Athlete preparation and performance continue to increase in complexity and costs. Modern coaches are shifting from reliance on personal memory, experience, and opinion to evidence from collected training-load data. Training-load monitoring may hold vital information for developing systems of monitoring that follow the training process with such precision that both performance prediction and day-to-day management of training become adjuncts to preparation and performance. Time-series data collection and analyses in sport are still in their infancy, with considerable efforts being applied in “big data” analytics, models of the appropriate variables to monitor, and methods for doing so. Training monitoring has already garnered important applications but lacks a theoretical framework from which to develop further. As such, we propose a framework involving the following: analyses of individuals, trend analyses, rules-based analysis, and statistical process control.
William A. Sands, Ashley A. Kavanaugh, Steven R. Murray, Jeni R. McNeal and Monèm Jemni
Pitre C. Bourdon, Marco Cardinale, Andrew Murray, Paul Gastin, Michael Kellmann, Matthew C. Varley, Tim J. Gabbett, Aaron J. Coutts, Darren J. Burgess, Warren Gregson and N. Timothy Cable
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.
Robin T. Thorpe, Greg Atkinson, Barry Drust and Warren Gregson
The increase in competition demands in elite team sports over recent years has prompted much attention from researchers and practitioners to the monitoring of adaptation and fatigue in athletes. Monitoring fatigue and gaining an understanding of athlete status may also provide insights and beneficial information pertaining to player availability, injury, and illness risk. Traditional methods used to quantify recovery and fatigue in team sports, such as maximal physical-performance assessments, may not be feasible to detect variations in fatigue status throughout competitive periods. Faster, simpler, and nonexhaustive tests such as athlete self-report measures, autonomic nervous system response via heart-rate-derived indices, and to a lesser extent, jump protocols may serve as promising tools to quantify and establish fatigue status in elite team-sport athletes. The robust rationalization and precise detection of a meaningful fluctuation in these measures are of paramount importance for practitioners working alongside athletes and coaches on a daily basis. There are various methods for arriving at a minimal clinically important difference, but these have been rarely adopted by sport scientists and practitioners. The implementation of appropriate, reliable, and sensitive measures of fatigue can provide important information to key stakeholders in team-sport environments. Future research is required to investigate the sensitivity of these tools to fundamental indicators such as performance, injury, and illness.
Carl Foster, Jose A. Rodriguez-Marroyo and Jos J. de Koning
Training monitoring is about keeping track of what athletes accomplish in training, for the purpose of improving the interaction between coach and athlete. Over history there have been several basic schemes of training monitoring. In the earliest days training monitoring was about observing the athlete during standard workouts. However, difficulty in standardizing the conditions of training made this process unreliable. With the advent of interval training, monitoring became more systematic. However, imprecision in the measurement of heart rate (HR) evolved interval training toward index workouts, where the main monitored parameter was average time required to complete index workouts. These measures of training load focused on the external training load, what the athlete could actually do. With the advent of interest from the scientific community, the development of the concept of metabolic thresholds and the possibility of trackside measurement of HR, lactate, VO2, and power output, there was greater interest in the internal training load, allowing better titration of training loads in athletes of differing ability. These methods show much promise but often require laboratory testing for calibration and tend to produce too much information, in too slow a time frame, to be optimally useful to coaches. The advent of the TRIMP concept by Banister suggested a strategy to combine intensity and duration elements of training into a single index concept, training load. Although the original TRIMP concept was mathematically complex, the development of the session RPE and similar low-tech methods has demonstrated a way to evaluate training load, along with derived variables, in a simple, responsive way. Recently, there has been interest in using wearable sensors to provide high-resolution data of the external training load. These methods are promising, but problems relative to information overload and turnaround time to coaches remain to be solved.
Sean Williams, Grant Trewartha, Matthew J. Cross, Simon P.T. Kemp and Keith A. Stokes
Numerous derivative measures can be calculated from the simple session rating of perceived exertion (sRPE), a tool for monitoring training loads (eg, acute:chronic workload and cumulative loads). The challenge from a practitioner’s perspective is to decide which measures to calculate and monitor in athletes for injury-prevention purposes. The aim of the current study was to outline a systematic process of data reduction and variable selection for such training-load measures.
Training loads were collected from 173 professional rugby union players during the 2013–14 English Premiership season, using the sRPE method, with injuries reported via an established surveillance system. Ten derivative measures of sRPE training load were identified from existing literature and subjected to principal-component analysis. A representative measure from each component was selected by identifying the variable that explained the largest amount of variance in injury risk from univariate generalized linear mixed-effects models.
Three principal components were extracted, explaining 57%, 24%, and 9% of the variance. The training-load measures that were highly loaded on component 1 represented measures of the cumulative load placed on players, component 2 was associated with measures of changes in load, and component 3 represented a measure of acute load. Four-week cumulative load, acute:chronic workload, and daily training load were selected as the representative measures for each component.
The process outlined in the current study enables practitioners to monitor the most parsimonious set of variables while still retaining the variation and distinct aspects of “load” in the data.
