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Originally Published 27 March 2017
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Dose–Response Association Between Physical Activity and Incident Hypertension: A Systematic Review and Meta-Analysis of Cohort Studies

Xuejiao Liu, Dongdong Zhang, Yu Liu, Xizhuo Sun, Chengyi Han, Bingyuan Wang, Yongcheng Ren, Junmei Zhou, Yang Zhao, Yuanyuan Shi, Dongsheng Hu, and Ming ZhangAuthor Info & Affiliations
Hypertension
Volume 69, Number 5
https://doi.org/10.1161/HYPERTENSIONAHA.116.08994
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Abstract

Despite the inverse association between physical activity (PA) and incident hypertension, a comprehensive assessment of the quantitative dose–response association between PA and hypertension has not been reported. We performed a meta-analysis, including dose–response analysis, to quantitatively evaluate this association. We searched PubMed and Embase databases for articles published up to November 1, 2016. Random effects generalized least squares regression models were used to assess the quantitative association between PA and hypertension risk across studies. Restricted cubic splines were used to model the dose–response association. We identified 22 articles (29 studies) investigating the risk of hypertension with leisure-time PA or total PA, including 330 222 individuals and 67 698 incident cases of hypertension. The risk of hypertension was reduced by 6% (relative risk, 0.94; 95% confidence interval, 0.92–0.96) with each 10 metabolic equivalent of task h/wk increment of leisure-time PA. We found no evidence of a nonlinear dose–response association of PA and hypertension (Pnonlinearity=0.094 for leisure-time PA and 0.771 for total PA). With the linear cubic spline model, when compared with inactive individuals, for those who met the guidelines recommended minimum level of moderate PA (10 metabolic equivalent of task h/wk), the risk of hypertension was reduced by 6% (relative risk, 0.94; 95% confidence interval, 0.92–0.97). This meta-analysis suggests that additional benefits for hypertension prevention occur as the amount of PA increases.

Introduction

Hypertension, a key risk factor for cardiovascular diseases, is also the leading cause of premature death and the third cause of disability. It affects 1 billion people worldwide, leading to heart attacks, strokes, and renal failure.1–4 The total number of people with hypertension is expected to increase to 1.56 billion by 2025.5 Therefore, preventive approaches focused on modifying risk factors for hypertension are essential to control this growing epidemic.
Over the past 4 to 5 decades, accumulating data have yielded consistent findings on the protective effects of physical activity (PA) in preventing hypertension.6–17 Thus, PA is considered a major, protective factor for hypertension. Recent public health guidelines18,19 recommend a minimum of 150 minutes of moderate PA or 75 minutes of vigorous PA a week to maintain general health. However, self-reporting data suggest that about one third of adults globally are not meeting these recommendations.20
A recent systematic review of prospective cohort studies suggested an inverse association between PA and incident hypertension.21 However, it only reported the pooled high versus low effect size between PA and incident hypertension. Given the different definitions of PA exposure among studies, the association between PA and risk of hypertension could not be analyzed precisely. Thus, we investigated the dose–response association between PA and incident hypertension in cohort studies by a systematic review and meta-analysis.

Methods

Literature Search Strategy

We searched the electronic databases PubMed and Embase for all articles of cohort studies investigating the association between PA and incident hypertension by using a combination of medical subject heading terms and free texts (details in Table S1 in the online-only Data Supplement). The reference lists of all included articles6–17,22–31 and previous systematic reviews21,32–37 were manually searched for further studies. All published studies in English and Chinese were considered, with no restrictions on date of publication; new results were included up to November 1, 2016.

Study Selection

Cohort studies were included if they (1) followed a cohort of adults (>18 years of age at baseline); (2) excluded people with hypertension at baseline; (3) reported relative risks (RRs), odds ratios or hazard ratios with 95% confidence intervals (CIs) or data to calculate them; and (4) ascertained levels of PA at baseline, number of cases, exposed person-years, or participant numbers for the dose–response analysis. Exclusion criteria were (1) insufficient details of PA assessment to estimate PA dose in terms of metabolic equivalent of task (MET) h/wk, (2) reporting PA as a dichotomous variable, and (3) duplicate data. If multiple articles were published from the same cohort, we included data from the study with the most informative reporting of PA levels or the larger sample size. The excluded articles and the reasons for exclusion are listed in Table S2.

