Insufficient Physical Activity Among Adults and Human Development Index: A Global Study

AUTHORS

Victoria Momenabadi 1 , Elham Goodarzi 2 , Maryam Seraji 3 , Ahmad Naghibzadeh-Tahami 4 , Reza Beiranvand 5 , Elham Nejadsadeghi 6 , Maryam Zahmatkeshan 7 , Leili Moayed 8 , Zaher Khazaei ORCID 9 , *

1 Department of Public Health, Bam University of Medical Sciences, Bam, Iran

2 Social Determinants of Health Research Center, Lorestan University of Medical Sciences, Khorramabad, Iran

3 Health Promotion Research Center, Zahedan University of Medical Sciences, Zahedan, Iran

4 Social Determinants of Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran

5 Department of Public Health, School of Medicine, Dezful University of Medical Sciences, Dezful, Iran

6 Behbahan University of Medical Sciences, Behbahan, Iran

7 Noncommunicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran

8 Iranian Research Center on Healthy Aging, Sabzevar University of Medical Sciences, Sabzevar, Iran

9 Department of Epidemiology, School of Public Health, Ilam University of Medical Sciences, Ilam, Iran

How to Cite: Momenabadi V, Goodarzi E, Seraji M, Naghibzadeh-Tahami A, Beiranvand R, et al. Insufficient Physical Activity Among Adults and Human Development Index: A Global Study, Iran Red Crescent Med J. 2020 ; 22(7):e103602. doi: 10.5812/ircmj.103602.

ARTICLE INFORMATION

Iranian Red Crescent Medical Journal: 22 (7); e103602
Published Online: August 25, 2020
Article Type: Research Article
Received: April 12, 2020
Revised: July 4, 2020
Accepted: July 10, 2020
Crossmark
Crossmark
CHECKING
READ FULL TEXT

Abstract

Background: Insufficient physical activity, particularly in low- and middle-income countries, plays an important role in the spread of non-communicable diseases.

Objectives: The purpose of this study is to investigate the insufficient physical activity and its relationship with the human development index (HDI) in the world.

Methods: This is an ecological study, and the study data, including the human development index and the incidence of insufficient physical activity, were extracted from the World Bank’s database. The descriptive analysis included mean and standard deviation. The inferential analysis consisted of two-way correlation and ANOVA at a significance level of less than 0.05. The analyses were performed using Stata-14 software.

Results: The highest insufficient physical activity in both sexes (39.26 [37.42, 40.95]) was found in the Americas, especially in high-income regions. There was a significant positive correlation between the incidence of insufficient physical inactivity and HDI in the world (r = 0.446, P < 0.0001). This correlation was also significant in Asia and Africa (P < 0.05). The results showed a positive correlation between components of HDI (i.e., gross national income per 1000 capita, mean years of schooling, life expectancy at birth, and expected years of schooling) and insufficient activity (P < 0.0001). The results of ANOVA also exhibited a significant relationship between the mean prevalence of physical inactivity and the level of development (P < 0.0001).

Conclusions: Given the significant correlation between the incidence of insufficient physical inactivity and HDI, understanding this correlation and its components, especially in low- and middle-income countries can alleviate the impact of physical inactivity epidemics in the future, thereby contributing to the effective global prevention of non-communicable diseases.

Copyright © 2020, Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited

1. Background

Regular physical activity is recognized as a major health indicator of populations. Each year, two million lives are lost due to insufficient physical activity (IPA) worldwide (1, 2). A comparative risk assessment by the Global Burden of Disease suggested that IPA accounted for 3.2 million deaths, and 2.8% of years lived with disability in 2010. It is also ranked tenth among the top 20 risk factors attributable to the burden of diseases (3). Evidence suggests that 30 min of moderate-intensity exercise each day (equivalent to 4.2 MJ/wk or 1000 kcal/wk) is associated with a significant drop in cardiovascular diseases (4). Research has shown the link between physical activity and the risk of stroke. Many studies suggest that physical activity can decrease the risk of stroke by about 25% - 30% in active individuals (5).

