









































CAJGH_Template


 

 

New articles in this journal are licensed under a Creative Commons Attribution 4.0 United States License. 

 

 

This journal is published by theUniversity Library System of the University of Pittsburgh as part  

of its D-Scribe Digital Publishing Program and is cosponsored by the University of Pittsburgh Press. 

 

 

 

 

 

 

 

 

 

Patterns of Physical Activity and 

Self-rated Health Among Adult 

Populations in South Asia 

 

Sanni Yaya1*, Ghose Bishwajit1,2* 
 
1Faculty of Social Sciences, School of 
International Development and Global 
Studies, University of Ottawa, Canada; 
2Institute of Nutrition and Food science, 
University of Dhaka, Bangladesh 

*Equal contributors 

 

 

Vol. 9, No. 1 (2020)   |   ISSN 2166-7403 (online)  

DOI 10.5195/cajgh.2020.347 |   http://cajgh.pitt.edu 

http://www.library.pitt.edu/
http://www.pitt.edu/
http://www.library.pitt.edu/articles/digpubtype/index.html
http://www.upress.pitt.edu/upressIndex.aspx
http://creativecommons.org/licenses/by/3.0/us/


 

 

YAYA 

 

 

This work is licensed under a Creative Commons Attribution 4.0 United States License. 

 

This journal is published by theUniversity Library System of the University of Pittsburgh as 

part  

of its D-Scribe Digital Publishing Program and is cosponsored by the University of Pittsburgh 

Press. 

 

Central Asian Journal of Global Health 

Volume 9, No. 1 (2020) |  ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2020.347|http://cajgh.pitt.edu 

 

 

Abstract 

Introduction: Although South Asians are considered to be at high risk for cardiovascular diseases, research evidence on the health 

impacts of physical activity (PA) remains very limited. In this study we aimed to explore the patterns of PA and to investigate 

whether engaging in regular PA is associated with better Self-Rated Health (SRH) among South Asians.  

Methods: Cross-sectional data on population health were drawn from the World Health Survey of WHO. Subjects were 28,020 

male and female South Asians (from Bangladesh, India, Nepal, and Sri Lanka) aged 18 years and above. Data were analysed using 

descriptive and multivariable logistic regression analyses.  

Results: The proportion of the sample population reported good SRH was 44.3%, 58.7%, 37.7%, and 73.7% in Bangladeshis, 

Indians, Nepalese, and Sri Lankans, respectively. Regular engagement in moderate PA was highest in Nepal (69.7%) and lowest 

in Bangladesh (37.4%). Vigorous PA was highest in India (29.9%) and lowest in Bangladesh (17.9%). In  Bangladesh, compared 

to those never engaged in MPA, those who engaged for 1-2, 3-4, 5-6, or 7 days a week were 30% [AOR=1.306; 95%CI 1.085-

1.572], 33% [AOR=1.326; 95%CI 1.093-1.609], 39% [AOR=1.389; 95%CI 1.125-1.716], and 46% [AOR=1.459; 95%CI 1.249-

1.705] more likely to report being in good health, respectively.  

Conclusions: We found that self-reported engagement in physical activities varies in South Asian countries. Since engaging in PA 

may help improve subjective and objective health status, health policy makers need to focus on designing exercise-friendly 

neighbourhoods in an attempt to promote population health. 

Keywords: Moderate Physical Activity; Vigorous Physical Activity; Self-Rated Health; South Asia; World Health Survey 

Patterns of Physical Activity and Self-

rated Health Among Adult 

Populations in South Asia 

 

Sanni Yaya1*, Ghose Bishwajit1,2* 
 
1Faculty of Social Sciences, School of 
International Development and Global 
Studies, University of Ottawa, Canada; 
2Institute of Nutrition and Food science, 
University of Dhaka, Bangladesh 
 

Research 

Introduction 

The construct of self-rated health (SRH) is an 

inclusive measure of public health, and it is used as a 

reliable predictor of quality of life, subjective well-being, 

disability, morbidity, and mortality1,2. SRH is one of the 

most widely used predictors of health risk and prognosis 

compared with/to other objective measures3. Possible 

explanations for the efficacy of self-assessments of 

health include its multifaceted representation of an 

individual’s general perception of health, including 

biological, psychosocial, and cultural dimensions of 

health and expressiveness4. SRH has also been found to 

be related to clinical measures of health3, and it was 

proposed that general practitioners can utilize SRH 

measures in clinical encounters5. Moreover, SRH can be 

regarded as a more inclusive measure of health status 

than clinical diagnosis, as it tends to be sensitive to social 

determinants of health such as education, socioeconomic 

status, and living conditions, which have direct 

influences on health and well-being and on shaping 

individuals perception of health and illness4,5. SRH is 

therefore able to provide information above and beyond 

typical clinical evaluation and thus offers a 

http://www.library.pitt.edu/
http://www.pitt.edu/
http://www.library.pitt.edu/articles/digpubtype/index.html
http://www.upress.pitt.edu/upressIndex.aspx
http://www.upress.pitt.edu/upressIndex.aspx


 

 

CENTRAL ASIAN JOURNAL OF GLOBAL HEALTH 

 

 

This work is licensed under a Creative Commons Attribution 4.0 United States License. 

 

This journal is published by theUniversity Library System of the University of Pittsburgh as part  

of its D-Scribe Digital Publishing Program and is cosponsored by the University of Pittsburgh Press. 

 

Central Asian Journal of Global Health 

Volume 9, No. 1 (2020) |  ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2020.347|http://cajgh.pitt.edu 

 

 

comprehensive way of assessing a patients’ overall 

health status4,6. 

