Functional Translation of Exercise Responses from Exercise Testing to Exercise Training: The Test of a Model.

Tristan Tyrrell,Andrea Fusco,Jessica Pavlock,John P Porcari,Scott T Doberstein,Cristina Cortis,Carl Foster,Susan Bramwell

doi:10.3390/jfmk6030066

Abstract

Exercise prescription based on exercise test results is complicated by the need to downregulate the absolute training intensity to account for cardiovascular drift in order to achieve a desired internal training load. We tested a recently developed generalized model to perform this downregulation using metabolic equivalents (METs) during exercise testing and training. A total of 20 healthy volunteers performed an exercise test to define the METs at 60, 70, and 80% of the heart rate (HR) reserve and then performed randomly ordered 30 min training bouts at absolute intensities predicted by the model to achieve these levels of training intensity. The training HR at 60 and 70% HR reserve, but not 80%, was significantly less than predicted from the exercise test, although the differences were small. None of the ratings of perceived exertion (RPE) values during training were significantly different than predicted. There was a strong overall correlation between predicted and observed HR (r = 0.88) and RPE (r = 0.52), with 92% of HR values within ±10 bpm and 74% of RPE values within ±1 au. We conclude that the generalized functional translation model is generally adequate to allow the generation of early absolute training loads that lead to desired internal training loads.

Highlights

Exercise is a lifestyle factor that is beneficial to health [1,2,3,4]
In patients with chronic diseases, exercise forms a cornerstone of the treatment scheme of rehabilitation programs being of value both in terms of accelerating the rate of recovery [6], secondary prevention [7,8,9], and is even of value after contemporary medical care is accounted for [10]
The overall experimental approach was to start with MaxMETs, which is a normally measured variable during exercise testing, and apply our predictive model for converting MaxMETs into the workload for exercise training [31] to test the degree to which exercise training responses (HR and rating of perceived exertion (RPE)) during training fell within the desired range

Summary

Introduction

Exercise is a lifestyle factor that is beneficial to health [1,2,3,4] It is beneficial on a dose–response basis, up to amounts of training of several times professional society guidelines [1,2] and only associated with health problems at levels consistent with heavy athletic training in middle-aged and older individuals [1,2,5]. High-intensity exercise has recently become popular [15] and has well-documented efficacy [16], even in clinical populations [17,18,19], it is associated with at least a potentially increased risk of complications and reductions in program enjoyment and compliance [20]

Methods

Results

Conclusion