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Association of physical activity with blood pressure and blood glucose among Malaysian adults: a population-based study

  • Chien Huey Teh1Email author,
  • Ying Ying Chan2,
  • Kuang Hock Lim2,
  • Chee Cheong Kee1,
  • Kuang Kuay Lim2,
  • Pei Sien Yeo2,
  • Omar Azahadi2,
  • Yusoff Fadhli2,
  • Aris Tahir2,
  • Han Lim Lee1 and
  • Wasi Ahmad Nazni1
BMC Public Health201515:1205

https://doi.org/10.1186/s12889-015-2528-1

Received: 17 September 2014

Accepted: 18 November 2015

Published: 3 December 2015

Abstract

Background

The health-enhancing benefits of physical activity (PA) on hypertension and diabetes have been well documented for decades. This study aimed to determine the association of PA with systolic and diastolic blood pressure as well as blood glucose in the Malaysian adult population.

Methods

Data were extracted from the 2011 National Health and Morbidity Survey (NHMS), a nationally representative, cross-sectional study. A two-stage stratified sampling method was used to select a representative sample of 18,231 Malaysian adults aged 18 years and above. The PA levels of the respondents were categorised as low, moderate or high according to the International Physical Activity Questionnaire (IPAQ)-short form. Blood pressure and fasting blood glucose levels were measured using a digital blood pressure-measuring device and finger-prick test, respectively.

Results

Systolic blood pressure (SBP) level was positively associated with PA level (p = 0.02) whilst no significant association was noted between PA level and diastolic blood pressure (DBP). In contrast, respondents with low (adjusted coefficient = 0.17) or moderate (adjusted coefficient = 0.03) level of PA had significantly higher blood glucose level as compared to those who were highly active (p = 0.04).

Conclusions

A significant negative association was observed between PA level and blood glucose only. Future studies should employ an objective measurement in estimating PA level in order to elucidate the actual relationship between PA, hypertension and diabetes for the development of effective interventions to combat the increasing burden of premature-mortality and cardiovascular disease-related morbidity in Malaysia.

Keywords

HypertensionDiabetesMalaysiaNHMSPhysical activity

Background

Hypertension and diabetes are major and common risk factors for cardiovascular disease. Cardiovascular diseases (CVDs) account for approximately one third of deaths worldwide, with high blood pressure being the leading cause of cardiovascular death in the South East Asia Region. Together with a number of lifestyle risk factors (alcohol use, tobacco use, high body mass index (BMI), high cholesterol, low fruit and vegetable intake and physical activity), high blood pressure and high blood glucose account for 61 % of cardiovascular deaths and more than 75 % of deaths from ischaemic and hypertensive heart disease globally [1].

In Malaysia, the prevalence of hypertension among adults aged 18 years and above has been persistent at 32.2 % in 2006 [2] and 32.7 % in 2011 [3], whereas the prevalence of diabetes has been increasing steadily from 11.6 % in 2006 [2] to 15.2 % in 2011 [3]. The high prevalence of hypertension and diabetes are anticipated to cause an epidemic of cardiovascular disease. The Malaysian Second Burden of Disease and Injury Study (BOD) in 2012 reported that ischaemic heart disease was the top killer and ranked first in the top 10 causes of premature mortality (Years of Life Lost, YLLs) and top 10 based on burden of disease (Disability Adjusted Life Years, DALYs) [4]. Therefore, the implementation of intensive targeted strategies based on scientific evidence to prevent and control these conditions is of paramount importance.

For decades, a plethora of epidemiological studies have demonstrably proven the metabolic and cardiovascular benefits of physical activity (PA), either alone or in conjunction with dietary changes in reducing the risk of hypertension and diabetes [57]. Findings from prospective studies [8, 9] and meta-analysis of randomised controlled trials (RCTs) [10, 11] indicated that regular moderate-intensity activity and resistance exercise lowered the blood pressure in both normotensive and hypertensive individuals. Similarly, studies [7, 12, 13] also demonstrated that moderate- and high-intensity PA conferred evident health benefits amongst both non-diabetic and diabetic populations by improving insulin sensitivity and glycaemic control. A RCT reported that the incidence of diabetes among high-risk individuals was reduced by 40–60 % through proper diet control and programmed PA over 3 to 4 years [13].