Data Extraction and Exposure Harmonization

Two authors (X.L. and D.Z.) extracted data on the first author, publication year, location, study name, cohort size, follow-up period, sex and age, method of hypertension assessment, method and unit of PA assessment, reported levels of PA exposure, case number per category of PA exposure, total persons or person-years per PA category, odds ratios/RRs/hazard ratios for hypertension with 95% CIs for each PA category, and covariates on which the analyses were adjusted. In studies reporting >1 type of PA, leisure-time PA (LTPA) was preferentially included for analysis. The study quality was assessed by the Newcastle-Ottawa Scale, which allows for a total score of ≤9 points38 summarizing 8 aspects of each study. Any disagreements were discussed until agreement was reached.
Initially, we harmonized group-level exposure estimates to the common unit of MET h/wk, thereby allowing for integration of activities differing in intensity and duration accumulated over a week. To assign specific intensities to categories PA, the mean intensity of light PA, moderate PA, moderate to vigorous PA, and vigorous PA was assigned as 3, 4, 4.5, and 8 METs, respectively.39,40 Articles reporting data separately for men and women11,12,14,17,27,30 or reporting on multiple cohorts within an article8 were treated as independent studies. For studies7,11,14,17,22,27,30 reporting risk estimates relative to the highest category of PA, the risk estimates were recalculated to set the lowest PA category as the reference.41
When not directly reported, PA volume (MET h/wk) was calculated by multiplying the median or midpoint duration of the reported category by its assigned MET value. If the highest category for PA duration was open ended, the width of the interval was assumed to be the same as in the closest category.42 When the lowest category was open ended, the lower boundary was set to 0. For 7 studies10,16,22,25,27,30 reporting PA only as frequency of sessions per week, we converted the frequency of PA per week to h/wk by assigning a dose of 45 minutes per session in the main analysis with an assumption of 30 minutes tested in sensitivity analysis.40,43,44 Likewise, if the intensity of PA was not reported,6,25,26,30 we assumed that the intensity was 4.5 METs. An overview of dose assignment calculations is listed in Table S3. When the required data were not reported in the original articles, we emailed authors of the identified cohorts to acquire further details (eg, average or median MET/h of PA, definition of PA and number of hypertension cases for each PA exposure category). After this correspondence, 1 detail25 on the definition of PA was obtained.

Statistical Methods

For cohort studies reporting hazard ratios or odds ratios for hypertension, we assumed that the hazard ratios and odds ratios were approximately RRs.45 If the number of cases in each category was missing, these data were inferred on the basis of the number of total cases and the reported effect size. If the exposed person-years or participant numbers were not reported in each category, groups were assumed to be of equal sizes.46 A random effects model,47 which considers both within- and between-study variation, was used to calculate the summarized RR estimates. Data separately reporting the results by race were pooled with the fixed-effects model before inclusion in the meta-analysis.30
Generalized least squares regression was used to estimate study-specific dose–response association. Generalized least squares regression model estimates the linear dose–response coefficient taking into account the covariance for each exposure category within each study because they are estimated relative to a common referent PA exposure category.48,49 The DerSimonian and Laird random effects model47 was used to pool the study-specific dose–response RR estimates. First, a linear association was assumed; study-specific RR estimates were calculated per 10 MET h/wk of LTPA increment and per 50 MET h/wk of TPA increment and then pooled, respectively. In addition, we examined possible nonlinear associations by modeling PA using a restricted cubic spline with 3 knots located at the 25th, 50th, and 75th percentiles of the distribution.50 Only studies reporting risk estimates for at least 3 PA exposure levels for incident hypertension were included in this analysis. The P value for nonlinearity was calculated by testing the null hypothesis that the coefficient of the second spline is equal to zero.
Heterogeneity was tested by Cochran Q and I2 statistics.51 For the Q statistic, P<0.1 was considered statistically significant; and for the I2 statistic, I2 values of ≈25%, 50%, and 75% were considered to reflect low, moderate, and high heterogeneity, respectively. Subgroup analyses involved the variables, sex, age, nationality, number of cases, body mass index (BMI), PA intensity, study quality, publication year, follow-up time, MET h/wk, and the covariates (age, smoking, alcohol drinking, education, income, and baseline chronic diseases) adjusted in the analysis. Mediation by BMI was explored according to the degree of adjustment (adjusted or not for BMI) and participant overweight (BMI <25 versus ≥25 kg/m2). We performed a sensitivity analysis by excluding 1 study at a time to assess the stability of results and potential sources of heterogeneity. Potential publication bias was evaluated by the Egger test,52 and publication bias was indicated at P<0.10. All analyses involved use of Stata 12.1 (Stata Corp, College Station, TX).

