Food selection, cardiorespiratory fitness and the longitudinal association with adiposity among South African school children: ExAMIN Youth SA study

Food selection, cardiorespiratory fitness and the longitudinal association with adiposity among South African school children: ExAMIN Youth SA study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Food selection, cardiorespiratory fitness and the longitudinal association with adiposity among South African school children: ExAMIN Youth SA study Herculina Kruger, Cristian Ricci, Anita Pienaar, Makama Monyeki, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7371897/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background: The global pandemic of paediatric obesity, unhealthy diets and physical inactivity are important future health challenges. The purpose of this study was to determine 4-year changes in food selection, cardiorespiratory fitness (CRF), and the longitudinal association withadiposity among South African children. Methods: School children aged 5 to 9 years old in 2017 (n=950) were followed up four years later. Parents indicated selection from healthy and unhealthy food groups in a validated questionnaire. Weight and height of the children were measured, and WHO BMI z-score (BAZ) was calculated. CRF was determined by the 20-m shuttle run test. Changes in food selection frequency, CRF and BAZ were assessed. The association between frequency of food selection, age, household income and CRF with 4-year change in BAZ was determined using mixed linear models. Results: CRF and BAZ increased over four years (both p<0.001). The frequency of sugar-sweetened beverages (p<0.001) and milk intake (p<0.02) decreased, while the intake from fast-foods increased (p=0.001). Daily intakes of vegetables and milk were associated with decreases in BAZ. A trend of a positive association was found between frequency of SSB intake and BAZ. CRF showed a strong negative association with BAZ over time (p<0.001). Conclusions: Higher daily vegetable and milk intakes, as well as higher levels of CRF were protective against increasing adiposity among school-age children, whereas daily SSB intake was associated with an increased adiposity. The promotion of good eating habits and measures to improve CRF among school children are important policy change priorities. Health sciences/Risk factors Biological sciences/Developmental biology Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The global pandemic of paediatric obesity, unhealthy diets and physical inactivity are important future health challenges. A recent meta-analysis of studies in healthy children, including national surveys, showed that the global prevalence of obesity among children and adolescents increased from 7.1% among studies performed from year 2000 to 2011, to 11.3% among studies performed from 2012-to 2023. In the same meta-analysis, the prevalence of obesity was significantly higher among those who reported insufficient physical activity (PA) (12.1%) than in participants reporting sufficient PA according to recommendations (7.7%).[ 1 ] In a systematic review of longitudinal studies among children and adolescents, a decline in PA with time was found across age groups.[ 2 ] Low levels of cardiorespiratory fitness (CRF) have been linked to excessive adiposity, as well as cardiovascular disease risk markers such as serum lipids, systolic blood pressure, and diabetes risk.[ 3 ] Therefore, early detection of overweight and obesity during childhood and timely prevention may curb the long-lasting adverse health effects of excessive adiposity in childhood.[ 1 ] Evidence on the effects of consuming unhealthy foods and beverages in longitudinal studies among children aged < 11 years across middle- and high-income countries was synthesised in a recent systematic review.[ 4 ] Consumption of sugar-sweetened beverages (SSB) was positively associated with percentage body fat (%BF) but showed no association with change in body mass index-for-age z-score (BAZ) at follow-up. Ten studies on the effects of unhealthy food intake, such as ultra-processed foods, salty fried snacks and foods with a high sugar content, on increases in adiposity or the odds of overweight/obesity among children 5 to 11years old were found. Of these, the results of three studies showed a positive association between snack foods and adiposity measures, whereas seven showed no association, and overall certainty of evidence was low.[ 4 ] Based on these findings, the authors pointed out the need for prospective studies in low- and middle-income countries on the types of foods, in particular unhealthy foods and SSB, consumed in relation to nutritional outcomes. Such data are needed to make nutritional recommendations aimed at preventing paediatric overweight and obesity.[ 4 ] The decline in PA levels in children over time has been linked to a high prevalence of overweight and obesity, as well as low levels of CRF.[ 5 ] The latter is regarded as a marker of regular physical activity, but also of genetic predisposition.[ 6 ] Recent regional studies in South Africa found a high prevalence of both overweight and physical inactivity among children.[ 5 , 7 , 8 ] The prevalence of paediatric overweight and obesity increases over time and is more prevalent in girls compared to boys in Sub-Saharan Africa.[ 9 , 10 ] A review of South African studies indicated that 50% of children are not meeting the recommended average of 60 minutes of moderate-to-vigorous PA per day.[ 11 ] Urbanisation and the shift away from active transport particularly in rural areas, appear to contribute to decreasing PA.[ 12 ] Given the lack of evidence for longitudinal associations between unhealthy food intake, SSB and CRF with adiposity in South African primary school children, the aim of this study was to determine 4-year changes in food selection and CRF and the longitudinal association of these variables with the development of adiposity among South African primary school-age children. Methods This study is part of the longitudinal Exercise, Arterial Modulation and Nutrition in Youth South Africa (ExAMIN Youth SA) study among 5- to 9-year-old children, aimed to determine changes in the prevalence of childhood hypertension and obesity.[ 8 , 13 ] The study protocol was registered in a clinical trials registry (ClinicalTrials.gov Identifier: NCT04056377), following the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) guidelines.[ 14 ] Baseline data were collected from 1,103 children during 2017 and 2018. The original sample size calculation indicated that 1,000 children should be included. After excluding dropouts, incomplete data and absentees, baseline data of 950 children were available for the analysis for this part of the study and 672 children were followed up four years later (2021–2022).[ 13 ] Population and setting The research team received permission from the provincial Department of Education and principals of 10 urban schools from the Dr. Kenneth Kaunda district in the North West province of South Africa to conduct this research project.[ 8 , 13 ] Five schools were from quintile three and five schools from quintiles four to five, based on the level of government funding for schools and the area in which the school is located. Quintile one represents communities with the lowest employment rate and literacy, whereas Quintile five represents the most affluent.[ 15 ] The parents of 1,103 children gave informed consent. The only exclusion criterion was children with minor ailments on the day of data collection. Each child < 7years old signed an informed assent form and those ≥ 7 years signed an informed consent form before measurements. After excluding children with missing data for key variables for this part of the study, 950 children were included at baseline. Complete baseline and endline sociodemographic, body composition and food group data were obtained from 672 of these children, while fitness data were available for 323 children. Figure 1 shows the participant flow diagram for this longitudinal study. The Health Research Ethics Committee (NWU-00091-16-A1) approved the study. A day before baseline data collection, children received an information pamphlet, a General Health and Demographics Questionnaire, and a food intake questionnaire to take home. Parents had to complete the questionnaires and return them on the day of participation, as reported in detail elsewhere.[ 13 ] Anthropometric measurements and physical activity Weight was taken on a Seca 813 digital scale (Birmingham, United Kingdom). Height was measured barefoot to the nearest 0.1 cm with a Seca 213 stadiometer (Birmingham, United Kingdom) according to the International Society for the Advancement of Kinanthropometry protocol.[ 16 ] We calculated body mass index (BMI), height-for-age and BAZ using the WHO 2006 AnthroPlus growth software [ 17 , 18 ] to identify underweight (BAZ 1 to 2) and obesity (BAZ > 2).[ 17 ] %BF was estimated using bio-electric impedance analysis (BIA, BodyStat 1500MDD, Multiscan 5000 model, Douglas, United Kingdom). An equation developed for children and based on Bodystat 1500MD measurements was used to calculate fat-free mass, fat mass and %BF.[ 19 ] CRF was assessed using the 20-m Shuttle Run Test (20-m SRT), which is a valid and recognised field-based endurance test that shows reliability in children aged six to 16 years old. The test involves running back and forth across a 20-m distance.[ 20 ] The starting speed is 8.0 km/h, with a 0.5 km/h rise every minute, paced by beeps on a stereo. A final score is taken when a participant drops out because of exhaustion or cannot cross the 20-m line at the point of the beep for two consecutive 20-m laps.[ 20 ] Food intake questionnaire Children’s usual dietary intake from ten food groups was assessed using a validated unquantified food frequency questionnaire.