Association Between Physical Activity Indicators and Human Development Index at a National Level: Information From Global Matrix 4.0 Physical Activity Report Cards for Children and Adolescents

Click name to view affiliation

Diego Augusto Santos Silva Research Center in Kinanthropometry and Human Performance, Physical Education Department, Sports Center, Federal University of Santa Catarina, Florianópolis, SC, Brazil
Faculty of Health Sciences, Universidad Autónoma de Chile, Providencia, Chile

Search for other papers by Diego Augusto Santos Silva in
Current site
Google Scholar
PubMed
Close
*
,
Salomé Aubert Active Healthy Kids Global Alliance, Ottawa, ON, Canada

Search for other papers by Salomé Aubert in
Current site
Google Scholar
PubMed
Close
,
Kwok Ng Physical Activity for Health Research Cluster, Department of Physical Education and Sport Sciences, University of Limerick, Limerick, Ireland
School of Educational Sciences and Psychology, University of Eastern Finland, Joensuu, Finland
Faculty of Education, University of Turku,, Rauma, Finland

Search for other papers by Kwok Ng in
Current site
Google Scholar
PubMed
Close
,
Shawnda A. Morrison Faculty of Sport, University of Ljubljana, Ljubljana, Slovenia

Search for other papers by Shawnda A. Morrison in
Current site
Google Scholar
PubMed
Close
,
Jonathan Y. Cagas Department of Sports Science, University of the Philippines Diliman, Quezon City, Philippines

Search for other papers by Jonathan Y. Cagas in
Current site
Google Scholar
PubMed
Close
,
Riki Tesler Department of Health Systems Management, School of Health Sciences, Ariel University, Ariel, Israel

Search for other papers by Riki Tesler in
Current site
Google Scholar
PubMed
Close
,
Dawn Tladi Department of Sports Science, University of Botswana, Gaborone, Botswana

Search for other papers by Dawn Tladi in
Current site
Google Scholar
PubMed
Close
,
Taru Manyanga Division of Medical Sciences, University of Northern British Columbia, Prince George, BC, Canada
Department of Physical Therapy, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada

Search for other papers by Taru Manyanga in
Current site
Google Scholar
PubMed
Close
,
Silvia A. González Active Healthy Kids Global Alliance, Ottawa, ON, Canada
School of Medicine, Universidad de los Andes, Bogotá, Colombia

Search for other papers by Silvia A. González in
Current site
Google Scholar
PubMed
Close
,
Eun-Young Lee School of Kinesiology & Health Studies, Queen’s University, Kingston, ON, Canada

Search for other papers by Eun-Young Lee in
Current site
Google Scholar
PubMed
Close
, and
Mark S. Tremblay Active Healthy Kids Global Alliance, Ottawa, ON, Canada
Healthy Active Living and Obesity Research Group, Children’s Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada
Department of Pediatrics, University of Ottawa, Ottawa, ON, Canada
Department of Health Sciences, Carleton University, Ottawa, ON, Canada

Search for other papers by Mark S. Tremblay in
Current site
Google Scholar
PubMed
Close
Open access

Background: The aim of this study was to explore the associations between the 10 key indicators of the Global Matrix 4.0 project and human development index (HDI) at a national level according to sex, age, area of residence, and ability levels. Methods: Information from the 57 countries/localities included in the Global Matrix 4.0 project was compiled and presented according to the HDI of each country/locality for each of the 10 key indicators. Grades were assigned based on the benchmarks of the Global Matrix 4.0 project ranged between “A+” (best performance) and “F” (worst performance). Results: The population subgroups of females, children, rural residents, with/without disabilities from countries/localities with higher HDI performed better in the organized sport and physical activity indicator than their peers from countries/localities with lower HDI. Children and adolescents living in rural areas of countries/localities with higher HDI showed better performance for active play, and children and adolescents living in urban areas of countries/localities with lower HDI showed better performance for the active transportation. Countries/localities with higher HDI showed better grades for sources of influence than the countries/localities with lower HDI. Conclusions: Physical activity patterns in some population subgroups of children and adolescents differed according to the development level of countries/localities.

The promotion of physical activity (PA) for children and adolescents has become a worldwide concern and is now included in global action plans to improve the health of future generations.13 The World Health Organization has developed a Global Action Plan on Physical Activity 2018–2030, which targets a 15% reduction in the prevalence of insufficient physical inactivity among adults and adolescents from 2016 to 2030.1 While specific targets for children are not identified in the Global Action Plan on Physical Activity, the fourth edition of the Active Healthy Kids Global Alliance (AHKGA) Global Matrix project aims to monitor the PA of school-aged children and adolescents at global, regional, and local levels, using a harmonized approach.46

Some of the recognized benefits of PA for children and adolescents include maintaining and improving indicators of cardiorespiratory fitness, muscular fitness, adiposity, bone health, and cardiometabolic health, reduced depressive symptoms, and positive effects on cognitive function and academic outcomes.7 Although these health benefits are well established, there is still a high prevalence of insufficient PA in children and adolescents around the world.7 Aubert et al8 recently published a review on the intercontinental PA surveillance initiatives for children and adolescents and found significant differences in the prevalence of PA across sexes (lower levels of PA among girls in comparison with same age boys) and age (diminution of PA level with age), yet details of extrapolated differences of social characteristics were lacking.

