Physical activity and sedentary behaviour in the Middle East and North Africa: An overview of systematic reviews and meta-analysis


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.


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 NCDs5,6,7,8,9,10,11,12,13,14,15 leading to premature mortality10,11,16,17,18. 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).


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)34,35,36. 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 century39,40,41,42. 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 contexts50,51,52,53,54,55. 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.


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.

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 S8S11. 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.

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.

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.

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 studies63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84 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.


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 regionally52,53,56,57,58 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.

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 debatable101,102,103,104 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 youth111,112,113,114,115. 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.
Table 7 Evidence gap and future research.

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.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.


  1. 1.

    The World Health Organization (WHO). Noncommunicable diseases, (2018).

  2. 2.

    International Diabetes Federation (IDF). IDF Diabetes Atlas. (2017).

  3. 3.

    Hurt, R. T., Kulisek, C., Buchanan, L. A. & McClave, S. A. The Obesity Epidemic: Challenges, Health Initiatives, and Implications for Gastroenterologists. Gastroenterology & Hepatology 6, 780–792 (2010).

    Google Scholar 

  4. 4.

    Nikoloski, Z. & Williams, G. In Metabolic Syndrome (ed R.S. Ahima) (Springer International Publishing Switzerland 2016, Philadelphia, PA, USA).

  5. 5.

    Warburton, D. E. R., Nicol, C. W. & Bredin, S. S. D. Health benefits of physical activity: the evidence. Canadian Medical Association Journal 174, 801–809, (2006).

    Article  PubMed  Google Scholar 

  6. 6.

    World Health Organization (WHO). In Global Recommendations on Physical Activity for Health (World Health Organization 2010, Geneva, 2010).

  7. 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. 8.

    González, K., Fuentes, J. & Márquez, J. L. Physical Inactivity, Sedentary Behavior and Chronic Diseases. Korean Journal of Family Medicine 38, 111–115, (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Hallal, P. C. et al. Global physical activity levels: surveillance progress, pitfalls, and prospects. The Lancet 380, 247–257, (2012).

    ADS  Article  Google Scholar 

  10. 10.

    Kyu, H. H. 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 354, i3857, (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Lee, C. D., Folsom, A. R. & Blair, S. N. Physical activity and stroke risk: a meta-analysis. Stroke 34, 2475–2481, (2003).

    Article  PubMed  Google Scholar 

  12. 12.

    Lee, I. M. et al. Impact of Physical Inactivity on the World’s Major Non-Communicable Diseases. Lancet 380, 219–229, (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  13. 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).

  14. 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 41, S240–S265, (2016).

    Article  PubMed  Google Scholar 

  15. 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, (2018).

  16. 16.

    Berlin, J. A. & Colditz, G. A. A meta-analysis of physical activity in the prevention of coronary heart disease. American journal of epidemiology 132, 612–628 (1990).

    CAS  PubMed  Article  Google Scholar 

  17. 17.

    Sofi, F., Capalbo, A., Cesari, F., Abbate, R. & Gensini, G. F. 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 15, 247–257, (2008).

    Article  Google Scholar 

  18. 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 33, S530–S550 (2001).

    CAS  Article  Google Scholar 

  19. 19.

    Street, S. J., Wells, J. C. K. & Hills, A. P. Windows of opportunity for physical activity in the prevention of obesity. Obesity Reviews 16, 857–870, (2015).

    CAS  Article  PubMed  Google Scholar 

  20. 20.

    Biddle, S. J. H. & Asare, M. Physical activity and mental health in children and adolescents: a review of reviews. British Journal of Sports Medicine 45, 886–895, (2011).

    Article  PubMed  Google Scholar 

  21. 21.

    Khan, N. A. & Hillman, C. H. The Relation of Childhood Physical Activity and Aerobic Fitness to Brain Function and Cognition: A Review. Pediatric Exercise Science 26, 138–146, (2014).

    Article  PubMed  Google Scholar 

  22. 22.

    World Health Organization (WHO). Global Strategy on Diet, Physical Activity and Health, (2018).

  23. 23.

    Hallal, P. C., Martins, R. C. & Ramírez, A. The Lancet Physical Activity Observatory: promoting physical activity worldwide. The Lancet 384, 471–472, (2014).

    Article  Google Scholar 

  24. 24.

    Varela, A. R. et al. Mapping the historical development of physical activity and health research: A structured literature review and citation network analysis. Preventive Medicine 111, 466–472, (2018).

    Article  PubMed  Google Scholar 

  25. 25.

    Sallis, J. F. et al. Progress in physical activity over the Olympic quadrennium. The Lancet 388, 1325–1336, (2016).

    Article  Google Scholar 

  26. 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&#xb7;9 million participants. The Lancet Global Health, (2018).

  27. 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 7, 94, (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Booth, A. et al. PROSPERO at one year: an evaluation of its utility. Systematic Reviews 2, 4, (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Moher, D., Liberati, A., Tetzlaff, J. & Altman, D. G. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. J Clin Epidemiol 62, 1006–1012, (2009).