Given the strong relationship between physical activity and major non-communicable diseases, one of the nine goals set by WHO member states for improving, preventing, and treating non-communicable diseases is to achieve 10% reduction in the incidence of IPA by 2025 (6).

Globally, many adults and children have inadequate physical activities to maintain their health (2, 7). The prevalence of IPA is growing at a slow pace and is even deteriorating in some countries (8). Risk factors, including the non-communicable diseases induced by IPA are also on the rise in low-income countries in addition to developed countries. Understanding the behavioral causes of physical activity is crucial for progress and public health interventions (9).

Studies have exhibited that demographic and biological variables are significantly correlated with physical activity, and there is a positive correlation between physical activity and socioeconomic status in low- and middle-income countries (10-13).

IPA plays a key role in the spread of non-communicable diseases in high-income countries, and its impact is increasing in low- and middle-income countries. Hence, deeper insights into the causes of physical inactivity can have a huge influence on evidence-based planning for public health interventions because these programs target the main contributors of IPA (9). Therefore, the purpose of this study is to investigate the incidence of IPA in the world and its association with human development index (HDI).

2. Objectives

The purpose of this study is to investigate the insufficient physical activity and its relationship with HDI in the world.

3. Methods

The estimates are based on self-reported physical activity data derived from the Global Physical Activity Questionnaire (GPAQ), the International Physical Activity Questionnaire (IPAQ), or similar questionnaires that cover activities at workplace/home, transportation, and leisure time. Where necessary, the reported definition was modified (if different from the indicator’s definition) to know the over-reporting of activities in the IPAQ, to survey coverage (of the survey only covered urban areas), and to account for age coverage (in case the age range was narrower than 18+ years). No estimates are offered for countries for which no data was available (14).

3.1. Method of Estimation of Global and Regional Aggregates

We have offered global estimates as well as estimates of WHO regions and the World Bank’s income groups. The estimates of countries were combined in each group and weighed by the population size of each country. Countries with no estimate were excluded from the analysis. We also used the World Bank’s 2010 income groups report, as 2010 represented the estimation year (14).

3.2. Definition

The percentage of a specific population performing less than 150 min of moderate-intensity physical activity per week, or less than 75 min of moderate-intensity physical activity per week, or an equivalent combination (14).

3.3. Human Development Index (HDI)

With a numerical value between 0 and 1, HDI exhibits the extent of a country’s progress in achieving the highest value (HDI = 1), which allows inter-country comparisons. In other words, HDI is a summary measure of the average achievement in three dimensions of human development, including a long and healthy life, schooling, and decent standards of living. HDI is the geometric mean of the normalized indices for each of these three dimensions and measures the degree of achievements in each dimension. Life expectancy is measured by life expectancy at birth, education is measured by mean years of schooling (elementary, secondary, and higher education), and standard of living is measured by gross national income (GNI) per capita (15, 16) .

3.4. Statistical Analysis

In this study, data analysis was conducted using Stata software (Ver. 14). The descriptive analysis involved mean and standard deviation. The correlation method was used to evaluate the association between the IPA and the HDI components. The significance level was set to 0.05.

4. Results

According to the results, the highest prevalence of IPA in both sexes (39.26, CI95% [37.42, 40.95]) in men (33.14, CI95% [30.82, 34.01]) and women (45.15, CI95% [42.87, 48.58]) was observed in the Americas (Table 1).