[ 21 ] The questionnaire was developed based on validated questionnaires,[ 22 – 26 ] and finalised based on studies from South Africa [ 22 , 27 – 29 ]. The questionnaire included four healthy food groups, namely fruits, vegetables, milk, meat/fish/poultry/eggs and six unhealthy food groups, tea and coffee with sugar, cold SSB, sweets, salty snacks, cakes and fast foods. The five different responses for frequency of intake were never (0 days), 1–2 days, 3–4 days, 5–6 days, or 7 days per week. Healthy foods were defined as nutrient dense foods containing essential nutrients for child health,[ 22 ] whereas unhealthy foods were defined as foods high in energy, sugar, salt and fats, but with low nutrient density.[ 30 ] The same measurements of the children were repeated in 2021–2022, after 4 years of follow-up since 2017–2018, as described in detail elsewhere.[ 8 , 31 ] Statistical analysis Descriptive analyses of age, household income, anthropometric data, physical fitness and frequency of intake from food groups were performed. Results were reported as mean and standard deviation for continuous data and counts and percentages for categorical data. The responses of frequency of intake (0, 1–2 days, 3–4 days, 5–6 days, or 7 days per week) were coded as 0, 1.5, 3.5, 5.5 and 7. Tests were performed for cases with complete data for each test. Changes over time were assessed using a random intercept generalized linear mixed model adjusting for baseline age, sex of the child and household income. The model was based on the negative binomial distribution with logarithmic link to account for the potential overdispersion of the outcomes. Exponentiated least squares mean at baseline and endpoint were reported and compared by a Wald t-test.[ 32 ] Firstly, the negative binomial distribution has been chosen to account for potential overdispersion. Secondly, a random intercept effect was chosen to better consider individual variability. The same model with interaction terms between frequencies of intake from the different food groups, age, sex of the child, household income and time (factors) has been applied to investigate the effect of the above factors on BAZ and %BF, respectively, over the observational time. The effect of CRF on BAZ and %BF over time was assessed in similar models. All statistical tests were two tailed with a type-I error rate of 5% as threshold for statistical significance. Analysis was performed using SPSS version 30 for Windows (SPSS, Chicago, IL, USA) and SAS version 9.04 (proc glimmix SA, S, Cary, NC, USA). Results The characteristics of the children at baseline are shown in Table 1. The %BF and BAZ (p < 0.001, Fig. 2), as well as CRF increased at follow-up (p < 0.001, Fig. 3). The frequency of SSB (p < 0.001), as well as milk intake (p < 0.02) decreased, while the intake from fast foods increased (p = 0.001). The frequency of selection from other food groups remained unchanged (Fig. 4). The prevalence of overweight (14.9 to 18.2%) and obesity (4.2 to 14.3%) among the children increased at follow-up. In mixed models, with adjustment for age, sex of the children and household income, selection from the vegetable group 7 times/week vs 0 was associated with a decrease in %BF (estimated reduction=-2.7%, p = 0.03) and showed a trend of a decrease in BAZ units (estimated reduction=-0.3, p = 0.06). Daily intake from the milk group vs no milk showed an estimated reduction of -0.4 BAZ units (p = 0.01). In contrast, daily intake of SSB vs 0 was associated with a trend of an estimated increase in BAZ of 0.29 units (p = 0.07). Unexpectedly, daily intake of cake or biscuits vs 0 was associated with an estimated decrease in BAZ of 0.38 units (p = 0.02). Overall, the frequency of intake of biscuits were low and few children ate biscuits daily. No other associations with intakes from the food groups were found. CRF, according to the number of pacer laps completed, showed a negative correlation with BAZ (r = -0.43, p < 0.0001), as well as with %BF (r -0.38, p < 0.0001) over time, after adjustment for age, sex of the child and household income. Each increase in number of pacer laps completed was associated with an estimated reduction of -0.04 BAZ units (p < 0.0001) and a reduction in %BF of -0.2% (p < 0.0001) at follow-up. Discussion Overweight and obesity prevalence increased over four years among the primary school-age children, whereas an age-related increase in CRF was found. Although the overall frequency of SSB intake decreased, only those children with no SSB intake vs daily intake had lower estimated BAZ and %BF. The intake from fast foods increased, but there was no association between fast food intakes and the increase in BAZ. Instead, daily selection from the vegetable and milk groups vs none were associated with lower BAZ. Number of pacer laps completed in the CRF test was negatively associated with age-adjusted BAZ and %BF over time. After four years of follow-up almost one-third of the children were in the overweight/obesity category. This prevalence is higher than the national prevalence for this age group, estimated at 12.5% in a recent meta-analysis of studies in primary school-age children.[ 33 ] Another local study also showed that overweight and obesity prevalence increased from early childhood up to mid-childhood.[ 9 ] The marked increase in overweight and obesity could partly be explained by the frequency of SSB intake in the present study. Furthermore, it is important to note that baseline data were collected before the onset of the COVID-19 pandemic, while follow-up in 2021–2022 took place after most COVID restrictions were relaxed. A systematic review of the impact of the COVID-19 lockdown on childhood obesity showed that the abrupt interruption of organized sport led to decreased PA among children. Simultaneously, screen time increased due to distance learning and confinement at home. Studies reported that these behavioural shifts were accompanied by increased consumption of fast foods, unhealthy snacks and SSB. Apparently, the higher energy intake and expected lower energy expenditure may have contributed to the weight gain, a trend supported by studies that reported significant increases in body weight.[ 34 ] For example, in the only longitudinal study included in this systematic review, Chinese children aged 7–12 years old at baseline in 2019 were recruited before lockdown and their weight and height were measured. The same children were followed up after the strict lockdown period 9 months later and the prevalence of overweight and obesity among the children increased while 42.4% of the overweight children became obese.[ 35 ] Unhealthy food marketing directed at children contributes to a lifelong preference for such foods and influences family food purchases.[ 36 ] Therefore, regulation of marketing of unhealthy food to children should be mandatory, monitored, and enforced in South Africa,[ 37 ] where there is currently no legal restriction on the marketing of food to children. The Food and Beverage Advertising Code (2008) and the South African Marketing to Children Pledge (2009) are private sector self-regulatory measures to protect children from unhealthy food and beverage marketing. These codes had no clear impact in reducing unhealthy food and beverage marketing to children due to weak enforcement.[ 37 ] The increasing frequency of consumption of fast foods is of concern and in line with findings from other studies in South Africa. Frequent intakes of unhealthy snack foods and SSB have been reported across household income groups.[ 27 , 38 , 39 ] Low intakes of fruit and vegetables have been reported among South African children, with possible adverse health implications.[ 39 ] The age-related increase in CRF was expected due to the children's physical development. CRF depends on both regular physical activity and genetic predisposition.[ 6 ] Differences in CRF within the same age groups of boys and girls could be an indication of regular physical activity of the children. This is supported by a study in Spanish children, 9–11 years old, where CRF acted as a partial mediator in the negative relationship between dietary factors (energy intake/weight, carbohydrate intake/weight, protein intake/weight, and fat intake/weight) and fat mass. Thus, Spanish schoolchildren with higher levels of energy and macronutrients intake had lower adiposity levels, when they had good levels of CRF, confirming both dietary intakes and CRF as key variables for maintaining the energy balance.[ 40 ] Physical inactivity among young children remains a major public health problem in many countries.[ 41 ] Data from several studies assessing the association between PA and adiposity among children are available, but findings are inconsistent.[ 42 , 43 ] Most studies on the association between objectively measured PA and adiposity were conducted in high-income countries. A few studies in South African children focused on preschool children and adolescents,[ 44 , 45 ] while studies among primary school-age children had inconsistent results, which may be due to the challenges related to accurate measurement of physical activity in this age group.[ 5 , 7 ] A study in 7 to 10 year old urban children from a high-income setting showed no association between PA and BMI.[ 7 ] Other studies in South African children aged 9 to 13 years, and 5 to 13 years, respectively, indicated that the children’s PA level correlated negatively with %BF, calculated by using a prediction equation based on two skinfold thicknesses,[ 46 ] and by BIA.[ 5 ] Another study found significant positive associations between body mass and sedentary time among an ethnically diverse urban group of South African school children 5 to 18 years old. The same study indicated an age-related decline in PA and increase in sedentary time among school-age children, whereas overweight and obesity prevalence increased up to mid-childhood.