Abstract
To support the global strategy to reduce risk factors for obesity, we synthesized the evidence on physical activity (PA) and sedentary behaviour in the Middle East and North Africa (MENA) region. Our systematic overview included seven systematic reviews reporting 229 primary studies. The meta-analysis included 125 prevalence measures from 20 MENA countries. After 2000, 50.8% of adults (ranging from 13.2% in Sudan to 94.9% in Jordan) and 25.6% of youth (ranging from 8.3% in Egypt to 51.0% in Lebanon) were sufficiently active. Limited data on PA behaviours is available for MENA countries, with the exception of Gulf Cooperation Council countries. The meta-regression identified gender and geographical coverage among youth, and the PA measurement as predictors of PA prevalence for both adults and youth. Our analysis suggests a significant PA prevalence increase among adults over the last two decades. The inconsistency in sedentary behaviour measurement is related to the absence of standardized guidelines for its quantification and interpretation. The global epidemic of insufficient PA is prevalent in MENA. Lower PA participation among youth and specifically females should be addressed by focused lifestyle interventions. The recognition of sedentary behaviour as a public health issue in the region remains unclear. Additional data on PA behaviours is needed from low- and middle-income countries in the region.
Subject terms: Epidemiology, Risk factors
Introduction
Non-communicable diseases (NCDs) kill 41 million people worldwide each year – equivalent to 71% of all deaths1. The Middle East and North Africa (MENA) region has one of the highest rates of NCDs in the world. In 2017, the region reported the second highest prevalence of diabetes in the world (10.8%)2 and is recording a rapid increase in obesity3,4. Insufficient physical activity (PA) and sedentary behaviour are key risk factors for obesity and other NCDs5–15 leading to premature mortality10,11,16–18. It has been suggested that PA has the potential to effectively control and reduce the burden of obesity during the various phases of human development19. Regular PA can also improve self-esteem, cognitive performance, and academic achievement in young people7,20,21 and is positively related to cardiorespiratory and metabolic health6. Recently, sedentary behaviour has received global attention as prolonged sedentary time is associated with an increased risk of chronic disease and an increase in all-cause mortality, regardless of individuals meeting the recommended levels of PA13,15.
The World Health Organization (WHO) and the Global Observatory for Physical Activity (GoPA) are targeting a relative reduction of 10% in the global prevalence of physical inactivity among adults by 202522,23. Currently, one of the most pressing needs to improve population health is to develop appropriate policies and implement interventions to address the global pandemic of physical inactivity24,25. However, to support this action, country-level evidence on PA behaviour in various population groups is essential. Both regional-and country-level data contribute to the continuous surveillance of PA participation and are essential to track progress towards the global PA target.
In the MENA region, the proportion of the population not engaging in sufficient levels of PA (as per the recommendation of standardized international guidelines) remains unclear. Recent reports indicate physical inactivity prevalence measures of 32.8% for adults across the MENA and Central Asia regions26 and 78.4% in boys and 84.4% in girls globally25. However, gender-stratified measures in adults and youth are needed to develop evidence-based interventions informed by local data.
The aim of our study is to: 1) synthesize evidence from published systematic reviews (SRs) on PA behaviour in MENA countries, 2) quantify country-specific PA prevalence measures and assess demographic variations among youth and the general adult population within the region, 3) summarize measurement variations of PA and sedentary behaviour in the region, and 4) identify research gaps and provide specific recommendations pertaining to PA for the region. We tested the following null hypotheses that there is no difference in the PA prevalence between males and females, before and after 2000, and among nationals and non-nationals in the Gulf Cooperation Council countries (GCC).
Methods
We conducted a systematic overview of published SRs on PA and sedentary behaviour in the MENA region. The current review is a co-product of a protocol planned for a systematic overview reporting the grey literature in systematic reviews on population health in the Middle East and North Africa (PROSPERO registration number CRD42017076736)27,28. This manuscript follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Supplementary, Table S1)29, and the Preferred Reporting Items for Overviews of Systematic Reviews (PRIO-harms) tool (Supplementary, Table S2).
Search strategy and selection criteria
A broad search strategy was developed to systematically identify any type of review on all health issues in any MENA country. Search terms related to countries’ names, MENA populations’ names, and MENA sub-regions’ names, such as North Africa, East Africa, and the Middle East, were used. No restrictions to a specific health condition or intervention and language of publication were applied at this stage. The full search strategy with search criteria is available in the published overview protocol27. Two independent reviewers (AA and HA) systematically searched the Medical Literature Analysis and Retrieval System Online (MEDLINE) through the search engine PubMed. We included publications since 2008 – the publication year of the first version of the Cochrane Handbook for Systematic Reviews of Interventions30 up to February 21, 2017. Additionally, we also searched grey and non-grey literature sources with no date or language restriction including Google Scholar, Epistemonikos, ProQuest, OpenGrey, Bioline International, E-Marefa, ALMANHAL platform, governmental websites of all MENA countries, and the WHO website for systematic reviews relevant to our topic. The literature search was then updated to identify recent SRs published up to November 2019.
A systematic review (SR) was defined as a literature review that has explicitly used a systematic literature search of at least one electronic database to identify all studies that meet pre-defined eligibility criteria along with a study selection30. Reviews not reporting a systematic methodology, such as narrative reviews, were excluded.
Based on the relevance of grey literature in the region when studying population health outcomes27,31, we included MENA countries where Arabic, English, French, and/or Urdu are the primary official languages and/or the medium of instruction in the colleges/universities. These languages are also those spoken by the authors of this overview (see the overview’s protocol27). The 20 MENA countries included are Algeria, Bahrain, Djibouti, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Pakistan, Palestine, Qatar, Saudi Arabia, Sudan, Syria, Tunisia, the United Arab Emirates (UAE), and Yemen.
Data screening
Records were downloaded into Endnote (version X8.2), and duplicates were removed. Using Rayyan software32, two independent reviewers (AA and HA) conducted the multi-stage screening following a standard process. Discrepancies in the inclusion of SRs were resolved through discussion, with the involvement of a third reviewer (AA, HA, and KC).
Retrieved SRs were then categorized based on the reported population health outcome. For the purpose of this overview, we included any SR reporting measurable PA-related outcomes including physical activity or inactivity prevalence and/or sedentary behaviour pertaining to MENA population of any age group residing in a MENA country.
Data extraction
Data extraction was conducted by SC1 and checked for accuracy by KC. Extracted data included characteristics of the included SRs as well as the primary studies and were matched to PICOTS items (Population, Outcomes, Time of the study and Setting; Control and Intervention were not applicable to our overview question). From each included SR, the following characteristics were extracted: literature search terms and time period, geographical coverage, data literature sources, MENA countries with retrieved data, along with the number of included studies, inclusion and exclusion criteria, targeted review population, and reported PA-related outcomes. From each primary study included in a SR, the following characteristics were collected: study design and sample size, sampling method, study setting, years of data collection, population characteristics (type, age, gender, proportion of males and females, and response rate), and all PA-related outcomes (definition, measurement tool/administration, prevalence, and barriers to PA). In case of discordance between the reported data by the SR and data available in the primary study, the latter was retained. A consensus meeting with SC1, KC, and SC2 was held to resolve any disagreements.
In order to assess the methodological quality of the included primary studies and conduct the quantitative analyses, any relevant study characteristics not reported by the SR were extracted from the primary study publication. In addition, any additional data on PA-related outcomes from a MENA country found in an included primary study but not reported by the SR (usually not part of the objectives) was extracted and reported.
Qualitative synthesis
The qualitative synthesis was done at two levels:
Qualitative synthesis of the SRs’ data
A summary of the geographical coverage, the methodology used by each SR, the main conclusions on reported outcomes, limitations and strengths, research gaps, and recommendations from each SR were synthesized in Supplementary, Tables S3 and S4. A synthesis of the methodological quality of the included SRs and our own overview was conducted.
Qualitative synthesis of the primary studies data
The characteristics of primary studies that reported PA in the SRs were synthesized. These included physical inactivity prevalence measures and/or sedentary behaviour data among youth (≤19-year-old) and adults (>19-years-old). If these were not reported, the prevalence of the outcome among the total study population (males and/or females) was calculated using raw data available in the primary study.
We synthesized the measurement tools and definitions of PA, physical inactivity and sedentary behaviour used by the primary studies as well as the characteristics of the study population and the official primary language of the MENA country where the study was conducted. These characteristics were contrasted with the population and linguistic validation parameters of each used tool. A measurement tool was considered to be validated in a specific population or language if a validation record in the same population or language was retrieved in the literature. The objective of this synthesis was to review the variability in the outcome measurements and appropriateness of their use in the primary study population33.
Quantitative synthesis
By definition, a participant who does not meet the PA recommendations of 150 min of moderate physical activity/week or equivalent for adults and 60 min of moderate to vigorous PA daily for youth or equivalent is considered physically inactive and vice versa. Hence, in all included studies reporting physical inactivity outcomes, participants who did not meet the physical inactivity criteria were considered meeting the recommended level for PA. These measures were included in the meta-analysis as a PA-prevalence measure as long as they were not overlapping with other included data in the meta-analysis. The “poor level”, “mild level” or “low score” of PA was reported in some studies where there was a low PA participation level (lower than standard thresholds)34–36. These PA prevalence measures were considered as physical inactivity prevalence measures. All physical inactivity prevalence measures along with reasons for conversion or non-conversion to a PA prevalence measures are listed in Supplementary, Table S5. When two levels of PA (moderate and vigorous) were reported in the same study34,36, both levels were merged to one level of PA.
A meta-analysis of PA prevalence (proportion of participants meeting recommended levels of PA as defined by the primary studies) was conducted for the MENA countries for which data was available. The GCC subregion includes the following countries: Bahrain, Qatar, Oman, Kuwait, Saudi Arabia, and UAE. The PA-prevalence measures stratified by gender and age were prioritized for the inclusion in the meta-analysis rather than the overall measures for the entire study population. As there is no consensus on the standard definition and a threshold to quantify sedentary behaviour, no meta-analysis was conducted for this outcome. A publication of a primary study included in more than one SR and/or publications with overlapping data points were included once and all the replicates were excluded from the meta-analysis. A list of primary studies excluded from the meta-analysis with reasons is detailed in Supplementary, Table S6.
A subgroup meta-analysis of PA participation by gender (males and females) and year of data collection (before 2000 and after 2000) was then conducted for the MENA and GCC regions and for each MENA country with available data. A high proportion of the population residing in the GCC is non-national37; while in the other MENA countries, the populations are predominantly nationals. Therefore, meta-analyses according to the type of population (national population and mixed populations of nationals and non-nationals) were relevant for GCC countries only.
The threshold of data collection time was established at the year 2000 based on the demographic, socioeconomic, migration background in the GCC countries37,38, urbanization, and lifestyle transition observed in many MENA countries by the beginning of the 21st century39–42. For the PA prevalence measures where the year of data collection was missing, we considered this as prior to the publication year and the prevalence measure was then classified accordingly for the analyses.
If not reported, the total or strata sample sizes were calculated based on the percentage of males and females in the sample. Random effects modelling was used to conduct the meta-analyses. We generated forest plots using the ‘meta/metaprop’ package in R (version 3.5.0) and inspected them visually to assess the variability of prevalence estimates across subgroups. Furthermore, the heterogeneity of the prevalence estimates was assessed using: (1) the I2 statistic, (2) the Cochran’s Q statistic, and (3) the Cochran’s Q between-subgroups. The I2 statistic was used to describe the percentage of variability in the prevalence estimates resulting from the between-study heterogeneity rather than chance43. The statistical significance (p-value < 0.05) of the Cochran’s Q statistic was used to test for evidence of the overall heterogeneity between prevalence estimates44. The statistical significance (p-value < 0.05) of the Cochran’s Q between-subgroups statistic was used to test for differences between prevalence estimates across subgroups45. The prediction interval estimated the 95% interval in which the true PA prevalence in a new PA prevalence study will lie.
To continue exploring the statistical heterogeneity, univariable random-effects meta-regression analyses were conducted to identify associations of higher PA prevalence and sources of between-study heterogeneity. Associations were assessed using odds ratios (ORs), 95% Confidence Intervals (CIs), and t-tests. Relevant covariates were specified a priori and included in the models separately: gender (males vs. females), years of data collection (after 2000 vs. before 2000), geographical coverage (country level vs. local level), and PA measurement tool (standard tools vs. non-standard tools). Standard PA measurement tools included: 1) the pedometer, accelerometer and continuous heart rate monitor (gold standard tool), and 2) validated PA questionnaires among adults and youth included the International Physical Activity Questionnaire (IPAQ), the Global Physical Activity Questionnaire (GPAQ), the Arab Teens Lifestyle Student Questionnaire (ATLS), the Nurses’ Health Study II Activity and Inactivity Questionnaire (NHS II-PAQ), “How physically active are you” questionnaire, the Health Behaviour in School-aged Children Survey (HBSC), and the Adolescent Physical Activity Measure Questionnaire (PACE+)46. Any other used measurement tool that was not included in the National Institutes of Health (NIH) Database of Standardized Questionnaires About Walking & Bicycling or does not reference to a published validation study of the used tool was considered as non-validated measurement tool.
Factors with p-value <0.05 in the univariate meta-regression were considered as statistically significant covariates. These covariates were explored separately in the adult and youth MENA general populations. Meta-analyses and meta-regressions were conducted using the ‘meta/metareg’ package in the R software (version 3.5.0).
Methodological quality assessment
The methodological quality of the included SRs and primary studies was assessed by two independent reviewers.
The AMSTAR measurement tool47 was used by two independent reviewers (SC1 and AA) to perform the quality assessment of the included SRs. Discrepancies were resolved through discussion, with the involvement of a third reviewer (SC1, AA, and KC) when necessary. The methodological quality of the included SRs was then discussed according to the 11 criteria of the AMSTAR checklist48.
At primary study-level, an adapted quality assessment checklist was developed based on the PICOTS framework49 and other published tools utilized for similar contexts50–55. Our quality assessment checklist included criteria related to outcome definition, outcome measurement, population characteristics, and sampling method since the quality of PA and sedentary behavior measures are directly related to the study’s quantification method and its population characteristics. We utilized the guidelines of the World Health Organization (WHO), the Physical Activity Guidelines Advisory Committee, the Centers for Disease Control and Prevention (CDC), the American College of Sports Medicine, and the Sedentary Behavior Research Network (SBRN) to establish a list of standard definitions and valid methods utilized for PA-participation assessment. The detailed checklist and the definition of each criterion and the scoring system is presented in Supplementary, Table S7. Each of the seven included criteria was rated with a maximum score of three (0 = “Not defined”, 3 = “Clearly defined and appropriate”). Hence, a maximum quality score of 21 can be reported for one assessed PA-related outcome. The PA-related outcomes in each population subgroup were evaluated and scored by SC1 and checked by KC. Any discrepancies were resolved by discussion with the involvement of a third reviewer (SC1, KC, and SC2). The higher the quality score that was reported, the better the methodological quality of the reported outcome.
Results
A total of seven SRs and 229 primary studies on the epidemiology of PA, physical inactivity, and/or sedentary behaviour in at least one of the 20 selected MENA countries were included in the qualitative synthesis of this overview (Fig. 1). After exclusion of primary studies included in more than one SR (n = 15), primary studies with overlapping data (n = 8), primary studies reporting only sedentary behaviour data or barriers to PA data (n = 65), and those using non-standard definitions for PA or inactivity, excluded from the meta-analyses (n = 59), a total of 82 studies, including 125 PA prevalence measures were considered for the quantitative analyses.
Figure 1.
PRISMA 2009 flowchart of the systematic reviews inclusion.
Characteristics of the included systematic reviews
The seven SRs included in our overview are described in Supplementary, Table S3. The summary of qualitative results, strengths and limitations, research gaps, and recommendations are synthesized in Supplementary, Table S4. PA-related outcomes were found for all the 20 MENA countries. The physical inactivity and sedentary behaviour measures were the primary outcomes in three SRs54. The other SRs reported these outcomes only if included in the primary studies on PA. No SR restricted the primary study inclusion to one specific definition of PA. All the identified data was based on the general population. Of note, only Yammine et al.56 conducted a meta-analysis of prevalence measures of different levels of PA among UAE adolescents. All the included SRs excluded clinical populations and three SR53,57,58 excluded primary studies based on their methodological quality of sampling and measurement methodology.
Methodological quality assessment of the included systematic reviews and primary studies
No SR reported the list of excluded studies, assessed publication bias, or included the source of funding for the SR itself and of the included primary studies, as per the AMSTAR recommendations48 (Table 1). Two SRs56,58 searched for grey literature sources (Table 1).