In recent years, there has been an increasing 

research interest on the impact of various lifestyle factors 

and health related behaviours on SRH7,8. Behavioural 

aspects, such as tobacco smoking, alcohol drinking, 

dietary habits, and engaging in physical activity (PA) are 

explored in relation to how they correlate with SRH 

among people of different age groups and socioeconomic 

backgrounds. Physical inactivity is regarded as a growing 

public health issue both in developed and developing 

countries. In Europe and other industrialised societies, 

increasing sedentary lifestyle has been shown to be 

associated with worse health and all-cause mortality, 

independent of level of PA9-11. According to WHO, 

public health burden of physical inactivity is high and 

causes an estimated 600,000 deaths per year in Europe 

alone11. Worldwide, physical inactivity is responsible for 

6% of the burden of coronary heart disease, 7% of type 2 

diabetes, and 10% of breast and colon cancer12. It has also 

been identified as the fourth leading risk factor for global 

mortality in 2010, accounting for roughly 13.4 million 

disability adjusted life years (DALYs)15 and 6% of all 

deaths13. 

There is a growing consensus that moderate- to 

vigorous- intensity PA has a key preventive role in non-

communicable diseases (NCDs), including obesity, 

cardiovascular disease, type-2 diabetes, and some 

cancers9,10. Lack of PA during adolescence was reported 

to be a significant predictor of abdominal obesity in 

young adulthood leading to a self-perpetuating vicious 

circle of obesity and physical inactivity14. In addition to 

its contribution to increased morbidity and mortality, 

physical inactivity is also responsible for a substantial 

economic burden. Epidemiological evidence on the role 

of PA on SRH is necessary for making informed health 

policies that can promote PA in the general population.  

South Asians are people who identify with the 

cultures of Bangladesh, India, Sri Lanka, and Nepal, and 

account for about a quarter of the global population with 

a unique epidemiological and sociodemographic profile. 

Though South Asians are considered at-risk population 

for cardiovascular diseases, research evidence on the 

health impacts of PA in this population remains very 

limited. Therefore, epidemiological evidence from other 

regions may not be applicable for the population in this 

region. This study was carried out to provide insights on 

the pattern of PA in South Asians, and to investigate the 

association between frequency of PA and SRH among 

the adult population. Data used in this study were 

extracted from the World Health Survey program of 

WHO conducted during 2002-2004 that included four 

South Asian countries: Bangladesh, India, Nepal, and Sri 

Lanka.  

 

Methods 

Data source 

This study was based on the data extracted from 

WHO World Health Survey conducted between 2002 and 

2004, available from WHO upon request. Objectives of 

the WHO-funded survey were to provide reliable and 

nationally comparable data on a wide range of health and 

socioeconomic indicators to facilitate evidence-based 

health policy making. These data are utilized by many 

researchers due to lack of more recent data on health 

behaviour and self-rated health in South Asian 

population. The program is operational in 70 countries 

including four South Asian countries: Bangladesh, India, 

Nepal, and Sri Lanka. Further details regarding the 

original survey study are published elsewhere16.  

Variables of interest 

Self-rated health status was the outcome 

variable in this study. Emerging evidence suggests 

predictability of SRH for both non-clinical and clinical 

outcomes, and it is being proposed to family physicians 

as an efficient yet simple way for therapeutic decision 

making28,29. Respondents were asked to rate their health 

on a scale from 1 to 5 with the following response  

http://www.library.pitt.edu/
http://www.pitt.edu/
http://www.library.pitt.edu/articles/digpubtype/index.html
http://www.upress.pitt.edu/upressIndex.aspx


 

 

YAYA 

 

 

This work is licensed under a Creative Commons Attribution 4.0 United States License. 

 

This journal is published by theUniversity Library System of the University of Pittsburgh as 

part  

of its D-Scribe Digital Publishing Program and is cosponsored by the University of Pittsburgh 

Press. 

 

Central Asian Journal of Global Health 

Volume 9, No. 1 (2020) |  ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2020.347|http://cajgh.pitt.edu 

 

 

 

Variables 

Bangladesh India Nepal Sri Lanka 

 (n=5462) (n=8853) (n=8031) (n=5674) 

Age, Mean (SD) 38.47 (14.81) 38.69 (15.07) 38.55 (15.33) 40.78 (15.22) 

18-29 1688 30.9 2771 31.3 2666 33.2 1515 26.7 

30-39 1502 27.5 2275 25.7 2008 25 1367 24.1 

40-49 1103 20.2 1611 18.2 1462 18.2 1231 21.7 

50-59 574 10.5 1054 11.9 867 10.8 823 14.5 

60+ 595 10.9 1151 13 1028 12.8 743 13.1 

Sex         

Female 2917 53.4 4515 51 4602 57.3 2968 52.3 

Male 2545 46.6 4338 49 3429 42.7 2706 47.7 

Currently married         

No 1218 22.3 2036 23 1413 17.6 1765 31.1 

Yes 4244 77.7 6817 77 6618 82.4 3909 68.9 

Educational 

attainment 
      

 
 

