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A Systems Model of the Effects of Training on Physical Performance
Abstract
A systems model is proposed to relate a profile of athletic performance to a profile of training. The general model assumes that performance has four components: endurance, strength, skill, and psychological factors. Each of these factors is discussed and ascribed a transfer function. A major problem is the quantification of both training and performance. The case of a swimmer is studied in detail. It is shown that if a time series of training impulses is used as input, his performance in 100 m criterion performances can be modeled rather well with a simple linear system. The major conclusion is that performance appears to be related to the difference between fitness and fatigue functions. The fitness function is related to training by a first-order system with time constant 50 days, whereas the fatigue function is related to training by a similar system with time constant 15 days. An appendix is provided to show how these systems can be simulated on a simple electronic calculator. The relationship of these relatively short-term efects on the individual performer (six months) to longer term effects on the same indiviudal is also discussed.
... Training-performance models provide a science-based, quantitative method for understanding the effects of training on performance and the relationship between training and performance, as well as predicting an athlete's performance over time during the training course and on the competition day. Since the introduction of the Banister impulse-response (IR) model (the earliest proposed training-performance model) [7,8], a number of training-performance models modified from the Banister IR model have been subsequently proposed [9][10][11][12][13]. One of the main advantages of training-performance models is that they can be applied to design training programs for an individual, since the model input is the data collected from an individual and therefore the model output is specific to that individual [3]. ...
... It describes how performance decays over time naturally, and how training can either slow the decay of performance or improve performance; in other words, this model intends to describe the effects of training on performance. In the paper in which the Banister IR model was first proposed [7,8], this model was used to describe the relationship between training and performance of a competitive swimmer. Since then, the Banister IR model and the subsequent models modified based on it have been applied to numerous kinds of sports and have started to attract more interest in recent years owing to their important roles in commercially available portable devices for real-time exercise monitoring [3]. ...
... During the training course, performance can increase or decrease due to the effects of a number of factors (including regulation of training load). Hence, performance can be assumed to be the difference between the overall positive effects on performance (termed "fitness") and the overall negative effects on performance (termed "fatigue") plus the performance on the initial day [8], i.e., ...
... The first widely acknowledged quantitative model of athletic performance in the academic literature is the Banister et al. Fitness-Fatigue Impulse Response Model (FFM) [3,15,33,11]. Since 1975, this, and variants, have been applied to: swimming [3,34,24,43], running [33], weight lifting [14], cycling [12,13], hammer throwing [11], triathlons [4,32], gymnastics [40] and judo [1]. ...
... As such, the integral over continuous time in (1) is mostly zero and the non-zero contributions can be captured as a discrete sum of one-dimensional training impulses. In Banister et al.'s original work [3,15], the authors studied the change in performance of a swimmer and quantified the training impulse using a 3-tier weighting system based on intensity for each 100m swum. Warm-up intensity was awarded 1 arbitrary training unit (ATU) per 100m swum, low intensity was awarded 2 ATUs per 100m swum and high intensity was awarded 3 ATUs per 100m swum. ...
... We underscore the importance and practicality of the simplifying assumption introduced by Banister et al. in [3,15], that both training load and performance are expressed as a function of discrete days where training is summarised by a one-dimensional variable w. ...
We conduct a mathematical optimisation of the training load to maximise performance for two seminal athletic performance models: the Banister et al. 1975 Fitness-Fatigue Impulse Response Model and the Busso 2003 Variable Dose-Response Model. We discuss discrepancies in the general trends of the optimised training loads compared to common training practices recommended in the sports science literature, such as tapering and periodisation. We then propose a set of interpretable nonlinear modifications in the magnitude and time response to training in the fitness-fatigue model such that the optimised training load demonstrates these trends.
... In a systems model, performance (the output or response) can be predicted from the training completed (the input or impulse). Banister and Calvert were the first to introduce and validate a systems model to explain how performance changes in response to adjustments in training load (Banister et al., 1975;Calvert et al., 1976). After some initial models involving multiple components, they reduced their performance model to be a function of two variables: fitness and fatigue (Banister & Calvert, 1980;Calvert et al., 1976). ...
... Banister and Calvert were the first to introduce and validate a systems model to explain how performance changes in response to adjustments in training load (Banister et al., 1975;Calvert et al., 1976). After some initial models involving multiple components, they reduced their performance model to be a function of two variables: fitness and fatigue (Banister & Calvert, 1980;Calvert et al., 1976). This impulseresponse model, often referred to simply as the Banister model, survives to the present day and is, in various forms, the basis for current approaches to monitoring and predicting fitness, fatigue, and performance. ...
... With varying success, the impulse-response model has been used to relate training load to changes in performance in different sports, including swimming (Calvert et al., 1976;Hellard et al., 2005;Mujika et al., 1996), triathlon (Millet et al., 2002), cycling Busso et al., 1991;Vermeire et al., 2021), and running Wallace et al., 2014;Wood et al., 2005). However, substantial uncertainty remains around the model's ability to predict performance, which might be in part explained by the difficulty of quantifying training load as outlined in the prior sections. ...
Athletic training is characterized by physiological systems responding to repeated exercise-induced stress, resulting in gradual alterations in the functional properties of these systems. The adaptive response leading to improved performance follows a remarkably predictable pattern that may be described by a systems model provided that training load can be accurately quantified and that the constants defining the training-performance relationship are known. While various impulse-response models have been proposed, they are inherently limited in reducing training stress (the impulse) into a single metric, assuming that the adaptive responses are independent of the type of training performed. This is despite ample evidence of markedly diverse acute and chronic responses to exercise of different intensities and durations. Herein, we propose an alternative, three-dimensional impulse-response model that uses three training load metrics as inputs and three performance metrics as outputs. These metrics, represented by a three-parameter critical power model, reflect the stress imposed on each of the three energy systems: the alactic (phosphocreatine/immediate) system; the lactic (glycolytic) system; and the aerobic (oxidative) system. The purpose of this article is to outline the scientific rationale and the practical implementation of the three-dimensional impulse-response model.
... We adapt the Fitness-Fatigue theory as a framework for comprehending an individual's performance and response to exercise workload. The Fitness-Fatigue theory, as conceptualized by Calvert et al., 17 provides a foundational framework for understanding the impact of training on exercise performance combining the fundamental principles of exercise science and training theory. It comes with the assumption that every exercise session generates two opposing responses: "Fitness", which signifies the underlying adaptations contributing to long-term performance improvements, and "Fatigue", which relates to transitory reductions in exercise performance. ...
... In this context, "Fatigue" can manifest in both physical and mental aspects and typically refers to the immediate effects of exercise, including muscle fatigue, decreased coordination, and a feeling of exhaustion. 17 Thirdly, the evaluation of the performance of the digital health service is dependent on the user's adherence to the exercise plans. 18 The user may struggle with self-control problems, resulting in failure to complete the daily plans. ...
... For instance, Calvert et al. proposed analytical research on exercise performance using a Fitness-fatigue model that predicts exercise performance based on exercise intensity. 17 This model has been applied to predict exercise performance in various endurance sports such as running, 22,39,40 swimming. 17 Nevertheless, in digital health service where the focus is on persuasive information rather than direct control of exercise processes, mathematical models are not directly applicable. ...
Background
The utilization of digital health has increased recently, and these services provide extensive guidance to encourage users to exercise frequently by setting daily exercise goals to promote a healthy lifestyle. These comprehensive guides evolved from the consideration of various personalized behavioral factors. Nevertheless, existing approaches frequently neglect the users’ dynamic behavior and the changing in their health conditions.
Objective
This study aims to fill this gap by developing a machine learning algorithm that dynamically updates auto-suggestion exercise goals using retrospective data and realistic behavior trajectory.
Methods
We conducted a methodological study by designing a deep reinforcement learning algorithm to evaluate exercise performance, considering fitness-fatigue effects. The deep reinforcement learning algorithm combines deep learning techniques to analyze time series data and infer user's exercise behavior. In addition, we use the asynchronous advantage actor-critic algorithm for reinforcement learning to determine the optimal exercise intensity through exploration and exploitation. The personalized exercise data and biometric data used in this study were collected from publicly available datasets, encompassing walking, sports logs, and running.
Results
In our study, we conducted the statistical analyses/inferential tests to compare the effectiveness of machine learning approach in exercise goal setting across different exercise goal-setting strategies. The 95% confidence intervals demonstrated the robustness of these findings, emphasizing the superior outcomes of the machine learning approach.
Conclusions
Our study demonstrates the adaptability of machine learning algorithm to users’ exercise preferences and behaviors in exercise goal setting, emphasizing the substantial influence of goal design on service effectiveness.
... Equation 1 -Original Fitness-Fatigue model proposed by Banister et al. (1975) Whilst previously prescribing the training process relied largely on the experience of either the athlete or the coach, the aim of this model was to better prescribe training leading to an optimal performance at a given time. Calvert et al. (1976) then proposed a multicomponent model which encompassed four key training 'elements' and aimed to explain their effect on performance. The elements proposed were; endurance, strength, skill, and psychological factors (Figure 2.2). ...
... Calvert et al. (1976) then proposed a multicomponent model which encompassed four key training 'elements' and aimed to explain their effect on performance. The elements proposed were; endurance, strength, skill, and psychological factors (Figure 2.2). Calvert et al. (1976) admittedly stated that this initial model was a speculative attempt at assessing performance and should only be viewed as a 'skeleton' of what a complete model may eventually be. There is difficulty in using this model in quantifying values of the inputs and assessing the effects of the separate input components. ...
... At the time the limitations of this model were accepted, but it should be noted that these components account for the major determinants of performance, and ensuring these inputs are logical will lead to a more realistic output. Utilising a case study of a highly developed swimmer, Calvert et al. (1976) proposed that there was significant interplay between fatigue and fitness to Future models were developed over time in distance runners (Banister and Hamilton, 1985), elite weightlifters (Busso et al., 1990) and in recreational runners (Morton et al., 1990). Busso et al. (1994) then compared two differing methods of estimating fatigue and fitness to model resultant performance. ...
... Largely studied for years, the theoretical physiological mechanisms related to exercise allows us to understand and estimate what physiological adaptations are susceptible to occur following a training session and their aftereffects on athletic performance. Scientists attempted to model the effects of training on physical performance on a physiological basis, initially using system model frameworks (Banister, Calvert, Savage, et al. 1975;Calvert, Banister, Savage, et al. 1976). Sometimes named "biocybernetics" models, they aim at describing and predicting performance outcomes using states features, built from more or less elaborated functions intended to represent some basics of biological processes (e.g. ...
... The first models of training effects were developed in the seventies, notably with the work of Banister, Calvert, Savage, et al. 1975 and Calvert, Banister, 1. State of the art -1.2. Modelling the effects of training Savage, et al. 1976. Initially, Calvert, Banister, Savage, et al. 1976 designed a system model of the effects of training on physical performance using a general transfer function that applies on various inputs. ...