Table 1. Prevalence of IPA According to WHO (Source: WHO)
WHO IPA Among Adults Aged ≥ 18 Years
Both SexesMaleFemale
Africa22.1 (19.92, 24.01)18.4 (15.81, 20.9)25.63 (22.82, 28.18)
Americas39.26 (37.42, 40.95)33.14 (30.82, 34.01)45.15 (42.87, 48.58)
South-East Asia30.49 (21.57, 46.84)22.9 (15.09, 49.79)38.62 (27.04, 63.96)
Europe29.37 (27.91, 32.14)26.17 (23.88, 29.54)32.4 (30.52, 36.99)
Eastern Mediterranean34.92 (32.14, 39.21)26.94 (25.35, 30.56)43.48 (41.36, 46.55)
Western Pacific18.64 (16.53, 23.5)18.8 (16.31, 25.13)18.49 (15.54, 27.28)

The highest incidence of IPA in both sexes was reported in Kuwait (69.66%), Saudi Arabia (53.14%), and Iraq (52.03%) in Asia, Brazil (47.02%), Costa Rica (46.06%) and Suriname (44.43%) in the Americas, Cyprus (44.35%), Portugal (43.4%),, and Germany (42.21%) in Europe; Mauritania (41.31%), Mali (40.42%), and South Africa (38.17%) in Africa, and New Zealand (42.38%) in Oceania. (Table 2 and Figure 1).