[ 47 ] These results are in line with the results of the current study, indicating that physical activity may prevent excessive weight gain over time during childhood. A recent reanalysis of data from two Cochrane reviews showed that the greatest obesity prevention effects were for interventions in children targeting physical activity alone compared with diet alone. The most effective combination was school setting interventions with individualised physical activity of short duration and high intensity and involving behaviour modification.[ 48 ] Early detection and prevention of obesity among children is important to improve their future health and to relief the growing burden of adult comorbidities. Poor diets and physical inactivity are modifiable risk factors, that could be addressed in school-based interventions.[ 49 ] The conclusion from a recent systematic review of the effectiveness of school-based obesity prevention interventions on the health behaviour of children was that obesity prevention programmes had a positive impact on fruit and vegetable intakes and PA and decreased SSB intake.[ 50 ] Limitations of this study include that CRF could only be measured using a field-based test and in a subsample of children due to logistical reasons. The study participants attended South African quintile groups 3 to 5 schools, indicating medium to high employment rate and literacy of the communities. Quintiles 1 and 2 represents the poorest schools,[ 15 ] and these participants were excluded from this study for logistical reasons. The results may therefore not apply to children from the lowest socio-economic status groups in South Africa. Despite these limitations the strength of the study was that it was the first longitudinal study of intakes from healthy and unhealthy food groups in a relatively large sample of primary school aged South African children over a 4-year period, and the association with adiposity. In conclusion, higher daily vegetable and milk intakes, as well as higher levels of CRF were protective against increasing adiposity among school-age children, whereas daily SSB intake was associated with an increased overweight and obesity prevalence. These findings indicate that policy changes to promote good eating habits and measures to improve CRF should be prioritised among school children. Declarations Data Availability Statement The datasets generated and analysed during the current study are available from the corresponding author on reasonable request. Acknowledgments The authors are grateful towards all parents and children participating voluntarily in the study, and the goodwill of the school principals. The following research staff and postgraduate students are acknowledged for their dedication to acquiring the data: Mrs Bianca Petersen, Hypertension in Africa Research Team and Ms Persuade Makore, Centre of Excellence for Nutrition, North-West University, South Africa. Author Contributions HSK, RK, AEP and MAM designed the study. CR, TvZ and HR contributed to data analysis and interpretation. All authors helped write and review the manuscript. Funding The ExAMIN Youth SA study was funded by the South African Medical Research Council (SAMRC) Extra Mural Unit and the National Research Foundation (NRF) of South Africa for Competitive Support for Y-Rated Researchers (Unique Identification Number: 112141) and the NRF Equipment Related Training and Travel Grant (Unique Identification Number: 109905). Research reported in this paper was also supported by a South African Medical Research Council under a Self-Initiated Research Grant, and the South African Research Chairs Initiative (SARChI) of the Department of Science and Technology and National Research Foundation (NRF) of South Africa (Unique Identification Number: 86895). The support of International Atomic Energy Agency (IAEA) is greatly appreciated. Ethical Approval The study protocol was approved by the Centre of Excellence Scientific Review Committee of the North-West University. The researchers obtained approval from the Health Research Ethics Committee (NWU-00091-16-A1) of the North-West University to conduct the study. The parents and caregivers signed informed consent forms for the children while children, whose parents consented, signed assent forms for their participation in the study after they were provided with information about this study and their role and rights as participants. Competing Interests HSK is a member of the Research Grants Panel of the South African Sugar Association and receives a honorarium for the review of grant applications. The South African Sugar Association played no role in the design of the study, collection and analysis of data and the decision to submit this manuscript for publication. The other authors declare no competing interests. References Zhang X, Liu J, Ni Y, Yi C, Fang Y, Ning Q, et al (2024) Global prevalence of overweight and obesity in children and adolescents: a systematic review and meta-analysis. JAMA Pediatr 178:800-813. doi: 10.1001/jamapediatrics.2024.1576 Farooq A, Martin A, Janssen X, Wilson MG, Gibson AM, Hughes A, et al (2020) Longitudinal changes in moderate-to-vigorous-intensity physical activity in children and adolescents: A systematic review and meta-analysis. 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Int J Obes 45:2132-2136 doi: 10.1038/s41366-021-00871-w Qutteina Y, De Backer C, Smits T (2019) Media food marketing and eating outcomes among pre-adolescents and adolescents: A systematic review and meta-analysis. Obes Rev 20:1708-1719 doi: 10.1111/obr.12929 Erzse A, Karim SA, Foley L, Hofman KJ (2022) A realist review of voluntary actions by the food and beverage industry and implications for public health and policy in low- and middle-income countries. Nat Food 3:650-663 doi: 10.1038/s43016-022-00552-5 Feeley AB, Norris SA (2014) Added sugar and dietary sodium intake from purchased fast food, confectionery, sweetened beverages and snacks among Sowetan adolescents. South African Journal of Child Health 8:88-91. Steyn NP, de Villiers A, Gwebushe N, Draper CE, Hill J, de Waal M, et al (2015) Did HealthKick, a randomised controlled trial primary school nutrition intervention improve dietary quality of children in low-income settings in South Africa? BMC Publ Health 15:948. Lahoz-Garcia N, Garcia-Hermoso A, Milla-Tobarra M, Diez-Fernandez A, Soriano-Cano A, Martinez-Vizcaino V (2018) Cardiorespiratory fitness as a mediator of the influence of diet on obesity in children. Nutrients 10(3) doi: 10.3390/nu10030358 Bernhardsen GP, Stensrud T, Hansen BH, Steene-Johannesen J, Kolle E, Nystad W, et al (2020) Birth weight, cardiometabolic risk factors and effect modification of physical activity in children and adolescents: pooled data from 12 international studies. Int J Obes 44:2052-2063 doi: 10.1038/s41366-020-0612-9 Collings PJ, Westgate K, Väistö J, Wijndaele K, Atkin AJ, Haapala EA, et al (2016) Cross-Sectional Associations of Objectively-Measured Physical Activity and Sedentary Time with Body Composition and Cardiorespiratory Fitness in Mid-Childhood: The PANIC Study. Sports Med 47:769-780 doi: 10.1007/s40279-016-0606-x Reisberg K, Riso EM, Jurimae J (2020) Associations between physical activity, body composition, and physical fitness in the transition from preschool to school. Scand J Med Sci Sports 30:2251-2263 doi: 10.1111/sms.13784 Draper CE, Tomaz SA, Jones RA, Hinkley T, Twine R, Kahn K, et al (2019) Cross-sectional associations of physical activity and gross motor proficiency with adiposity in South African children of pre-school age. Publ Health Nutr 22:614-623 doi: 10.1017/S1368980018003579 Tomaz SA, Prioreschi A, Watson ED, McVeigh JA, Rae DE, Jones RA, et al (2019) Body mass index, physical activity, sedentary behavior, sleep, and gross motor skill proficiency in preschool children from a low- to middle-income urban setting. J Phy Act Health 16:525-532 doi: 10.1123/jpah.2018-0133 Moselakgomo KV, Monyeki A, Toriola AL (2015) Relationship between physical activity and risk factors of body weight disorders among South African primary school children. Biomed Res 26:730-738 McVeigh J, Meiring R (2014) Physical activity and sedentary behavior in an ethnically diverse group of South African school children. J Sports Sci Med 13:371-378 Davies AL, Spiga F, Caldwell DM, Savovic J, Palmer JC, Tomlinson E, et al (2025) Factors associated with the effectiveness of interventions to prevent obesity in children: a synthesis of evidence from 204 randomised trials. BMJ Publ Health 3:e001707 doi: 10.1136/bmjph-2024-001707 Pulimeno M, Piscitelli P, Colazzo S, Colao A, Miani A (2020) School as ideal setting to promote health and wellbeing among young people. Health Promot Perspect 10:316-324 doi: 10.34172/hpp.2020.50 McDiarmid K, Clinton-McHarg T, Wolfenden L, O'Brien K, Lee DCW, Stuart A, et al (2025) The effectiveness of school-based obesity prevention interventions on the health behaviours of children aged 6-18 years: A secondary data analysis of a systematic review. Prev Med Rep 53:103053 doi: 10.1016/j.pmedr.2025.103053 Table Table 1. Baseline descriptive characteristics of the children (n=950) Characteristic Boys Girls All children Mean/ frequency Standard deviation/% Mean/ frequency Standard deviation/% Mean/ frequency Standard deviation/% Age, years 7.5 0.93 7.4 0.95 7.4 0.94 Female sex, n (%) 507 53.4 Race: Black, n (%) 216 49.8 304 59.0 520 54.7 White, n (%) 188 43.3 222 43.1 410 43.2 Asian and mixed race, n (%) 9 2.1 11 2.1 20 2.1 School quintile: n (%) Quintile 3, no-school fee schools,government funded 134 30.9 178 34.6 312 32.8 Quintile 4, fee-paying schools, second highest income quintile 176 40.6 212 41.2 388 40.8 Quintile 5, fee-paying schools, highest income quintile 124 28.6 125 24.3 249 26.2 Anthropometric data: BMI for age z-score 0.03 1.2 0.04 1.1 0.03 1.1 BMI for age z-score categories Underweight, n (%) 19 4.4 17 3.3 36 3.8 Lean, n (%) 331 76.3 399 77.5 730 76.8 Overweight, n (%) 63 14.2 78 15.1 142 14.9 Obese, n (%) 21 4.8 21 4.1 42 4.4 Cardiorespiratory fitness : number of completed shuttle run laps 29.5 14.4 24.9 11.3 27.0 13.0 Abbreviations : BMI, body mass index Additional Declarations Yes there is potential conflict of interest. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: revise 05 Feb, 2026 Review # 1 received at journal 20 Oct, 2025 Review # 2 received at journal 16 Oct, 2025 Reviewer # 2 agreed at journal 30 Sep, 2025 Reviewer # 1 agreed at journal 21 Sep, 2025 Reviewers invited by journal 08 Sep, 2025 Editor assigned by journal 18 Aug, 2025 Submission checks completed at journal 18 Aug, 2025 First submitted to journal 16 Aug, 2025 Unknown event 15 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7371897","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":511903970,"identity":"3f782e77-7da7-4dab-a6f5-33f1f3938074","order_by":0,"name":"Herculina 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06:55:06","extension":"xml","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":123090,"visible":true,"origin":"","legend":"","description":"","filename":"2025EJCN09110structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7371897/v1/b8db8c2e25e254ac4df0038a.xml"},{"id":91488083,"identity":"fcfcfcc6-5bcb-4357-bc84-0382953c7c78","added_by":"auto","created_at":"2025-09-17 05:07:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":34870,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant flow diagram for this longitudinal study\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7371897/v1/0f09d840c3f7b83f6676c497.png"},{"id":91816888,"identity":"6747f49a-34dc-48df-9058-ac1205920ed4","added_by":"auto","created_at":"2025-09-22 06:52:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":11896,"visible":true,"origin":"","legend":"\u003cp\u003eBaseline and follow-up body mass index-for-age z-score of the children (n=790)\u003c/p\u003e\n\u003cp\u003eValues are mean and standard deviation\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7371897/v1/523575b4abb5ce88310a7cf7.png"},{"id":91488085,"identity":"131633cd-6716-436d-802d-d3b50a585d42","added_by":"auto","created_at":"2025-09-17 05:07:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":23884,"visible":true,"origin":"","legend":"\u003cp\u003eBaseline and follow-up cardiorespiratory fitness (CRF) of the boys (n=133) and girls (n=188)\u003c/p\u003e\n\u003cp\u003eValues are mean and standard deviation\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7371897/v1/b3656c0a5946275c86dd6894.png"},{"id":91488087,"identity":"94716fa9-c602-4242-bf79-64bcc5fe4af1","added_by":"auto","created_at":"2025-09-17 05:07:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":41514,"visible":true,"origin":"","legend":"\u003cp\u003eThe frequency of intakes (number of days/week) from healthy and unhealthy food groups at baseline and follow-up (least squares estimates from mixed model analysis)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7371897/v1/6f0c994fd325a72d4e7de92e.png"},{"id":91818059,"identity":"84bf4e02-aaaf-402d-b924-9bfef72f1450","added_by":"auto","created_at":"2025-09-22 07:01:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":660040,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7371897/v1/c38e842e-447f-4137-a7ef-090d3233f571.pdf"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential conflict of interest.","formattedTitle":"Food selection, cardiorespiratory fitness and the longitudinal association with adiposity among South African school children: ExAMIN Youth SA study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe global pandemic of paediatric obesity, unhealthy diets and physical inactivity are important future health challenges. A recent meta-analysis of studies in healthy children, including national surveys, showed that the global prevalence of obesity among children and adolescents increased from 7.1% among studies performed from year 2000 to 2011, to 11.3% among studies performed from 2012-to 2023. In the same meta-analysis, the prevalence of obesity was significantly higher among those who reported insufficient physical activity (PA) (12.1%) than in participants reporting sufficient PA according to recommendations (7.7%).[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] In a systematic review of longitudinal studies among children and adolescents, a decline in PA with time was found across age groups.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Low levels of cardiorespiratory fitness (CRF) have been linked to excessive adiposity, as well as cardiovascular disease risk markers such as serum lipids, systolic blood pressure, and diabetes risk.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] Therefore, early detection of overweight and obesity during childhood and timely prevention may curb the long-lasting adverse health effects of excessive adiposity in childhood.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eEvidence on the effects of consuming unhealthy foods and beverages in longitudinal studies among children aged\u0026thinsp;\u0026lt;\u0026thinsp;11 years across middle- and high-income countries was synthesised in a recent systematic review.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] Consumption of sugar-sweetened beverages (SSB) was positively associated with percentage body fat (%BF) but showed no association with change in body mass index-for-age z-score (BAZ) at follow-up. Ten studies on the effects of unhealthy food intake, such as ultra-processed foods, salty fried snacks and foods with a high sugar content, on increases in adiposity or the odds of overweight/obesity among children 5 to 11years old were found. Of these, the results of three studies showed a positive association between snack foods and adiposity measures, whereas seven showed no association, and overall certainty of evidence was low.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] Based on these findings, the authors pointed out the need for prospective studies in low- and middle-income countries on the types of foods, in particular unhealthy foods and SSB, consumed in relation to nutritional outcomes. Such data are needed to make nutritional recommendations aimed at preventing paediatric overweight and obesity.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eThe decline in PA levels in children over time has been linked to a high prevalence of overweight and obesity, as well as low levels of CRF.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] The latter is regarded as a marker of regular physical activity, but also of genetic predisposition.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] Recent regional studies in South Africa found a high prevalence of both overweight and physical inactivity among children.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] The prevalence of paediatric overweight and obesity increases over time and is more prevalent in girls compared to boys in Sub-Saharan Africa.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] A review of South African studies indicated that 50% of children are not meeting the recommended average of 60 minutes of moderate-to-vigorous PA per day.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Urbanisation and the shift away from active transport particularly in rural areas, appear to contribute to decreasing PA.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eGiven the lack of evidence for longitudinal associations between unhealthy food intake, SSB and CRF with adiposity in South African primary school children, the aim of this study was to determine 4-year changes in food selection and CRF and the longitudinal association of these variables with the development of adiposity among South African primary school-age children.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study is part of the longitudinal Exercise, Arterial Modulation and Nutrition in Youth South Africa (ExAMIN Youth SA) study among 5- to 9-year-old children, aimed to determine changes in the prevalence of childhood hypertension and obesity.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] The study protocol was registered in a clinical trials registry (ClinicalTrials.gov Identifier: NCT04056377), following the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) guidelines.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] Baseline data were collected from 1,103 children during 2017 and 2018. The original sample size calculation indicated that 1,000 children should be included. After excluding dropouts, incomplete data and absentees, baseline data of 950 children were available for the analysis for this part of the study and 672 children were followed up four years later (2021\u0026ndash;2022).[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePopulation and setting\u003c/h2\u003e\u003cp\u003e The research team received permission from the provincial Department of Education and principals of 10 urban schools from the Dr. Kenneth Kaunda district in the North West province of South Africa to conduct this research project.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] Five schools were from quintile three and five schools from quintiles four to five, based on the level of government funding for schools and the area in which the school is located. Quintile one represents communities with the lowest employment rate and literacy, whereas Quintile five represents the most affluent.