Literature DB >> 31063607

A new approach to estimate aerobic fitness using the NHANES dataset.

Kim D Lu1, Ronen Bar-Yoseph1, Shlomit Radom-Aizik1, Dan M Cooper1.   

Abstract

INTRODUCTION: Physical activity and fitness are essential for healthy growth in children. The National Health and Nutrition Examination Survey (NHANES) evaluated fitness by estimating V̇O2 max from submaximal measurements of heart rate (HR) during graded treadmill exercise. Our aims were (a) to examine how well NHANES methodology used to estimate V̇O2 max correlated with actual VO2 max and (b) to evaluate a novel fitness metric using actual data collected during exercise and its relationship to physical activity and sedentary time, lipid profiles, and body composition.
METHODS: Fifty-three adolescents completed NHANES submaximal exercise protocol and maximal graded cardiopulmonary exercise testing. We used a novel approach to quantifying fitness (Δvelocity × incline × body mass (VIM)/ΔHR slopes) and evaluated its relationship to physical activity and sedentary time using NHANES data (n = 4498). In a subset (n = 740), we compared ΔVIM/ΔHR slopes to NHANES estimated V̇O2 max and examined their relationship to cardiovascular risk factors (BMI percentiles and lipid levels).
RESULTS: Measured V̇O2 peak was moderately correlated with NHANES estimated V̇O2 max (r = 0.53, P < 0.01). Significantly higher ΔVIM/ΔHR slopes were associated with increased physical activity and decreased sedentary time. ΔVIM/ΔHR slopes were negatively associated with LDL, triglycerides, and BMI percentiles (P < 0.01). In general, the two fitness models were similar; however, ΔVIM/ΔHR was more discriminating than NHANES in quantifying the relationship between fitness and LDL levels.
CONCLUSION: We found that the NHANES estimated V̇O2 max accounted for approximately 28% of the variability in the measured V̇O2 peak. Our approach to estimating fitnessVIM/ΔHR slopes) using actual data provided similar relationships to lipid levels. We suggest that fitness measurements based on actually measured data may produce more accurate assessments of fitness and, ultimately, better approaches linking exercise to health in children.
© 2019 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Entities:  

Keywords:  adolescents; cardiorespiratory fitness; children; exercise

Mesh:

Substances:

Year:  2019        PMID: 31063607      PMCID: PMC6860366          DOI: 10.1111/sms.13461

Source DB:  PubMed          Journal:  Scand J Med Sci Sports        ISSN: 0905-7188            Impact factor:   4.221


  32 in total

1.  Mathematical coupling can undermine the statistical assessment of clinical research: illustration from the treatment of guided tissue regeneration.

Authors:  Yu-Kang Tu; Ian H Maddick; Gareth S Griffiths; Mark S Gilthorpe
Journal:  J Dent       Date:  2004-02       Impact factor: 4.379

2.  Cardiorespiratory fitness, LDL cholesterol, and CHD mortality in men.

Authors:  Stephen W Farrell; Carrie E Finley; Scott M Grundy
Journal:  Med Sci Sports Exerc       Date:  2012-11       Impact factor: 5.411

3.  Prediction of maximum oxygen consumption from walking, jogging, or running.

Authors:  Gary E Larsen; James D George; Jeffrey L Alexander; Gilbert W Fellingham; Steve G Aldana; Allen C Parcell
Journal:  Res Q Exerc Sport       Date:  2002-03       Impact factor: 2.500

4.  Cardiopulmonary Exercise Testing in Children and Adolescents with High Body Mass Index.

Authors:  Dan M Cooper; Szu-Yun Leu; Candice Taylor-Lucas; Kim Lu; Pietro Galassetti; Shlomit Radom-Aizik
Journal:  Pediatr Exerc Sci       Date:  2015-12-29       Impact factor: 2.333

5.  The clinical translation gap in child health exercise research: a call for disruptive innovation.

Authors:  Naveen Ashish; Marcas M Bamman; Frank J Cerny; Dan M Cooper; Pierre D'Hemecourt; Joey C Eisenmann; Dawn Ericson; John Fahey; Bareket Falk; Davera Gabriel; Michael G Kahn; Han C G Kemper; Szu-Yun Leu; Robert I Liem; Robert McMurray; Patricia A Nixon; J Tod Olin; Paolo T Pianosi; Mary Purucker; Shlomit Radom-Aizik; Amy Taylor
Journal:  Clin Transl Sci       Date:  2014-08-11       Impact factor: 4.689

6.  Common pitfalls in statistical analysis: The use of correlation techniques.

Authors:  Rakesh Aggarwal; Priya Ranganathan
Journal:  Perspect Clin Res       Date:  2016 Oct-Dec

Review 7.  Pediatric Exercise Testing: Value and Implications of Peak Oxygen Uptake.

Authors:  Paolo T Pianosi; Robert I Liem; Robert G McMurray; Frank J Cerny; Bareket Falk; Han C G Kemper
Journal:  Children (Basel)       Date:  2017-01-24

8.  Sparsifying machine learning models identify stable subsets of predictive features for behavioral detection of autism.

Authors:  Sebastien Levy; Marlena Duda; Nick Haber; Dennis P Wall
Journal:  Mol Autism       Date:  2017-12-19       Impact factor: 7.509

9.  Tracking and Variability in Childhood Levels of BMI: The Bogalusa Heart Study.

Authors:  David S Freedman; Hannah G Lawman; Deborah A Galuska; Alyson B Goodman; Gerald S Berenson
Journal:  Obesity (Silver Spring)       Date:  2018-06-11       Impact factor: 5.002

10.  Reference Values of Skeletal Muscle Mass for Korean Children and Adolescents Using Data from the Korean National Health and Nutrition Examination Survey 2009-2011.

Authors:  Kirang Kim; Sangmo Hong; Eun Young Kim
Journal:  PLoS One       Date:  2016-04-13       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.