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

  • Gabbett Performance Solutions
  • Memphis Grizzlies Basketball Club


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.
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
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.
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.
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
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
BJSM Online First, published on June 23, 2017 as 10.1136/bjsports-2016-097298
Copyright Article author (or their employer) 2017. Produced by BMJ Publishing Group Ltd under licence. on June 23, 2017 - Published by from
2Gabbett TJ, etal. Br J Sports Med Month 2017 Vol 0 No 0
Figure 1 The athlete monitoring cycle.
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
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
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.
© Article author(s) (or their employer(s) unless
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/
Accepted 30 May 2017
Br J Sports Med 2017;0:1
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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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... The quantification of training and match load/demands on soccer players is a common practice [1][2][3]. Specifically, the monitorisation of athletes include the quantification of training/match demands (e.g., locomotor/ mechanical and psychophysiological), and the wellness and readiness of players [4]. On the one hand, locomotor/ mechanical demands or just physical demands are associated with external load monitoring using global positioning system (GPS) variables (e.g., distances covered at various running speeds or accelerations). ...
... While, wellness is usually measured by questionnaires as previously proposed by Hooper and Mackinnon [6] and McLean et al. [7]. These questionnaires assess various items such as fatigue, quality of sleep, muscle soreness, mood and stress [7], that may vary depending on the specific load applied [4]. Thus, internal load can be estimated from questionnaires whose initial validity presented some concerns, although these issues have recently been resolved due to numerous scientific studies demonstrating individual response sensitivity to training-load changes [8][9][10]. ...
Full-text available
Background The aims of the study were to: (i) compare accumulated load and wellness between starters and non-starters of a European professional soccer team; (ii) analyze the relationships between wellness and load measures and; (iii) compare training/match ratio (TMr) of external and internal load between starters and non-starters. Methods Ten players were considered starters while seven were classified as non-starters over a 16-week period in which six training sessions and match day (MD) were considered in each weekly micro-cycle. The following measures were used: wellness (fatigue, quality of sleep, muscle soreness, stress, and mood); load (rated of perceived exertion (RPE), session-RPE (s-RPE), high-speed running (HSR), sprinting, accelerations (ACC) and decelerations (DEC)). Accumulated wellness/load were calculated by summing all training and match sessions, while TMr was calculated by dividing accumulated training load by match data for all load measures and each player. Mann–Whitney U test was used for wellness variables, while independent T-test was used for the remaining variables to compare groups. Moreover, relationships among variables were explored using the Spearman’s Rho correlation coefficient. Results The main results showed that non-starters presented higher significant values for fatigue (p < 0.019; g = 0.24) and lower significant values for duration (p < 0.006; ES = 1.81) and s-RPE (p < 0.001; ES = 2.69) when compared to starters. Moreover, positive and very large correlation was found between quality of sleep and RPE, while negative and very large correlation were found between stress and deceleration, and mood and deceleration (all, p < 0.05). Finally, non-starters presented higher values in all TMr than starters, namely, RPE (p = 0.001; g = 1.96), s-RPE (p = 0.002; g = 1.77), HSR (p = 0.001; g = 2.02), sprinting (p = 0.002; g = 4.23), accelerations (p = 0.001; g = 2.72), decelerations (p < 0.001; g = 3.44), and duration (p = 0.003; g = 2.27). Conclusions In conclusion, this study showed that non-starters produced higher TMr in all examined variables despite the lower match and training durations when compared with starters, suggesting that physical load was adjusted appropriately. Additionally, higher RPE was associated with improved sleep while higher number of decelerations were associated with decreased wellness, namely, stress and mood for non-starters.
... Quantifying wellness, training and match load/demands in soccer players is a common practice [1][2][3]. Specifically, the monitorisation of athletes include quantifying training/match demands (e.g., locomotor/mechanical and psychophysiological) and the wellness and readiness of players [4]. On the one hand, wellness is usually measured by questionnaires, as previously proposed by Hooper and Mackinnon [5] using the Hooper Index or by McLean et al. [6]. ...
... While the Hooper Index includes fatigue, quality of sleep, muscle soreness and stress, measured via a seven-point scale [6], the wellness questionnaire by McLean et al. [6] includes the same subjective items plus mood status, measured on a five-point scale. Regardless of the questionnaire, wellness variables depend on the load applied [4]. Meanwhile, locomotor/mechanical demands or physical demands are associated with external load/intensity monitoring using global positioning system (GPS) variables (e.g., distances covered at various running speeds or accelerations), whereas psychophysiological demands are associated with internal load/intensity monitoring using subjective or objective measures such as rating of perceived exertion (RPE) and heart rate [2,7]. ...
Full-text available
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... 4,5,7,[68][69][70][71] My Jump 2 was developed to evaluate vertical jump performance and to obtain estimated lower limb power. Moreover, My Jump 2 app allows the athlete to monitor the cycle and objective measure of the state of readiness to train/compete, 78 while FitLight was developed for evaluating reaction time and response to different sports situations. TReaction, Hysko, and Corner apps were developed specifically for evaluating the strikes performed by athletes in combat sports. ...
... Thus, the athlete's goal can be set based on a combination of minimal detectable change and coefficient of variation to provide the greatest certainty in performance change. 78,79 Additionally, the present research allows readers, researchers, and professionals that work with combat sports to understand the proposed new technologies, which of these technologies present reliable and validated methods, and how to use the methods to determine biomechanical parameters. Based on that, a flowchart ( Figure 5) suggesting operational guidelines for research and the development of new technologies for biomechanical assessment is presented to assist readers in developing a methodology for future research. ...
New technologies have amplified the possibilities for processing and incorporating data and scientific methods in algorithms through the integration of the use of mobile technology and a wide range of wearables that allow acquisition metrics in real-time. These technologies arise as a possible alternative to supply market demand and to present practical solutions to problems that coaches and athletes face in their daily routines. Concerning biomechanical assessment in combat sports (i.e. reaction time, velocity, and force), the literature is scarce regarding studies that carried out surveys of new assessments and monitoring technologies, with solutions for coaches and athletes. Therefore, the current study aimed to investigate, through a literature review, mobile technologies available on the market for biomechanical analyses in combat sports modalities. Significant growth has been observed in the number of studies involving mobile technologies with practical tools for biomechanical assessment in combat sports athletes. However, only seven technological proposals presented scientific reliability studies, and six assessed validity, showing the necessity of more original articles to investigate scientific validation. As a suggestion, a flowchart is presented with operational guidelines for the research and development of new technologies for biomechanical assessment and monitoring in combat sports in real-time.
... (2022) MLSSR, 2(2), 36-53 El monitoreo de la fatiga es una estrategia a partir de la cual con el uso de distintas herramientas, se pueden conocer las respuestas de los atletas a una determinada carga de ejercicio físico (Gabbett et al., 2017). De esta forma se puede saber si las adaptaciones al entrenamiento son las buscadas durante las distintas fases de la periodización (Halson, 2014), con el fin de evitar una acumulación de estrés que podría derivar en sobreentrenamiento o incluso un perjuicio sobre la salud (Drew & Finch, 2016;Hamlin et al., 2019). ...
... El objetivo principal de este trabajo fue analizar el proceso de monitoreo de la fatiga de una boxeadora profesional, en la búsqueda de maximizar los efectos positivos de las cargas de entrenamiento y minimizar los negativos (Gabbett et al., 2017). ...
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... Making predictions about future scenarios and analyzing current situations for the sports sector (Cumps et al., 2008), which utilizes data this extensively (Aughey and Falloon, 2010), will guide teams in match tactics and long league journeys. Observing injury risks of athletes (Gabbe et al., 2003), monitoring athlete performances, making predictions about league outcomes (Gabbett et al., 2017), and analyzing competitors are essential insights derived from data (Gayles, 2009). Methods applied in predicting injury risks, match outcomes, and league results can vary, leading to different accuracy rates. ...
... Sports medicine emphasizes the need for a more individualized and holistic approach to the rehabilitation and coaching of recreational and competitive athletes (Gabbett et al. 2017;Vanrenterghem et al. 2017;O'Sullivan et al. 2018;Gabbett 2020). Similarly, in the field of pelvic health, Moore et al. (2021) and Donnelly et al. (2022) highlighted the importance of a whole-body biopsychosocial approach to facilitating a return to running postpartum. ...
Conference Paper
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This paper discusses the management of pelvic floor dysfunction (PFD) in an active female patient. Pelvic health physiotherapy is a relatively new specialty in some countries, and many healthcare systems only prescribe pelvic floor muscle (PFM) exercises for the treatment of this condition. The importance of a holistic approach to the management of PFD in physically active women is addressed. A biopsychosocial perspective is needed for the assessment and treatment of these individuals. If this is integrated with an understanding of musculoskeletal dysfunction and sports medicine, the physiotherapist can improve their female patients’ pelvic health, and individual fitness and sporting performance. “Silent” symptoms (e.g. incontinence and pelvic heaviness) are a major challenge in the management of PFD because women can be too embarrassed to discuss these outside the clinic. The physiotherapist’s role must extend beyond the assessment of the pelvis and the PFMs, and a variety of skills are required to treat PFD throughout the course of a woman’s life.
... To customize the training process, it is necessary to regularly monitor the effects of training on players through frequent strati ed tracking, which is done by controlling the training load and measuring the level of players' readiness (Gabbett et al., 2017). Hence, training load quanti cation has become an important area of study in sports science departments (Bourdon et al., 2017). ...
Full-text available
The present study was designed to investigate two aspects. Firstly, analysis of variations in acute load, chronic load, acute-chronic load ratio, monotony, and training strain during the preseason (PS), first and second half of the season (1st HS, 2nd HS). Secondly, comparing these indicators of training load in players' positions. Twenty-two elite soccer players from the Premier League of Iran (age: 27.2 4.5 years, professional experience: 6.2 4.3 years) were involved in this study. Players were monitored daily for 45 weeks through an 18 Hz global positioning system (GPS), to gather data on distance running (total running), distance running at 14 km/h (moderate intensity running), and distance running at a speed above 19.8 km/h (high-intensity running). One-way analysis of variance, followed by Tukey HSD post hoc test to analyze data. The pre-season has a significantly higher amount of acute load, chronic load, monotony and training strain, total running, and moderate-intensity running than the first half ( p < 0.05) and the second half ( p < 0.05). When comparing these training load indicators for high-intensity running, a significant difference was only observed between the PS and the 2nd HS (p 0.05, moderate ES). Also, no significant differences were observed between positions in total running and moderate intensity running. However, training load indices based on high-intensity running between positions showed external defenders vs center backs ( p < 0.05), midfielders ( p < 0.05) and strikers ( p < 0.05) were significantly different. Furthermore, wingers had a significant difference in high-intensity running compared to central defenders ( p < 0.05) and midfielders ( p < 0.05). To conclude, this study demonstrated that acute load, chronic load, monotony, and training strain were more prevalent in the pre-season and slowly decreased during the season. External defenders and wingers experienced more acute load, chronic load, monotony, and training strain for high-intensity running during the season compared to other positions. Therefore, the results indicate that pre-season had a higher physical load than competition season, and players' positions experienced varying physical loads.
... Considering the relationship between injuries and performance in athletes, monitoring athletes is a crucial element in sports that require high performance. There are various monitoring tools available to track an athlete's training load, their response to that load, and their relative fitness or fatigue status [138]. By collecting data from athletes, it is possible to identify their strengths and weaknesses and monitor progress toward improving performance or providing information for proper training programs. ...
Full-text available
Wearable technology is increasingly vital for improving sports performance through real-time data analysis and tracking. Both professional and amateur athletes rely on wearable sensors to enhance training efficiency and competition outcomes. However, further research is needed to fully understand and optimize their potential in sports. This comprehensive review explores the measurement and monitoring of athletic performance, injury prevention, rehabilitation, and overall performance optimization using body wearable sensors. By analyzing wearables’ structure, research articles across various sports, and commercial sensors, the review provides a thorough analysis of wearable sensors in sports. Its findings benefit athletes, coaches, healthcare professionals, conditioners, managers, and researchers, offering a detailed summary of wearable technology in sports. The review is expected to contribute to future advancements in wearable sensors and biometric data analysis, ultimately improving sports performance. Limitations such as privacy concerns, accuracy issues, and costs are acknowledged, stressing the need for legal regulations, ethical principles, and technical measures for safe and fair use. The importance of personalized devices and further research on athlete comfort and performance impact is emphasized. The emergence of wearable imaging devices holds promise for sports rehabilitation and performance monitoring, enabling enhanced athlete health, recovery, and performance in the sports industry.
... A previous monitoring cycle model has been proposed which includes the external and internal workloads, the perceptual well-being, and the readiness of the athlete in a closed loop. 16 While we recognize its merits, we suggest that this model needs to be expanded to meet the necessity of practitioners to adapt monitoring practices to their specific reality while allowing a better diagnosis and predictability. ...
Full-text available
Purpose: Monitoring is a fundamental part of the training process to guarantee that the programmed training loads are executed by athletes and result in the intended adaptations and enhanced performance. A number of monitoring tools have emerged during the last century in sport. These tools capture different facets (eg, psychophysiological, physical, biomechanical) of acute training bouts and chronic adaptations while presenting specific advantages and limitations. Therefore, there is a need to identify what tools are more efficient in each sport context for better monitoring of training process. Methods and results: We present and discuss the fine-tuning approach for training monitoring, which consists of identifying and combining the best monitoring tools with experts' knowledge in different sport settings, designed to improve (1) the control of actual training loads and (2) understanding of athletes' training adaptations. Instead of using single-tool approaches or merely subjective decision making, the identification of the best combination of monitoring tools to assist experts' decisions in each specific context (ie, triangulation) is necessary to better understand the link between acute and chronic adaptations and their impact on health and performance. Future studies should elaborate on the identification of the best combination of monitoring tools for each specific sport setting. Conclusion: The fine-tuning monitoring approach requires the simultaneous use of several valid and practical tools, instead of a single tool, to improve the effectiveness of monitoring practices when added to experts' knowledge.
... Load monitoring consists of training/match demand quantification as well as wellness and readiness to maximize the likelihood of optimal athletic performance [1]. The literature divides load into two dimensions: internal and external. ...
Full-text available
Load monitoring consists of training/match demand quantification as well as wellness and readiness to maximize the likelihood of optimal athletic performance [...]
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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.
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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 …
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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.
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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.
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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.
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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 (, 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.
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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.
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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.
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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.