Nonetheless, despite established evidence of the beneficial relationship between PA, hypertension and diabetes, there is a lack of national data reporting on the association of PA with systolic and diastolic blood pressure as well as blood glucose level among the Malaysian population. Therefore, in the present study, we aimed to determine these associations by analysing data obtained from the National Health and Morbidity Survey (NHMS) 2011 among Malaysian adults aged 18 years and above, while adjusted for other potential confounders. Findings from this study could provide evidence-based information to assist policy-making decisions pertaining to primary prevention and treatment of hypertension and diabetes, as well as interventional strategies.

Methods

Study sample

Data were extracted from a cross-sectional multi-ethnic population-based study, the 2011 NHMS, which was conducted from April to July 2011, across 13 states (Penang, Perlis, Kedah, Perak, Selangor, Negeri Sembilan, Melaka, Johor, Kelantan, Terengganu, Pahang, Sabah, Sarawak) and 2 Federal Territories (Kuala Lumpur and Putrajaya) in Malaysia. Samples were selected using a two-stage stratified sampling method, by which the first stage stratification was performed by states and the second stage stratification was performed by urban/rural localities. The urban area is defined as a gazetted administrative area (a carefully mapped area with definite boundaries which had been notified in the government gazette for public information) with adjoining built-up areas of more than 10,000 people while a gazetted administrative area of less than 10,000 people is defined as a rural area. The Malaysian Department of Statistics (DOS) provided the Primary Sampling Units (PSUs) or Enumeration Blocks (EBs) for first stage sampling and Secondary Sampling Units (SSUs) or Living Quarters (LQs) for second stage sampling according to the 2010 census.

A total of 794 EBs (484 urban and 310 rural EBs) were systematically selected from all EBs in Malaysia via a probability-proportional-to-size sampling technique. Subsequently, 12 Living Quarters (LQs) or Secondary Sampling Units (SSUs) were randomly selected from each EB. Finally, all households and eligible household members within the selected LQs were included in the sample. Details of the sampling method and calculation of sample size have been described elsewhere [14]. For the present study, a total of 18,231 eligible respondents (aged 18-year-old and above) were recruited in the study and written consent for participation was obtained before they were interviewed. The study protocol was approved by the Medical Review and Ethics Committee (MREC), Ministry of Health Malaysia.

Data collection

The data collection was conducted from April 2011 to July 2011 by trained interviewers via face-to-face interviews using a standardised questionnaire; whilst clinical assessment, which included measurement of blood pressure and blood glucose level, was performed by trained staff nurses. To ensure a higher response rate, only selected respondents who were not at home after at least 3 attempted visits were excluded from the survey.

Physical activity

The International Physical Activity Questionnaire-short form (IPAQ-SF) estimates the overall PA level of an individual in MET-minutes/week by determining the duration (in minutes) and number of days (in one week) of engagement in three specific types of activity (walking, moderate-intensity and high-intensity activities) across a comprehensive set of domains (leisure time, work-related and transport-related physical activities, domestic and gardening activities) in the past seven days. MET or metabolic equivalent is a unit that is used to estimate the amount of oxygen used by the body during physical activity. A MET-minutes/week is computed by multiplying the MET score of an activity (3.3 for walking, 4.0 for moderate-intensity and 8.0 for vigorous-intensity) by the minutes and days (or sessions) of engagement. Respondents were classified into three levels of PA as low, moderate or high according to the cutoff of total MET-minutes/week in each category [15] as follow:
  • Category 1 Low

Individuals who do not meet the criteria for Category 2 (moderately active) and 3 (highly active) were considered to have a ‘low’ PA level or were physically inactive.
  • Category 2 Moderate
    1. a)

      3 or more days of vigorous-intensity activity of at least 20 min per day, or

       
    2. b)

      5 or more days of moderate-intensity activity and/or walking of at least 30 min per day, or

       
    3. c)

      5 or more days of any combination of walking, moderate- or vigorous-intensity activities achieving a minimum total PA of at least 600 MET-minutes/week

       
  • Category 3 High
    1. a)

      vigorous-intensity activity on at least 3 days achieving a minimum total PA of at least 1500 MET-minutes/week, or

       
    2. b)

      7 or more days of any combination of walking, moderate- or vigorous-intensity activities achieving a minimum total PA of at least 3000 MET-minutes/week.

       

Hypertension

Blood pressure level was measured twice in an interval of 10 min by a trained nurse using a calibrated digital blood pressure-measuring device, OMRON HEM-907. This device was evaluated for compliance to both the American Association for the Advancement Medical Instrumentation (AAMI) and British Hypertension Society (BHS) protocols for accuracy for a non-invasive blood pressure-monitoring device using single observer readings [16]. Both known hypertension and undiagnosed hypertension constituted the overall prevalence of hypertension. “Known hypertension” is defined as self-report of being told they have hypertension by a doctor or medical assistant; “undiagnosed hypertension” is defined as self-perceived as non-hypertensive and had a SBP of 140 mmHg or more and/or DBP of 90 mmHg or more [17].