Results

Characteristics of Studies

We identified 22 articles (29 independent studies) in PubMed and Embase for the meta-analysis; 24 studies provided information on the association between LTPA and hypertension,7–10,12–14,16,17,22–27,29–31 5 yielding findings on TPA6,11,15,28 (Figure S1). In total, the review included 330 222 individuals and 67 698 incident cases of hypertension.
The descriptive characteristics of the cohort studies are in Table S4. In all, 7 articles did not distinguish between sex, 6 articles described stratified analyses by sex, and 9 described analysis of only men or women. Overall, 13 studies were conducted in the United States, 8 in Asia, 6 in Europe, 1 in Australia, and 1 in Mauritius. Analyses of the quality of studies yielded an average Newcastle-Ottawa Scale score of 8.0 (Table S5). Eleven articles defined hypertension as systolic blood pressure (BP) ≥140 mm Hg or diastolic BP ≥90 mm Hg or use of antihypertensive medication; 2 as systolic BP ≥160 or diastolic BP ≥95 mm Hg or use of antihypertensive medication, and 8 as self-reported hypertension or linked records; 1 did not provide the definition. Five of the 29 studies did not adjust for BMI, a key factor mediating the association between PA and hypertension.

High Versus Low LTPA and TPA Analysis

We included 21 studies in the meta-analysis of LTPA; 3 studies were excluded8,9 because they provided only a continuous risk estimate of LTPA. When comparing high versus low LTPA level, the pooled RR for hypertension was 0.84 (95% CI, 0.78–0.90; I2=68.9%; Pheterogeneity<0.001; Figure S2). We found no evidence of publication bias by Egger test (P=0.369).
Four studies were included in the meta-analysis of TPA; 1 study was excluded28 because it provided only a continuous risk estimate of TPA. When comparing high versus low TPA level, the pooled RR was 0.71 (95% CI, 0.58–0.87; I2=78.3%; Pheterogeneity=0.003; Figure S3). We found no evidence of publication bias by Egger test (P=0.895).

Dose–Response Association Between LTPA and Incident Hypertension

Data from 24 cohort studies were included in the linear dose–response analysis of LTPA. The pooled RR for hypertension was 94% (95% CI, 0.92–0.96) with each 10 MET h/wk increment of LTPA, with significant heterogeneity (I2=65.3%; Pheterogeneity<0.001; Figure 1). The risk was slightly weaker with than without BMI adjustment (RR, 0.94; 95% CI, 0.92–0.96 versus RR, 0.91; 95% CI, 0.89–0.93), although the studies for each analysis were not completely consistent. On restricting the analysis to studies providing risk estimates for both adjusted and unadjusted BMI, the pooled RR for hypertension per 10 MET h/wk was 0.94 (95% CI, 0.92–0.97) with adjustment and 0.90 (95% CI, 0.87–0.93) without adjustment (Figure S4).
Figure 1. Forest plot of study-specific relative risk statistics for hypertension per 10 metabolic equivalent of task (MET) h/wk increment of leisure-time physical activity (LTPA).
Three studies8,9 reported only continuous risk estimates, which did not meet the requirement of nonlinear dose–response analysis, so 21 studies were included in the analysis. We found no evidence of nonlinear association between LTPA and hypertension (Pnonlinearity=0.094), so restricted cubic splines were adopted to model the linear dose–response association. We found a linear negative correlation between degree of LTPA and risk of hypertension (Figure 2). The shape of the linear dose–response curve was steeper in the analysis restricted to studies without than with BMI adjustment.
Figure 2. A–C, Linear dose–response association between leisure-time physical activity (LTPA) and incident hypertension modeled by using restricted cubic splines and comparison of predicted relative risk (RR) point estimates for hypertension. (A, Results pooled in all studies; B, results pooled for studies unadjusted for BMI; and C, results pooled for studies adjusted for BMI). MET indicates metabolic equivalent of task.
Results from the cubic spline model suggested that when compared with inactive participants, for those who met the guidelines recommended minimum PA levels of 150 min/wk (10 MET h/wk), the risk of hypertension was reduced by 6% (RR, 0.94, 95% CI, 0.91–0.96). The magnitude of the protective effect for hypertension was substantially greater with increasing levels of PA. For example, for participants with PA at 20 MET h/wk (equivalent to double the recommended minimum PA level) and 60 MET h/wk (6× the recommended minimum level), the risk of hypertension was reduced by 12% (RR, 0.88; 95% CI, 0.83–0.92) and 33% (RR, 0.67; 95% CI, 0.58–0.78), respectively.

