A Deep Dive into Athlete Monitoring: Background, Context and Practical Applications

Within current professional sport, the monitoring of athletes has become a cornerstone of the daily training schedule. The monitoring systems implemented by the sports science & medicine departments within clubs are dependent on its ability to reliability and accurately quantify the training dose and the individual response to the training dose of each player. This information in turn is then used to inform and assist the actions that are taken in terms of future planning of the training process to maximise player availability and performance through understanding the complex relationships between the training dose, performance outcomes, injury, and illness ¹⁷.

The ultimate/continuous aim of the training process is to progressively develop the required qualities of the sport (soccer) in order to improve performance ³. This is achieved via the balance between the application of an appropriate training dose and the time afforded to facilitate adequate recovery for sustained adaptations ^{8, 9}.

In essence, for adaptations to occur, an overloading training dose must be applied to the individual and homeostasis must be disturbed resulting in reduced performance (fatigue) ¹⁰. If appropriate recovery is afforded, adaptations occur which are protective against further fatigue arising from a similar training dose ^11-13.

In essence, the training-recovery cycle can be partly explained based on three governing theories ¹⁴:

1. 1. Selye’s general adaptation syndrome theory ¹⁰, which describes the body’s response to a stressor/stimulus.
   2. The stimulus-fatigue-recovery-adaptation theory ^15-17, which proposes that the accumulation of fatigue is in proportion to the duration, volume and/or intensity of the stressor/stimulus.

      a. Furthermore, the frequency of subsequent stressors/stimuli is also crucial in order to optimise performance outcomes, with any subsequent training occurring during the supercompensation phase ^{13, 18-20}, if the subsequent stressors/stimuli is applied too soon – the additional fatigue may accumulate which results in a prolonged return to homeostasis and reduced performance ^{18, 21, 22}. However, when the subsequent stressors/stimuli are applied at the appropriate time, during the supercompensation phase, performance can be enhanced ⁹.

   3. The fitness-fatigue model ^{14, 23, 24}, which advises that training doses which minimise fatigue accumulation while also maximising adaptation will have the greatest transfer to performance outcomes.

However, to fully explain the training-recovery cycle, practitioners must also account for the multitude of additional internal psycho-physiological responses /adaptations that also occur during training which dilute the accuracy of quantifying the training response ^25-27, resulting in the complex relationship between the training dose, performance outcomes, injury and illness ^{28, 29}. Therefore, a multi-dimensional monitoring approach to evaluate the individual response to the implied training stressor has become an essential part of informing the training process.

A key component of any successful monitoring system is its ability to accurately & reliably quantify the training load (Figure 1). This is achieved through the quantification of both external load (Figure 1) and the internal load (Figure 1) of the athlete. The collection of these measures forms the foundations of a training process framework (Figure 2) ²⁶ to inform the monitoring process. Ultimately, it is the athletes internal load that determines the stress that governs the individual response to the implied external load training stressor ²⁶.

Figure 1: Training Load & Injury Causation Relevant nomenclature ^{25, 30}

Figure 2: Training Load Framework ²⁶

Even though internal load measures are associated with adaptations, it is imperative that we understand and quantify the external loads that contribute to an athlete’s internal response. Due to advancements in technology, the process of measuring external loads has become widespread across the sporting landscape ³¹ and is quantified depending on the context of the sport or mode of training ³².

The advantage of monitoring external loads is that it allows the practitioners to prescribe training more precisely in advance of the session being carried out based on the valid and reliable variables collected during previous sessions and the athletes prior response ³³ to the external load that was prescribed. As mentioned, how external load is collected and prescribed is context specific however in invasion team sports the most commonly technologies include both local and global positioning systems, along with accelerometers, inertial measurement units, and camera-based measurements ³.

Detailing each of these systems and their variables is beyond the scope of this section however, they have been extensively researched within the literature and the reader is directed to the following ^{31, 34-38} for more information on this topic (Figure 3). Nonetheless, it is not necessarily how (i.e. the technology used) the external load is quantified but rather the why (i.e. what is this tool used, is it valid?, is it reliable?), and that is to inform the training process in order to achieve the desired adaptations imposed by the external load prescribed ²⁶. As a result, these adaptations, either positive or negative, are what correspond to the internal load ^{25, 26}.