... A simplification of that system model of training effects on performance is the so-called FFM (Calvert, Banister, Savage, et al. 1976). The model came with a single input but two transfer functions providing two antagonistic features (fitness and fatigue states) that affect positively and negatively performance outcomes, respectively. ...
The first models of training effects on athletic performance emerged with the work of Banister and Calvert through the so-called Fitness-Fatigue model (FFM). One major drawback of FFMs is that the features stem from a single source of data. That is not in line with the existing consensus about a multifactorial aspect of athletic performance. Hence, multivariate modelling approaches from statistics and machine-learning (ML) emerged.
A research issue arises from the quantification of training Loads (TL) in resistance training (RT) which lack of physiological evidence. In the first study, we provided a new method of TL quantification in RT based on physiological observations. To achieve that, we initially modelled the torque-velocity profiles of fifteen participants during an isokinetic leg extension task and assessed a set of physiological responses to various resistance exercises intensities. Each session was volume-equated according to the formulation of volume load (i.e. the product of the number of repetitions and the relative intensity).
Higher led to greater muscular fatigue described by neuromuscular impairments. Conversely, systemic and local pulmonary responses (measured through oxygen uptake) and metabolic changes (according to blood lactate concentrations) were more significant at low intensities, suggesting different contributions of metabolic pathways.
From these results, we provided a new index of TL based on the neuromuscu- lar impairments observed at exercise. We showed that to exponentially weight TL by the average rate decay of force development rate yielded better correla- tions with any of the significant physiological responses to exercise. In addition, information compressed within a principal component could be a valuable TL index.
In the second study, we provided a robust modelling methodology that relies on model generalisation. Using data from elite speed skaters, we compared a dose-response model to regularisation methods and machine-learning models.
Regularisation procedures provided the greatest performances in both generalisa- tion and accuracy. Also, we highlighted the pertinence of computing one model over the group of athletes instead of a model per athlete in a context of a small sample size.
Finally, ML approaches could be a way of improving FFMs through ensemble learning methods.
In the third study, we modelled acceleration-velocity directly from global posi- tioning system (GPS) measurements and attempted to predict the coefficients of the relationship between acceleration and velocity.
First, a baseline model was defined by time-series forecasting using game data only. Then, we proceeded to multivariate modelling using commercial features. A regularised linear regression and a long short term memory neural network were compared. Finally, we extracted features directly from raw GPS data and compared these features to the commercial ones for prediction purposes.
The results showed only slight differences between model accuracy, and no models significantly outperformed the baseline in the prediction task. Given the multi- factorial nature of athletic performance, using only GPS data for predicting such athletic performance criterion provided an acceptable accuracy. Using time-domain and frequency-domain features extracted from raw data led to similar performances compared to the commercial ones, despite being evidence-based. It suggests that raw data should be considered for future athletic performance and injury occurrence analysis.
Lastly, we developed an athlete management system for long-distance runners. This application provided an athlete monitoring module and a predictive module based on a physiological model of running performance.
A second development was realised under the SAP analytics cloud solution. Team management and automated dashboards were provided herein, in close collaboration with a professional Rugby team.
... This may have introduced bias, although this is inevitable when developing conceptual frameworks given the qualitative nature of the process. From the examination of the literature (conducted via a literature search and consultation with experts in the field), we identified and selected two other potentially relevant models specifically proposed for the training process: the Banister Impulse-Response (IR) [24,25] and the Performance Potential Metamodel (PerPot) [26,27]. The main criterion for selecting reference conceptual frameworks and models was the presence of potentially measurable components and concepts. ...
... The model of Banister considered athletic performance to be measurable as the net outcome of two key training responses, also called fitness and fatigue, from the application of a training impulse. The original works by Banister and colleagues [24,25] applied a systems theory to evaluate the response to physical training using a mathematical function. In this mathematical model, performance is the output, with the athlete regarded as the system, and the training impulse as the input [24,25]. ...
... The original works by Banister and colleagues [24,25] applied a systems theory to evaluate the response to physical training using a mathematical function. In this mathematical model, performance is the output, with the athlete regarded as the system, and the training impulse as the input [24,25]. The functional relationship between the training impulse and the system response is expressed by two differential first-order equations attributed to the antagonist effects that were called fitness and fatigue [24,25]. ...
A conceptual framework has a central role in the scientific process. Its purpose is to synthesize evidence, assist in understanding phenomena, inform future research and act as a reference operational guide in practical settings. We propose an updated conceptual framework intended to facilitate the validation and interpretation of physical training measures. This revised conceptual framework was constructed through a process of qualitative analysis involving a synthesis of the literature, analysis and integration with existing frameworks (Banister and PerPot models). We identified, expanded, and integrated four constructs that are important in the conceptualization of the process and outcomes of physical training. These are: (1) formal introduction of a new measurable component ‘training effects’, a higher-order construct resulting from the combined effect of four possible responses (acute and chronic, positive and negative); (2) explanation, clarification and examples of training effect measures such as performance, physiological, subjective and other measures (cognitive, biomechanical, etc.); (3) integration of the sport performance outcome continuum (from performance improvements to overtraining); (4) extension and definition of the network of linkages (uni and bidirectional) between individual and contextual factors and other constructs. Additionally, we provided constitutive and operational definitions, and examples of theoretical and practical applications of the framework. These include validation and conceptualization of constructs (e.g., performance readiness), and understanding of higher-order constructs, such as training tolerance, when monitoring training to adapt it to individual responses and effects. This proposed conceptual framework provides an overarching model that may help understand and guide the development, validation, implementation and interpretation of measures used for athlete monitoring.
... To our knowledge, this is the first study to examine this effect in young football players, simulating a football training for full 90 minutes duration. Understanding the magnitude of this effect on sRPE is crucial for accurately assessing TL and optimizing training programs for football players (Calvert et al. 1976;Foster et al. 2001;Impellizzeri et al. 2019). This finding is confirmed by previous investigations that showed similar results with swimming athletes (Impellizzeri et al. 2019). ...
... Coaches and practitioners who utilize sRPE as a marker of exercise intensity should be cautious when interpreting sRPE values in long-duration training sessions, as the perceived exertion may be influenced by the duration of exercise (Green et al. 2007;Haddad et al. 2014). This means that TL may be calculated twice, once based on the exercise intensity measured by sRPE and the second time based on the exercise duration in minutes (Calvert et al. 1976;Foster et al. 2001). Coaches who utilize sRPE as a marker of exercise intensity need to be aware of this possibility, especially during longduration training sessions. ...
In sports, session rating of perceived exertion (sRPE) is used to reflect internal training load (TL). TL reflects the body physiological strains during exercise sessions. The extent to which exercise intensity and duration affect sRPE during successive football training sessions is unclear. The study investigated the impact of exercise intensity and duration on the session rating of perceived exertion (sRPE) in relation to physiological variables during football training sessions. A sample of 47 youth male football players underwent two high-intensity exercise trainings with a 15-minute break in between. The first training consisted of three 15-minute "walk-sprint-jog" sessions, followed by three minutes of recovery. The second training continued until exhaustion. The levels of sRPE, physiological data, blood and urine analysis were assessed pre-exercise, after each session, and after exhaustion. Results showed a progressively significant increase in sRPE, physiological, blood and urine parameters from the first session until exhaustion. The impact of cumulative duration on the holistic perception of workload showed a linear increment during consecutive exercise sessions. The study concludes that sRPE demonstrates sensitivity to the accumulation of perceived fatigue resulting from exercise duration during football training sessions, even with consistently maintained exercise intensity.
... Athletic performance is shaped by the interaction of two training after-effects: fitness, or the positive, chronic adaptation resulting from a training stimulus, and fatigue, a negative response that occurs immediately after training. 1,3 This relationship is known as the fitness-fatigue model. 1,3 The athlete monitoring cycle that allows for the observation of the fitness fatigue model includes data collection on the workload the athlete performs (external workload; e.g. ...
... 1,3 This relationship is known as the fitness-fatigue model. 1,3 The athlete monitoring cycle that allows for the observation of the fitness fatigue model includes data collection on the workload the athlete performs (external workload; e.g. high-speed yards), the response to the workload (internal workload; e.g. ...
American football athletes require the development of workload capacity for repeated high-intensity efforts, and successful athletes are adept at accelerating, decelerating, and changing directions. The prescription of appropriate training volume stimulus can be difficult to determine, as there are very few guidelines for prescribing sport-specific acceleration, deceleration, and maximum velocity efforts. Preparatory training stimulus has to closely match in-game demands, but at the same time, practitioners need to avoid excessive workloads and undertraining to mitigate workload progression-related injuries and maximize roster availability. The acute-to-chronic workload ratio (ACWR) approach is based upon the fitness: fatigue ratio, which allows practitioners to monitor workloads. New technology allows for in-game positional tracking and these advancements are accessible to the public. By measuring in-game movement, coaches can quantify key metrics like the number of accelerations and average distance covered. These metrics provide a snapshot of in-game demands and performance requirements. Using a reverse engineering approach, coaches can utilize ACWRs to calculate predefined targets to ensure athletes are adequately prepared for gameplay. Here we use the ACWR concept and previously reported in-game data derived from the National Football League to show how to reverse engineer the targeted number of efforts and distances to assist in preparatory pre-season training program design. This approach, which we term the Reverse ACWR Method, can be used to set guidelines for training volumes and workload progressions and provides a systematic, quantitative approach that complements periodization. As such, the Reverse ACWR Method allows practitioners to calculate target sport-specific workloads and training progressions derived from scientific-grounded methodology, which may enhance performance, readiness, and roster availability. Although this paper presents an example of how to use positional in-game data to prescribe American football training workloads, this model can be applied to any sport and team that has access to positional in-game movement data.
... External and internal loads are therefore linked by a causal relationship [5]. The Training Impulse (TRIMP) is one of the indices for integrating external and internal training loads [6]. It combines the heart rate (HR) reserve method (the internal load), the duration of training (the external load) and a weighting factor [6]. ...
... The Training Impulse (TRIMP) is one of the indices for integrating external and internal training loads [6]. It combines the heart rate (HR) reserve method (the internal load), the duration of training (the external load) and a weighting factor [6]. The Edward TRIMP model (eTRIMP) is an alternative of TRIMP for quantifying the physiological impact of training by using the accumulated duration in five arbitrary HR zones multiplied by a weighting factor [7]. ...
This study aimed to create a training load index to measure physiological stress during breath-hold (BH) training and examine its relationship with memory performance. Eighteen well-trained BH divers (Age: 35.8±6.6 years, BH training practice: 5.3±4.5 years) participated in this study. During a standard 1.5-hour BH training in the pool, perceived exertion, heart rate, distance, and duration were measured. The training load index was modelled on the basis of a TRIMP (TRaining IMPulse) with four different equations and was used to measure the stress induced by this BH training. A reference value, based on the ratio between the average heart rate during all BHs and the lowest heart rate during BH training, was used for comparing training load index. Memory assessment was conducted both before and after this training. Of the four equations proposed, equation no. 4, named aTRIMP for “apnoea,” showed the strongest correlation with our reference value (r=0.652, p<0.01). No difference was found between any of the memory tests before and after the BH training. The aTRIMP was a new representative index for monitoring habitual training of well-trained BH divers. Furthermore, this training had no negative impact on memory performance.