    Article  PubMed  Google Scholar 

  30. 30.

    Higgins J & Green S. (The Cochrane Collaboration, 2011).

  31. 31.

    Quenby, M., Dwayne, V. E. & Emma, I. Searching for grey literature for systematic reviews: challenges and benefits. Research Synthesis Methods 5, 221–234, (2014).

    Article  Google Scholar 

  32. 32.

    Ouzzani, M., Hammady, H., Fedorowicz, Z. & Elmagarmid, A. Rayyan—a web and mobile app for systematic reviews. Systematic Reviews 5, 210, (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Troiano, R. P., Gabriel, K. K. P., Welk, G. J., Owen, N. & Sternfeld, B. Reported Physical Activity and Sedentary Behavior: Why Do You Ask? Journal of Physical Activity and Health 9, S68–S75, (2012).

    Article  PubMed  Google Scholar 

  34. 34.

    Mehairi, A. E. et al. Metabolic syndrome among Emirati adolescents: a school-based study. PLoS One 8, e56159, (2013).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Muhairi, S. J. et al. Vitamin D deficiency among healthy adolescents in Al Ain, United Arab Emirates. BMC Public Health 13, 33, (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Wasfi, A. S., El-Sherbiny, A. A., Gurashi, E. & Al Sayegh, F. U. Sport practice among private secondary-school students in Dubai in 2004. East Mediterr Health J 14, 704–714 (2008).

    CAS  PubMed  Google Scholar 

  37. 37.

    Chaabna, K., Cheema, S. & Mamtani, R. Migrants, healthy worker effect, and mortality trends in the Gulf Cooperation Council countries. PLOS ONE 12, e0179711, (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Jakovljevic, M. M. 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 14, 30, (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Farrag, N. S., Cheskin, L. J. & Farag, M. K. 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 4, 8, (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    UN news. Malnutrition among children in Yemen at ‘all-time high,’ warns UNICEF, (2016).

  41. 41.

    World Bank. Middle East and North Africa., (2018).

  42. 42.

    Khan, H. T. A., Hussein, S. & Deane, J. Nexus Between Demographic Change and Elderly Care Need in the Gulf Cooperation Council (GCC) Countries: Some Policy Implications. Ageing International 42, 466–487, (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Higgins, J. P. T., Thompson, S. G., Deeks, J. J. & Altman, D. G. Measuring inconsistency in meta-analyses. BMJ 327, 557–560, (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Michael Borenstein, Larry V. Hedges, Julian P.T. Higgins & Rothstein, H. R. Borenstein, M. Introduction to meta-analysis.. (John Wiley & Son, 2009).

  45. 45.

    Schwarzer, G. (CRAN, 2018).

  46. 46.

    National Institutes of Health: Division of Cancer Control and Population Sciences. List of physical activity standard questionnaires validations studies,

  47. 47.

    Shea, B. J. et al. Development of AMSTAR: a measurement tool to assess the methodological quality of systematic reviews. BMC Medical Research Methodology 7, 10, (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Shea, B. J. et al. AMSTAR is a reliable and valid measurement tool to assess the methodological quality of systematic reviews. Journal of Clinical Epidemiology 62, 1013–1020, (2009).

    Article  PubMed  Google Scholar 

  49. 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).

  50. 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. 51.

    Davids, E. & Roman, N. A systematic review of the relationship between parenting styles and children’s physical activity. Vol. 20 (2014).

  52. 52.

    Mabry, R., Koohsari, M. J., 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 16, 1003, (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Mabry, R. M., Reeves, M. M., Eakin, E. G. & 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 11, 457–464, (2010).

    CAS  Article  Google Scholar 

  54. 54.

    Ranasinghe, C. D., Ranasinghe, P., Jayawardena, R. & Misra, A. Physical activity patterns among South-Asian adults: a systematic review. International Journal of Behavioral Nutrition and Physical Activity 10, 116, (2013).

    Article  PubMed  Google Scholar 

  55. 55.

    Boyle, M. H. Guidelines for evaluating prevalence studies. Evidence Based Mental Health 1, 37–39, (1998).

    Article  Google Scholar 

  56. 56.

    Yammine, K. The prevalence of physical activity among the young population of UAE: a meta-analysis. Perspectives in Public Health 137, 275–280, (2016).

    Article  PubMed  Google Scholar 

  57. 57.

    Al-Hazzaa, H. M. Physical inactivity in Saudi Arabia revisited: A systematic review of inactivity prevalence and perceived barriers to active living. Int J Health Sci (Qassim) 12, 50–64 (2018).

    Google Scholar 

  58. 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 18, 639, (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  59. 59.

    International Physical Activity Questionnaire, (2005).

  60. 60.

    World Health Organization (WHO). In Working version 5 (Geneva 2014).

  61. 61.