Improving the health of populations involves increasing young people’s PA levels to a point where they will attain consistent health benefits, higher physical fitness, and better physical literacy into their adulthood, these improvements are directly related to the sustainable development of the planet.9 Investing in policies to promote PA will contribute directly to achieving many of the United Nations’ 2030 Sustainable Development Goals.1 However, the promotion of PA needs to be equitable among global population subgroups to achieve successful and sustained reductions of insufficient PA worldwide.9 The promotion of healthy behaviors, including PA in children and youth is critical to avoiding increased cost and mortality burden, and it is incumbent on society to reach as many people as possible, many of whom are from resource-limited communities, vulnerable, and equity-deserving groups, genders, or those facing other barriers to accessing safe, effective PA options.1,9

The objective of the Global Matrix project is to advance knowledge on the monitoring of different PA indicators in different countries considering differences within and between countries. The 10 indicators common to all countries included in Global Matrix 4.0 were: overall PA, organized sport and PA, active play, active transportation, sedentary behavior, physical fitness, family and peers, school, community and environment, and government investments and strategies.10 According to the methodology defined for the Global Matrix project,4,10 each of these indicators has benchmarks that are evaluated in relation to the performance of each indicator in each country/locality. Using on these benchmarks, grades are assigned based on the best evidence found in each of the countries/localities. The letter grades ranged between “A+” (best performance) and “F” (worst performance).4,10 For indicators with incomplete (INC), inadequate, or insufficient data, the INC grade is assigned.4,10 To align with the achievement of the Sustainable Development Goals through PA monitoring and promotion, this study aims to explore the associations between the 10 key indicators of the Global Matrix 4.0 project and human development index (HDI) at the national level according to sex, age, area of residence, and ability levels. The main hypothesis of this study is that in countries/localities with better HDI, PA indicators will be better than in countries/localities with lower HDI for all subgroups analyzed.

Methods

The present research is a cross-sectional, ecological observation study, and used information from Global Matrix 4.0 project.4,10 As has been done in 3 previous editions of the Global Matrix initiatives,3,5,6 the AHKGA led the development of the Global Matrix 4.0, bringing together 57 countries/territories from 6 continents.4,10 Each of these countries or territories had one Report Card team comprising national experts (researchers, professors, stakeholders, students, staff, and practitioners) led by one or more Report Card leader(s) following an established, harmonized process.10 Thus, the project had a direct involvement of nearly 700 people.4 The development of the Global Matrix 4.0 project started in 2019 and included data up to early 2022. Each national Report Card team gathered the best available evidence to systematically grade each of the 10 common indicators included in Global Matrix 4.0. This common process of evidence gathering, and report card development generated 570 grades on key indicators of PA for children and youth around the world. The 10 indicators common to all countries included in Global Matrix 4.0 were: overall PA, organized sport and PA, active play, active transportation, sedentary behavior, physical fitness, family and peers, school, community and environment, and government investments and strategies. Several Report Card teams included additional indicators related to the health: (eg, sleep, obesity, mental health, physical literacy, seasonal variation in PA) of children and adolescents, expanding beyond the Global Matrix 4.0 framework.10 Some teams also had specialized teams to grade the disability specific data in Para Report Cards.11

The AHKGA provides leadership for the Global Matrix 4.0 project,4 including providing due dates and work guidelines that each country must follow. Each country or territory has its own Report Card committee that is in charge of collecting the best available information. The process is important for the harmonization of concepts, metrics, and information to be compiled into the different Report Cards for each country or territory. To ensure uniformity during the Report Card development process, each team from all participating countries/localities, were required to submit their final grades to AHKGA headquarters where the grades were audited by 2 independent reviewers. The audit process included (1) ensuring that the teams followed the AHKGA grading rubric and benchmarks,10 (2) providing a rationale for the assigned grades, (3) providing details of the studies that were used to inform the grading decisions, and (4) providing the bibliographic references consulted in each country, for every common indicator.

The grades ranged between “A” (excellent) and “F” (failing) and were assigned to each indicator using a standardized grading rubric and participating countries/localities adhered to the same benchmarks for grade assignment.10 In cases in which data were insufficient to accurately assign a grade, an incomplete was assigned to that indicator.10 Each grade corresponds to a numeral that was defined in the previous version of the Global Matrix project and helps in the descriptive analysis of the research.6 Supplementary Material S1 (available online) presents the detailed information corresponding to each grade used in the project.

The auditing process of submitted grades was an independent peer-review process. The auditors were researchers with expertise in child PA and included representatives from 6 continents involved in the Global Matrix 4.0 project. At least 2 independent auditors evaluated the information for each country, and they provided comments/suggestions/reviews for every grade indicator, rationale, characteristics of the consulted studies, and bibliographic references consulted in each country/jurisdiction. The auditors carried out at least 2 rounds of analysis/review for each country in order to harmonize the grade results for all participating nations as much as possible, and based on the reviewers’ comments or suggestions, the country grades and information were either maintained or modified. Only after the approval by the auditors, the grades of each indicator and the rationale addressed for each country/locality moved on to the next stage of the project, which was the preparation of the reports for each country that are published on the AHKGA website (https://www.activehealthykids.org/) as well as the development of the Global Matrix. Additional information about the new edition of the Global Matrix project can be found by consulting the literature.10

The analysis conducted in this article had 3 sources of information. First, the grades and rationales approved by the auditors for each of the 57 countries/locations to analyze the indicators “Physical fitness,” “Family and Peers,” “School,” “Community and Environment,” and “Government.” Second, all the leaders of the 57 Report Cards were invited to provide information on the prevalence of the behavioral indicators (overall PA, organized sport and PA, active play, active transportation, and sedentary behavior) according to each subgroup analyzed (sex [male and female], age groups [children, 5–12 y old and adolescents, ≥13 y old], ability levels [with/without disabilities], and residence area [urban and rural]). The Report Card leaders reported the prevalence/grades based on their interpretation of the best evidence for each of the countries/localities. In the Supplementary Material S2 (available online) are the bibliographic references on which the Report Cards leaders relied on to submit their country/locality information. And third, information on the ability level breakdown for each indicator was also taken from a companion paper specific to data on children and adolescents with disabilities.11

Findings are presented stratified according to the HDI of each country/locality (very high: ≥0.800; high: 0.700–0.799; medium: 0.550–0.699; low < 0.550).12 Ethiopia was the only country/locality participating in the Global Matrix 4.0 that was classified as low HDI and was grouped with the medium HDI group. The evidence of the personal characteristics (“Physical fitness”) and sources of influence indicators (“Family and Peers,” “School,” “Community and Environment,” and “Government”) were analyzed according to the distribution of the 57 countries/localities and according to HDI. For England, Scotland, and Wales, the official data of the HDI are from the United Kingdom. For Basque Country, Region of Murcia, and Extremadura, the official data of the HDI are from Spain. For Chinese Taipei, the official data of the HDI are from China.