Table 2. Prevalence of IPA Among Adults Aged +18 Years (Age-Standardized Estimate) Worldwide by Countries in 2016 (Source: WHO)
CountryPrevalence (%)CountryPrevalence (%)
BothMaleFemaleBothMaleFemale
AsiaAfrica
Kuwait66.9661.3474.64Egypt31.0223.2338.79
Jordan11.8710.3313.5Libya36.431.241.54
United Arab Emirates41.3538.9649.35South Africa38.1728.4747.26
Saudi Arabia53.1444.9165.1Tunisia30.3626.4234.1
Israel------Morocco26.220.7531.35
Qatar36.8333.1648.7Mauritius29.7727.6331.82
Bahrain-------Algeria33.5826.9240.25
West Bank and Gaza Strip------Swaziland------
Syrian Arab Republic------Gabon25.3317.6733.19
Lebanon36.4139.832.97Cameroon28.5121.7535.18
Iraq52.0339.4664.56Mauritania41.3136.5346.07
Turkey30.5621.7438.8Djibouti------
Kazakhstan27.526.128.74Cabo Verde19.6814.0924.99
Oman32.953040.19Zimbabwe26.8422.7730.68
Uzbekistan19.0613.324.42Botswana21.7417.1126.3
Georgia17.9617.2518.58Namibia33.3628.8937.4
Azerbaijan------Equatorial Guinea------
Iran (Islamic Republic of)33.2223.0643.38Lesotho6.256.526
Armenia22.6423.3422.09Nigeria27.124.729.57
Turkmenistan------Ghana21.818.624.84
Kyrgyzstan13.8810.8616.74Côte d'Ivoire33.0629.0637.28
Mongolia18.617.7719.39Benin15.8913.6118.11
Malaysia38.7534.6442.79Sierra Leone14.2910.1518.29
Yemen------Senegal23.0917.5628.12
Darussalam Brunei27.3421.7833.89Guinea14.510.6118.38
Singapore36.534.338.61South Sudan------
Republic of Korea35.3529.5540.99United Republic of Tanzania6.495.87.15
Japan35.4733.8439.89Liberia25.1221.7228.5
Tajikistan29.3219.9538.68Sudan------
China14.1115.9612.19Comoros14.329.6319.01
Maldives30.3325.7734.78Congo28.0324.7631.27
Thailand24.5821.7927.25Uganda5.525.25.82
Philippines39.6630.1349.08Somalia------
Bhutan22.9817.729.46Mozambique5.565.056.02
Democratic People's Republic of Korea35.3529.5540.99Angola------
Pakistan33.6524.3843.3Guinea-Bissau------
Sri Lanka28.9520.2336.75Kenya15.4413.9316.93
Indonesia22.5723.4631.96Togo9.89.2310.34
India34.0324.743.89Malawi15.6112.9918.17
Lao People's Democratic Republic16.2711.720.65Gambia21.0715.9825.85
Nepal13.41214.61Rwanda14.5611.0217.58
Myanmar10.728.1313.14Mali40.4233.7347.1
Timor-Leste17.7910.2625.48Burundi------
Afghanistan------Chad23.319.6226.93
Cambodia------Zambia22.0619.0824.97
Viet Nam25.3919.930.56Central African Republic14.3112.615.94
Bangladesh27.7616.1339.46Niger22.3819.7225.02
AmericaMadagascar17.1712.8321.41
Puerto Rico------Burkina Faso20.317.7422.72
United States of America40.0131.7248.02Eritrea22.3713.7730.71
Argentina41.5837.6245.25Democratic Republic of the Congo23.8520.8326.78
Canada28.625.7231.38Ethiopia14.8611.3418.28
Mexico28.8925.4732.19Europe
Bahamas43.2630.0455.58Czech Republic------
Belize------Malta41.7536.2147.25
Chile26.5824.4228.65Spain26.8122.8730.52
Barbados42.929.2954.95Cyprus44.3538.3850.53
Venezuela (Bolivarian Republic of)31.4329.4933.31Luxembourg28.4326.4230.43
Trinidad and Tobago38.1827.2148.65Germany42.2140.1844.13
Uruguay22.4218.7425.72Ireland32.7328.2637.08
Costa Rica46.0637.6854.32Hungary38.5433.1343.3
Panama------Slovenia32.2227.6936.61
Suriname44.4338.1350.62Poland32.4631.4833.36
Nicaragua------United Kingdom------
El Salvador------Portugal43.437.5548.48
Paraguay37.4238.0636.76Serbia39.4634.8243.78
Ecuador27.1824.629.7Lithuania26.5323.2229.23
Brazil47.0240.3753.28Belgium35.7530.6340.62
Colombia44.0138.7848.93Slovakia34.931.1438.36
Peru------Finland16.5617.1915.96
Guatemala37.1237.1237.13Bosnia and Herzegovina25.5422.828.05
Dominican Republic38.9734.4343.38Austria30.0926.4333.57
Honduras------Iceland------
Cuba36.8830.9542.79Croatia31.0925.8735.83
Bolivia (Plurinational State of)------Switzerland23.7521.7125.71
Guyana------Italy41.3936.1746.23
Jamaica32.5628.3736.62Greece37.6633.9341.4
Haiti------Bulgaria38.6335.5841.44
OceaniaMontenegro------
Samoa12.588.2117.19Latvia29.5425.3532.94
Australia30.3727.0433.64France29.3224.2633.99
New Zealand42.3839.2742.25Russian Federation17.1216.5517.6
Solomon Islands18.213.2523.17Netherlands27.1825.2929.01
Vanuatu87.218.78The former Yugoslav Republic of Macedonia------
Fiji17.4110.8524.09Norway31.729.5833.83
Papua New Guinea14.811.4418.22Albania------
Sweden23.1321.5124.73
Belarus14.0813.6814.42
Romania35.3532.138.32
Estonia31.9928.8634.64
Denmark28.525.7331.19
Ukraine19.6318.6820.42
Republic of Moldova11.4812.1310.9
Figure 1. Prevalence of IPA among adults aged ≥ 18 years (age-standardized estimate) (%) in both sexes in 2016 [Source WHO]

According to the results, the highest incidence of IPA was reported in high-income areas. This figure was 41.58% for women, 32% for men, and 36.76% for both sexes. The lowest incidence of IPA (in women, men, and both sexes) was observed in low-income areas (Figure 2).

Figure 2. Prevalence of IPA among adults aged 18+ years (%) by income (Source: WHO)

The results exhibited a significant positive correlation between the incidence of IPA and HDI in the world (r = 0.446, P < 0.0001). According to the results, there was also a significant positive correlation between the prevalence of IPA and the HDI in Asia (r = 0.334, P < 0.05) and Africa (r = 0.446). But the correlation observed was not significant in the Americas and Europe (P > 0.05; Figure 3).