    Al-Hazzaa, H. M., Musaiger, A. O. & Group, A. R. Arab Teens Lifestyle Study (ATLS): objectives, design, methodology and implications. Diabetes Metab Syndr Obes 4, 417–426, (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Al-Hazzaa, H. M. Pedometer-determined physical activity among obese and non-obese 8- to 12-year-old Saudi schoolboys. J Physiol Anthropol 26, 459–465 (2007).

    PubMed  Article  Google Scholar 

  63. 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 7, 7593–7615, (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  64. 64.

    El-Aty, M. A. et al. Metabolic Syndrome and Its Components: Secondary analysis of the World Health Survey, Oman. Sultan Qaboos Univ Med J 14, e460–467 (2014).

    PubMed  PubMed Central  Google Scholar 

  65. 65.

    Mabry, R. M., Winkler, E. A., Reeves, M. M., Eakin, E. G. & Owen, N. Correlates of Omani adults’ physical inactivity and sitting time. Public Health Nutr 16, 65–72, (2013).

    Article  PubMed  Google Scholar 

  66. 66.

    WHO-STEPS Survey Kuwait. World Health Organization (WHO) Stepwise Approach to NCD Surveillance, Kuwait. (MOH & WHO, Kuwait City, Kuwait, 2008).

  67. 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. 68.

    Al-Hazzaa, H. M., Abahussain, N. A., Al-Sobayel, H. I., Qahwaji, D. M. & Musaiger, A. O. Physical activity, sedentary behaviors and dietary habits among Saudi adolescents relative to age, gender and region. International Journal of Behavioral Nutrition and Physical Activity 8, 140 (2011).

    PubMed  Article  Google Scholar 

  69. 69.

    Al-Hazzaa, H. M. 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 10, 6701–6720, (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Al-Hazzaa, H. M. & Al-Rasheedi, A. A. Adiposity and physical activity levels among preschool children in Jeddah, Saudi Arabia. Saudi medical journal 28, 766–773 (2007).

    PubMed  Google Scholar 

  71. 71.

    Al-Hazzaa, H. M. et al. Association of dietary habits with levels of physical activity and screen time among adolescents living in Saudi Arabia. J Hum Nutr Diet 27(Suppl 2), 204–213, (2013).

    Article  PubMed  Google Scholar 

  72. 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 9, 1490–1506, (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Al-Nuaim, A. A. 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, 417589, (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Allafi, A. et al. Physical activity, sedentary behaviours and dietary habits among Kuwaiti adolescents: gender differences. Public Health Nutr 17, 2045–2052, (2013).

    Article  PubMed  Google Scholar 

  75. 75.

    Farghaly, N. F., Ghazali, B. M., Al-Wabel, H. M., Sadek, A. A. & Abbag, F. I. Life style and nutrition and their impact on health of Saudi school students in Abha, Southwestern region of Saudi Arabia. Saudi medical journal 28, 415–421 (2007).

    PubMed  Google Scholar 

  76. 76.

    Gharib, N. M. & Rasheed, P. Obesity Among Bahraini Children and Adolescents: Prevalence And Associated Factors. Journal of the Bahrain Medical Society 20 (2008).

  77. 77.

    Guthold, R., Cowan, M. J., Autenrieth, C. S., Kann, L. & Riley, L. M. Physical activity and sedentary behavior among schoolchildren: a 34-country comparison. J Pediatr 157(43-49), e41, (2010).

    Article  Google Scholar 

  78. 78.

    Guthold, R., Ono, T., Strong, K. L., Chatterji, S. & Morabia, A. Worldwide variability in physical inactivity a 51-country survey. American journal of preventive medicine 34, 486–494, (2008).

    Article  PubMed  Google Scholar 

  79. 79.

    Kilani, H., Al-Hazzaa, H., Waly, M. I. & Musaiger, A. Lifestyle Habits: Diet, physical activity and sleep duration among Omani adolescents. Sultan Qaboos Univ Med J 13, 510–519 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  80. 80.

    Mahfouz, A. A. et al. Obesity and related behaviors among adolescent school boys in Abha City, Southwestern Saudi Arabia. J Trop Pediatr 54, 120–124, (2008).

    Article  PubMed  Google Scholar 

  81. 81.

    Mahfouz, A. A. et al. Nutrition, physical activity, and gender risks for adolescent obesity in Southwestern Saudi Arabia. Saudi J Gastroenterol 17, 318–322, (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Musaiger, A. O. & Zagzoog, N. Dietary and lifestyle habits among adolescent girls in Saudi Arabia. Nutrition & Food Science 43, 605–610 (2013).

    Article  Google Scholar 

  83. 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 18, 203–207, (2013).

    Article  Google Scholar 

  84. 84.

    Youssef, R. M., 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 19, 759–768 (2013).

    CAS  PubMed  Article  Google Scholar 

  85. 85.

    Pate, R. R., Pratt, M. & Blair, S. N. 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 273, 402–407, (1995).

    CAS  Article  PubMed  Google Scholar 

  86. 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).

  87. 87.

    Tremblay, M. S. et al. New Canadian physical activity guidelines. Appl Physiol Nutr Metab 36(36-46), 47–58, (2011).

