PurposeTo investigate the effects of different recovery strategies on fatigue markers following a prolonged running exercise.Methods46 recreational male runners completed a half-marathon, followed by active recovery (ACT), cold water immersion (CWI), massage (MAS) or passive recovery (PAS). Countermovement jump height, muscle soreness and perceived recovery and stress were measured 24h before the half-marathon (pre), immediately after the recovery intervention (postrec) and 24h after the race (post24). In addition, muscle contractile properties and blood markers of fatigue were determined at pre and post24.ResultsMagnitude-based inferences revealed substantial differences in the changes between the groups. At postrec, ACT was harmful to perceived recovery (ACT vs. PAS: effect size [ES] = −1.81) and serum concentration of creatine kinase (ACT vs. PAS: ES = 0.42), with CWI being harmful to jump performance (CWI vs. PAS: ES = −0.98). It was also beneficial for reducing muscle soreness (CWI vs. PAS: ES = −0.88) and improving perceived stress (CWI vs. PAS: ES = −0.64), with MAS being beneficial for reducing muscle soreness (MAS vs. PAS: ES = −0.52) and improving perceived recovery (MAS vs. PAS: ES = 1.00). At post24, both CWI and MAS were still beneficial for reducing muscle soreness (CWI vs. PAS: ES = 1.49; MAS vs. PAS: ES = 1.12), with ACT being harmful to perceived recovery (ACT vs. PAS: ES = −0.68), serum concentration of creatine kinase (ACT vs. PAS: ES = 0.84) and free-testosterone (ACT vs. PAS: ES = −0.91).ConclusionsIn recreational runners, a half-marathon results in fatigue symptoms lasting at least 24h. To restore subjective fatigue measures, the authors recommend CWI and MAS, as these recovery strategies are more effective than PAS, with ACT being even disadvantageous. However, runners must be aware that neither the use of ACT nor CWI or MAS had any beneficial effect on objective fatigue markers.

Tracking and predicting the performance of athletes is of great interest, not only in training science but also, increasingly, for serious hobbyists. The increasing availability and use of smart watches and fitness trackers means that abundant data is becoming available, and the interest to optimally use this data for performance tracking and training optimization is great. One competitive model in this domain is the 3-time-constant fitness-fatigue model by Busso based on the model by Banister and colleagues. In the following, we will show that this model can be written equivalently as a linear, time-variant state-space model. With this understanding, it becomes clear that all methods for optimum tracking in statespace models are also directly applicable here. As an example, we show how a Kalman filter can be combined with the fitness-fatigue model in a mathematically consistent fashion. This gives us the opportunity to optimally consider measurements of performance to adapt the fitness and fatigue estimates in a datadriven manner. Results show that this approach is capable of clearly improving performance tracking and prediction over a range of different scenarios.

In mathematical modeling by means of performance models, the Fitness-Fatigue Model (FF-Model) is a common approach in sport and exercise science to study the training performance relationship. The FF-Model uses an initial basic level of performance and two antagonistic terms (for fitness and fatigue). By model calibration, parameters are adapted to the subject’s individual physical response to training load. Although the simulation of the recorded training data in most cases shows useful results when the model is calibrated and all parameters are adjusted, this method has two major difficulties. First, a fitted value as basic performance will usually be too high. Second, without modification, the model cannot be simply used for prediction. By rewriting the FF-Model such that effects of former training history can be analyzed separately – we call those terms preload – it is possible to close the gap between a more realistic initial performance level and an athlete's actual performance level without distorting other model parameters and increase model accuracy substantially. Fitting error of the preload-extended FF-Model is less than 32% compared to the error of the FF-Model without preloads. Prediction error of the preload-extended FF-Model is around 54% of the error of the FF-Model without preloads.

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