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

June 2017
British Journal of Sports Medicine 51(20):bjsports-2016-097298

DOI:10.1136/bjsports-2016-097298

Authors:

Tim Gabbett

Gabbett Performance Solutions

George P Nassis

Eric Oetter

Memphis Grizzlies Basketball Club

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I Want to Monitor My Athlete But Where Do I Start? 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 optimizing 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 emphasizes the importance of having a clear rationale for athlete monitoring. The aim of this paper is to provide a practical guide to strategic planning, analyzing, interpreting, and applying athlete monitoring data in the sporting environment irrespective of data management software.

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Gabbett TJ, etal. Br J Sports Med Month 2017 Vol 0 No 0

The athlete monitoring cycle: a practical

guide to interpreting and applying

training monitoringdata

Tim J Gabbett,1,2 George P Nassis,3 Eric Oetter,4 Johan Pretorius,5

Nick Johnston,6 Daniel Medina,7 Gil Rodas,7 Tom Myslinski,8

Dan Howells,9 Adam Beard,10 Allan Ryan11

I WANT TO MONITOR MY ATHLETE BUT

WHERE DO I START?

Given the relationships among athlete

workloads, injury1 and performance,2

athlete monitoring has become critical in

the high-performance sporting environ-

ment. 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 prac-

titioners 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.

WHAT SHOULD I DO WITH ALL OF

THESE DATA AND HOW DO I CHOOSE

WHAT TO MEASURE?

When deciding on the athlete moni-

toring 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, readiness and performance) and

minimise the negative effects (eg, exces-

sive fatigue, injury and illness) of training.

Once practitioners know the reasons for

athlete monitoring, appropriate tools can

be chosen in order to answer the athlete

monitoring question.

For 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) will be very different from an

American football player (eg, a maximum

strength test). On the other hand, the

measurement of external load and

response to this load in baseball pitchers

will likely require counting balls thrown

(and speed) and the internal response to

that external load (eg, ‘arm health’). High-

speed running is important for football,

but less important for a baseball pitcher.

In this respect, the ideal performance test

and workload ‘metric’ should be context

and sport-specific. Thus, understanding

the physical demands of the sport and the

physiological capacities required of the

sport is critical in this decision-making

process. Database management, data

cleaning and statistical analysis skills are

important for practitioners, but when first

starting with a question, “What do I want

to achieve through athlete monitoring?”,

analysing and interpreting the data

become much easier.

HOW DO I ANALYSE AND INTERPRET

THE DATA?

Sports medicine and science practitioners

can now use global positioning technology,3

inertial measurement sensors4 5 and quan-

tify a range of physiological responses

(eg, heart rate variability, testosterone and

cortisol concentrations, creatine kinase

and the duration and quality of sleep).

With such a range of monitoring tools

available and no agreement on the most

appropriate athlete monitoring ‘system’, it

is difficult for practitioners to evaluate the

available evidence and develop a process

to effectively monitor athletes. A second

challenge facing practitioners is how to

(1) manage the ‘large’ amounts of data

collected, (2) make meaningful interpreta-

tions of these data to inform subsequent

training prescription and (3) translate

these interpretations into actionable steps

for all relevant stakeholders (eg, sport

coaches, performance and medical staff).

In clinical practice, and the high perfor-

mance sport setting, practitioners typically

work with individual patients and athletes

(even in team sports) and are therefore

interested in individual responses and

whether these changes are practically

meaningful. In these environments, tradi-

tional null hypothesis testing (ie, using

a p<0.05 statistical significance test) is

limited as even a small change (which

may have a potentially positive or nega-

tive effect) may be interpreted as having

no effect (ie, p>0.05) due to factors

such as small sample size. We would

suggest the use of SDs, z scores, and the

smallest worthwhile change statistic (also

commonly referred to as the minimum

clinically important difference)6 to deter-

mine whether athletes have deviated

(either positively or negatively) from

‘normal’, although practitioners should be

aware of the potential limitations of these

approaches.7

THE ATHLETE MONITORING CYCLE

Below we provide a step-by-step strategy

for interpreting athlete monitoring data

from the exposure of athletes to a single

external training stimulus, through to the

subsequent exposure of another training

stimulus (figure 1). The inner cycle

describes (1) the workload the athlete

performed (ie, external load), (2) the

athlete’s response to the workload (ie,

internal load), (3) whether the athlete

is tolerating the workload (ie, percep-

tual well-being) and finally (4) whether

the athlete is physically and/or mentally

prepared for exposure to another training

stimulus (ie, readiness to train/compete).