Table 1.
Quality assessment of the included systematic reviews and the actual overview using AMSTAR checklist.
| Systematic review | 1.Was an ‘a priori’ design provided? | 2.Was there duplicate study selection and data extraction? | 3.Was a comprehensive literature search performed? | 4.Was the status of publication (i.e. grey literature) used as an inclusion criterion? | 5.Was a list of studies (included and excluded) provided? | 6.Were the characteristics of the included studies provided? | 7.Was the scientific quality of the included studies assessed and documented? | 8.Was the scientific quality of the included studies used appropriately in formulating conclusions? | 9.Were the methods used to combine the findings of studies appropriate? | 10.Was the likelihood of publication bias assessed? | 11.Was the conflict of interest included? † |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Sisson, 200888 | No | No | No | No | No | Yes | No | No | N/A | N/A | No |
| Mabry, 201053 | No | No | Yes | No | No | Yes | Yes | Yes | N/A | N/A | No |
| Ranasinghe, 201354 | No | Yes | Yes | No | No | Yes | Yes | No | N/A | N/A | No |
| Yammine, 201656 | No | No | Yes | Yes | No | Yes | No | No | Yes | No | No |
| Mabry, 201652 | No | Yes | No | No | No | Yes | Yes | Yes | N/A | N/A | No |
| Al-Hazzaa, 201857 | No | No | Yes | No | No | Yes | No | No | N/A | N/A | No |
| Sharara, 201858 | Yes | No | Yes | Yes | No | Yes | No | No | N/A | N/A | Yes |
| Current Overview | Yes | Yes | Yes | N/A | Yes | Yes | Yes | Yes | Yes | No | Yes |
Notes: We used the AMSTAR checklist with additional notes made by Michelle Weir, Julia Worswick, and Carolyn Wayne based on conversations with Bev Shea and/or Jeremy Grimshaw in June and October 2008 and July and September 2010. Available on https://amstar.ca/docs/AMSTARguideline.pdf
The absence of a statement regarding any criteria of the quality assessment was considered as not done. The unit of analysis for the systematic reviews is the study. The unit of analysis for the actual overview is the systematic review.
Abbreviations: N/A: Not applicable;
† To get a ‘yes’ for the included SRs, the conflict of interest should be clearly acknowledged for the SR and the included original studies.
To get a ‘yes’ for the actual overview, the conflict of interest should be clearly acknowledged for the actual overview and all included SRs.
The checklist used for the quality assessment of the included studies is presented in Supplementary, Table S5. The quality assessment scores of each reported PA related outcome are reported in Supplementary, Tables S8–S11. The methodological quality scores varied between 11 and 20 out of a maximum score of 21. Most of the reported outcomes had a quality score closer to the upper score limit. Most of the primary studies were cross-sectional clearly describing the population characteristics and study setting, used PA definition consistent with international recommendations, utilized validated measurement tools, used probability-based sampling method, had an equal male and female ratio, and country-level coverage.
Overview of studies with physical activity data
Forty-seven primary studies with 75 PA prevalence measures on adults and thirty-five primary studies with 50 PA prevalence measures on youth were included in the meta-analysis. The study characteristics and PA prevalence measures among the adult and youth population are summarized in Supplementary, Tables S8 and S9.
For both populations, limited data on PA was reported in MENA countries with the exception of the GCC countries (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and UAE). In most included studies pertaining to GCC countries, the distinction between the national and non-national population was unclear. A wide range of PA prevalence measures were reported for the MENA region. A higher PA prevalence measure among males than females was observed for all included MENA countries except among adults in Lebanon and Jordan. Considering all retrieved data (before and after 2000) among adult males, the highest pooled prevalence measures of sufficient PA participation was found in Jordan [94.2% (95% CI = 93.1–95.2%) and the lowest in Bahrain [23.1% (95% CI = 20.9–25.5%)]. Among adult females, the highest pooled prevalence measures of sufficient PA participation were found in Jordan [95.5% (95% CI = 94.4–96.4%)], and the lowest in Bahrain 1.3% (95% CI = 0.7–2.3%) (Table 2).
Table 2.
Meta-analysis of physical activity prevalence in MENA countries among the adult general population.
| Studied subgroups | Years of data collection | Number of prevalence measures | Total sample size | Prevalence range (%)a | Effect size | Subgroup comparison | Heterogeneity between studies | |||
|---|---|---|---|---|---|---|---|---|---|---|
| Weighted average prevalence (%)b | 95% CI (%) |
Q between-subgroups test p-value | Cochrane Q test’s p-value |
I² (confidence limits) | 95% Prediction interval (%) | |||||
| MENA | ||||||||||
| All* | 1995–<2017 | 75 | 124,702 | 1.3–95.5 | 47.9 | 40.8–55.0 | N/A | 0.0000 | 99.8% (99.8–99.8%) | 1.8–96.9 |
| Malesc | 1995–<2015 | 29 | 49,343 | 6.1–94.2 | 54.1 | 42.3–65.7 | 0.2286 | 0.0000 | 99.9% (99.8–99.9%) | 2.1–99.7 |
| Femalesc | 1995–<2016 | 32 | 44,167 | 1.3–95.5 | 43.6 | 31.7–55.9 | 0.0000 | 99.8% (99.8–99.9%) | 0.0–98.8 | |
| <2000 | 1995–<2000 | 4 | 19,593 | 1.3–23.1 | 6.2 | 1.7–13.3 | <0.0001 | <0.0001 | 99.6% (99.4–99.7%) | 0.0–56.0 |
| >2000 | 2002–<2017 | 71 | 105,109 | 5.2–95.5 | 50.8 | 45.6–56.0 | 0.0000 | 99.7% (99.6–99.7%) | 11.5–89.6 | |
| GCC | ||||||||||
| All* | 1995–2017 | 49 | 72,489 | 1.3–69.2 | 41.7 | 33.7–50 | N/A | 0.0000 | 99.8% (99.8–99.8%) | 0.9–93.1 |
| Malesc | 1995–<2015 | 19 | 34,270 | 6.1–69.2 | 47 | 33.1–61.2 | 0.3836 | 0.0000 | 99.8% (99.8–99.9%) | 0.4–98.3 |
| Femalesc | 1995–<2016 | 22 | 27,256 | 1.3–65.7 | 38.6 | 26.6–51.3 | 0.0000 | 99.8% (99.7–99.8%) | 0.0–93.9 | |
| <2000 | 1995–2000 | 4 | 19,593 | 1.3–23.1 | 6.2 | 1.7–13.3 | <0.0001 | <0.0001 | 99.6% (99.4–99.7%) | 0.0–56.0 |
| >2000 | 2000–2017 | 45 | 52,896 | 13.3–69.2 | 45.7 | 40.5–51.0 | 0.0000 | 99.3% (99.2–99.4%) | 13.8–79.8 | |
| National population | 1995–<2014 | 23 | 44,711 | 1.3–68.0 | 36.8 | 25.9–48.4 | 0.1737 | 0.0000 | 99.8% (99.8–99.8%) | 0.1–90.8 |
| National and non-national populations | <1996–<2016 | 26 | 27,778 | 13.3–69.2 | 46.2 | 38.9–53.6 | 0.0000 | 99.2% (99.1–99.3%) | 11.6–83.1 | |
| Saudi Arabia | ||||||||||
| All* | 1995–2017 | 30 | 52,221 | 1.9–69.2 | 39.9 | 28.8–51.5 | N/A | 0.0000 | 99.8% (99.8–99.9%) | 0.0–95.7 |
| Malesc | 1995–<2015 | 11 | 25,990 | 6.1–69.2 | 42.9 | 22.7–64.4 | 0.8969 | 0.0000 | 99.9% (99.9–99.9%) | 0.0–100.0 |
| Femalesc | 1995–<2016 | 14 | 18,145 | 1.9–65.7 | 41 | 24.5–58.6 | 0.0000 | 99.8% (99.7–99.8%) | 0.0–98.8 | |
| <2000 | 1995–2000 | 2 | 17,395 | 1.9–6.1 | 3.7 | 0.7–9.0 | <0.0001 | <0.0001 | 99.5% (99.2–99.7%) | N/A |
| >2000 | 2003–2017 | 28 | 34,826 | 13.3–69.2 | 43.3 | 36.0–50.8 | 0.0000 | 99.4% (99.4–99.5%) | 8.7–82.2 | |
| National population | 1995–<2014 | 10 | 30,296 | 1.9–65.7 | 29.5 | 16.5–44.4 | 0.0712 | 0.0000 | 99.8% (99.8–99.9%) | 0.0–85.2 |
| National and non-national populations | 2005–2017 | 20 | 21,925 | 13.3–69.2 | 45.2 | 36.5–54.1 | 0.0000 | 99.3% (99.1–99.4%) | 9.3–84.4 | |
| UAE | ||||||||||
| All* | 2000–2003 | 5 | 2,459 | 43.2–67.4 | 57.8 | 49.1–66.3 | N/A | < 0.0001 | 94.4% (89.6–96.9%) | 25.2–87.0 |
| Malesc | 2002–2003 | 2 | 1,646 | 62.1–66.5 | 64.3 | 59.9–68.6 | <0.0001 | 0.0621 | 71.3% (0.0–93.5%) | N/A |
| Femalesc | 2002–2003 | 2 | 638 | 43.2–49.4 | 46.3 | 40.3–52.4 | 0.1165 | 59.4% (0.0%–90.5%) | N/A | |
| <2000 | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2000–2003 | 5 | 2,459 | 43.2–67.4 | 57.8 | 49.1–66.3 | <0.0001 | 94.4% (89.6–96.9%) | 25.2–87.0 | |
| National population | 2003 | 2 | 1,180 | 49.4–66.5 | 58.2 | 41.2–74.2 | 0.9558 | <0.0001 | 96.5% (90.5–98.7%) | N/A |
| National and non-national populations | 2000–2003 | 3 | 1,279 | 43.2–67.4 | 57.6 | 44.0–70.6 | <0.0001 | 94.9% (88.4–97.7%) | 0.0–100.0 | |
| Oman | ||||||||||
| All | 2008 | 2 | 3,137 | 59.5–68.0 | 63.8 | 55.3–71.9 | N/A | < 0.0001 | 95.9% (88.4–98.6%) | N/A |
| Males | 2008 | 1 | 1,459 | N/A | 68.0 | 65.5–70.4 | N/A | N/A | N/A | N/A |
| Females | 2008 | 1 | 1,678 | N/A | 59.5 | 57.1–61.8 | N/A | N/A | N/A | |
| <2000 | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2008 | 2 | 3,137 | 59.5–68.0 | 63.8 | 55.3–71.9 | <0.0001 | 95.9% (88.4–98.6%) | N/A | |
| National population | 2008 | 2 | 3,137 | 59.5–68.0 | 63.8 | 55.3–71.9 | N/A | < 0.0001 | 95.9% (88.4–98.6%) | N/A |
| National and non-national populations | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | |
| Bahrain | ||||||||||
| All | 1995–1996 | 2 | 2,198 | 1.3–23.1 | 9.3 | 0.0–40.2 | N/A | < 0.0001 | 99.7% (99.5–99.8%) | N/A |
| Males | 1995–1996 | 1 | 1,297 | N/A | 23.1 | 20.9–25.5 | N/A | N/A | N/A | |
| Females | 1995–1996 | 1 | 901 | N/A | 1.3 | 0.7–2.3 | N/A | N/A | N/A | |
| <2000 | 1995–1996 | 2 | 2,198 | 1.3–23.1 | 9.3 | 0.0–40.2 | N/A | < 0.0001 | 99.7% (99.5–99.8%) | N/A |
| >2000 | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | |
| National population | 1995–1996 | 2 | 2,198 | 1.3–23.1 | 9.3 | 0.0–40.2 | N/A | < 0.0001 | 99.7% (99.5–99.8%) | N/A |
| National and non-national populations | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | |
| Qatar | ||||||||||
| All* | 2003–<2015 | 5 | 4,382 | 39.5–62.6 | 49.3 | 41.5–57.1 | N/A | < 0.0001 | 96.3% (93.7–97.8%) | 20.6–78.2 |
| Malesc | 2003 | 2 | 1537 | 49.1–62.6 | 56.0 | 42.7–68.9 | 0.0782 | <0.0001 | 96.0% (88.9–98.6%) | N/A |
| Femalesc | 2003–2012 | 2 | 2,113 | 39.5–45.8 | 42.8 | 36.7–49 | 0.0058 | 86.8% (48.2% 96.7%) | N/A | |
| <2000 | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2003–<2015 | 5 | 4,382 | 39.5–62.6 | 49.3 | 41.5–57.1 | <0.0001 | 96.3% (93.7–97.8%) | 20.6–78.2 | |
| National population | 2003–2012 | 4 | 3,650 | 39.5–62.6 | 49.3 | 39.4–59.3 | 0.9982 | <0.0001 | 97.2% (95.1–98.4%) | 8.3–90.9 |
| National and non-national populations | <2015 | 1 | 732 | N/A | 49.3 | 45.7–52.9 | N/A | N/A | N/A | |
| Kuwait | ||||||||||
| All* | 2006–2014 | 5 | 8,092 | 27.2–48.6 | 36.5 | 28.7–44.7 | N/A | < 0.0001 | 98.2% (97.3–98.9%) | 9.8–68.8 |
| Malesc | 2006–2014 | 2 | 2341 | 42.1–48.6 | 45.4 | 39.1–51.9 | <0.0001 | 0.0020 | 89.6% (61.2%–97.2%) | N/A |
| Femalesc | 2006–2014 | 2 | 3,781 | 27.2–28.4 | 27.7 | 26.2–29.1 | 0.4233 | 0.0% | N/A | |
| <2000 | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2006–2014 | 5 | 8,092 | 27.2–48.6 | 36.5 | 28.7–44.7 | <0.0001 | 98.2% (97.3–98.9%) | 9.8–68.8 | |
| National population | 2006–<2013 | 3 | 4,250 | 28.4–42.1 | 35.7 | 28.5–43.2 | 0.8676 | <0.0001 | 96.0% (91.4–98.1%) | 0.0–100.0 |
| National and non-national populations | 2014 | 2 | 3,842 | 27.2–48.6 | 37.6 | 18.3–59.3 | N/A | N/A | N/A | |
| Pakistan | ||||||||||
| All* | 2002–<2010 | 4 | 6,144 | 30.2–87.2 | 61.0 | 41.4–78.9 | N/A | < 0.0001 | 99.5% (99.3–99.6%) | 0.0–100.0 |
| Males | 2002–<2010 | 2 | 3,114 | 47.9–87.2 | 69.5 | 27.7–97.7 | 0.5557 | <0.0001 | 99.5% (99.2–99.7%) | N/A |
| Females | 2002–<2010 | 2 | 3,030 | 30.2–72.7 | 51.7 | 13.2–89.1 | <0.0001 | 99.4% (99.0–99.7%) | N/A | |
| <2000 | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2002–<2010 | 4 | 6,144 | 30.2–87.2 | 61.0 | 41.4–78.9 | <0.0001 | 99.5% (99.3–99.6%) | 0.0–100.0 | |
| Tunisia | ||||||||||
| All* | 2002–2009 | 3 | 6,212 | 55.6–89.0 | 76.8 | 55.9–92.5 | N/A | < 0.0001 | 99.7% (99.6–99.8%) | 0.0–100.0 |
| Malesc | 2002–2003 | 1 | 2,149 | N/A | 89.0 | 87.6–90.3 | N/A | N/A | N/A | N/A |
| Femalesc | 2002–2003 | 1 | 2,183 | N/A | 81.8 | 80.1–83.4 | N/A | N/A | N/A | |
| <2000 | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2002–2009 | 3 | 6,212 | 55.6–89.0 | 76.8 | 55.9–92.5 | <0.0001 | 99.7% (99.6–99.8%) | 0.0–100.0 | |
| Lebanon | ||||||||||
| All* | 2000–2014 | 4 | 5,461 | 24.0–59.8 | 41.5 | 27.0–56.7 | N/A | < 0.0001 | 99.2% (98.9–99.5%) | 0.0–98.6 |
| Malesc | 2008–2009 | 1 | 893 | N/A | 47.8 | 44.5–51.2 | N/A | N/A | N/A | N/A |
| Femalesc | 2008–2009 | 1 | 1,089 | N/A | 59.8 | 56.8–62.7 | N/A | N/A | N/A | N/A |
| <2000 | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2000–2014 | 4 | 5,461 | 24.0–59.8 | 41.5 | 27.0–56.7 | N/A | < 0.0001 | 99.2% (98.9–99.5%) | 0.0–98.6 |
| Morocco | ||||||||||
| All* | 2008 | 3 | 5,233 | 54.6–83.5 | 69.3 | 50.6–85.2 | N/A | < 0.0001 | 99.5% (99.2–99.6%) | 0.0–100.0 |
| Malesc | 2008 | 1 | 1,359 | N/A | 67.6 | 65.1–70.1 | N/A | N/A | N/A | N/A |
| Femalesc | 2008 | 1 | 1,261 | 54.6 | 51.8–57.4 | N/A | N/A | N/A | ||
| <2000 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2008 | 3 | 5,233 | 54.6–83.5 | 69.3 | 50.6–85.2 | <0.0001 | 99.5% (99.2–99.6%) | 0.0–100.0 | |
| Egypt | ||||||||||
| All* | 2012 | 1 | 5,300 | N/A | 67.9 | 66.6–69.2 | N/A | N/A | N/A | N/A |
| Males | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Females | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| <2000 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2012 | 1 | 5,300 | N/A | 67.9 | 66.6–69.2 | N/A | N/A | N/A | |
| Palestine | ||||||||||
| All* | 2010–2011 | 1 | 6,957 | N/A | 53.5 | 52.3–54.7 | N/A | N/A | N/A | N/A |
| Males | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Females | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| <2000 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2010–2011 | 1 | 6,957 | N/A | 53.5 | 52.3–54.7 | N/A | N/A | N/A | N/A |
| Sudan | ||||||||||
| All* | 2005–2006 | 2 | 1,573 | 5.2–24.2 | 13.2 | 0.8–36.7 | N/A | N/A | N/A | N/A |