Martin Buchheit and Ben Michael Simpson
With the ongoing development of microtechnology, player tracking has become one of the most important components of load monitoring in team sports. The 3 main objectives of player tracking are better understanding of practice (provide an objective, a posteriori evaluation of external load and locomotor demands of any given session or match), optimization of training-load patterns at the team level, and decision making on individual players’ training programs to improve performance and prevent injuries (eg, top-up training vs unloading sequences, return to play progression). This paper discusses the basics of a simple tracking approach and the need to integrate multiple systems. The limitations of some of the most used variables in the field (including metabolic-power measures) are debated, and innovative and potentially new powerful variables are presented. The foundations of a successful player-monitoring system are probably laid on the pitch first, in the way practitioners collect their own tracking data, given the limitations of each variable, and how they report and use all this information, rather than in the technology and the variables per se. Overall, the decision to use any tracking technology or new variable should always be considered with a cost/benefit approach (ie, cost, ease of use, portability, manpower/ability to affect the training program).
Twan ten Haaf, Selma van Staveren, Erik Oudenhoven, Maria F. Piacentini, Romain Meeusen, Bart Roelands, Leo Koenderman, Hein A.M. Daanen, Carl Foster and Jos J. de Koning
To investigate whether monitoring of easily measurable stressors and symptoms can be used to distinguish early between acute fatigue (AF) and functional overreaching (FOR).
The study included 30 subjects (11 female, 19 male; age 40.8 ± 10.8 y, VO2max 51.8 ± 6.3 mL · kg–1 · min–1) who participated in an 8-d cycling event over 1300 km with 18,500 climbing meters. Performance was measured before and after the event using a maximal incremental test. Subjects with decreased performance after the event were classified as FOR, others as AF. Mental and physical well-being, internal training load, resting heart rate, temperature, and mood were measured daily during the event. Differences between AF and FOR were analyzed using mixed-model ANOVAs. Logistic regression was used to determine the best predictors of FOR after 3 and 6 d of cycling.
Fifteen subjects were classified as FOR and 14 as AF (1 excluded). Although total group changes were observed during the event, no differences between AF and FOR were found for individual monitoring parameters. The combination of questionnaire-based changes in fatigue and readiness to train after 3 d cycling correctly predicted 78% of the subjects as AF or FOR (sensitivity = 79%, specificity = 77%).
Monitoring changes in fatigue and readiness to train, using simple visual analog scales, can be used to identify subjects likely to become FOR after only 3 d of cycling. Hence, we encourage athlete support staff to monitor not only fatigue but also the subjective integrated mental and physical readiness to perform.
Daniel Martínez-Silván, Jaime Díaz-Ocejo and Andrew Murray
To analyze the influence of training exposure and the utility of self-report questionnaires on predicting overuse injuries in adolescent endurance athletes.
Five adolescent male endurance athletes (15.7 ± 1.4 y) from a full-time sports academy answered 2 questionnaires (Recovery Cue; RC-q and Oslo Sports Trauma Research questionnaire; OSTRC-q) on a weekly basis for 1 season (37 wk) to detect signs of overtraining and underrecovery (RC-q) and early symptoms of lower-limb injuries (OSTRC-q). All overuse injuries were retrospectively analyzed to detect which variations in the questionnaires in the weeks preceding injury were best associated. Overuse incidence rates were calculated based on training exposure.
Lower-limb overuse injuries accounted for 73% of total injuries. The incidence rate for overuse training-related injuries was 10 injuries/1000 h. Strong correlations were observed between individual running exposure and overuse injury incidence (r 2 = .66), number of overuse injuries (r 2 = .69), and days lost (r 2 = .66). A change of 20% or more in the RC-q score in the preceding week was associated with 67% of the lower-limb overuse injuries. Musculoskeletal symptoms were only detected in advance by the OSTRC-q in 27% of the episodes.
Training exposure (especially running exposure) was shown to be related to overuse injuries, suggesting that monitoring training load is a key factor for injury prevention. Worsening scores in the RC-q (but not the OSTRC) may be an indicator of overuse injury in adolescent endurance runners when used longitudinally.
Training quantification is basic to evaluate an endurance athlete’s responses to training loads, ensure adequate stress/recovery balance, and determine the relationship between training and performance. Quantifying both external and internal workload is important, because external workload does not measure the biological stress imposed by the exercise sessions. Generally used quantification methods include retrospective questionnaires, diaries, direct observation, and physiological monitoring, often based on the measurement of oxygen uptake, heart rate, and blood lactate concentration. Other methods in use in endurance sports include speed measurement and the measurement of power output, made possible by recent technological advances such as power meters in cycling and triathlon. Among subjective methods of quantification, rating of perceived exertion stands out because of its wide use. Concurrent assessments of the various quantification methods allow researchers and practitioners to evaluate stress/recovery balance, adjust individual training programs, and determine the relationships between external load, internal load, and athletes’ performance. This brief review summarizes the most relevant external- and internal-workload-quantification methods in endurance sports and provides practical examples of their implementation to adjust the training programs of elite athletes in accordance with their individualized stress/recovery balance.
Samuel Robertson, Jonathan D. Bartlett and Paul B. Gastin
Decision-support systems are used in team sport for a variety of purposes including evaluating individual performance and informing athlete selection. A particularly common form of decision support is the traffic-light system, where color coding is used to indicate a given status of an athlete with respect to performance or training availability. However, despite relatively widespread use, there remains a lack of standardization with respect to how traffic-light systems are operationalized. This paper addresses a range of pertinent issues for practitioners relating to the practice of traffic-light monitoring in team sports. Specifically, the types and formats of data incorporated in such systems are discussed, along with the various analysis approaches available. Considerations relating to the visualization and communication of results to key stakeholders in the team-sport environment are also presented. In order for the efficacy of traffic-light systems to be improved, future iterations should look to incorporate the recommendations made here.