- On May 18, 2017
- athlete monitoring, data, injury, performance, training load
It was easily the most useful conference I had been to: “Monitoring Athlete Training Loads — The Hows and the Whys” in Doha, Qatar. Therefore, when a consensus statement around best practice of monitoring athlete training loads (from some of the best minds in the field) was released I was very intrigued. In this blog, we look to present how this consensus statement can be applied to your organisation straight away!
Please note: much of this material was pulled directly from the original article and should acknowledge the authors on their great work. Credit goes to Pitre C. Bourdon, Marco Cardinale, Andrew Murray, Paul Gastin, Michael Kellmann, Matthew C. Varley, Tim J. Gabbett, Aaron J. Coutts, Darren J. Burgess, Warren Gregson, and N. Timothy Cable.
You may download and read the full article for free here.
Defining Training Load
Training load is broadly categorised as either:
- Internal: the relative biological (both physiological & psychological) stressors imposed on the athlete. I.e. heart rate, blood lactate and rating perceived exertion (RPE)
- External: objective measures of the work performed. I.e. power output, speed and acceleration derived from GPS and accelerometer devices.
The experts suggest it is extremely valuable to take an INTEGRATED approach when capturing training load in your setting — athletes repeating the exact same session (i.e. 800m of high speed running) on different days may experience different internal responses. See below this great illustration taken from Burgess2 on an integrated approach to high-speed run (external) and % max heart rate (internal). Here, an uncoupling of these two metrics, a low external output but high internal cost, may indicate a state of fatigue and require action.
How is Training Load Monitored?
Depending on your sport and the setting in which you train, training load is measured in several ways. See below some tips around typical training modalities and how to best capture information.
GPS Measures in Team Sports
- Smoothing algorithms for velocity and acceleration data are common. Understand their manufacture-specific use and extract raw data if required.
- Interpret acceleration, deceleration and directional change with caution — these are the least valid metrics.
- Use the same unit to monitor the athlete.
- Clearly communicate your metrics of choice — know exactly what they capture and report that in laymen terms to the coach.
An abundance of metrics are often applied in this setting; ensure your system includes the following key features when selecting the metrics that are applicable to your situation.
- Ease of use & intuitive design
- Translatability — ensure the coach will understand
- Flexibility and adaptability when working across several sports and training modalities
- Ability to effectively identify meaningful change
- An assessment of cognitive function
- Capability of providing both individual and group responses
Strength and Power Training
As noted in the paper, strength and power training is an integral component of most training programs. Methods of quantification include:
- Volume load (number of repetitions x external load [kg]) is still the most common method to quantify load.
- Measures of mechanical work during the resistance exercise (such as the force and displacement of the barbell and/or ground-reaction force during jumping-type activities). Although these metrics are expanding, further validation, reliability and a review of their applicability in a gym is still required.
Load Monitoring in Young Athletes
To ensure longevity in the sport, load monitoring in young athletes is crucial and the following tips may help.
- Encourage athletes to keep training diaries to understand their training loads but also understand the loading implications of their attendance, performance and health.
- RPE-related measures should be used with caution as the ability of young athletes to self-assess their perception of load and effort can be unreliable.
- Therefore, RPE should not be considered in isolation but should be linked to objective methods such as volume, loads, jumps, or pitch counts.
- Lifestyle factors should be quantified — many non-training or non-competition related stressors are linked to burnout and/ or abandoning the sport.
Psychological Measures for Monitoring Training Loads
With the cost-effective, rapid reporting and individualised nature of psychological measures, they are found quite commonly in most organisations. Below are some key points to consider when selecting psychological instruments.
- Measure should be valid and reliable — with specific details on its application, development process and theoretical background.
- If it will be used for research or applied feedback to the athletes and coaches
- Timeframe — global (past 3 days/nights) or very specific (right now)
- Ensure there is a clear feedback loop to the coach with set outcomes
- Don’t use it for competitive selection purposes – athletes will catch on and report incorrectly
- Important to know how frequently data needs to be collected; i.e. daily vs weekly measure, with perhaps more detailed reports for specific phases or competition
Why is Athlete-Load Monitoring Important?
Applying load monitoring to facilitate coach decision making
As outlined, a primary goal of load monitoring should be to assist and inform coach/manager decision making on player availability for training. Some key tips to facilitate this:
- Information should be simplified, with reporting limited to a few metrics.
- Practitioners must provide feedback to players and recommendations to coaches in the context of their specific circumstances (e.g. sprint output may be low due to wet weather).
- Place monitoring reports appropriately (locker room etc.) with individual feedback, rather than just group means.
- Select both internal and external monitoring tools that suit your specific situation.
- Be wary of the misuse of RPE by poorly educated or sceptical training groups – they may use this to falsely influence subsequent training sessions.
Analysing training-load data
The ability to make meaningful inferences on the efficacy of the training processes for individual athletes and coaches is critical. See below the more common approaches practitioners have taken to analyse training load data:
- Fitness-Fatigue Model: a systems-theory approach reported by Bannister to analyse the response physical training:
Modelled performance = fitness from training model — K(fatigue from training model)
Where K is the constant that adjusts for the magnitude of the fatigue relative to the fitness effect. Essentially the model suggests a training load elicits a fitness response that increases performance and also produces fatigue responses that decrease performance.
- Acute:Chronic Workload Ratio: A simplification of the above Banister model, which uses rolling averages to compare training loads completed in a recent period (usually ~ 5-10 days) with the chronic training completed over a longer period (usually ~ 4-6 weeks).
Recent research suggests that the acute:chronic workload ratio may be useful in monitoring injury risk. However, to account for the decaying nature of fitness and fatigue effects over time, an exponentially weighted moving average approach may be more appropriate.
- Internal: External-Load Ratio:
As presented above (see Burgess2 figure), an integrated approach to monitoring may prove more useful for determining an athlete’s training status. It is important to be wary of the environment in which external loads were collected when looking to do comparative ratios.
Modelling Training Loads With a View to Enhance or Predict Athletic Performance
As highlighted in the consensus statement:
“Performance responses to training are nonlinear; they are influenced by myriad training-and-non-training-related factors and they are difficult to accurately predict.”
To date, there has been little evidence of approaches that consistently predict an individual athlete’s performance. Suggestions provided here included complex machine learning methods such as nonlinear, multilayer, perception neural networks. However, we must be wary of the “black box” nature of such complex methods.
Athlete Load Monitoring and Injury Prevention
See the key summary points below from this section below for consideration in your injury risk assessment:
- There is a significant body of evidence to suggest high chronic training loads protect athletes against injury. Appropriate overloading patterns (by keeping the ACWR between 0.80-1.30) will allow you to get there.
- A spike in load (ACWR > 1.50), relative to your chronic base is associated with an increase in injury risk.
- A “trough” in training load, resulting in lower chronic load foundation may also result in an increased likelihood of injury. See paper by Malone3.
What are the Challenges to Load Monitoring?
Load monitoring has its challenges. To ensure it’s successful in your program, the following suggestions were presented:
- Ensure tools demonstrate acceptable validity and reliability — do your own in-house tests!
- Look for methods that directly quantify a unit of measure, or can count occurrences or repetitions, to allow for ease of interpretation.
- Do your research. Implement for a period of time, ensuring you take a consistent and rigorous approach, then reflect on its influence on your practice.
What is the Future of Athlete-Load Monitoring?
The growth of miniature technology, wearable analytics tools and apps is undeniable. The expert insight from this consensus suggests the future of athlete-load monitoring is bright, with advancements including:
- Integrated devices (clothing) to chart all aspects of mechanical, physiological and psychological load in real time
- Complex analytic models involving pattern recognition, advanced neural networks and machine learning. As discussed, we will need to be wary of the interpretation of such approaches.
- Visualisation will become key to ensure data is correctly interpreted more accurately and faster. Athlete management systems such as SMARTABASE are leading the way.
Is it Worth Investing in an Athlete Monitoring System?
We think so! As do many of our current clients.
SMARTABASE provides you with a one-stop shop to collate all your training load data for automated analysis and provide your coaches with actionable information.
If you are interested in hearing more about the SMARTABASE system, do not hesitate to contact us at firstname.lastname@example.org.
- Bourdon PC, Cardinale M, Murray A, et al. Monitoring Athlete Training Loads: Consensus Statement Int J Sports Physiol Perform Performance 2017 12: Suppl 2, S2-161-S2-170.
- Burgess DJ. The Research Doesn’t AlwaysApply: Practical Solutions to Evidence-Based Training-Load Monitoring in Elite Team Sports. Int J Sports Physiol Perform 2017 12: Suppl 2, S2-136-S2-141.
- Malone, S, Roe M, Doran DA, et al. High chronic training loads and exposure to bouts of maximal velocity running reduce injury risk in elite Gaelic football. J Sci Med Sport 2016: 20, 3: 250 – 254.
By Marcus Colby, PhD Candidate at The University of Western Australia