Diabetes

Respondents were asked to fast for at least 6 h for the measurement of fasting blood glucose level using a validated CardioChek PA Analyser [18] via finger-prick test by trained nurses. Respondents with known diabetes or undiagnosed diabetes constituted the overall prevalence of diabetes. “Known diabetes” is defined as self-report of being told they have diabetes by a doctor or medical assistant; a respondent was classified as having ‘undiagnosed diabetes’ when the respondent was not known to have diabetes and had a fasting capillary blood glucose (FBG) of 6.1 mmol/L or more or non-fasting blood glucose of more than 11.1 mmol/L [19].

Anthropometric measurements and obesity

SECA bodymeter 206 (SECA, Germany) and TANITA318 digital weighing scale (TANITA, Japan) were used to measure the height and weight of respondents to the nearest 0.1 cm and 0.1 kg, respectively. Anthropometric measurements were taken twice and the average values were used for data analysis. Body mass index (BMI, weight/height2 in kg/m2) was used to classify respondents into underweight, normal, overweight and obese based on the cut-off points recommended by the World Health Organization (WHO) [20].

Smoking

Respondents were classified as non-smokers if they answered “No” to the question “Have you ever smoked shisha, cigarettes, cigars and pipes?”; whilst respondents who answered “Yes” to both questions “Have you ever smoked shisha, cigarettes, cigars and pipes?” and “Do you currently smoke?” were classified as smokers.

Data analysis

SPSS version 19.0 with add-on complex sample analysis was used to analyse the data after the adjustment for stratification using post-stratified weights. Descriptive statistics were used to illustrate the characteristics of the study population by PA level (low, moderate or high) and socio-demographic variables (gender, locality, age, ethnicity, educational level, household monthly income, employment and marital status). Multivariable linear regression (MLR) or ordinary least square was performed to elucidate the associations of PA levels with SBP, DBP and blood glucose while controlling for gender, locality, age, ethnicity, educational level, household monthly income, employment and marital status, body mass index status and smoking status. Missing data were excluded from analyses and it was ignorable since the NHMS 2011 had taken a 20 % non-response rate into consideration at the sample size calculation stage. All statistical tests were conducted at a 95 % confidence interval (CI).

Results

Of the 18,231 eligible respondents aged 18 years and above, 99.1, 99.3 and 97.5 % of them responded to the PA, hypertension and diabetes modules of the questionnaire, respectively. The mean age (SD) of the respondents was 42.0 (16.0) years. The characteristics of respondents are provided in Table 1. The population was almost equally composed of men (51.1 %) and women (48.9 %). A large portion of respondents were urban dwellers (73.1 %). Almost half of the Malaysian population was constituted by the Malays, followed by the Chinese (25.4 %), other ethnic groups (Serani, Iban, Kadazan, Dusun, Bidayuh, Melanau and Bumiputras from the State of Sabah and Sarawak, as well as the aborigines, 17.8 %) and Indians (7.0 %). Most of the respondents attained a tertiary level of education (college/university graduates). Men accrued a higher level of high-intensity PA whilst women were engaged more in low- and moderate-intensity activities. Almost 1 in every 3 adults was overweight or hypertensive and 2 in every 13 were diabetic.
Table 1

Characteristics of the study population, Malaysia, April-June 2011

Socio-demographics

Male

Female

All

 

n

N (%)

n

N (%)

n

N (%)

 

8536

51.1

9695

48.9

18231

100

Locality

      

 Urban

4874

73.0

5730

73.3

10604

73.1

 Rural

3662

27.0

3965

26.7

7627

26.9

Age

      

 18-24

1445

20.3

1448

20.1

2893

20.2

 25-34

1889

26.7

2097

25.7

3986

26.2

 35-44

1643

19.9

2000

20.2

3643

20.0

 45-54

1620

16.4

1862

16.0

3482

16.2

 55-64

1159

10.0

1301

10.2

2460

10.1

 65 and above

780

6.7

987

7.8

1767

7.3

Ethnicity

      

 Malay

4858

48.5

5532

51.0

10390

49.8

 Chinese

1666

25.9

1856

25.0

3522

25.4

 Indian

651

6.8

808

7.3

1459

7.0

 Others

1361

18.8

1499

16.8

2860

17.8

Educational Level

      