When combined with each previous

step, the subsequent step of the cycle

provides insight into how to interpret the

data and prescribe an intervention (eg,

additional training or extra recovery) to

facilitate appropriate training adaptations.

To assist decision-making for the practi-

tioner, we have produced a matrix at each

step of the cycle. These matrices are inter-

preted using magnitude-based inferential

statistics, such as the smallest worthwhile

change (for more detail see refs 6 and 7).

1Gabbett Performance Solutions, Queensland, Australia

2Institute for Resilient Regions, University of Southern

Queensland, Australia

3National Sports Medicine Programme, Excellence

in Football Project, Aspetar Orthopaedic and Sports

Medicine Hospital, Doha, Qatar

4Memphis Grizzlies, Tennessee, USA

5Sharks Super Rugby, Durban, South Africa

6Nick Johnston Lifestyle and Sport Consultancy,

Birmingham, UK

7Sport Science and Medical Department, FC Barcelona,

Barcelona, Spain

8Jacksonville Jaguars, Florida, USA

9Rugby Football Union, London, UK

10Cleveland Browns, Ohio, USA

11Bath Rugby, Bath, UK

Correspondence to Dr Tim J Gabbett, Gabbett

Performance Solutions, Brisbane, 4011, Qld, Australia;

tim@ gabbettperformance. com. au

Editorial

BJSM Online First, published on June 23, 2017 as 10.1136/bjsports-2016-097298

group.bmj.com on June 23, 2017 - Published by http://bjsm.bmj.com/Downloaded from

2Gabbett TJ, etal. Br J Sports Med Month 2017 Vol 0 No 0

Figure 1 The athlete monitoring cycle.

Editorial

For example, we first examine the

relationship between external load and

internal load (figure 1A). If an athlete has

performed a greater external workload

than planned and their internal workload is

also higher than expected, it may be neces-

sary to decrease workload. Maladaptive

training responses may also be identified.

Combining measures of workload with

perceptual well-being scores8 provides

insight into whether the athlete is toler-

ating training (figure 1B). For example,

factors other than high workloads can

contribute to poor well-being; if athletes

report that they are not coping with the

training programme despite performing

low workloads, investigation of additional

life stressors and lifestyle factors may be

warranted. High workloads are not the

only reason why an athlete may be experi-

encing poor well-being.

Finally, many programmes include

either a subjective or objective measure of

‘readiness to train/compete’. These objec-

tive markers may include short (~3–6 s)

maximal effort cycle ergometer tests,9

counter-movement jumps9 or submax-

imal heart rate recovery tests.8 Combining

perceptual well-being scores with these

‘physical readiness’ measures provides the

final step in the training monitoring cycle

(figure 1C). Depending on the combina-

tion of perceptual well-being and physical

readiness, athletes may be ready to train/

compete, require additional mental or

physical preparation, or extra recovery

before exposure to another training stim-

ulus. Music, relaxation (eg, brief naps and

meditation), nutrition (eg, caffeine) and

soft tissue therapy (eg, physiotherapy,

massage or foam rolling) may form some

of the physiological and/or psychological

strategies available to athletes. Athlete

monitoring should not be viewed as a

means of managing athletes away from

training; if athletes are experiencing

lower than normal ‘readiness’, then extra

recovery is not the only option available to

practitioners.

USE DATA TO SUPPORT COACHES,NOT

REPLACE THEM

Because athlete monitoring has wide

acceptance, practitioners risk becoming

a pariah if they do not implement some

form of athlete monitoring system. The

proposed monitoring cycle discussed

above provides a practical road map for

informing performance decision-making.

We would suggest that viewing external

workload, internal workload, perceptual

well-being and readiness to train/compete

data in combination provides more mean-

ingful individual training prescriptions

than making interpretations based on data

from any single athlete monitoring tool in

isolation.

It is likely that the proposed monitoring

cycle will have greater impact if accompa-

nied by an education programme designed

to encourage involvement from key stake-

holders (eg, sport coaches) as well as

complement the intuition (ie, ‘gut feel’)

of these individuals. But the real challenge

arises in creating tailored and palatable

dissemination strategies for the relevant

stakeholders involved in sport.

Contributors TJG proposed the initial concept and

draft of the paper. All authors contributed equally to

subsequent versions of the paper and approved the

submission of the ﬁnal version of the paper.

Competing interests None declared.

