A Simplified Approach to Estimating the Maximal Lactate Steady State
| Autor: | R. Welsh, Ann C. Snyder, Carl Foster, T. Woulfe |
|---|---|
| Rok vydání: | 1994 |
| Předmět: |
Adult
Male medicine.medical_specialty Steady state (electronics) Physical Therapy Sports Therapy and Rehabilitation Physical exercise Models Biological Running Heart Rate Endurance training Internal medicine Heart rate medicine Humans Orthopedics and Sports Medicine Lactic Acid Mathematics digestive system diseases Confidence interval Bicycling Intensity (physics) Skating Lactates Exercise intensity Physical therapy Cardiology Female Exercise prescription human activities Sports |
| Zdroj: | International Journal of Sports Medicine. 15:27-31 |
| ISSN: | 1439-3964 0172-4622 |
| DOI: | 10.1055/s-2007-1021015 |
| Popis: | The exercise intensity associated with an elevated but stable blood lactate (HLa) concentration during constant load work (the maximal steady state, MSS) has received attention as a candidate for the "optimal" exercise intensity for endurance training. Identification of MSS ordinarily demands direct measurement of HLa or respiratory metabolism. The purpose of this study was to test the ability of heart rate (HR) to identify MSS during steady state exercise, similar to that used in conventional exercise prescription. Trained runners (n = 9) and cyclists (n = 12) performed incremental and steady state exercise. MSS was defined as the highest intensity in which blood lactate concentration increased1.0 mM from minutes 10 to 30. The next higher intensity workbout completed was defined asMSS. HR models related to the presence or absence of steady state conditions were developed from the upper 95% confidence interval of MSS and the lower 95% confidence interval ofMSS. Cross validation of the model to predict MSS was performed using 21 running and 45 cycling exercise bouts in a separate group. Using the MSS upper 95% confidence interval model 84% and 76% of workbouts were correctly predicted in cyclists and runners, respectively. Using theMSS lower 95% confidence interval model, 76% and 81% of workbouts were correctly predicted in cyclists and runners, respectively. Prediction errors tended to incorrectly predict non-steady state conditions when steady state had occurred (16/26) (62%). We conclude that use of these simple HR models may predict MSS with sufficient accuracy to be useful when direct HLa measurement is not available. |
| Databáze: | OpenAIRE |
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