Monitoring Athlete Training Loads: Consensus Statement.
01 Apr 2017-International Journal of Sports Physiology and Performance (Human Kinetics)-Vol. 12, pp 161-170
TL;DR: This consensus statement brings together the key findings and recommendations from a conference on monitoring Athlete Training Loads in a shared conceptual framework for use by coaches, sport-science and -medicine staff, and other related professionals who have an interest in monitoring athlete training loads.
Abstract: Monitoring the load placed on athletes in both training and competition has become a very hot topic in sport science. Both scientists and coaches routinely monitor training loads using multidisciplinary approaches, and the pursuit of the best methodologies to capture and interpret data has produced an exponential increase in empirical and applied research. Indeed, the field has developed with such speed in recent years that it has given rise to industries aimed at developing new and novel paradigms to allow us to precisely quantify the internal and external loads placed on athletes and to help protect them from injury and ill health. In February 2016, a conference on "Monitoring Athlete Training Loads-The Hows and the Whys" was convened in Doha, Qatar, which brought together experts from around the world to share their applied research and contemporary practices in this rapidly growing field and also to investigate where it may branch to in the future. This consensus statement brings together the key findings and recommendations from this conference in a shared conceptual framework for use by coaches, sport-science and -medicine staff, and other related professionals who have an interest in monitoring athlete training loads and serves to provide an outline on what athlete-load monitoring is and how it is being applied in research and practice, why load monitoring is important and what the underlying rationale and prospective goals of monitoring are, and where athlete-load monitoring is heading in the future.
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TL;DR: Meta-analytic estimates of the relationships, as determined by a correlation coefficient, between internal and external measures of load and intensity during team-sport training and competition are provided.
Abstract: The associations between internal and external measures of training load and intensity are important in understanding the training process and the validity of specific internal measures. We aimed to provide meta-analytic estimates of the relationships, as determined by a correlation coefficient, between internal and external measures of load and intensity during team-sport training and competition. A further aim was to examine the moderating effects of training mode on these relationships. We searched six electronic databases (Scopus, Web of Science, PubMed, MEDLINE, SPORTDiscus, CINAHL) for original research articles published up to September 2017. A Boolean search phrase was created to include search terms relevant to team-sport athletes (population; 37 keywords), internal load (dependent variable; 35 keywords), and external load (independent variable; 81 keywords). Articles were considered for meta-analysis when a correlation coefficient describing the association between at least one internal and one external measure of session load or intensity, measured in the time or frequency domain, was obtained from team-sport athletes during normal training or match-play (i.e., unstructured observational study). The final data sample included 122 estimates from 13 independent studies describing 15 unique relationships between three internal and nine external measures of load and intensity. This sample included 295 athletes and 10,418 individual session observations. Internal measures were session ratings of perceived exertion (sRPE), sRPE training load (sRPE-TL), and heart-rate-derived training impulse (TRIMP). External measures were total distance (TD), the distance covered at high and very high speeds (HSRD ≥ 13.1–15.0 km h−1 and VHSRD ≥ 16.9–19.8 km h−1, respectively), accelerometer load (AL), and the number of sustained impacts (Impacts > 2–5 G). Distinct training modes were identified as either mixed (reference condition), skills, metabolic, or neuromuscular. Separate random effects meta-analyses were conducted for each dataset (n = 15) to determine the pooled relationships between internal and external measures of load and intensity. The moderating effects of training mode were examined using random-effects meta-regression for datasets with at least ten estimates (n = 4). Magnitude-based inferences were used to interpret analyses outcomes. During all training modes combined, the external load relationships for sRPE-TL were possibly very large with TD [r = 0.79; 90% confidence interval (CI) 0.74 to 0.83], possibly large with AL (r = 0.63; 90% CI 0.54 to 0.70) and Impacts (r = 0.57; 90% CI 0.47 to 0.64), and likely moderate with HSRD (r = 0.47; 90% CI 0.32 to 0.59). The relationship between TRIMP and AL was possibly large (r = 0.54; 90% CI 0.40 to 0.66). All other relationships were unclear or not possible to infer (r range 0.17–0.74, n = 10 datasets). Between-estimate heterogeneity [standard deviations (SDs) representing unexplained variation; τ] in the pooled internal–external relationships were trivial to extremely large for sRPE (τ range = 0.00–0.47), small to large for sRPE-TL (τ range = 0.07–0.31), and trivial to moderate for TRIMP (τ range= 0.00–0.17). The internal–external load relationships during mixed training were possibly very large for sRPE-TL with TD (r = 0.82; 90% CI 0.75 to 0.87) and AL (r = 0.81; 90% CI 0.74 to 0.86), and TRIMP with AL (r = 0.72; 90% CI 0.55 to 0.84), and possibly large for sRPE-TL with HSRD (r = 0.65; 90% CI 0.44 to 0.80). A reduction in these correlation magnitudes was evident for all other training modes (range of the change in r when compared with mixed training − 0.08 to − 0.58), with these differences being unclear to possibly large. Training mode explained 24–100% of the between-estimate variance in the internal–external load relationships. Measures of internal load derived from perceived exertion and heart rate show consistently positive associations with running- and accelerometer-derived external loads and intensity during team-sport training and competition, but the magnitude and uncertainty of these relationships are measure and training mode dependent.