For statistical analyses, the grades for each indicator were converted into their respective numerals, as conducted previously by Aubert et al,6,10 which allows to perform descriptive and inferential analyses. For each subgroup analyzed in this study (eg, male and female, children and adolescents, living with/without disability, urban and rural areas), the median (interquartile range) were calculated. We used the median value of each subgroup of countries/localities according to HDI to assign the final grade. In addition, we used Spearman correlation coefficient (ρ) to verify the relationships between the respective numerals of the grades of the indicators and the HDI of the countries/localities according to subgroups. We adopted a significance level of 5%.

Results

Of the 57 countries/localities included in the Global Matrix 4.0 project, 31 provided information according to at least one of the investigated subgroups, 21 countries/localities provided information on indicators according to sex, 21 countries/localities provided information according to age, 8 countries/localities provided information according to area of residence, and 18 countries/localities provided information according to ability level (see Supplementary Material S3 [available online]).

Overall, males had higher grades for overall PA and organized sport and PA than females. Adolescents had higher grades than children for overall PA; however, children had better grades than adolescents for organized sport and PA, active play, and sedentary behavior indicators. Individuals living in urban areas had higher grades than those living in rural areas for active transportation and sedentary behavior indicators. Children and adolescents without disabilities had higher grades than those with disabilities for overall PA, organized sport and PA, active play, and sedentary behavior indicators (Table 1).

Table 1

Grades of Behavioural Indicators According to Subgroups

SubgroupsOverall PAOrganized sport and PAActive playActive transportationSedentary behavior
GradeMedian (IQR)GradeMedian (IQR)GradeMedian (IQR)GradeMedian (IQR)GradeMedian (IQR)
Sex
 MaleC−7.0 (5.0–9.0)C+9.0 (7.3–11.0)C8.0 (4.0–8.0)C+8.5 (7.0–11.3)D+5.5 (3.5–7.3)
 FemaleD+6.0 (4.0–9.0)C8.0 (6.0–9.0)C7.5 (3.5–8.3)C+8.5 (7.0–11.0)D+6.0 (3.5–8.0)
Age groups
 ChildrenD+5.5 (2.0–10.5)C+8.5 (6.0–11.0)D+6.0 (3.5–11.3)B−10.0 (7.0–12.7)C−7.0 (4.0–7.5)
 AdolescentsC8.0 (4.0–12.0)C−6.5 (5.3–9.0)F2.0 (2.0–11.5)B−10.0 (6.5–13.5)D+6.0 (3.0–10.5)
Residence area
 UrbanD+6.0 (5.0–8.0)C−7.0 (5.0–10.0)D−3.0 (2.0–12.3)C+8.5 (6.5–12.0)D+5.5 (2.8–12.8)
 RuralD+6.4 (4.3–7.8)C−7.0 (4.3–8.5)D−3.0 (2.0–4.7)D5.0 (3.5–10.5)F2.0 (2.0–8.0)
Disabilities
 NoD+6.0 (5.0–10.0)C−7.0 (6.0–8.0)C−7.0 (2.0–10.0)C+9.0 (8.0–12.0)C−7.0 (4.0–10.0)
 YesD-4.0 (2.0–5.0)F2.0 (2.0–6.0)F2.0 (2.0–5.5)D−4.0 (3.5–5.0)D−4.0 (2.0–6.0)

Abbreviations: IQR, interquartile range (percentiles 25–75); PA, physical activity. Note: Global Matrix 4.0 Physical Activity Report Cards for children and adolescents.

When comparing grades of males according to the HDI of countries/localities, it was observed that those living in countries with low/medium HDI performed better than those living in countries/localities with very high and high HDI for organized sport and PA, active play, active transportation, and sedentary behavior indicators. Among female children and youth, those living in countries/localities with low/medium HDI performed better than those living in countries/localities with very high and high HDI for active play and sedentary behavior. On the other hand, females living in countries/localities with very high HDI performed better for the overall PA indicator than those living in other countries/localities. Comparing the grades by sex, no differences between males and females were evident for overall PA, active play, and sedentary behaviors among very high HDI countries. Also, no differences by sex were observed for active transportation and sedentary behavior among high HDI countries (Table 2).

Table 2

Difference Between Countries for Behavioral Indicators According to Sex and Age Groups

