Key result
Translating exercise test responses to steady state exercise training using METs showed a highly correlated linear relationship (r = 0.89), with training METs averaging 71.8% of test METs.
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A generalized approach using METs demonstrates a consistent, linear stepdown (averaging 71.8%) from testing to training, enabling accurate exercise prescription.
Although there is evidence supporting the benefit of regular exercise, and recommendations about exercise and physical activity, the process of individually prescribing exercise following exercise testing is more difficult. Guidelines like % heart rate (HR) reserve (HRR) require an anchoring maximal test and do not always provide a homogenous training experience. When prescribing HR on the basis of % HRR, rating of perceived exertion or Talk Test, cardiovascular/perceptual drift during sustained exercise makes prescription of the actual workload difficult. To overcome this issue, we have demonstrated a strategy for "translating" exercise test responses to steady state exercise training on the basis of % HRR or the Talk Test that appeared adequate for individuals ranging from cardiac patients to athletes. However, these methods depended on the nature of the exercise test details. In this viewpoint, we combine these data with workload expressed as Metabolic Equivalent Task (METs). We demonstrate that there is a regular stepdown between the METs during training to achieve the same degree of homeostatic disturbance during testing. The relationship was linear, was highly-correlated (r = 0.89), and averaged 71.8% (Training METs/Test METs). We conclude that it appears possible to generate a generalized approach to correctly translate exercise test responses to exercise training.
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Foster et al. (2020) studied Cardiac patients to athletes. Translating exercise test responses to steady state exercise training using METs was evaluated on Relationship between METs during training and testing (r = 0.89). Translating exercise test responses to steady state exercise training using METs showed a highly correlated linear relationship (r = 0.89), with training METs averaging 71.8% of test METs.
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