Figure 3. Correlation between HDI and the incidence of IPA in the world for each continent in 2016

The results indicated that the prevalence of IPA in both sexes was positively correlated with GNI (r = 0.410, P < 0.0001), MYS (r = 0.304, P < 0.0001), LEB (r = 0.418, P < 0.0001),, and EYS (r = 0.315, P < 0.0001) (Table 3).

Table 3. Pearson Correlation Between HDI and the Dependent Variablea
HD ComponentsPearson Correlation Between HDI Component and Dependent Variable
BothMaleFemale
rPrPrP
Gross national income per 1000 capita0.410< 0.00010.491< 0.00010.343< 0.0001
Mean years of schooling0.304< 0.00010.374< 0.00010.228< 0.001
Life expectancy at birth0.418< 0.00010.473< 0.00010.352< 0.0001
Expected years of schooling0.315< 0.00010.393< 0.00010.235< 0.0001

aDependent variables: Prevalence of IPA

The results of the analysis of variance (ANOVA) showed that the highest mean prevalence of IPA in both sexes (35.5 ± 1.09) belonged to countries with very high human development and the lowest (20.3 ± 9.6) to countries with low human development, and their difference was statistically significant (P < 0.0001). The highest mean prevalence of IPA in men (30.24 ± 8.7) belonged to countries with very high human development and the lowest (16.7 ± 8.4) to countries with low human development, and their difference was statistically significant (P < 0.0001). In women, the highest mean prevalence of IPA (37.05 ± 12.1) was associated with very high human development and the lowest (23.79 ± 11.1) with low human development, and their difference was statistically significant (P < 0.0001; Table 4).

Table 4. Mean IPA in Different HDI Regions in 2016a
HDI ComponentsMean IPA (%)
BothMaleFemale
Very high human development35.5 ± 1.0930.24 ± 8.737.05 ± 12.1
High human development31.7 ± 9.1721.15 ± 7.736.21 ± 11.5
Medium human development26.3 ± 9.921.03 ± 8.532.14 ± 12.2
Low human development20.3 ± 9.616.7 ± 8.423.79 ± 11.1
P (F-test)< 0.0001< 0.0001< 0.0001

aStatistical method: ANOVA

5. Discussion

Physical activity represents an integral part of different facets of daily life, including work, transportation, and leisure. This distinction is particularly relevant in the developing countries that are in a state of transition, where recreational and leisure activities account for a lower share of total costs compared to occupational or transport activities (17). Measuring the level of physical activity in the population is essential for health promotion and policy formulation as well as the assessment of the impact of large-scale policies and programs designed to amplify activity (18).

As noted by the results of this study, the highest prevalence of physical inactivity was observed in high-income regions (41.58% in men and 32% in women) and the lowest prevalence of physical inactivity (in women, men, and both sexes) was reported in low-income regions. A significant positive correlation was found between the prevalence of IPA and HDI (r = 0.446, P < 0.0001) in Asia (r = 0.334, P < 0.05) and Africa (r = 0.446, P < 0.05).

In some countries such as the United States, Finland, and Canada, the standard physical activity in adults has been monitored for several decades (19, 20). The Asia-Pacific region covers a spectrum of diverse and predominantly low-income countries (LMICs) from large and densely-populated states in Asia to small Pacific Islands. Considering the dramatic rise of NCD in this region, risk factors, including physical activity, should be closely monitored (21, 22).

In the developing countries outside the Asia-Pacific region, there is a similar situation with regard to measures of physical activity monitoring. In two Brazilian studies that employed IPAQ Multiple Domain Questionnaire, the incidence of IPA was estimated at 41.1% in Brazil (23) and 26.1% in a national sample (19). According to the results of another study in Brazil, the incidence of activity during leisure time was only 3% in adults (24). Similarly, in Saudi Arabia, the incidence of physical inactivity was estimated at an alarming rate of 96% (25).

The physical activity transfer program is a theoretical paradigm that manifests the link between the incidence of physical inactivity and higher levels of economic and social development in a country. This can be explained in terms of occupational shifts from high-activity to low-activity occupations (26).

