6) | 160 098 (78.8) |
| Separated, divorced, or widowed | 2355 (13.9) | 1560 (11.2) | 2001 (9.9) | 4349 (9.5) | 8212 (7.7) | 18 477 (9.1) |
| Employment status, n (%) | ||||||
| Unknown | 40 (0.2) | 25 (0.2) | 39 (0.2) | 80 (0.2) | 241 (0.2) | 425 (0.2) |
| Employed | 8635 (50.8) | 7531 (54.2) | 10 858 (53.6) | 23 805 (52.0) | 65 258 (61.5) | 116 087 (57.2) |
| Retired | 1412 (8.3) | 1141 (8.2) | 1758 (8.7) | 5027 (11.0) | 7971 (7.5) | 17 309 (8.5) |
| Students | 621 (3.7) | 1029 (7.4) | 1576 (7.8) | 3571 (7.8) | 3113 (2.9) | 9910 (4.9) |
| Unemployed | 6277 (37.0) | 4162 (30.0) | 6035 (29.8) | 13 318 (29.1) | 29 585 (27.9) | 59 377 (29.2) |
| Smoke status, n (%) | ||||||
| Unknown | 317 (1.9) | 262 (1.9) | 327 (1.6) | 805 (1.8) | 1910 (1.8) | 3621 (1.8) |
| Nonsmokers | 12 244 (72.1) | 10 072 (72.5) | 15 435 (76.2) | 34 786 (76.0) | 76 913 (72.4) | 149 450 (73.6) |
| Current smokers | 3803 (22.4) | 3036 (21.9) | 3789 (18.7) | 8514 (18.6) | 23577 (22.2) | 42 719 (21.0) |
| Past smokers | 621 (3.7) | 518 (3.7) | 715 (3.5) | 1696 (3.7) | 3768 (3.6) | 7318 (3.6) |
| Drink status, n (%) | ||||||
| Unknown | 25 (0.2) | 13 (0.1) | 12 (0.1) | 72 (0.2) | 371 (0.4) | 493 (0.2) |
| No | 14 419 (84.9) | 11646 (83.9) | 17338 (85.6) | 39069 (85.3) | 87919 (82.8) | 170391 (83.9) |
| Yes | 2541 (15.0) | 2229 (16.1) | 2916 (14.4) | 6660 (14.5) | 17878 (16.8) | 32224 (15.9) |
| BMI category, n (%) | ||||||
| Unknown | 351 (2.1) | 136 (1) | 188 (0.9) | 392 (0.9) | 767 (0.7) | 1834 (0.9) |
| Underweight or normal weight | 9927 (58.5) | 8164 (58.8) | 11 711 (57.8) | 25 903 (56.6) | 57 806 (54.5) | 113 511 (55.9) |
| Overweight | 4828 (28.4) | 4081 (29.4) | 6163 (30.4) | 14 234 (31.1) | 34 712 (32.7) | 64 018 (31.5) |
| Obese | 1879 (11.1) | 1507 (10.9) | 2204 (10.9) | 5272 (11.5) | 12 883 (12.1) | 23 745 (11.7) |
| Hypertension, n (%) | ||||||
| No | 11 485 (67.6) | 9916 (71.4) | 14577 (71.9) | 32 656 (71.3) | 76 060 (71.6) | 144 694 (71.2) |
| Yes | 5500 (32.4) | 3972 (28.6) | 5689 (28.1) | 13 145 (28.7) | 30 108 (28.4) | 58 414 (28.8) |
| Coronary artery disease, n (%) | ||||||
| No | 16 857 (99.3) | 13 799 (99.4) | 20 132 (99.3) | 45 471 (99.3) | 105 610 (99.5) | 201 869 (99.4) |
| Yes | 128 (0.8) | 89 (0.6) | 134 (0.7) | 330 (0.7) | 558 (0.5) | 1239 (0.6) |
| Stroke, n (%) | ||||||
| No | 16 489 (97.1) | 13 674 (98.5) | 19 942 (98.4) | 45 019 (98.3) | 104 801 (98.7) | 199 925 (98.4) |
| Yes | 496 (2.9) | 214 (1.5) | 324 (1.6) | 782 (1.7) | 1367 (1.3) | 3183 (1.6) |
| Family history of hypertension, n (%) | ||||||
| No | 13 401 (78.9) | 10 443 (75.2) | 15 243 (75.2) | 33 803 (73.8) | 77 260 (72.8) | 150 150 (73.9) |
| Yes | 3584 (21.1) | 3445 (24.8) | 5023 (24.8) | 11 998 (26.2) | 28 908 (27.2) | 52 958 (26.1) |