Nil 2245 41.1 3400 38.4 4819 60 318 5.6 

Less than primary 

school 
1000 18.3 832 9.4 883 11 

431 
7.6 

Primary complete 1360 24.9 44 0.5 1108 13.8 1430 25.2 

Secondary complete 404 7.4 1567 17.7 819 10.2 2236 39.4 

High school/ 

equivalent complete 
218 4 1142 12.9 257 3.2 

1123 
19.8 

Pre-university/ 

University 
235 4.3 894 10.1 137 1.7 

136 
2.4 

Employment status         

Govt. employee 197 3.6 336 3.8 249 3.1 511 9 

Private employee 333 6.1 974 11 169 2.1 647 11.4 

Employer 2081 38.1 3621 40.9 5381 67 1923 33.9 

Not working for 

payment 
2851 52.2 3940 44.5 2225 27.7 

2599 
45.8 

Smoking habit         

Daily 2021 37 2780 31.4 3212 40 726 12.8 

Yes. not daily 300 5.5 266 3 385 4.8 460 8.1 

Non-smoker 3141 57.5 5808 65.6 4433 55.2 4494 79.2 

http://www.library.pitt.edu/
http://www.pitt.edu/
http://www.library.pitt.edu/articles/digpubtype/index.html
http://www.upress.pitt.edu/upressIndex.aspx
http://www.upress.pitt.edu/upressIndex.aspx


 

 

CENTRAL ASIAN JOURNAL OF GLOBAL HEALTH 

 

 

This work is licensed under a Creative Commons Attribution 4.0 United States License. 

 

This journal is published by theUniversity Library System of the University of Pittsburgh as part  

of its D-Scribe Digital Publishing Program and is cosponsored by the University of Pittsburgh Press. 

 

Central Asian Journal of Global Health 

Volume 9, No. 1 (2020) |  ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2020.347|http://cajgh.pitt.edu 

 

 

Table 1. Sample characteristics. 

options: 1. Very good; 2. Good; 3. Moderate; 4. Bad; and 

5. Very Bad. SRH was analysed dichotomously as: 1) 

Good SRH (Very good and Good), and 2) Poor SRH 

(Moderate, Bad and Very Bad)30. The validity of the 

single-item tool to measure subjective health was 

mentioned in previous studies31,32. 

The predictor variable of primary interest was 

PA. The two types of PA used in this study were 

moderate PA (MPA) and vigorous PA (VPA). VPA was 

measured by the following question: “Vigorous activities 

make you breathe much harder than normal and may 

include heavy lifting, digging, aerobics, or fast bicycling. 

Think only about those physical activities that you 

performed for at least 10 minutes at a time. During the 

last 7 days, on how many days did you do vigorous 

physical activities?” MPA was measured by the 

following question: “Moderate physical activities make 

you breathe somewhat harder than normal and may 

include carrying light loads, bicycling at a regular pace, 

or doubles tennis. Do not include walking. Again, think 

about only those physical activities that you performed 

for at least 10 minutes at a time. During the last 7 days, 

on how many days did you do moderate physical 

activities?” 

Answers ranged from 0 to 7 days and were 

categorised as follows: 0 days (never), 1-2 days, 3-4 days, 

5-6 days, and every day. 

The other potential predictor variables included 

in the study were: Age (18-29/30-39/40-49/50-59/60+ 

years); Sex (Female/Male); Currently married (No/Yes); 

Educational attainment (Nil/ Less than primary school/ 

Primary complete/ Secondary complete/ High 

school/equivalent complete/ Pre-university/ University); 

Employment status (Government employee/ Private 

employee/ Employer/ Unemployed); Smoking habit 

(Daily/Yes, but not daily/Non-smoker); Ever drank 

alcohol (Yes/No).  

Ethical considerations 

Informed Consent was obtained from the 

recruited participants before their participation in the 

survey. Participation was completely voluntary, and the 

respondent had the choice to refuse to take part in the 

interview. The data used in this study were secondary, 

Ever drank alcohol         

Yes 360 6.6 965 10.9 2883 35.9 965 17 

No 5102 93.4 7888 89.1 5148 64.1 4709 83 

Days of MPA          

0 1262 23.1 1505 17 1108 13.8 1010 17.8 

1-2 852 15.6 558 6.3 369 4.6 460 8.1 

3-4 748 13.7 629 7.1 498 6.2 630 11.1 

5-6 557 10.2 1062 12 450 5.6 755 13.3 

7 2043 37.4 5099 57.6 5598 69.7 2820 49.7 

Days of VPA         

0 2709 49.6 3824 43.2 3975 49.5 2582 45.5 

1-2 754 13.8 735 8.3 771 9.6 545 9.6 

3-4 606 11.1 646 7.3 899 11.2 499 8.8 

5-6 410 7.5 1000 11.3 426 5.3 681 12 

7 978 17.9 2647 29.9 1960 24.4 1367 24.1 

http://www.library.pitt.edu/
http://www.pitt.edu/
http://www.library.pitt.edu/articles/digpubtype/index.html
http://www.upress.pitt.edu/upressIndex.aspx


 

 

YAYA 

 

 

This work is licensed under a Creative Commons Attribution 4.0 United States License. 

 

This journal is published by theUniversity Library System of the University of Pittsburgh as 

part  

of its D-Scribe Digital Publishing Program and is cosponsored by the University of Pittsburgh 

Press. 