 Primary

2106

22.1

2263

20.8

4369

21.4

 Secondary

4091

48.6

4191

45.6

8282

47.1

 Tertiary

1825

23.7

2040

23.9

3865

23.8

 No formal education

514

5.6

1201

9.7

1715

7.6

Household Income/montha

      

 Less than RM2000

3102

32.3

4038

38.4

7140

35.3

 RM2000-RM2999

1526

17.5

1562

16.1

3088

16.8

 RM3000-RM3999

1172

14.0

1236

13.2

2408

13.6

 At least RM4000

2736

36.2

2859

32.4

5595

34.3

Employment Status

      

 Government/semi-government

1075

12.3

1073

10.4

2148

11.4

 Private

3389

51.0

2308

33.9

5697

42.7

 Self-employed

2376

27.4

1243

13.2

3619

20.5

 Homemaker/unpaid worker

100

1.3

3094

33.9

3194

17.1

 Retiree

789

8.1

949

8.6

1738

8.3

Marital Status

      

 Single

2356

32.9

1875

24.0

4231

28.6

 Married

5954

65.2

6554

65.6

12508

65.4

 Widow/Widower/Divorcee

216

1.9

1254

10.4

1470

6.0

BMI Status

      

 Underweight

638

8.4

639

8.2

1277

8.3

 Normal

3833

48.0

3797

46.4

7630

47.2

 Overweight

2538

30.9

2615

27.8

5153

29.4

 Obese

1021

12.7

1729

17.6

2750

15.1

Smoking Status

      

 Current smoker

3973

46.9

160

1.9

4133

25

 Non-current smoker

4510

53.1

9430

98.1

13940

75

Physical Activity Level

      

 Low

2581

30.1

3899

40.4

6480

35.2

 Moderate

2729

32.5

3898

41.2

6627

36.7

 High

3122

37.4

1841

18.4

4963

28.1

Hypertension

      

 Yes

3130

33.7

3570

31.6

6700

32.7

 No

5330

66.3

6068

68.4

11398

67.3

Diabetes

      

 Yes

1555

15.8

1647

14.5

3202

15.2

 No

6754

84.2

7826

85.5

14580

84.8

Abbreviations: RM Ringgit Malaysia

a1 RM ≈ 0.24 US Dollar

Generally, both simple and multivariable linear regression (MLR) analyses demonstrated that PA level was positively associated with systolic blood pressure (SBP) (P < 0.05). However, such association was not significant between PA level and diastolic blood pressure (SDP) in the adjusted MLR analyses (Table 2). In contrast, both simple and multivariable linear regression (MLR) analyses revealed that there was a significant dose response relationship between PA level and blood glucose level (P < 0.05). Respondents with low level of PA had significantly higher blood glucose level as compared to those who were moderately or highly active (reference group), and also, those who were moderately active had significantly higher blood glucose level than their highly active counterparts (Table 3).
Table 2

Coefficients from multiple linear logistic regression of systolic and diastolic blood pressure on physical activity level

Physical activity level

Systolic blood pressure

Diastolic blood pressure

Crude

Adjusted

Crude

Adjusted

Coefficient

Standard error

*p-value

Coefficient

Standard error

*p-value

Coefficient

Standard error

*p-value

Coefficient

Standard error

p-value

Low (high)

−2.30

0.54

<0.001

−1.35

0.49

0.02

−1.20

0.33

0.001

−0.49

0.33

0.31

Moderate (high)

−1.72

0.51

−0.57

0.48

−0.41

0.32

−0.24

0.32

n

17,568

14,923

17,567

14,922

aCoefficient values were adjusted for gender, locality, age, ethnicity, educational level, household monthly income, employment and marital status, body mass index status and smoking status

*Significant p values at 95 % confidence interval

Table 3

Coefficients from multiple linear logistic regression of blood glucose on physical activity level

Physical activity level

Crude

Adjusted

Coefficient

Standard error

*p-value

Coefficienta

Standard error

*p-value

Low (High)

0.26

0.07

0.002

0.17

0.07

0.04

Moderate (High)

0.12

0.06

0.03

0.06

n

16,181

13,973

aCoefficient values were adjusted for gender, locality, age, ethnicity, educational level, household monthly income, employment and marital status, body mass index status and smoking status

*Significant p values at 95 % confidence interval

Discussion

This is the first national, multi-ethnic study to cross-sectionally examine the effects of PA against SBP, DBP and blood glucose among Malaysians aged 18 years and above, while controlling for other potential confounders. The present findings demonstrated a positive association between physical activity and systolic blood pressure. However, observations from several well-controlled cohort studies [8, 9] and randomised control trials (RCTs) [10, 11] had proven unequivocally an independent, negative association between PA and blood pressure.