... 6 Yet, considering the cycle as a continuum of hormonal fluctuations, where every single day (or time point) would be worth testing, has been preconised in medicine 7 but has not yet been done in sports setting. Studies relying on objective data measurements such as inertial data, [8][9][10] which captures movement and acceleration, have shown significant impact of MC on training or competition load parameters. Yet, most sports, such as skiing or rowing, face the challenge of quantifying workload through subjective measures, making even more difficult to estimate the complex effect of MC or OC on athletes' daily responses to training. ...
... Previous studies have shown an impaired postexercise recovery during the mid-luteal phase with an impaired ventilatory efficiency. 2 Correspondingly, other studies have shown greater workload capacity during football competitions, measured through inertial devices during late follicular phase in comparison with mid-luteal and early luteal phases, but without further distinction in between phases. 8 We showed similar results based on daily measurements in the field. The cyclic dynamics evidenced here are associated with better workload responses on the follicular phase, that is, the first half of the MC in comparison with the luteal phase. ...
Objectives
Develop the Markov Index Load State (MILS) model, based on hidden Markov chains, to assess athletes’ workload responses and investigate the effects of menstrual cycle (MC)/oral contraception (OC), sex steroids hormones and wellness on elite athletes’ training.
Methods
On a 7-month longitudinal follow-up, daily training (volume and perceived effort, n=2200) and wellness (reported sleep quality and quantity, fitness, mood, menstrual symptoms, n=2509) data were collected from 24 female rowers and skiers preparing for the Olympics. 51 MC and 54 OC full cycles relying on 214 salivary hormone samples were analysed. MC/OC cycles were normalised, converted in % from 0% (first bleeding/pill withdrawal day) to 100% (end).
Results
MILS identified three chronic workload response states: ‘easy’, ‘moderate’ and ‘hard’. A cyclic training response linked to MC or OC (95% CI) was observed, primarily related to progesterone level (p=8.23e-03 and 5.72e-03 for the easy and hard state, respectively). MC athletes predominantly exhibited the ‘easy’ state during the cycle’s first half (8%–53%), transitioning to the ‘hard’ state post-estimated ovulation (63%–96%). OC users had an increased ‘hard’ state (4%–32%) during pill withdrawal, transitioning to ‘easy’ (50%–60%) when on the pill. Wellness metrics influenced the training load response: better sleep quality (p=5.20e-04), mood (p=8.94e-06) and fitness (p=6.29e-03) increased the likelihood of the ‘easy’ state. Menstrual symptoms increased the ‘hard’ state probability (p=5.92e-02).
Conclusion
The MILS model, leveraging hidden Markov chains, effectively analyses cumulative training load responses. The model identified cyclic training responses linked to MC/OC in elite female athletes.
... Vorbereitungszustand) auswirken. Zum Beispiel werden mit dem Fitness-Fatigue-Modell die Fitness als leistungsaufbauender Ef-fekt und die Ermüdung (Fatigue) als leistungsabbauender Effekt in Folge von körperlichem Training mathematisch über Exponentialfunktionen beschrieben (Banister et al. 1975;Calvert et al. 1976). Die Bestimmung von variablen Modellparametern (z. ...
... Ermüdung) für jeden Trainierenden ermöglicht dabei eine individuelle Anpassung des Modells. Die Prognose der Leistungsfähigkeit mit Hilfe des Fitness-Fatigue-Modells konnte bereits bei unterschiedlichen Zielgruppen (Leistungssportler, Untrainierte) überprüft werden (Busso et al. 1991;Calvert et al. 1976;Hellard et al. 2006). Kritiker dieses Modells geben jedoch zu bedenken, dass die Modellparameter selbst auf Ebene des Individuums variabel sind (Borresen und Lambert 2009;Pfeiffer 2008) und Bezüge zu physiologischen Strukturen und Anpassungsmechanismen fehlen (Taha und Thomas 2003). ...
Neben Leistungs- und Spitzensport widmet sich die Trainingswissenschaft auch vermehrt neuen Anwendungsfeldern im Freizeit-, Breiten- und Gesundheitssport, um die Trainingspraxis wissenschaftlich zu fundieren. Das vorliegende Kapitel führt in die tradierten Gegenstandsbereiche der Trainingswissenschaft ein und diskutiert, inwieweit ausgewählte Aspekte (z. B. Dosis-Wirkungs-Beziehungen) auch für den Gesundheitssport Gültigkeit haben. Diesbezüglich soll insbesondere auch die inhaltliche Schnittmenge zwischen Trainingswissenschaft und Sportmedizin beleuchtet werden. Dieser Beitrag ist Teil der Sektion Sportmedizin, herausgegeben vom Teilherausgeber Holger Gabriel, innerhalb des Handbuchs Sport und Sportwissenschaft, herausgegeben von Arne Güllich und Michael Krüger.
... The aim of physical training is to systematically induce the optimal relevant psychological, physiological or biomechanical response (fitness) from minimum energy cost (fatigue) (Calvert et al., 1976;Impellizzeri, Marcora, et al., 2019a). It is this doseresponse that corresponds to the internal load which is generally considered a reflection of the athlete's ability to cope with the requirements elicited by external load (Impellizzeri, Marcora, et al., 2019a). ...
... endurance, team-sport activity and resistance training); and thus requires informed selection from a myriad of possible variables (Bourdon et al., 2017;Coyne et al., 2018;Gabbett & Whiteley, 2017). Although the method of monitoring internal load may vary between activities, it is common that these variables will be analysed using a training impulse, derived as a product of intensity and volume/duration (Calvert et al., 1976;Coyne et al., 2018). Several approaches have been used to measure internal training load, with likely the most popular being ratings of perceived exertion (RPE), originally employed by (Fanchini et al., 2016;Impellizzeri et al., 2004;Weston, 2013). ...
The period surrounding the adolescent growth spurt is a turbulent but crucial stage of development for young footballers in their pursuit of becoming full-time athletes. At a time of almost constant talent (re)selection which coincides with major physical and physiological changes players experience large fluctuations in performance and a heightened injury incidence. Adding to the complexity of this period, the timing and tempo of biological maturation varies between individuals causing a diversity in physical and physiological capabilities, influencing the dose-response to training. Although differences in biological maturation and the links with injury are acknowledged in literature, little evidence exists to quantify the magnitude and extent to which these impacts perceptions of load and subsequent performance. This thesis aims to quantify the maturity-specific responses to load using ecologically valid approaches to aid the enhancement of provision offered to young academy players.
To provide a context and informed backdrop for the rest of the thesis, it was deemed important to first identify the current practices of, and perceived barriers to monitoring training load and biological maturation in academies. A cross-sectional survey design was used to ascertain perceptions of staff from male (EPPP) and female (RTC) academies during the 2017/18 soccer season. In total, 49 respondents completed the survey who advocated injury prevention as highest importance for conducting training load and maturation monitoring across academy groups, with overall athletic development, load management, coach and player feedback considered important. However, there were clear differences in monitoring strategies that academies of different categories adopted, which were often associated with resources or staffing. Survey responses suggest that despite routine monitoring of biological maturation and training load being commonplace within adolescent soccer the communication and dissemination of this information is often lacking, which may ultimately impede the impact of the monitoring practices for the players. Resource and environmental constraints create natural diversity around the strategies adopted, but academies are recommended to adopt sustainable and consistent approaches to monitor key variables to inform the coaching, selection, and development process.
The survey chapter identified that most clubs employ one of the various ‘non-invasive’, somatic equations to estimate biological maturation. However, the methodological differences associated with calculations often mean they provide variable estimations, even when using the same anthropometrical data. Therefore, it was deemed important to this thesis to observe the agreement of maturity estimations and compare concordance between methods when looking to estimate maturity status. Thus, anthropometric data from 57 participants was collected from a single assessment point during the 2017-18 season, with an additional 55 participants providing three repeated measurements during the 2018-19 season, resulting in 222 somatic estimations observed. Results indicated that all methods of maturity-offset (MO) produced an identical estimate of age of peak height velocity (13.3 years) with mean prediction of adult height (PAH%) providing a mean estimate of 93.6%, which also aligns closely. However, when looking to identify circa-PHV individuals there is greater concordance when using conservative thresholds (44-67%) than when using more stringent bandwidth thresholds (31-60%), with both being considered moderate concordance at best. Therefore, although overall findings indicate that there is very high to near perfect agreement between all approaches when predicting APHV, concordance of categorisation between these methods is less useful. Therefore, this chapter indicates that PAH% and MO methods are not interchangeable, and practitioners should utilise one approach routinely for all maturity-specific interventions.
Academy squads are comprised of players within chronological parameters but often present significant variations in physical characteristics including body mass (~50%), stature (~17%), percentages of predicted adult height (10-15%) and fat free mass (~21%). These maturational changes likely influence performance and dose-responses to load, but limited studies using standardised activity profiles have directly observed this influence. Therefore, this thesis aimed to quantify the neuromuscular performance (CMJ, RSI absolute and relative stiffness) and psycho-physiological (d-RPE) responses to a simulated soccer-specific activity profile (Y-SAFT60) and analyse whether this dose-response was moderated by maturation in EPPP academy players. Data illustrated an interaction between perceived psycho-physiological load (RPE-T) and maturation, with absolute stiffness, relative stiffness and playerload (PL) showing slope significance across various stages of maturation (~86-96% PAH). These interactions suggest that psycho-physiological dose responses are influenced by maturation and should be considered for training prescription purposes, which is likely a result of the musculotendinous changes that occur around peak height velocity (PHV). Therefore, practitioners are urged to consider the maturational load-response variation to reduce injury incidence from inappropriate levels of physical and cognitive stress, which are likely compounded chronically with multiple weekly sessions.
Typically, players experience between 3-4 acute bouts of specific training on a weekly basis, proposing that the maturity-specific load-responses observed above may be exacerbated over the course of a season. 55 male soccer players from a Category 2 EPPP academy were monitored during the 2018-19 season. Self-reported perceptions of psycho-physiological (d-RPE) intensity were collected approximately 15-minutes after each training session for a period of 40-weeks using the CR100® centi-Max scale. Analysis indicated that a 5% increase in PAH%, resulted in a reduction of ~7AU per session, with a ~14AU difference for a 10% difference in PAH%. Therefore, players less biologically mature are consistently working harder just to compete with more biologically advanced teammates of a similar chronological age. Again, these changes are mostly attributed to musculotendinous changes because of maturation and therefore a higher relative mechanical load experienced by less mature individuals. When accrued, these small inter-individual differences lead to a substantial variation in training load (~40-50%) over the 40-week season. This has the potential to undermine the whole developmental pathway, as the assumption that players of a similar chronological age are experiencing similar load-responses is precarious. Failure to act, by adopting more maturity sensitive ways of working for example, will result in a ‘survival of the fittest’ environment, rather than the systematic, considered, and individualised approach to optimal loading proposed in policy documents and literature.