Dose–Response Association Between TPA and Incident Hypertension

Five studies were included in the linear association analysis of TPA. For each 50 MET h/wk increment of TPA, the pooled risk for hypertension was reduced by 7% (RR, 0.93; 95% CI, 0.88–0.98; I2=69.9%; Pheterogeneity=0.010). Because the above-mentioned studies were all adjusted for BMI, we reanalyzed the studies without BMI adjustment. The pooled protective effect size seemed to be stronger without than with BMI adjustment (Figure S5).
We excluded 1 study28 reporting only a continuous estimate and included 4 studies in the nonlinear dose–response analysis. We found no evidence of nonlinearity (Pnonlinearity=0.7713). The risk of hypertension decreased with increasing TPA for studies with BMI adjustment, with a milder association when compared with the analysis of studies without BMI adjustment (Figure S5). Results from the cubic spline model suggested a 10% reduced hypertension risk for participants with TPA of 64.5 MET h/wk.

Subgroup, Sensitivity Analyses, and Publication Bias

To explore the sources of heterogeneity of LTPA, we performed subgroup analyses by sex, age, nationality, number of cases, PA intensity, study quality, publication year, follow-up time, MET h/wk, and the covariates (age, smoking, alcohol drinking, education, income, and baseline chronic diseases) adjusted in the analysis (Table). In general, the association was consistent in most analyses. The heterogeneity seemed to be lower in American and European populations, with I2 reduced to 30.5% and 4.5%, respectively. No significant changes of heterogeneity occurred in other subgroup analyses. We found no high heterogeneity sources of TPA in subgroup analyses.
Table. Dose–Response Subgroup Analysis of Risk of Hypertension With LTPA and TPA
CharacteristicsLTPA (per 10 MET h/wk)TPA (per 50 MET h/wk)
nRR (95% CI)I 2P ValuenRR (95% CI)I 2P Value
All studies240.94 (0.92–0.96)65.3050.93 (0.88–0.98)69.90.01
Sex
 M/W60.96 (0.95–0.98)33.90.18220.67 (0.26–1.78)790.029
 M80.93 (0.88–0.98)70.60.00120.91(0.85–0.97)82.20.018
 W100.93 (0.91–0.96)71.5010.95 (0.91–1.00)……
Age
 <50 y150.94 (0.92–0.96)75.5030.90 (0.76–1.08)720.028
 ≥50 y40.97 (0.94–1.00)00.7220.95 (0.92–0.97)00.714
Nationality
 American100.96 (0.95–0.97)30.50.16520.67 (0.26–1.78)790.029
 Asian60.94 (0.87–1.01)81.2030.93 (0.88–0.97)74.30.021
 European60.91 (0.88–0.94)4.50.388…………
 Other20.89 (0.85–0.94)00.602…………
Follow-up
 <10 y100.96 (0.93–0.99)69.40.00140.92 (0.87–0.97)74.70.008
 ≥10 y140.93 (0.90–0.95)64.5011.01 (0.88–1.16)……
BMI
 <25 kg/m2120.94 (0.91–0.96)74.90.00040.93 (0.89–0.97)67.40.027
 ≥25 kg/m290.95 (0.92–0.99)54.70.02410.37 (0.15–0.90)……
PA intensity
 MPA only21.1 (0.86–1.40)00.329…………
 VPA only30.96 (0.92–0.99)73.50.023…………
Study quality
 High200.94 (0.92–0.96)690.000…………
 Medium or low40.94 (0.91–0.97)10.70.339…………
Publication year
 <200060.90 (0.82–0.99)54.90.050…………
 >2000180.94 (0.93–0.96)68.20.000…………
Cases
 <50070.93 (0.84–1.04)57.70.02820.67 (0.25–1.78)790.029
 ≥500150.94 (0.92–0.96)69.30.00030.92 (0.88–0.97)74.30.021
MET h/wk
 Reported90.94 (0.91–0.97)73.10.00020.94 (0.90–0.99)54.60.110
 Assigned150.95 (0.92–0.97)42.20.08630.93 (0.81–1.06)70.50.065
Adjustment
 Age
  No40.94 (0.91–0.97)10.70.3390………
  Yes200.94 (0.92–0.96)69.00.00050.93 (0.88–0.98)69.90.01
 Smoking
  No80.95 (0.91–0.99)31.30.17810.37 (0.15–0.90)……
  Yes160.94 (0.92–0.96)72.70.00040.93 (0.89–0.97)67.40.027
 Alcohol drinking
  No70.94 (0.91–0.97)15.60.31110.37 (0.15–0.90)……
  Yes170.94 (0.92–0.96)72.30.00040.93 (0.89–0.97)67.40.027
 Baseline chronic disease
  No120.95 (0.93–0.98)34.80.11230.94 (0.90–0.99)54.60.110
  Yes120.93 (0.91–0.96)76.00.00020.93 (0.81–1.06)70.50.065
 Education
  No110.95 (0.92–0.97)53.20.01930.90 (0.76–1.07)72.00.028
  Yes130.94 (0.91–0.97)71.90.00020.95 (0.92–0.97)00.714
 Income
  No200.94 (0.92–0.96)56.40.00140.93 (0.89–0.97)67.40.027
  Yes40.94 (0.88–1.01)83.80.00010.37 (0.15–0.90)……
BMI indicates body mass index; CI, confidence interval; LTPA, leisure-time physical activity; M, men; MET, metabolic equivalent of task; MPA, moderate physical activity; PA, physical activity; RR, relative risk; TPA, total physical activity; VPA, vigorous physical activity; and W, women.
When performing sensitivity analyses of LTPA by removing 1 study at a time, none of the individual studies changed the pooled risk substantially. A similar finding was observed in sensitivity analysis of TPA. We repeated the cubic spline model with an assumed duration of 30 minutes in sensitivity analysis, and the shape of the dose–response curve was similar to the main analysis (Figure S6). No publication bias was detected by Egger test for LTPA (P=0.388) or TPA (P=0.989; Figure S7).