    Article  Google Scholar 

  88. 88.

    Sisson, S. B. & Katzmarzyk, P. T. International prevalence of physical activity in youth and adults. Obesity reviews: an official journal of the International Association for the Study of Obesity 9, 606–614, (2008).

    CAS  Article  Google Scholar 

  89. 89.

    Khuwaja, A. K. & Kadir, M. M. Gender differences and clustering pattern of behavioural risk factors for chronic non-communicable diseases: community-based study from a developing country. Chronic Illn 6, 163–170, (2010).

    Article  PubMed  Google Scholar 

  90. 90.

    Cleland, C. L. 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 14, 1255, (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  91. 91.

    Craig, C. L. et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc 35, 1381–1395, (2003).

    Article  PubMed  Google Scholar 

  92. 92.

    Ainsworth, B. E. et al. Comparison of the 2001 BRFSS and the IPAQ Physical Activity Questionnaires. Medicine & Science in Sports & Exercise 38, 1584–1592, (2006).

    Article  Google Scholar 

  93. 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 9, 258–265, (2006).

    Article  PubMed  Google Scholar 

  94. 94.

    Lee, P. H., Macfarlane, D. J., Lam, T. H. & Stewart, S. M. Validity of the international physical activity questionnaire short form (IPAQ-SF): A systematic review. The International Journal of Behavioral Nutrition and Physical Activity 8, 115–115, (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  95. 95.

    Rzewnicki, R., Auweele, Y. V. & Bourdeaudhuij, I. D. Addressing overreporting on the International Physical Activity Questionnaire (IPAQ) telephone survey with a population sample. Public Health Nutrition 6, 299–305, (2003).

    Article  PubMed  Google Scholar 

  96. 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. 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. 98.

    Alkahtani, S. A. Convergent validity: agreement between accelerometry and the Global Physical Activity Questionnaire in college-age Saudi men. BMC Research Notes 9, 436, (2016).

    MathSciNet  Article  PubMed  PubMed Central  Google Scholar 

  99. 99.

    Healy, G. N. et al. Measurement of Adults’ Sedentary Time in Population-Based Studies. American journal of preventive medicine 41, 216–227, (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  100. 100.

    Sallis, J. F. Measuring Physical Activity: Practical Approaches for Program Evaluation in Native American Communities. Journal of public health management and practice: JPHMP 16, 404–410, (2010).

    Article  PubMed  Google Scholar 

  101. 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 2, 52, (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  102. 102.

    Tudor-Locke, C. et al. BMI-referenced standards for recommended pedometer-determined steps/day in children. Preventive Medicine 38, 857–864, (2004).

    Article  PubMed  Google Scholar 

  103. 103.

    Beets, M., Beighle, A., Bottai, M., Rooney, L. & Tilley, F. Pedometer-Determined Step-Count Guidelines for Afterschool Programs. Vol. 9 (2012).

  104. 104.

    Harrington, D. M. et al. Step-based translation of physical activity guidelines in the Lower Mississippi Delta. Applied Physiology, Nutrition, and Metabolism 36, 583–585, (2011).

    Article  PubMed  Google Scholar 

  105. 105.

    Tudor-Locke, C., Williams, J. E., Reis, J. P. & Pluto, D. Utility of pedometers for assessing physical activity: convergent validity. Sports Med 32, 795–808, (2002).

    Article  PubMed  Google Scholar 

  106. 106.

    Rowlands, A. V. & Eston, R. G. Comparison of accelerometer and pedometer measures of physical activity in boys and girls, ages 8-10 years. Research quarterly for exercise and sport 76, 251–257, (2005).

    Article  PubMed  Google Scholar 

  107. 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).

  108. 108.

    McNamara, E., Hudson, Z. & Taylor, S. J. C. Measuring activity levels of young people: the validity of pedometers. British Medical Bulletin 95, 121–137, (2010).

    Article  PubMed  Google Scholar 

  109. 109.

    Al-Hazzaa, H. M., Al-Sobayel, H. I. & Musaiger, A. O. Convergent Validity of the Arab Teens Lifestyle Study (ATLS) Physical Activity Questionnaire. International Journal of Environmental Research and Public Health 8, 3810 (2011).

    PubMed  PubMed Central  Article  Google Scholar 

  110. 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 Med 47, (2017).

  111. 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 One 10, (2015).

  112. 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 Quart 33, (2016).

  113. 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 Soc 63, (2015).

  114. 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 Health 38, (2015).

  115. 115.

    Trost, S. G., McIver, K. L. & Pate, R. R. Conducting accelerometer-based activity assessments in field-based research. Med Sci Sports Exerc 37, S531–543 (2005).

    PubMed  PubMed Central  Article  Google Scholar 

  116. 116.

    Tudor-Locke, C., Craig, C. L., Thyfault, J. P. & Spence, J. C. A step-defined sedentary lifestyle index: <5000 steps/day. Applied Physiology, Nutrition, and Metabolism 38, 100–114, (2012).