 

Central Asian Journal of Global Health 

Volume 9, No. 1 (2020) |  ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2020.347|http://cajgh.pitt.edu 

 

 

 Bangladesh India Nepal Sri Lanka 

 
Good SRH 

(44.3) 

Poor SRH 

(55.7) 

Good SRH 

(58.7) 

Poor SRH 

(41.3) 

Good SRH 

(37.7) 

Poor SRH 

(62.3) 

Good SRH 

(73.7) 

Poor SRH 

(26.3) 

 n % n % n % n % n % n % n % n % 

Age                 

18-29 2092 38.3 1371 25.1 3444 38.9 1815 20.5 3100 38.6 1952 24.3 1838 32.4 607 10.7 

30-39 1639 30 1393 25.5 2461 27.8 2010 22.7 2120 26.4 1831 22.8 1515 26.7 942 16.6 

40-49 1005 18.4 1180 21.6 1478 16.7 1788 20.2 1446 18 1486 18.5 1265 22.3 1135 20 

50-59 404 7.4 705 12.9 859 9.7 1328 15 747 9.3 1068 13.3 664 11.7 1265 22.3 

60+ 322 5.9 814 14.9 611 6.9 1921 21.7 626 7.8 1687 21 392 6.9 1725 30.4 

P <0.0001 <0.0001 <0.0001 <0.0001 

Sex                 

Female 2797 51.2 3015 55.2 4241 47.9 4905 55.4 4578 57 4642 57.8 2865 50.5 3257 57.4 

Male 2665 48.8 2447 44.8 4612 52.1 3948 44.6 3453 43 3389 42.2 2809 49.5 2417 42.6 

P 0.002 <0.0001 0.175 <0.0001 

Currently 

married 
                

No 1191 21.8 1240 22.7 2125 24 1903 21.5 1333 16.6 1542 19.2 1765 31.1 1770 31.2 

Yes 4271 78.2 4222 77.3 6728 76 6950 78.5 6698 83.4 6489 80.8 3909 68.9 3904 68.8 

P  <0.0001 <0.0001 <0.0001 <0.0001 

Educational 

attainment 
                

Nil 2103 38.5 2354 43.1 2939 33.2 4046 45.7 4634 57.7 5116 63.7 227 4 579 10.2 

Less than 

primary 

school 

945 17.3 1043 19.1 691 7.8 1036 11.7 916 11.4 835 10.4 340 6 692 12.2 

Primary 

complete 
1431 26.2 1305 23.9 35 0.4 44 0.5 1205 15 956 11.9 1316 23.2 1748 30.8 

Secondary 

complete 
415 7.6 393 7.2 1470 16.6 1709 19.3 851 10.6 771 9.6 2326 41 1963 34.6 

High school/ 

equivalent  
262 4.8 186 3.4 1328 15 876 9.9 297 3.7 201 2.5 1305 23 613 10.8 

Pre-

university/ 

University 

306 5.6 180 3.3 1142 12.9 531 6 137 1.7 145 1.8 159 2.8 74 1.3 

P <0.0001 <0.0001 <0.0001 <0.0001 

http://www.library.pitt.edu/
http://www.pitt.edu/
http://www.library.pitt.edu/articles/digpubtype/index.html
http://www.upress.pitt.edu/upressIndex.aspx
http://www.upress.pitt.edu/upressIndex.aspx


 

 

CENTRAL ASIAN JOURNAL OF GLOBAL HEALTH 

 

 

This work is licensed under a Creative Commons Attribution 4.0 United States License. 

 

This journal is published by theUniversity Library System of the University of Pittsburgh as part  

of its D-Scribe Digital Publishing Program and is cosponsored by the University of Pittsburgh Press. 

 

Central Asian Journal of Global Health 

Volume 9, No. 1 (2020) |  ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2020.347|http://cajgh.pitt.edu 

 

 

Employment 

status 
                

Govt. 

employee 
235 4.3 169 3.1 425 4.8 204 2.3 257 3.2 233 2.9 550 9.7 397 7 

Private 

employee 
388 7.1 289 5.3 1107 12.5 779 8.8 177 2.2 161 2 721 12.7 437 7.7 

Employer 2687 49.2 2021 37 4621 52.2 3444 38.9 5590 69.6 5051 62.9 2480 43.7 1923 33.9 

Not working 

for payment 
2157 39.5 2683 

54. 

6 
3594 40.6 4427 50 2008 25 2586 32.2 1918 33.8 2916 51.4 

P <0.0001 <0.0001 <0.0001 <0.0001 

Smoking 

habit 
                

Daily 2092 38.3 2261 41.4 2470 27.9 3222 36.4 2883 35.9 3116 38.8 704 12.4 783 13.8 

Yes. not 

daily 
262 4.8 257 4.7 257 2.9 274 3.1 418 5.2 482 6 443 7.8 494 8.7 

Non-smoker 3108 56.9 2944 53.9 6126 69.2 5356 60.5 4730 58.9 4433 55.2 4528 79.8 4397 77.5 

P <0.0001 <0.0001 <0.0001 <0.0001 

Ever drank 

alcohol 
                

Yes 328 6 388 7.1 983 11.1 956 10.8 2819 35.1 2980 37.1 993 17.5 891 15.7 

No 5134 94 5074 92.9 7870 88.9 7897 89.2 5212 64.9 5051 62.9 4681 82.5 4783 84.3 

P <0.0001 <0.0001 <0.0001 <0.0001 

Days of 

MPA 
                

0 863 15.8 2807 51.4 505 5.7 1726 19.5 353 4.4 4538 56.5 448 7.9 1566 27.6 

1-2 1010 18.5 787 14.4 1346 15.2 637 7.2 883 11 715 8.9 811 14.3 488 8.6 

3-4 770 14.1 590 10.8 593 6.7 673 7.6 498 6.2 795 9.9 635 11.2 596 10.5 

5-6 590 10.8 410 7.5 992 11.2 1151 13 385 4.8 450 5.6 817 14.4 596 10.5 

7 2228 40.8 874 16 5409 61.1 4666 52.7 5903 73.5 1534 19.1 2956 52.1 2428 42.8 

P <0.0001 <0.0001 <0.0001 <0.0001 

Days of VPA                 

0 716 13.1 1469 26.9 735 8.3 4241 47.9 811 10.1 1478 18.4 601 10.6 3387 59.7 

1-2 2584 47.3 847 15.5 3523 39.8 744 8.4 3638 45.3 402 5 2292 40.4 386 6.8 

3-4 634 11.6 726 13.3 664 7.5 629 7.1 956 11.9 498 6.2 499 8.8 511 9 

5-6 415 7.6 530 9.7 930 10.5 1098 12.4 418 5.2 562 7 755 13.3 482 8.5 

7 1114 20.4 1890 34.6 3001 33.9 2142 24.2 2217 27.6 5084 63.3 1532 27 908 16 

P <0.0001 <0.0001 <0.0001 <0.0001  

Table 2. Self-rated Health (SRH) results breakdown in Bangladesh, India, Nepal and Sri Lanka, WHS 2002-03 