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] The parents of 1,103 children gave informed consent. The only exclusion criterion was children with minor ailments on the day of data collection. Each child\u0026thinsp;\u0026lt;\u0026thinsp;7years old signed an informed assent form and those\u0026thinsp;\u0026ge;\u0026thinsp;7 years signed an informed consent form before measurements. After excluding children with missing data for key variables for this part of the study, 950 children were included at baseline. Complete baseline and endline sociodemographic, body composition and food group data were obtained from 672 of these children, while fitness data were available for 323 children. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the participant flow diagram for this longitudinal study.\u003c/p\u003e\u003cp\u003e The Health Research Ethics Committee (NWU-00091-16-A1) approved the study. A day before baseline data collection, children received an information pamphlet, a General Health and Demographics Questionnaire, and a food intake questionnaire to take home. Parents had to complete the questionnaires and return them on the day of participation, as reported in detail elsewhere.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAnthropometric measurements and physical activity\u003c/h3\u003e\n\u003cp\u003eWeight was taken on a Seca 813 digital scale (Birmingham, United Kingdom). Height was measured barefoot to the nearest 0.1 cm with a Seca 213 stadiometer (Birmingham, United Kingdom) according to the International Society for the Advancement of Kinanthropometry protocol.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] We calculated body mass index (BMI), height-for-age and BAZ using the WHO 2006 AnthroPlus growth software [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] to identify underweight (BAZ \u0026lt;-2), normal weight (BAZ \u0026minus;\u0026thinsp;2 to 1) overweight (BAZ\u0026thinsp;\u0026gt;\u0026thinsp;1 to 2) and obesity (BAZ\u0026thinsp;\u0026gt;\u0026thinsp;2).[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] %BF was estimated using bio-electric impedance analysis (BIA, BodyStat 1500MDD, Multiscan 5000 model, Douglas, United Kingdom). An equation developed for children and based on Bodystat 1500MD measurements was used to calculate fat-free mass, fat mass and %BF.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eCRF was assessed using the 20-m Shuttle Run Test (20-m SRT), which is a valid and recognised field-based endurance test that shows reliability in children aged six to 16 years old. The test involves running back and forth across a 20-m distance.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] The starting speed is 8.0 km/h, with a 0.5 km/h rise every minute, paced by beeps on a stereo. A final score is taken when a participant drops out because of exhaustion or cannot cross the 20-m line at the point of the beep for two consecutive 20-m laps.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\n\u003ch3\u003eFood intake questionnaire\u003c/h3\u003e\n\u003cp\u003eChildren\u0026rsquo;s usual dietary intake from ten food groups was assessed using a validated unquantified food frequency questionnaire.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] The questionnaire was developed based on validated questionnaires,[\u003cspan additionalcitationids=\"CR23 CR24 CR25\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and finalised based on studies from South Africa [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The questionnaire included four healthy food groups, namely fruits, vegetables, milk, meat/fish/poultry/eggs and six unhealthy food groups, tea and coffee with sugar, cold SSB, sweets, salty snacks, cakes and fast foods. The five different responses for frequency of intake were never (0 days), 1\u0026ndash;2 days, 3\u0026ndash;4 days, 5\u0026ndash;6 days, or 7 days per week. Healthy foods were defined as nutrient dense foods containing essential nutrients for child health,[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] whereas unhealthy foods were defined as foods high in energy, sugar, salt and fats, but with low nutrient density.[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] The same measurements of the children were repeated in 2021\u0026ndash;2022, after 4 years of follow-up since 2017\u0026ndash;2018, as described in detail elsewhere.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eDescriptive analyses of age, household income, anthropometric data, physical fitness and frequency of intake from food groups were performed. Results were reported as mean and standard deviation for continuous data and counts and percentages for categorical data. The responses of frequency of intake (0, 1\u0026ndash;2 days, 3\u0026ndash;4 days, 5\u0026ndash;6 days, or 7 days per week) were coded as 0, 1.5, 3.5, 5.5 and 7. Tests were performed for cases with complete data for each test.\u003c/p\u003e\u003cp\u003eChanges over time were assessed using a random intercept generalized linear mixed model adjusting for baseline age, sex of the child and household income. The model was based on the negative binomial distribution with logarithmic link to account for the potential overdispersion of the outcomes. Exponentiated least squares mean at baseline and endpoint were reported and compared by a Wald t-test.[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] Firstly, the negative binomial distribution has been chosen to account for potential overdispersion. Secondly, a random intercept effect was chosen to better consider individual variability. The same model with interaction terms between frequencies of intake from the different food groups, age, sex of the child, household income and time (factors) has been applied to investigate the effect of the above factors on BAZ and %BF, respectively, over the observational time. The effect of CRF on BAZ and %BF over time was assessed in similar models. All statistical tests were two tailed with a type-I error rate of 5% as threshold for statistical significance. Analysis was performed using SPSS version 30 for Windows (SPSS, Chicago, IL, USA) and SAS version 9.04 (proc glimmix SA, S, Cary, NC, USA).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe characteristics of the children at baseline are shown in Table 1. The %BF and BAZ (p \u0026lt; 0.001, Fig. 2), as well as CRF increased at follow-up (p \u0026lt; 0.001, Fig. 3). The frequency of SSB (p \u0026lt; 0.001), as well as milk intake (p \u0026lt; 0.02) decreased, while the intake from fast foods increased (p = 0.001). The frequency of selection from other food groups remained unchanged (Fig. 4). The prevalence of overweight (14.9 to 18.2%) and obesity (4.2 to 14.3%) among the children increased at follow-up.\u003c/p\u003e\n\u003cp\u003eIn mixed models, with adjustment for age, sex of the children and household income, selection from the vegetable group 7 times/week vs 0 was associated with a decrease in %BF (estimated reduction=-2.7%, p = 0.03) and showed a trend of a decrease in BAZ units (estimated reduction=-0.3, p = 0.06). Daily intake from the milk group vs no milk showed an estimated reduction of -0.4 BAZ units (p = 0.01). In contrast, daily intake of SSB vs 0 was associated with a trend of an estimated increase in BAZ of 0.29 units (p = 0.07). Unexpectedly, daily intake of cake or biscuits vs 0 was associated with an estimated decrease in BAZ of 0.38 units (p = 0.02). Overall, the frequency of intake of biscuits were low and few children ate biscuits daily. No other associations with intakes from the food groups were found.\u003c/p\u003e\n\u003cp\u003eCRF, according to the number of pacer laps completed, showed a negative correlation with BAZ (r = -0.43, p \u0026lt; 0.0001), as well as with %BF (r -0.38, p \u0026lt; 0.0001) over time, after adjustment for age, sex of the child and household income. Each increase in number of pacer laps completed was associated with an estimated reduction of -0.04 BAZ units (p \u0026lt; 0.0001) and a reduction in %BF of -0.2% (p \u0026lt; 0.0001) at follow-up.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOverweight and obesity prevalence increased over four years among the primary school-age children, whereas an age-related increase in CRF was found. Although the overall frequency of SSB intake decreased, only those children with no SSB intake vs daily intake had lower estimated BAZ and %BF. The intake from fast foods increased, but there was no association between fast food intakes and the increase in BAZ. Instead, daily selection from the vegetable and milk groups vs none were associated with lower BAZ. Number of pacer laps completed in the CRF test was negatively associated with age-adjusted BAZ and %BF over time.\u003c/p\u003e\u003cp\u003eAfter four years of follow-up almost one-third of the children were in the overweight/obesity category. This prevalence is higher than the national prevalence for this age group, estimated at 12.5% in a recent meta-analysis of studies in primary school-age children.[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] Another local study also showed that overweight and obesity prevalence increased from early childhood up to mid-childhood.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] The marked increase in overweight and obesity could partly be explained by the frequency of SSB intake in the present study. Furthermore, it is important to note that baseline data were collected before the onset of the COVID-19 pandemic, while follow-up in 2021\u0026ndash;2022 took place after most COVID restrictions were relaxed. A systematic review of the impact of the COVID-19 lockdown on childhood obesity showed that the abrupt interruption of organized sport led to decreased PA among children. Simultaneously, screen time increased due to distance learning and confinement at home. Studies reported that these behavioural shifts were accompanied by increased consumption of fast foods, unhealthy snacks and SSB. Apparently, the higher energy intake and expected lower energy expenditure may have contributed to the weight gain, a trend supported by studies that reported significant increases in body weight.