244 citations
Cites background from "Monitoring Athlete Training Loads: ..."
...Specifically, changes in internal load with respect to a standard external load may be used to infer an athletes fitness or fatigue over time or in comparison with that of their peers [14]....
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TL;DR: It is demonstrated that well-developed lower-body strength, RSA and speed are associated with better tolerance to higher workloads and reduced risk of injury in team-sport athletes.
102 citations
TL;DR: Current limitations of heart rate monitoring are outlined, methodological considerations of univariate and multivariate approaches are discussed, the influence of different analytical concepts on assessing meaningful changes in heart rate responses are illustrated, and case examples for contextualizing heart rate measures using simple heuristics are provided.
Abstract: A comprehensive monitoring of fitness, fatigue, and performance is crucial for understanding an athlete's individual responses to training to optimize the scheduling of training and recovery strategies. Resting and exercise-related heart rate measures have received growing interest in recent decades and are considered potentially useful within multivariate response monitoring, as they provide non-invasive and time-efficient insights into the status of the autonomic nervous system (ANS) and aerobic fitness. In team sports, the practical implementation of athlete monitoring systems poses a particular challenge due to the complex and multidimensional structure of game demands and player and team performance, as well as logistic reasons, such as the typically large number of players and busy training and competition schedules. In this regard, exercise-related heart rate measures are likely the most applicable markers, as they can be routinely assessed during warm-ups using short (3-5 min) submaximal exercise protocols for an entire squad with common chest strap-based team monitoring devices. However, a comprehensive and meaningful monitoring of the training process requires the accurate separation of various types of responses, such as strain, recovery, and adaptation, which may all affect heart rate measures. Therefore, additional information on the training context (such as the training phase, training load, and intensity distribution) combined with multivariate analysis, which includes markers of (perceived) wellness and fatigue, should be considered when interpreting changes in heart rate indices. The aim of this article is to outline current limitations of heart rate monitoring, discuss methodological considerations of univariate and multivariate approaches, illustrate the influence of different analytical concepts on assessing meaningful changes in heart rate responses, and provide case examples for contextualizing heart rate measures using simple heuristics. To overcome current knowledge deficits and methodological inconsistencies, future investigations should systematically evaluate the validity and usefulness of the various approaches available to guide and improve the implementation of decision-support systems in (team) sports practice.
101 citations
TL;DR: Rapid increases in training and competition workloads and low chronic workloads are associated with greater injury risk and the findings suggest that appropriately staged training programmes may reduce injury risk in athletes.
Abstract: A substantial amount of research has tested the relationship between training load and injury.1 Given that sports injuries compromise team success,2 3 team administrators, players and coaches are now interested in this field. As team injury data are widely available through various internet sources (eg, Man Games Lost, https://www.mangameslost.com/), sports medicine staff are commonly evaluated based on the number of injuries sustained (or not sustained) by their playing rosters.
A search of the ‘PubMed’ database shows that in the past 18 years, there has been a rapid growth in ‘training load’ and ‘injury’ research, increasing from 9 papers in 2000 to 145 in 2017 (figure 1). Despite this growing body of literature, evidence-based guidelines to reduce workload-related injury are often poorly implemented due to the level of expertise or understanding of the high-performance team (including the skill coaches, strength and conditioning or medical staff) or their individual beliefs and experiences (cognitive biases, confirmation biases). This can lead to a disconnect between the evidence supporting training load and its role in injury, and the actual training programmes prescribed.4 Five common myths and misconceptions about training load and its role in injury and performance are reviewed in this paper.
Figure 1
Growth in research including the keywords ‘training’ AND ‘injury’ since 2000.