| Males | 2005–2006 | 1 | 652 | N/A | 24.2 | 21.0–27.7 | N/A | N/A | N/A | N/A |
| Females | 2005–2006 | 1 | 921 | N/A | 5.2 | 3.9–6.9 | N/A | N/A | N/A | N/A |
| <2000 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2005–2006 | 2 | 1,573 | 5.2–24.2 | 13.2 | 0.8–36.7 | N/A | N/A | N/A | N/A |
| Algeria | ||||||||||
| All* | 2003 | 2 | 4,101 | 54.2–67.5 | 61.0 | 47.7–73.5 | N/A | N/A | N/A | N/A |
| Males | 2003 | 1 | 1,599 | N/A | 67.5 | 65.2–69.8 | N/A | N/A | N/A | N/A |
| Females | 2003 | 1 | 2,502 | N/A | 54.2 | 52.3–56.2 | N/A | N/A | N/A | |
| <2000 | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2003 | 2 | 4,101 | 54.2–67.5 | 61.0 | 47.7–73.5 | N/A | N/A | N/A | N/A |
| Libya | ||||||||||
| All* | 2009 | 2 | 3,590 | N/A | 56.2 | 40.8–71.1 | N/A | N/A | N/A | N/A |
| Males | 2009 | 1 | 1,800 | N/A | 64.0 | 61.7–66.2 | N/A | N/A | N/A | N/A |
| Females | 2009 | 1 | 1,790 | N/A | 48.3 | 46.0–50.7 | N/A | N/A | N/A | |
| <2000 | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2009 | 2 | 3,590 | N/A | 56.2 | 40.8–71.1 | N/A | N/A | N/A | N/A |
| Jordan | ||||||||||
| All* | 2007 | 2 | 3,654 | N/A | 94.9 | 93.5–96.1 | N/A | N/A | N/A | N/A |
| Males | 2007 | 1 | 1,939 | N/A | 94.2 | 93.1–95.2 | N/A | N/A | N/A | N/A |
| Females | 2007 | 1 | 1,715 | N/A | 95.5 | 94.4–96.4 | N/A | N/A | N/A | N/A |
| <2000 | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2007 | 2 | 3,654 | N/A | 94.9 | 93.5–96.1 | N/A | N/A | N/A | N/A |
| Iraq | ||||||||||
| All* | 2015 | 2 | 3,988 | N/A | 52.6 | 28.4–76.2 | N/A | N/A | N/A | N/A |
| Males | 2015 | 1 | 1,568 | N/A | 65.1 | 62.7–67.5 | N/A | N/A | N/A | N/A |
| Females | 2015 | 1 | 2,420 | N/A | 40.0 | 38.0–42.0 | N/A | N/A | N/A | N/A |
| <2000 | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2015 | 2 | 3,988 | N/A | 52.5 | 28.4–76.2 | N/A | N/A | N/A | N/A |
Notes: Mean effect size reported as weighted average prevalence measure with their corresponding 95% confidence interval (CI); the p-value of the Cochran’s Q statistic is used to inform about the statistical significance of the heterogeneity in PA prevalence estimates within the group; the p-value of the Cochran’s Q between-subgroups statistic is used to inform about the statistical significance of the heterogeneity in PA prevalence estimates between the results of subgroups if applicable; the I2 is used to assess the magnitude of between-study variation that is due to differences in PA prevalence estimates across studies rather than chance; the prediction interval is used to estimate the 95% interval in which the true PA prevalence in a new PA prevalence study will lie. No meta-analyses according to the time of data collection were then performed. p-value <0.05 was considered statistically significant. Meta-analyses according to the type of population (nationals and non-national populations) were only relevant in the GCC countries as per the type of setting of the GCC population. Years of data collection were considered as prior to the publication year in studies where this information was missing. The prevalence measures were then classified accordantly for the meta-analyses. <2000: this analysis includes all studies where the data was collected before 2000. > 2000: this analysis includes all studies where the data was collected before 2000.
Symbols:
*Include converted data from physical inactivity to physical activity not overlapping with other included data.
aStratified prevalence measures were used to conduct the meta-analyses.
bWeighted average prevalence measures were obtained using random-effect model.
cExcluded the studies with a mixed male and female population.
The highest pooled prevalence measures of sufficient PA participation among youth males was observed in Kuwait [70.4% (95% CI = 66.0–74.5%)] and the lowest in Egypt [14.0% (95% CI = 12.5–15.6%)]. The highest pooled prevalence measures of sufficient PA participation among youth females was observed in Kuwait 39.3% (95% CI = 34.7–44.0%) and the lowest in Egypt 4.0% (95% CI = 3.1–5.1. %) (Table 3).
Table 3.
Meta-analysis of physical activity prevalence in MENA countries among the youth general population.
| Studied subgroups | Years of data collection | Number of prevalence measures | Total sample size |
Prevalence range (%) a | Effect size | Subgroup comparison | Heterogeneity between studies | |||
|---|---|---|---|---|---|---|---|---|---|---|
| Weighted average prevalence (%) b | 95% CI (%) |
Q between-subgroups test p-value |
Cochrane Q test’s p-value |
I² (confidence limits) | 95% Prediction interval (%) | |||||
| MENA | ||||||||||
| All* | <1993-<2018 | 50 | 78,915 | 4.0–79.9 | 25.4 | 21.8–29.1 | N/A | 0.0000 | 99.3% (99.2–99.3%) | 4.9–54.7 |
| Malesc | <1993-<2015 | 19 | 20,811 | 14.0–79.9 | 37.1 | 28.8–45.8 | <0.0001 | 0.0000 | 99.4% (99.3–99.5%) | 5.2–77.7 |
| Femalesc | 2004-<2018 | 16 | 18,606 | 4.0–52.2 | 16.5 | 12.2–21.2 | <0.0001 | 98.5% (98.2–98.8%) | 2.1–40.4 | |
| <2000 | <1993- <2000 | 1 | 212 | N/A | 15.1 | 10.6–20.6 | 0.0014 | N/A | N/A | N/A |
| >2000 | 2002-<2018 | 49 | 78,703 | 4.0–79.9 | 25.6 | 22.0–29.4 | 0.0000 | 99.3% (99.2–99.4%) | 5.0–55.0 | |
| GCC | ||||||||||
| All* | <1993-<2018 | 25 | 34,323 | 4.0–79.9 | 33.3 | 26.7–40.3 | N/A | 0.0000 | 99.4% (99.4–99.5%) | 5.1–71.0 |
| Malesc | <1993-<2015 | 12 | 12,522 | 15.1–79.9 | 48.9 | 36.7–61.2 | 0.0002 | <0.0000 | 99.4% (99.3–99.5%) | 7.1–91.7 |
| Femalesc | 2004-<2018 | 9 | 10,637 | 4.0–52.2 | 21.7 | 15.1–29.1 | <0.0001 | 98.5% (98.0–98.9%) | 2.6–52.1 | |
| <2000 | <1993 | 1 | 212 | N/A | 15.1 | 10.6–20.6 | <0.0001 | N/A | N/A | N/A |
| >2000 | 2004-<2018 | 24 | 34,111 | 4.0–79.9 | 34.1 | 27.3–41.3 | 0.0000 | 99.5% (99.4–99.5%) | 5.4–72.0 | |
| National population | 2005–2010 | 5 | 4,110 | 21.9–70.4 | 47.7 | 29.3–66.6 | 0.0771 | <0.0001 | 99.3% (99.0–99.5%) | 0.0–99.8 |
| National and non-national populations | <1993- <2018 | 20 | 30,213 | 4.0–79.9 | 29.9 | 23.4–36.8 | 0.0000 | 99.4% (99.3–99.5%) | 4.7–64.9 | |
| Saudi Arabia | ||||||||||
| All* | <1993-<2018 | 9 | 6,884 | 4.0–79.9 | 34.1 | 19.7–50.1 | N/A | < 0.0001 | 99.4% (99.3–99.5%) | 0.0–89.7 |
| Malesc | <1993-<2015 | 6 | 4,360 | 15.1–79.9 | 47.2 | 33.0–61.7 | 0.0007 | <0.0001 | 98.9% (98.4–99.2%) | 4.6–92.8 |
| Femalesc | 2009-<2018 | 3 | 2,524 | 4.0–21.9 | 12.2 | 3.1–26.1 | <0.0001 | 98.6% (97.5–99. 2%) | 0.0–100.0 | |
| <2000 | <1993 | 1 | 212 | N/A | 15.1 | 10.6–20.6 | 0.0083 | N/A | N/A | N/A |
| >2000 | 2005-<2018 | 8 | 6,672 | 4.0–79.9 | 36.7 | 21.1–53.9 | <0.0001 | 99.5% (99.4–99.6%) | 0.0–92.8 | |
| National population | 2005–2010 | 3 | 3,204 | 21.9–55.5 | 42.9 | 19.2–68.4 | 0.4506 | <0.0001 | 99.5% (99.2–99.7%) | 0.0–100.0 |
| National and non-national populations | <1993- <2018 | 6 | 3,680 | 4.0–79.9 | 29.8 | 11.0–53.1 | <0.0001 | 99.5% (99.4–99.6%) | 0.0–99.1 | |
| UAE | ||||||||||
| All*** | 2004–2010 | 7 | 14,447 | 18.0–74.5 | 35.0 | 24.7–46.1 | N/A | < 0.0001 | 99.4% (99.2–99.5%) | 4.6–75.2 |
| Malesc | 2004–2010 | 3 | 6,206 | 21.4–74.5 | 44.2 | 17.1–73.3 | 0.3889 | <0.0001 | 99.7% (99.6–99.8%) | 0.0–100.0 |
| Femalesc | 2004–2010 | 3 | 6,203 | 18.0–52.2 | 29.0 | 12.9–48.4 | <0.0001 | 99.2% (98.7–99.5%) | 0.0–100.0 | |
| <2000 | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2004–2010 | 7 | 14,447 | 18.0–74.5 | 35.0 | 24.7–46.1 | <0.0001 | 99.4% (99.2–99.5%) | 4.6–75.2 | |
| National population | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| National and non-national populations | 2004–2010 | 7 | 14,447 | 18.0–74.5 | 35.0 | 24.7–46.1 | <0.0001 | 99.4% (99.2–99.5%) | 4.6–75.2 | |
| Oman | ||||||||||
| All** | 2005–2015 | 5 | 6,428 | 11.7–66.7 | 28.8 | 14.6–45.6 | N/A | < 0.0001 | 99.4% (99.2–99.5%) | 0.0–90.6 |
| Malesc | 2005–2010 | 2 | 1,493 | 34.0–66.7 | 50.3 | 19.8–80.6 | 0.0532 | <0.0001 | 99.2% (98.4–99.6%) | N/A |
| Femalesc | 2005–2010 | 2 | 1,467 | 16.0–23.1 | 19.3 | 12.8–26.7 | 0.0016 | 90.0% (63.2–97.3%) | N/A | |
| <2000 | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2005–2015 | 5 | 6,428 | 11.7–66.7 | 28.8 | 14.6–45.6 | <0.0001 | 99.4% (99.2–99.5%) | 0.0–90.6 | |
| National population | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| National and non-national populations | 2005–2015 | 5 | 6,428 | 11.7–66.7 | 28.8 | 14.6–45.6 | <0.0001 | 99.4% (99.2–99.5%) | 0.0–90.6 | |
| Kuwait | ||||||||||
| All* | 2009–2015 | 3 | 4,543 | 17.1–70.4 | 41.4 | 11.7–75.2 | N/A | < 0.0001 | 99.7% (99.5–99.8%) | 0.0–100.0 |
| Malesc | 2009 | 1 | 463 | N/A | 70.4 | 66.0–74.5 | N/A | N/A | N/A | N/A |
| Femalesc | 2009 | 2 | 443 | N/A | 39.3 | 34.7–44.0 | N/A | N/A | N/A | |
| <2000 | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2009–2015 | 3 | 4,543 | 17.1–70.4 | 41.4 | 11.7–75.2 | <0.0001 | 99.7% (99.5–99.8%) | 0.0–100.0 | |
| National population | 2009 | 2 | 906 | 39.3–70.4 | 55.1 | 25.1–83.2 | 0.0099 | <0.0001 | 98.9% (97.8–99.5%) | N/A |
| National and non-national populations | 2015 | 1 | 3637 | N/A | 17.1 | 15.9–18.3 | N/A | N/A | N/A | |
| Lebanon | ||||||||||
| All* | 2009–2011 | 2 | 3154 | 34.6–67.4 | 51.0 | 20.4–81.3 | N/A | < 0.0001 | 99.6% (99.4–99.8%) | N/A |
| Malesc | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Femalesc | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | |
| <2000 | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2009–2011 | 2 | 3154 | 34.6–67.4 | 51.0 | 20.4–81.3 | <0.0001 | 99.6% (99.4–99.8%) | N/A | |
| Palestine | ||||||||||
| All* | 2002–2010 | 4 | 13,784 | 18.3–24.2 | 21 | 18.4–23.7 | N/A | < 0.0001 | 89.7% (76.5–95.5%) | 10.4–34.1 |
| Malesc | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Femalesc | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | |
| <2000 | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2002–2010 | 4 | 13,784 | 18.3–24.2 | 21 | 18.4–23.7 | <0.0001 | 89.7% (76.5–95.5%) | 10.4–34.1 | |
| Iraq | ||||||||||
| All* | 2012 | 1 | 2,038 | N/A | 20.0 | 18.3–21.8 | N/A | N/A | N/A | N/A |
| Malesc | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Femalesc | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | |
| <2000 | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2012 | 1 | 2,038 | N/A | 20.0 | 18.3–21.8 | N/A | N/A | N/A | |
| Qatar | ||||||||||
| All* | 2011 | 1 | 2,021 | N/A | 15.0 | 13.5–16.5 | N/A | N/A | N/A | N/A |
| Malesc | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Femalesc | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | |
| <2000 | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2011 | 1 | 2,021 | N/A | 15.0 | 13.5–16.5 | N/A | N/A | N/A | |
| National population | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| National and non-national populations | 2011 | 1 | 2,021 | N/A | 15.0 | 13.5–16.5 | N/A | N/A | N/A | |
| Morocco | ||||||||||
| All** | 2006–2010 | 4 | 4,659 | 13.0–20.8 | 16.6 | 12.6–21 | N/A | < 0.0001 | 93.3% (86.2–96.8%) | 2.2–40.5 |
| Malesc | 2006–2010 | 2 | 2,546 | 20.0–20.8 | 20.5 | 19.0–22.1 | <0.0001 | 0.6257 | 0.0% | N/A |
| Femalesc | 2006–2010 | 2 | 2,113 | 13.0–13.3 | 13.2 | 11.7–14.6 | 0.8581 | 0.0% | N/A | |
| <2000 | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2006–2010 | 4 | 4,659 | 13.0–20.8 | 16.6 | 12.6–21 | <0.0001 | 93.3% (86.2–96.8%) | 2.2–40.5 | |
| Libya | ||||||||||
| All** | 2007 | 2 | 2,242 | 11.6–21.5 | 16.2 | 7.8–27.0 | N/A | < 0.0001 | 97.5% (93.8–99.0%) | N/A |
| Malesc | 2007 | 1 | 1,123 | N/A | 21.5 | 19.1–24.0 | N/A | N/A | N/A | N/A |
| Femalesc | 2007 | 1 | 1,119 | N/A | 11.6 | 9.8–13.6 | N/A | N/A | N/A | |
| <2000 | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2007 | 2 | 2,242 | 11.6–21.5 | 16.2 | 7.8–27.0 | <0.0001 | 97.5% (93.8–99.0%) | N/A | |
| Jordan | ||||||||||
| All** | 2004–2007 | 4 | 3,916 | 11.0–20.0 | 15.9 | 12.1–20.1 | N/A | < 0.0001 | 91.8% (82.2–96.2%) | 2.2–38.6 |
| Malesc | 2004–2007 | 2 | 1,938 | 18.2–20.0 | 19.0 | 17.3–20.8 | 0.0078 | 0.3143 | 1.2% | N/A |
| Femalesc | 2004–2007 | 2 | 1,978 | 11.0–14.9 | 12.9 | 9.3–17.0 | 0.0098 | 85% (39.1–96.3%) | N/A | |
| <2000 | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2004–2007 | 4 | 3,916 | 11.0–20.0 | 15.9 | 12.1–20.1 | <0.0001 | 91.8% (82.2–96.2%) | 2.2–38.6 | |
| Sudan | ||||||||||
| All* | 2012 | 1 | 2,211 | N/A | 11 | 9.7–12.4 | N/A | N/A | N/A | N/A |
| Malesc | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Femalesc | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | |
| <2000 | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2012 | 1 | 2,211 | N/A | 11 | 9.7–12.3 | N/A | N/A | N/A | |
| Egypt | ||||||||||
| All** | 2006 | 2 | 3,664 | 4.0–14.0 | 8.3 | 1.3–20.5 | N/A | < 0.0001 | 99.2% (98.4–99.6%) | N/A |
| Malesc | 2006 | 1 | 1,975 | N/A | 14.0 | 12.5–15.6 | 0.0000 | N/A | N/A | N/A |
| Femalesc | 2006 | 1 | 1,689 | N/A | 4.0 | 3.1–5.1 | N/A | N/A | N/A | |
| <2000 | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2006 | 2 | 3,664 | 4.0–14.0 | 8.3 | 1.3–20.5 | <0.0001 | 99.2% (98.4–99.6%) | N/A | |
| Djibouti | ||||||||||
| All** | 2007 | 2 | 1,777 | 9.2–18.8 | 13.6 | 5.6–24.4 | N/A | < 0.0001 | 97.1% (92.3–98.9%) | N/A |
| Malesc | 2c007 | 1 | 707 | N/A | 18.8 | 16.0–21.9 | <0.0000 | N/A | N/A | N/A |
| Females | 2007 | 1 | 1,070 | N/A | 9.2 | 7.5–11.0 | N/A | N/A | N/A | |
| <2000 | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2007 | 2 | 1,777 | 9.2–18.8 | 13.6 | 5.6–24.4 | <0.0001 | 97.1% (92.3–98.9%) | N/A | |
| Syria | ||||||||||
| All* | 2010 | 1 | 3,102 | N/A | 15.1 | 13.8–16.4 | N/A | N/A | N/A | N/A |
| Malesc | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Femalesc | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | |
| <2000 | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2010 | 1 | 3,102 | N/A | 15.1 | 13.8–16.4 | N/A | N/A | N/A | |
| Tunisia | ||||||||||
| All* | 2008 | 1 | 2,870 | N/A | 18.5 | 17.1–20.0 | N/A | N/A | N/A | N/A |
| Malesc | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Femalesc | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | |
| <2000 | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2008 | 1 | 28,70 | N/A | 18.5 | 17.1–20.0 | N/A | N/A | N/A | |
| Yemen | ||||||||||
| All* | 2008 | 1 | 1,175 | N/A | 15.2 | 13.2–17.4 | N/A | N/A | N/A | N/A |
| Malesc | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| Femalesc | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | |
| <2000 | N/A | 0 | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
| >2000 | 2008 | 1 | 1,175 | N/A | 15.2 | 13.2–17.4 | N/A | N/A | N/A | |
Notes: Mean effect size reported as weighted average prevalence measure with their corresponding 95% confidence interval (CI); the p-value of the Cochran’s Q statistic is used to inform about the statistical significance of the heterogeneity in PA prevalence estimates within the group; the p-value of the Cochran’s Q between-subgroups statistic is used to inform about the statistical significance of the heterogeneity in PA prevalence estimates between the results of subgroups if applicable; the I2 is used to assess the magnitude of between-study variation that is due to differences in PA prevalence estimates across studies rather than chance; the prediction interval is used to estimate the 95% interval in which the true PA prevalence in a new PA prevalence study will lie. p-value <0.05 was considered statistically significant. <2000: this analysis includes all studies where the data was collected before 2000. > 2000: this analysis includes all studies where the data was collected before 2000.
Meta-analyses according to the type of population (nationals and non-national populations) were only relevant in the GCC countries as per the type of setting of the GCC population. Years of data collection were considered as prior to the publication year in studies where this information was missing. The prevalence measures were then classified accordantly for the meta-analyses. All data among youth was collected after 2000.
Symbols:
* Include converted data from physical inactivity to physical activity not overlapping with other included data.
**include data from an included primary study not reported by the SR.
*** Data on moderate and vigorous physical activity levels were merged in the studies of Wasfi, 200836 and Mehairi, 201334, respectively, to ovoid partial overlapping of denominators data from the same study.
aStratified prevalence measures were used to conduct the meta-analyses.
bWeighted average prevalence measures were obtained using random-effect model.
cExcluded the studies with a mixed male and female population.
In most of the MENA countries among both adult and youth populations, more males engage in PA than females except for adults in Lebanon and Jordan (Tables 2, 3, Fig. 2). In MENA as a whole and within GCC countries, greater PA participation was observed among adults than in youth. However, caution must be practiced in interpreting these results given the limited number of included studies and the high heterogeneity between the included prevalence measures.
Figure 2.
Map of the physical activity prevalence measures, using data collected after 2000: (A) In the adult MENA population, (B) in the youth MENA population, and (C) in the national GCC adult and youth populations. All PA data were collected after 2000. MENA countries with no PA prevalence measures among adults include Djibouti, Yemen, Bahrain, and Syria. MENA countries with no PA prevalence measures among youth include Algeria, Bahrain and Pakistan. Heterogeneity (I2) between the PA prevalence measures varied between 99.6–99.7% in adults and 99.2–99.4% in youth in MENA countries. Among GCC nationals, heterogeneity (I2) between the PA prevalence measures varied between 99.8–99.8% in adults and 99.0–99.5% in youth. N/A is used to indicate the non-availability of disaggregated prevalence data according to gender in (A,B) and age (adult/youth) in (C).
Only the SR of Yammine et al. 2016 pooled the PA prevalence measures among adolescents in the UAE56 for moderate and vigorous levels of PA [19.2% (95% CI = 18.5–19.9%, I2 = 98.6%) and 24.7% (95% CI = 23.2–26.3%, I2 = 97.8%)]. This pooled prevalence was higher among males than females for both moderate and vigorous levels of PA [ORpooled: 1.23% (95% CI = 1.13–1.35%, I2 = 89.5%) and ORpooled: 2.6% (95% CI = 2.14–3.14%, I2 = 84.5%), respectively]. Our pooled prevalence of both moderate and vigorous levels of PA in the UAE among youth was 36.0% (95% CI = 23.9–49.9%).
For adults, most studies used PA definitions consistent with international recommendations of i) at least 150 minutes of moderate-intensity activity per week or ii) at least 600 or more metabolic equivalent of task (MET)-minutes per week of vigorous or moderate activity6,59,60. For youth, most studies used a PA definition corresponding to 60 minutes of moderate intensity activity as per the Arab Teens Lifestyle Study (ATLS) questionnaire PA measurement and compilation (Supplementary, Table S12)61.
Only one study62 used a daily threshold of ≥13,000 steps using the pedometer to quantify sufficient PA participation (Supplementary, Table S12). Most studies pertaining to adults and youth utilized validated tools along with a validated version in Arabic or Urdu language, depending on the primary language of the country.
The results from univariate meta-regression analyses assessing the relationship between study-level covariates and the PA prevalence measures are summarized in Table 4. Factors associated with a higher prevalence of PA among adults were the use of the two validated questionnaires- IPAQ and GPAQ, and data collected after the year 2000. Factors associated with a higher prevalence of PA among youth were male gender and the use of IPAQ. The HBSC, PACE+ questionnaires and country level coverage of the study are associated with a lower PA prevalence among youth (Table 4).
Table 4.
Univariate meta-regression models for physical activity prevalence in MENA adult and youth general population.
| Meta-regression variables | Variables categories | Number of prevalence measures | Total sample size | Effect size | Univariable analyses | Proportion of explained true variance | |||
|---|---|---|---|---|---|---|---|---|---|
| Weighted average prevalence (%) | 95% CI (%) |
OR | 95% CI | p-value† | R2 (%) | ||||
| Adults | |||||||||
| Gender | Male | 29 | 49,343 | 54.1 | 42.3–65.7 | 1.11 | 0.94–1.32 | 0.2286 | 2.72 |
| Female | 32 | 44,167 | 43.6 | 31.7–55.9 | Ref. | Ref. | |||
| Data collection time | > 2000 | 71 | 105,109 | 50.8 | 45.6–56.0 | 1.72 | 1.38–2.13 | <.0001 | 52.89 |
| <2000 | 4 | 19,593 | 6.2 | 1.7–13.3 | Ref. | Ref. | |||
| PA measurement tools | IPAQ | 19 | 19,317 | 57.4 | 47.2–67.1 | 1.32 | 1.12–1.57 | 0.0012 | 41.42 |
| “How physically active are you” questionnaire | 2 | 322 | 21.4 | 17.3–26.2 | 0.92 | 0.64–1.32 | 0.6408 | ||
| GPAQ | 37 | 74,049 | 51.1 | 42.8–59.3 | 1.25 | 1.07–1.45 | 0.0040 | ||
| Nurses’ Health Study II | 1 | 175 | 67.4 | 60.1–74.0 | 1.48 | 0.91–2.42 | 1.5641 | ||
| ATLS | 3 | 1,177 | 55.8 | 48.1–63.1 | 1.31 | 0.97–1.78 | 0.0782 | ||
| Non-validated | 13 | 29,662 | 23.9 | 11.9–42.1 | Ref. | Ref. | Ref. | ||
| Geographical coverage | Country level | 42 | 98,662 | 50.4 | 39.8–60.9 | 1.08 | 0.93–1.25 | 0.3034 | 0.00 |
| Local level | 33 | 26,040 | 42.7 | 36.1–49.7 | Ref. | Ref. | |||
| Youth | |||||||||
| Gender | Male | 19 | 20,811 | 37.1 | 28.8–45.8 | 1.27 | 1.13–1.41 | <0.0001 | 19.37 |
| Female | 16 | 18,606 | 16.5 | 12.2–21.2 | Ref. | Ref. | |||
| Data collection time | > 2000 | 49 | 78,703 | 25.6 | 22.0–29.4 | 1.14 | 0.84–1.54 | 0.4050 | 0.00 |
| <2000 | 1 | 212 | 15.1 | 10.6–20.3 | Ref. | Ref. | |||
| PA measurement tools | Objective measurement | 2 | 508 | 32.6 | 3.9–72.3 | 0.91 | 0.75–1.11 | 0.3569 | 35.35 |
| ATLS | 8 | 5,886 | 38.9 | 22.7–56.4 | 0.97 | 0.85–1.10 | 0.6381 | ||
| HBSC | 2 | 9199 | 20.1 | 19.1–21.0 | 0.80 | 0.66–0.97 | 0.0233 | ||
| PACE + | 30 | 57,763 | 17.1 | 14.9–19.5 | 0.76 | 0.68–0.84 | <0.0001 | ||
| IPAQ | 2 | 1,018 | 63.7 | 41.0–83.6 | 1.25 | 1.03–1.52 | 0.0272 | ||
| Non-validated | 6 | 4,541 | 28.0 | 13.9–48.3 | Ref. | Ref. | Ref. | ||
| Geographical coverage | Country level | 34 | 68,422 | 20.3 | 17.3–23.3 | 0.83 | 0.76–0.89 | <0.0001 | 21.70 |
| Local level | 16 | 10,493 | 37.4 | 26.7–48.8 | Ref. | Ref. | |||
Notes: Significant results are highlighted in bold. Association between the meta-regression variable and the prevalence of PA is reported as an odds ratio (OR) with its corresponding 95% confidence interval. Mean effect size reported as weighted average prevalence measure with their corresponding 95% confidence interval (CI). Weighted average prevalence measures were obtained using random-effect model. Proportion of total between-study variance explained by the model (R2) is used to assess the magnitude of the covariates relationship with effect size30. All data among youth was collected after 2000. No meta-analyses according to the time of data collection were then performed. The category “Country level” includes all prevalence measures based on a national sample of individuals representative of the entire country where the study was conducted (e.g. all cities of the country). To be included in this category, a study should clearly mention that the study was conducted at the national level. Any study based on a sample from a restricted part of the country (one or few cities, specific university, specific school, etc.) is considered in the “Local level” category.
†p-value <0.05 was considered statistically significant.
Abbreviations: PA: Physical activity; MENA: Middle East and North Africa; IPAQ: International physical activity questionnaire; GPAQ: Global physical activity questionnaire; ATLS: Arab Teens Lifestyle Study questionnaire; HBSC: Health Behaviour in School-aged Children Survey, PACE+: Adolescent physical activity measure questionnaire.
Overview of studies with physical inactivity data
To define physical inactivity, we used physical activity levels below the recommended levels of PA. Hence, the validated measurement tools used to assess PA participation were also used to assess physical inactivity (Supplementary, Table S12).
Two studies used a daily threshold of <10,000 steps using the pedometer to quantify insufficient PA participation. Two studies used a daily threshold of a daily heart rate <159 bpm for at least 20 min and a daily heart rate <140 bpm for at least 30 min to quantify physical inactivity.
Among adults and youths, a total of 134 primary studies reported data on physical inactivity (Supplementary, Tables S10 and S11). Studies using a standard definition for physical inactivity and not overlapping with an already included data/study were included in the meta-analysis and are listed in Supplementary, Tables S6 and S7. Overall, higher prevalence measures of physical inactivity among female adults compared to male adults was noted.
A total of 58 primary studies reported factors positively correlated to physical inactivity and barriers to physical activity.
Among the included SRs, only the SR of Ranasinghe 201354 reported an OR comparing the prevalence of physical inactivity between males and females in Pakistan. This SR concluded that females were significantly more inactive than males [OR: 2.1 (95% CI = 1.5–3.1), p-value<0.001]. In UAE, Yammine et al.56 concluded that adolescent females had a significantly higher mild level of PA compared to males [ORpooled: 0.82 (95% CI = 0.698–0.961%); I2 = 98.0%; n = 2]56.
Overview of studies with sedentary behaviour
A total of 22 primary studies63–84 on sedentary behaviour among adults and youth were included (Supplementary, Tables S8 and S9). Adult sedentary behaviour data was reported for Kuwait, Oman, Qatar, and Saudi Arabia and youth sedentary behaviour measures were available for Bahrain, Djibouti, Jordan, Kuwait, Libya, Morocco, Oman, Saudi Arabia, and the UAE. However, the wide variability in sedentary behaviour measurement prevents measurable conclusions on age and sex-related differences.
Sedentary behaviour definitions varied for adults (three definitions) and youth (17 definitions). Definition variations were related to the type of sedentary activities considered (screen time, sitting time, game time, reading time, standing time, talking time, or total sedentary time) and the measurement unit (minutes or hours per day or week). Sedentary behaviour was assessed using the mean time (minutes per day or week) or the proportion of the population exhibiting sedentary behaviour (prevalence measure, %). The threshold used for sedentary behaviour prevalence measures (6+ hours per day, 3+ hours per day, or 2+ hours per day) also varied among the studies. Within the 13 sedentary behaviour measures in adults, 54% were reported as a sedentary behaviour prevalence measure and 46% as the mean amount of time spent sitting (Supplementary, Table S10). Among the 79 sedentary behaviour measures in youth, 63% were reported as a sedentary behaviour prevalence measure and 37% as the mean amount of time spent sitting (Supplementary, Table S11). The validated tools utilized for sedentary behaviour measurement were the same tools used for PA assessment among adults and youth.