[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] For example, in the only longitudinal study included in this systematic review, Chinese children aged 7\u0026ndash;12 years old at baseline in 2019 were recruited before lockdown and their weight and height were measured. The same children were followed up after the strict lockdown period 9 months later and the prevalence of overweight and obesity among the children increased while 42.4% of the overweight children became obese.[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eUnhealthy food marketing directed at children contributes to a lifelong preference for such foods and influences family food purchases.[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] Therefore, regulation of marketing of unhealthy food to children should be mandatory, monitored, and enforced in South Africa,[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] where there is currently no legal restriction on the marketing of food to children. The Food and Beverage Advertising Code (2008) and the South African Marketing to Children Pledge (2009) are private sector self-regulatory measures to protect children from unhealthy food and beverage marketing. These codes had no clear impact in reducing unhealthy food and beverage marketing to children due to weak enforcement.[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] The increasing frequency of consumption of fast foods is of concern and in line with findings from other studies in South Africa. Frequent intakes of unhealthy snack foods and SSB have been reported across household income groups.[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] Low intakes of fruit and vegetables have been reported among South African children, with possible adverse health implications.[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eThe age-related increase in CRF was expected due to the children's physical development. CRF depends on both regular physical activity and genetic predisposition.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] Differences in CRF within the same age groups of boys and girls could be an indication of regular physical activity of the children. This is supported by a study in Spanish children, 9\u0026ndash;11 years old, where CRF acted as a partial mediator in the negative relationship between dietary factors (energy intake/weight, carbohydrate intake/weight, protein intake/weight, and fat intake/weight) and fat mass. Thus, Spanish schoolchildren with higher levels of energy and macronutrients intake had lower adiposity levels, when they had good levels of CRF, confirming both dietary intakes and CRF as key variables for maintaining the energy balance.[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/p\u003e\u003cp\u003ePhysical inactivity among young children remains a major public health problem in many countries.[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] Data from several studies assessing the association between PA and adiposity among children are available, but findings are inconsistent.[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] Most studies on the association between objectively measured PA and adiposity were conducted in high-income countries. A few studies in South African children focused on preschool children and adolescents,[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] while studies among primary school-age children had inconsistent results, which may be due to the challenges related to accurate measurement of physical activity in this age group.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] A study in 7 to 10 year old urban children from a high-income setting showed no association between PA and BMI.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] Other studies in South African children aged 9 to 13 years, and 5 to 13 years, respectively, indicated that the children\u0026rsquo;s PA level correlated negatively with %BF, calculated by using a prediction equation based on two skinfold thicknesses,[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] and by BIA.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] Another study found significant positive associations between body mass and sedentary time among an ethnically diverse urban group of South African school children 5 to 18 years old. The same study indicated an age-related decline in PA and increase in sedentary time among school-age children, whereas overweight and obesity prevalence increased up to mid-childhood.[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] These results are in line with the results of the current study, indicating that physical activity may prevent excessive weight gain over time during childhood.\u003c/p\u003e\u003cp\u003eA recent reanalysis of data from two Cochrane reviews showed that the greatest obesity prevention effects were for interventions in children targeting physical activity alone compared with diet alone. The most effective combination was school setting interventions with individualised physical activity of short duration and high intensity and involving behaviour modification.[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] Early detection and prevention of obesity among children is important to improve their future health and to relief the growing burden of adult comorbidities. Poor diets and physical inactivity are modifiable risk factors, that could be addressed in school-based interventions.[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] The conclusion from a recent systematic review of the effectiveness of school-based obesity prevention interventions on the health behaviour of children was that obesity prevention programmes had a positive impact on fruit and vegetable intakes and PA and decreased SSB intake.[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eLimitations of this study include that CRF could only be measured using a field-based test and in a subsample of children due to logistical reasons. The study participants attended South African quintile groups 3 to 5 schools, indicating medium to high employment rate and literacy of the communities. Quintiles 1 and 2 represents the poorest schools,[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and these participants were excluded from this study for logistical reasons. The results may therefore not apply to children from the lowest socio-economic status groups in South Africa. Despite these limitations the strength of the study was that it was the first longitudinal study of intakes from healthy and unhealthy food groups in a relatively large sample of primary school aged South African children over a 4-year period, and the association with adiposity.\u003c/p\u003e\u003cp\u003eIn conclusion, higher daily vegetable and milk intakes, as well as higher levels of CRF were protective against increasing adiposity among school-age children, whereas daily SSB intake was associated with an increased overweight and obesity prevalence. These findings indicate that policy changes to promote good eating habits and measures to improve CRF should be prioritised among school children.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful towards all parents and children participating voluntarily in the study, and the goodwill of the school principals. The following research staff and postgraduate students are acknowledged for their dedication to acquiring the data: Mrs Bianca Petersen, Hypertension in Africa Research Team and Ms Persuade Makore, Centre of Excellence for Nutrition, North-West University, South Africa.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHSK, RK, AEP and MAM designed the study. CR, TvZ and HR contributed to data analysis and interpretation. All authors helped write and review the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe ExAMIN Youth SA study was funded by the South African Medical Research Council (SAMRC) Extra Mural Unit and the National Research Foundation (NRF) of South Africa for Competitive Support for Y-Rated Researchers (Unique Identification Number: 112141) and the NRF Equipment Related Training and Travel Grant (Unique Identification Number: 109905). Research reported in this paper was also supported by a South African Medical Research Council under a Self-Initiated Research Grant, and the South African Research Chairs Initiative (SARChI) of the Department of Science and Technology and National Research Foundation (NRF) of South Africa (Unique Identification Number: 86895). The support of International Atomic Energy Agency (IAEA) is greatly appreciated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Centre of Excellence Scientific Review Committee of the North-West University. The researchers obtained approval from the Health Research Ethics Committee (NWU-00091-16-A1) of the North-West University to conduct the study. The parents and caregivers signed informed consent forms for the children while children, whose parents consented, signed assent forms for their participation in the study after they were provided with information about this study and their role and rights as participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHSK is a member of the Research Grants Panel of the South African Sugar Association and receives a honorarium for the review of grant applications. The South African Sugar Association played no role in the design of the study, collection and analysis of data and the decision to submit this manuscript for publication. \u0026nbsp; The other authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhang X, Liu J, Ni Y, Yi C, Fang Y, Ning Q, et al (2024) Global prevalence of overweight and obesity in children and adolescents: a systematic review and meta-analysis. JAMA Pediatr 178:800-813. doi: 10.1001/jamapediatrics.2024.1576\u003c/li\u003e\n\u003cli\u003eFarooq