Discussion

To our knowledge, this is the first meta-analysis of cohort studies to quantify the dose–response relation between PA and incident hypertension. We found a linear, inverse association between risk of hypertension and both LTPA and TPA, with a reduction of 6% per 10 MET h/wk increment and 7% per 50 MET h/wk increment, respectively. With the linear cubic spline model, the risk of hypertension was reduced by 6% (RR, 0.94; 95% CI, 0.91–0.96) for people who met the guidelines recommended minimum PA levels of 150 min/wk (10 MET h/wk) when compared with inactive people. With twice this amount of activity, the risk reduced by 12% (RR, 0.88; 95% CI, 0.83–0.92), with a further 33% reduction (RR, 0.67; 95% CI, 0.58–0.78) at higher doses (60 MET h/wk).
The results are consistent with another meta-analysis21 also finding an inverse association between levels of recreational PA and risk of hypertension. In contrast to the previous meta-analysis, we explored the association between PA and risk of hypertension quantitatively by dose–response analyses. Moreover, the present analysis included more comprehensive original research and had a larger sample size, which increased the accuracy and reliability of the effect estimates.
Recently, several reviews have examined the effects of various forms of exercise training on BP.53–56 The reduction in systolic and diastolic BP is reported between 2.4 to 5.2 mm Hg and 2.2 to 4.1 mm Hg, respectively, which provides experimental evidence that PA has a non-negligible role as a single or additive treatment for hypertension. However, the mechanisms of PA in preventing hypertension are elusive and controversial in large part because the cause of hypertension is multifactorial, and how these factors interact to contribute to the development of hypertension is unclear. The possible mechanisms are as follows. First, PA may affect hypertension by reducing cardiac output, sympathetic nerve activity, plasma norepinephrine levels, and total peripheral resistance and improving endothelial function.57–59 A meta-analysis of 72 trials and 105 study groups found reduced systemic vascular resistance, plasma norepinephrine level, and plasma renin activity as the main reasons for the decrease in BP after exercise.60 In addition, several studies have shown impaired endothelial function in hypertensive patients,61,62 and improved endothelial function is another possible mediator of the hypotensive response with exercise training.63,64 Second, hyperinsulinemia and insulin resistance may contribute to hypertension via the effects of insulin on the retention of sodium increasing sympathetic nervous system activity and vascular smooth muscle proliferation.65 As well, exercise improves insulin sensitivity,66,67 for another possible mechanism of the antihypertensive effect of exercise. In addition, PA may decrease plasma viscosity,68,69 which can contribute to the peripheral vascular resistance influencing blood flow.
Except for the mechanisms mentioned above, PA may decrease the risk of hypertension by improving energy balance and reducing adiposity,70 the main risk factor for hypertension.71 However, we found that risk estimates were only about 3% to 4% weaker from studies with than without BMI adjustment. Furthermore, results from the subgroup analysis by BMI <25 and ≥25 kg/m2 showed no significant difference between overweight and normal-weight participants. Moreover, significant reduction remained after BMI adjustment. Hence, the effect of PA on hypertension may be independent of obesity.
To discover potential sources of heterogeneity, we performed various subgroup analyses, and the results generally supported our overall findings. Results seemed to be more stable in American and European populations, perhaps because of varied measurement of PA type and level among different studies,6,8,22 which can result in a distinct degree of reporting accuracy of PA in different populations. Furthermore, we should take education into account in that populations in Europe and America are more educated than Asian populations,7,8,14,24 which can contribute to the degree of accuracy and consistency of the results. The different lifestyle and constitution of Asians and Whites may also explain the different amount of PA and magnitude of reduced hypertension risk.