Provenance and peer review Not commissioned;

externally peer reviewed.

otherwise stated in the text of the article) 2017. All

rights reserved. No commercial use is permitted unless

otherwise expressly granted.

To cite GabbettTJ, NassisGP, OetterE,

etal. Br J Sports Med Published Online First:

[please include Day Month Year]. doi:10.1136/

bjsports-2016-097298

Accepted 30 May 2017

Br J Sports Med 2017;0:1

doi:10.1136/bjsports-2016-097298

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monitoring data

guide to interpreting and applying training

The athlete monitoring cycle: a practical

Beard and Allan Ryan

Johnston, Daniel Medina, Gil Rodas, Tom Myslinski, Dan Howells, Adam

Tim J Gabbett, George P Nassis, Eric Oetter, Johan Pretorius, Nick

published online June 23, 2017Br J Sports Med

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Infographic: The training-injury prevention paradox: Should athletes be training smarter and harder?

Article

Full-text available

Jan 2017
BRIT J SPORT MED

Tim Gabbett

Relationships Between Training Load Indicators and Training Outcomes in Professional Soccer

Article

Full-text available

Mar 2017
SPORTS MED

Background: In professional senior soccer, training load monitoring is used to ensure an optimal workload to maximize physical fitness and prevent injury or illness. However, to date, different training load indicators are used without a clear link to training outcomes. Objective: The aim of this systematic review was to identify the state of knowledge with respect to the relationship between training load indicators and training outcomes in terms of physical fitness, injury, and illness. Methods: A systematic search was conducted in four electronic databases (CINAHL, PubMed, SPORTDiscus, and Web of Science). Training load was defined as the amount of stress over a minimum of two training sessions or matches, quantified in either external (e.g., duration, distance covered) or internal load (e.g., heart rate [HR]), to obtain a training outcome over time. Results: A total of 6492 records were retrieved, of which 3304 were duplicates. After screening the titles, abstracts and full texts, we identified 12 full-text articles that matched our inclusion criteria. One of these articles was identified through additional sources. All of these articles used correlations to examine the relationship between load indicators and training outcomes. For pre-season, training time spent at high intensity (i.e., >90 % of maximal HR) was linked to positive changes in aerobic fitness. Exposure time in terms of accumulated training, match or combined training, and match time showed both positive and negative relationships with changes in fitness over a season. Muscular perceived exertion may indicate negative changes in physical fitness. Additionally, it appeared that training at high intensity may involve a higher injury risk. Detailed external load indicators, using electronic performance and tracking systems, are relatively unexamined. In addition, most research focused on the relationship between training load indicators and changes in physical fitness, but less on injury and illness. Conclusion: HR indicators showed relationships with positive changes in physical fitness during pre-season. In addition, exposure time appeared to be related to positive and negative changes in physical fitness. Despite the availability of more detailed training load indicators nowadays, the evidence about the usefulness in relation to training outcomes is rare. Future research should implement continuous monitoring of training load, combined with the individual characteristics, to further examine their relationship with physical fitness, injury, and illness.

Is workload associated with injuries and performance in elite football? A call for action

Article

Full-text available

Mar 2016

What would a Premier League team pay for software that allowed it to optimise performance while reducing injuries? There are emerging data that would allow such software to be developed (and indeed, some software companies who already claim they can predict injuries before they occur), but no product is ready for prime time yet. In this editorial, we briefly direct the reader to data showing how workload is associated with injuries, highlight the challenges in training and match load monitoring and call for a consensus meeting to agree on the variables to be used to assess training and match load in football (soccer). To date, few studies have assessed the effect of decreased recovery days between matches (ie, fixture congestion) as an index of match load on injury and performance. Running performance itself appears unaffected by fixture congestion,1 ,2 but injury rates may be higher1 or similar2 when playing two matches …

The Relationship Between Training Load and Injury, Illness and Soreness: A Systematic and Literature Review

Article

Full-text available

Jun 2016
SPORTS MED

Background Clinically it is understood that rapid increases in training loads expose an athlete to an increased risk of injury; however, there are no systematic reviews to qualify this statement. Objective The aim of this systematic review was to determine training and competition loads, and the relationship between injury, illness and soreness. Methods The MEDLINE, SPORTDiscus, CINAHL and EMBASE databases were searched using a predefined search strategy. Studies were included if they analysed the relationship between training or competition loads and injury or illness, and were published prior to October 2015. Participants were athletes of any age or level of competition. The quality of the studies included in the review was evaluated using the Newcastle–Ottawa Scale (NOS). The level of evidence was defined as strong, ‘consistent findings among multiple high-quality randomised controlled trials (RCTs)’; moderate, ‘consistent findings among multiple low-quality RCTs and/or non-randomised controlled trials (CCTs) and/or one high-quality RCT’; limited, ‘one low-quality RCT and/or CCTs, conflicting evidence’; conflicting, ‘inconsistent findings among multiple trials (RCTs and/or CCTs)’; or no evidence, ‘no RCTs or CCTs’. ResultsA total of 799 studies were identified; 23 studies met the inclusion criteria, and a further 12 studies that were not identified in the search but met the inclusion criteria were subsequently added to the review. The largest number of studies evaluated the relationship between injuries and training load in rugby league players (n = 9) followed by cricket (n = 5), football (n = 3), Australian Football (n = 3), rugby union (n = 2),volleyball (n = 2), baseball (n = 2), water polo (n = 1), rowing (n = 1), basketball (n = 1), swimming (n = 1), middle-distance runners (n = 1) and various sports combined (n = 1). Moderate evidence for a significant relationship was observed between training loads and injury incidence in the majority of studies (n = 27, 93 %). In addition, moderate evidence exists for a significant relationship between training loads and illness incidence (n = 6, 75 %). Training loads were reported to have a protective effect against injury (n = 9, 31 %) and illness (n = 1, 13 %). The median (range) NOS score for injury and illness was 8 (5–9) and 6 (5–9), respectively. LimitationsA limitation of this systematic review was the a priori search strategy. Twelve further studies were included that were not identified in the search strategy, thus potentially introducing bias. The quality assessment was completed by only one author. Conclusions The results of this systematic review highlight that there is emerging moderate evidence for the relationship between the training load applied to an athlete and the occurrence of injury and illness. ImplicationsThe training load applied to an athlete appears to be related to their risk of injury and/or illness. Sports science and medicine professionals working with athletes should monitor this load and avoid acute spikes in loads. It is recommended that internal load as the product of the rate of perceived exertion (10-point modified Borg) and duration be used when determining injury risk in team-based sports. External loads measured as throw counts should also be monitored and collected across a season to determine injury risk in throwing populations. Global positioning system-derived distances should be utilised in team sports, and injury monitoring should occur for at least 4 weeks after spikes in loads.

The training-injury prevention paradox: Should athletes be training smarter and harder?

Article

Full-text available

Jan 2016
BRIT J SPORT MED

Tim Gabbett

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.

Validation of a Six Second Cycle Test for the Determination of Peak Power Output

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Full-text available

Feb 2015

The present study examined the agreement between peak power output during a standard Wingate anaerobic test (WAnT) and a six second 'all-out' test on a Wattbike Pro. Nine males (40.7 ± 19.4 yrs, 1.76 ± 0.03 cm, 82.11 ± 8.9 kg) underwent three testing protocols on separate days. The protocols consisted 30 second WAnT (WAnT30), a modified WAnT over 6 seconds (WAnT6) and a 6 second peak power test (PPT6). PPT6 was correlated with WAnT30 (r = 0.9; p < 0.001) with a mean bias of 105 W. PPT6 correlated with WAnT6 (r = 0.95; p < 0.001) with a mean bias of 74 W. WAnT6 correlated with WAnT30 (r = 0.99; p < 0.001) with a mean bias of 31 W. There was no difference in time to peak power between any trial. PPT6 resulted in significantly greater power outputs than in WAnT30 and WAnT6 (p < 0.001). We conclude that PPT6 and WAnT6 are valid measures of peak power output compared with WAnT30. This identifies that PPT6 and WAnT6 as short duration 'all-out' tests that have practical applications for researchers and coaches who wish to assess peak power output without the fatiguing effects associated with a standard WAnT.

"Magnitude-Based Inference": A Statistical Review.

Article

Full-text available

Jul 2014

Purpose: We consider "magnitude-based inference" and its interpretation by examining in detail its use in the problem of comparing two means. Methods: We extract from the spreadsheets, which are provided to users of the analysis (http://www.sportsci.org/), a precise description of how "magnitude-based inference" is implemented. We compare the implemented version of the method with general descriptions of it and interpret the method in familiar statistical terms. Results and conclusions: We show that "magnitude-based inference" is not a progressive improvement on modern statistics. The additional probabilities introduced are not directly related to the confidence interval but, rather, are interpretable either as P values for two different nonstandard tests (for different null hypotheses) or as approximate Bayesian calculations, which also lead to a type of test. We also discuss sample size calculations associated with "magnitude-based inference" and show that the substantial reduction in sample sizes claimed for the method (30% of the sample size obtained from standard frequentist calculations) is not justifiable so the sample size calculations should not be used. Rather than using "magnitude-based inference," a better solution is to be realistic about the limitations of the data and use either confidence intervals or a fully Bayesian analysis.

The Validity of Microsensors to Automatically Detect Bowling Events and Counts in Cricket Fast Bowlers

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Full-text available

Jun 2014

Purpose: Bowling workload is linked to injury risk in cricket fast bowlers. This study investigated the validity of microtechnology in the automated detection of bowling counts and events, including run-up distance and velocity, in cricket fast bowlers. Method: Twelve highly skilled fast bowlers (mean ± SD age 23.5 ± 3.7 y) performed a series of bowling, throwing, and fielding activities in an outdoor environment during training and competition while wearing a microtechnology unit (MinimaxX). Sensitivity and specificity of a bowling-detection algorithm were determined by comparing the outputs from the device with manually recorded bowling counts. Run-up distance and run-up velocity were measured and compared with microtechnology outputs. Results: No significant differences were observed between direct measures of bowling and nonbowling events and true positive and true negative events recorded by the MinimaxX unit (P = .34, r = .99). The bowling-detection algorithm was shown to be sensitive in both training (99.0%) and competition (99.5%). Specificity was 98.1% during training and 74.0% during competition. Run-up distance was accurately recorded by the unit, with a percentage bias of 0.8% (r = .90). The final 10-m (-8.9%, r = .88) and 5-m (-7.3%, r = .90) run-up velocities were less accurate. Conclusions: The bowling-detection algorithm from the MinimaxX device is sensitive to detect bowling counts in both cricket training and competition. Although specificity is high during training, the number of false positive events increased during competition. Additional bowling workload measures require further development.

Physical collisions and injury during professional rugby league skills training

Article

Full-text available

Nov 2010

This study described the number and intensity of collisions experienced by professional rugby league players during pre-season and in-season skills training sessions using microtechnology (e.g. accelerometers, gyroscopes). Short, medium, and long recovery periods between matches were accounted for and the incidence of collision injuries sustained in the training environment was also assessed. Thirty professional rugby league players (mean±SD age, 23.6±3.8yr) participated in this study. The number and intensity of collisions and the incidence of collision injuries were monitored during 117 skills training sessions. Over the course of the season, an average of 77 collisions occurred per player, per session. The average number of mild, moderate, and heavy collisions performed by each member of the squad per session was 24, 46, and 7, respectively. A total of 37 collision injuries were recorded during training over the season, equating to an injury incidence of 6.4 per 10,000 collisions. Over half (54.1%) of all collision injuries resulted in no loss of training time, and less than 14% of collision injuries resulted in a missed match. The greatest number of collisions occurred during training sessions in the weeks with the longest recovery between matches (10 days). The incidence of collision injuries also peaked during the 10 day between match recovery cycle. These findings demonstrate that while significant physiological demands are placed on rugby league players as a result of the large number and intensity of physical collisions performed in training, these collisions are associated with minimal injury risk.

Making Meaningful Inferences About Magnitudes

Article

Full-text available

Apr 2006

A study of a sample provides only an estimate of the true (population) value of an outcome statistic. A report of the study therefore usually includes an inference about the true value. Traditionally, a researcher makes an inference by declaring the value of the statistic statistically significant or nonsignificant on the basis of a P value derived from a null-hypothesis test. This approach is confusing and can be misleading, depending on the magnitude of the statistic, error of measurement, and sample size. The authors use a more intuitive and practical approach based directly on uncertainty in the true value of the statistic. First they express the uncertainty as confidence limits, which define the likely range of the true value. They then deal with the real-world relevance of this uncertainty by taking into account values of the statistic that are substantial in some positive and negative sense, such as beneficial or harmful. If the likely range overlaps substantially positive and negative values, they infer that the outcome is unclear; otherwise, they infer that the true value has the magnitude of the observed value: substantially positive, trivial, or substantially negative. They refine this crude inference by stating qualitatively the likelihood that the true value will have the observed magnitude (eg, very likely beneficial). Quantitative or qualitative probabilities that the true value has the other 2 magnitudes or more finely graded magnitudes (such as trivial, small, moderate, and large) can also be estimated to guide a decision about the utility of the outcome.

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

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