Figure 3: Tracking Systems in Team Sports ^{39, 40}

In view of that, when monitoring athletes, it is recommended that measures of internal load are used as the primary means of determining the training outcomes (adaptations) ²⁶. This is since the internal load borne by an individual athlete corresponding to a specific external load will vary depending on the specific contextual factors as shown in (Figure 2), along with the nature of the sport ^{26, 41-44}. Hence, in a practical sense estimating one’s internal load response prior to training or performance is at best, what all great scientists have, an educated guess!

Nonetheless, internal load (Figure 4) can be psychological/subjective or physiological/objective in nature ^{2, 26, 27} with the methods of assessment being context specific ²⁶ based on the nature of the sport and when used in combination reflects the type of fatigue experienced which will enhance a practitioners understanding of the internal load experienced to better inform the training process ^{25, 26}. However it is important to note that when it comes to the level of evidence of objective and subjective measures, current research ^45-47 demonstrates that there is little relationship between objective and subjective measures and suggests that subjective measures of internal load were more stable and responsive to training load.

Figure 4: Internal Training Load Monitoring ^48-50

As aforementioned, the key to any monitoring system is to find the appropriate balance between an athletes level of fatigue & their ability to perform ^{9, 14, 23-26}, while also taking the numerous contextual factors that influence this equation (Figure 2) ²⁶. Fatigue as a concept is extremely difficult to define due to its multifaceted origins ³ with numerous definitions proposed in the literature ^{7, 51}. Despite this ambiguity, there is mutual agreement that a central component of fatigue is the failure to produce or maintain the required force or power output for a given task that was previously attainable resulting from both central and peripheral factors ⁵¹, including activation of the motor command, propagation of the action potential through the descending motor pathway, myofilament excitation-contraction coupling and the status of the intracellular milieu ⁵², which can persist for days at a time if not addressed ⁵³.

However, despite this agreement, there is a failure to acknowledge the mental component of fatigue which must be considered ^{51, 53} due to the suggestion that when fatigue is reported as a symptom by an individual, it can only be evaluated by self-reporting and categorised as a trait characteristic or state variable ⁵³. Despite this, physiology remains the central focus of the numerous models proposed to define fatigue ^{51, 53, 54}, all of which fail to account for the perception of effort required to maintain the required force or power output for a given task (i.e. the definition of fatigue) ⁵⁵. To address this omission of perception of effort two main concepts have been developed: the Psychobiological Model of Fatigue (PBM) ⁵⁵ and Integrative Governor Theory (IGT) ⁵⁶ which are defined below, however it must be noted that there is some criticisms of these concepts ⁵⁷. Nevertheless, quantifying an athlete’s fatigue status and its time-course remains a central component of any monitoring system ^{56, 58}, however it must be noted that there is no current gold-standard of measurement and misconceptions are still prevalent within the literature ²⁶. To account for the ambiguities of the definition, of fatigue, the term “Readiness” has utilised in athletic populations ^{2, 59} and has been defined as a state “where an athlete has no impairment of physical performance, no mental fatigue or excessive psychological distress” ⁴.

One method of assessing readiness is through the use of athlete self-reported measures (ASRMs) ^{22, 46, 59-62} due to their sensitivity and consistency in reflecting changes in training load ^{46, 59}. Numerous ASRMs have been developed (Figure 5) to assess an athlete’s subjective response to training, although originally used in research to detect early symptoms of over-reaching and over-training ^{47, 63}. The most common ASRMs are described and outlined below including the Recovery Stress Questionnaire for Athletes (RESTQ-S) ⁶⁴, Daily Analyses of Life Demands of Athletes (DALDA) ⁶⁵, the Multi-Component Training Distress Scale ⁶⁶ and the Profile of Mood State questionnaire (POMS) ⁶⁷. Unfortunately however, many of these tools are extensive and time consuming in nature which limits their use in a team sports environment ⁴⁸ and thus, shorter customised ASRMs have been developed to address this fact ⁶¹, to provide valuable information to inform the training process despite the inherent lack of their validity within the literature ^{48, 68}. To address this, recommendations have been published to minimise error when developing and administering these questionnaires in a practical setting (Figure 5) ⁴⁶.