Our meta-analysis contains several strengths. Primarily, the study quantified the PA amount and converted the categorical data into continuous data. We expressed the PA exposure dose in MET h/wk, which is a promising method to aggregate the exposure with different intensity and duration characteristics and an appropriate approach to achieve data harmonizing. Also, the meta-analysis included only cohort studies, so we could minimize the recall bias and obtained sufficient statistical power to detect the association. Third, we evaluated risk estimates from models with and without adjusted BMI to identify its meditation of PA and hypertension risk.
Our study also contains several potential limitations. Almost all of the PA exposure was measured by self-reporting questionnaires except in the White et al6 study. Because of the measurement error inherent in the questionnaires and the accuracy being subjected to recall bias, some study participants may have had their level of PA misclassified. Nevertheless, the type of misclassification would likely be random, and the measurement bias tends toward the null hypothesis. The measurements of PA varied among the 29 included studies in terms of frequency, intensity, and duration, for different definitions of low-, moderate-, and high-level PA. Moreover, the METs were assigned at a study level rather than an individual level, so the amount of PA that should be consumed to reduce risk of hypertension could not be evaluated precisely. Furthermore, our meta-analysis focused more on LTPA than TPA. Possibly, the measurement of multiple domains of PA (eg, home activity, occupation, self-care, and communication) may better illustrate the association of PA and hypertension risk.6,9,72 Further studies are needed to clarify diverse domains of PA associated with incident hypertension. In addition, physically active people are usually younger than inactive people and have other healthy behaviors such as healthier diets, lower rate of obesity, and less smoking and drinking of alcohol. However, most of the included studies had adjusted for these confounding factors and the pooled risk estimates persisted in the corresponding subgroup analyses.
The results from the meta-analysis provide evidence supporting that the PA has a clinically meaningful role in primary prevention of hypertension in the general population. Overall, we found that the dose–response curve for PA and incident hypertension is linear, which suggests that health benefits of PA can be achieved even at relatively low levels of PA (<150 min/wk) but also that considerable additional decreases in risk for hypertension are afforded when substantially exceeding the minimum recommendations.

Perspectives

Our meta-analysis supports the generally accepted notion of an inverse association between PA and health maintenance. There is no cutoff at which benefits are not achieved and more benefits occur with increasing PA. Future studies involving multiple domains of PA are needed to explore the optimal dose of PA for hypertension prevention.

Novelty and Significance

What Is New?

•
To our knowledge, this is the first meta-analysis based on cohort studies to quantify the dose–response association of physical activity (PA) and hypertension (previous meta-analyses of cohort studies have only reported the association of high and moderate versus low PA and incident hypertension, rather than quantified the reduction in risk, let alone the specific doses of PA required).
•
We converted PA exposure with different intensity and duration characteristics into a consistent unit (metabolic equivalent of task h/wk), which is appropriate to achieve data harmonization.

What Is Relevant?

•
We found an inverse linear dose–response association between risk of hypertension and PA.
•
Results of this study suggested that when compared with inactive individuals, those who met the guidelines recommended minimum PA levels of 150 min/wk (10 metabolic equivalent of task h/wk) had 6% lower risk of hypertension.
•
Achieving twice this amount was associated with a risk reduction of 12%, with a further 33% reduction at higher doses (60 metabolic equivalent of task h/wk).

Summary

In conclusion, we observed an inverse linear dose–response association between PA and risk of hypertension. The results of this meta-analysis suggest that additional benefits for hypertension prevention occur with increasing amount of physical activity.

