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A Meta-Analysis of High-Intensity Interval Training on Glycolipid Metabolism in Children With Metabolic Disorders

Meng Cao, Shu Li, Yucheng Tang and 1 more

Frontiers in Pediatrics · 2022

doi:10.3389/fped.2022.887852 (opens in a new tab)

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Abstract

ObjectiveMetabolic disorders are common among children and adolescents with obesity and are associated with insulin resistance, hyperlipidemia, hypertension, and other cardiovascular risk factors. High-intensity interval training (HIIT) is a time-efficient method to improve cardiometabolic health. We performed a meta-analysis to determine the effects of HIIT on glycolipid metabolism in children with metabolic disorders.MethodsMeta-analyses were conducted to determine the effect of HIIT on glycolipid metabolism markers. Subgroup analysis with potential moderators was explored [i.e., training intensity standard and work/rest time ratio (WRR)].ResultsEighteen trials involving 538 participants were included. HIIT showed positive effects on glycolipid metabolism, such as triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), blood glucose (BG), blood insulin (BI), and homeostasis model assessment (HOMA)-IR, when compared to the non-training control group (CON); in addition to BG (p = 0.257), the combined results of other indicators have high heterogeneity (p = 0.000). HIIT showed no superior effects when compared to moderate-intensity training (MIT). Subgroup analysis demonstrated that HIIT protocol with a WRR of 1:1 was superior to MIT for reducing TG and LDL-C and used %maximal aerobic speed (MAS) as the exercise intensity was superior to MIT for reducing TG. HIIT protocol used %heart rate (HR) as the exercise intensity was superior to MIT for increasing HDL-C, decreasing BI, and HOMA-IR.ConclusionHIIT improved glycolipid metabolism in children with metabolic disorders. WRR and training intensity can affect the intervention effects of HIIT.Systematic Review Registration[https://www.crd.york.ac.uk/], identifier [CRD42021291473]

Abstract licence: CC BY

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