Figure 5: Athlete Self-Reported Measures (ASRM) ^{7, 46}

The assessment of heart rate (HR) (Figure 6) derived measurements including resting HR (RHR), exercising HR (HRex), HR variability (HRV), and HR recovery (HRR) has also received some attention within the literature ^{59, 69} due to their ability to be utilised as surrogate markers of an individual’s autonomic nervous system (ANS) and cardiovascular fitness ⁷⁰ in a time-efficient, inexpensive and non-invasive manner ^69-73. However, despite the relative ease of data collection, HR measures remain cautiously implemented ^{69, 70} as ambiguity exists in the interpretation of the data collected ^{70, 74} due to contradictory results ^{72, 74}, inconsistent methods ⁷⁵ and misinterpretation of the data reported within the literature ⁷⁰. Also, as the majority of the research has been conducted in endurance sports ⁶⁹ the interpretation of the results remains speculative due to the inherent complex nature of team sport ^{32, 70}. Furthermore, despite the use of HRex & HRR to infer changes in fitness from standardised protocols ^{59, 69, 70}, morning resting HRV is currently the only viable option to guide the training process ^{69, 76, 77} in a non-invasive and non-fatiguing manner, although it is important to state the lack of sensitivity of HR measures to perturbations of other internal load measures as HRV is solely a marker of cardiac ANS function and cannot infer any additional information ⁷⁸. Despite this ambiguity, ⁶⁹ has provided an excellent overview of currently utilised HR measures and their context for interpretation in practice.

Figure 6: Heart Rate Monitoring in Team Sport ^{69, 70, 72}

Physical performance assessments are another tool that have been extensively researched within the literature to assess an athletes readiness ^{4, 59, 79}, with a focus placed on neuromuscular performance ^{2, 59, 61}. This is due to the mutual agreement that a central component of fatigue is the failure to produce or maintain the required force or power output for a given task ⁵¹. Nonetheless, it must be noted that although neuromuscular performance tests to assess fatigue have become common place in sport ^{2, 59, 61}, the validity of many of the protocols remains indeterminate ³⁸. Within the literature, neuromuscular fatigue is categorised based on its origin, with fatigue likely occurring either distal to the neuromuscular junction (peripheral neuromuscular fatigue) or proximal to the neuromuscular junction (central neuromuscular fatigue) ^{54, 80, 81}. An emerging area of interest for practitioners for assessing neuromuscular fatigue is low-frequency neuromuscular fatigue (LFF). LFF is the result of high-intensity, moderate-to-high force, cyclical eccentric, or stretch-shortening cycle activities which has a prolonged, detrimental effect on low-frequency force-generating capacity ^{57, 80, 82, 83}. Currently, the gold standard of assessment for LFF is via percutaneous nerve or muscle stimulation ⁸⁴, however, this method of assessment remains impractical for regular assessment in the sporting environment ⁸², thus, to infer LFF, the focus has shifted to using either ASRMs ⁴⁸ or single or repeated explosive activities ^85-87 as a more practical method of assessment. However, when examining neuromuscular fatigue research has demonstrated a level of task specificity ⁸⁸, and thus it may not be possible to decipher the origins of fatigue during assessment although, promising research is emerging within this field ⁸⁰.

Biochemical, hormonal and immunological assessments have also been examined within the literature to assess fatigue and adaptation ⁸⁹ and have been used extensively within the research for monitoring training and recovery ^{2, 86, 90}. Unfortunately, to this date, a definitive biochemical, hormonal, or immunological marker of fatigue has yet to be established ^{2, 59, 79, 86}. Furthermore, the time-consuming and expensive nature of their analysis coupled with a lack of longitudinal data in team sports and the inherent logistical issues of data sampling ^{2, 3, 59} fail to justify its use in daily monitoring. However, these measures may become beneficial for athletes displaying prolonged symptoms of overtraining and underperformance ³.