    Article  PubMed  Google Scholar 

  117. 117.

    Al-Hazzaa Physical activity profile of college male students. King Saud University Journal (Educational Sciences) 2, 383–396 (1990).

    Google Scholar 

  118. 118.

    Gawwad, E. S. A. Stages of change in physical activity, self efficacy and decisional balance among saudi university students. Journal of family & community medicine 15, 107–115 (2008).

    Google Scholar 

  119. 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 10, 3502–3517, (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  120. 120.

    Awadalla, N. J. et al. Assessment of physical inactivity and perceived barriers to physical activity among health college students, south-western Saudi Arabia. East Mediterr Health J 20, 596–604 (2014).

    CAS  PubMed  Article  Google Scholar 

  121. 121.

    Amin, T. T., 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 8, 775–784, (2011).

    Article  PubMed  Google Scholar 

  122. 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 10, 188–196, (2015).

    Article  Google Scholar 

  123. 123.

    Al-Otaibi, H. H. Measuring stages of change, perceived barriers and self efficacy for physical activity in Saudi Arabia. Asian Pac J Cancer Prev 14, 1009–1016, (2013).

    Article  PubMed  Google Scholar 

  124. 124.

    Mandil, A. M., Alfurayh, N. A., Aljebreen, M. A. & Aldukhi, S. A. Physical activity and major non-communicable diseases among physicians in Central Saudi Arabia. Saudi medical journal 37, 1243–1250, (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  125. 125.

    Bajamal, E. et al. Physical Activity Among Female Adolescents in Jeddah, Saudi Arabia: A Health Promotion Model-Based Path Analysis. Nurs Res 66, 473–482, (2017).

    Article  PubMed  Google Scholar 

  126. 126.

    Al-Nozha, M. M. et al. Prevalence of physical activity and inactivity among Saudis aged 30-70 years. A population-based cross-sectional study. Saudi medical journal 28, 559–568 (2007).

    PubMed  Google Scholar 

  127. 127.

    Alam, A. A. Obesity among female school children in North West Riyadh in relation to affluent lifestyle. Saudi medical journal 29, 1139–1144 (2008).

    PubMed  Google Scholar 

  128. 128.

    Alsubaie, A. S. R. & Omer, E. O. M. Physical Activity Behavior Predictors, Reasons and Barriers among Male Adolescents in Riyadh, Saudi Arabia: Evidence for Obesogenic Environment. Int. J Health Sci (Qassim) 9, 400–408 (2015).

    Google Scholar 

  129. 129.

    AlQuaiz, A. M. & Tayel, S. A. Barriers to a healthy lifestyle among patients attending primary care clinics at a university hospital in Riyadh. Annals of Saudi medicine 29, 30–35, (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  130. 130.

    Samara, A., Nistrup, A., Al-Rammah, T. Y. & Aro, A. R. Lack of facilities rather than sociocultural factors as the primary barrier to physical activity among female Saudi university students. Int. J Womens Health 7, 279–286, (2015).

    Article  Google Scholar 

  131. 131.

    Al-Hazzaa, H. M. et al. Patterns and determinants of physical activity among Saudi adolescents. J Phys Act Health 11, 1202–1211, (2014).

    Article  PubMed  Google Scholar 

  132. 132.

    Berger, G. & Peerson, A. Giving young Emirati women a voice: participatory action research on physical activity. Health Place 15, 117–124, (2009).

    Article  PubMed  Google Scholar 

  133. 133.

    Kim, H. J., Choi-Kwon, S., Kim, H., Park, Y. H. & Koh, C. K. Health-promoting lifestyle behaviors and psychological status among Arabs and Koreans in the United Arab Emirates. Res Nurs Health 38, 133–141, (2015).

    Article  PubMed  Google Scholar 

  134. 134.

    Mabry, R. M., Al-Busaidi, Z. Q., Reeves, M. M., Owen, N. & Eakin, E. G. Addressing physical inactivity in Omani adults: perceptions of public health managers. Public Health Nutr 17, 674–681, (2014).

    Article  PubMed  Google Scholar 

  135. 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 15, 836 (2018).

    PubMed Central  Article  Google Scholar 

  136. 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 159, 362–373, (2017).

    ADS  CAS  Article  PubMed  Google Scholar 

  137. 137.

    McCloskey, M. L. 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 14, 90, (2017).

    Article  PubMed  Google Scholar 

  138. 138.

    Eime, R. M. 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 41, 248–255, (2017).

    Article  PubMed  Google Scholar 

  139. 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 13, e0190737, (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  140. 140.

    Al-Rukban, M. O. Obesity among Saudi male adolescents in Riyadh, Saudi Arabia. Saudi medical journal 24, 27–33 (2003).

    PubMed  Google Scholar 

  141. 141.

    Alquaiz, A. M. et al. Correlates of cardiovascular disease risk scores in women in Riyadh, Kingdom of Saudi Arabia. Women Health 55, 103–117, (2015).

    Article  PubMed  Google Scholar 

  142. 142.