A, Martin A, Janssen X, Wilson MG, Gibson AM, Hughes A, et al (2020) Longitudinal changes in moderate-to-vigorous-intensity physical activity in children and adolescents: A systematic review and meta-analysis. Obes Rev 21(1):e12953. doi: 10.1111/obr.12953\u003c/li\u003e\n\u003cli\u003eHurtig-Wennlof A, Ruiz JR, Harro M, Sjostrom M (2007) Cardiorespiratory fitness relates more strongly than physical activity to cardiovascular disease risk factors in healthy children and adolescents: the European Youth Heart Study. Eur J Cardiovasc Prev Rehabil 14:575-81 doi: 10.1097/HJR.0b013e32808c67e3 \u003c/li\u003e\n\u003cli\u003eRousham EK, Goudet S, Markey O, Griffiths P, Boxer B, Carroll C, et al (2022) Unhealthy food and beverage consumption in children and risk of overweight and obesity: A systematic review and meta-analysis. Adv Nutr 13:1669-1696 doi: 10.1093/advances/nmac032 \u003c/li\u003e\n\u003cli\u003eNqweniso S, Walter C, du Randt R, Adams L, Beckmann J, Degen J, et al. (2021) Physical activity, cardiorespiratory fitness and clustered cardiovascular risk in South African primary schoolchildren from disadvantaged communities: a cross-sectional study. Int J Environ Res Public Health 18:2080 doi: 10.3390/ijerph18042080\u003c/li\u003e\n\u003cli\u003eBallin M, Nordstrom A, Nordstrom P, Ahlqvist VH (2025) Cardiorespiratory fitness in adolescence and premature mortality: widespread bias identified using negative control outcomes and sibling comparisons. Eur J Prev Cardiol doi: 10.1093/eurjpc/zwaf267\u003c/li\u003e\n\u003cli\u003eBaard ML, Bezuidenhout HP, Kramer M, Venter DJL (2019) Prevalence of overweight and obesity in six to nine years old primary school children in Mpumalanga province, South Africa. African Journal for Physical Activity and Health Sciences (AJPHES). 25:313-328.\u003c/li\u003e\n\u003cli\u003eKruger R, Kruger HS, Monyeki MA, Pienaar AE, Roux SB, Gafane-Matemane LF, et al (2021) A demographic approach to assess elevated blood pressure and obesity in prepubescent children: the ExAMIN Youth South Africa study. 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South African Journal of Child Health 13:130-136\u003c/li\u003e\n\u003cli\u003eChoukem SP, Tochie JN, Sibetcheu AT, Nansseu JR, Hamilton-Shield JP (2020) Overweight/obesity and associated cardiovascular risk factors in sub-Saharan African children and adolescents: a scoping review. Int J Pediatr Endocrinol 2020:6 doi: 10.1186/s13633-020-0076-7\u003c/li\u003e\n\u003cli\u003eKruger R, Monyeki MA, Schutte AE, Smith W, Mels CMC, Kruger HS, et al (2020) The Exercise, Arterial Modulation and Nutrition in Youth South Africa Study (ExAMIN Youth SA). Front Pediatr 8:212 doi: 10.3389/fped.2020.00212 \u003c/li\u003e\n\u003cli\u003eChan AW, Tetzlaff JM, Altman DG, Laupacis A, Gotzsche PC, Krleza-Jeric K, et al (2013) SPIRIT 2013 statement: defining standard protocol items for clinical trials. Ann Intern Med 158:200-207 doi: 10.7326/0003-4819-158-3-201302050-00583\u003c/li\u003e\n\u003cli\u003evan Dyk H, White CJ (2019) Theory and practice of the quintile ranking of schools in South Africa: A financial management perspective. S Afr J Educat 39(Supplement 1):S1-S9.\u003c/li\u003e\n\u003cli\u003eStewart A, Marfell-Jones M, Olds T, 7 De Ridder H (2011) International Standards for Anthropometric Assessment. ISAK, Lower Hutt, New Zealand.\u003c/li\u003e\n\u003cli\u003eDe Onis M (2006) WHO Child Growth Standards Length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age. Methods and development. https://www.who.int/publications/i/item/924154693X. Accessed 8 August 2025\u003c/li\u003e\n\u003cli\u003eWHO (2007) Growth reference data for children from 5 to 19 years. https://www.who.int/tools/growth-reference-data-for-5to19-years. Accessed 8 August 2025\u003c/li\u003e\n\u003cli\u003eKourkoumelis N, Grujic VR, Grabez M, Vidic A, Siksna I, Lazda I, et al (2021) New bioelectrical impedance analysis equations for children and adolescents based on the deuterium dilution technique. Clin Nutr ESPEN 44:402-409 doi: 10.1016/j.clnesp.2021.05.001\u003c/li\u003e\n\u003cli\u003eTomkinson GR, Lang JL, Blanchard J, Tremblay MS (2019) The 20-m shuttle run: Assessment and interpretation of data in relation to youth aerobic fitness and health. Pediatr Exerc Sci 31(2):152-163 doi: 10.1123/pes.2018-0179\u003c/li\u003e\n\u003cli\u003eKruger HS, Makore P, van Zyl T, Faber M, Ware LJ, Monyeki MA, et al (2014) Validation of a short food group questionnaire to determine intakes from healthy and unhealthy food groups in 5-9-year-old South African children. J Hum Nutr Diet 37:234-245 doi: 10.1111/jhn.13249\u003c/li\u003e\n\u003cli\u003eDabon\u0026eacute; C, Delisle H, Receveur O (2013) Predisposing, facilitating and reinforcing factors of healthy and unhealthy food consumption in schoolchildren: a study in Ouagadougou, Burkina Faso. Global Health Promot 20:68-77 doi: 10.1177/1757975913476905\u003c/li\u003e\n\u003cli\u003eSanigorski AM, Bell AC, Swinburn BA (2007) Association of key foods and beverages with obesity in Australian schoolchildren. Publ Health Nutr 10:152-157 doi: 10.1017/S1368980007246634\u003c/li\u003e\n\u003cli\u003eAndaya AA, Arredondo EM, Alcaraz JE, Lindsay SP, Elder JP (2011) The association between family meals, TV viewing during meals, and fruit, vegetables, soda, and chips intake among Latino children. J Nutr Educ Behav 43:308-315 doi: 10.1016/j.jneb.2009.11.005\u003c/li\u003e\n\u003cli\u003eLarsen AL, McArdle JJ, Robertson T, Dunton G (2015) Four dietary items of the School Physical Activity and Nutrition (SPAN) questionnaire form a robust latent variable measuring healthy eating patterns. J Nutr Educ Behav 47:253-258 doi: 10.1016/j.jneb.2014.12.005\u003c/li\u003e\n\u003cli\u003eMoreira CC, Moreira EA, Fiates GM (2015) Perceived purchase of healthy foods is associated with regular consumption of fruits and vegetables. J Nutr Educ Behav 47:248-252 doi: 10.1016/j.jneb.2014.12.003\u003c/li\u003e\n\u003cli\u003ePedro TM, MacKeown JM, Norris SA (2008) Variety and total number of food items recorded by a true longitudinal group of urban black South African children at five interceptions between 1995 and 2003: the Birth-to-Twenty (Bt20) Study. Publ Health Nutr 11:616-623 doi: 10.1017/S1368980007000936\u003c/li\u003e\n\u003cli\u003eFeeley AB, Musenge E, Pettifor JM, Norris SA (2013) Investigation into longitudinal dietary behaviours and household socio-economic indicators and their association with BMI Z-score and fat mass in South African adolescents: the Birth to Twenty (Bt20) cohort. Publ Health Nutr 16:693-703 doi: 10.1017/S1368980012003308\u003c/li\u003e\n\u003cli\u003eFeeley A, Musenge E, Pettifor JM, Norris SA (2012) Changes in dietary habits and eating practices in adolescents living in urban South Africa: The birth to twenty cohort. Nutr 28:e1-e6 doi: 10.1016/j.nut.2011.11.025\u003c/li\u003e\n\u003cli\u003eBarragan M, Luna V, Hammons AJ, Olvera N, Greder K, Drumond Andrade FC, et al (2022) Reducing obesogenic eating behaviors in Hispanic children through a family-based, culturally-tailored RCT: Abriendo Caminos. Int J Environ Res Public Health 19:1917 doi: 10.3390/ijerph19041917\u003c/li\u003e\n\u003cli\u003eKruger HS, van Zyl T, Monyeki MA, Ricci C, Kruger R (2025) Decreased frequency of sugar-sweetened beverages intake among young children following the implementation of the health promotion levy in South Africa. Publ Health Nutr 28:e23 doi: 10.1017/S1368980024002623\u003c/li\u003e\n\u003cli\u003ePayne EH, Hardin JW, Egede LE, Ramakrishnan V, Selassie A, Gebregziabher M (2017) Approaches for dealing with various sources of overdispersion in modeling count data: Scale adjustment versus modeling. Stat Methods Med Res 26:1802-1823 doi: 10.1177/0962280215588569\u003c/li\u003e\n\u003cli\u003eKruger HS, Visser M, Malan L, Zandberg L, Wicks M, Ricci C, et al (2023) Anthropometric nutritional status of children (0-18 years) in South Africa 1997-2022: a systematic review and meta-analysis. Publ Health Nutr 26:2226-2242 doi: 10.1017/S1368980023001994\u003c/li\u003e\n\u003cli\u003eKaratzi K, Poulia KA, Papakonstantinou E, Zampelas A (2021) The impact of nutritional and lifestyle changes on body weight, body composition and cardiometabolic risk factors in children and adolescents during the pandemic of COVID-19: A systematic review. Children (Basel) 8(12) doi: 10.3390/children8121130\u003c/li\u003e\n\u003cli\u003eQiu N, He H, Qiao L, Ding Y, Ji S, Guo X, et al. Sex differences in changes in BMI and blood pressure in Chinese school-aged children during the COVID-19 quarantine. Int J Obes 45:2132-2136 doi: 10.1038/s41366-021-00871-w\u003c/li\u003e\n\u003cli\u003eQutteina Y, De Backer C, Smits T (2019) Media food marketing and eating outcomes among pre-adolescents and adolescents: A systematic review and meta-analysis. Obes Rev 20:1708-1719 doi: 10.1111/obr.12929\u003c/li\u003e\n\u003cli\u003eErzse A, Karim SA, Foley L, Hofman KJ (2022) A realist review of voluntary actions by the food and beverage industry and implications for public health and policy in low- and middle-income countries. Nat Food 3:650-663 doi: 10.1038/s43016-022-00552-5\u003c/li\u003e\n\u003cli\u003eFeeley AB, Norris SA (2014) Added sugar and dietary sodium intake from purchased fast food, confectionery, sweetened beverages and snacks among Sowetan adolescents. South African Journal of Child Health 8:88-91.\u003c/li\u003e\n\u003cli\u003eSteyn NP, de Villiers A, Gwebushe N, Draper CE, Hill J, de Waal M, et al (2015) Did HealthKick, a randomised controlled trial primary school nutrition intervention improve dietary quality of children in low-income settings in South Africa? BMC Publ Health 15:948.