Bio-banding is a method to group individuals based on biological maturation rather than chronological age. Supplementing the chronological programme with bio-banded activities may offer practitioners a practical method to better control load exposure and ultimately mechanical load related injury risk. Therefore, the final thesis study explored effects of standardised chronological and bio-banded training sessions on neuromuscular performance and psycho-physiological perceptions of intensity in 55 male soccer players from a single academy. Players participated in bio-banded and chronologically categorised bouts (x5) of 5-minute 6v6 (including GK) SSG on a playing area 45 x 36 m (135m2 per player). Prior to and following this, players performed a standardised sub-maximal run using the audio controlled 30-15IFT wearing foot-mounted inertial devices. Findings indicate that the introduction of bio-banded training sessions minimises the decrement in neuromuscular and locomotor markers and psycho-physiological ratings of intensity for players across the maturation spectrum. From a load management point of view, the relatively smaller pre-post changes observed in bio-banded SSGs offer promising early indications that biologically categorising training may help to stabilise the stress-response for players across maturity groups and facilitate a load management option for practitioners. Based on this, practitioners should actively seek opportunities to integrate biologically classified training activity alongside chronologically categorised sessions within their training schedules. In doing so they may alleviate the consistent stress placed on less mature players as part of standard chronologically categorised sessions without compromising the development of those more mature and able to tolerate greater workloads.
... The physiology literature concerning evaluating exercise performance is vast. Analytical research on practice performance was proposed by Calvert et al. (1976). In their study, practice performance is associated with training intensity by a positive effect ("fitness") and a negative effect ("fatigue"). ...
... In their study, practice performance is associated with training intensity by a positive effect ("fitness") and a negative effect ("fatigue"). The fitness-fatigue model has been used to predict athletes' performance in a variety of endurance sports, including running (Mcgregor et al., 2009;Roels, 2020), swimming (Calvert et al., 1976), soccer (Jaspers et al., 2017), among others. The existing literature on practice performance is extensive and focuses particularly on the process optimization perspective (Schmidt & Bjork, 1992). ...
Mobile health (mHealth) information service makes healthcare management easier for users, who want to increase physical activity and improve health. However, the differences in activity preference among the individual, adherence problems, and uncertainty of future health outcomes may reduce the effect of the mHealth information service. The current health service system usually provides recommendations based on fixed exercise plans that do not satisfy the user specific needs. This paper seeks an efficient way to make physical activity recommendation decisions on physical activity promotion in personalised mHealth information service by establishing data-driven model. In this study, we propose a real-time interaction model to select the optimal exercise plan for the individual considering the time-varying characteristics in maximising the long-term health utility of the user. We construct a framework for mHealth information service system comprising a personalised AI module, which is based on the scientific knowledge about physical activity to evaluate the individual exercise performance, which may increase the awareness of the mHealth artificial intelligence system. The proposed deep reinforcement learning (DRL) methodology combining two classes of approaches to improve the learning capability for the mHealth information service system. A deep learning method is introduced to construct the hybrid neural network combing long-short term memory (LSTM) network and deep neural network (DNN) techniques to infer the individual exercise behavior from the time series data. A reinforcement learning method is applied based on the asynchronous advantage actor-critic algorithm to find the optimal policy through exploration and exploitation.
... Modelling the effect of training is a major challenge for the sport community since the apparition of the first mathematical models five decades ago [1]. A simplified version of the one from Banister et al. [1], the so-called Fitness-Fatigue model (FFM) [2], describes the effect of training on athletic performance relying on some basics of exercise sciences and training theory. It comes with the assumption that each training dose induces two antagonistic responses. ...
... Based on more relevant physiological and practical assumptions, models were seeking a better interpretability of parameters and for more accurate predictions. Hence, the underlying impulse responses framework relates to a collection of FFMs [1][2][3][4][5][6][7][8][9]. ...
The emergence of the first Fitness-Fatigue impulse responses models (FFMs) have allowed the sport science community to investigate relationships between the effects of training and performance. In the models, athletic performance is described by first order transfer functions which represent Fitness and Fatigue antagonistic responses to training. On this basis, the mathematical structure allows for a precise determination of optimal sequence of training doses that would enhance the greatest athletic performance, at a given time point. Despite several improvement of FFMs and still being widely used nowadays, their efficiency for describing as well as for predicting a sport performance remains mitigated. The main causes may be attributed to a simplification of physiological processes involved by exercise which the model relies on, as well as a univariate consideration of factors responsible for an athletic performance. In this context, machine-learning perspectives appear to be valuable for sport performance modelling purposes. Weaknesses of FFMs may be surpassed by embedding physiological representation of training effects into non-linear and multivariate learning algorithms. Thus, ensemble learning methods may benefit from a combination of individual responses based on physiological knowledge within supervised machine-learning algorithms for a better prediction of athletic performance.
In conclusion, the machine-learning approach is not an alternative to FFMs, but rather a way to take advantage of models based on physiological assumptions within powerful machine-learning models.
... Essentially, ACWR aims to record both internal and external loads, with a primary focus on investigating data for their quantification. This research is based on findings that athletes' performance can be calculated as the difference between fitness and fatigue [14]. The acute chronic workload ratio (ACWR) is based on this research, with subsequent research data focusing on the potential relationship between ACWR and injury rather than performance [10,[15][16][17][18][19][20]. ...
The aim of this study was to record and interpret external load parameters in professional soccer players in competitive microcycles with one or two matches per week, while investigating the interaction between training load and non-contact musculoskeletal injuries during training and matches. Musculoskeletal injuries in athletes are closely associated with workload fluctuations, particularly the acute:chronic workload ratio (ACWR) over preceding weeks. This study analyzed the physical workload of 40 high-level soccer players competing in the Greek championship across two seasons, encompassing 50 competitive microcycles, 60 official matches, and 300 training sessions. GPS-based assessments recorded total distance (TD), running speeds (15–20 km/h, 20–25 km/h, 25–30 km/h), accelerations (>2.5 m/s²), and decelerations (>2.5 m/s²). An independent sample t-test was conducted to compare injured and uninjured athletes, with statistical significance set at p < 0.05. Results showed that 20 injured athletes frequently exceeded the ACWR threshold (>1.3) compared to uninjured players. Analysis of the four weeks preceding the injury revealed that increased workload in high-intensity categories significantly contributed to non-contact injuries. Specifically, high running speeds of 15–20 km/h (p = 0.015), 20–25 km/h (p = 0.045) and >25 km/h (p = 0.008), as well as accelerations (p = 0.010), were linked to a higher risk of injury. The three-week ACWR model indicated statistically significant differences in the ACWR index for total distance (p = 0.033), runs at 15–20 km/h (p = 0.007), >25 km/h (p = 0.004), accelerations (p = 0.009), and decelerations (p = 0.013). In the two-week model, significant differences were found in runs at 15–20 km/h (p = 0.008) and >25 km/h (p = 0.012). In the final week, significant differences were observed in runs at 15–20 km/h (p = 0.015), >25 km/h (p = 0.016), and accelerations (p = 0.049). Additionally, running speeds of 25–30 km/h (p values between 0.004 and 0.016) played a key role in injury risk when limits were exceeded across all weekly blocks. These findings highlight the importance of monitoring ACWR to prevent injuries, particularly by managing high-intensity workload fluctuations in elite athletes.
... This study proposed to use the fitness-fatigue model to objectively quantify performance and its relative effect on match day physical and technical performances. The use of statistical models to explain the relationship between training and performance has existed for a long time [4,27,28]. Interestingly, in the context of team sport, the interest of such an approach has been limited due to the so-called multifactorial nature of performance reducing its application to mainly endurance sports (e.g., running, swimming, cycling) where physical performance is central. ...
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The article describes the process to build a performance score and investigates the association with physical and tactical parameters.
Abstract
Elite football players face increasing physical and tactical demands due to rising match schedules emphasizing the need for effective load monitoring strategies to optimize performance and reduce injury risk. This study integrates fitness and fatigue indices derived from a machine learning approach to develop a performance score based on Banister’s fitness–fatigue model. Data were collected over two seasons (2022/23 and 2023/24) from 23 elite players of an Italian professional team. Fitness was assessed via heart rate collected during small-sided games, while fatigue was evaluated through PlayerLoad recorded during training sessions; both were normalized using z-scores. Match outcomes, including physical (e.g., total distance, high-sprint distance) and tactical metrics (e.g., field tilt, territorial domination), were analyzed in relation to performance conditions (optimal, intermediate, poor). Results revealed that players in the optimal performance condition exhibited significantly higher second-half physical outputs, including total distance (z-TD2ndHalf: p < 0.05, ES = 0.29) and distance covered at >14.4 km/h (z-D14.42ndHalf: p < 0.01, ES = 0.52), alongside improved match tactical parameters as territorial domination (%TDO2ndHalf: p < 0.01, r = 0.431). This study underscores the utility of invisible monitoring in football, providing actionable insights for weekly training periodization. This research establishes a foundation for integrating data-driven strategies to enhance physical and tactical performance in professional football.
... The training impulse (TRIMP) was calculated as described by e.g. (Calvert et al., 1976). For TRIMPs, we calculate three versions: TRIMP (all), TRIMP (>4), and TRIMP (<5): The heart frequency zones from 1-9 described above were used to calculate the three TRIMPs. ...
This study aimed to identify relationships between external and internal load parameters with subjective ratings of perceived exertion (RPE). Consecutively, these relationships shall be used to evaluate different machine learning models and design a deep learning architecture to predict RPE in highly trained/national level soccer players. From a dataset comprising 5402 training sessions and 732 match observations, we gathered data on 174 distinct parameters, encompassing heart rate, GPS, accelerometer data and RPE (Borg’s 0-10 scale) of 26 professional male professional soccer players. Nine machine learning algorithms and one deep learning architecture was employed. Rigorous preprocessing protocols were employed to ensure dataset equilibrium and minimize bias. The efficacy and generalizability of these models were evaluated through a systematic 5-fold cross-validation approach. The deep learning model exhibited highest predictive power for RPE (Mean Absolute Error: 1.08 ± 0.07). Tree-based machine learning models demonstrated high-quality predictions (Mean Absolute Error: 1.15 ± 0.03) and a higher robustness against outliers. The strongest contribution to reducing the uncertainty of RPE with the tree-based machine learning models was maximal heart rate (determining 1.81% of RPE), followed by maximal acceleration (determining 1.48%) and total distance covered in speed zone 10-13 km/h (determining 1.44%). A multitude of external and internal parameters rather than a single variable are relevant for RPE prediction in highly trained/national level soccer players, with maximum heart rate having the strongest influence on RPE. The ExtraTree Machine Learning model exhibits the lowest error rates for RPE predictions, demonstrates applicability to players not specifically considered in this investigation, and can be run on nearly any modern computer platform.