 

http://www.library.pitt.edu/
http://www.pitt.edu/
http://www.library.pitt.edu/articles/digpubtype/index.html
http://www.upress.pitt.edu/upressIndex.aspx


 

 

YAYA 

 

 

This work is licensed under a Creative Commons Attribution 4.0 United States License. 

 

This journal is published by theUniversity Library System of the University of Pittsburgh as 

part  

of its D-Scribe Digital Publishing Program and is cosponsored by the University of Pittsburgh 

Press. 

 

Central Asian Journal of Global Health 

Volume 9, No. 1 (2020) |  ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2020.347|http://cajgh.pitt.edu 

 

 

which are available in the public domain in anonymised 

form. Additional approval was therefore was not 

necessary according to WHO regulations 

(https://www.who.int/healthinfo/survey/en/). 

Statistical analysis 

Datasets were checked for missing values and 

outliers. Data were cleaned to retain the maximum 

number of observations. Sample characteristics were 

analysed through univariate analysis. Cross tabulation 

was used to measure the distribution of the 

sociodemographic variables across the outcome SRH 

variable. Chi-square tests were conducted to assess the 

group differences for Good vs. Poor self-rated health. 

Variables that had a p-value below 0.25 were entered into 

the final regression analysis17. Four separate regression 

models were run for each country. The outcomes of the 

regression (binary logistic) analyses were reported in 

terms of adjusted odds ratios (AOR) and corresponding 

95% confidence intervals. All analyses were performed 

with SPSS version 22. 

 

Results 

Descriptive sample characteristics were 

provided in Table 1. In short, the mean age was highest 

in Sri Lankans (40.78, SD 15.22) and lowest in 

Bangladeshis (38.47, SD 14.81). The majority of the 

participants were between 18 and 29 years of age, female, 

and currently married. Rate of literacy was highest for Sri 

Lanka (94.4%) and lowest in Nepal (40%). However, the 

rate of pre-university/university level education was 

highest in India (10.1%), followed by Bangladesh (4.3%) 

and Sri Lanka (2.4%). Regular engagement in MPA was 

highest for Nepal (69.7%), followed by India (57.6%), 

Sri Lanka (49.7%), and Bangladesh (37.4%), and that for 

VPA was highest in India (29.9%) followed by Nepal 

(24.4%), Sri Lanka (24.1%), and Bangladesh (17.9%). 

Table 2 shows that the prevalence of good SRH 

was highest in Sri Lanka (73.7%) and lowest in Nepal 

(37.7%), while in Bangladesh over two-fifth (44.3%) and 

in India (58.7%) a little less than three-fifth of the 

population reported being in good health. Results of 

cross-tabulation also showed that people who reported 

good SRH were more likely to be in the younger age 

groups, female, currently married, having no formal 

education (except for Sri Lanka), self-employed, non-

smoker, and never drinking alcohol. Those who reported 

engaging in any type of physical activities were also 

more likely to report being in good health. 

Results of multivariable regression are shown in 

Table 3. Results indicate that Bangladeshis who engaged 

in 1-2, 3-4, 5-6, and 7 days a week were respectively 31% 

[AOR=1.306; 95%CI 1.085-1.572], 33% [AOR=1.326; 

95%CI 1.093-1.609], 39% [AOR=1.389; 95%CI 1.125-

1.716], and 46% [AOR=1.459; 95%CI 1.249-1.705] 

more likely to report being in good health compared to 

those who never engaged in MPA. In Sri Lanka, the odds 

of being in good health were respectively 49% 

[AOR=1.490; 95%CI 1.164-1.908], 80% [AOR=1.802; 

95%CI=1.433-2.266], 2.3 times [AOR=2.255; 

95%CI=1.805-2.817], and 86% [AOR=1.854; 

95%CI=1.579-2.177] higher among those who those who 

engaged in MPA for 1-2, 3-4, 5-6 and 7 days a week. 

In Bangladesh, India, and Nepal, those who 

engaged in VPA on daily basis were respectively 21% 

[AOR=1.212; 95%CI 1.033-1.421], 34% [AOR=1.340; 

95%CI 1.186-1.512], 36% [AOR=1.363; 95%CI 1.201-

1.547], 22% [AOR=2.224; 95%CI 1.879-2.633] more 

likely to report being in good SRH compared to those 

who never engaged in VPA. Among Sri Lankans, the 

odds of being in good SRH were respectively 2 times 

[AOR=1.995; 95%CI 1.575-2.527], 25% [AOR=1.255; 

95%CI 1.007-1.564], 2.04 times [AOR=2.036; 

95%CI=1.644-2.521], and 2.22 times [AOR=2.224; 

http://www.library.pitt.edu/
http://www.pitt.edu/
http://www.library.pitt.edu/articles/digpubtype/index.html
http://www.upress.pitt.edu/upressIndex.aspx
http://www.upress.pitt.edu/upressIndex.aspx
https://www.who.int/healthinfo/survey/en/


 

 

CENTRAL ASIAN JOURNAL OF GLOBAL HEALTH 

 

 

This work is licensed under a Creative Commons Attribution 4.0 United States License. 

