DOI QR코드

DOI QR Code

Estimating excess post-exercise oxygen consumption using multiple linear regression in healthy Korean adults: a pilot study

  • Jung, Won-Sang (Physical Activity and Performance Institute (PAPI), Konkuk University) ;
  • Park, Hun-Young (Physical Activity and Performance Institute (PAPI), Konkuk University) ;
  • Kim, Sung-Woo (Physical Activity and Performance Institute (PAPI), Konkuk University) ;
  • Kim, Jisu (Physical Activity and Performance Institute (PAPI), Konkuk University) ;
  • Hwang, Hyejung (Physical Activity and Performance Institute (PAPI), Konkuk University) ;
  • Lim, Kiwon (Physical Activity and Performance Institute (PAPI), Konkuk University)
  • Received : 2021.02.24
  • Accepted : 2021.03.15
  • Published : 2021.03.31

Abstract

[Purpose] This pilot study aimed to develop a regression model to estimate the excess post-exercise oxygen consumption (EPOC) of Korean adults using various easy-to-measure dependent variables. [Methods] The EPOC and dependent variables for its estimation (e.g., sex, age, height, weight, body mass index, fat-free mass [FFM], fat mass, % body fat, and heart rate_sum [HR_sum]) were measured in 75 healthy adults (31 males, 44 females). Statistical analysis was performed to develop an EPOC estimation regression model using the stepwise regression method. [Results] We confirmed that FFM and HR_sum were important variables in the EPOC regression models of various exercise types. The explanatory power and standard errors of estimates (SEE) for EPOC of each exercise type were as follows: the continuous exercise (CEx) regression model was 86.3% (R2) and 85.9% (adjusted R2), and the mean SEE was 11.73 kcal, interval exercise (IEx) regression model was 83.1% (R2) and 82.6% (adjusted R2), while the mean SEE was 13.68 kcal, and the accumulation of short-duration exercise (AEx) regression models was 91.3% (R2) and 91.0% (adjusted R2), while the mean SEE was 27.71 kcal. There was no significant difference between the measured EPOC using a metabolic gas analyzer and the predicted EPOC for each exercise type. [Conclusion] This pilot study developed a regression model to estimate EPOC in healthy Korean adults. The regression model was as follows: CEx = -37.128 + 1.003 × (FFM) + 0.016 × (HR_sum), IEx = -49.265 + 1.442 × (FFM) + 0.013 × (HR_sum), and AEx = -100.942 + 2.209 × (FFM) + 0.020 × (HR_sum).

Keywords

Acknowledgement

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2019S1A5B8099542).