\u003c/li\u003e\n\u003cli\u003eLahoz-Garcia N, Garcia-Hermoso A, Milla-Tobarra M, Diez-Fernandez A, Soriano-Cano A, Martinez-Vizcaino V (2018) Cardiorespiratory fitness as a mediator of the influence of diet on obesity in children. Nutrients 10(3) doi: 10.3390/nu10030358\u003c/li\u003e\n\u003cli\u003eBernhardsen GP, Stensrud T, Hansen BH, Steene-Johannesen J, Kolle E, Nystad W, et al (2020) Birth weight, cardiometabolic risk factors and effect modification of physical activity in children and adolescents: pooled data from 12 international studies. Int J Obes 44:2052-2063 doi: 10.1038/s41366-020-0612-9\u003c/li\u003e\n\u003cli\u003eCollings PJ, Westgate K, V\u0026auml;ist\u0026ouml; J, Wijndaele K, Atkin AJ, Haapala EA, et al (2016) Cross-Sectional Associations of Objectively-Measured Physical Activity and Sedentary Time with Body Composition and Cardiorespiratory Fitness in Mid-Childhood: The PANIC Study. Sports Med 47:769-780 doi: 10.1007/s40279-016-0606-x\u003c/li\u003e\n\u003cli\u003eReisberg K, Riso EM, Jurimae J (2020) Associations between physical activity, body composition, and physical fitness in the transition from preschool to school. Scand J Med Sci Sports 30:2251-2263 doi: 10.1111/sms.13784\u003c/li\u003e\n\u003cli\u003eDraper CE, Tomaz SA, Jones RA, Hinkley T, Twine R, Kahn K, et al (2019) Cross-sectional associations of physical activity and gross motor proficiency with adiposity in South African children of pre-school age. Publ Health Nutr 22:614-623 doi: 10.1017/S1368980018003579\u003c/li\u003e\n\u003cli\u003eTomaz SA, Prioreschi A, Watson ED, McVeigh JA, Rae DE, Jones RA, et al (2019) Body mass index, physical activity, sedentary behavior, sleep, and gross motor skill proficiency in preschool children from a low- to middle-income urban setting. J Phy Act Health 16:525-532 doi: 10.1123/jpah.2018-0133\u003c/li\u003e\n\u003cli\u003eMoselakgomo KV, Monyeki A, Toriola AL (2015) Relationship between physical activity and risk factors of body weight disorders among South African primary school children. Biomed Res 26:730-738\u003c/li\u003e\n\u003cli\u003eMcVeigh J, Meiring R (2014) Physical activity and sedentary behavior in an ethnically diverse group of South African school children. J Sports Sci Med 13:371-378\u003c/li\u003e\n\u003cli\u003eDavies AL, Spiga F, Caldwell DM, Savovic J, Palmer JC, Tomlinson E, et al (2025) Factors associated with the effectiveness of interventions to prevent obesity in children: a synthesis of evidence from 204 randomised trials. BMJ Publ Health 3:e001707 doi: 10.1136/bmjph-2024-001707\u003c/li\u003e\n\u003cli\u003ePulimeno M, Piscitelli P, Colazzo S, Colao A, Miani A (2020) School as ideal setting to promote health and wellbeing among young people. Health Promot Perspect 10:316-324 doi: 10.34172/hpp.2020.50\u003c/li\u003e\n\u003cli\u003eMcDiarmid K, Clinton-McHarg T, Wolfenden L, O\u0026apos;Brien K, Lee DCW, Stuart A, et al (2025) The effectiveness of school-based obesity prevention interventions on the health behaviours of children aged 6-18 years: A secondary data analysis of a systematic review. Prev Med Rep 53:103053 doi: 10.1016/j.pmedr.2025.103053\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Baseline descriptive characteristics of the children (n=950)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"936\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eBoys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eGirls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAll children\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean/\u003c/p\u003e\n \u003cp\u003efrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStandard deviation/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean/\u003c/p\u003e\n \u003cp\u003efrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStandard deviation/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean/\u003c/p\u003e\n \u003cp\u003efrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStandard deviation/%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale sex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eRace:\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBlack, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e54.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWhite, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAsian and mixed race, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eSchool quintile:\u003c/em\u003e n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQuintile 3,\u0026nbsp;no-school fee schools,government funded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQuintile 4, fee-paying schools, second highest income quintile\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e41.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e40.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQuintile 5, fee-paying schools, highest income quintile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eAnthropometric data:\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI for age z-score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMI for age z-score categories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnderweight, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLean, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e76.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e77.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e76.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOverweight, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eObese, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCardiorespiratory fitness\u003c/em\u003e: number of completed shuttle run laps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations\u003c/em\u003e: BMI, body mass index\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-clinical-nutrition","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ejcn","sideBox":"Learn more about [European Journal of Clinical Nutrition](http://www.nature.com/ejcn/)","snPcode":"41430","submissionUrl":"https://mts-ejcn.nature.com/cgi-bin/main.plex","title":"European Journal of Clinical Nutrition","twitterHandle":"@ejcneditor","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7371897/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7371897/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e The global pandemic of paediatric obesity, unhealthy diets and physical inactivity are important future health challenges. The purpose of this study was to determine 4-year changes in food selection, cardiorespiratory fitness (CRF), and the longitudinal association withadiposity among South African children.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e School children aged 5 to 9 years old in 2017 (n=950) were followed up four years later. Parents indicated selection from healthy and unhealthy food groups in a validated questionnaire. Weight and height of the children were measured, and WHO BMI z-score (BAZ) was calculated. CRF was determined by the 20-m shuttle run test. Changes in food selection frequency, CRF and BAZ were assessed. The association between frequency of food selection, age, household income and CRF with 4-year change in BAZ was determined using mixed linear models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e CRF and BAZ increased over four years (both p\u0026lt;0.001). The frequency of sugar-sweetened beverages (p\u0026lt;0.001) and milk intake (p\u0026lt;0.02) decreased, while the intake from fast-foods increased (p=0.001). Daily intakes of vegetables and milk were associated with decreases in BAZ. A trend of a positive association was found between frequency of SSB intake and BAZ. CRF showed a strong negative association with BAZ over time (p\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Higher daily vegetable and milk intakes, as well as higher levels of CRF were protective against increasing adiposity among school-age children, whereas daily SSB intake was associated with an increased adiposity. The promotion of good eating habits and measures to improve CRF among school children are important policy change priorities.\u003c/p\u003e","manuscriptTitle":"Food selection, cardiorespiratory fitness and the longitudinal association with adiposity among South African school children: ExAMIN Youth SA study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-17 05:07:19","doi":"10.21203/rs.3.rs-7371897/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2026-02-05T13:51:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-10-20T22:54:10+00:00","index":1,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-10-16T12:04:19+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-09-30T20:55:12+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-09-21T21:03:49+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-09-08T12:27:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-18T10:53:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-18T10:47:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Clinical Nutrition","date":"2025-08-16T08:44:17+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2025-08-15T09:28:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-clinical-nutrition","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ejcn","sideBox":"Learn more about [European Journal of Clinical Nutrition](http://www.nature.com/ejcn/)","snPcode":"41430","submissionUrl":"https://mts-ejcn.nature.com/cgi-bin/main.plex","title":"European Journal of Clinical Nutrition","twitterHandle":"@ejcneditor","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"eee08970-660c-4617-a389-78283890112d","owner":[],"postedDate":"September 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":54367486,"name":"Health sciences/Risk factors"},{"id":54367487,"name":"Biological sciences/Developmental biology"}],"tags":[],"updatedAt":"2026-03-12T13:36:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-17 05:07:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7371897","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7371897","identity":"rs-7371897","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}