 

This journal is published by theUniversity Library System of the University of Pittsburgh as part  

of its D-Scribe Digital Publishing Program and is cosponsored by the University of Pittsburgh Press. 

 

Central Asian Journal of Global Health 

Volume 9, No. 1 (2020) |  ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2020.347|http://cajgh.pitt.edu 

 

 

95%CI 1.879-2.633] higher among those who engaged in 

VPA for 1-2, 3-4, 5-6, and 7 days a week. 

 

Discussion 

This is one of the first studies that reports on the 

association between PA and SRH in a South Asian 

sample population. Findings of this study indicate a 

suboptimal level of PA among the adult population in

Variables Bangladesh India Nepal Sri Lanka 

 Odds ratio (95%CI) Odds ratio (95%CI) Odds ratio (95%CI) Odds ratio (95%CI) 

Days of MPA     

0 - - - - 

1-2 1.306 0.913 1.186 1.490 

 (1.085-1.572) (0.654-1.010) (0.925-1.519) (1.164-1.908) 

3-4 1.326 0.926 1.289 1.802 

 (1.093-1.609) (0.752-1.142) (1.028-1.615) (1.433-2.266) 

5-6 1.389 0.894 0.930 2.255 

 (1.125-1.716) (0.693-1.004) (0.739-1.169) (1.805-2.817) 

7 1.459 1.055 1.478 1.854 

 (1.249-1.705) (0.919-1.211) (1.283-1.702) (1.579-2.177) 

Days of VPA     

0 - - - - 

1-2 0.854 1.024 1.110 1.995 

 (0.721-1.012) (0.857-1.222) (0.939-1.313) (1.575-2.527) 

3-4 1.025 1.103 1.178 1.255 

 (0.852-1.233) (0.922-1.343) (1.003-1.382) (1.007-1.564) 

5-6 0.962 0.959 0.894 2.036 

 (0.772-1.198) (0.815-1.129) (0.722-1.106) (1.644-2.521) 

7 1.212 1.340 1.363 2.224 

 (1.033-1.421) (1.186-1.512) (1.201-1.547) (1.879-2.633) 

Table 3. Multivariable analysis on the association between PA and SRH in selected South Asian countries, WHS 

2002-03. 

South Asia. Within subgroups, variations were observed 

in PA. Findings showed that participants from 

Bangladesh had the lowest proportion of engaging in any 

type of PA. Similar results on low prevalence of PA were 

reported by previous studies from Bangladesh33 and 

India34.  Findings suggest that the percentage of good 

SRH decreased with age in all countries. Female 

participants were more likely to report good SRH 

compared to/with males in all countries except for in 

India. A noticeable variation was observed in the 

prevalence of SRH among the four countries. Another 

important disparity was that having higher frequency of 

participation in PA did not always relate to higher rate of 

good SRH. For instance, compared to Sri Lanka, 

participants from Nepal had lower rates of reporting good 

SRH despite their higher frequency of involvement in 

both VPA and MPA. A possible connection might be 

higher living standards of Sri Lanka compared to the 

other countries measured in terms of Human 

Development Index (HDI). The correlation between 

higher educational status and better health outcomes are 

well documented across countries18,19. Our results further 

http://www.library.pitt.edu/
http://www.pitt.edu/
http://www.library.pitt.edu/articles/digpubtype/index.html
http://www.upress.pitt.edu/upressIndex.aspx


 

YAYA 

 

This work is licensed under a Creative Commons Attribution 4.0 United States License. 

 

This journal is published by theUniversity Library System of the University of Pittsburgh as part  

of its D-Scribe Digital Publishing Program and is cosponsored by the University of Pittsburgh Press. 

 

Central Asian Journal of Global Health 

Volume 9, No. 1 (2020) |  ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2020.347|http://cajgh.pitt.edu 

 

 

indicate that the rate of both moderate and vigorous type 

physical inactivity was highest in Bangladesh, followed 

by Sri Lanka, and India. Surprisingly, Nepal had lowest 

SRH despite its highest prevalence of MPA and second 

highest prevalence of VPA. As expected, engaging in 

regular PA was associated with higher odds of good SRH 

for most of the countries. In Bangladesh and Nepal, those 

who participated in MPA on a daily basis, and in Sri 

Lanka, those took MPA 5-6 days a week, had the highest 

odds of reporting good SRH. For VPA, highest odds of 

reporting good SRH were reported among those who 

exercised on daily basis, compared with those who 

exercised at a lesser frequency. Among all the countries, 

the strongest associations between SRH and PA in both 

categories was observed in Sri Lanka. 

A major barrier to reporting association between 

SRH and PA is the lack of comparable studies reporting 

prevalence at the national level and the absence of 

standardised and validated instruments in studied 

countries24. Previous studies based on USA (86.2%)20 

and Canada (89.9%)21 concluded that participants who 

rated their health as poor to average were less likely to 

take PA compared with those who rated their general 

health as good to excellent21. Similar findings were 

observed in South Korea, where an independent 

association between lower level of PA and poor SRH was 

reported22. Regarding the prevalence of PA, a study 

encompassing 76 countries reported that the prevalence 

of physical inactivity among individuals aged 15 years or 

older ranged from 3 to 62%23, which varied substantially 

from the worldwide prevalence of physical inactivity in 

adults of 31%24.  