Supplemental Material

File (data supplement.pdf)

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Hypertension
Volume 69 • Number 5 • May 2017
Pages: 813 - 820
PubMed: 28348016

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© 2017 American Heart Association, Inc.

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History

Received: 26 December 2016
Revision received: 11 January 2017
Accepted: 20 February 2017
Published online: 27 March 2017
Published in print: May 2017

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Keywords

  1. cohort studies
  2. exercise
  3. hypertension
  4. meta-analysis
  5. risk

Subjects

  1. Epidemiology
  2. Exercise
  3. Hypertension

Authors

Affiliations

Xuejiao Liu*
From the Department of Preventive Medicine, Shenzhen University Health Sciences Center, Guangdong, China (X.L., D.Z., C.H., B.W., Y.R., J.Z., Y.Z., D.H., M.Z.); The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Guangdong, China (Y.L., X.S., C.H., B.W., Y.R., J.Z., Y.Z., D.H.); and Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Henan, China (C.H., B.W., Y.R., Y.Z., Y.S.).
View all articles by this author
Dongdong Zhang*
From the Department of Preventive Medicine, Shenzhen University Health Sciences Center, Guangdong, China (X.L., D.Z., C.H., B.W., Y.R., J.Z., Y.Z., D.H., M.Z.); The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Guangdong, China (Y.L., X.S., C.H., B.W., Y.R., J.Z., Y.Z., D.H.); and Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Henan, China (C.H., B.W., Y.R., Y.Z., Y.S.).
View all articles by this author
Yu Liu
From the Department of Preventive Medicine, Shenzhen University Health Sciences Center, Guangdong, China (X.L., D.Z., C.H., B.W., Y.R., J.Z., Y.Z., D.H., M.Z.); The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Guangdong, China (Y.L., X.S., C.H., B.W., Y.R., J.Z., Y.Z., D.H.); and Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Henan, China (C.H., B.W., Y.R., Y.Z., Y.S.).
View all articles by this author
Xizhuo Sun
From the Department of Preventive Medicine, Shenzhen University Health Sciences Center, Guangdong, China (X.L., D.Z., C.H., B.W., Y.R., J.Z., Y.Z., D.H., M.Z.); The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Guangdong, China (Y.L., X.S., C.H., B.W., Y.R., J.Z., Y.Z., D.H.); and Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Henan, China (C.H., B.W., Y.R., Y.Z., Y.S.).
View all articles by this author
Chengyi Han
From the Department of Preventive Medicine, Shenzhen University Health Sciences Center, Guangdong, China (X.L., D.Z., C.H., B.W., Y.R., J.Z., Y.Z., D.H., M.Z.); The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Guangdong, China (Y.L., X.S., C.H., B.W., Y.R., J.Z., Y.Z., D.H.); and Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Henan, China (C.H., B.W., Y.R., Y.Z., Y.S.).
View all articles by this author
Bingyuan Wang
From the Department of Preventive Medicine, Shenzhen University Health Sciences Center, Guangdong, China (X.L., D.Z., C.H., B.W., Y.R., J.Z., Y.Z., D.H., M.Z.); The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Guangdong, China (Y.L., X.S., C.H., B.W., Y.R., J.Z., Y.Z., D.H.); and Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Henan, China (C.H., B.W., Y.R., Y.Z., Y.S.).
View all articles by this author
Yongcheng Ren
From the Department of Preventive Medicine, Shenzhen University Health Sciences Center, Guangdong, China (X.L., D.Z., C.H., B.W., Y.R., J.Z., Y.Z., D.H., M.Z.); The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Guangdong, China (Y.L., X.S., C.H., B.W., Y.R., J.Z., Y.Z., D.H.); and Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Henan, China (C.H., B.W., Y.R., Y.Z., Y.S.).
View all articles by this author
Junmei Zhou
From the Department of Preventive Medicine, Shenzhen University Health Sciences Center, Guangdong, China (X.L., D.Z., C.H., B.W., Y.R., J.Z., Y.Z., D.H., M.Z.); The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Guangdong, China (Y.L., X.S., C.H., B.W., Y.R., J.Z., Y.Z., D.H.); and Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Henan, China (C.H., B.W., Y.R., Y.Z., Y.S.).
View all articles by this author
Yang Zhao
From the Department of Preventive Medicine, Shenzhen University Health Sciences Center, Guangdong, China (X.L., D.Z., C.H., B.W., Y.R., J.Z., Y.Z., D.H., M.Z.); The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Guangdong, China (Y.L., X.S., C.H., B.W., Y.R., J.Z., Y.Z., D.H.); and Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Henan, China (C.H., B.W., Y.R., Y.Z., Y.S.).
View all articles by this author
Yuanyuan Shi
From the Department of Preventive Medicine, Shenzhen University Health Sciences Center, Guangdong, China (X.L., D.Z., C.H., B.W., Y.R., J.Z., Y.Z., D.H., M.Z.); The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Guangdong, China (Y.L., X.S., C.H., B.W., Y.R., J.Z., Y.Z., D.H.); and Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Henan, China (C.H., B.W., Y.R., Y.Z., Y.S.).
View all articles by this author
Dongsheng Hu
From the Department of Preventive Medicine, Shenzhen University Health Sciences Center, Guangdong, China (X.L., D.Z., C.H., B.W., Y.R., J.Z., Y.Z., D.H., M.Z.); The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Guangdong, China (Y.L., X.S., C.H., B.W., Y.R., J.Z., Y.Z., D.H.); and Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Henan, China (C.H., B.W., Y.R., Y.Z., Y.S.).
View all articles by this author
Ming Zhang
From the Department of Preventive Medicine, Shenzhen University Health Sciences Center, Guangdong, China (X.L., D.Z., C.H., B.W., Y.R., J.Z., Y.Z., D.H., M.Z.); The Affiliated Luohu Hospital of Shenzhen University Health Sciences Center, Guangdong, China (Y.L., X.S., C.H., B.W., Y.R., J.Z., Y.Z., D.H.); and Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Henan, China (C.H., B.W., Y.R., Y.Z., Y.S.).
View all articles by this author