... The correction should account for exercise intensity and duration and individual differences in body characteristics and aerobic fitness. Such models are not new in the literature; for instance, Banister in Calvert et al. (1976) introduced a model of cardiovascular endurance performance (Eq. 2), which is as follows: ...
Introduction: This study aimed to model below and above anaerobic threshold exercise-induced heart rate (HR) drift, so that the corrected HR could better represent V ̇ O 2 kinetics during and after the exercise itself.
Methods: Fifteen healthy subjects (age: 28 ± 5 years; V ̇ O 2 M a x : 50 ± 8 mL/kg/min; 5 females) underwent a maximal and a 30-min submaximal (80% of the anaerobic threshold) running exercises. A five-stage computational (i.e., delay block, new training impulse-calculation block, Sigmoid correction block, increase block, and decrease block) model was built to account for instantaneous HR, fitness, and age and to onset, increase, and decrease according to the exercise intensity and duration.
Results: The area under the curve (AUC) of the hysteresis function, which described the differences in the maximal and submaximal exercise-induced V ̇ O 2 and HR kinetics, was significantly reduced for both maximal (26%) and submaximal (77%) exercises and consequent recoveries.
Discussion: In conclusion, this model allowed HR drift instantaneous correction, which could be exploited in the future for more accurate V ̇ O 2 estimations.
... At a simplistic level of understanding, there is hypothesised to exist a 'sweet region' of ideal training load, below which prior adaptations may be lost (i.e., disuse) and above which the negative consequences (e.g., tissue damage) of activity may exceed any beneficial adaptations (i.e., overuse) [37,38]. Additionally, the balance between training stimulus and inter-session recovery feeds into a 'fitness-fatigue' model of performance [39]. These simplistic models can be applied to various biological systems and their related performance and/or injury effects [40]. ...
E-textiles have emerged as a fast-growing area in wearable technology for sports and fitness due to the soft and comfortable nature of textile materials and the capability for smart functionality to be integrated into familiar sports clothing. This review paper presents the roles of wearable technologies in sport and fitness in monitoring movement and biosignals used to assess performance, reduce injury risk, and motivate training/exercise. The drivers of research in e-textiles are discussed after reviewing existing non-textile and textile-based commercial wearable products. Different sensing components/materials (e.g., inertial measurement units, electrodes for biosignals, piezoresistive sensors), manufacturing processes, and their applications in sports and fitness published in the literature were reviewed and discussed. Finally, the paper presents the current challenges of e-textiles to achieve practical applications at scale and future perspectives in e-textiles research and development.
... These improvements in training adaptations may be a consequence of both the ability to adjust training loads during an acute training session, but also over the course of training cycles. As 1RMs should improve over the course of a training cycle, the use of VBT will also limit the need for repeated reassessment of maximal strength (3,17). ...
The purpose of this investigation was to determine the validity and reliability of the Humac360 linear position transducer (LPT) as compared to Tendo Weightlifting Analyzer. Seventeen recreationally active men and women completed three visits. Visit one included maximal strength assessments via one-repetition maximum (1RM) for the barbell back squat. On visits two and three, participants completed two sets of three repetitions at 30-, 50-, 60-, and 70% 1RM. Mean Concentric Velocity (MCV), Peak Velocity (PV), Displacement (D), and Duration (T) were collected. Repetition data agreement was assessed with Intraclass Correlation Coefficients (ICCs) and were categorized as poor (<0.50), moderate (0.50-0.75), good (0.75-0.90), and excellent (>0.90). Significance was accepted at an alpha (p) value < 0.05. Repetition-to-repetition comparisons between devices demonstrate varying degrees of agreement, with significant differences between devices across all intensities and all measurements (p < 0.001). Inter-set reliability was excellent for MCV, PV, D, and T with the exceptions of MCV and PV at 70% 1RM (ICC 2,k = 0.548 and 0.816). Inter-session reliability data demonstrated reduced agreeableness in an intensity-dependent manner, with ICCs decreasing and SEMs increasing with increases in intensity. The Humac360 LPT does not appear to be valid when compared to the criterion method, though we contend it maintains construct validity. Coaches may use the Humac360 LPT as a tool to monitor fatigue, and the associated changes in trainee movement velocity on an inter-set and inter-session basis.
... The first seminal work in modelling training is the Fitness-Fatigue model (FFM) [36,37]. The FFM was developed based on a systems theory approach to model the relationship between fitness and fatigue -the two key outcomes of a training stimulus [37,38]. This relationship was originally described by a set of first-order differential equations and further simplified into the FFM depicted in Figure 9.3. ...
... This method for CE resembles the way in which training load has historically been quantified. For example, common measures such as training impulse (TRIMP) [50][51][52] and session-rating of perceived exertion [53] are based on the multiplication of intensity (eventually time weighted) by duration. For the American College of Sports Medicine, the weekly volume formula (for aerobic activities) is the product of frequency, session duration and intensity [54]. ...
Various terms used in sport and exercise science, and medicine, are derived from other fields such as epidemiology, pharmacology and causal inference. Conceptual and nomological frameworks have described training load as a multidimensional construct manifested by two causally related subdimensions: external and internal training load. In this article, we explain how the concepts of training load and its subdimensions can be aligned to classifications used in occupational medicine and epidemiology, where exposure can also be differentiated into external and internal dose. The meanings of terms used in epidemiology such as exposure, external dose, internal dose and dose–response are therefore explored from a causal perspective and their underlying concepts are contextualised to the physical training process. We also explain how these concepts can assist in the validation process of training load measures. Specifically, to optimise training (i.e. within a causal context), a measure of exposure should be reflective of the mediating mechanisms of the primary outcome. Additionally, understanding the difference between intermediate and surrogate outcomes allows for the correct investigation of the effects of exposure measures and their interpretation in research and applied settings. Finally, whilst the dose–response relationship can provide evidence of the validity of a measure, conceptual and computational differentiation between causal (explanatory) and non-causal (descriptive and predictive) dose–response relationships is needed. Regardless of how sophisticated or “advanced” a training load measure (and metric) appears, in a causal context, if it cannot be connected to a plausible mediator of a relevant response (outcome), it is likely of little use in practice to support and optimise the training process.
... In humans, training load has been determined through several methods based on the product of volume and training intensity, which are called "training impulses (TRIMP)". These are obtained from heart rate responses 20,21 , blood lactate concentration ([La]) 13,22 , and perceived exertion 23 . In animal models, the same rationale has been used [24][25][26] . ...
This study investigated the physiological and molecular responses of Wistar Hannover rats, submitted to three 5-week chronic training models, with similar training loads. Twenty-four Wistar Hanover rats were randomly divided into four groups: control (n = 6), low-intensity training (Z1; n = 6), moderate-intensity training (Z2; n = 6) and high-intensity training (Z3; n = 6). The three exercise groups performed a 5-week running training three times a week, with the same prescribed workload but the intensity and the volume were different between groups. An increase in maximal speed was observed after four weeks of training for the three groups that trained, with no difference between groups. Higher rest glycogen was also observed in the soleus muscle after training for the exercise groups compared to the control group. We also found that the Z2 group had a higher protein content of total and phosphorylated GSK3-β compared to the control group after five weeks of training. In conclusion, the present study shows that five weeks of treadmill training based on intensity zones 1, 2, and 3 improved performance and increased resting glycogen in the soleus muscle, therefore intensity modulation does not change the training program adaptation since the different program loads are equalized.
... This method for CE resembles the manner in which training load has historically been quantified. For example, common measures such as training impulse (TRIMP) [34][35][36] and session-Rating of Perceived Exertion 37 are based on the multiplication of intensity (eventually time-weighted) by duration. For the American College of Sports Medicine, the weekly volume formula (for aerobic activities) is the product of frequency, session duration and intensity. ...
... The training dose, defined by the frequency, volume and intensity of exercise, determines the type and magnitude of the training response [2,3]. The prescription of training by coaches remains largely instinctive, consequently research is needed to further understand training programme design [2,4,5]. The monitoring of training has become common practice in elite sport, with the primary purpose being to guide and inform training prescription [6], which is typically quantified with reference to frequency, intensity, time, and type of each session [7]. ...
Male academy rugby league players are required to undertake field and resistance training to develop the technical, tactical and physical qualities important for success in the sport. However, limited research is available exploring the training load of academy rugby league players. Therefore, the purpose of this study was to quantify the field and resistance training loads of academy rugby league players during a pre-season period and compare training loads between playing positions (i.e., forwards vs. backs). Field and resistance training load data from 28 adolescent male (age 17 ± 1 years) rugby league players were retrospectively analysed following a 13-week pre-season training period (85 total training observations; 45 field sessions and 40 resistance training sessions). Global positioning system microtechnology, and estimated repetition volume was used to quantify external training load, and session rating of perceived exertion (sRPE) was used to quantify internal training load. Positional differences (forwards n = 13 and backs n = 15) in training load were established using a linear mixed effect model. Mean weekly training frequency was 7 ± 2 with duration totaling 324 ± 137 minutes, and a mean sRPE of 1562 ± 678 arbitrary units (AU). Backs covered more high-speed distance than forwards in weeks two (p = 0.024), and 11 (p = 0.028). Compared to the forwards, backs completed more lower body resistance training volume in week one (p = 0.02), more upper body volume in week three (p< 0.001) and week 12 (p = 0.005). The findings provide novel data on the field and resistance-based training load undertaken by academy rugby league players across a pre-season period, highlighting relative uniformity between playing positions. Quantifying training load can support objective decision making for the prescription and manipulation of future training, ultimately aiming to maximise training within development pathways.
... However, the hypothesis that TRIMPs provide a valid means of quantifying a training dose was never tested by Banister et al in their original or subsequent work. 1,12,17,18 Specifically, where criterion validity was considered, Morton et al 12 examined the use of TRIMPs to predict changes in performance, but they did not evaluate whether TRIMPs provided a reasonable estimate of their participants' TL. Furthermore, as mentioned above, in this study the authors introduced heart rate as a new measure from which to calculate TRIMPs and added an arbitrary nonlinear correction to this data. ...
Training load (TL) is a widely used concept in training prescription and monitoring and is also recognized as as an important tool for avoiding athlete injury, illness, and overtraining. With the widespread adoption of wearable devices, TL metrics are used increasingly by researchers and practitioners worldwide. Conceptually, TL was proposed as a means to quantify a dose of training and used to predict its resulting training effect. However, TL has never been validated as a measure of training dose, and there is a risk that fundamental problems related to its calculation are preventing advances in training prescription and monitoring. Specifically, we highlight recent studies from our research groups where we compare the acute performance decrement measured following a session with its TL metrics. These studies suggest that most TL metrics are not consistent with their notional training dose and that the exercise duration confounds their calculation. These studies also show that total work done is not an appropriate way to compare training interventions that differ in duration and intensity. We encourage scientists and practitioners to critically evaluate the validity of current TL metrics and suggest that new TL metrics need to be developed.