Discussion
Our overview included seven SRs, 229 primary studies and 203,617 participants spanning the 20 MENA countries. Our meta-analysis on PA participation after the year 2000 demonstrates that 49.2% of adults and 74.4% of the youth population were not sufficiently active. Insufficient evidence is available on sedentary behaviour due to the absence of clear measurement guidelines.
Our regional pooled PA prevalence of 50.8% among adults was similar to the recent estimate in the region of MENA and Central Asia (67.2%)26 but lower than the global estimates of 72.5–77%25,26. Minor differences in PA prevalence estimates can be explained by the differences in the number of countries considered for the calculation, the measurement of PA, and data management25. Evolving PA recommendations could also be an explanation; previously, sufficient PA was defined as undertaking at least 30 minutes per day of moderate intensity activity for at least five days per week or at least 20 minutes per day of vigorous intensity activity for at least three days per week or an equivalent combination achieving 600 METs-minutes per week85,86. This recommendation is likely more difficult to be achieved when compared to the updated recommendation of 150 minutes of moderate intensity activity or 75 minutes of vigorous intensity activity per week, or an equivalent combination regardless of the weekly frequency6,7, since this has more flexibility.
Our regional pooled PA prevalence of 25.6% among youth is consistent with the global estimate of about 20% of youth who are sufficiently active as per the international recommendation for PA9,25. The lack of awareness pertaining to the recommendation of 60 minutes of moderate- to vigorous-intensity PA daily for children and youth6,7,87 and its benefits can explain the very low PA participation in the region57,58and globally. Our findings in MENA confirm the lower PA participation of youth in comparison to adults which is observed globally9,25. This could partially be due to the higher recommended levels of PA for youth and in part due to the tools utilized to measure PA. Because PA habits developed during youth may persist in adulthood, young people should be encouraged to participate in a variety of physical activity that supports their natural development6. Implementation of locally informed, evidence-based interventions promoting PA participation at the standardized recommended levels and addressing barriers for PA would be a step in the right direction.
Our findings confirm the higher PA participation among males as compared to females which is observed globally9,22,88,89 and regionally52,53,56–58 among both adults and youth. Hence, the female youth should be a target population for future interventions to increase PA participation in the region. Understanding the barriers and determinants of PA in the MENA population is essential for developing culturally- and age-appropriate PA interventions (Table 5). The sex differences in adults may be attributable to the sociocultural role of men and women in the region54,58, however, barriers related to the lower participation of female youth in PA requires further investigation (Table 5). Physical activity interventions should be inspired from countries with the highest PA prevalence measures among girls, for example in China (75%)88, where walking and biking are a common form of commuting. Our analyses provide some indication of an increased PA participation among adults after 2000 in all MENA countries with available data in both periods. Although definitions used for sufficient PA participation were similar between the studies conducted before and after 2000, caution must be practiced while interpreting these results given the limited data collected before 2000. In addition, none of the studies conducted before 2000 assessed PA participation using a standard PA questionnaire (GPAQ, IPAQ) as these questionnaires were developed in 200290 and 200391 respectively.
Table 5.
Barriers to physical activity and correlates of physical inactivity in MENA.
| In high income countries* | In low- and middle-income income countries** |
|---|---|
| • Lower levels of urbanization and built environment (eg: Lack of parks, greenery, squares, playgrounds, sports venues)57,68–70,72,117–139. | • Rapid urbanization (eg: Access to motorization; Highdensity traffic; Low air quality; Pollution)22,25,168–180,77 |
| • The physical environment factors (eg: Proximity to | • The physical environment factors (eg: Proximity to |
| destinations; Neighbourhood aesthetics; Access toopen space)25,140. | The physical environment factors (eg: Proximity to destinations; Neighborhood aesthetics; Access to open space)25,176,180. |
| • Psychological and social factors (eg: Unperceived | • Psychological and social factors (eg: Unperceived |
| benefits of PA and healthy status; Low self-efficacy; | benefits of PA and healthy status; Low self-efficacy; |
| Absence of social support from friends and peers; | Absence of social support from friends and peers; |
| Lack of exercise partner)25,57,69,70,72,118–121,123–125,128–130,134,141,142. | Lack of exercise partner)25,176,177,179–182. |
| • Hot arid climate53,57,143–148. | • Hot arid climate176,180. |
| • The employment of domestic helpers53. | • Cultural expectations (traditional role of women in taking care of household work and supporting extended family members)54,171,174–180,183–190. |
| • Conservative social norms particularly relevant for women53,57,81,121,132–134,146,148–153. | • Sociodemographic factors (eg: Age; Sex; Education level)78,176,186,187,191–202. |
| • Work-related factors (eg: Working long hours; Working in private sector)124,149,154: | • Lifestyle factors (eg: Lack of time; Poor sleeping habits; High screen time) 203,183,181,192,204,192,191,193,182,205,176,206,207,142,180. |
| • Sociodemographic factors (eg: Age; Sex; Education level)62,70,78,121,126,131,142,149,153,155–165. | |
| • Lifestyle factors (eg: Lack of time; Poor sleeping habits; High screen time)62,65,70,72,81,94,117–126,129,131,132,134,140,141,145,149,151,154,155,157,158,164,166,167. |
*Including GCC countries.
**Including MENA countries other than GCC.
Unlike other MENA countries, a substantial proportion of the population in the GCC countries is non-national37. Physical activity data differentiating nationals and non-nationals is scarce. The increase in PA in the GCC after 2000 may be explained by urbanization and socioeconomic improvement, see Table 5. Additional studies considering the demographic specificities in the GCC are needed to provide evidence on PA behaviour to develop, implement, and monitor public health programs.
We found a high proportion of heterogeneity between PA prevalence measures attributable to the type of questionnaire used. Prevalence estimates using the IPAQ were higher than those using the GPAQ. This is consistent with previous findings demonstrating PA over-reporting using the IPAQ compared to other similar questionnaires such as the GPAQ27, the Behavioural Risk Factor Surveillance System (BRFSS)92, or the accelerometer93,94. This results in an overestimation of the PA prevalence measures and consequently an underestimation of the physical inactivity prevalence measures95. Moreover, the GPAQ and IPAQ measure the total PA for a typical week making comparison difficult with the BRFSS (non-occupational PA for the past month)96 and the Active Australia Survey (leisure-time PA in the week preceding interview)97 used in Western countries.
The GPAQ is a valid measurement tool to assess moderate-to-vigorous PA endorsed by the World Health Organization90. However, this tool demonstrated a weak agreement with accelerometer use in young Saudi men98. Adapting the current GPAQ to build a regional PA questionnaire has been recommended98. Both measurement tool-based factors (question phrasing, time period coverage, and single or multipart questions)99 and individual-based factors (demographic and socioeconomic characteristics)98 should be considered in its development.
The international PA recommendations for youth require a mix of moderate and vigorous intensity activity (vigorous-intensity activity, including those that strengthen muscle and bone, at least three days per week)6,7,87. Physical activity participation among youth assessed using the ATLS questionnaire was based on the conversion of all moderate and vigorous activities during the week to a summary measure in MET-minutes with a cut-off of 1,680 METs-minutes per week corresponding to 60 minutes of moderate intensity activity every day of the week61. This methodology of quantifying PA participation is inclusive of participants engaging in solely moderate activities while the standard definition includes only those participants who engage in a combination of moderate activities most days of the week and vigorous activities three times per week6,7,87. The ATLS questionnaire could then overestimate the proportion of the population which is physically active. Nevertheless, the ATLS questionnaire was the only PA questionnaire validated among youth in MENA countries.
Objectively measured PA using accelerometer, pedometer or continuous heart rate monitor can avoid recall bias and is practical for use particularly among youth when compared to self-reported data100. However, the minimum number of steps measured by the pedometer required for health benefits is still debatable101–104 and no health-based criteria for steps per day have been developed for adults and youth88,100,102,104,105. Despite attempts to establish the steps per day threshold equivalent to the daily recommended PA level for youth106, validation studies are inconsistent on the standard threshold steps count62,102,103,105,107,108. Moreover, none of the international guidelines use the steps count to define sufficient PA6,7,49,86. Additionally, even if it was used as a gold standard in the ATLS validation study109, the pedometer is not designed to capture pattern, intensity, or type of PA and most are sensitive to ambulatory activities105.
The most basic dimensions of PA assessment are frequency, intensity, time (duration), and type of activities100. Currently, no single tool can adequately assess all these dimensions101. A combination of self-reported measures (to capture important domain- and behaviour-specific sedentary time information) and device-based measures (to measure both total sedentary time and patterns of sedentary time accumulation) is recommended99,110.
Sedentary behaviour is a different concept than physical inactivity and is expressed as a total number of minutes or hours spent in sedentary activities on a typical day60. The variability in the type of sedentary activities considered in the sedentary behaviour questionnaires prevent comparisons between countries in the region and globally. To date there is no well-accepted threshold to categorize sedentary behaviour data59.
The accelerometer is acknowledged as a valid and reliable instrument for objective measurement of sedentary behaviour in adults and youth111–115. However, it is a costly option for researchers. Moreover, it cannot provide information on the type and setting of sedentary behaviour and there is no consensus on the number of steps to identify individuals with sedentary behavior110,116. Sedentary behaviour has been recognized as a public health issue only in the past decade9 and therefore, only minimal standardized instruments exist for its assessment with no clear interpretation guidelines.
To our knowledge, this is the most comprehensive systematic overview and meta-analysis covering several dimensions of PA behaviour in the MENA region. Our overview is the first to bring together the available evidence on sedentary behaviour and PA. Moreover, most of the included primary studies have assessed PA prevalence using validated measurement tools in random national-level samples supporting the quality of the reported estimates. The country-, time-, age-, and sex-specific pooled PA prevalence measures are a major addition to the evidence on PA. This compilation will serve as a benchmark for epidemiologists and public health interventionists (Table 6). This overview identifies barriers (Table 5) and research gaps (Table 7) that could help direct future funding and investigation.
Table 6.
Public health implications and recommendations.
| • Public health efforts to increase PA and decrease sedentary time require standardized PA surveillance among all countries in the region. These measures are necessary to understand locally informed interventional strategies and to identify target populations affected by physical inactivity. This requires building and strengthening country capacity with a systems approach to scale up efforts aimed at increasing PA. |
| • Health-care providers can adopt a comprehensive curriculum that potentially closes the gap in medical schools, residency programs, graduate education, and nursing curricula on topics related to PA, and exercise prescription and lifestyle health57. |
| • Current data suggests that MENA youth population is insufficiently active. Physical environment interventions like parks, playgrounds, sports venues, proximity to destinations, and neighbourhood aesthetics can be beneficial. Additionally, psychosocial factors such as self-efficacy and social support from peers should also be considered while planning interventions. |
| • Identifying and considering PA-relevant domains (e.g. transportation, leisure-time PA) and barriers specific to the MENA populations are essential for adapting national actions and policies to tackle obesity and other non-communicable diseases. |
| • Considering the increasing urbanization in the region115, urban design favourable for PA has been suggested as a potential strategy to mitigate the impacts of urbanization on PA in low- and middle-income MENA countries25. |
| • Clear guidelines for sedentary behaviour measurement and interpretation in order to characterize patterns of sedentary behaviour in the MENA countries are required. |
Table 7.
Evidence gap and future research.
| There is a need to: |
| • Clarify standardized methods of measuring PA, inactivity and sedentary behaviour for future studies. |
| • Conduct large and up-to-date country-level studies using standardized tools and definitions to quantify PA participation52–54,56 and sedentary behavior52,54. |
| • Generate data on PA-related outcomes in MENA countries other than the GCC countries. |
| • Explore differences in PA participation between nationals and non-nationals in the GCC countries. |
| • Further understand the personal, social and environmental barriers to PA, particularly in relation to the different domains of PA (leisure time, occupational, transports, and households PA) specific to the region53,56 to facilitate effective locally informed interventions. |
| • Identify target population groups (girls, youth, obese) for focused interventions52,53. |
| • Follow PA trends over time53. |
Limitations include the restriction of the search strategy to PubMed/MEDLINE. However, we searched additional sources indexing grey and non-grey literature relevant to the region. Moreover, the included SRs have searched in total 21 literature sources for primary studies including grey literature sources, which are potentially important data sources for MENA countries27,31. This minimizes the risk of publication bias in our overview. We also extracted the data from the primary studies to complete the missing information and provide a complete overview of the available data.
The data that we used on PA and sedentary behaviour was collected before 2018; new PA data from MENA countries may have been published since. The limited data on PA behaviour in the MENA countries could be explained by the targeted population and outcomes of the included SRs. To optimize the quantitative synthesis, we converted physical inactivity data, using a standard definition, to PA measures. Despite attempts to minimize the heterogeneity using several subgroup analyses, a high heterogeneity between the PA prevalence measures persisted. Although most of the primary studies used validated PA-measurement tools, differences in PA domains captured by these measurement tools contributes to the heterogeneity between estimates. Another limitation of our metanalysis could be the minor differences in the definitions of the ‘active’ category among youth (e.g. number of days per week or steps). However, since only a limited number of studies had these differences, we feel this may have a minimal impact on the results. Differences according to the area of residence (rural versus urban) and the study coverage (country versus local coverage) might have been additional sources of heterogeneity in the reported prevalence measures.
In comparison to the global average estimates, our regional PA prevalence estimates for MENA were lower among both adults and youth. The lower PA participation by females and youth observed globally was also found in several MENA countries. More recent data suggests an increased PA participation in the region as a whole. Some of the variability in the PA measures might be attributable to the type of questionnaires used for data collection. Trends and patterns need to be confirmed in future investigations. Whether sedentary behaviour is a public health issue in the MENA region remains unclear, due to the absence of guidelines to quantify and interpret it. Harmonizing definitions of sedentary behaviour would be useful to generate evidence for the region. Promoting PA and limiting sedentary behaviour should be health priorities for both adults and the youth to reduce the overall burden of obesity and other non-communicable diseases. Additionally, promoting PA surveillance and identifying and investigating sedentary behaviour trends utilizing validated and reliable measurement tools consistent at national and international levels is essential to allow meaningful comparisons and to implement effective evidence-based interventions.
Supplementary information
Acknowledgements
We would like to thank Sinéad O’Rourke, Content Development Specialist, Weill Cornell Medicine-Qatar for editing the manuscript, Aida Tariq Nasir, Project Coordinator for formatting and checking the tables and Hekmat Alrouh, Project Specialist for his help in the literature search and the screening step (both previously employed at the Institute for Population Health, Weill Cornell Medicine - Qatar). We would also like to thank Sapna Bhagat, Temporary Research Assistant and Anupama Jithesh, Research Coordinator for helping with the data extraction, formatting, and checking the tables and Sathyanarayanan Doraiswamy, Assistant Director for reviewing the revised manuscript (all are employed at the Institute for Population Health, Weill Cornell Medicine-Qatar). The work was funded by the Institute for Population Health, Weill Cornell Medicine - Qatar and Qatar Foundation through the Weill Cornell Medicine - Qatar Biomedical Research Program.
Author contributions
Sonia Chaabane (S.C.1), Karima Chaabna (K.C.), Amit Abraham (A.A.), Ravinder Mamtani (R.M.) and Sohaila Cheema (S.C.2) collectively contributed to the conception of the study. S.C.1, K.C., A.A., and S.C.2 were involved in the literature search, screening, and extraction steps. Analysis and manuscript drafting were implemented by S.C.1 with support from K.C., A.A., S.C.2, and R.M. All authors read, edited, and approved the final manuscript.
Data availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
is available for this paper at 10.1038/s41598-020-66163-x.
References
- 1.The World Health Organization (WHO). Noncommunicable diseases, https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases (2018).
- 2.International Diabetes Federation (IDF). IDF Diabetes Atlas. (2017).
- 3.Hurt RT, Kulisek C, Buchanan LA, McClave SA. The Obesity Epidemic: Challenges, Health Initiatives, and Implications for Gastroenterologists. Gastroenterology & Hepatology. 2010;6:780–792. [PMC free article] [PubMed] [Google Scholar]
- 4.Nikoloski, Z. & Williams, G. In Metabolic Syndrome (ed R.S. Ahima) (Springer International Publishing Switzerland 2016, Philadelphia, PA, USA).
- 5.Warburton DER, Nicol CW, Bredin SSD. Health benefits of physical activity: the evidence. Canadian Medical Association Journal. 2006;174:801–809. doi: 10.1503/cmaj.051351. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.World Health Organization (WHO). In Global Recommendations on Physical Activity for Health (World Health Organization 2010, Geneva, 2010).