References

  1. Fletcher GF, Landolfo C, Niebauer J, Ozemek C, Arena R, Lavie CJ. Promoting physical activity and exercise: JACC health promotion series. J Am Coll Cardiol. 2018;72:1622-39. https://doi.org/10.1016/j.jacc.2018.08.2141
  2. Piercy KL, Troiano RP. Physical activity guidelines for Americans from the US department of health and human services. Circ Cardiovasc Qual Outcomes. 2018;11:e005263. https://doi.org/10.1161/CIRCOUTCOMES.118.005263
  3. Denay KL, Breslow RG, Turner MN, Nieman DC, Roberts WO, Best TM. ACSM call to action statement: COVID-19 considerations for sports and physical activity. Curr Sports Med Rep. 2020;19:326-8. https://doi.org/10.1249/JSR.0000000000000739
  4. Bushman BA. Physical activity guidelines for Americans: the relationship between physical activity and health. ACSMs Health Fit. J. 2019;23:5-9. https://doi.org/10.1249/FIT.0000000000000472
  5. ACSM. ACSM's guidelines for exercise testing and prescription: Lippincott Williams & Wilkins; 2020.
  6. Yang YJ. An overview of current physical activity recommendations in primary care. Korean J Fam Med. 2019;40:135. https://doi.org/10.4082/kjfm.19.0038
  7. Lazzer S, Lafortuna C, Busti C, Galli R, Agosti F, Sartorio A. Effects of low-and high-intensity exercise training on body composition and substrate metabolism in obese adolescents. J Endocrinol Invest. 2011;34:45-52. https://doi.org/10.1007/bf03346694
  8. Trombold JR, Christmas KM, Machin DR, Kim IY, Coyle EF. Acute high-intensity endurance exercise is more effective than moderate-intensity exercise for attenuation of postprandial triglyceride elevation. J Appl Physiol. 2013;114:792-800. https://doi.org/10.1152/japplphysiol.01028.2012
  9. Bagley L, Slevin M, Bradburn S, Liu D, Murgatroyd C, Morrissey G, Carroll M, Piasecki M, Gilmore WS, McPhee JS. Sex differences in the effects of 12 weeks sprint interval training on body fat mass and the rates of fatty acid oxidation and VO2max during exercise. BMJ Open Sport Exerc Med. 2016;2:e000056. https://doi.org/10.1136/bmjsem-2015-000056
  10. Hazell TJ, Olver TD, Hamilton CD, Lemon PW. Two minutes of sprint-interval exercise elicits 24-hr oxygen consumption similar to that of 30 min of continuous endurance exercise. Int J Sport Nutr Exerc Metab. 2012;22:276-83. https://doi.org/10.1123/ijsnem.22.4.276
  11. Gaesser GA, Brooks CA. Metabolic bases of excess post-exercise oxygen. Med Sci Sports Exerc. 1984;16:29-43.
  12. Mann TN, Webster C, Lamberts RP, Lambert MI. Effect of exercise intensity on post-exercise oxygen consumption and heart rate recovery. Eur J Appl Physiol. 2014;114:1809-20. https://doi.org/10.1007/s00421-014-2907-9
  13. Laforgia J, Withers RT, Gore CJ. Effects of exercise intensity and duration on the excess post-exercise oxygen consumption. J Sports Sci. 2006;24:1247-64. https://doi.org/10.1080/02640410600552064
  14. Bahr R, Gronnerod O, Sejersted O. Effect of supramaximal exercise on excess postexercise O2 consumption. Med Sci Sports Exerc. 1992;24:66-71.
  15. Townsend JR, Stout JR, Morton AB, Jajtner AR, Gonzalez AM, Wells AJ, Mangine GP, McCormack WP, Emerson NS, Robinson EH, Hoffman JR, Fragala MS, Cosio-Lima L. Excess post-exercise oxygen consumption (EPOC) following multiple effort sprint and moderate aerobic exercise. Kinesiology. 2013;45:16.
  16. Jung WS, Hwang H, Kim J, Park HY, Lim K. Effect of interval exercise versus continuous exercise on excess post-exercise oxygen consumption during energy-homogenized exercise on a cycle ergometer. J Exerc Nutr Biochem. 2019;23:45-50.
  17. Macfarlane DJ. Automated metabolic gas analysis systems. Sports Med. 2001;31:841-61. https://doi.org/10.2165/00007256-200131120-00002
  18. Levine JA. Measurement of energy expenditure. Public Health Nutr. 2005;8:1123-32. https://doi.org/10.1079/PHN2005800
  19. Keytel L, Goedecke J, Noakes TD, Hiiloskorpi H, Laukkanen R, van der Merwe L, Lambert EV. Prediction of energy expenditure from heart rate monitoring during submaximal exercise. J Sports Sci. 2005;23:289-97. https://doi.org/10.1080/02640410470001730089
  20. Montgomery PG, Green DJ, Etxebarria N, Pyne DB, Saunders PU, Minahan CL. Validation of heart rate monitor-based predictions of oxygen uptake and energy expenditure. J Strength Cond. 2009;23:1489-95. https://doi.org/10.1519/JSC.0b013e3181a39277
  21. Bahr R, Maehlum S. Excess post-exercise oxygen consumption. A short review. Acta Physiol Scand Suppl. 1986;556:99-104.
  22. Borsheim E, Bahr R. Effect of exercise intensity, duration and mode on post-exercise oxygen consumption. Sports Med. 2003;33:1037-60. https://doi.org/10.2165/00007256-200333140-00002
  23. Miller WC, Wallace JP, Eggert KE. Predicting max HR and the HR-VO2 relationship for exercise prescription in obesity. Med Sci Sports Exerc. 1993;25:1077-81.
  24. Charlot K, Cornolo J, Borne R, Brugniaux JV, Richalet JP, Chapelot D, Pichon A. Improvement of energy expenditure prediction from heart rate during running. Physiol Meas. 2014;35:253. https://doi.org/10.1088/0967-3334/35/2/253
  25. Garcia-Prieto JC, Martinez-Vizcaino V, Garcia-Hermoso A, Sanchez-Lopez M, Arias-Palencia N, Fonseca JFO, Mora-Rodriguez R. Energy expenditure in playground games in primary school children measured by accelerometer and heart rate monitors. Int J Sport Nutr Exerc Metab. 2017;27:467-74. https://doi.org/10.1123/ijsnem.2016-0122
  26. Romero-Ugalde HM, Garnotel M, Doron M, Jallon P, Charpentier G, Franc S, Huneker E, Simon C, Bonnet S. An original piecewise model for computing energy expenditure from accelerometer and heart rate signals. Physiol Meas. 2017;38:1599. https://doi.org/10.1088/0967-3334/38/8/1599
  27. Hernando D, Garatachea N, Almeida R, Casajus JA, Bailon R. Validation of heart rate monitor polar RS800 for heart rate variability analysis during exercise. J Strength Cond. 2018;32:716-25. https://doi.org/10.1519/JSC.0000000000001662
  28. Strath SJ, Bassett DR, Swartz AM, Thompson DL. Simultaneous heart rate-motion sensor technique to estimate energy expenditure. Med Sci Sports Exerc. 2001;33:2118-23. https://doi.org/10.1097/00005768-200112000-00022
  29. Bot S, Hollander A. The relationship between heart rate and oxygen uptake during non-steady state exercise. Ergonomics. 2000;43:1578-92. https://doi.org/10.1080/001401300750004005
  30. Lothian F, Farrally M. A comparison of methods for estimating oxygen uptake during intermittent exercise. J Sports Sci. 1995;13:491-7. https://doi.org/10.1080/02640419508732266