Tim J Gabbett

The athlete monitoring cycle: a practical guide to interpreting and applying training monitoring data

Given the relationships among athlete workloads, injury1 and performance,2 athlete monitoring has become critical in the high-performance sporting environment. Sports medicine and science staff have a suite of monitoring tools available to track how much ‘work’ an athlete has performed, the response to that ‘work’ and whether the athlete is in a relative state of fitness or fatigue. The volume of literature, coupled with clever marketing around the ‘best approaches’ to optimising athlete performance, has resulted in practitioners having more choices than ever before. Furthermore, the range of different practices used in sport and the lack of agreement between parties emphasise the importance of having a clear rationale for athlete monitoring. The aim of this paper is to provide a practical guide to strategic planning, analysing, interpreting and applying athlete monitoring data in the sporting environment irrespective of data management software.nnWhen deciding on the athlete monitoring tools to use with your athletes, the first question one should ask is “What do I want to achieve through athlete monitoring?” Quite commonly, the answer is to maximise the positive effects (eg, fitness, readinessxa0and performance) and minimise the negative effects (eg, excessive fatigue, injuryxa0and illness) of training. Once practitioners know the reasons for athlete monitoring, appropriate tools can be chosen in order to answer the athlete monitoring question.nnFor example, if practitioners wish to maximise ‘fitness’ and minimise ‘fatigue’, then appropriate monitoring tools to measure these outcomes are necessary. Measurement of fitness improvements for a Premier League football player (eg, a Yo-Yo test) …

High-speed running and sprinting as an injury risk factor in soccer: Can well-developed physical qualities reduce the risk?

OBJECTIVESnThis study investigated the association between high-speed running (HSR) and sprint running (SR) and injuries within elite soccer players. The impact of intermittent aerobic fitness as measured by the end speed of the 30-15 intermittent fitness test (30-15VIFT) and high chronic workloads (average 21-day) as potential mediators of injury risk were also investigated.nnnDESIGNnObservational Cohort Study.nnnMETHODSn37 elite soccer players from one elite squad were involved in a one-season study. Training and game workloads (session-RPE×duration) were recorded in conjunction with external training loads (using global positioning system technology) to measure the HSR (>14.4kmh-1) and SR (>19.8kmh-1) distance covered across weekly periods during the season. Lower limb injuries were also recorded. Training load and GPS data were modelled against injury data using logistic regression. Odds ratios (OR) were calculated with 90% confidence intervals based on 21-day chronic training load status (sRPE), aerobic fitness, HSR and SR distance with these reported against a reference group.nnnRESULTSnPlayers who completed moderate HSR (701-750-m: OR: 0.12, 90%CI: 0.08-0.94) and SR distances (201-350-m: OR: 0.54, 90%CI: 0.41-0.85) were at reduced injury risk compared to low HSR (≤674-m) and SR (≤165-m) reference groups. Injury risk was higher for players who experienced large weekly changes in HSR (351-455-m; OR: 3.02; 90%CI: 2.03-5.18) and SR distances (between 75-105-m; OR: 6.12, 90%CI: 4.66-8.29). Players who exerted higher chronic training loads (≥2584 AU) were at significantly reduced risk of injury when they covered 1-weekly HSR distances of 701-750m compared to the reference group of <674m (OR=0.65, 90% CI 0.27-0.89). When intermittent aerobic fitness was considered based on 30-15VIFT performance, players with poor aerobic fitness had a greater risk of injury than players with better-developed aerobic fitness.nnnCONCLUSIONSnExposing players to large and rapid increases in HSR and SR distances increased the odds of injury. However, higher chronic training loads (≥2584 AU) and better intermittent aerobic fitness off-set lower limb injury risk associated with these running distances in elite soccer players.

Pain and fatigue in sport: are they so different?

Pain and fatigue are common reasons for athletes to avoid, or reduce, sporting participation. Despite commonly coexisting, they are usually treated as distinct entities. Both sensations are often interpreted by medical staff as indicating that physical activity should be reduced or avoided, either due to tissue damage (pain) or excessive training (fatigue). But paradoxically, that management plan—relative rest—means that athletes avoid what keeps them healthy, fit and resilient—physical activity.nnCoaches sometimes view the sensations of pain and fatigue as indicators of physical and/or psychological weakness; they should be ignored to ‘toughen up’ athletes, sometimes leading to athletes unhelpfully provoking symptoms. These opposing views between medical staff and coaches—which often reflect limited understanding regarding the interaction of training load, beliefs and other external factors on pain and fatigue—often place the athlete in a conflicted state. ‘Should I tell (the medical team) or should I remain stoic’ (figure 1). We discuss the parallels between pain and fatigue, and how their management reflects the lens through which these …