- 7.Committee, U. S. D. O. H. A. H. S. P. A. G. A. 2018 Physical Activity Guidelines Advisory Committee Scientific Report.. (Washington, DC:).
- 8.González K, Fuentes J, Márquez JL. Physical Inactivity, Sedentary Behavior and Chronic Diseases. Korean Journal of Family Medicine. 2017;38:111–115. doi: 10.4082/kjfm.2017.38.3.111. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Hallal PC, et al. Global physical activity levels: surveillance progress, pitfalls, and prospects. The Lancet. 2012;380:247–257. doi: 10.1016/S0140-6736(12)60646-1. [DOI] [PubMed] [Google Scholar]
- 10.Kyu HH, et al. Physical activity and risk of breast cancer, colon cancer, diabetes, ischemic heart disease, and ischemic stroke events: systematic review and dose-response meta-analysis for the Global Burden of Disease Study 2013. BMJ. 2016;354:i3857. doi: 10.1136/bmj.i3857. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Lee CD, Folsom AR, Blair SN. Physical activity and stroke risk: a meta-analysis. Stroke. 2003;34:2475–2481. doi: 10.1161/01.Str.0000091843.02517.9d. [DOI] [PubMed] [Google Scholar]
- 12.Lee IM, et al. Impact of Physical Inactivity on the World’s Major Non-Communicable Diseases. Lancet. 2012;380:219–229. doi: 10.1016/S0140-6736(12)61031-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Biswas A O. P. et al. Sedentary time and its association with risk for disease incidence, mortality, and hospitalization in adults a systematic review and meta-analysis of sedentary time and disease incidence, mortality, and hospitalization. 162(2):123–32 (2015). [DOI] [PubMed]
- 14.Carson V, et al. Systematic review of sedentary behaviour and health indicators in school-aged children and youth: an update. Applied Physiology, Nutrition, and Metabolism. 2016;41:S240–S265. doi: 10.1139/apnm-2015-0630. [DOI] [PubMed] [Google Scholar]
- 15.Patterson, R. et al. Sedentary behaviour and risk of all-cause, cardiovascular and cancer mortality, and incident type 2 diabetes: a systematic review and dose response meta-analysis. European Journal of Epidemiology, 10.1007/s10654-018-0380-1 (2018). [DOI] [PMC free article] [PubMed]
- 16.Berlin JA, Colditz GA. A meta-analysis of physical activity in the prevention of coronary heart disease. American journal of epidemiology. 1990;132:612–628. doi: 10.1093/oxfordjournals.aje.a115704. [DOI] [PubMed] [Google Scholar]
- 17.Sofi F, Capalbo A, Cesari F, Abbate R, Gensini GF. Physical activity during leisure time and primary prevention of coronary heart disease: an updated meta-analysis of cohort studies. European Journal of Cardiovascular Prevention & Rehabilitation. 2008;15:247–257. doi: 10.1097/HJR.0b013e3282f232ac. [DOI] [PubMed] [Google Scholar]
- 18.Thune I, Furberg A-S. Physical activity and cancer risk: dose-response and cancer, all sites and site-specific. Medicine & Science in Sports & Exercise. 2001;33:S530–S550. doi: 10.1097/00005768-200106001-00025. [DOI] [PubMed] [Google Scholar]
- 19.Street SJ, Wells JCK, Hills AP. Windows of opportunity for physical activity in the prevention of obesity. Obesity Reviews. 2015;16:857–870. doi: 10.1111/obr.12306. [DOI] [PubMed] [Google Scholar]
- 20.Biddle SJH, Asare M. Physical activity and mental health in children and adolescents: a review of reviews. British Journal of Sports Medicine. 2011;45:886–895. doi: 10.1136/bjsports-2011-090185. [DOI] [PubMed] [Google Scholar]
- 21.Khan NA, Hillman CH. The Relation of Childhood Physical Activity and Aerobic Fitness to Brain Function and Cognition: A Review. Pediatric Exercise Science. 2014;26:138–146. doi: 10.1123/pes.2013-0125. [DOI] [PubMed] [Google Scholar]
- 22.World Health Organization (WHO). Global Strategy on Diet, Physical Activity and Health, http://www.who.int/dietphysicalactivity/factsheet_inactivity/en/ (2018).
- 23.Hallal PC, Martins RC, Ramírez A. The Lancet Physical Activity Observatory: promoting physical activity worldwide. The Lancet. 2014;384:471–472. doi: 10.1016/S0140-6736(14)61321-0. [DOI] [PubMed] [Google Scholar]
- 24.Varela AR, et al. Mapping the historical development of physical activity and health research: A structured literature review and citation network analysis. Preventive Medicine. 2018;111:466–472. doi: 10.1016/j.ypmed.2017.10.020. [DOI] [PubMed] [Google Scholar]
- 25.Sallis JF, et al. Progress in physical activity over the Olympic quadrennium. The Lancet. 2016;388:1325–1336. doi: 10.1016/S0140-6736(16)30581-5. [DOI] [PubMed] [Google Scholar]
- 26.Guthold, R., Stevens, G. A., Riley, L. M. & Bull, F. C. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1·9 million participants. The Lancet Global Health, 10.1016/S2214-109X(18)30357-7 (2018). [DOI] [PubMed]
- 27.Chaabna K, et al. Gray literature in systematic reviews on population health in the Middle East and North Africa: protocol of an overview of systematic reviews and evidence mapping. Systematic Reviews. 2018;7:94. doi: 10.1186/s13643-018-0751-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Booth A, et al. PROSPERO at one year: an evaluation of its utility. Systematic Reviews. 2013;2:4. doi: 10.1186/2046-4053-2-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. J Clin Epidemiol. 2009;62:1006–1012. doi: 10.1016/j.jclinepi.2009.06.005. [DOI] [PubMed] [Google Scholar]
- 30.Higgins J & Green S. (The Cochrane Collaboration, 2011).
- 31.Quenby M, Dwayne VE, Emma I. Searching for grey literature for systematic reviews: challenges and benefits. Research Synthesis Methods. 2014;5:221–234. doi: 10.1002/jrsm.1106. [DOI] [PubMed] [Google Scholar]
- 32.Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan—a web and mobile app for systematic reviews. Systematic Reviews. 2016;5:210. doi: 10.1186/s13643-016-0384-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Troiano RP, Gabriel KKP, Welk GJ, Owen N, Sternfeld B. Reported Physical Activity and Sedentary Behavior: Why Do You Ask? Journal of Physical Activity and Health. 2012;9:S68–S75. doi: 10.1123/jpah.9.s1.s68. [DOI] [PubMed] [Google Scholar]
- 34.Mehairi AE, et al. Metabolic syndrome among Emirati adolescents: a school-based study. PLoS One. 2013;8:e56159. doi: 10.1371/journal.pone.0056159. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Muhairi SJ, et al. Vitamin D deficiency among healthy adolescents in Al Ain, United Arab Emirates. BMC Public Health. 2013;13:33. doi: 10.1186/1471-2458-13-33. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Wasfi AS, El-Sherbiny AA, Gurashi E, Al Sayegh FU. Sport practice among private secondary-school students in Dubai in 2004. East Mediterr Health J. 2008;14:704–714. [PubMed] [Google Scholar]
- 37.Chaabna K, Cheema S, Mamtani R. Migrants, healthy worker effect, and mortality trends in the Gulf Cooperation Council countries. PLOS ONE. 2017;12:e0179711. doi: 10.1371/journal.pone.0179711. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Jakovljevic MM, et al. Population aging and migration - history and UN forecasts in the EU-28 and its east and south near neighborhood - one century perspective 1950-2050. Globalization and health. 2018;14:30. doi: 10.1186/s12992-018-0348-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Farrag NS, Cheskin LJ, Farag MK. A systematic review of childhood obesity in the Middle East and North Africa (MENA) region: Prevalence and risk factors meta-analysis. Advances in pediatric research. 2017;4:8. doi: 10.12715/apr.2017.4.8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.UN news. Malnutrition among children in Yemen at ‘all-time high,’ warns UNICEF, https://news.un.org/en/story/2016/12/547632-malnutrition-among-children-yemen-all-time-high-warns-unicef#.WI_-ifl97IU (2016).
- 41.World Bank. Middle East and NorthAfrica., http://www.worldbank.org/en/region/mena/overview (2018).
- 42.Khan HTA, Hussein S, Deane J. Nexus Between Demographic Change and Elderly Care Need in the Gulf Cooperation Council (GCC) Countries: Some Policy Implications. Ageing International. 2017;42:466–487. doi: 10.1007/s12126-017-9303-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Higgins JPT, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327:557–560. doi: 10.1136/bmj.327.7414.557. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Michael Borenstein, Larry V. Hedges, Julian P.T. Higgins & Rothstein, H. R. Borenstein, M. Introduction to meta-analysis.. (John Wiley & Son, 2009).
- 45.Schwarzer, G. (CRAN, 2018).
- 46.National Institutes of Health: Division of Cancer Control and Population Sciences. List of physical activity standard questionnaires validations studies, https://epi.grants.cancer.gov/paq/validation.html(
- 47.Shea BJ, et al. Development of AMSTAR: a measurement tool to assess the methodological quality of systematic reviews. BMC Medical Research Methodology. 2007;7:10. doi: 10.1186/1471-2288-7-10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Shea BJ, et al. AMSTAR is a reliable and valid measurement tool to assess the methodological quality of systematic reviews. Journal of Clinical Epidemiology. 2009;62:1013–1020. doi: 10.1016/j.jclinepi.2008.10.009. [DOI] [PubMed] [Google Scholar]
- 49.Institute of Medicine of the National Academies. Finding What Works in Health Care: Standards for Systematic Reviews. (Washington, DC: THE NATIONAL ACADEMIES PRESS 2011). [PubMed]
- 50.National Heart, L., and Blood Institute (NHLBI)/National Institutes of Health (NIH),. In Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies (2018).
- 51.Davids, E. & Roman, N. A systematic review of the relationship between parenting styles and children’s physical activity. Vol. 20 (2014).
- 52.Mabry R, Koohsari MJ, Bull F, Owen N. A systematic review of physical activity and sedentary behaviour research in the oil-producing countries of the Arabian Peninsula. BMC Public Health. 2016;16:1003. doi: 10.1186/s12889-016-3642-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.Mabry RM, Reeves MM, Eakin EG, Owen N. Evidence of physical activity participation among men and women in the countries of the Gulf cooperation council: a review. Obesity reviews: an official journal of the International Association for the Study of Obesity. 2010;11:457–464. doi: 10.1111/j.1467-789X.2009.00655.x. [DOI] [PubMed] [Google Scholar]
- 54.Ranasinghe CD, Ranasinghe P, Jayawardena R, Misra A. Physical activity patterns among South-Asian adults: a systematic review. International Journal of Behavioral Nutrition and Physical Activity. 2013;10:116. doi: 10.1186/1479-5868-10-116. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Boyle MH. Guidelines for evaluating prevalence studies. Evidence Based Mental Health. 1998;1:37–39. doi: 10.1136/ebmh.1.2.37. [DOI] [Google Scholar]
- 56.Yammine K. The prevalence of physical activity among the young population of UAE: a meta-analysis. Perspectives in Public Health. 2016;137:275–280. doi: 10.1177/1757913916675388. [DOI] [PubMed] [Google Scholar]
- 57.Al-Hazzaa HM. Physical inactivity in Saudi Arabia revisited: A systematic review of inactivity prevalence and perceived barriers to active living. Int J Health Sci (Qassim) 2018;12:50–64. [PMC free article] [PubMed] [Google Scholar]
- 58.Sharara E, Akik C, Ghattas H, Makhlouf Obermeyer C. Physical inactivity, gender and culture in Arab countries: a systematic assessment of the literature. BMC Public Health. 2018;18:639. doi: 10.1186/s12889-018-5472-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59.International Physical Activity Questionnaire, https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnx0aGVpcGFxfGd4OjE0NDgxMDk3NDU1YWRlZTM (2005).
- 60.World Health Organization (WHO). In Working version 5 (Geneva 2014).
- 61.Al-Hazzaa HM, Musaiger AO, Group AR. Arab Teens Lifestyle Study (ATLS): objectives, design, methodology and implications. Diabetes Metab Syndr Obes. 2011;4:417–426. doi: 10.2147/DMSO.S26676. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 62.Al-Hazzaa HM. Pedometer-determined physical activity among obese and non-obese 8- to 12-year-old Saudi schoolboys. J Physiol Anthropol. 2007;26:459–465. doi: 10.2114/jpa2.26.459. [DOI] [PubMed] [Google Scholar]
- 63.Al-Thani M, et al. Lifestyle Patterns Are Associated with Elevated Blood Pressure among Qatari Women of Reproductive Age: A Cross-Sectional National Study. Nutrients. 2015;7:7593–7615. doi: 10.3390/nu7095355. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 64.El-Aty MA, et al. Metabolic Syndrome and Its Components: Secondary analysis of the World Health Survey, Oman. Sultan Qaboos Univ Med J. 2014;14:e460–467. [PMC free article] [PubMed] [Google Scholar]
- 65.Mabry RM, Winkler EA, Reeves MM, Eakin EG, Owen N. Correlates of Omani adults’ physical inactivity and sitting time. Public Health Nutr. 2013;16:65–72. doi: 10.1017/s1368980012002844. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 66.WHO-STEPS Survey Kuwait. World Health Organization (WHO) Stepwise Approach to NCD Surveillance, Kuwait. (MOH & WHO, Kuwait City, Kuwait, 2008).
- 67.WHO-STEPS Survey Saudi Arabia. World Heath Organization (WHO) Stepwise Approach to NCD Surveillance - Country-Specific Standard Report. (MOH, WHO & EMRO, Riyadh, Saudi Arabia, 2005).
- 68.Al-Hazzaa HM, Abahussain NA, Al-Sobayel HI, Qahwaji DM, Musaiger AO. Physical activity, sedentary behaviors and dietary habits among Saudi adolescents relative to age, gender and region. International Journal of Behavioral Nutrition and Physical Activity. 2011;8:140. doi: 10.1186/1479-5868-8-140. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 69.Al-Hazzaa HM, et al. A cross-cultural comparison of health behaviors between Saudi and British adolescents living in urban areas: gender by country analyses. Int J Environ Res Public Health. 2013;10:6701–6720. doi: 10.3390/ijerph10126701. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 70.Al-Hazzaa HM, Al-Rasheedi AA. Adiposity and physical activity levels among preschool children in Jeddah, Saudi Arabia. Saudi medical journal. 2007;28:766–773. [PubMed] [Google Scholar]
- 71.Al-Hazzaa HM, et al. Association of dietary habits with levels of physical activity and screen time among adolescents living in Saudi Arabia. J Hum Nutr Diet. 2013;27(Suppl 2):204–213. doi: 10.1111/jhn.12147. [DOI] [PubMed] [Google Scholar]
- 72.Al-Nakeeb Y, et al. Obesity, physical activity and sedentary behavior amongst British and Saudi youth: a cross-cultural study. Int J Environ Res Public Health. 2012;9:1490–1506. doi: 10.3390/ijerph9041490. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 73.Al-Nuaim AA, et al. The Prevalence of Physical Activity and Sedentary Behaviours Relative to Obesity among Adolescents from Al-Ahsa, Saudi Arabia: Rural versus Urban Variations. J Nutr Metab. 2012;2012:417589. doi: 10.1155/2012/417589. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 74.Allafi A, et al. Physical activity, sedentary behaviours and dietary habits among Kuwaiti adolescents: gender differences. Public Health Nutr. 2013;17:2045–2052. doi: 10.1017/S1368980013002218. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 75.Farghaly NF, Ghazali BM, Al-Wabel HM, Sadek AA, Abbag FI. Life style and nutrition and their impact on health of Saudi school students in Abha, Southwestern region of Saudi Arabia. Saudi medical journal. 2007;28:415–421. [PubMed] [Google Scholar]
- 76.Gharib, N. M. & Rasheed, P. Obesity Among Bahraini Children and Adolescents: Prevalence And Associated Factors. Journal of the Bahrain Medical Society20 (2008).