Indicators/countriesMale

(n = 21)
Female

(n = 21)
Children

(n = 21)
Adolescents

(n = 21)
GradeMedian (IQR)GradeMedian (IQR)GradeMedian (IQR)GradeMedian (IQR)
Overall PA
 Very high human developmentC8.0 (4.3–10.7)C7.5 (2.0–9.0)D5.0 (2.0–12.5)C−6.5 (2.0–10.8)
 High human developmentC−7.0 (5.0–7.0)D5.0 (4.0–7.0)C−6.5 (3.0–7.0)D5.0 (4.0–12.0)
 Medium/low human developmentC7.5 (7.0–8.0)C−7.0 (6.0–7.0)F2.0 (2.0–2.0)A13.5 (2.0–12.0)
Organized sport and PA
 Very high human developmentB−9.5 (8.0–11.0)C+8.5 (8.0–10.0)B11.0 (9.0–11.0)C−7.0 (5.3–9.0)
 High human developmentC−7.0 (6.0–9.5)D+6.0 (5.0–6.5)D+6.0 (3.0–7.5)D+6.0 (2.0–6.0)
 Medium/low human developmentA−13.0 (13.0–13.0)C+9.0 (9.0–9.0)F2.0 (2.0–2.0)A+15.0 (15.0–15.0)
Active play
 Very high human developmentC8.0 (7.0–8.0)C8.0 (6.3–8.3)C+9.0 (6.0–15.0)*D+6.0 (2.0–11.5)
 High human developmentD−4.0 (2.0–4.0)F2.0 (2.0–2.0)D−4.0 (2.0–4.0)F2.0 (2.0–2.0)
 Medium/low human developmentA−13.0 (13.0–13.0)C+9.0 (9.0–9.0)F2.0 (2.0–2.0)A+15.0 (15.0–15.0)
Active transportation
 Very high human developmentC+8.5 (7.2–11.5)C8.0 (7.0–11.5)B10.5 (7.3–13.8)C+9.0 (5.5–12.5)
 High human developmentC+8.5 (7.2–0.5)C+9.0 (8.2–10.5)B−10.0 (7.0–10.0)B−10.0 (5.3–14.0)
 Medium/Low human developmentB10.5 (7.0–15.0)C+9.0 (7.0–9.0)F2.0 (2.0–2.0)A−12.5 (10.0–12.5)
Sedentary behavior
 Very high human developmentD+5.5 (2.5–7.7)D+6.0 (2.5–8.0)C−7.0 (4.0–9.0)D+6.0 (2.0–8.0)
 High human developmentD+5.5 (2.8–6.8)D+5.5 (2.8–8.3)C−7.0 (4.8–7.0)D+5.5 (2.8–12.8)
 Medium/low human developmentC+9.0 (4.0–9.0)C−7.0 (5.0–7.0)D−3.5 (2.0–3.5)B−9.5 (4.0–9.5)

Abbreviations: IQR, interquartile range (percentiles 25–75); PA, physical activity. Note: Global Matrix 4.0 Physical Activity Report Cards for children and adolescents.

Children living in very high HDI countries/localities performed better than those living in high and medium/low HDI countries/localities for overall PA, participation in organized sport and PA, active play, and active transportation indicators. Adolescents living in countries/localities with medium/low HDI performed better than those living in countries/localities with very high and high HDI for all the behavioral indicators (Table 2).

When comparing the grades of children and adolescents living in urban areas according to the HDI of countries/localities, it was observed that those living in medium/low HDI countries/localities performed better than those living in very high and high HDI countries/localities across all behavioral indicators. Regarding children and adolescents living in rural areas, it was observed that those from countries/localities with very high HDI performed better than those from countries/localities with low/medium HDI for overall PA, organized sport and PA, active play, and active transportation indicators. Comparing grades between children living in urban and rural areas, children living in rural areas had higher grades than their counterparts for most of the indicators in very high and high HDI countries. While children living in urban settings of low and medium HDI had higher grades than their rural counterparts for all indicators (Table 3).

Table 3

Difference Between Countries for Behavioral Indicators According to Residence Area and Ability Status

Urban

(n = 8)
Rural

(n = 8)
Without disabilities

(n = 18)
With disabilities

(n = 18)
Indicators/countriesGradeMedian (IQR)GradeMedian (IQR)GradeMedian (IQR)GradeMedian (IQR)
Overall PA
 Very high human developmentD+6.0 (6.0–6.0)C−7.0 (7.0–7.0)B−10.0 (7.0–12.0)D−4.0 (2.0–5.0)
 High human developmentD5.0 (4.3–5.8)D+6.0 (5.0–10.0)D5.0 (5.0–5.0)
 Medium/low human developmentB+11.5 (8.0–15.0)D4.5 (2.0–7.0)D+6.0 (6.0–6.0)
Organized sport and PA
 Very high human developmentB−10.0 (10.0–10.0)B−10.0 (10.0–10.0)C8.0 (8.0–10.0)D4.5 (2.0–7.0)
 High human developmentD+6.0 (3.5–7.5)C−6.5 (2.3–8.0)D+6.0 (6.0–7.0)F2.0 (2.0–2.0)
 Medium/low human developmentB−10.0 (10.0–10.0)D5.0 (5.0–5.0)C−7.0 (7.0–7.0)F2.0 (2.0–2.0)
Active play
 Very high human developmentD−4.0 (4.0–4.0)D5.0 (5.0–5.0)C+9.0 (7.0–11.0)D−3.5 (2.0–6.0)
 High human developmentF2.0 (2.0–2.0)D−3.0 (2.0–4.0)F2.0 (2.0–2.0)
 Medium/low human developmentA+15.0 (15.0–15.0)F2.0 (2.0–2.0)C+9.0 (9.0–9.0)F2.0 (2.0–2.0)
Active transportation
 Very high human developmentD5.0 (2.0–8.0)D5.0 (5.0–5.0)B+12.0 (6.0–13.0)D−4.0 (4.0–5.0)
 High human developmentC+9.0 (8.0–11.0)B10.5 (10.0–11.0)C+8.5 (8.0–9.0)
 Medium/low human developmentA+15.0 (15.0–15.0)F2.0 (2.0–2.0)C8.0 (8.0–8.0)F2.0 (2.0–2.0)
Sedentary behavior
 Very high human developmentC+9.0 (7.0–11.0)D4.5 (2.0–6.3)
 High human developmentD5.0 (2.0–6.0)D5.0 (2.0–8.0)D−4.0 (2.0–6.0)
 Medium/low human developmentA+15.0 (15.0–15.0)F2.0 (2.0–2.0)C8.0 (8.0–8.0)F2.0 (2.0–2.0)

Abbreviations: IQR, interquartile range (percentiles 25–75); PA, physical activity. Note: Global Matrix 4.0 Physical Activity Report Cards for children and adolescents.