As discussed, the acute and transient fatigue disturbances resulting from training and competition, remain present during the following days depending on the recovery timeline of the fatigue marker and the athletes capacity to deal with the incurred training load and thus, if regeneration and recovery strategies are limited, can expose athletes to performing whilst not fully recovered ^{79, 90}, prompting the need to attention to be payed to monitoring practices ^{4, 59, 79, 90, 91}. Consequently, practitioners utilise a vast array of monitoring methods to evaluate recovery and athlete readiness for future performance ^{4, 59, 79, 90, 91}. However, as it pertains to the real-world setting, uncertainty exists around the tangible impact of athlete monitoring and its practical applications for informing player readiness ⁷⁹ in professional sport. For example, doubts remain over whether athletes are actually exposed to competition congestion ⁹², with competition performance, in professional soccer players who competed in a minimum of 75 minutes over successive matches, remaining generally unaffected, prompting the questioning of the real-world necessity for athlete monitoring ^93-95. Similarly, and most importantly, carling debates that there is currently no evidence to demonstrate that insufficient recovery of fatigue markers relate to underperformance during match play. Therefore, as the main justifications for implementing monitoring systems in professional sport pertains to 1: promoting player readiness (fatigue dissipation) and 2: underperformance related to fatigue during competition, can it really be justified, particularly if the ultimate/continuous goal of the training process is to progressively develop the required qualities of the sport (soccer) in order to perform ^{3, 26}. In addition, barriers to athlete monitoring, for both athletes and practitioners, are constant over the course of the competition period ^{5, 79, 96}.

In professional settings, recurrent disconnect between monitoring opportunities, the monitoring methods that can administered and the practical literature exists, with much of this literature focusing on acute (<24 hours) responses ^{5, 79, 97}. As it is commonplace for athletes to have a day off on the day following competition ^{79, 98-100}, acute monitoring may not be viable during the regular training cycle. However, during congested competition periods readiness monitoring may be viable, however, practitioners tend to prioritise implementing recovery strategies during these periods over data collection, to aid fatigue dissipation and restoration of homeostasis ^{93, 101}. Additionally, in the days following competition, especially in team sport, logistical issues become apparent including travel, media duties, additional recovery strategies, athlete meetings including video analysis, performance reviews, coach-athlete meetings coupled with subsequent competition preparation and training considerably limit monitoring opportunities ⁷⁹. Furthermore, on loading days (days of highest training load following competition), the majority of coaches want full athlete availability and participation frequently disregarding any individual recovery considerations ⁷⁹.

Likewise, due to the inter-individual differences in athlete monitoring and recovery potential ^{90, 97} full participation is required to gain the greatest insight into athlete readiness (Figure 7) ^{79, 86, 90, 97}, given the inherent logistical burden, athlete availability and buy-in from both the athletes and the coaching staff themselves, this can be quite a burdensome and limits monitoring consistency and replicability ⁷⁹ thus a focus should shift from readiness monitoring to facilitating recovery potential through scientifically valid and reliable modalities ^{59, 102}. To further this point, if there is a lack of a clear link between the monitoring systems and its relationship with performance between the athletes, coaching staff, and sports science practitioners, this can lead to negative perceptions associated with monitoring ⁵. Coupled with this, not knowing whether a decision based on the data observed will achieve the desired outcome, encourages risk-adverse behaviours that may be limiting an athlete’s ability to adapt and perform ^{5, 79}. Moreover, building trust in the data that is collected when monitoring athletes and understanding the limitations, error, validity and reliability, and the measure of what represent true change of both the technologies used and the data collected ^{5, 103, 104}, may help limit risk-adverse this risk adverse decision-making behaviour. As a detailed discussion of all challenges and contextual factors is beyond the scope of this section, (Figure 8) illustrates the many contextual factors, challenges faced and some considerations when implementing an athlete monitoring system (Figure 9).

Figure 7: Using Technology to Inform Performance ⁵

Figure 8: Contextual Factors Influencing Technology Use to Inform Performance ⁵

Figure 9: Example Monitoring System for High- Performance Team Sport ¹

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