    Al-Gelban, K. S. Dietary habits and exercise practices among the students of a Saudi Teachers’ Training College. Saudi medical journal 29, 754–759 (2008).

    PubMed  Google Scholar 

  143. 143.

    Memish, Z. A. et al. Burden of disease, injuries, and risk factors in the Kingdom of Saudi Arabia, 1990-2010. Prev Chronic Dis 11, E169, (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  144. 144.

    Al-Baghli, N. A. et al. Overweight and obesity in the eastern province of Saudi Arabia. Saudi medical journal 29, 1319–1325 (2008).

    PubMed  Google Scholar 

  145. 145.

    Al-Mutairi, R. L., Bawazir, A. A., Ahmed, A. E. & Jradi, H. Health beliefs related to diabetes mellitus prevention among adolescents in Saudi Arabia. Sultan Qaboos University Medical Journal 15, e398 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  146. 146.

    Musaiger, A. O. et al. Perceived barriers to weight maintenance among university students in Kuwait: the role of gender and obesity. Environ Health Prev Med 19, 207–214, (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  147. 147.

    Rahim, H. F. et al. Non-communicable diseases in the Arab world. Lancet 383, 356–367, (2014).

    Article  PubMed  Google Scholar 

  148. 148.

    Baglar, R. “Oh God, save us from sugar”: an ethnographic exploration of diabetes mellitus in the United Arab Emirates. Med Anthropol 32, 109–125, (2013).

    Article  PubMed  Google Scholar 

  149. 149.

    Al-Rafaee, S. A. & Al-Hazzaa, H. M. Physical activity profile of adult males in Riyadh City. Saudi medical journal 22, 784–789 (2001).

    CAS  PubMed  Google Scholar 

  150. 150.

    Al-Kandari, F. & Vidal, V. L. Correlation of the health-promoting lifestyle, enrollment level, and academic performance of College of Nursing students in Kuwait. Nurs Health Sci 9, 112–119, (2007).

    Article  PubMed  Google Scholar 

  151. 151.

    Ali, H. I., Baynouna, L. M. & Bernsen, R. M. Barriers and facilitators of weight management: perspectives of Arab women at risk for type 2 diabetes. Health Soc Care Community 18, 219–228, (2010).

    Article  PubMed  Google Scholar 

  152. 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) 37, 68–74, (2013).

    CAS  Article  Google Scholar 

  153. 153.

    Ng, S. W. et al. Nutrition transition in the United Arab Emirates. Eur J Clin Nutr 65, 1328–1337, (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  154. 154.

    Albawardi, N. M., Jradi, H. & Al-Hazzaa, H. M. Levels and correlates of physical activity, inactivity and body mass index among Saudi women working in office jobs in Riyadh city. BMC Womens Health 16, 33, (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  155. 155.

    Amin, T. T., Al Khoudair, A. S., Al Harbi, M. A. & Al Ali, A. R. Leisure time physical activity in Saudi Arabia: prevalence, pattern and determining factors. Asian Pac J Cancer Prev 13, 351–360, (2012).

    Article  PubMed  Google Scholar 

  156. 156.

    Al-Hazzaa, H. M. Health-enhancing physical activity among Saudi adults using the International Physical Activity Questionnaire (IPAQ). Public Health Nutr 10, 59–64, (2007).

    Article  PubMed  Google Scholar 

  157. 157.

    Taha, A. Z. Self-reported knowledge and pattern of physical activity among school students in Al Khobar, Saudi Arabia. East Mediterr Health J 14, 344–355 (2008).

    CAS  PubMed  Google Scholar 

  158. 158.

    Al-Sobayel, H., Al-Hazzaa, H. M., Abahussain, N. A., Qahwaji, D. M. & Musaiger, A. O. Gender differences in leisure-time versus non-leisure-time physical activity among Saudi adolescents. Ann Agric Environ Med 22, 344–348, (2015).

    Article  PubMed  Google Scholar 

  159. 159.

    Collison, K. S. et al. Sugar-sweetened carbonated beverage consumption correlates with BMI, waist circumference, and poor dietary choices in school children. BMC Public Health 10, 234, (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  160. 160.

    Al-Kandari, Y. Y. 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 7, 147–154, (2006).

    CAS  Article  Google Scholar 

  161. 161.

    Carter, A. O., Saadi, H. F., Reed, R. L. & Dunn, E. V. Assessment of obesity, lifestyle, and reproductive health needs of female citizens of Al Ain, United Arab Emirates. J Health Popul Nutr 22, 75–83 (2004).

    PubMed  Google Scholar 

  162. 162.

    Garawi, F., Ploubidis, G. B., 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 15, 254–254, (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  163. 163.

    Amin, T. T. et al. Physical activity and cancer prevention: awareness and meeting the recommendations among adult Saudis. Asian Pac J Cancer Prev 15, 2597–2606, (2014).

    Article  PubMed  Google Scholar 

  164. 164.