For countries/localities that disaggregated information by the ability level of children and adolescents, it was observed that countries with very high HDI performed better than countries with high and low/medium HDI for overall PA, organized sport and PA, active play, active transportation, and sedentary behavior indicators (Table 3).

For the Physical Fitness indicator, no information was found for children and adolescents living in low/medium HDI countries/localities. Children and adolescents living in very high HDI countries/localities had higher grades for Physical Fitness than those living in high HDI countries/localities. Regarding Family and Peers indicator children and adolescents from low/medium HDI countries/localities performed better than those from other countries/localities. In relation to the school, community and environment, and government indicators, countries/localities with very high HDI performed better than other countries/localities (Table 4).

Table 4

Difference Between Countries for the Physical Fitness, Family and Peers, School, Community and Environment, and Government Indicators

Physical fitness

(n = 26)
Family and peers

(n = 44)
School

(n = 52)
Community and environment

(n = 48)
Government

(n = 53)
CountriesGradeMedian

(IQR)
GradeMedian

(IQR)
GradeMedian

(IQR)
GradeMedian

(IQR)
GradeMedian

(IQR)
Very high human developmentC8.0 (7.0–9.0)C−7.0 (6.0–10.0)B11.0 (8.0–12.0)B11.0 (8.0–12.0)B−10.0 (8.0–11.0)
High human developmentD+6.0 (3.0–9.0)C−7 (7.0–10.0)C−7.0 (5.0–11.0)C−7.0 (5.0–8.0)C8.0 (5.0–10.0)
Medium/low human developmentC+9 (2.0–15.0)C8.0 (8.0–12.0)C−7.0 (6.0–8.0)C−7.0 (3.0–9.0)

Abbreviation: IQR, interquartile range (percentiles 25–75). Note: Global Matrix 4.0 Physical Activity Report Cards for children and adolescents.

We found a direct association (P < .05) between the grades of the organized sport and PA indicators and HDI of the countries/localities for the subgroups female (ρ = .65), children (ρ = .69), rural residents (ρ = .88), without disabilities (ρ = .74), and with disability (ρ = .67). For the active play indicator, the grades of the rural residents were directly associated with the HDI of the countries/localities (ρ = .94, P < .01). For the active transportation indicator, the grades of the urban residents were inversely associated with the HDI of the countries/localities (ρ = −.87, P < .01) (Table 5). In addition, the grades of the indicators community and environment (ρ = .62, P < .05) and government (ρ = .36, P < .05) were directly related to HDI of the countries/localities (Table 6).

Table 5

Association Between Human Development Index of the Countries/Localities and PA Indicators (Overall PA, Organized Sport and PA, Active Play, Active Transportation, and Sedentary Behavior) According to Subgroups

IndicatorsHuman development index
MaleFemaleChildrenAdolescentsUrbanRuralWithout disabilitiesWith disabilities
ρ
Overall PA.08−.02.08−.32−.63.61.69−.06
Organized sport and PA.37.65*.69*.31.29.88*.74*.67*
Active play.07.11.56−.18−.31.94*.15.22
Active transportation.02.02.18−.27−.87*.05.54.46
Sedentary behavior−.32−.40.13−.27−.63.00.15.57

Abbreviations: ρ, Spearman correlation coefficient; PA, physical activity. Note: Global Matrix 4.0 Physical Activity Report Cards for children and adolescents.

*P < .05.

Table 6

Association Between Human Development Index of the Countries/Localities and Physical Activity Indicators (Physical Fitness, Family and Peers, School, Community and Environment, and Government)

Human development index
Indicatorsρ
Physical fitness.09
Family and peers.01
School.20
Community and environment.62*
Government.36*

Abbreviation: ρ, Spearman correlation coefficient. Note: Global Matrix 4.0 Physical Activity Report Cards for children and adolescents.

*P < .05.

Discussion

The present study found a direct relationship between females’ involvement in organized sports and PA and countries’ HDI, which demonstrates that in countries with better HDI, females were more engaged in organized sports and PA. On the other hand, in countries with lower HDI, the engagement of females in organized sports and PA was lower. This result demonstrates that in countries/localities with lower HDI intrinsic sociocultural norms that girls face more barriers to engaging in PA such as issues with body image, lack of opportunities for PA, stereotypes, and prejudices toward girls being “sporty” and “active”13 seems to be more evident than in countries/localities with higher HDI. Combating these sex inequalities in low HDI countries and promoting the participation of females in organized sports involves joint actions from government, school, family, and society in general, supporting greater engagement of girls in organized sports and demystifying social prejudices since childhood.

Regarding differences by age group, the present study found that there are differences between the HDI of countries/localities and performance in Organized sports and PA indicator. In countries with very high and high HDI, there was a tendency for children to have higher grades in Organized sports and PA indicator. On the other hand, in countries/localities with low HDI, children’s performance for this indicator was lower. In general, elementary schools in very high and high HDI countries/localities have structures and opportunities for organized sports and PA, which are not observed for children in low-income countries.14

We found that children and adolescents living in urban areas of the countries/localities with low/medium HDI achieved better grades than those living in urban areas of the countries/localities with very high and high HDI for the Active transportation indicator. However, when analyzing only children and adolescents living in rural areas, the results were the opposite, in which those from very high HDI countries had higher grades than those from lower HDI countries for the Organized sport and PA and Active play indicators. Most of the evidence on this topic comes from countries with very high HDI and, therefore, results are largely inconclusive.15,16 From the results of the present study, it could be assumed that even rural areas of very high HDI countries/localities have more opportunities for PA than rural areas of low/medium HDI countries/localities and that social inequity is more accentuated in these locations than in urban areas. An alternate explanation is that the PA measurement questionnaires/instruments fail to capture the diversity of PA habitual to rural living in low/medium HDI countries for children, including active domestic chores and routine non-school-related active transportation. This study compiled information on children and adolescents living in urban and rural areas. The results found should be interpreted with caution because only 8 countries/localities provided information according to area of residence, and of these only 2 countries/localities (ie, India and Nepal) classified as with low/medium HDI presented information on these subgroups.