... Banister et al. [9] introduced a systems model based on the assumption that each training load induces a negative effect 1 (fatigue) and a positive effect (fitness) on performance. As the original paper cannot be found easily, we refer to Calvert et al. [19] for a description of the model. The ordinary differential equation (ODE) model has been adopted for various settings and several modifications have been proposed. ...
Individualized resistance training is necessary to optimize training results. A model-based optimization of loading schemes could provide valuable impulses for practitioners and complement the predominant manual program design by customizing the loading schemes to the trainee and the training goals. We compile a literature overview of model-based approaches used to simulate or optimize the response to single resistance training sessions or to long-term resistance training plans in terms of strength, power, muscle mass, or local muscular endurance by varying the loading scheme. To the best of our knowledge, contributions employing a predictive model to algorithmically optimize loading schemes for different training goals are nonexistent in the literature. Thus, we propose to set up optimal control problems as follows. For the underlying dynamics, we use a phenomenological model of the time course of maximum voluntary isometric contraction force. Then, we provide mathematical formulations of key performance indicators for loading schemes identified in sport science and use those as objective functionals or constraints. We then solve those optimal control problems using previously obtained parameter estimates for the elbow flexors. We discuss our choice of training goals, analyze the structure of the computed solutions, and give evidence of their real-life feasibility. The proposed optimization methodology is independent from the underlying model and can be transferred to more elaborate physiological models once suitable ones become available.
... Personal Modeling Layer: To build a personal module for the cardiac PHN system, we retrieve from the knowledge layer about how increasing intensity and duration of exercise is lowers risk of cardiovascular disease [76]. We further extract from the layer advanced physiologic cardiovascular endurance training strategies in bioenergetics science [5,8,18,25,30,82] to build a personalized rule-based model for daily exercise guidance. Table 1 explains the definitions used in the rule-based model and the following rules are the key rules we used in the guidance module: ...
It is well understood that an individual's health trajectory is influenced by choices made in each moment, such as from lifestyle or medical decisions. With the advent of modern sensing technologies, individuals have more data and information about themselves than any other time in history. How can we use this data to make the best decisions to keep the health state optimal? We propose a generalized Personal Health Navigation (PHN) framework. PHN takes individuals towards their personal health goals through a system which perpetually digests data streams, estimates current health status, computes the best route through intermediate states utilizing personal models, and guides the best inputs that carry a user towards their goal. In addition to describing the general framework, we test the PHN system in two experiments within the field of cardiology. First, we prospectively test a knowledge-infused cardiovascular PHN system with a pilot clinical trial of 41 users. Second, we build a data-driven personalized model on cardiovascular exercise response variability on a smartwatch data-set of 33,269 real-world users. We conclude with critical challenges in health computing for PHN systems that require deep future investigation.
... While several mathematical models have been proposed to quantify the training and performance relationship [28,29], the commonly used fitness-fatigue equation presents that (Eq. 1) [30,31]: ...
It has been suggested that improper post-exercise recovery or improper sequence of training may result in an ‘accumulation’ of fatigue. Despite this suggestion, there is a lack of clarity regarding which physiological mechanisms may be proposed to contribute to fatigue accumulation. The present paper explores the time course of the changes in various fatigue-related measures in order to understand how they may accumulate or lessen over time following an exercise bout or in the context of an exercise program. Regarding peripheral fatigue, the depletion of energy substrates and accumulation of metabolic byproducts has been demonstrated to occur following an acute bout of resistance training; however, peripheral accumulation and depletion appear unlikely candidates to accumulate over time. A number of mechanisms may contribute to the development of central fatigue, postulating the need for prolonged periods of recovery; however, a time course is difficult to determine and is dependent on which measurement is examined. In addition, it has not been demonstrated that central fatigue measures accumulate over time. A potential candidate that may be interpreted as accumulated fatigue is muscle damage, which shares similar characteristics (i.e., prolonged strength loss). Due to the delayed appearance of muscle damage, it may be interpreted as accumulated fatigue. Overall, evidence for the presence of fatigue accumulation with resistance training is equivocal, making it difficult to draw the conclusion that fatigue accumulates. Considerable work remains as to whether fatigue can accumulate over time. Future studies are warranted to elucidate potential mechanisms underlying the concept of fatigue accumulation.
... To meet the optimal energy need of the body and to withstand fatigue due to competition, the human body systems need to be operating in full potential. (Calvert et al., 1976). Basketball, football, and cricket are games that are performed at very high tempo. ...
Aims: The purpose of this study was assessment of body type, characteristics of male basketball, football, and cricket Indian university-level
participation players. Materials and Methods: There were seventy-five (n = 75) trained male basketball, football, and cricket players between
the ages of 17 and 25 years who volunteered for this study. In other words, twenty-five players participated in the study from each game,
i.e. basketball, football, and cricket. Little is done to observe the effects of the training of each game on anthropometric profile or body size
of male basketball, football, and cricket players. Results: The mean age, height, and weight of male basketball, football, and cricket players
was 22.12 ± 1.61 years, 183.32 ± 12.82 cm and 78.08 ± 12.18 kg, 22.12 ± 1.61 years, 183.32 ± 12.82 cm and 78.08 ± 12.18 kg, and 21.12
± 1.66 years, 174.40 ± 6.66 cm and 63.28 ± 3.92 kg, respectively. It was observed that the mean arm length of basketball players was 34.76
± 5.91 cm, cricket players was 32.10 ± 1.79 cm, and football players was 31.12 ± 1.34 cm. It was observed that the mean forearm length of
basketball players was 27.62 ± 2.48 cm, cricket players was 26.54 ± 1.71 cm, and football players was 25.60 ± 1.40cm. It was observed that the
mean hand length of basketball players was 20.00 ± 1.79 cm, cricket players was 19.70 ± 1.35 cm, and football players was 18.73 ± 0.80 cm. It
was observed that the mean tibial length of basketball players was 37.90 ± 5.58 cm, cricket players was 36.54 ± 2.05 cm, and football players
was 35.12 ± 1.79 cm. It was observed that the mean iliospinale base height of basketball players was 43.08 ± 3.55 cm, cricket players was 42.48
± 3.33 cm, and football players was 40.70 ± 2.42 cm. It was observed that the mean trochanterion base height of basketball players was 35.00
± 3.86 cm, cricket players was 33.42 ± 2.00 cm, and football players was 32.20 ± 2.50 cm. It was observed that the mean arm thigh length of
basketball players was 46.60 ± 5.63 cm, cricket players was 46.26 ± 2.21 cm, and football players was 44.56 ± 3.24cm. It was observed that
the mean tibiale laterale-base height of basketball players was 49.38 ± 5.16cm, cricket players was 45.98 ± 2.69cm, and football players was
45.10 ± 2.32 cm. It was observed that the mean foot length of basketball players was 27.22 ± 2.06 cm, cricket players was 24.86 ± 1.92 cm,
and football players was 26.00 ± 1.15 cm, respectively. Conclusion: The physique characteristics mainly arm length, forearm length, hand
length, tibial length, foot length, and thigh length are significantly distinct in basketball, cricket, and football players performance.
... To meet the optimal energy need of the body and to withstand fatigue due to competition, the human body systems need to be operating in full potential. (Calvert et al., 1976). Basketball, football, and cricket are games that are performed at very high tempo. ...
The purpose of this study is to investigate the status of management information systems (MISs) in sports and youth departments of
Ardabil Province. The present research is descriptive-analytical due to the purpose of the applied type and in terms of the type of research
data collection, which has been conducted by field method. The statistical population of this study was all managers, deputies and experts
of sports and youth departments of Ardabil province, that 48 people were selected as the statistical sample of the study using Morgan
table and random sampling. The measurement tool was Eidi et al.’s (2014) questionnaire with a reliability of 0.86. Univariate Chi-square
test was used to analyze the data and Friedman test was used for ranking. The results show that the ranking of the group of obstacles to
the implementation of MISs in sports and youth departments of Ardabil Province is as follows: (1) Cultural barriers, (2) environmental
barriers, (3) educational barriers, (4) individual barriers, (5) barriers to change management, (6) technical barriers, (7) managerial barriers
(8) economic barriers, and (9) structural barriers.
Keywords: Barriers, Management Information System, Sports and Youth Organization, Technology.
... We fit the following dose-response model which asserts that the response to a single exercise training dose has both a positive physiological response referred to as "fitness" and a negative response referred to as "fatigue" [2], represented mathematically in the following manner: ...
Optimizing athletic training programs with the support of predictive models is an active research topic, fuelled by a consistent data collection. The Fitness-Fatigue Model (FFM) is a pioneer for modelling responses to training on performance based on training load exclusively. It has been subject to several extensions and its methodology has been questioned. In this article, we leveraged a Bayesian framework involving biologically meaningful priors to diagnose the fit and predictive ability of the FFM. We used cross-validation to draw a clear distinction between goodness-of-fit and predictive ability. The FFM showed major statistical flaws. On the one hand, the model was ill-conditioned, and we illustrated the poor identifiability of fitness and fatigue parameters using Markov chains in the Bayesian framework. On the other hand, the model exhibited an overfitting pattern, as adding the fatigue-related parameters did not significantly improve the model’s predictive ability (p-value > 0.40). We confirmed these results with 2 independent datasets. Both results question the relevance of the fatigue part of the model formulation, hence the biological relevance of the fatigue component of the FFM. Modelling sport performance through biologically meaningful and interpretable models remains a statistical challenge.
The increasing number of competitions in professional basketball has increased the interest in controlling player loads. The Acute:Chronic Workload Ratio is a very common tool for controlling load variation in professional teams. However, there are specific situations in which the ACWR is limited if no historical load values are available. The objective of this intervention was to analyze the workload of a professional basketball team through the ACWR, including a retrospective review of its curve for those scenarios in which it is desired to obtain accurate values of injury risk without players’ previous load values. A ten-player professional men’s team participated in this study. WIMU Pro brand inertial devices were used to quantify player load during training. The variables in this study were objective and subjective external load, acute load and chronic load. The results show the existence of injuries when the load is disproportionately increased and enters very high risk values. The incidence of injury is 20% when the risk values are exceeded. The study corroborates that the Acute:Chronic Workload Ratio is a practical and useful tool to monitor the load and its evolution throughout the mesocycle without further statistical analysis. In addition, it is useful to know when to control the players' loads from an eminently practical point of view, finding non-causal relationships with the appearance of injuries. A retrospective review of the values is recommended if it is desired to refine the injury risk value in those measurements where pre-intervention load rates are not available.