    Al-Isa, A. N., Campbell, J., Desapriya, E. & Wijesinghe, N. Social and Health Factors Associated with Physical Activity among Kuwaiti College Students. J Obes 2011, 512363, (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  165. 165.

    Sabri, S. et al. Some risk factors for hypertension in the United Arab Emirates. East Mediterr Health J 10, 610–619 (2004).

    CAS  PubMed  Google Scholar 

  166. 166.

    Al-Nakeeb, Y., Lyons, M., Dodd, L. J. & Al-Nuaim, A. An investigation into the lifestyle, health habits and risk factors of young adults. Int J Environ Res Public Health 12, 4380–4394, (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  167. 167.

    Khalid, M. E. M. 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 19, 776–780 (1995).

    CAS  Google Scholar 

  168. 168.

    Assah, F. K., Ekelund, U., Brage, S., Mbanya, J. C. & Wareham, N. J. Urbanization, physical activity, and metabolic health in sub-Saharan Africa. Diabetes Care 34, 491–496, (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  169. 169.

    Sullivan, R. et al. Socio-demographic patterning of physical activity across migrant groups in India: results from the Indian Migration Study. PLoS One 6, e24898, (2011).

    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

  170. 170.

    Monda, K. L., Gordon-Larsen, P., Stevens, J. & Popkin, B. M. China’s transition: The effect of rapid urbanization on adult occupational physical activity. Social Science & Medicine 64, 858–870, (2007).

    Article  Google Scholar 

  171. 171.

    Shediac-Rizkallah, M. C., Soweid, R. A. A., Farhat, T. M. & Yeretzian, J. Adolescent health-related behaviors in postwar Lebanon: findings among students at the American University of Beirut. International quarterly of community health education 20, 115–131 (2000).

    Article  Google Scholar 

  172. 172.

    Fazah, A. et al. Activity, inactivity and quality of life among Lebanese adolescents. Pediatr Int 52, 573–578, (2010).

    Article  PubMed  Google Scholar 

  173. 173.

    Jabre, P., Sikias, P., Khater-Menassa, B., Baddoura, R. & Awada, H. Overweight children in Beirut: prevalence estimates and characteristics. Child Care Health Dev 31, 159–165, (2005).

    CAS  Article  PubMed  Google Scholar 

  174. 174.

    Rguibi, M. & Belahsen, R. High blood pressure in urban Moroccan Sahraoui women. J Hypertens 25, 1363–1368, (2007).

    CAS  Article  PubMed  Google Scholar 

  175. 175.

    Rguibi, M. & Belahsen, R. Overweight and obesity among urban Sahraoui women of South Morocco. Ethn Dis 14, 542–547 (2004).

    PubMed  Google Scholar 

  176. 176.

    El-Gilany, A. H., Badawi, K., El-Khawaga, G. & Awadalla, N. Physical activity profile of students in Mansoura University, Egypt. East Mediterr Health J 17, 694–702 (2011).

    CAS  PubMed  Article  Google Scholar 

  177. 177.

    Abolfotouh, M. A., Bassiouni, F. A., Mounir, G. M. & Fayyad, R. Health-related lifestyles and risk behaviours among students living in Alexandria University Hostels. East Mediterr Health J 13, 376–391 (2007).

    CAS  PubMed  Google Scholar 

  178. 178.

    Nsour, M., Mahfoud, Z., Kanaan, M. N. & Balbeissi, A. Prevalence and predictors of nonfatal myocardial infarction in Jordan. East Mediterr Health J 14, 818–830 (2008).

    CAS  PubMed  Google Scholar 

  179. 179.

    Madanat, H. & Merrill, R. M. Motivational factors and stages of change for physical activity among college students in Amman, Jordan. Promotion & education 13, 185–190 (2006).

    Article  Google Scholar 

  180. 180.

    Musaiger, A. O. et al. Perceived barriers to healthy eating and physical activity among adolescents in seven Arab countries: a cross-cultural study. ScientificWorldJournal 2013, 232164, (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  181. 181.

    Musharrafieh, U. et al. Determinants of university students physical exercise: a study from Lebanon. Int J Public Health 53, 208–213, (2008).

    Article  PubMed  Google Scholar 

  182. 182.

    Hallaj, F. A., El Geneidy, M. M., Mitwally, H. H. & Ibrahim, H. S. Activity patterns of residents in homes for the elderly in Alexandria, Egypt. East Mediterr Health J 16, 1183–1188 (2010).

    CAS  PubMed  Article  Google Scholar 

  183. 183.

    Aounallah-Skhiri, H. et al. Health and behaviours of Tunisian school youth in an era of rapid epidemiological transition. East Mediterr Health J 15, 1201–1214 (2009).

    CAS  PubMed  Google Scholar 

  184. 184.

    Lachheb, M. Religion in practice. The veil in the sporting space in Tunisia. Social Compass 59, 120–135 (2012).

    Article  Google Scholar 

  185. 185.

    Rguibi, M. & Belahsen, R. Fattening practices among Moroccan Saharawi women. East Mediterr Health J 12, 619–624 (2006).