| Season of the study, n (%) | ||||||
| Spring | 2917 (17.2) | 2369 (17.1) | 3223 (15.9) | 6706 (14.6) | 14 414 (13.6) | 29 629 (14.6) |
| Summer | 7148 (42.1) | 5201 (37.5) | 8294 (40.9) | 18 947 (41.4) | 48 107 (45.3) | 87 697 (43.2) |
| Fall | 4366 (25.7) | 3399 (24.5) | 5153 (25.4) | 12 422 (27.1) | 29 020 (27.3) | 54 360 (26.8) |
| Winter | 2554 (15.0) | 2919 (21.0) | 3596 (17.7) | 7726 (16.9) | 14 627 (13.8) | 31 422 (15.5) |
| Continuous variables | ||||||
| Total physical activity, MET-min/week, median (IQR) | 0.0 (0.0) | 386.0 (240.0) | 840.0 (290.0) | 1920.0 (867.0) | 5508.0 (3504.0) | 3213.0 (4545.0) |
| Occupation and housework physical activity, MET-min/week, median (IQR) | 0.0 (0.0) | 0.0 (240.0) | 420.0 (720.0) | 960.0 (1200.0) | 4560.0 (2040.0) | 1680.0 (4720.0) |
| Leisure-time physical activity, MET-min/week, median (IQR) | 0.0 (0.0) | 0.0 (0.0) | 0.0 (0.0) | 0.0 (360.0) | 0.0 (720.0) | 0.0 (0.0) |
| Transportation physical activity (walking or cycling), MET-min/week, median (IQR) | 0.0 (0.0) | 132.0 (330.0) | 297.0 (693.0) | 577.5 (990.0) | 693.0 (1386.0) | 396.0 (1188.0) |
| Moderate physical activity, MET-min/week, median (IQR) | 0.0 (0.0) | 120.0 (320.0) | 480.0 (800.0) | 1200.0 (1080.0) | 3360.0 (3000.0) | 1680.0 (3240.0) |
| Vigorous physical activity, MET-min/week, median (IQR) | 0.0 (0.0) | 0.0 (0.0) | 0.0 (0.0) | 0.0 (0.0) | 0.0 (2880.0) | 0.0 (0.0) |
| SBP, mmHg, mean (SD) | 129.7 (20.4) | 127.5 (19.7) | 127.0 (19.4) | 127.1 (19.4) | 127.6 (19.0) | 127.6 (19.3) |
| DBP, mmHg, mean (SD) | 76.2 (10.7) | 75.3 (10.7) | 75.1 (10.6) | 75.0 (10.5) | 75.4 (10.6) | 75.3 (10.6) |
Categorical variables are presented as numbers (percentages); continuous variables are presented as the means (standard deviations) or medians (interquartile ranges).
IQR, interquartile range; MET, metabolic equivalent of task; SD, standard deviation.
In our study population, the mean (standard deviation, SD) SBP and DBP were 127.6 (19.3) mmHg and 75.3 (10.6) mmHg, respectively; the crude and standardized prevalence of hypertension were 28.8% and 22.5%, respectively. Regarding demographic factors, the unadjusted prevalence of physical inactivity was significantly high among individuals located in the eastern region (17.9%), urban residents (16.8%), males (17.8%), individuals aged over 65 years (21.1%), separated, divorced, or widowed individuals (21.2%), and individuals with a high school education or above (17.9%). In terms of risk factors, the corresponding prevalence rates were the highest among individuals with stroke (22.3%), coronary artery disease (17.5%), and hypertension (16.2%), followed by current smokers (16.0%) and nondrinkers (15.3%).