Optimizing athletic training programs with the support of predictive models is an active research topic, fuelled by a consistent data collection. The Fitness-Fatigue Model (FFM) is a pioneer for modelling responses to training on performance based on training load, exclusively. It has been subject to several extensions and its methodology has been questioned. In this article, we leveraged a Bayesian framework involving biologically meaningful priors to diagnose the fit and predictive ability of the FFM. We used cross-validation to draw a clear distinction between goodness-of-fit and predictive ability. The FFM showed major statistical flaws. On the one hand, the model was ill-conditioned, and we illustrated the poor identifiability of fitness and fatigue parameters using Markov chains in the Bayesian framework. On the other hand, the model exhibited an overfitting pattern, as adding the fatigue-related parameters did not significantly improve the model's predictive ability (p-value > 0.40). We confirmed these results with 2 independent datasets. Both results question the relevance of the fatigue part of the model formulation, hence the biological relevance of the fatigue component of the FFM. Modelling sport performance through biologically meaningful and interpretable models remains a statistical challenge.
In recent years, a large focus has been placed on managing training load for injury prevention. To minimise injuries, training recommendations should be based on research that examines causal relationships between load and injury risk. While observational studies can be used to estimate causal effects, conventional methods to study the relationship between load and injury are prone to bias. The target trial framework is a valuable tool that requires researchers to emulate a hypothetical randomised trial using observational data. This framework helps to explicitly define research questions and design studies in a way that estimates causal effects. This article provides an overview of the components of the target trial framework as applied to studies on load and injury and describes various considerations that should be made in study design and analyses to minimise bias.
In many physical activities, exercise is not continuous, but intermittent: it involves a sequence of exercise fractions at varying intensities, some higher than others. In planned training, this type of exercise is found in the form of high intensity interval training (HIIT), which is an effective and time-efficient approach that has been popular in high-performance sports over the last century, and in clinical settings for the past twenty years. Models are available to predict performance during continuous exercise (without intensity variation) over various durations. The ecological validity of some continuous exercise models has been reported. However, this is not the case for intermittent exercise, which has several parameters that can be modified, leading to a large variation in individual responses. The purpose of this master’s thesis is to compare the major models of intermittent exercise and determine their strengths and weaknesses, the constructs on which they are based, and their applicability to various physical activities. The master’s thesis also reviews the evolution of continuous exercise models to better understand the elements that need to be considered to improve the validity of intermittent exercise modelling. Due to the lack of quality data to compare a set of HIIT sessions of the same degree of difficulty, the thesis presents a study that uses simulations to identify the main limitations of the intermittent exercise models included in commercial applications, i.e., the Coggan and Skiba models. The study reveals the limitations of these models in prescribing sessions with a low number of repetitions performed at supramaximal intensity, interspersed with long recovery periods. The main intermittent exercise models have limitations that restrict their widespread use. In order for intermittent exercise modelling to evolve into more valid models that improve understanding of the physiological phenomena involved, it is crucial that the models be tested against a robust set of comparable intermittent exercise data. The thesis draws a detailed portrait of the continuous and intermittent exercise models, accounts for their evolution over time, and provides elements to guide future exercise modelling. Finally, the thesis identifies the limits of the current intermittent exercise models, makes recommendations to sports practitioners to promote their good use, and proposes a modification to the Coggan model that reduces its limitations.
Key words: modelling, critical power, anaerobic reserve, training load
Dans plusieurs activités physiques, l’exercice n’est pas continu, mais intermittent : il comprend un enchaînement de fractions d’exercice à des intensités variées, certaines plus élevées que d’autres. Dans l’entraînement planifié, on retrouve ce type d’exercice sous la forme de l’entraînement par intervalles (EPI), qui est une approche efficace et économe en temps, très populaire dans les milieux sportifs depuis plus d’un siècle, et dans les milieux cliniques depuis plus d’une vingtaine d’années. Des modèles sont disponibles permettant de prédire les performances lors de l’exercice continu (sans variation d’intensité) sur des durées variées. La validité écologique de certains modèles de l’exercice continu a été rapportée, montrant leur capacité à s’appliquer aux situations observées sur le terrain. Ce n’est toutefois pas le cas pour l’exercice intermittent, qui comporte plusieurs paramètres pouvant être modifiés, et menant à une grande variation des réponses individuelles. L’objectif du mémoire est de comparer les principaux modèles de l’exercice intermittent et déterminer leurs forces et leurs faiblesses, les construits sur lesquels ils sont fondés, et leur applicabilité dans diverses activités physiques. Il s’agit aussi de revoir l’évolution des modèles de l’exercice continu pour mieux comprendre les éléments à considérer pour améliorer la validité de la modélisation de l’exercice intermittent. Face au manque de données de qualité permettant de comparer un ensemble de séances d’EPI de même degré de difficulté, le mémoire présente une étude qui procède par simulations pour identifier les principales limites des modèles de l’exercice intermittent inclus dans des applications commerciales, soit les modèles de Coggan et de Skiba. L’étude révèle les limites de ces modèles quant à la prescription de séances comprenant un faible nombre de répétitions effectuées à intensité supramaximale, entrecoupées de longues périodes de récupération. Les principaux modèles de l’exercice intermittent présentent des limites restreignant leur utilisation généralisée. Pour que la modélisation de l’exercice intermittent évolue vers des modèles plus valides, permettant d’améliorer la compréhension des phénomènes physiologiques en jeu, il est crucial de confronter les modèles à un ensemble robuste de données comparables de l’exercice intermittent. Le mémoire dresse un portrait détaillé des modèles de l’exercice continu et
intermittent, fait état de leur évolution au fil du temps, et propose des éléments pour guider la suite des travaux de modélisation. Enfin, le mémoire identifie les limites des modèles de courants de l’exercice intermittent, présente des recommandations aux intervenants sportifs pour favoriser la bonne utilisation de ceux-ci, en plus de fournir une modification du modèle de Coggan qui diminue les limites de celui-ci.
Mots-clés : modélisation, puissance critique, réserve anaérobie, charge d’entraînement
Eine Überprüfung der Leistungsentwicklung im Radsport geht bis heute mit der Durchführung einer spezifischen Leistungsdiagnostik unter Verwendung vorgegebener Testprotokolle einher. Durch die zwischenzeitlich stark gestiegene Popularität von »wearable devices« ist es gleichzeitig heutzutage sehr einfach, die Herzfrequenz im Alltag und bei sportlichen Aktivitäten aufzuzeichnen. Doch eine geeignete Modellierung der Herzfrequenz, die es ermöglicht, Rückschlüsse über die Leistungsentwicklung ziehen zu können, fehlt bislang. Die Herzfrequenzaufzeichnungen in Kombination mit einer phänomenologisch interpretierbaren Modellierung zu nutzen, um auf möglichst direkte Weise und ohne spezifische Anforderungen an die Trainingsfahrten Rückschlüsse über die Leistungsentwicklung ziehen zu können, bietet die Chance, sowohl im professionellen Radsport wie auch in der ambitionierten Radsportpraxis den Erkenntnisgewinn über die eigene Leistungsentwicklung maßgeblich zu vereinfachen. In der vorliegenden Arbeit wird ein neuartiges und phänomenologisch interpretierbares Modell zur Simulation und Prädiktion der Herzfrequenz beim Radsport vorgestellt und im Rahmen einer empirischen Studie validiert. Dieses Modell ermöglicht es, die Herzfrequenz (sowie andere Beanspruchungsparameter aus Atemgasanalysen) mit adäquater Genauigkeit zu simulieren und bei vorgegebener Wattbelastung zu prognostizieren. Weiterhin wird eine Methode zur Reduktion der Anzahl der kalibrierbaren freien Modellparameter vorgestellt und in zwei empirischen Studien validiert. Nach einer individualisierten Parameterreduktion kann das Modell mit lediglich einem einzigen freien Parameter verwendet werden. Dieser verbleibende freie Parameter bietet schließlich die Möglichkeit, im zeitlichen Verlauf mit dem Verlauf der Leistungsentwicklung verglichen zu werden. In zwei unterschiedlichen Studien zeigt sich, dass der freie Modellparameter grundsätzlich in der Lage zu sein scheint, den Verlauf der Leistungsentwicklung über die Zeit abzubilden.
The factors explaining variance in thermoneutral maximal oxygen uptake (V˙O2max) adaptation to heat acclimation (HA) were evaluated, with consideration of HA programme parameters, biophysical variables and thermo-physiological responses. Seventy-one participants consented to perform iso-intensity training (range: 45%-55% V˙O2max) in the heat (range: 30°C-38°C; 20%-60% relative humidity) on consecutive days (range: 5-days-14-days) for between 50-min and-90 min. The participants were evaluated for their thermoneutral V˙O2max change pre-to-post HA. Participants' whole-body sweat rate, heart rate, core temperature, perceived exertion and thermal sensation and plasma volume were measured, and changes in these responses across the programme determined. Partial least squares regression was used to explain variance in the change in V˙O2max across the programme using 24 variables. Sixty-three percent of the participants increased V˙O2max more than the test error, with a mean ± SD improvement of 2.6 ± 7.9%. A two-component model minimised the root mean squared error and explained the greatest variance (R2; 65%) in V˙O2max change. Eight variables positively contributed (P < 0.05) to the model: exercise intensity (%V˙O2max), ambient temperature, HA training days, total exposure time, baseline body mass, thermal sensation, whole-body mass losses and the number of days between the final day of HA and the post-testing day. Within the ranges evaluated, iso-intensity HA improved V˙O2max 63% of the time, with intensity- and volume-based parameters, alongside sufficient delays in post-testing being important considerations for V˙O2max maximisation. Monitoring of thermal sensation and body mass losses during the programme offers an accessible way to gauge the degree of potential adaptation.
Purpose:
To manage physical performance in soccer, practitioners monitor the training load (TL) and the resulting fatigue. A method frequently used to assess performance is the countermovement jump (CMJ). However, the efficacy of CMJ to detect fatigue from soccer matches and training remains uncertain, as does the relationship between TL and change in CMJ performance. The aims of the present study were 2-fold. One was to observe the changes of CMJ force-time components and jump height (JH). The second was to examine dose-response relationships between TL measures and CMJ over a 6-week preseason.
Methods:
Twelve male academy soccer players (17 [1] y, 71.2 [5.6] kg, and 178 [5.8] cm) were recruited. Daily changes in CMJ were assessed against baseline scores established before preseason training, along with internal and external TL measures. A series of Bayesian random intercept models were fitted to determine probability of change above/below zero and greater than the coefficient of variation established at baseline. Jumps were categorized into match day minus (MD-) categories where the higher number indicated more time from a competitive match.
Results:
JH was lowest on MD - 3 (28 cm) and highest on MD - 4 (34.6 cm), with the probability of change from baseline coefficient of variation highly uncertain (41% and 61%, respectively). Changes to force-time components were more likely on MD - 3 (21%-99%), which provided less uncertainty than JH. Bayes R2 ranged from .22 to .57 between TL measures and all CMJ parameters.