This study has some important limitations. 

Number of days of VPA and MPA (at least 10 minutes at 

a time) was used as a proxy for level of PA instead of 

exact duration. However, similar methodology was used 

in some other studies21,26. Another limitation is the 

absence of several necessary covariates which are 

commonly correlated with the level of PA, such as 

presence of disease conditions (diseased people are less 

likely to engage in PA), place of residency (urban and 

rural residences have differing patterns of engaging in 

PA), and other community level variables (e.g. 

neighbourhood cleanliness, safety, availability of public 

spaces for exercise). As the data were secondary, we had 

no control over the choice of selecting the covariates and 

the ways they were measured. For instance, we could not 

distinguish between work and leisure physical activity, 

which could have affected the associations. With the data 

being self-reported, there remains a possibility of under- 

and over-reporting, as well as the recall bias. Moreover, 

there are also differences in the way in which men and 

women describe their health. Women are more likely to 

report poorer functioning and worse overall health than 

men27. Last but not least, the results cannot be generalised 

to all age groups, since no data were available on 

participants below 18 years of age. 

Further research is needed to better understand 

subgroup variations with larger sample sizes to address 

the heterogeneity found within South Asian groups in this 

study, who may have different motivations to 

undertaking and increasing their PA levels.  At policy 

making level, attempts should be taken to encourage PA 

by improving the provision of necessary infrastructure 

and environment for exercise. This should be facilitated 

by developing national PA guidelines for people of 

different ages. Further studies should investigate the 

predictive factors of PA in the population and monitor the 

trends in PA to improve public health. 

 

References  

1.     Jennifer Beam Dowd, Anna Zajacova. Does Self-

Rated Health Mean the Same Thing Across 

Socioeconomic Groups? Evidence from Biomarker 

Data. Ann Epidemiol. 2010 Oct; 20(10): 743–749. 

2.    Lee S, McClain C, Webster N, Han S. Question 

order sensitivity of subjective well-being measures: 

focus on life satisfaction, self-rated health, and 

http://www.library.pitt.edu/
http://www.pitt.edu/
http://www.library.pitt.edu/articles/digpubtype/index.html
http://www.upress.pitt.edu/upressIndex.aspx


 

 

CENTRAL ASIAN JOURNAL OF GLOBAL HEALTH 

 

 

This work is licensed under a Creative Commons Attribution 4.0 United States License. 

 

This journal is published by theUniversity Library System of the University of Pittsburgh as part  

of its D-Scribe Digital Publishing Program and is cosponsored by the University of Pittsburgh Press. 

 

Central Asian Journal of Global Health 

Volume 9, No. 1 (2020) |  ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2020.347|http://cajgh.pitt.edu 

 

 

subjective life expectancy in survey instruments. 

Qual Life Res. 2016 Oct;25(10):2497-510. 

3.    Irina L.G. Todorova, Katherine L. Tucker, Marcia 

Pescador Jimenez et al. Determinants of self-rated 

health and the role of acculturation: Implications 

for health inequalities. Ethn Health. 2013 Dec; 

18(6): 10. 

4.    Jylhä M. What is self-rated health and why does it 

predict mortality? Towards a unified conceptual 

model. Soc Sci Med. 2009 Aug; 69(3):307-16.  

5.    Göran Waller. Self-rated health in general practice: 

a plea for subjectivity. Br J Gen Pract. 2015 Mar; 

65(632): 110–111.  

6.    Undén AL, Elofsson S. Health from the patient's 

point of view. How does it relate to the physician's 

judgement? Fam Pract. 2001 Apr; 18(2):174-80. 

7. Kwaśniewska M, Kaleta D, Dziankowska-

Zaborszczyk E, Drygas W, Makowiec-Dabrowska 

T. Lifestyle index and self-rated health status. Int J 

Occup Med Environ Health. 2007;20(4):349-56. 

8. Sargent-Cox K, Cherbuin N, Morris L, Butterworth 

P, Anstey KJ. The effect of health behavior change 

on self-rated health across the adult life course: a 

longitudinal cohort study. Prev Med. 2014 

Jan;58:75-80. 

9. Warburton, D.E.; Nicol, C.W.; Bredin, S.S. Health 

benefits of physical activity: The evidence. Can. 

Med Assoc. J.2006,174, 801–809. 

10. Humphreys, B.R.; McLeod, L.; Ruseski, J.E. 

physical activity and health outcomes: Evidence 

from Canada Health Econ. 2014,23, 33–54. 

11. Morris, J.N.; Heady, J.A.; Raffle, P.A.; Roberts, 

C.G.; Parks, J.W. Coronary heart-disease and 

physical activity of work. Lancet1953,265, 1111–

1120. 

12. I-Min Lee, MBBS, ScD, Eric J Shiroma, MSc, 

Felipe Lobelo et al. Impact of Physical Inactivity on 

the World’s Major Non-Communicable Diseases. 

Lancet. 2012 Jul 21; 380(9838): 219–229. 

13. Global Recommendations on physical activity for 

Health. Geneva: World Health Organization; 2010. 

Bookshelf ID: NBK305049. 

14. Pietiläinen KH1, Kaprio J, Borg P, Plasqui G et al. 

Physical inactivity and obesity: a vicious circle. 

Obesity (Silver Spring). 2008 Feb;16(2):409-14. 

15. Ding D, Lawson KD, Kolbe-Alexander TL et al. 

The economic burden of physical inactivity: a 

global analysis of major non-communicable 

diseases. Lancet. 2016 Sep 24;388(10051):1311-24. 

