Race, Ethnicity, and Geography as Determinants of Excessive Weight and Low Physical Activity in Pediatric Population: Protocol for Systematic Review and Meta-Analysis

The rationale for the current study is the sparsity of data on the combined effect of the environmental and individual risks of obesity and sedentary lifestyle in children of different races/ethnicities from different regions. An effective weight management strategy is hard to design due to insuffic...

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Published in:Healthcare (Basel) Vol. 12; no. 18; p. 1830
Main Authors: Statsenko, Yauhen, Smetanina, Darya, Simiyu, Gillian Lylian, Belghali, Maroua, Ghenimi, Nadirah, Mannaerts, Guido Hein Huib, Almaramah, Leena, Alhashmi, Maryam, Chun Mohammad, Nazia, Al Hamed, Rahaf, Alblooshi, Sara F, Talbi, Khawla, Albreiki, Maitha, Alkaabi, Fatima, Ponomareva, Anna, Ljubisavljevic, Milos
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 13-09-2024
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Summary:The rationale for the current study is the sparsity of data on the combined effect of the environmental and individual risks of obesity and sedentary lifestyle in children of different races/ethnicities from different regions. An effective weight management strategy is hard to design due to insufficient evidence. This work was initiated to study race, ethnicity, and geography as determinants of excessive weight and low physical activity in the pediatric population. To achieve this aim, we systematically review publications on daily length of physical activity of light, moderate, and vigorous intensity, as well as sedentary time and BMI and its dynamics in children of different races/ethnicities and geographies. The extracted data are stratified into six major geographic regions and six races/ethnicities. Then, a random-effects meta-analysis is used to calculate the pooled mean of each outcome measure. A ridge regression is constructed to explore age-related change in BMI. A Kruskal-Wallis H test is applied to compare the pooled duration of physical activity and sedentary time in the subgroups. Finally, we calculate paired correlation coefficients between BMI and physical activity/inactivity for each group. The findings can be further used in public health surveillance to clarify the epidemiology of obesity, to guide priority setting and planning, and to develop and evaluate public health policy and strategy.
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These authors contributed equally to this work.
ISSN:2227-9032
2227-9032
DOI:10.3390/healthcare12181830