Conclusions:
Force-time components were more likely to change than JH. Practitioners should also be cautious when manipulating TL measures to influence CMJ performance.
This study aims to provide a transferable methodology in the context of sport performance modelling, with a special focus to the generalisation of models. Data were collected from seven elite Short track speed skaters over a three months training period. In order to account for training load accumulation over sessions, cumulative responses to training were modelled by impulse, serial and bi-exponential responses functions. The variable dose-response (DR) model was compared to elastic net (ENET), principal component regression (PCR) and random forest (RF) models, while using cross-validation within a time-series framework. ENET, PCR and RF models were fitted either individually (M_I) or on the whole group of athletes (M_G). Root mean square error criterion was used to assess performances of models. ENET and PCR models provided a significant greater generalisation ability than the DR model ( p = 0.018, p < 0.001, p = 0.004 and p < 0.001 for ENET_I , ENET_G, PCR_I and PCR_G, respectively). Only ENET_G and RF_G were significantly more accurate in prediction than DR (p < 0.001 and p < 0.012). In conclusion, ENET achieved greater generalisation and predictive accuracy performances. Thus, building and evaluating models within a generalisation enhancing procedure is a prerequisite for any predictive modelling.
Health and athletic performance models can be used to prescribe an individualized training regimen to maximize improvement over a given period (e.g., preparing for a peak event). The Banister impulse-response (IR) model is a popular approach to describe the cumulative dynamic effects of training on athletic performance. The model describes both positive and negative training effects (PTEs and NTEs, respectively) that occur from a single training impulse, each having magnitude and decay parameters determined via testing of individual athletes. This work proposes that PTE and NTE responses from a single training impulse can similarly be described by the response of a single degree of freedom (SDOF) viscoelastic system. Cumulative training effects can then be modeled using the discrete form of the convolution integral in a manner similar to the IR model. The pedagogical advantage of this approach is to offer students studying mechanical vibrations a physical sense for how cumulative impulses relate to the response of a system. The other advantage of this approach is to initiate the discussion on how concepts from structural dynamics can be used to solve problems in exercise physiology.
Introduction
Sports coaches are guided by empirical evidence at the moment of planning the training loads, and, many times, these loads are not recorded for posterior analysis. A validated scientific instrument could help coaches to plan, register, and analyse sports training load.
Aim
The study aimed to develop and assess the content validity of a catalog of exercises for speed track events.
Methods
Through interviews, a group of expert coaches elaborated a catalog of exercises. Two groups of raters evaluated the content validity of these exercises, producing a coefficient of content validity (CCV) for such validity indicators as clarity of language, practical pertinence and theoretical relevance. Additionally, raters assessed the specificity level of each exercise by deciding if the exercise was general, special or specific to speed track events.
Results
These CCV results confirmed the content validity of a 75-exercise catalog with satisfactory validity indicators, meaning the exercises are understandable for athletic coaches (CCV CL =0,93), pertinent for speed track training (CCV PP =0,84) and relevant (CCV RT =0,83).
Conclusion
This catalog may help athletic coaches to plan, implement and analyze their players’ sports training loads.
World records for running provide data of physiological significance. In this article, I shall provide a theory of running that is simple enough to be analyzed and yet allows one to determine certain physiological parameters from the records. The theory, which is based on Newton's second law and the calculus of variations, also provides an optimum strategy for running a race. Using simple dynamics one can correlate the physiological attributes of runners with world track records and determine the optimal race strategy.
Physiological models are frequently designed to study a single-organ system or to investigate the differ ences in response of alternative representations of the same system. In this study, an interactive model portrays the human circulatory, thermoregulatory, and energy-exchange systems as an intercoupled set and serves as a means of communication between mem bers of an interdisciplinary research team. The assumptions necessary to couple these systems are described, as are the research team's techniques for using the model. The goal is to develop the model to the point of accurately simulating the real-world behavior of the coupled systems in normal human beings and in those with certain diseases. The model serves to focus the team's attention on areas requiring additional experimentation, which in turn leads to modifications to improve the model.
The effects of various regimes of bicycle ergometer exercise varying in intensity, duration and frequency of effort on directly measured maximum aerobic power ( max) have been studied on 28 healthy male subjects aged 18–38 years.
Analysis of the results showed that the two most important factors in training the max were intensity and duration; these parameters being interdependent. No subject who trained at or below 50% of max showed an improvement in his maximum aerobic power output. Even at the highest intensities and longest durations of effort the improvement in max was quite small (1–9 ml/kg/min). The responses to submaximal work mirrored in part these changes: and for a given work load remained constant whereas cardiac frequency (fH) decreased after training.
It would seem that in order to effect an improvement in max an individual must be prepared to work at or close to his maximum for prolonged periods of time; even then the improvement may be disappointingly small.
The steady-state human respiratory system was simulated with the aid of a digital computer and a Calcomp model 565 digital plotter. Equations of the model were divided into a controlling system and a controlled system. The classical controller equation of Gray (1950) was compared to the more recent work of Lloyd and Cunningham (1963). Simulation results for alveolar carbon dioxide and oxygen partial pressures as functions of a wide range of alveolar ventilation are presented (controlled system). Simulation results for alveolar ventilation, tidal volume, and respiratory frequency as functions of a wide range of alveolar carbon dioxide and oxygen partial pressures are also presented (controlling system). Similarly, simulation results for alveolar ventilation, tidal volume, and respiratory frequency are presented for a wide range of inspired carbon dioxide and oxygen partial pressures (complete model).
For purposes of theoretical analysis of experimental results and evaluation of hypothetical concepts a mathematical model of thermoregulation in man is presented. The human body is represented by three cylinders: the head, the trunk, and the extremities. Each cylinder is divided into two or more concentric layers to represent anatomical and functional differences in so far as they are of primary importance in thermoregulation. Heat flow between adjacent layers is by conduction, and all layers exchange heat by convection with a central blood compartment. All three skin layers exchange heat with the environment by conduction, convection, radiation, and evaporation. Signals which are proportional to temperature deviations in the brain and to deviations in average skin temperature are supplied to the regulator portion of the model. The regulator then causes evaporative heat loss, heat production by shivering or changes in the peripheral blood flow to occur in the appropriate locations in the body. If a proposed mechanism of thermoregulation is expressed in quantitative form it describes the relationships between the input signals and the resulting thermoregulatory response; the model can be used to compare the quantitative response resulting from a proposed mechanism with the responses obtained by measurement. A number of experimental results are compared with predictions furnished by the mathematical model using a regulator with an output which is proportional to the product of the input signals. It is emphasized that models of this type should be used in close connection with an experimental program to attain their full usefulness.
The interrelationships of depression, age, height, weight, percent body fat, strength of grip, and physical working capacity were evaluated in 67 normal adult males. None of the correlations were statistically significant (P > .05). Also, the multiple correlation between depression and the physical variables was low (R = .28). In addition, 101 adult males participated in an exercise program which consisted of circult training (N = 18), jogging (N = 23), swimming (N = 27), treadmill running and bicycle ergometry (N = 17), and 16 subjects served as controls. It was found that six weeks of exercise did not produce a significant reduction in depression for any of the groups. However, a significant (P < .01) reduction in depression was observed in those subjects (N = 11) who were depressed initially. It was concluded that depression was not signifcantly correlated with variables such as age, height, weight, percent fat, strength of grip, and physical work capacity in normal adult males. Also, depressed adult males can experience a significant reduction in depression following six weeks of chronic exercise somatotherapy. (C)1970The American College of Sports Medicine
A theory concerning relative running performance measurement is proposed and a model for this theory is derived which generates a reference time for any given distance. Construction of scoring tables from the reference time is presented. These “mathematically based” tables provide a rating system for all performances at a given distance. The algorithm which constructs the scoring tables was programmed for a computer, and sample output is given. Comparisons are made between many of the current world records. These comparisons indicate that these tables are more consistent than other previously existing tables, although a thorough validation still needs to be performed. Finally, a modification is developed which generates tables of sub-maximum running speeds that may be utilized in interval training.
Summary A group of 39 sedentary subjects were trained on the treadmill, using one of three graded intensities of effort (walking at 3.5 m.p.h., 0% slope, equivalent to 39% of their aerobic power; running at 5 m.p.h., 1% slope, equivalent to 75% of their aerobic power; running at 5 m.p.h., 6% slope, equivalent to 96% of their aerobic power). Exercise was undertaken one, three, or five times per week, and was maintained for 5, 10, or 20 min per session. The main factor influencing the extent of training achieved was the intensity of effort relative to the subject's initial aerobic power. However, training was also influenced by the frequency of exercise and (marginally) by its duration. Some training was achieved even at the lowest intensity of exercise, but the most effective regime involved the combination of maximum intensity, frequency, and duration of effort.
This Memorandum expresses the basic material balance relationships for the lung-blood-tissue gas transport and exchange system in a set of differential-difference equations containing a number of dependent time delays. Additional equations define the chemical details of transport and acid-base buffering, concentration equilibria, and blood flow behavior. Finally, a control function is included defining the dependence of ventilation upon CSF (H ), and PO2 at the carotid chemoceptors. A Fortran program was written for convenient digital simulation of the responses of the system to a wide variety of forcings, including C02 inhalation, hypoxia at: sea level, altitude hypoxia, and metabolic disturbances in acid-base balance. Both dynamic and steady-state behavior of the model were reasonably realistic. U.S. Air Force
Comments on 'A Theory of Competitive Running'," logical effects of acute physical activity
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- Feinerman
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Steady state simulation of the determinants of the response to a training regimen
- H T Milhorn
- D R Brown
H. T. Milhorn and D. R. Brown, "Steady state simulation of the determinants of the response to a training regimen," Int. Z. human respiratory system," Computers and Biomedical Research, angew Physiol., vol. 26, pp. 272-278, 1968. vol. 3, p. 604, 1967.
Principles of Skill Acquisition
- E A Bilodeau
- Ed
E. A. Bilodeau, Ed., Principles of Skill Acquisition. New York: Medicine and Science in Sports, vol. 1, pp. 50-56, Mar. 1969. Academic, 1969.
Computerized Running Training oxygen uptake from measurements made during submaximal Programs. Los Altos, CA: Tafnews, 1974. work
- J B Gardner
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J. B. Gardner and J. G. Purdy, Computerized Running Training oxygen uptake from measurements made during submaximal Programs. Los Altos, CA: Tafnews, 1974. work," Ph.D. dissertation, Univ. Minn., Minneapolis, 1962.
[illegible text]ematical analysis and digital simulation of the respiratory con[illegible text]system
- F S Grodins
- J Buellard
- A J Bart
Specific training, taper and fatigue
- C W Zauner
- E C Reese
Comments on 'A Theory of Competitive Running'
- D C Carey
- J W Prothero
- F Osterle
- H Borovetz
- J Hammerly
Jokl The Physiological Basis of Athletic Records
- E Jokl
The Physiological Basis of Athletic Records
- E Jokl