Development is characterized by an economic shift from the agricultural-based economy, including changes in the occupational structure, the level of urbanization, and lower levels of work-related and domestic activities. Katzmarzyk and Mason argue that changes in daily work, social climate, and the nature of work (particularly outdoor activities) have contributed to the sedentary behaviors and constrained pattern of disease from communicable to chronic illnesses. In addition, economic changes may lead to lower levels of physical activity (27). Guthold et al. evaluated the outcomes of physical activity in 22 African countries, reporting a linear relationship between the level of urbanization in a country and the physical inactivity of its population. In other words, increased urbanization has diminished the level of physical activity (28).

Research shows that higher-income groups can increase leisure-time activities by diminishing work-related activities (29). However, low-income groups have to deal with decreased physical activity because they often lack the financial resources to engage in leisure activities. Nevertheless, lower-income groups that are more economically vulnerable maintain higher levels of physical activity at work and transportation compared to higher-income groups (30).

5.1. Conclusion

According to the results of this study, there is a significant correlation between HDI and the incidence of IPA. Therefore, considering factors that can influence the incidence of IPA in different countries can be helpful in reducing its incidence rate. By curbing the prevalence of IPA, the risk factors for non-communicable diseases could be reduced. Monitoring the current level and trend of IPA to track the extent of progress toward this global goal is essential to identify high-risk populations, assess the effectiveness of policies, and guide policy-making and future planning.

Footnotes

References

  • 1.

    Leites GT, Bastos GAN, Bastos JP. Prevalence of insufficient physical activity in adolescents in South Brazil. Revista Brasileira de Cineantropometria & Desempenho Humano. 2013;15(3):286-95.

  • 2.

    Kavousi E, Khazaei Z, Amini A, Fattahi E, Pnahi A, Sohrabivafa M, et al. Promoting behaviors of healthiness in two domains of physical activity and nutrition statue in high school students. International journal of pediatrics. 2017;5(5):4839-47.

  • 3.

    Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. The lancet. 2012;380(9859):2224-60.

  • 4.

    Inoue S, Sugiyama T, Takamiya T, Oka K, Owen N, Shimomitsu T. Television viewing time is associated with overweight/obesity among older adults, independent of meeting physical activity and health guidelines. Journal of Epidemiology. 2012;1(2):256.

  • 5.

    Physical Activity Guidelines Advisory Committee. Physical activity guidelines advisory committee report, 2008. Washington, DC: US Department of Health and Human Services; 2008. NaN p.

  • 6.

    World Health Organization. Global action plan for the prevention and control of noncommunicable diseases 2013-2020. World Health Organization; 2013. Contract No.: 9241506237.

  • 7.

    Ngandu T, Lehtisalo J, Solomon A, Levälahti E, Ahtiluoto S, Antikainen R, et al. A 2 year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): a randomised controlled trial. The Lancet. 2015;385(9984):2255-63.

  • 8.

    Rodgers A, Ezzati M, Vander Hoorn S, Lopez AD, Lin R, Murray CJ, et al. Distribution of major health risks: findings from the Global Burden of Disease study. PLoS medicine. 2004;1(1).

  • 9.

    Bauman AE, Reis RS, Sallis JF, Wells JC, Loos RJ, Martin BW, et al. Correlates of physical activity: why are some people physically active and others not? The lancet. 2012;380(9838):258-71.

  • 10.

    do Paranс C. Physical Activity 2 Correlates of physical activity: why are some people physically active and others not? Lancet. 2012;2(2).

  • 11.

    Trost SG, Owen N, Bauman AE, Sallis JF, Brown W. Correlates of adults’ participation in physical activity: review and update. Medicine & science in sports & exercise. 2002;34(12):1996-2001.

  • 12.

    Macniven R, Bauman A, Abouzeid M. A review of population-based prevalence studies of physical activity in adults in the Asia-Pacific region. BMC public health. 2012;12(1):41.