- 77.Guthold R, Cowan MJ, Autenrieth CS, Kann L, Riley LM. Physical activity and sedentary behavior among schoolchildren: a 34-country comparison. J Pediatr. 2010;157(43-49):e41. doi: 10.1016/j.jpeds.2010.01.019. [DOI] [PubMed] [Google Scholar]
- 78.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;34:486–494. doi: 10.1016/j.amepre.2008.02.013. [DOI] [PubMed] [Google Scholar]
- 79.Kilani H, Al-Hazzaa H, Waly MI, Musaiger A. Lifestyle Habits: Diet, physical activity and sleep duration among Omani adolescents. Sultan Qaboos Univ Med J. 2013;13:510–519. doi: 10.12816/0003309. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 80.Mahfouz AA, et al. Obesity and related behaviors among adolescent school boys in Abha City, Southwestern Saudi Arabia. J Trop Pediatr. 2008;54:120–124. doi: 10.1093/tropej/fmm089. [DOI] [PubMed] [Google Scholar]
- 81.Mahfouz AA, et al. Nutrition, physical activity, and gender risks for adolescent obesity in Southwestern Saudi Arabia. Saudi J Gastroenterol. 2011;17:318–322. doi: 10.4103/1319-3767.84486. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 82.Musaiger AO, Zagzoog N. Dietary and lifestyle habits among adolescent girls in Saudi Arabia. Nutrition & Food Science. 2013;43:605–610. [Google Scholar]
- 83.Yousef S, Eapen V, Zoubeidi T, Mabrouk A. Behavioral correlation with television watching and videogame playing among children in the United Arab Emirates. Int J Psychiatry Clin Pract. 2013;18:203–207. doi: 10.3109/13651501.2013.874442. [DOI] [PubMed] [Google Scholar]
- 84.Youssef RM, Al Shafie K, Al-Mukhaini M, Al-Balushi H. Physical activity and perceived barriers among high-school students in Muscat, Oman. East Mediterr Health J. 2013;19:759–768. [PubMed] [Google Scholar]
- 85.Pate RR, Pratt M, Blair SN, et al. Physical activity and public health: A recommendation from the centers for disease control and prevention and the american college of sports medicine. JAMA. 1995;273:402–407. doi: 10.1001/jama.1995.03520290054029. [DOI] [PubMed] [Google Scholar]
- 86.Human energy requirements. Scientific background papers from the Joint FAO/WHO/UNU Expert Consultation. October 17-24, 2001.. Report No. 1368-9800 (Print) 1368-9800, 929-1228 (Rome, Italy, 2005). [DOI] [PubMed]
- 87.Tremblay MS, et al. New Canadian physical activity guidelines. Appl Physiol Nutr Metab. 2011;36(36-46):47–58. doi: 10.1139/h11-009. [DOI] [PubMed] [Google Scholar]
- 88.Sisson SB, Katzmarzyk PT. International prevalence of physical activity in youth and adults. Obesity reviews: an official journal of the International Association for the Study of Obesity. 2008;9:606–614. doi: 10.1111/j.1467-789X.2008.00506.x. [DOI] [PubMed] [Google Scholar]
- 89.Khuwaja AK, Kadir MM. Gender differences and clustering pattern of behavioural risk factors for chronic non-communicable diseases: community-based study from a developing country. Chronic Illn. 2010;6:163–170. doi: 10.1177/1742395309352255. [DOI] [PubMed] [Google Scholar]
- 90.Cleland CL, et al. Validity of the Global Physical Activity Questionnaire (GPAQ) in assessing levels and change in moderate-vigorous physical activity and sedentary behaviour. BMC Public Health. 2014;14:1255. doi: 10.1186/1471-2458-14-1255. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 91.Craig CL, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35:1381–1395. doi: 10.1249/01.Mss.0000078924.61453.Fb. [DOI] [PubMed] [Google Scholar]
- 92.Ainsworth BE, et al. Comparison of the 2001 BRFSS and the IPAQ Physical Activity Questionnaires. Medicine & Science in Sports & Exercise. 2006;38:1584–1592. doi: 10.1249/01.mss.0000229457.73333.9a. [DOI] [PubMed] [Google Scholar]
- 93.Ekelund U, et al. Criterion-related validity of the last 7-day, short form of the International Physical Activity Questionnaire in Swedish adults. Public Health Nutrition. 2006;9:258–265. doi: 10.1079/PHN2005840. [DOI] [PubMed] [Google Scholar]
- 94.Lee PH, Macfarlane DJ, Lam TH, Stewart SM. Validity of the international physical activity questionnaire short form (IPAQ-SF): A systematic review. The International Journal of Behavioral Nutrition and Physical Activity. 2011;8:115–115. doi: 10.1186/1479-5868-8-115. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 95.Rzewnicki R, Auweele YV, Bourdeaudhuij ID. Addressing overreporting on the International Physical Activity Questionnaire (IPAQ) telephone survey with a population sample. Public Health Nutrition. 2003;6:299–305. doi: 10.1079/PHN2002427. [DOI] [PubMed] [Google Scholar]
- 96.Centers for Disease Control and Prevention: National Center for Cahronic Disease Prevention and Health Promotion. The Behavioral Risk Factor Surveillance System (BRFSS) questionnaire. (U.S 2018).
- 97.Australian Institute of Health and Welfare (AIHW). The Active Australia Survey: a guide and manual for implementation, analysis and reporting. (Canberra, Australia 2003).
- 98.Alkahtani SA. Convergent validity: agreement between accelerometry and the Global Physical Activity Questionnaire in college-age Saudi men. BMC Research Notes. 2016;9:436. doi: 10.1186/s13104-016-2242-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 99.Healy GN, et al. Measurement of Adults’ Sedentary Time in Population-Based Studies. American journal of preventive medicine. 2011;41:216–227. doi: 10.1016/j.amepre.2011.05.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 100.Sallis JF. Measuring Physical Activity: Practical Approaches for Program Evaluation in Native American Communities. Journal of public health management and practice: JPHMP. 2010;16:404–410. doi: 10.1097/PHH.0b013e3181d52804. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 101.Graf C, et al. Feasibility and acceptance of exercise recommendations (10,000 steps a day) within routine German health check (Check-Up 35/GOÄ29)—study protocol. Pilot and Feasibility Studies. 2016;2:52. doi: 10.1186/s40814-016-0092-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 102.Tudor-Locke C, et al. BMI-referenced standards for recommended pedometer-determined steps/day in children. Preventive Medicine. 2004;38:857–864. doi: 10.1016/j.ypmed.2003.12.018. [DOI] [PubMed] [Google Scholar]
- 103.Beets, M., Beighle, A., Bottai, M., Rooney, L. & Tilley, F. Pedometer-Determined Step-Count Guidelines for Afterschool Programs. Vol. 9 (2012). [DOI] [PubMed]
- 104.Harrington DM, et al. Step-based translation of physical activity guidelines in the Lower Mississippi Delta. Applied Physiology, Nutrition, and Metabolism. 2011;36:583–585. doi: 10.1139/h11-053. [DOI] [PubMed] [Google Scholar]
- 105.Tudor-Locke C, Williams JE, Reis JP, Pluto D. Utility of pedometers for assessing physical activity: convergent validity. Sports Med. 2002;32:795–808. doi: 10.2165/00007256-200232120-00004. [DOI] [PubMed] [Google Scholar]
- 106.Rowlands AV, Eston RG. Comparison of accelerometer and pedometer measures of physical activity in boys and girls, ages 8-10 years. Research quarterly for exercise and sport. 2005;76:251–257. doi: 10.1080/02701367.2005.10599296. [DOI] [PubMed] [Google Scholar]
- 107.Beets, M. et al. Convergent Validity of Pedometer and Accelerometer Estimates of Moderate-to-Vigorous Physical Activity of Youth. Vol. 8 Suppl 2 (2011). [PubMed]
- 108.McNamara E, Hudson Z, Taylor SJC. Measuring activity levels of young people: the validity of pedometers. British Medical Bulletin. 2010;95:121–137. doi: 10.1093/bmb/ldq016. [DOI] [PubMed] [Google Scholar]
- 109.Al-Hazzaa HM, Al-Sobayel HI, Musaiger AO. Convergent Validity of the Arab Teens Lifestyle Study (ATLS) Physical Activity Questionnaire. International Journal of Environmental Research and Public Health. 2011;8:3810. doi: 10.3390/ijerph8093810. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 110.Hidding, L. M., Altenburg, T. M., Mokkink, L. B., Terwee, C. B. & Chinapaw, M. J. M. Systematic review of childhood sedentary behavior questionnaires: What do we know and what is next? Sports Med47, 10.1007/s40279-016-0610-1 (2017). [DOI] [PMC free article] [PubMed]
- 111.Kim, Y., Welk, G. J., Braun, S. I. & Kang, M. Extracting objective estimates of sedentary behavior from accelerometer data: measurement considerations for surveillance and research applications. PLoS One10, 10.1371/journal.pone.0118078 (2015). [DOI] [PMC free article] [PubMed]
- 112.Klaren, R. E., Hubbard, E. A., Zhu, W. & Motl, R. W. Reliability of accelerometer scores for measuring sedentary and physical activity behaviors in persons with multiple sclerosis. Adapt Phys Act Quart33, 10.1123/apaq.2015-0007 (2016). [DOI] [PubMed]
- 113.Manns, P., Ezeugwu, V., Armijo-Olivo, S., Vallance, J. & Healy, G. N. Accelerometer-Derived Pattern of Sedentary and Physical Activity Time in Persons with Mobility Disability: National Health and Nutrition Examination Survey 2003 to 2006. J Am Geriatr Soc63, 10.1111/jgs.13490 (2015). [DOI] [PubMed]
- 114.Peterson, N. E., Sirard, J. R., Kulbok, P. A., DeBoer, M. D. & Erickson, J. M. Validation of accelerometer thresholds and inclinometry for measurement of sedentary behavior in young adult University students. Res Nurs Health38, 10.1002/nur.21694 (2015). [DOI] [PMC free article] [PubMed]
- 115.Trost SG, McIver KL, Pate RR. Conducting accelerometer-based activity assessments in field-based research. Med Sci Sports Exerc. 2005;37:S531–543. doi: 10.1249/01.mss.0000185657.86065.98. [DOI] [PubMed] [Google Scholar]
- 116.Tudor-Locke C, Craig CL, Thyfault JP, Spence JC. A step-defined sedentary lifestyle index: <5000 steps/day. Applied Physiology, Nutrition, and Metabolism. 2012;38:100–114. doi: 10.1139/apnm-2012-0235. [DOI] [PubMed] [Google Scholar]
- 117.Al-Hazzaa Physical activity profile of college male students. King Saud University Journal (Educational Sciences) 1990;2:383–396. [Google Scholar]
- 118.Gawwad ESA. Stages of change in physical activity, self efficacy and decisional balance among saudi university students. Journal of family & community medicine. 2008;15:107–115. [PMC free article] [PubMed] [Google Scholar]
- 119.Khalaf A, et al. Female university students’ physical activity levels and associated factors–a cross-sectional study in southwestern Saudi Arabia. Int J Environ Res Public Health. 2013;10:3502–3517. doi: 10.3390/ijerph10083502. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 120.Awadalla NJ, et al. Assessment of physical inactivity and perceived barriers to physical activity among health college students, south-western Saudi Arabia. East Mediterr Health J. 2014;20:596–604. [PubMed] [Google Scholar]
- 121.Amin TT, Suleman W, Ali A, Gamal A, Al Wehedy A. Pattern, prevalence, and perceived personal barriers toward physical activity among adult Saudis in Al-Hassa, KSA. J Phys Act Health. 2011;8:775–784. doi: 10.1123/jpah.8.6.775. [DOI] [PubMed] [Google Scholar]
- 122.Majeed F. Association of BMI with diet and physical activity of female medical students at the University of Dammam, Kingdom of Saudi Arabia. Journal of Taibah University Medical Sciences. 2015;10:188–196. doi: 10.1016/j.jtumed.2014.11.004. [DOI] [Google Scholar]
- 123.Al-Otaibi HH. Measuring stages of change, perceived barriers and self efficacy for physical activity in Saudi Arabia. Asian Pac J Cancer Prev. 2013;14:1009–1016. doi: 10.7314/apjcp.2013.14.2.1009. [DOI] [PubMed] [Google Scholar]
- 124.Mandil AM, Alfurayh NA, Aljebreen MA, Aldukhi SA. Physical activity and major non-communicable diseases among physicians in Central Saudi Arabia. Saudi medical journal. 2016;37:1243–1250. doi: 10.15537/smj.2016.11.16268. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 125.Bajamal E, et al. Physical Activity Among Female Adolescents in Jeddah, Saudi Arabia: A Health Promotion Model-Based Path Analysis. Nurs Res. 2017;66:473–482. doi: 10.1097/nnr.0000000000000244. [DOI] [PubMed] [Google Scholar]
- 126.Al-Nozha MM, et al. Prevalence of physical activity and inactivity among Saudis aged 30-70 years. A population-based cross-sectional study. Saudi medical journal. 2007;28:559–568. [PubMed] [Google Scholar]
- 127.Alam AA. Obesity among female school children in North West Riyadh in relation to affluent lifestyle. Saudi medical journal. 2008;29:1139–1144. [PubMed] [Google Scholar]
- 128.Alsubaie ASR, Omer EOM. Physical Activity Behavior Predictors, Reasons and Barriers among Male Adolescents in Riyadh, Saudi Arabia: Evidence for Obesogenic Environment. Int. J Health Sci (Qassim) 2015;9:400–408. [PMC free article] [PubMed] [Google Scholar]
- 129.AlQuaiz AM, Tayel SA. Barriers to a healthy lifestyle among patients attending primary care clinics at a university hospital in Riyadh. Annals of Saudi medicine. 2009;29:30–35. doi: 10.4103/0256-4947.51818. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 130.Samara A, Nistrup A, Al-Rammah TY, Aro AR. Lack of facilities rather than sociocultural factors as the primary barrier to physical activity among female Saudi university students. Int. J Womens Health. 2015;7:279–286. doi: 10.2147/ijwh.S80680. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 131.Al-Hazzaa HM, et al. Patterns and determinants of physical activity among Saudi adolescents. J Phys Act Health. 2014;11:1202–1211. doi: 10.1123/jpah.2012-0427. [DOI] [PubMed] [Google Scholar]
- 132.Berger G, Peerson A. Giving young Emirati women a voice: participatory action research on physical activity. Health Place. 2009;15:117–124. doi: 10.1016/j.healthplace.2008.03.003. [DOI] [PubMed] [Google Scholar]
- 133.Kim HJ, Choi-Kwon S, Kim H, Park YH, Koh CK. Health-promoting lifestyle behaviors and psychological status among Arabs and Koreans in the United Arab Emirates. Res Nurs Health. 2015;38:133–141. doi: 10.1002/nur.21644. [DOI] [PubMed] [Google Scholar]
- 134.Mabry RM, Al-Busaidi ZQ, Reeves MM, Owen N, Eakin EG. Addressing physical inactivity in Omani adults: perceptions of public health managers. Public Health Nutr. 2014;17:674–681. doi: 10.1017/s1368980012005678. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 135.Huang N-C, Kung S-F, Hu S. The Relationship between Urbanization, the Built Environment, and Physical Activity among Older Adults in Taiwan. International Journal of Environmental Research and Public Health. 2018;15:836. doi: 10.3390/ijerph15050836. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 136.Kabisch N, van den Bosch M, Lafortezza R. The health benefits of nature-based solutions to urbanization challenges for children and the elderly - A systematic review. Environmental research. 2017;159:362–373. doi: 10.1016/j.envres.2017.08.004. [DOI] [PubMed] [Google Scholar]
- 137.McCloskey ML, et al. Disparities in dietary intake and physical activity patterns across the urbanization divide in the Peruvian Andes. International Journal of Behavioral Nutrition and Physical Activity. 2017;14:90. doi: 10.1186/s12966-017-0545-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 138.Eime RM, et al. The relationship of sport participation to provision of sports facilities and socioeconomic status: a geographical analysis. Australian and New Zealand Journal of Public Health. 2017;41:248–255. doi: 10.1111/1753-6405.12647. [DOI] [PubMed] [Google Scholar]
- 139.O’Donoghue G, et al. Socio-economic determinants of physical activity across the life course: A “Determinants of DIet and Physical ACtivity” (DEDIPAC) umbrella literature review. PLOS ONE. 2018;13:e0190737. doi: 10.1371/journal.pone.0190737. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 140.Al-Rukban MO. Obesity among Saudi male adolescents in Riyadh, Saudi Arabia. Saudi medical journal. 2003;24:27–33. [PubMed] [Google Scholar]