16. WHO WORLD HEALTH SURVEY, Survey 

manual, World Health Organization 2002. 

17. Yaya S, Bishwajit G, Danhoundo G, Shah V, 

Ekholuenetale M. Trends and determinants of 

HIV/AIDS knowledge among women in 

Bangladesh. BMC Public Health. 2016 Aug 

17;16(1):812. 

18. Baker DP, Leon J, Smith Greenaway EG, Collins J, 

Movit M. The Education Effect on Population 

Health: A Reassessment. Population and 

Development Review. 2011;37(2):307–332. 

19. Goesling B. The Rising Significance of Education 

for Health? Social Forces. 2007;85(4):1621–1644. 

20. James Tsai, Earl S Ford, Chaoyang Li, Guixiang 

Zhao and Lina S Balluz. Physical activity and 

optimal self-rated health of adults with and without 

diabetes. BMC Public Health 2010, 10:365. 

21. Catherine Hudon, Hassan Soubhi and Martin 

Fortin. Relationship between multimorbidity and 

physical activity: Secondary analysis from the 

Quebec health survey. BMC Public Health 2008, 

8:304. 

22. M.A. Han, K.S. Kim, J. Park, M.G. Kang, S.Y. 

Ryu. Association between levels of physical 

activity and poor self-rated health in Korean adults: 

The Third Korea National Health and Nutrition 

Examination Survey (KNHANES), 2005. Public 

Health. October 2009 Volume 123, Issue 10, pages 

665–669. 

23. Dumith SC, Hallal PC, Reis RS, Kohl Iii HW. 

Worldwide prevalence of physical inactivity and its 

http://www.library.pitt.edu/
http://www.pitt.edu/
http://www.library.pitt.edu/articles/digpubtype/index.html
http://www.upress.pitt.edu/upressIndex.aspx


 

YAYA 

 

This work is licensed under a Creative Commons Attribution 4.0 United States License. 

 

This journal is published by theUniversity Library System of the University of Pittsburgh as part  

of its D-Scribe Digital Publishing Program and is cosponsored by the University of Pittsburgh Press. 

 

Central Asian Journal of Global Health 

Volume 9, No. 1 (2020) |  ISSN 2166-7403 (online) | DOI 10.5195/cajgh.2020.347|http://cajgh.pitt.edu 

 

 

association with human development index in 76 

countries. Prev Med2011;53(1e2):24e8. 

24. Bauman A, Bull F, Chey T, Craig C, Ainsworth B, 

Sallis J, et al. The international prevalence study on 

physical activity: results from 20 countries. Int J 

Behav Nutr Phys Activity 2009;6(1):21. 

25. M. Moniruzzamana, M. Mostafa Zamana, M.S. 

Islalm, H.A.M.N. Ahasanc, H. Kabird, R. Yasmin. 

physical activity levels in Bangladeshi adults: 

results from STEPS survey 2010. Public Health 137 

(2016) 131e138. 

26. Adrian Bauman, Fiona Bull, Tien Chey, Cora L 

Craig et al. The International Prevalence Study on 

PHYSICAL ACTIVITY: results from 20 countries. 

Int J Behav Nutr Phys Act. 2009; 6: 21. 

27. Hosseinpoor AR, Stewart Williams J, Amin A, 

Araujo de Carvalho I, Beard J, Boerma T, Kowal P, 

Naidoo N, Chatterji S. Social determinants of self-

reported health in women and men: understanding 

the role of gender in population health. PLoS One. 

2012; 7(4):e34799. 

28. Gilles  Ambresin,  Physical activitytty  Chondros,  

Christopher  Dowrick,  Helen Herrman, Jane M. 

Gunn. Self-Rated Health and Long-Term Prognosis 

of Depression. Ann Fam Med. 2014 Jan; 12(1): 57–

65. 

29. Bishwajit G, Tang S, Yaya S, He Z, Feng Z. 

Lifestyle Behaviors, Subjective Health, and Quality 

of Life Among Chinese Men Living With Type 2 

Diabetes. Am J Mens Health. 2017 Mar;11(2):357-

364. 

30. Carin Warnoff, Mats Lekander, Tomas 

Hemmingsson et al. Is poor self-rated health 

associated with low-grade inflammation in 43 110 

late adolescent men of the general population? A 

cross-sectional study. BMJ Open. 2016; 6(4): 

e009440. 

31.  Mucci LA, Wood PA, Cohen B, Clements KM, 

Brawarsky P, Brooks DR. Validity of self-reported 

health plan information in a population-based 

health survey. J Public Health Manag Pract. 2006 

Nov-Dec;12(6):570-7.  

32.  Fosse NE, Haas SA. Validity and stability of self-

reported health among adolescents in a 

longitudinal, nationally representative survey. 

Pediatrics. 2009 Mar;123(3):e496-501. 

33.  Moniruzzaman M, Mostafa Zaman M, Islalm MS, 

Ahasan HA, Kabir H, Yasmin R. Physical activity 

levels in Bangladeshi adults: results from STEPS 

survey 2010. Public Health. 2016 Aug;137:131-8. .  

34.  Ranjit M Anjana, Rajendra Pradeepa, Ashok K Das 

et al. Physical activity and inactivity patterns in 

India – results from the ICMR-INDIAB study 

(Phase-1) [ICMR-INDIAB-5]. Int J Behav Nutr 

Phys Act. 2014; 11: 26. 

 

 

 

http://www.library.pitt.edu/
http://www.pitt.edu/
http://www.library.pitt.edu/articles/digpubtype/index.html
http://www.upress.pitt.edu/upressIndex.aspx

