Load Management

Recent evidence suggests a fine balance between undertraining, overtraining and recognising when an athlete is fatigued or fresh (Hulin, Gabbett, Caputi, Lawson & Sampson, 2016). The process of balancing this state at an optimal level is known as load management and will have large consequences on player performance and injury rates.

To assess and manage load, it needs to be quantified (Brukner et al., 2017). There are two components to athletic loads, internal and external load. Both need to be examined individually and as a collective to effectively evaluate an athlete’s state of fatigue or readiness (Halson, 2014).

Internal load is the direct impact that training has on an athlete (Brukner et al., 2017). This can be measured with objective data such as heart rate, training data, blood-lactate levels, heart rate recovery and variability and biochemical markers (Halson, 2014). Subjective data can measure internal load via self-reported questionnaires such as Rating of Perceived Exertion (RPE), Profile of Mood States, Recovery Stress Questionnaire for Athletes and Daily Analysis of Life Demands of Athletes (Saw, Main & Gastin, 2015). These subjective measures have been found to have superior responsiveness to the changes in short and long-term training workloads compared to objective measures of internal load. This is particularly evident when measuring an athlete’s mood disturbance, perceived stress and recovery (Saw, Main & Gastin, 2015).

External load is the measure of physical output completed by the athlete (Halson, 2014). It is raw data which doesn’t account for internal factors such as an athlete’s fatigue levels. Common ways to measure external load include using activity counting (e.g. number of throws), GPS devices, power output, training duration, speed or dynamometry (Halson, 2014).

 “Maximising training output (external load) while minimising training cost (internal load) is the goal of most training programs” (Brukner et al., 2017, p. 151). It is extremely important that both external and internal loads are measured, or estimated, as accurately as possible. When this is done, load can be calculated using both internal and external components, to effectively measure different types of training with the same unit of measurement. This is known as sessional-RPE and works by taking the RPE (internal load) and multiplying that by the session duration (external load) to derive a number of ‘arbitrary units’ (Fanchini, Ghielmetti, Coutts, Schena & Impellizzeri, 2015).

Once established measurements of load are determined, focus can shift toward identifying longer-term trends and using the data to guide other variables within training. Large differences in week-to-week training loads, specifically spikes in weekly loads, are predictive of injury regardless of the athlete or team’s level of fitness (Brukner et al., 2017; Gabbett, 2016). Important to note is that this heightened risk of injury doesn’t just occur in the short-term, rather there may be a latent effect where an injury occurs a few weeks after the unaccustomed load. The acute:chronic workload ratio (ACWR) is used to compare an athletes performed load to what they are prepared for (Gabbet, 2016). This ratio is defined as an acute load (for example, total load over 1 week) compared to chronic load, which is average load over a set period of time (for example, average weekly load over 4 weeks)(Eckard, Padua, Hearn, Pexa & Frank, 2018). It is suggested that if this ratio is <0.8 or >1.3, an athlete is at risk of undertraining or overtraining for that particular week and therefore at an increased risk of injury (Hulin, Gabbett, Lawson, Caputi & Sampson, 2015). If the player falls within the above figures, this is within the ‘sweet spot’ where injury risk is minimised (Gabbet, 2016). 

Dr Paolo Menaspà (2017) challenged the ACWR by explaining that rolling averages don’t truly reflect the daily workload in respect to the average. However, the authors of the original paper acknowledge the ACWR isn’t a magic number and that one figure should not be used to analyse an athlete’s injury risk (Drew, Blanch, Purdam & Gabbett, 2016). Instead, a number of different metrics of workload should be analysed before determining injury risk. For example, RPE, kilometres covered or high-speed running distance. A further calculation has been suggested to account for Menaspà’s (2017) second concern to address the decaying effect of fitness and fatigue over time (Williams, West, Cross & Stokes, 2016). The limitation here is the new formula has not been proven within the literature.

Determining injury risk based on undertraining or over training is a popular research topic and will be an area to keep an eye on. Based on the evidence, the ACWR seems to be an effective tool if understood and applied correctly. With simple metrics and easy to use equations, the everyday athlete and coach can use numbers to help determine injury risk when planning training sessions. If you would like to learn more about training loads and their application to reduce your injury risk, book in to see one of our Titled Sports Physiotherapists.

References:

1. Gabbett, T. (2016). The training—injury prevention paradox: should athletes be training smarter and harder?. British Journal Of Sports Medicine, 50(5), 273-280. doi: 10.1136/bjsports-2015-095788
2. Brukner, P., Khan, K., Clarsen, B., Cook, J., Cools, A., & Crossley, K. et al. (2017). Brukner & Khan's clinical sports medicine. North Ryde, N.S.W.: McGraw-Hill Education (Australia).
3. Hulin, B., Gabbett, T., Caputi, P., Lawson, D., & Sampson, J. (2016). Low chronic workload and the acute:chronic workload ratio are more predictive of injury than between-match recovery time: a two-season prospective cohort study in elite rugby league players. British Journal Of Sports Medicine, 50(16), 1008-1012. doi: 10.1136/bjsports-2015-095364
4. Halson, S. (2014). Monitoring Training Load to Understand Fatigue in Athletes. Sports Medicine, 44(S2), 139-147. doi: 10.1007/s40279-014-0253-z
5. Saw, A., Main, L., & Gastin, P. (2015). Monitoring the athlete training response: subjective self-reported measures trump commonly used objective measures: a systematic review. British Journal Of Sports Medicine, 50(5), 281-291. doi: 10.1136/bjsports-2015-094758
6. Fanchini, M., Ghielmetti, R., Coutts, A., Schena, F., & Impellizzeri, F. (2015). Effect of Training-Session Intensity Distribution on Session Rating of Perceived Exertion in Soccer Players. International Journal Of Sports Physiology And Performance, 10(4), 426-430. doi: 10.1123/ijspp.2014-0244
7. Coles, P. (2017). An injury prevention pyramid for elite sports teams. British Journal Of Sports Medicine, 52(15), 1008-1010. doi: 10.1136/bjsports-2016-096697
8. Eckard, T., Padua, D., Hearn, D., Pexa, B., & Frank, B. (2018). The Relationship Between Training Load and Injury in Athletes: A Systematic Review. Sports Medicine, 48(8), 1929-1961. doi: 10.1007/s40279-018-0951-z
9. Hulin, B., Gabbett, T., Lawson, D., Caputi, P., & Sampson, J. (2015). The acute:chronic workload ratio predicts injury: high chronic workload may decrease injury risk in elite rugby league players. British Journal Of Sports Medicine, 50(4), 231-236. doi: 10.1136/bjsports-2015-094817
10. Menaspà, P. (2016). Are rolling averages a good way to assess training load for injury prevention?. British Journal Of Sports Medicine, 51(7), 618.1-619. doi: 10.1136/bjsports-2016-096131
11. Drew, M., Blanch, P., Purdam, C., & Gabbett, T. (2016). Yes, rolling averages are a good way to assess training load for injury prevention. Is there a better way? Probably, but we have not seen the evidence. British Journal Of Sports Medicine, 51(7), 618.2-619. doi: 10.1136/bjsports-2016-096609
12. Williams, S., West, S., Cross, M., & Stokes, K. (2017). Better way to determine the acute:chronic workload ratio?. British Journal Of Sports Medicine, 51(3), 209-210. doi: 10.1136/bjsports-2016-096589