- 141.Alquaiz AM, et al. Correlates of cardiovascular disease risk scores in women in Riyadh, Kingdom of Saudi Arabia. Women Health. 2015;55:103–117. doi: 10.1080/03630242.2014.972020. [DOI] [PubMed] [Google Scholar]
- 142.Al-Gelban KS. Dietary habits and exercise practices among the students of a Saudi Teachers’ Training College. Saudi medical journal. 2008;29:754–759. [PubMed] [Google Scholar]
- 143.Memish ZA, et al. Burden of disease, injuries, and risk factors in the Kingdom of Saudi Arabia, 1990-2010. Prev Chronic Dis. 2014;11:E169. doi: 10.5888/pcd11.140176. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 144.Al-Baghli NA, et al. Overweight and obesity in the eastern province of Saudi Arabia. Saudi medical journal. 2008;29:1319–1325. [PubMed] [Google Scholar]
- 145.Al-Mutairi RL, Bawazir AA, Ahmed AE, Jradi H. Health beliefs related to diabetes mellitus prevention among adolescents in Saudi Arabia. Sultan Qaboos University Medical Journal. 2015;15:e398. doi: 10.18295/squmj.2015.15.03.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 146.Musaiger AO, et al. Perceived barriers to weight maintenance among university students in Kuwait: the role of gender and obesity. Environ Health Prev Med. 2014;19:207–214. doi: 10.1007/s12199-013-0377-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 147.Rahim HF, et al. Non-communicable diseases in the Arab world. Lancet. 2014;383:356–367. doi: 10.1016/s0140-6736(13)62383-1. [DOI] [PubMed] [Google Scholar]
- 148.Baglar R. “Oh God, save us from sugar”: an ethnographic exploration of diabetes mellitus in the United Arab Emirates. Med Anthropol. 2013;32:109–125. doi: 10.1080/01459740.2012.671399. [DOI] [PubMed] [Google Scholar]
- 149.Al-Rafaee SA, Al-Hazzaa HM. Physical activity profile of adult males in Riyadh City. Saudi medical journal. 2001;22:784–789. [PubMed] [Google Scholar]
- 150.Al-Kandari F, Vidal VL. Correlation of the health-promoting lifestyle, enrollment level, and academic performance of College of Nursing students in Kuwait. Nurs Health Sci. 2007;9:112–119. doi: 10.1111/j.1442-2018.2007.00311.x. [DOI] [PubMed] [Google Scholar]
- 151.Ali HI, Baynouna LM, Bernsen RM. Barriers and facilitators of weight management: perspectives of Arab women at risk for type 2 diabetes. Health Soc Care Community. 2010;18:219–228. doi: 10.1111/j.1365-2524.2009.00896.x. [DOI] [PubMed] [Google Scholar]
- 152.Al Junaibi A, Abdulle A, Sabri S, Hag-Ali M, Nagelkerke N. The prevalence and potential determinants of obesity among school children and adolescents in Abu Dhabi, United Arab Emirates. Int J Obes (Lond) 2013;37:68–74. doi: 10.1038/ijo.2012.131. [DOI] [PubMed] [Google Scholar]
- 153.Ng SW, et al. Nutrition transition in the United Arab Emirates. Eur J Clin Nutr. 2011;65:1328–1337. doi: 10.1038/ejcn.2011.135. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 154.Albawardi NM, Jradi H, Al-Hazzaa HM. Levels and correlates of physical activity, inactivity and body mass index among Saudi women working in office jobs in Riyadh city. BMC Womens Health. 2016;16:33. doi: 10.1186/s12905-016-0312-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 155.Amin TT, Al Khoudair AS, Al Harbi MA, Al Ali AR. Leisure time physical activity in Saudi Arabia: prevalence, pattern and determining factors. Asian Pac J Cancer Prev. 2012;13:351–360. doi: 10.7314/apjcp.2012.13.1.351. [DOI] [PubMed] [Google Scholar]
- 156.Al-Hazzaa HM. Health-enhancing physical activity among Saudi adults using the International Physical Activity Questionnaire (IPAQ) Public Health Nutr. 2007;10:59–64. doi: 10.1017/S1368980007184299. [DOI] [PubMed] [Google Scholar]
- 157.Taha AZ. Self-reported knowledge and pattern of physical activity among school students in Al Khobar, Saudi Arabia. East Mediterr Health J. 2008;14:344–355. [PubMed] [Google Scholar]
- 158.Al-Sobayel H, Al-Hazzaa HM, Abahussain NA, Qahwaji DM, Musaiger AO. Gender differences in leisure-time versus non-leisure-time physical activity among Saudi adolescents. Ann Agric Environ Med. 2015;22:344–348. doi: 10.5604/12321966.1152091. [DOI] [PubMed] [Google Scholar]
- 159.Collison KS, et al. Sugar-sweetened carbonated beverage consumption correlates with BMI, waist circumference, and poor dietary choices in school children. BMC Public Health. 2010;10:234. doi: 10.1186/1471-2458-10-234. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 160.Al-Kandari YY. Prevalence of obesity in Kuwait and its relation to sociocultural variables. Obesity reviews: an official journal of the International Association for the Study of Obesity. 2006;7:147–154. doi: 10.1111/j.1467-789X.2006.00231.x. [DOI] [PubMed] [Google Scholar]
- 161.Carter AO, Saadi HF, Reed RL, Dunn EV. Assessment of obesity, lifestyle, and reproductive health needs of female citizens of Al Ain, United Arab Emirates. J Health Popul Nutr. 2004;22:75–83. [PubMed] [Google Scholar]
- 162.Garawi F, Ploubidis GB, Devries K, Al-Hamdan N, Uauy R. Do routinely measured risk factors for obesity explain the sex gap in its prevalence? Observations from Saudi Arabia. BMC public health. 2015;15:254–254. doi: 10.1186/s12889-015-1608-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 163.Amin TT, et al. Physical activity and cancer prevention: awareness and meeting the recommendations among adult Saudis. Asian Pac J Cancer Prev. 2014;15:2597–2606. doi: 10.7314/apjcp.2014.15.6.2597. [DOI] [PubMed] [Google Scholar]
- 164.Al-Isa AN, Campbell J, Desapriya E, Wijesinghe N. Social and Health Factors Associated with Physical Activity among Kuwaiti College Students. J Obes. 2011;2011:512363. doi: 10.1155/2011/512363. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 165.Sabri S, et al. Some risk factors for hypertension in the United Arab Emirates. East Mediterr Health J. 2004;10:610–619. [PubMed] [Google Scholar]
- 166.Al-Nakeeb Y, Lyons M, Dodd LJ, Al-Nuaim A. An investigation into the lifestyle, health habits and risk factors of young adults. Int J Environ Res Public Health. 2015;12:4380–4394. doi: 10.3390/ijerph120404380. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 167.Khalid MEM. The association between strenuous physical activity and obesity in high and low altitude populations in southern Saudi Arabia. International journal of obesity and related metabolic disorders: journal of the International Association for the Study of Obesity. 1995;19:776–780. [PubMed] [Google Scholar]
- 168.Assah FK, Ekelund U, Brage S, Mbanya JC, Wareham NJ. Urbanization, physical activity, and metabolic health in sub-Saharan Africa. Diabetes Care. 2011;34:491–496. doi: 10.2337/dc10-0990. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 169.Sullivan R, et al. Socio-demographic patterning of physical activity across migrant groups in India: results from the Indian Migration Study. PLoS One. 2011;6:e24898. doi: 10.1371/journal.pone.0024898. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 170.Monda KL, Gordon-Larsen P, Stevens J, Popkin BM. China’s transition: The effect of rapid urbanization on adult occupational physical activity. Social Science & Medicine. 2007;64:858–870. doi: 10.1016/j.socscimed.2006.10.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 171.Shediac-Rizkallah MC, Soweid RAA, Farhat TM, Yeretzian J. Adolescent health-related behaviors in postwar Lebanon: findings among students at the American University of Beirut. International quarterly of community health education. 2000;20:115–131. [Google Scholar]
- 172.Fazah A, et al. Activity, inactivity and quality of life among Lebanese adolescents. Pediatr Int. 2010;52:573–578. doi: 10.1111/j.1442-200X.2009.03021.x. [DOI] [PubMed] [Google Scholar]
- 173.Jabre P, Sikias P, Khater-Menassa B, Baddoura R, Awada H. Overweight children in Beirut: prevalence estimates and characteristics. Child Care Health Dev. 2005;31:159–165. doi: 10.1111/j.1365-2214.2004.00458.x. [DOI] [PubMed] [Google Scholar]
- 174.Rguibi M, Belahsen R. High blood pressure in urban Moroccan Sahraoui women. J Hypertens. 2007;25:1363–1368. doi: 10.1097/HJH.0b013e3280f31b83. [DOI] [PubMed] [Google Scholar]
- 175.Rguibi M, Belahsen R. Overweight and obesity among urban Sahraoui women of South Morocco. Ethn Dis. 2004;14:542–547. [PubMed] [Google Scholar]
- 176.El-Gilany AH, Badawi K, El-Khawaga G, Awadalla N. Physical activity profile of students in Mansoura University, Egypt. East Mediterr Health J. 2011;17:694–702. [PubMed] [Google Scholar]
- 177.Abolfotouh MA, Bassiouni FA, Mounir GM, Fayyad R. Health-related lifestyles and risk behaviours among students living in Alexandria University Hostels. East Mediterr Health J. 2007;13:376–391. [PubMed] [Google Scholar]
- 178.Nsour M, Mahfoud Z, Kanaan MN, Balbeissi A. Prevalence and predictors of nonfatal myocardial infarction in Jordan. East Mediterr Health J. 2008;14:818–830. [PubMed] [Google Scholar]
- 179.Madanat H, Merrill RM. Motivational factors and stages of change for physical activity among college students in Amman, Jordan. Promotion & education. 2006;13:185–190. [PubMed] [Google Scholar]
- 180.Musaiger AO, et al. Perceived barriers to healthy eating and physical activity among adolescents in seven Arab countries: a cross-cultural study. ScientificWorldJournal. 2013;2013:232164. doi: 10.1155/2013/232164. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 181.Musharrafieh U, et al. Determinants of university students physical exercise: a study from Lebanon. Int J Public Health. 2008;53:208–213. doi: 10.1007/s00038-008-7037-x. [DOI] [PubMed] [Google Scholar]
- 182.Hallaj FA, El Geneidy MM, Mitwally HH, Ibrahim HS. Activity patterns of residents in homes for the elderly in Alexandria, Egypt. East Mediterr Health J. 2010;16:1183–1188. [PubMed] [Google Scholar]
- 183.Aounallah-Skhiri H, et al. Health and behaviours of Tunisian school youth in an era of rapid epidemiological transition. East Mediterr Health J. 2009;15:1201–1214. [PubMed] [Google Scholar]
- 184.Lachheb M. Religion in practice. The veil in the sporting space in Tunisia. Social Compass. 2012;59:120–135. [Google Scholar]
- 185.Rguibi M, Belahsen R. Fattening practices among Moroccan Saharawi women. East Mediterr Health J. 2006;12:619–624. [PubMed] [Google Scholar]
- 186.El Rhazi K, et al. Prevalence of obesity and associated sociodemographic and lifestyle factors in Morocco. Public Health Nutr. 2011;14:160–167. doi: 10.1017/s1368980010001825. [DOI] [PubMed] [Google Scholar]
- 187.Abdul-Rahim HF, et al. Obesity in a rural and an urban Palestinian West Bank population. International journal of obesity and related metabolic disorders: journal of the International Association for the Study of Obesity. 2003;27:140–146. doi: 10.1038/sj.ijo.0802160. [DOI] [PubMed] [Google Scholar]
- 188.Sirdah MM, Al Laham NA, Abu Ghali AS. Prevalence of metabolic syndrome and associated socioeconomic and demographic factors among Palestinian adults (20-65 years) at the Gaza Strip. Diabetes Metab Syndr. 2011;5:93–97. doi: 10.1016/j.dsx.2012.02.024. [DOI] [PubMed] [Google Scholar]
- 189.Gharaibeh M, Al-Ma’aitah R, Al Jada N. Lifestyle practices of Jordanian pregnant women. Int Nurs Rev. 2005;52:92–100. doi: 10.1111/j.1466-7657.2005.00257.x. [DOI] [PubMed] [Google Scholar]
- 190.Haddad LG, Owies A, Mansour A. Wellness appraisal among adolescents in Jordan: a model from a developing country: a cross-sectional questionnaire survey. Health Promot Int. 2009;24:130–139. doi: 10.1093/heapro/dap013. [DOI] [PubMed] [Google Scholar]
- 191.Sibai AM, Costanian C, Tohme R, Assaad S, Hwalla N. Physical activity in adults with and without diabetes: from the ‘high-risk’ approach to the ‘population-based’ approach of prevention. BMC Public Health. 2013;13:1002. doi: 10.1186/1471-2458-13-1002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 192.Al-Tannir M, Kobrosly S, Itani T, El-Rajab M, Tannir S. Prevalence of physical activity among Lebanese adults: a cross-sectional study. Journal of Physical Activity and Health. 2009;6:315–320. doi: 10.1123/jpah.6.3.315. [DOI] [PubMed] [Google Scholar]
- 193.Najdi A, et al. Correlates of physical activity in Morocco. Preventive medicine. 2011;52:355–357. doi: 10.1016/j.ypmed.2011.03.009. [DOI] [PubMed] [Google Scholar]
- 194.Fouad M, Rastam S, Ward K, Maziak W. Prevalence of obesity and its associated factors in Aleppo, Syria. Prev Control. 2006;2:85–94. doi: 10.1016/j.precon.2006.09.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 195.Al Ali R, Rastam S, Fouad FM, Mzayek F, Maziak W. Modifiable cardiovascular risk factors among adults in Aleppo, Syria. International journal of public health. 2011;56:653–662. doi: 10.1007/s00038-011-0278-0. [DOI] [PubMed] [Google Scholar]
- 196.Al Sabbah H, Vereecken C, Kolsteren P, Abdeen Z, Maes L. Food habits and physical activity patterns among Palestinian adolescents: findings from the national study of Palestinian schoolchildren (HBSC-WBG2004) Public health nutrition. 2007;10:739–746. doi: 10.1017/S1368980007665501. [DOI] [PubMed] [Google Scholar]
- 197.Al-Tawil NG, Abdulla MM, Abdul Ameer AJ. Prevalence of and factors associated with overweight and obesity among a group of Iraqi women. East Mediterr Health J. 2007;13:420–429. [PubMed] [Google Scholar]
- 198.Centers for Disease Control and Prevention Prevalence of selected risk factors for chronic disease–Jordan, 2002. MMWR. Morbidity and mortality weekly report. 2003;52:1042. [PubMed] [Google Scholar]
- 199.Moukhyer M, Van Eijk J, De Vries N, Bosma H. Health-related behaviors of Sudanese adolescents. Education for health. 2008;21:184. [PubMed] [Google Scholar]
- 200.Chamieh MC, et al. Diet, physical activity and socio-economic disparities of obesity in Lebanese adults: findings from a national study. BMC Public Health. 2015;15:279. doi: 10.1186/s12889-015-1605-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 201.Batnitzky A. Obesity and household roles: gender and social class in Morocco. Sociol Health Illn. 2008;30:445–462. doi: 10.1111/j.1467-9566.2007.01067.x. [DOI] [PubMed] [Google Scholar]
- 202.Aounallah-Skhiri H, et al. Nutritional status of Tunisian adolescents: associated gender, environmental and socio-economic factors. Public Health Nutr. 2008;11:1306–1317. doi: 10.1017/s1368980008002693. [DOI] [PubMed] [Google Scholar]
- 203.Maatoug J, et al. Clustering of risk factors with smoking habits among adults, Sousse, Tunisia. Preventing chronic disease. 2013;10:E211–E211. doi: 10.5888/pcd10.130075. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 204.Chacar HR, Salameh P. Public schools adolescents’ obesity and growth curves in Lebanon. J Med Liban. 2011;59:80–88. [PubMed] [Google Scholar]
- 205.Salazar-Martinez E, et al. Overweight and obesity status among adolescents from Mexico and Egypt. Arch Med Res. 2006;37:535–542. doi: 10.1016/j.arcmed.2005.10.014. [DOI] [PubMed] [Google Scholar]
- 206.Ammouri AA, Neuberger G, Nashwan AJ, Al‐Haj AM. Determinants of self‐reported physical activity among Jordanian adults. Journal of Nursing Scholarship. 2007;39:342–348. doi: 10.1111/j.1547-5069.2007.00191.x. [DOI] [PubMed] [Google Scholar]
- 207.Mahasneh SM. Health perceptions and health behaviours of poor urban Jordanian women. J Adv Nurs. 2001;36:58–68. doi: 10.1046/j.1365-2648.2001.01943.x. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.