Notes

*
X.L. and D.Z. contributed equally to this study.
The online-only Data Supplement is available with this article at http://hyper.ahajournals.org/lookup/suppl/doi:10.1161/HYPERTENSIONAHA.116.08994/-/DC1.
Correspondence to Ming Zhang, 3688 Nanhai Ave, Nanshan District, Shenzhen, Guangdong 518060, China. E-mail [email protected]

Disclosures

None.

Sources of Funding

This study was supported by the National Natural Science Foundation of China (grant numbers 81373074, 81402752, and 81673260); the Science and Technology Development Foundation of Shenzhen (grant number JCYJ20140418091413562, and 2016030715570); and the Natural Science Foundation of Shenzhen University (grant number 201404).

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    • Maria da C. C. de Almeida,
    • Maria J. M. Fonseca,
    • Maria del Carmen B. Molina,
    • Rosane H. Griep,
    • Cristiano P. S. Pitanga,
    • Francisco J. G. Pitanga,
    Leisure Time Physical Activity and Incidence of Arterial Hypertension in ELSA-Brasil Participants, Arquivos Brasileiros de Cardiologia, 121, 6, (2024).https://doi.org/10.36660/abc.20230734i
    Crossref
    • Tarcísio C. Souza,
    • Sheila M. A. Matos,
    • Maria da C. C. de Almeida,
    • Maria J. M. Fonseca,
    • Maria del Carmen B. Molina,
    • Rosane H. Griep,
    • Cristiano P. S. Pitanga,
    • Francisco J. G. Pitanga,
    Atividade Física no Tempo Livre e Incidência de Hipertensão Arterial em Participantes do ELSA-Brasil, Arquivos Brasileiros de Cardiologia, 121, 6, (2024).https://doi.org/10.36660/abc.20230734
    Crossref
    • Jung-Eun Lee,
    • Anthony Kityo,
    • Sang-Ah Lee,
    Lifestyle Factors, Sociodemographic Characteristics and Incident Hypertension: A Prospective Analysis of the Korean National Health Insurance Service Sample Cohort, Journal of Personalized Medicine, 14, 9, (959), (2024).https://doi.org/10.3390/jpm14090959
    Crossref
    • Narumi Kunimatsu,
    • Hayato Tsukamoto,
    • Shigehiko Ogoh,
    Exaggerated Blood Pressure Response to Exercise Is a Risk of Future Hypertension Even in Healthy, Normotensive Young Individuals—Potential Preventive Strategies for This Phenomenon?, Journal of Clinical Medicine, 13, 19, (5975), (2024).https://doi.org/10.3390/jcm13195975
    Crossref
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Dose–Response Association Between Physical Activity and Incident Hypertension
Hypertension
  • Vol. 69
  • No. 5

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  • Vol. 69
  • No. 5
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