  • 13.

    Giles-Corti B, Donovan RJ. Socioeconomic status differences in recreational physical activity levels and real and perceived access to a supportive physical environment. Preventive medicine. 2002;35(6):601-11.

  • 14.

    World Health Organization. The Global Health Observatory. 2018, [cited 2018 Jan 17]. Available from: https://www.who.int/data/gho.

  • 15.

    Goodarzi E, Moayed L, Sohrabivafa W, Adineh HA, Khazaei Z. Epidemiology incidence and mortality of breast cancer and its association with the body mass index and human development index in the asian population. World Cancer Research Journal. 2019;6(1):10.

  • 16.

    Khazaei Z, Sohrabivafa M, Darvishi I, Naemi H, Goodarzi E. Relation between obesity prevalence and the human development index and its components: an updated study on the Asian population. Journal of Public Health volume. 2020;1(28):323–329.

  • 17.

    Trinh OT, Nguyen ND, Dibley MJ, Phongsavan P, Bauman AE. The prevalence and correlates of physical inactivity among adults in Ho Chi Minh City. BMC public health. 2008;8(1):204.

  • 18.

    Bauman A, Phongsavan P, Schoeppe S, Owen N. Physical activity measurement-a primer for health promotion. Promotion & education. 2006;13(2):92-103.

  • 19.

    Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Medicine & science in sports & exercise. 2003;35(8):1381-95.

  • 20.

    Prättälä R, Helasoja V, Kasmel A, Klumbiene J, Pudule I. Finbalt health monitor. Global Behavioral Risk Factor Surveillance. Springer; 2003. p. 57-72.

  • 21.

    World Health Organization. Global status report on noncommunicable diseases 2014. World Health Organization; 2014. Report No.: WHO/NMH/NVI/15.1. Contract No.: 9241564857.

  • 22.

    Kanbur R. The role of the world bank in middle-income countries. Issues in Indian public policies. Springer; 2018. p. 167-80.

  • 23.

    Monteiro CA, Conde WL, Matsudo SM, Matsudo VR, Bonseñor IM, Lotufo PA. A descriptive epidemiology of leisure-time physical activity in Brazil, 1996-1997. Revista Panamericana de Salud Publica. 2003;14:246-54.

  • 24.

    Al-Nozha MM, Al-Hazzaa HM, Arafah MR, Al-Khadra A, Al-Mazrou YY, Al-Maatouq MA, et al. Prevalence of physical activity and inactivity among Saudis aged 30-70 years. Saudi Med J. 2007;28(4):559-68.

  • 25.

    Al-Hazzaa HM. Health-enhancing physical activity among Saudi adults using the International Physical Activity Questionnaire (IPAQ). Public health nutrition. 2007;10(1):59-64.

  • 26.

    Kohl 3rd HW, Craig CL, Lambert EV, Inoue S, Alkandari JR, Leetongin G, et al. The pandemic of physical inactivity: global action for public health. The lancet. 2012;380(9838):294-305.

  • 27.

    Katzmarzyk PT, Mason C. The physical activity transition. Journal of Physical activity and Health. 2009;6(3):269-80.

  • 28.

    Guthold R, Ono T, Strong KL, Chatterji S, Morabia A. Worldwide variability in physical inactivity: a 51-country survey. American journal of preventive medicine. 2008;34(6):486-94.

  • 29.

    Finger JD, Tylleskär T, Lampert T, Mensink GB. Physical activity patterns and socioeconomic position: the German National Health Interview and Examination Survey 1998 (GNHIES98). BMC Public Health. 2012;12(1):1079.

  • 30.

    Beenackers MA, Kamphuis CB, Giskes K, Brug J, Kunst AE, Burdorf A, et al. Socioeconomic inequalities in occupational, leisure-time, and transport related physical activity among European adults: a systematic review. International journal of behavioral nutrition and physical activity. 2012;9(1):116.

  • COMMENTS

    LEAVE A COMMENT HERE: