Home Training How to Practically use Athlete Monitoring and Training Workloads in your Practice (Mini-series part 3).
How to Practically use Athlete Monitoring and Training Workloads in your Practice (Mini-series part 3).

How to Practically use Athlete Monitoring and Training Workloads in your Practice (Mini-series part 3).


Part 3 of a collaborative mini-series (check out Part 1 and Part 2):

Just over a year ago, work brought forth by Tim Gabbett (2016) highlighted the importance, but also simplicity, of monitoring training workloads in athletes. His research has demonstrated fairly consistent relationships between athlete training workloads and soft-tissue injury development. The practicality of this work has empowered all domains of healthcare, and even sporting coaches, to be able to track an athletes total volume and intensity of training easily and use that information to adapt training programs. The goal of this work is to ultimately limit injuries in the training population before they begin.

In terms of injury prevention, let’s be clear, it isn’t easily researched. Many make claims of ‘injury prevention’ with various biomechanical methods or techniques of movement, but firm research rarely agrees. The work described below is a simple load-management tool used to monitor an athlete’s fitness and state-of-readiness and is beginning to have some solid evidence behind it. Below are practical means to incorporate this research into your practice regardless of whether you are a PT, coach, athlete, etc.

1) Know your athlete and your sport. Recognize what the demands are, what type of load is placed on the athlete, and what repetitive movements might cause overuse, injury, or place the athlete in a fatigued/unprepared state.

2) Track the athlete using a week-by-week approach – This can be accomplished by having the athlete or trainer fill out a daily log. For the majority of sports, using two simple variables will give a value: Duration & Intensity. Gabbett (2016) suggests using minutes of activity to measure duration and using a 0-10 RPE scale for intensity.

For other sports, perhaps total distance run (marathon runner) or pitch count (baseball) is used.
Sample Log Week 1:

Day Duration Intensity
Monday 60min 4/10
Tuesday 30min 7/10
Wednesday 60min 5/10
Thursday 90min 3/10
Friday 0min
Saturday 75min 5/10
Sunday 30min 7/10


3) Use this data to calculate a weekly “Training Load” experienced by the athlete. Multiply Duration (minutes) by Intensity (RPE) for each session, and the sum of the all days will give you the weekly total. Ex. Monday: 60 x 4 = 240
For the athlete above, after calculating each daily value and adding these together, we come to a value of 1605 AU (arbitrary units).

 4) You can use this number directly to compare with past or future weekly training loads.

(Gabbett, 2016)

The graph below demonstrates the relationship between changes in weekly training loads and the corresponding injury risk. As you can see, a change of >10% one week to the next results in a sharp increase in injury probability. Gabbett suggests that >15% increase in training load vs. the previous week resulted in a 21-49% injury risk. Try to limit increased weekly training by <10%.
Using the athlete above, if you know that their prior weeks training load was 1200 AUs, a 10% increase from that will be 120AUs. This would mean that the current week’s load of 1605 AU is well beyond the 10% buffer zone. 

5) Use the weekly training load to determine an ACUTE:CHRONIC workload ratio.

(Gabbett, 2016)

Gabbett describes this ratio as an “index for athlete preparedness”. In other words, it measures how hard the athlete is training this week (Acute) compared to their average of the most recent 3-6 weeks of training (Chronic). Makes sense right? If you spike an athlete’s training sharply compared to what they are used to, one would assume injury rates would spike as well. Once you have the athlete’s average training load over the past month, a simple calculator will determine their acute:chronic ratio. The research suggests that an athlete should avoid ratios of 1.5 or greater, as this is associated with an increased injury risk.
Use same athlete above. If the rolling average training load over the past 4 weeks is 1000 AUs, and the week above was calculated at 1605 AUs, the 1605/1000 = 1.6 A:C workload ratio.  This number tells you that the athlete was training at 1.6x their past month’s average on this given week. 

Keep in mind that the numbers used are what has been described in research thus far. It is not a consensus statement, and it will vary between individuals and sports as everyone has varying tolerance to load and adaptation. This model provides an easy framework to monitor your athletes to ensure a smooth training process with the intention of limiting injury risk. Personally, I use this framework with the UBC Men’s basketball program to track both off-season and in-season training loads. This provides the foundation for individualized care from both a therapy and coaching perspective as we can collaborate to modify training loads when needed for each athlete. I also use this model clinically with recreational athletes returning to sport post-injury. This can help to avoid the often-made mistake of returning to sport too soon without adequate adaptation to training, all while empowering the client to monitor their own loads, and understand injury risks.  I’d encourage everyone to check out both the original and more recent work on the subject to continue learning and incorporating this into your daily work.

By: Kevin Valcke, PT

Build to Perform.
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Gabbett, T. (2016). The training-injury prevention paradox: should athletes be training smarter and harder?  BJSM: 50:5.



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