The present study showed that in countries/localities with higher HDI, the grades for the Organized sport and PA indicator were better than in countries/localities with lower HDI for both subgroups with and without disabilities. This inequality in the grades between countries/localities according to HDI can be justified because there are few policies to promote sport and PA for children and adolescents with disabilities17 and without disabilities10 in countries/localities with lower HDI. In this project, only 18 countries/localities provided information according to the ability level of children and adolescents, highlighting the need to investigate PA-related behaviors of children and adolescents with disabilities, although data were missing on almost half the indicators.11

For the sources of influence grades, the results of this study are concerning, because for community and environment, and government indicators, countries/localities with very high and high HDI reported better grades than those with medium/low HDI. These findings, though perhaps not surprising, are aligned with the socioecological model of behavior change,18 which reinforces the sources of influence as important factors for the promotion of PA. In addition, it was observed that lower HDI countries/localities need to prioritize contextual actions for PA, because without government actions such as changing the school environment and legislation, restructuring the neighborhood and schools, there is limited opportunity to promote PA at sufficient levels for health benefits.19

This study has important limitations that should be noted and corrected going forward. First, not all countries included in the Global Matrix 4.0 project presented information stratified by subgroups analyzed in the present study. This lack of information from many countries limits comparisons made for each of the behavioral indicators investigated. Therefore, our conclusions are a first approach to understanding the associations between PA indicators and HDI among subgroups, but we recognize that the limited availability of stratified data in several countries s does not allow generalizations.

Another limitation of this study is related to the small amount of information from countries of low/medium HDI. Of the 4 countries classified with HDI < 0.700 (Ethiopia, India, Nepal, and Zimbabwe), only India and Nepal provided information on any of the subgroups analyzed. The lack of information from a greater number of countries classified as low/medium HDI makes it difficult to confidently decipher PA differences around the world and may be a consequence of some contextual factors such as: (1) low investment in scientific research in countries of medium and low HDI20; (2) lack of transparency by government agents about the real health conditions of the population, as the highest levels of antidemocratic actions and corruption are observed in low- and middle-income countries21; (3) health priorities in these countries are still focused on combating communicable diseases, such as AIDS, malaria, yellow fever, and monitoring and intervention actions on risk factors for noncommunicable diseases and disorders, such as physical inactivity, are not yet prioritized.22

Another limitation of this study is the fact that the distribution of the Global Matrix 4.0 indicators according to other population subgroups such as socioeconomic level (high vs low), race/ethnicity, and weight status were not investigated. Disaggregated information would demonstrate the global panorama of PA for more specific regional actions to combat sedentary lifestyle and promote healthy lifestyles,1 as well as checking health inequities for the topic of PA at the local and global level. In preparing this article, Report Cards leaders were asked for results stratified by socioeconomic status of children and adolescents, but only 4 countries/localities provided such information (Canada, Colombia, Scotland, and Spain [Extermadura]), which limited comparisons.

The involvement of countries/localities from different continents, climates, and with different HDIs is clearly a strength of this project. The investigation of differences for behavioral indicators between population subgroups is a priority in health studies and for PA, this information is important to design intervention strategies at global level aimed at promoting healthy lifestyles since childhood. This study also contributed to identifying gaps that are still persistent in studies on PA inequities. One of these gaps is that there is still little evidence regarding differences between school-age children living in urban and rural areas and there is still little evidence about comparisons regarding the ability level of children and adolescents. Furthermore, it is suggested that studies on PA inequities should include other factors that were not included in this project, such as race/ethnicity, and family, socioeconomic status.

Conclusions

Patterns of PA indicators differed according to the HDI, and specific circumstance of individual countries/localities and these patterns should be considered when studying PA in children and adolescents. The most evident differences are that the subgroups of females, children, rural residents, with/without disabilities from countries/localities with very high and high HDI performed better in the organized sport and PA indicator than their peers from countries/localities with lower HDI. In addition, differences between urban and rural areas were observed, mainly showing that children and adolescents living in rural areas of countries/localities with higher HDI showed better performance than those living in other countries/localities for the active play indicator. Also, children and adolescents living in urban areas of countries/localities with lower HDI showed better performance than those living in countries/localities with higher HDI the active transportation indicator. Finally, countries/localities with higher HDI reported better indicators for sources of influence (community and environment and government) than those with lower HDI. We hope this preliminary exploration will prompt better surveillance and reporting going forward.

Acknowledgments

The authors are grateful for the involvement of all leaders and co-leaders from the 57 countries involved in the AHKGA’s Global Matrix 4.0 project. The authors are grateful for the involvement of the AHKGA for their collaboration with this paper. Dr. Silva was supported in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)—Brazil (Finance Code 001), and also received partial support from by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)—Brazil (309589/2021-5).

References

  • 1.

    World Health Organization. Global Action Plan on Physical Activity 2018–2030: More Active People for a Healthier World. World Health Organization; 2018.

    • Search Google Scholar
    • Export Citation
  • 2.

    Organisation for Economic Co-operation and Development (OECD). Slovenia: Country Health Profile 2019, State of Health in the EU. OECD Publishing, Paris/European Observatory on Health Systems and Policies; 2019. doi:10.1787/79ba70a2-en

    • Search Google Scholar
    • Export Citation
  • 3.

    Tremblay MS, Gray CE, Akinroye K, et al. Physical activity of children: a Global Matrix of grades comparing 15 countries. J Phys Act Health. 2014;11(suppl 1):S113S125. doi:10.1123/jpah.2014-0177

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 4.

