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The association between physical activity and body fat percentage with adjustment for body mass index among middle-aged adults: China health and nutrition survey in 2015.

ABSTRACT: BACKGROUND:The inverse association between physical activity and body fat percentage (%) varies among different populations. We aim to examine whether the significant association between them was uniform across the subpopulations after taking into account body mass index (BMI). METHODS:Our study relied on data from China Health and Nutrition Surveys in 2015, including 5763 participants aged 40-64?years from 15 regions. Physical activity was calculated as metabolic equivalent task hours per day (MET·h/d). Body fat% was measured by bioelectrical impedance analysis. Body mass index

SUBMITTER: Zou Q

PROVIDER: S-EPMC7238529 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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The association between physical activity and body fat percentage with adjustment for body mass index among middle-aged adults: China health and nutrition survey in 2015.

Zou Qinpei Q Su Chang C Du Wenwen W Ouyang Yifei Y Wang Huijun H Wang Zhihong Z Ding Gangqiang G Zhang Bing B

BMC public health 20200519 1

<h4>Background</h4>The inverse association between physical activity and body fat percentage (%) varies among different populations. We aim to examine whether the significant association between them was uniform across the subpopulations after taking into account body mass index (BMI).<h4>Methods</h4>Our study relied on data from China Health and Nutrition Surveys in 2015, including 5763 participants aged 40-64 years from 15 regions. Physical activity was calculated as metabolic equivalent task ...[more]

PMID: 32429924

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Project description:BackgroundAccurate obesity classification is important so that appropriate intervention can be instituted to modify metabolic risk factors. Commonly utilized body mass index (BMI) and percentage body fat (PBF) are influenced by lean mass whereas fat mass index (FMI) measures only body fat. This study compares the prevalence of obesity and metabolic risk factors with FMI, BMI and PBF using DXA (dual-energy x-ray absorptiometry).Methods489 women randomly recruited from the electoral roll were stratified into 4 age groups; 40-49, 50-59, 60-69 and 70-79 years from 2000 to 2001. Clinical data and DXA body composition were obtained. Statistical analyses were performed using Medcalc v15 (Ostend, Belgium) with significance level at p = 0.05 (two-tailed).ResultsThere was higher prevalence of obesity using PBF compared to BMI and FMI (p<0.001). This difference was greater from age 50-59 (p<0.05) which may be explained by age-related lean mass loss. PBF over-classified obesity in over 35% of normal and 95% of overweight categories compared to FMI and BMI. BMI has a sensitivity of 78.9% and specificity of 98.3% for obesity using FMI as the standard. BMI under-classified obesity in the overweight category by 14.9% compared to FMI. There was no difference in diabetes, dyslipidemia, hypertension and metabolic syndrome prevalence within the BMI-obesity and FMI-obesity categories (p>0.05).ConclusionPBF classified more obesity than BMI and FMI because of its low pre-determined threshold. The greater difference with PBF compared to BMI and FMI from the 50-59 decade onwards can be attributed to age-related lean mass loss. BMI had the lowest sensitivity for obesity diagnosis. BMI under-classified obesity in the overweight category compared to FMI due to its inability to differentiate lean mass. However, there was no significant difference in the prevalence of metabolic risk factors between BMI and FMI-obesity categories indicating that fat location may influence metabolic dysregulation.

Project description:ObjectiveUsing meta-regression this paper sets out the minimum change in body mass index-SD score (BMI-SDS) required to improve adiposity as percentage body fat for children and adolescents with obesity.DesignMeta-regression.SettingStudies were identified as part of a large-scale systematic review of the following electronic databases: AMED, Embase, MEDLINE via OVID, Web of Science and CENTRAL via Cochrane library.ParticipantsIndividuals aged 4-19 years with a diagnosis of obesity according to defined BMI thresholds.InterventionsStudies of lifestyle treatment interventions that included dietary, physical activity and/or behavioural components with the objective of reducing obesity were included. Interventions of <2 weeks duration and those that involved surgical and/or pharmacological components (eg, bariatric surgery, drug therapy) were excluded.Primary and secondary outcome measuresTo be included in the review, studies had to report baseline and post-intervention BMI-SDS or change measurements (primary outcome measures) plus one or more of the following markers of metabolic health (secondary outcome measures): adiposity measures other than BMI; blood pressure; glucose; inflammation; insulin sensitivity/resistance; lipid profile; liver function. This paper focuses on adiposity measures only. Further papers in this series will report on other outcome measures.ResultsThis paper explores the potential impact of BMI-SDS reduction in terms of change in percentage body fat. Thirty-nine studies reporting change in mean percentage body fat were analysed. Meta-regression demonstrated that reduction of at least 0.6 in mean BMI-SDS ensured a mean reduction of percentage body fat mass, in the sense that the associated 95% prediction interval for change in mean percentage body fat was wholly negative.ConclusionsInterventions demonstrating reductions of 0.6 BMI-SDS might be termed successful in reducing adiposity, a key purpose of weight management interventions.Trial registration numberCRD42016025317.

Dataset Information

The association between physical activity and body fat percentage with adjustment for body mass index among middle-aged adults: China health and nutrition survey in 2015.

Publications

The association between physical activity and body fat percentage with adjustment for body mass index among middle-aged adults: China health and nutrition survey in 2015.

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