    CAS  PubMed  Google Scholar 

  186. 186.

    El Rhazi, K. et al. Prevalence of obesity and associated sociodemographic and lifestyle factors in Morocco. Public Health Nutr 14, 160–167, (2011).

    CAS  Article  PubMed  Google Scholar 

  187. 187.

    Abdul-Rahim, H. F. 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 27, 140–146, (2003).

    CAS  Article  Google Scholar 

  188. 188.

    Sirdah, M. M., Al Laham, N. A. & Abu Ghali, A. S. Prevalence of metabolic syndrome and associated socioeconomic and demographic factors among Palestinian adults (20-65 years) at the Gaza Strip. Diabetes Metab Syndr 5, 93–97, (2011).

    Article  PubMed  Google Scholar 

  189. 189.

    Gharaibeh, M., Al-Ma’aitah, R. & Al Jada, N. Lifestyle practices of Jordanian pregnant women. Int Nurs Rev 52, 92–100, (2005).

    CAS  Article  PubMed  Google Scholar 

  190. 190.

    Haddad, L. G., Owies, A. & Mansour, A. Wellness appraisal among adolescents in Jordan: a model from a developing country: a cross-sectional questionnaire survey. Health Promot Int 24, 130–139, (2009).

    Article  PubMed  Google Scholar 

  191. 191.

    Sibai, A. M., 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 13, 1002, (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  192. 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 6, 315–320 (2009).

    PubMed  Article  Google Scholar 

  193. 193.

    Najdi, A. et al. Correlates of physical activity in Morocco. Preventive medicine 52, 355–357 (2011).

    PubMed  Article  Google Scholar 

  194. 194.

    Fouad, M., Rastam, S., Ward, K. & Maziak, W. Prevalence of obesity and its associated factors in Aleppo, Syria. Prev Control 2, 85–94, (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  195. 195.

    Al Ali, R., Rastam, S., Fouad, F. M., Mzayek, F. & Maziak, W. Modifiable cardiovascular risk factors among adults in Aleppo, Syria. International journal of public health 56, 653–662 (2011).

    PubMed  Article  Google Scholar 

  196. 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 10, 739–746 (2007).

    CAS  PubMed  Article  Google Scholar 

  197. 197.

    Al-Tawil, N. G., Abdulla, M. M. & Abdul Ameer, A. J. Prevalence of and factors associated with overweight and obesity among a group of Iraqi women. East Mediterr Health J 13, 420–429 (2007).

    CAS  PubMed  Google Scholar 

  198. 198.

    Centers for Disease Control and Prevention. Prevalence of selected risk factors for chronic disease–Jordan, 2002. MMWR. Morbidity and mortality weekly report 52, 1042 (2003).

    Google Scholar 

  199. 199.

    Moukhyer, M., Van Eijk, J., De Vries, N. & Bosma, H. Health-related behaviors of Sudanese adolescents. Education for health 21, 184 (2008).

    PubMed  Google Scholar 

  200. 200.

    Chamieh, M. C. et al. Diet, physical activity and socio-economic disparities of obesity in Lebanese adults: findings from a national study. BMC Public Health 15, 279, (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  201. 201.

    Batnitzky, A. Obesity and household roles: gender and social class in Morocco. Sociol Health Illn 30, 445–462, (2008).

    Article  PubMed  Google Scholar 

  202. 202.

    Aounallah-Skhiri, H. et al. Nutritional status of Tunisian adolescents: associated gender, environmental and socio-economic factors. Public Health Nutr 11, 1306–1317, (2008).

    Article  PubMed  Google Scholar 

  203. 203.

    Maatoug, J. et al. Clustering of risk factors with smoking habits among adults, Sousse, Tunisia. Preventing chronic disease 10, E211–E211, (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  204. 204.

    Chacar, H. R. & Salameh, P. Public schools adolescents’ obesity and growth curves in Lebanon. J Med Liban 59, 80–88 (2011).

    PubMed  Google Scholar 

  205. 205.

    Salazar-Martinez, E. et al. Overweight and obesity status among adolescents from Mexico and Egypt. Arch Med Res 37, 535–542, (2006).

    Article  PubMed  Google Scholar 

  206. 206.

    Ammouri, A. A., Neuberger, G., Nashwan, A. J. & Al‐Haj, A. M. Determinants of self‐reported physical activity among Jordanian adults. Journal of Nursing Scholarship 39, 342–348 (2007).

    PubMed  Article  Google Scholar 

  207. 207.

    Mahasneh, S. M. Health perceptions and health behaviours of poor urban Jordanian women. J Adv Nurs 36, 58–68, (2001).

    CAS  Article  PubMed  Google Scholar 

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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.

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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.

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Correspondence to Karima Chaabna.

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Chaabane, S., Chaabna, K., Abraham, A. et al. Physical activity and sedentary behaviour in the Middle East and North Africa: An overview of systematic reviews and meta-analysis. Sci Rep 10, 9363 (2020).

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