    Tremblay MS, Barnes, J, Demchenko I, et al. Active healthy kids global alliance Global Matrix 4.0 — A resource for physical activity researchers. J Phys Act Health. Published online October 24, 2022. doi:10.1123/jpah.2022-0257

    • Search Google Scholar
    • Export Citation
  • 5.

    Tremblay MS, Barnes JD, González SA, et al. Global Matrix 2.0: report card grades on the physical activity of children and youth comparing 38 countries. J Phys Act Health. 2016;13(suppl 2):S343S366. doi:10.1123/jpah.2016-0594

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 6.

    Aubert S, Barnes JD, Abdeta C, et al. Global Matrix 3.0 physical activity report card grades for children and youth: results and analysis from 49 countries. J Phys Act Health. 2018;15(suppl 2):S251S273. PubMed ID: 30475137 doi:10.1123/jpah.2018-0472

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 7.

    Chaput JP, Willumsen J, Bull F, et al. 2020 WHO guidelines on physical activity and sedentary behaviour for children and adolescents aged 5–17 years: summary of the evidence. Int J Behav Nutr Phys Act. 2020;17(1):141. PubMed ID: 33239009 doi:10.1186/s12966-020-01037-z

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8.

    Aubert S, Brazo-Sayavera J, González SA, et al. Global prevalence of physical activity for children and adolescents; inconsistencies, research gaps, and recommendations: a narrative review. Int J Behav Nutr Phys Act. 2021;18(1):81. PubMed ID: 34187486 doi:10.1186/s12966-021-01155-2

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9.

    Salvo D, Garcia L, Reis RS, et al. Physical activity promotion and the United Nations sustainable development goals: building synergies to maximize impact. J Phys Act Health. 2021;18(10):11631180. PubMed ID: 34257157 doi:10.1123/jpah.2021-0413

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 10.

    Aubert S, Barnes J, Demchenko I, et al. Global Matrix 4.0 physical activity report card grades for children and adolescents: results and analysis from 57 countries. J Phys Act Health. Published online October 24, 2022. doi:10.1123/jpah.2022-0456

    • Search Google Scholar
    • Export Citation
  • 11.

    Ng K, Sit C, Arbour-Nicitopoulos K, et al. A Global Matrix of Para Report Cards for children and adolescents with disabilities. Adapt Phys Activ Q. Published online 2022.

    • Search Google Scholar
    • Export Citation
  • 12.

    United Nations Development Programme. Human Development Report 2020: The Next Frontier Human Development and the Anthropocene. Published 2020. https://hdr.undp.org/sites/default/files/hdr2020.pdf. Accessed March 25, 2022.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13.

    Ricardo LIC, Wendt A, Costa CDS, et al. Gender inequalities in physical activity among adolescents from 64 Global South countries. J Sport Health Sci. 2022:11(4):509520. PubMed ID: 35074485 doi:10.1016/j.jshs.2022.01.007

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 14.

    Nobre JNP, Morais RLS, Prat BV, et al. Physical environmental opportunities for active play and physical activity level in preschoolers: a multicriteria analysis. BMC Public Health. 2022;22(1):340. PubMed ID: 35177034 doi:10.1186/s12889-022-12750-8

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 15.

    Manyanga T, Pelletier C, Prince SA, Lee EY, Sluggett L, Lang JJ. A comparison of meeting physical activity and screen time recommendations between Canadian youth living in rural and urban communities: a nationally representative cross-sectional analysis. Int J Environ Res Public Health. 2022;19(7):4394. PubMed ID: 35410073 doi:10.3390/ijerph19074394

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 16.

    McCormack LA, Meendering J. Diet and physical activity in rural vs. urban children and adolescents in the United States: a narrative review. J Acad Nutr Diet. 2016;116(3):467480. PubMed ID: 26685123 doi:10.1016/j.jand.2015.10.024

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17.

    Sit C, Aubert S, Carty C, et al. Promoting physical activity among children and adolescents with disabilities: the translation of policy to practice internationally. J Phys Act Health. Published online October 24, 2022. doi:10.1123/jpah.2022-0351

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 18.

    Lee Y, Park S. Understanding of physical activity in social ecological perspective: application of multilevel model. Front Psychol. 2021;12:622929. PubMed ID: 33746840 doi:10.3389/fpsyg.2021.622929

    • Search Google Scholar
    • Export Citation
  • 19.

    Lambert EV, Kolbe-Alexander T, Adlakha D, et al. Making the case for “physical activity security:” the 2020 WHO guidelines on physical activity and sedentary behaviour from a Global South perspective. Br J Sports Med. 2020;54(24):14471448. PubMed ID: 33239348 doi:10.1136/bjsports-2020-103524

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 20.

    Acharya KP, Pathak S. Applied research in low-income countries: why and how? Front Res Metr Anal. 2019;4:3. PubMed ID: 33870035 doi:10.3389/frma.2019.00003

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 21.

    Becherair A, Tahtane M. The causality between corruption and human development in MENA countries: a panel data analysis. East-West J Econ Bus. 2017;20(2):6384.

    • Search Google Scholar
    • Export Citation
  • 22.

    Conalogue DM, Kinn S, Mulligan JA, McNeil M. International consultation on long-term global health research priorities, research capacity and research uptake in developing countries. Health Res Policy Syst. 2017;15(1):24. PubMed ID: 28327164 doi:10.1186/s12961-017-0181-0

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Collapse
  • Expand
  • 1.

    World Health Organization. Global Action Plan on Physical Activity 2018–2030: More Active People for a Healthier World. World Health Organization; 2018.

    • Search Google Scholar
    • Export Citation
  • 2.

    Organisation for Economic Co-operation and Development (OECD). Slovenia: Country Health Profile 2019, State of Health in the EU. OECD Publishing, Paris/European Observatory on Health Systems and Policies; 2019. doi:10.1787/79ba70a2-en

    • Search Google Scholar
    • Export Citation
  • 3.

