A Systems Model of the Effects of Training on Physical Performance

Calvert, Tom; Banister, E. W.; Savage, Margaret V.; Bach, Timothy Michael.

doi:10.1109/tsmc.1976.5409179

Cited by 147 publications

(131 citation statements)

References 10 publications

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“…Several other groups have modified this initial model to account for changes in training monotony (ie, variation in training-load stimulus) and increased fatigue, [44][45][46][47][48] but essentially each model suggests that the training impulse (or training load) elicits fitness responses that increase performance and also produce fatigue responses that decrease performance. This approach results in impulse-response models that relate training loads to performance, accounting for the dynamic and temporal characteristics of training and the effects of training over time.…”

Section: Analyzing Training-load Datamentioning

confidence: 99%

“…11,43,46 Indeed, studies that used the systems-model approach to predict performance have reported a significant relationship between the modeled and actual performance in a range of sports including swimming, running, cycling, triathlon, and hammer throwing (for review see Jobson et al 53 and Taha and Thomas 64 ). Unfortunately, however, the broader application of this approach to predict performance in highly trained athletes is limited due to the large amount of unexplained variance in the predictions for performance across a range of endurance sports.…”

Section: Modeling Training Loads With a View To Enhance Or Predict Atmentioning

confidence: 99%

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Monitoring Athlete Training Loads: Consensus Statement

Bourdon¹,

Cardinale²,

Murray³

et al. 2017

International Journal of Sports Physiology and Performance

755

735

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Monitoring the load placed on athletes in both training and competition has become a very hot topic in sport science. Both scientists and coaches routinely monitor training loads using multidisciplinary approaches, and the pursuit of the best methodologies to capture and interpret data has produced an exponential increase in empirical and applied research. Indeed, the field has developed with such speed in recent years that it has given rise to industries aimed at developing new and novel paradigms to allow us to precisely quantify the internal and external loads placed on athletes and to help protect them from injury and ill health. In February 2016, a conference on "Monitoring Athlete Training Loads-The Hows and the Whys" was convened in Doha, Qatar, which brought together experts from around the world to share their applied research and contemporary practices in this rapidly growing field and also to investigate where it may branch to in the future. This consensus statement brings together the key findings and recommendations from this conference in a shared conceptual framework for use by coaches, sport-science and -medicine staff, and other related professionals who have an interest in monitoring athlete training loads and serves to provide an outline on what athlete-load monitoring is and how it is being applied in research and practice, why load monitoring is important and what the underlying rationale and prospective goals of monitoring are, and where athlete-load monitoring is heading in the future.

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Section: Analyzing Training-load Datamentioning

confidence: 99%

Section: Modeling Training Loads With a View To Enhance Or Predict Atmentioning

confidence: 99%

Monitoring Athlete Training Loads: Consensus Statement

Bourdon¹,

Cardinale²,

Murray³

et al. 2017

International Journal of Sports Physiology and Performance

755

735

View full text Add to dashboard Cite

show abstract

“…Banister proposed a mathematical model that was based on a system of linear ordinary differential equations to quantify the effect of training on performance for collegiate swimmers [3,7]. The model accounted for two primary physiological components: the positive effects of training, called fitness, and the negative effects of training, called fatigue.…”

Section: Introductionmentioning

confidence: 99%

“…It is the incorporation of these well-known phenomena into a performance modeling framework that provides a more realistic and necessary counter balance; the inclusion of these phenomena also allows performance to be optimized. However, despite the potential concerns associated with the linear modeling approach, it has been used to successfully inform training strategies for many athletes by approximating optimal recovery times between workouts, predicting the success of training regimens, and determining how an athlete should taper before a competition [5,7,8,10,12,13,15].…”

Section: Introductionmentioning

confidence: 99%

A nonlinear model for the characterization and optimization of athletic training and performance

Turner

Mazzoleni

Little

et al. 2017

Biomedical Human Kinetics

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SummaryStudy aim: Mathematical models of the relationship between training and performance facilitate the design of training protocols to achieve performance goals. However, current linear models do not account for nonlinear physiological effects such as saturation and over-training. This severely limits their practical applicability, especially for optimizing training strategies. This study describes, analyzes, and applies a new nonlinear model to account for these physiological effects. Material and methods:This study considers the equilibria and step response of the nonlinear differential equation model to show its characteristics and trends, optimizes training protocols using genetic algorithms to maximize performance by applying the model under various realistic constraints, and presents a case study fitting the model to human performance data.Results: The nonlinear model captures the saturation and over-training effects; produces realistic training protocols with training progression, a high-intensity phase, and a taper; and closely fits the experimental performance data. Fitting the model parameters to subsets of the data identifies which parameters have the largest variability but reveals that the performance predictions are relatively consistent. Conclusions: These findings provide a new mathematical foundation for modeling and optimizing athletic training routines subject to an individual's personal physiology, constraints, and performance goals.

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“…In the past four decades, a number of attempts have been made to model training effects on performance by means of mathematical models, with the fitness-fatigue model (FFmodel) and its extensions being the most popular approach (Busso, 2003;Calvert, T. W., Banister, E. W., Savage, M. V., & Bach, T., 1976). In this model, athletes are understood as a system with training load as the input, equally feeding two antagonistic effects -fitness and fatigue -, which compromise the performance as the output.…”

Section: Introductionmentioning

confidence: 99%

Performance Estimation using the Fitness-Fatigue Model with Kalman Filter Feedback

Kolossa

Azhar

Rasche

et al. 2017

International Journal of Computer Science in Sport

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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.

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A Systems Model of the Effects of Training on Physical Performance

Cited by 147 publications

References 10 publications

Monitoring Athlete Training Loads: Consensus Statement

Monitoring Athlete Training Loads: Consensus Statement

A nonlinear model for the characterization and optimization of athletic training and performance

Performance Estimation using the Fitness-Fatigue Model with Kalman Filter Feedback

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