CrossFit Overview: Systematic Review and Meta-analysis

BackgroundCrossFit is recognized as one of the fastest growing high-intensity functional training modes in the world. However, scientific data regarding the practice of CrossFit is sparse. Therefore, the objective of this study is to analyze the findings of scientific literature related to CrossFit via systematic review and meta-analysis.MethodsSystematic searches of the PubMed, Web of Science, Scopus, Bireme/MedLine, and SciELO online databases were conducted for articles reporting the effects of CrossFit training. The systematic review followed the PRISMA guidelines. The Oxford Levels of Evidence was used for all included articles, and only studies that investigated the effects of CrossFit as a training program were included in the meta-analysis. For the meta-analysis, effect sizes (ESs) with 95% confidence interval (CI) were calculated and heterogeneity was assessed using a random-effects model.ResultsThirty-one articles were included in the systematic review and four were included in the meta-analysis. However, only two studies had a high level of evidence at low risk of bias. Scientific literature related to CrossFit has reported on body composition, psycho-physiological parameters, musculoskeletal injury risk, life and health aspects, and psycho-social behavior. In the meta-analysis, significant results were not found for any variables.ConclusionsThe current scientific literature related to CrossFit has few studies with high level of evidence at low risk of bias. However, preliminary data has suggested that CrossFit practice is associated with higher levels of sense of community, satisfaction, and motivation.

Can the workload–injury relationship be moderated by improved strength, speed and repeated-sprint qualities?

OBJECTIVESnThe aim of this study was to investigate potential moderators (i.e. lower body strength, repeated-sprint ability [RSA] and maximal velocity) of injury risk within a team-sport cohort.nnnDESIGNnObservational cohort study.nnnMETHODSnForty male amateur hurling players (age: 26.2±4.4 year, height: 184.2±7.1cm, mass: 82.6±4.7kg) were recruited. During a two-year period, workload (session RPE×duration), injury and physical qualities were assessed. Specific physical qualities assessed were a three-repetition maximum Trapbar deadlift, 6×35-m repeated-sprint (RSA) and 5-, 10- and 20-m sprint time. All derived workload and physical quality measures were modelled against injury data using regression analysis. Odds ratios (OR) were reported against a reference group.nnnRESULTSnModerate weekly loads between ≥1400 AU and ≤1900 AU were protective against injury during both the pre-season (OR: 0.44, 95% CI: 0.18-0.66) and in-season periods (OR: 0.59, 95% CI: 0.37-0.82) compared to a low load reference group (≤1200 AU). When strength was considered as a moderator of injury risk, stronger athletes were better able to tolerate the given workload at a reduced risk. Stronger athletes were also better able to tolerate larger week-to-week changes (>550-1000 AU) in workload than weaker athletes (OR=2.54-4.52). Athletes who were slower over 5-m (OR: 3.11, 95% CI: 2.33-3.87), 10-m (OR: 3.45, 95% CI: 2.11-4.13) and 20-m (OR: 3.12, 95% CI: 2.11-4.13) were at increased risk of injury compared to faster athletes. When repeated-sprint total time (RSAt) was considered as a moderator of injury risk at a given workload (≥1750 AU), athletes with better RSAt were at reduced risk compared to those with poor RSAt (OR: 5.55, 95%: 3.98-7.94).nnnCONCLUSIONSnThese findings demonstrate 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.

Is it all for naught? What does mathematical coupling mean for acute:chronic workload ratios?

Traditional calculations of the acute:chronic workload ratio (ACWR) are ‘mathematically coupled’, as the most recent week is included in estimates of both the acute and chronic workloads. As Lolli and colleagues rightly point out, this induces a spurious correlation between the acute and chronic loads ofxa0~0.50 (r=0.52 in their simulated data of 1000 athletes).1 They suggest that the simplest solution is to use uncoupled ACWRs (where the acute load is not part of the chronic load) insteadxa0(figure 1).nnnnFigure 1 nThe distinction between traditional ‘coupled’ and ‘uncoupled’ estimates of the acute:chronic workload ratio. The inclusion or exclusion of the most recent week in ‘chronic load’ calculations is the key distinction.nnnnNotably, at least two studies have already used uncoupled ACWR calculations, both demonstrating that rapid workload increases are associated with higher injury risks.2 3 To this end, Lolli and colleagues’ suggestion warrants consideration—should we use ‘uncoupled’ ACWRs instead of ‘coupled’? We have two aims in this editorial: (1) to further comment on how mathematical coupling affects ACWR estimates and (2) to encourage researchers and practitioners to use a critical approach to load management, wherein ACWRs may play a part.nn### Defining coupled and uncoupled ACWRsnnWe define mathematical coupling in the same manner as Lolli et al ,xa0where a number is represented in both the numerator and denominator of a ratio, contributing to a spurious correlation. In the case of the ACWR, both …