    Tremblay MS, Gray CE, Akinroye K, et al. Physical activity of children: a Global Matrix of grades comparing 15 countries. J Phys Act Health. 2014;11(suppl 1):S113S125. doi:10.1123/jpah.2014-0177

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 4.

    Tremblay MS, Barnes, J, Demchenko I, et al. Active healthy kids global alliance Global Matrix 4.0 — A resource for physical activity researchers. J Phys Act Health. Published online October 24, 2022. doi:10.1123/jpah.2022-0257

    • Search Google Scholar
    • Export Citation
  • 5.

    Tremblay MS, Barnes JD, González SA, et al. Global Matrix 2.0: report card grades on the physical activity of children and youth comparing 38 countries. J Phys Act Health. 2016;13(suppl 2):S343S366. doi:10.1123/jpah.2016-0594

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 6.

    Aubert S, Barnes JD, Abdeta C, et al. Global Matrix 3.0 physical activity report card grades for children and youth: results and analysis from 49 countries. J Phys Act Health. 2018;15(suppl 2):S251S273. PubMed ID: 30475137 doi:10.1123/jpah.2018-0472

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 7.

    Chaput JP, Willumsen J, Bull F, et al. 2020 WHO guidelines on physical activity and sedentary behaviour for children and adolescents aged 5–17 years: summary of the evidence. Int J Behav Nutr Phys Act. 2020;17(1):141. PubMed ID: 33239009 doi:10.1186/s12966-020-01037-z

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 8.

    Aubert S, Brazo-Sayavera J, González SA, et al. Global prevalence of physical activity for children and adolescents; inconsistencies, research gaps, and recommendations: a narrative review. Int J Behav Nutr Phys Act. 2021;18(1):81. PubMed ID: 34187486 doi:10.1186/s12966-021-01155-2

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 9.

    Salvo D, Garcia L, Reis RS, et al. Physical activity promotion and the United Nations sustainable development goals: building synergies to maximize impact. J Phys Act Health. 2021;18(10):11631180. PubMed ID: 34257157 doi:10.1123/jpah.2021-0413

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 10.

    Aubert S, Barnes J, Demchenko I, et al. Global Matrix 4.0 physical activity report card grades for children and adolescents: results and analysis from 57 countries. J Phys Act Health. Published online October 24, 2022. doi:10.1123/jpah.2022-0456

    • Search Google Scholar
    • Export Citation
  • 11.

    Ng K, Sit C, Arbour-Nicitopoulos K, et al. A Global Matrix of Para Report Cards for children and adolescents with disabilities. Adapt Phys Activ Q. Published online 2022.

    • Search Google Scholar
    • Export Citation
  • 12.

    United Nations Development Programme. Human Development Report 2020: The Next Frontier Human Development and the Anthropocene. Published 2020. https://hdr.undp.org/sites/default/files/hdr2020.pdf. Accessed March 25, 2022.

    • Search Google Scholar
    • Export Citation
  • 13.

    Ricardo LIC, Wendt A, Costa CDS, et al. Gender inequalities in physical activity among adolescents from 64 Global South countries. J Sport Health Sci. 2022:11(4):509520. PubMed ID: 35074485 doi:10.1016/j.jshs.2022.01.007

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 14.

    Nobre JNP, Morais RLS, Prat BV, et al. Physical environmental opportunities for active play and physical activity level in preschoolers: a multicriteria analysis. BMC Public Health. 2022;22(1):340. PubMed ID: 35177034 doi:10.1186/s12889-022-12750-8

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 15.

    Manyanga T, Pelletier C, Prince SA, Lee EY, Sluggett L, Lang JJ. A comparison of meeting physical activity and screen time recommendations between Canadian youth living in rural and urban communities: a nationally representative cross-sectional analysis. Int J Environ Res Public Health. 2022;19(7):4394. PubMed ID: 35410073 doi:10.3390/ijerph19074394

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 16.

    McCormack LA, Meendering J. Diet and physical activity in rural vs. urban children and adolescents in the United States: a narrative review. J Acad Nutr Diet. 2016;116(3):467480. PubMed ID: 26685123 doi:10.1016/j.jand.2015.10.024

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 17.

    Sit C, Aubert S, Carty C, et al. Promoting physical activity among children and adolescents with disabilities: the translation of policy to practice internationally. J Phys Act Health. Published online October 24, 2022. doi:10.1123/jpah.2022-0351

    • Search Google Scholar
    • Export Citation
  • 18.

    Lee Y, Park S. Understanding of physical activity in social ecological perspective: application of multilevel model. Front Psychol. 2021;12:622929. PubMed ID: 33746840 doi:10.3389/fpsyg.2021.622929

    • Search Google Scholar
    • Export Citation
  • 19.

    Lambert EV, Kolbe-Alexander T, Adlakha D, et al. Making the case for “physical activity security:” the 2020 WHO guidelines on physical activity and sedentary behaviour from a Global South perspective. Br J Sports Med. 2020;54(24):14471448. PubMed ID: 33239348 doi:10.1136/bjsports-2020-103524

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 20.

    Acharya KP, Pathak S. Applied research in low-income countries: why and how? Front Res Metr Anal. 2019;4:3. PubMed ID: 33870035 doi:10.3389/frma.2019.00003

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 21.

    Becherair A, Tahtane M. The causality between corruption and human development in MENA countries: a panel data analysis. East-West J Econ Bus. 2017;20(2):6384.

    • Search Google Scholar
    • Export Citation
  • 22.

    Conalogue DM, Kinn S, Mulligan JA, McNeil M. International consultation on long-term global health research priorities, research capacity and research uptake in developing countries. Health Res Policy Syst. 2017;15(1):24. PubMed ID: 28327164 doi:10.1186/s12961-017-0181-0

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 2925 1369 71
PDF Downloads 1418 490 35