Associations of physical activity levels with blood pressure
The associations between physical activity levels and BP are presented in Table 2. According to the fully adjusted model, compared with inactive participants (TPA ∼ 0 MET-min/week), the 1–599, 600–1199, 1200–2999, and at least 3000 MET-min/week TPA categories were associated with 0.80 [95% confidence interval (95% CI): 0.44–1.15], 0.54 (95% CI: 0.22–0.86), 0.57 (95% CI: 0.29–0.85), and 0.48 (95% CI: 0.22–0.74) mmHg decreases in SBP, respectively, and 0.53 (95% CI: 0.32–0.75), 0.52 (95% CI: 0.32–0.72), 0.66 (95% CI: 0.49–0.84), and 1.09 (95% CI: 0.93–1.25) mmHg decreases in DBP, respectively. Regarding hypertension, high levels of TPA (≥3000 MET-min/week) were associated with a 7% (95% CI: 3%–11%) reduction in hypertension risk compared with the 0 MET-min/week category. Higher levels of occupation and housework, leisure time, transportation, and moderate-intensity physical activity were associated with lower BP levels (Fig. 1). Similar patterns were also observed for associations between physical activity and hypertension risks, except in the transport physical activity-hypertension risk association. Engaging in vigorous-intensity physical activity was associated with decreases in DBP levels and hypertension risk but not in SBP levels.
TABLE 2.
Associations of total physical activity with SBP, DBP, and hypertension risk
| SBP | DBP | Hypertension | ||||
| Physical activity | Coefficients (95% CI) | P | Coefficients (95% CI) | P | Odds ratio (95% CI) | P |
| Total physical activity (MET-min/week) | ||||||
| Model 1 | ||||||
| 0 | Ref | Ref | Ref | |||
| 1–599 | -2.22 (-2.65, -1.78) | <0.001 | -0.91 (-1.15, -0.67) | <0.001 | 0.84 (0.80–0.88) | <0.001 |
| 600–1199 | -2.74 (-3.14, -2.35) | <0.001 | -1.11 (-1.33, -0.90) | <0.001 | 0.81 (0.78–0.85) | <0.001 |
| 1200–2999 | -2.57 (-2.91, -2.23) | <0.001 | -1.17 (-1.36, -0.98) | <0.001 | 0.84 (0.81–0.87) | <0.001 |
| ≥3000 | -2.13 (-2.44, -1.82) | <0.001 | -0.86 (-1.03, -0.69) | <0.001 | 0.83 (0.80–0.86) | <0.001 |
| Model 2 | ||||||
| 0 | Ref | Ref | Ref | |||
| 1–599 | -0.65 (-1.03, -0.27) | 0.001 | -0.44 (-0.66, -0.21) | <0.001 | 0.99 (0.93– 1.05) | 0.695 |
| 600–1199 | -0.36 (-0.71, -0.02) | 0.039 | -0.41 (-0.61, -0.20) | <0.001 | 1.02 (0.97–1.07) | 0.455 |
| 1200–2999 | -0.32 (-0.62, -0.02) | 0.037 | -0.51 (-0.69, -0.33) | <0.001 | 1.01 (0.97–1.06) | 0.567 |
| ≥3000 | -0.36 (-0.64, -0.08) | 0.011 | -0.99 (-1.15, -0.82) | <0.001 | 0.96 (0.92–1.00) | 0.072 |
| Model 3 | ||||||
| 0 | Ref | Ref | Ref | |||
| 1–599 | -0.80 (-1.15, -0.44) | <0.001 | -0.53 (-0.75, -0.32) | <0.001 | 0.98 (0.92–1.04) | 0.432 |
| 600–1199 | -0.54 (-0.86, -0.22) | 0.001 | -0.52 (-0.72, -0.32) | <0.001 | 1.00 (0.95–1.06) | 0.984 |
| 1200–2999 | -0.57 (-0.85, -0.29) | <0.001 | -0.66 (-0.84, -0.49) | <0.001 | 0.98 (0.93–1.02) | 0.296 |
| ≥3000 | -0.48 (-0.74, -0.22) | <0.001 | -1.09 (-1.25, -0.93) | <0.001 | 0.93 (0.89–0.97) | <0.001 |
Data were analyzed with linear regression (SBP and DBP) and logistic regression (hypertension risk) models. Model 1 was unadjusted with total physical activity as the only independent variable. Model 2 was adjusted for region, residence, season of the study, sex, age group, education attainment, marital status, employment status, smoke status, and drink status. Model 3 was additionally adjusted for BMI, cardiovascular disease, family history of hypertension, and antihypertensive medication (for the association of blood pressure).
CI, confidence interval; MET, metabolic equivalent of task.
FIGURE 1.
Associations of domain-specific and intensity-specific physical activity with SBP, DBP, and hypertension risk. Models were adjusted for region, residence, season of the study, sex, age group, education attainment, marital status, employment status, smoke status, drink status, BMI, cardiovascular disease, family history of hypertension, and antihypertensive medication (for the association of blood pressure).
Dose–response associations between physical activity and blood pressure
Figure 2 shows the estimated dose–response curves between TPA and SBP, DBP, and hypertension risk along with their 95% CIs. There was evidence of a nonlinear association for SBP (Pnonlinearity = 0.006) and DBP (Pnonlinearity < 0.001), respectively, but not for hypertension risk (Pnonlinearity = 0.180). Within the range of 0–2500 MET-min/week, an increase in TPA was associated with a steep decrease in BP, and both the dose–response curves were roughly linear over the exposures. The TPA-SBP curves plateaued over the range of 3000–9000 MET-min/week, that is, approximately the 50th percentile to the 90th percentile of the TPA level, and another decline in SBP appeared thereafter. In the case of DBP, the curves suggested a further decline with gentler slopes at a TPA level more than 2500 MET-min/week. We also established dose–response associations of domain-specific and intensity-specific physical activity with SBP, DBP, and hypertension risk, respectively, as shown in Supplemental Figures 2–6.
FIGURE 2.
Restricted cubic spline model for the associations of total physical activity with SBP, DBP, and hypertension risk. Restricted cubic splines were constructed with four knots located at the 5th, 35th, 65th, and 95th percentiles of the total physical activity in metabolic equivalent task-min/week. The solid line represents the point estimates; the dashed line represents the confidence intervals; and the gray line is the reference line. Models were adjusted for region, residence, season of the study, sex, age group, education attainment, marital status, employment status, smoke status, drink status, BMI, cardiovascular disease, family history of hypertension, and antihypertensive medication (for the association of blood pressure). MET, metabolic equivalent of task.
Associations between frequency, duration, and volume of physical activity and blood pressure
As summarized in Table 3, we examined whether physical activity interventions comprising different frequencies, durations, and intensities over the course of the week would have different BP-related effects. Here, we chose a TPA level of 2000–4000 MET-min/week as a typical range because the dose–response curves showed a maximum effect at a TPA level of approximately 2500 MET-min/week (Fig. 2), and this range covered the 40th--60th percentile of TPA level, and it is a typical range in populations. We found that within the same amount of TPA, participants who reported a higher frequency (5–7 days/week), that is, shorter in duration and/or lower in intensity, had a lower DBP (0.87 mmHg; 95% CI: 0.28--1.46) than those who engaged in physical activity 1–2 days/week; a longer TPA duration (≥180 min/day), that is, lower in frequency and/or lower in intensity, had a higher DBP (0.83 mmHg; 95% CI: 0.29--1.37) than those who reported a shorter duration (<90 min/day). A higher volume of physical activity was linked to a lower DBP, suggesting that a lower intensity had a beneficial effect. Similar associations were observed to those between SBP and hypertension risk, but they were not statistically significant. Overall, these results led to consistency in the beneficial BP effects from high-frequency, short-duration, and low-intensity physical activity within the same TPA amount.
TABLE 3.
Associations of frequency, duration and volume of total physical activity with systolic blood pressure, diastolic blood pressure, and hypertension risk among participants engaging in 2000–4000 MET-min/week
| SBP | DBP | Hypertension | ||||
| Total physical activity | Coefficients (95% CI) | P | Coefficients (95% CI) | P | Odds ratio (95% CI) | P |
| Frequency of total physical activity (d/week) | ||||||
| 1–2 | Ref | Ref | Ref | |||
| 3–4 | -0.38 (-1.43, 0.66) | 0.470 | -0.24 (-0.87, 0.40) | 0.465 | 1.00 (0.82–1.21) | 0.993 |
| 5–7 | -0.83 (-1.80, 0.15) | 0.097 | -0.87 (-1.46, -0.28) | 0.004 | 1.04 (0.87–1.25) | 0.671 |
| Duration of total physical activity (min/day) | ||||||
| <90 | Ref | Ref | Ref | |||
| 90–179 | 0.41 (-0.46, 1.27) | 0.357 | 0.55 (0.03, 1.08) | 0.039 | 1.03 (0.90–1.19) | 0.659 |
| ≥180 | 0.54 (-0.35, 1.43) | 0.231 | 0.83 (0.29, 1.37) | 0.002 | 1.02 (0.88–1.18) | 0.793 |
| Volume of total physical activity (min/week) | ||||||
| <450 | Ref | Ref | Ref | |||
| 450–749 | 0.03 (-0.83, 0.88) | 0.954 | -0.04 (-0.56, 0.48) | 0.867 | 1.07 (0.92–1.26) | 0.373 |
| ≥750 | -0.01 (-0.87, 0.85) | 0.980 | -0.24 (-0.77, 0.28) | 0.359 | 1.07 (0.91–1.25) | 0.396 |
Data were analyzed with linear regression (SBP and DBP) and logistic regression (hypertension risk) models. Models were adjusted for region, residence, season of the study, sex, age group, education attainment, marital status, employment status, smoke status, drink status, BMI, cardiovascular disease, family history of hypertension, and antihypertensive medication (for the association of blood pressure).
CI, confidence interval; MET, metabolic equivalent of task.
Subgroup and sensitivity analyses
We found that the estimated associations between TPA level and SBP, DBP, and hypertension risk remained robust, excluding participants with missing data regarding education, marital status, smoking, drinking, and BMI and participants with CVD (Supplemental Table 4). The inverse associations between TPA and BP were observed for both normotensive and hypertensive population, with a strong association for hypertensive population (Supplemental Table 5). The effects of frequency, duration, and volume of TPA on SBP, DBP, and hypertension risk, respectively, were also found to be robust (Supplemental Tables 6–8), excluding missing characteristic data, including education, marital status, smoking, drinking, and BMI, and excluding participants with CVD; and within alternative TPA ranges, including 2000–4500 and 1500–4500 MET-min/week.
DISCUSSION
Main findings
To the best of our knowledge, this is the first study to map the continuous distribution of total, domain-specific, and intensity-specific physical activity among Chinese general population, and we present more recent and up-to-date data on the prevalence of physical inactivity. This study is also the first to determine the nonlinear dose–response relationships between physical activity and BP in a large population-based survey, with a wide range of physical activity levels. We found that TPA was inversely associated with BP, with a relatively maximum effect at approximately 2500 MET-min/week; at the same weekly amount, more frequent, less vigorous, and shorter durations of physical activity could help decrease BP levels.
There is a lack of consistency across previous studies in China in terms of physical activity measures, and extrapolating data from restricted physical activity domains, geographical areas, and population groups to overall national estimates could be biased [32–37]. Our study benefited from a national survey with detailed quantification of total, domain-specific, and intensity-specific physical activity, which helped to reveal the detailed distribution of physical activity as well as epidemiological characteristics of physical inactivity among Chinese adults. Therefore, our results substantially improved the international comparability of prevalence estimates on physical inactivity across China. We estimated the overall prevalence of physical inactivity as 14.8%, which is consistent with the estimate (14.1%) from a worldwide estimation involving 358 surveys across 168 countries in 2016 [38]. In addition, we calculated the PAF to evaluate the impact of physical inactivity on hypertension prevalence in our study population, and the estimate was 0.65%. Based on the standardized prevalence of hypertension (22.5%) in our study and the number of Chinese adults aged at least 18 years in the 2010 Chinese population, we further estimated that over 1.59 million potential hypertensive patients could have been averted if people achieved sufficient physical activity levels in China. Our study reinforced the need to promote regular physical activity.
In contrast with most previous studies that ignored the possibility of nonlinear dose–response associations between physical activity and health effects [39–42], we established continuous nonlinear dose–response curves between physical activity and BP and found that increasing TPA levels was related to a steep decrease in BP, up to approximately 2500 MET-min/week, with a more modest decrease above that level of physical activity. Notably, our results remain consistent with two previous meta-analyses, which suggested L-shaped relationships between physical activity and the risk of ischemic heart disease, ischemic stroke, diabetes, breast cancer, and colon cancer, with most health gains occurring at 3000–4000 MET-min/week [12], and a U-shaped curve for all-cause mortality, with the maximum effect at 2000–3000 MET-min/week [13]. Although we found no evidence of a nonlinear dose–response association of physical activity with hypertension risk, these findings were also consistent with a meta-analysis involving cohort studies [43].
We observed that compared with inactive participants, increased physical activity, despite not meeting WHO guideline recommendations, was associated with decreases in BP levels. Our finding is consistent with previous studies [44], and the updated 2020 WHO guidelines suggest that if adults are not meeting recommendations, engaging in some physical activity will benefit their health [6]. Previous studies have provided the possible mechanisms, including that physical activity may reduce cardiac output, systemic vascular resistance, sympathetic nerve activity, plasma norepinephrine levels, homeostasis model assessment insulin resistance index scores, and weight and improve endothelial function and blood lipid levels [43,45–47]. In addition, it remains controversial whether a large amount of physical activity is deleterious [19,20]. Our real-world study found that people who achieved TPA levels four to five times higher than the current recommended minimum level of 600 MET min/week may have much lower BP levels, and a large amount of physical activity (>9000 MET min/week) was associated with a further decrease in BP levels and hypertension risk. This finding addressed an important research gap, which might still be biased due to the small proportion (approximately 10%) of participants engaged in more than 9000 MET min/week in the present study.
In our study, we observed the modest inverse associations between physical activity and BP. This can be due to following reasons. The study population in our study were general adults aged at least 18 years, of whom only 23% were older than 65 years. Our study showed a stronger association between physical activity and BP in older adults. Previous studies have suggested that there was a steady increase in SBP at older ages, which may lead to an increased risk for age-related hypertension among older population [48–50]. In addition, a longitudinal study, based on China Health and Nutrition Survey in Chinese adults at least 18 years, reported modest associations between physical activity and BP, which were similar to our results [51]. In our study, most participants (71%) had normal BP values, while 29% hypertensive individuals. We observed a stronger PA-BP association among hypertensive individuals. A network meta-analysis suggested that among hypertensive populations, physical activity interventions had the same effect as antihypertensive medications [52]. A systematic umbrella review, including 17 meta-analyses and one systematic review with 594 129 adults aged at least 18 years, reported that strong evidence has demonstrated that the magnitude of the BP response to physical activity varies by BP level, with greater BP reductions occurring among adults with hypertension than normal BP [53]. Another two reviews have reported similar results [50,54]. We have adjusted a series of confounders, which may attenuate the associations. Notably, even a small decrease in BP in the population can be able to reduce the prevalence of hypertension in the future [55], and may result in a substantial reduction in CVD mortality [56]. Furthermore, a network meta-analysis, including 93 randomized controlled trials, has suggested that although antihypertensive medications were more effective than exercise, there was insufficient evidence on significantly greater extent decreases in BP by medications than by exercise interventions [57]. In addition to pharmacological treatment, physical activity, as a modifiable lifestyle factor, could also have a great public health impact on the prevention and control of hypertension in the population.
Our study showed that occupation and housework, transportation and leisure time physical activity were negatively associated with BP, respectively. Two meta-analyses have reported similar results for leisure time physical activity-hypertension associations [41,43]. Although the pooled estimates were not significant between occupation physical activity and hypertension risk in one meta-analysis [41], three nationwide cohort study in China showed that occupation physical activity was inversely linked to CVD risk [33], moderate housework physical activity with a lower risk of hypertension [58], and occupation, and housework physical activity were inversely associated with CVD mortality [59]. One meta-analysis showed no evidence on transportation physical activity-hypertension associations [41], while another meta-analysis showed that active transportation physical activity was associated with a significantly lower risk of CVD incidence [60]. Our findings showed a U-shaped association between transportation physical activity and BP, which was consistent with a cohort study in China [61]. This may be due to the increased exposure to ambient air pollutants that had harmful effects on health [62]. Overall, evidence on associations of occupation, housework, and transportation physical activity with BP were remains scarce, especially in China. Our findings have added evidence on negative associations between domain-specific physical activity and BP.
There was insufficient evidence to determine whether accumulated physical activity (i.e., splitting a continuous duration of physical activity into several short durations) was more effective than continuous physical activity of the same amount [63,64]. Within an identical amount of TPA, we found that high-frequency, short-duration, and low-intensity physical activity was associated with lower BP levels to support the accumulated approach. In addition, a review reported that exercise three to five times per week for 30–60 min per session at a moderate intensity appeared to be effective with regard to BP reduction [42], which was consistent with our results. Given the multiple demands upon individuals’ time, the flexibility afforded by an accumulated approach may help to increase the proportion of the population meeting physical activity recommendations [65].
Strengths
Our study has several strengths. First, the study population were derived from a nationwide large-scale sample of Chinese adults, and individual-level physical activity was measured comprehensively across three domains and three intensities. Our study presented continuous distribution of total, domain-specific, and intensity-specific physical activity to provide the basic data for comprehensive and continuous distribution characteristics of physical activity in the Chinese general population. We also reported more recent and up-to-date data on the prevalence of physical inactivity according to 2020 WHO guideline, which can be used for international comparisons. Second, we portrayed the nonlinear dose–response relationships between physical activity and BP in a large population sample. The wide range of physical activity in our study can provide evidence on physical activity-BP associations not only for small amount of physical activity, but also for large amount of physical activity, which can also help to explore the threshold effects. Third, this study is the first to evaluate associations of frequency, duration, and volume of physical activity with BP when the amount of physical activity is fixed within a certain range to explore the relative role of frequency, duration, and intensity of physical activity.
Limitations
This study had several limitations. First, it was a cross-sectional study that could not provide causal associations. When we calculated PAF-associated physical inactivity for hypertension, we used the odds ratio related to physical inactivity for hypertension instead of the relative risk. Prospective longitudinal studies are needed for future research. Second, physical activity was assessed by self-reported adapted IPAQ responses, which may result in recall bias compared with objective measurements. However, the reliability and validity of the IPAQ has been demonstrated by several studies from different countries [26]. The use of the IPAQ benefits international comparability. On the other hand, the CHS study mainly focus on moderate-intensity and vigorous-intensity physical activity without walking information for occupation and housework and leisure-time physical activity, which may result in underestimating low-intensity physical activity. Third, residual confounding may still exist due to unknown or unadjusted (e.g., blood lipid and glucose levels and salt intake) factors.
CONCLUSION
This is the first study to quantify the nonlinear dose–response relationships between physical activity and BP with a wide range of physical activity levels in a large population-based survey and found that TPA is negatively associated with BP, and a greater risk reduction in BP is observed at moderate exposures (approximately 2500 MET-min/week) compared with higher exposures. Our findings may help to provide a comprehensive map of the continuous effects of physical activity on BP for general populations in the real world. We also reported the detailed continuous distribution of total, domain-specific, and intensity-specific physical activity and the prevalence of physical inactivity among Chinese adults. Within the given amount of physical activity, high-frequency, short-duration, and low-intensity physical activity was associated with lower BP levels.
ACKNOWLEDGEMENTS
The authors are grateful to all the staff working in the CHS.
M.C., T.G., and Z.W. designed the study. M.C. and T.G. developed the study methods, reviewed the literature, performed the analyses, and wrote the first draft of the manuscript. C.Z., H.Z., X.W., Z.C., L.Z., X.C., and Y.T. participated in the investigation and validated the data. T.G., Z.W., J.G., and X.H. critically revised the manuscript. All authors contributed to the interpretation of data and the final approved version.
Written informed consent was obtained from each participant. The Ethics Committee of Fuwai Hospital (Beijing, China) approved the study.
The data are not publicly available, as only authorized researchers can assess the database.
This study was funded by the CAMS Innovation Fund for Medical Sciences (2017-I2M-1–004), the China National Science & Technology Pillar Program (2011BAI11B01), the National Health and Family Planning Commission, China (201402002), and National Natural Science Foundation of China (41701591).
Conflicts of interest
There are no conflicts of interest.
Supplementary Material
T.G. and M.C. contributed equally to the study.
Abbreviations: BP, blood pressure; CHS, China Hypertension Survey; CI, confidence interval; CVD, cardiovascular disease; DBP, diastolic blood pressure; IPAQ, International Physical Activity Questionnaire; SBP, systolic blood pressure; TPA, total physical activity
Supplemental digital content is available for this article.
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