Load, capacity and health: critical pieces of the holistic performance puzzle

Relationships between load, load capacity, performance and health are topics of contemporary interest. At what intensity should an athlete train to achieve the best physiological response? How much (or little) can an athlete train without detrimentally affecting health? Most studies addressing such questions have used a reductionist approach wherein factors were studied in isolation, thereby ignoring the complex inter-relationships and balance between factors. This editorial discusses the association between load and load capacity, and their relationship with athlete performance and health. We illustrate the practical use of a model for the management of athlete performance and health, and provide directions for future practice and research.nnFigure 1 shows the intertwined relationships between load, load capacity, performance and health. To stimulate adaptation the basic principle of any training programme is to apply a load (ie, the amount of mechanical, physiological or mental stress) through training or competition that is greater than an athlete’s current load capacity (ie, the ability to tolerate load).1 With the optimal balance between both constructs, an appropriate training stimulus will …

Research, urban myths and the never ending story

In the modern technological age, practitioners are exposed to a wealth of information from many diverse sources. Social media has resulted in rapid distribution of research evidence. As soon as an article appears on the journal website, those with the fastest fingers and thumbs will have the paper ‘posted’, ‘tweeted’ or ‘blogged’. Without doubt, social media has assisted researchers to distribute their findings to (hopefully) enhance translation to the ‘real world’. But how well does a single 140-character ‘tweet’ encapsulate the findings of a complete research study (which may range from 3000 to 5000 words)?nnOften research evidence is equivocal; for every study there will be another study reporting an opposing view, and quite often, factors other than empirical evidence shape our beliefs.1 Wherever possible, practitioners should examine their beliefs and the sources those beliefs are formed upon. We should try to ascertain what is a hypothesis, what is evidence (myth, story, empirical), and based on the strength of the hypothesis or evidence, how much weight should be placed on each. Below, we have provided some examples of how people obtain information and …

Getting the most out of intensive longitudinal data: a methodological review of workload–injury studies

Objectives To systematically identify and qualitatively review the statistical approaches used in prospective cohort studies of team sports that reported intensive longitudinal data (ILD) (>20 observations per athlete) and examined the relationship between athletic workloads and injuries. Since longitudinal research can be improved by aligning the (1) theoretical model, (2) temporal design and (3) statistical approach, we reviewed the statistical approaches used in these studies to evaluate how closely they aligned these three components. Design Methodological review. Methods After finding 6 systematic reviews and 1 consensus statement in our systematic search, we extracted 34 original prospective cohort studies of team sports that reported ILD (>20 observations per athlete) and examined the relationship between athletic workloads and injuries. Using Professor Linda Collins’ three-part framework of aligning the theoretical model, temporal design and statistical approach, we qualitatively assessed how well the statistical approaches aligned with the intensive longitudinal nature of the data, and with the underlying theoretical model. Finally, we discussed the implications of each statistical approach and provide recommendations for future research. Results Statistical methods such as correlations, t-tests and simple linear/logistic regression were commonly used. However, these methods did not adequately address the (1) themes of theoretical models underlying workloads and injury, nor the (2) temporal design challenges (ILD). Although time-to-event analyses (eg, Cox proportional hazards and frailty models) and multilevel modelling are better-suited for ILD, these were used in fewer than a 10% of the studies (n=3). Conclusions Rapidly accelerating availability of ILD is the norm in many fields of healthcare delivery and thus health research. These data present an opportunity to better address research questions, especially when appropriate statistical analyses are chosen.

Physical fitness and peak running periods during female Australian football match-play

ABSTRACT Objective: To investigate the influence of physical fitness on peak periods of match-play. Methods: Forty-three female Australian footballers from three teams wore global positioning system units in matches during one competitive season. The Yo-Yo Intermittent Recovery Test (Level 1) was conducted as an estimate of physical fitness. One-, two-, three-, four- and five-minute rolling periods were analysed in order to determine the “peak” and “subsequent” periods during match-play. Results: Midfielders covered greater distances during peak periods than half-line players (Effect size, ES range = 0.33–0.86; likelihood ≥76%). Nomeaningful differences were reported between positional groups for high-speed distances during the peak periods, with the exception of half-liners covering greater distance during the 1-minute period (ES = 0.38; likelihood = 80%).Higher fitness players covered greater peak total and high-speed (ES range = 0.70–1.16; likelihood ≥94%) distances than lower fitness players, irrespective of position. Higher fitness midfielders covered greater high-speed distances during the 1 to 3-minute subsequent periods than lower fitness midfielders (ES range = 0.46–0.71; likelihood ≥81%). Half-liners with greater Yo-Yo performances covered greater relative total and low-speed (ES range = 0.47–0.70; likelihood ≥76%) distances during the subsequent periods than lower fitness players. Conclusion: Developing physical fitness may enable greater peak and subsequent period performances and improve players’ abilities to maintain higher average match intensities.