![]() ![]() The indices are then used in a proprietary algorithm to estimate accumulated fitness (a weighted running average of today’s stress relative to your past ~30-42 days), current fatigue (a weighted running average of today’s stress relative to your past ~7-10 days), and predicted performance (fitness minus fatigue). The HRSS and rTSS indices are proprietary formulae based on TRIMP theory that take into account the amount of time you have spent above the heart rate (HRSS) or pace (rTSS) that demarcate your anaerobic/lactate threshold. These are then used to estimate accumulated “Fitness” (aka chronic training load), current “Fatigue” (aka acute training load), and “Performance” (aka, form or training stress balance fitness minus fatigue), which can help you adjust your training and tapering periods in order to optimise your performance. Platforms like Strava (the paid subscription version only), Final Surge, Training Peaks, and Elevate calculate a training load score for each session in the form of a heart rate stress score (HRSS) or a running total stress score (rTSS). Scientists have used a systems modelling approach to quantify the dose-response nature of training for over 40-years and several companies have now exploited TRIMP theory to develop their own training load algorithms. TRIMP sounds great, right? Several commercial training platforms certainly think so. Therefore, in summary, the training impulse model is useful for demonstrating the basic principles of training theory. Furthermore, the “fitness” and “fatigue” impulses have also shown promise in small studies showing correlation with physiological variables that are related to fitness and fatigue, like iron status, muscle damage markers, and mood. Fortunately for us, the TRIMP model has also been applied to running where it has been shown use in predicting running performance. Without destroying your brains with too much with the mathematical modelling behind the fitness-fatigue impulse metric, you can read an in-depth overview by Drs David Clarke and Philip Skiba in the journal, Advances in Physiology Education.Īlthough Banister’s original paper only modelled TRIMP to predict the performance in a single athlete, TRIMP has since been applied to larger populations of swimmers, triathletes, cyclists, weightlifters, hammer throwers, and athletes in team sports, like football, demonstrating its utility for illustrating overload, overreaching, and reversibility or detraining. to find the day when fatigue is minimal and fitness is maximal). This also theoretically predicts the appropriate “taper” period required to maximise performance (i.e. He also posed that in response to a given load, fatigue initially outweighs fitness such that the subsequent performance capacity decreases, but that fatigue dissipates faster over time than fitness, suggesting that fitness eventually outweighs fatigue and performance capacity can be predicted to rise. By progressively increasing TRIMP over several weeks, the accumulation of fitness and fatigue can be modelled to predict “ performance” by subtracting current fatigue from the accumulated fitness. a positive and a negative training effect. y = 0.64e 1.92✕%HRR in men and 0.64e 1.67✕%HRR in women).īanister proposed that every exercise bout produces both a “ fitness” and a “ fatigue” impulse, i.e. In the original work, this relationship was different between men and women, so y is influenced by sex (i.e. TRIMP = Time (mins) × %HRR × weighting factor (y) Where %HRR = (mean HR during session − HR rest ) ÷ (HR max − HR rest ), and y = the nonlinear coefficient that models the relationship between the rise in blood lactate during exercise and the fractional elevation in HR during exercise above resting HR. ![]()
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