One of the plausible explanations for the above findings could be due to differential misclassification, in which either the hypertensive respondents were differentially misclassified as having high PA or those with low PA were differentially misclassified as non-hypertensive. However, the latter (misclassification of hypertensive status) was unlikely as blood pressure of respondents were measured twice at a 10-min interval using calibrated devices, OMRON HEM-907 by trained nurses. Therefore differential misclassification of hypertensive respondents as highly active, or normal respondents as lowly active could occurred as a result of information bias as the use of IPAQ in the estimation of PA level was rather subjective and subjected to recall bias and tendency of respondents to report a socially desirable response, which may lead to an over-estimation of PA. Lee and his colleagues who performed a systematic review on the validation of IPAQ-SF with objective measurements stated that IPAQ-SF typically overestimated physical activity by an average of 84 % [21]. Therefore, the possible presence of differential misclassification of PA due to poor PA ascertainment in attenuating the real effect of PA against blood pressure should not be overlooked. In addition, more than three-quarters of respondents with known hypertension claimed that they were on oral anti-hypertensive drugs within the past 2 weeks of the interview date (data not shown), and this may again attenuate the beneficial effect of PA against SBP. Furthermore, the cross-sectional nature of the present study had also limited the inference of a direct causal relationship between PA and blood pressure. Therefore, only a properly conducted prospective cohort on healthy individuals [22] or a RCT which employs objective measurement for PA, such as accelerometer, pedometer or doubly labelled water [21], could conclusively determine the causal as well as dose–response relationship between PA level and blood pressure.

In contrast, a significant negative association was observed between PA and blood glucose among Malaysian adults. The present findings were consistent with those reported from other cross-sectional studies [6, 7, 12] and were corroborated by prospective observational studies [8, 23]. Furthermore, an interventional study by Knowler et al. [24] had demonstrated a 58 % (96 % CI: 48–66) reduction of diabetes incidence among the intervention group, which targeted at least a 7 % weight loss and at least 150 min of PA per week, compared to placebo. Additionally, findings from a large cohort study among more than 70,000 healthy women aged 40–65 years in the United States had substantiated the beneficial effects of moderate- and vigorous-intensity PA against diabetes, whereby brisk walking and more vigorous exercise were found to be associated with a 25 % reduction in diabetes incidence [25].

Despite the cross-sectional nature of the study and the use of a subjective instrument (IPAQ) in the estimation of PA level, the large sample size and its representativeness as well as the high response rate were the major strengths of the present study. In addition, underestimations of the prevalence of hypertension and diabetes among the population were unlikely as measurements of blood pressure and fasting blood glucose levels were taken properly by trained nurses using calibrated devices rather than self-reported.

Conclusions

Generally, a significant dose–response relationship was observed between physical activity and blood glucose, but not between physical activity and blood pressure. Nonetheless, in order to elucidate the true association between physical activity and hypertension or diabetes, the use of an objective instrument for the estimation of the physical activity level is highly recommended for future study.

Abbreviations

AAMI: 

American Association for the Advancement Medical Instrumentation

BHS: 

British Hypertension Society

BMI: 

body mass index

BOD: 

burden of disease

CI: 

confidence interval

CVD: 

cardiovascular disease

DALYs: 

disability-adjusted life years

DBP: 

diastolic blood pressure

DOS: 

Department of Statistics

EB: 

enumeration block

FVs: 

fruits and vegetables

IPAQ: 

international physical activity questionnaire

LQ: 

living quarter

MET: 

metabolic equivalent

MLR: 

multivariable linear regression

MREC: 

Medical Review and Ethics Committee

NHMS: 

National Health and Morbidity Survey

PA: 

physical activity

PSU: 

primary sampling unit

RCT: 

randomised controlled trial

SBP: 

systolic blood pressure

SD: 

standard deviation

SSU: 

secondary sampling unit

WHO: 

World Health Organisation

YLLs: 

years of life lost

Declarations

Acknowledgements

The authors would like to thank the Director General of Health, Malaysia for his permission to publish this study. We would like to also convey our sincerest tribute to the field supervisors, nurses and data collectors for their dedicated efforts in conducting this survey. This research was supported by the Research and Development Fund, Ministry of Health Malaysia (NMRR-10-757-6837).

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Institute for Medical Research, Ministry of Health Malaysia
(2)
Institute for Public Health, Ministry of Health Malaysia

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