The relationship between training, performance and injury has been of interest to researchers and practitioners for considerable time.1 5–15 Both individual16 and team17–19 performance can be explained, at least in part by training load, with higher training loads generally associated with better performance. Equally, a large body of evidence has emerged suggesting that inappropriately prescribed training load may increase injury risk20–25 and pain.26 Based on these findings, a myopic view would be that ‘load’ explains all injuries.
The multifactorial determinants of both performance and …
98 citations
TL;DR: It is speculated that incorporating cognitive tasks into motor tasks, rather than separate training of mental and physical functions, is the most promising approach to efficiently enhance cognitive reserve.
Abstract: The demographic change in industrial countries, with increasingly sedentary lifestyles, has a negative impact on mental health. Normal and pathological aging leads to cognitive deficits. This development poses major challenges on national health systems. Therefore, it is necessary to develop efficient cognitive enhancement strategies. The combination of regular physical exercise with cognitive stimulation seems especially suited to increase an individual's cognitive reserve, i.e., his/her resistance to degenerative processes of the brain. Here, we outline insufficiently explored fields in exercise-cognition research and provide a classification approach for different motor-cognitive training regimens. We suggest to classify motor-cognitive training in two categories, (I) sequential motor-cognitive training (the motor and cognitive training are conducted time separated) and (II) simultaneous motor-cognitive training (motor and cognitive training are conducted sequentially). In addition, simultaneous motor-cognitive training may be distinguished based on the specific characteristics of the cognitive task. If successfully solving the cognitive task is not a relevant prerequisite to complete the motor-cognitive task, we would consider this type of training as (IIa) motor-cognitive training with additional cognitive task. In contrast, in ecologically more valid (IIb) motor cognitive training with incorporated cognitive task, the cognitive tasks are a relevant prerequisite to solve the motor-cognitive task. We speculate that incorporating cognitive tasks into motor tasks, rather than separate training of mental and physical functions, is the most promising approach to efficiently enhance cognitive reserve. Further research investigating the influence of motor(-cognitive) exercises with different quantitative and qualitative characteristics on cognitive performance is urgently needed.
98 citations
Cites background from "Monitoring Athlete Training Loads: ..."
..., individual level of blood lactate) (Bourdon et al., 2017; Burgess, 2017), because the same external training loads could lead to varying internal training loads (e....
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References
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Book•
15 Aug 1998
TL;DR: Borg's Perceived Exertion and Pain Scales as discussed by the authors is a complete theoretical and methodological guide to the field of human perception that provides guidance and direction on how and when to measure subjective somatic symptoms.
Abstract: Dr. Gunnar Borg introduced the field of perceived exertion in the 1950s. His ratings of perceived exertion (RPE) scale is used worldwide by professionals in medicine, exercise physiology, psychology, cardiology, ergonomy, and sports. Now, Dr. Borg presents the definitive source for using the latest RPE and CR10 scales correctly. "Borg's Perceived Exertion and Pain Scales" begins with an overview and history to introduce readers to the field of perceived exertion. The book then covers principles of scaling and applications of both the RPE and the CR10 scaling methods.This user-friendly, informative, and readable text-discusses the fundamental bases of perceived exertion, -presents information on uses and misuses of the scales, and-provides guidance and direction on how and when to measure subjective somatic symptoms.A special appendix in the back of the book includes tear-out cards containing three RPE scales and three CR10 scales. A scale and instructions for how the scale is used are printed on each two-sided card. "Borg's Perceived Exertion and Pain Scales" is the complete theoretical and methodological guide to the field of human perception.
3,228 citations
TL;DR: Monitoring of training load can provide important information to athletes and coaches; however, monitoring systems should be intuitive, provide efficient data analysis and interpretation, and enable efficient reporting of simple, yet scientifically valid, feedback.
Abstract: Many athletes, coaches, and support staff are taking an increasingly scientific approach to both designing and monitoring training programs. Appropriate load monitoring can aid in determining whether an athlete is adapting to a training program and in minimizing the risk of developing non-functional overreaching, illness, and/or injury. In order to gain an understanding of the training load and its effect on the athlete, a number of potential markers are available for use. However, very few of these markers have strong scientific evidence supporting their use, and there is yet to be a single, definitive marker described in the literature. Research has investigated a number of external load quantifying and monitoring tools, such as power output measuring devices, time-motion analysis, as well as internal load unit measures, including perception of effort, heart rate, blood lactate, and training impulse. Dissociation between external and internal load units may reveal the state of fatigue of an athlete. Other monitoring tools used by high-performance programs include heart rate recovery, neuromuscular function, biochemical/hormonal/immunological assessments, questionnaires and diaries, psychomotor speed, and sleep quality and quantity. The monitoring approach taken with athletes may depend on whether the athlete is engaging in individual or team sport activity; however, the importance of individualization of load monitoring cannot be over emphasized. Detecting meaningful changes with scientific and statistical approaches can provide confidence and certainty when implementing change. Appropriate monitoring of training load can provide important information to athletes and coaches; however, monitoring systems should be intuitive, provide efficient data analysis and interpretation, and enable efficient reporting of simple, yet scientifically valid, feedback.
1,082 citations
TL;DR: It was observed that a high percentage of illnesses could be accounted for when individual athletes exceeded individually identifiable training thresholds, mostly related to the strain of training.
Abstract: Purpose: Overtraining is primarily related to sustained high load training, often coupled with other stressors. Studies in animal models have suggested that unremittingly heavy training (monotonous training) may increase the likelihood of developing overtraining syndrome. The purpose of this study was to extend our preliminary observations by relating the incidence of illnesses and minor injuries to various indices of training. Methods: We report observations of the relationship of banal illnesses (a frequently cited marker of overtraining syndrome) to training load and training monotony in experienced athletes (N = 25). Athletes recorded their training using a method that integrates the exercise session RPE and the duration of the training session. Illnesses were noted and correlated with indices of training load (rolling 6 wk average), monotony (daily mean/standard deviation), and strain (load * monotony). Results: It was observed that a high percentage of illnesses could be accounted for when individual athletes exceeded individually identifiable training thresholds, mostly related to the strain of training. Conclusions: These results suggest that simple methods of monitoring the characteristics of training may allow the athlete to achieve the goals of training while minimizing undesired training outcomes.
1,067 citations
TL;DR: The appropriately graded prescription of high training loads should improve players’ fitness, which in turn may protect against injury, ultimately leading to greater physical outputs and resilience in competition, and a greater proportion of the squad available for selection each week.
Abstract: Background There is dogma that higher training load causes higher injury rates. However, there is also evidence that training has a protective effect against injury. For example, team sport athletes who performed more than 18 weeks of training before sustaining their initial injuries were at reduced risk of sustaining a subsequent injury, while high chronic workloads have been shown to decrease the risk of injury. Second, across a wide range of sports, well-developed physical qualities are associated with a reduced risk of injury. Clearly, for athletes to develop the physical capacities required to provide a protective effect against injury, they must be prepared to train hard. Finally, there is also evidence that under-training may increase injury risk. Collectively, these results emphasise that reductions in workloads may not always be the best approach to protect against injury.
Main thesis This paper describes the ‘Training-Injury Prevention Paradox’ model; a phenomenon whereby athletes accustomed to high training loads have fewer injuries than athletes training at lower workloads. The Model is based on evidence that non-contact injuries are not caused by training per se , but more likely by an inappropriate training programme. Excessive and rapid increases in training loads are likely responsible for a large proportion of non-contact, soft-tissue injuries. If training load is an important determinant of injury, it must be accurately measured up to twice daily and over periods of weeks and months (a season). This paper outlines ways of monitoring training load (‘internal’ and ‘external’ loads) and suggests capturing both recent (‘acute’) training loads and more medium-term (‘chronic’) training loads to best capture the player's training burden. I describe the critical variable—acute:chronic workload ratio—as a best practice predictor of training-related injuries. This provides the foundation for interventions to reduce players risk, and thus, time-loss injuries.
Summary The appropriately graded prescription of high training loads should improve players’ fitness, which in turn may protect against injury, ultimately leading to (1) greater physical outputs and resilience in competition, and (2) a greater proportion of the squad available for selection each week.
971 citations
TL;DR: The purpose of this paper is to exposit a control chart technique that may be of value to both manufacturing and continuous process quality control engineers: the exponentially weighted moving average (EWMA) control chart.
Abstract: The Shewhart and CUSUM control chart techniques have found wide application in the manufacturing industries. However, workpiece quality has also been greatly enhanced by rapid and precise individual item measurements and by improvements in automatic dynamic machine control. One consequence is a growing similarity in the control problems faced by the workpiece quality control engineer and his compatriot in the continuous process industries. The purpose of this paper is to exposit a control chart technique that may be of value to both manufacturing and continuous process quality control engineers: the exponentially weighted moving average (EWMA) control chart. The EWMA has its origins in the early work of econometricians, and although its use in quality control has been recognized, it remains a largely neglected tool. The EWMA chart is easy to plot, easy to interpret, and its control limits are easy to obtain. Further, the EWMA leads naturally to an empirical dynamic control equation.
856 citations