An international conference was held in 2016, where athletic experts and researchers discussed their findings related to training load. A collective review discussed the pros and cons of key findings shared at the conference. Here, as well as in Part 1, we will review tools and measures commonly used to monitor athlete training loads.
The fitness-fatigue model is used to analyze training load information (2). This method is used to advise training plans (8) and predict performance, fitness and fatigue levels (29, 30, 34, 37). The initial fitness-fatigue model suggests training loads give rise to fitness responses which increase performance and levels of fatigue (2). Increased levels of fatigue generally compute to decreased levels of performance (2). The model has been adjusted by various groups to better represent situations with variation in training load stimulus and increased fatigue levels (6, 7, 9, 10, 29). It is commonly used in the athletic community as a reliable measure for fatigue and fitness (29 30, 34, 37).
That being said, the fitness-fatigue model has been faulted to oversimplify complex relationships between training and performance (20, 24). As mentioned in Part 1, different factors and measures (e.g., psychological factors, training routines, health), can influence an athlete’s individual mental state and performance (2). As every athlete and training load is different, athletes’ progress and/or decline at different rates (2). Therefore, the simplification of the fitness-fatigue model has led to large variability in parameters and accuracy in performance predictions (20).
The acute:chronic-workload ratio (ACWR) is used to evaluate the relationship between an athlete’s previous training load and what an athlete is prepared for (2, 11, 23). ACWR uses rolling averages to compare recently completed training load periods (generally ~5-10 days) with completed chronic training load periods (generally ~4-6 weeks) (11, 23).
To add, the ACWR method can also be used to identify risk of injury (4, 23, 31). When ACWR scores between 0.8 and 1.3, risk for injury is suggested to be low (2). An ACWR score greater than 1.5 generally shows an exponential increase in injury risk (Figure 1) (2, 3). However, rolling averages do not consider the effects of declining fitness and fatigue over time. Therefore, the validity of using ACWR to evaluate risk of injury is questioned (28, 35).
The internal:external-load ratio estimates experienced psychophysiological stress during training in the context of the completed external training load. The internal:external-load ratio method can be used to interpret an athlete’s training status. You can find examples of internal and external loads in Table 1 from Part 1. (2)
For example, if an athlete experiences a reduction in heart rate (decrease internal load), they most likely are coping with training and/or gaining fitness (2).
This method can help advice upon the negative effects of training programs (32), identify fatigue in team-sport competition (1, 25), and identify changes in fitness and fatigue status (2, 5). However, this method can be difficult to utilize (2). Controlling and quantifying external loads, and managing the environment can be difficult. Therefore, this must be considered with care when using this method (2, 26).
There is high interest in knowing the optimal amount of training to ensure optimal performance but not overwork an athlete (2). Early studies suggest the harder an athlete works (the greater the training load), the greater their risk for injury (12, 16). For example, a 2012 study found when athletes increased amounts of high-speed running, they experienced greater risk for lower body, soft-tissue injuries (18).
However, when training load decreased, so did the presence of injury (13). That being said, recent studies have suggested intense chronic training could prevent injury in athletes (14, 17, 21-23, 31, 33). These studies suggest training load is like a “‘vehicle’ that drives athletes toward or away from injury” (2, 36).
For example, a 2014 study found cricket fast bowlers experienced a lower risk for injury when they bowled a greater number of balls in a 4-week period (chronic training load) compared to bowlers who did less (21).
The suggested “protective response” of training may result from the fact:
- training load exposure allows the body to begin tolerating the load, and
- training allows the body to better develop physically, in turn reducing injury risk (15, 19, 27).
These studies suggest 3 approaches to protect against injury:
- high chronic training loads
- exposure to high chronic training loads to better tolerate high-intensity units in training
- association of ACWR to injury risk is better than acute or chronic load alone. (2)
So, How Do I Choose?
With new technologies, and analytical methods, there are a multitude of ways to monitor training loads of athletes. When choosing a method, how efficiently it can be implemented should be considered (2). Using more than one tool or method is also suggested (2). Methods able to count repetitions and/or compute a unit of measure are easy to interpret and then translate into an appropriate training plan (2).
The Sportavida method is a quick and easy tool to help quantify athlete fatigue, muscle stress, and other physiological processes. Implementation is convenient, as Sportavida sampling includes a simple saliva sample which can be done by anyone, anywhere, at any time. Once samples are collected, they are shipped and then assessed by professionals in the lab. An accurate and comprehensive report is delivered to the athlete or coach – providing information on how training load and competition is affecting the athlete’s body.
Consider your goals when deciding on methods and tools to use to monitor training load (2). What do you want to know? Is it:
How much faster is the athlete? Is this equipment improving their strength?
Is the new high-intensity routine fatiguing the athlete? Is the athlete experiencing overtraining?
Use of several methods and tools can add more complexity in interpretation but generally bring more insight to what is occurring (2). Studies suggest to use a consistent approach in a program over an extended period of time in order to get meaningful data about the efficacy of the program (2).
Thank you for reading! I hope you feel more informed. If you have any questions or comments, please feel free to leave a shout out!
- Akubat I, Barrett S, Abt G. Integrating the internal and external training load in soccer. Int J Sports Physiol Perform. 2014;9(3):457–462. PubMed doi:10.1123/ijspp.2012-0347
- Bourdon, P. C., Cardinale, M., Murray, A., Gastin, P., Kellmann, M., Varley, M. C., … & Cable, N. T. (2017). Monitoring athlete training loads: consensus statement. International journal of sports physiology and performance, 12(s2), S2-161.
- Blanch P, Gabbett TJ. Has the athlete trained enough to return to play safely?: the acute:chronic workload ratio permits clinicians to quantify a player’s risk of subsequent injury. Br J Sports Med. 2016;50(8):471–475. PubMed doi:10.1136/bjsports-2015-095445
- Bowen L, Gross AS, Gimpel M, Li FX. Accumulated workloads and the acute:chronic workload ratio relate to injury risk in elite youth football players. Br J Sports Med. 2017;51(5):452–459. PubMed doi:10.1136/bjsports-2015-095820
- Buchheit M, Racinais S, Bilsborough JC, et al. Monitoring fitness, fatigue and running performance during a pre-season training camp in elite football players. J Sci Med Sport. 2013;16(6):550–555. PubMed doi:10.1016/j.jsams.2012.12.003
- Busso T. Variable dose-response relationship between exercise training and performance. Med Sci Sports Exerc. 2003;35(7):1188–1195. PubMed doi:10.1249/01.MSS.0000074465.13621.37
- Busso T, Carasso C, Lacour JR. Adequacy of a systems structure in the modeling of training effects on performance. J Appl Physiol. 1991;71(5):2044–2049. PubMed
- Busso T, Thomas L. Using mathematical modeling in training planning. Int J Sports Physiol Perform. 2006;1(4):400–405. PubMed doi:10.1123/ijspp.1.4.400
- Calvert TW, Banister EW, Savage MV, Bach T. A systems model of the effects of training on physical performance. IEEE Trans Syst Man Cybern. 1976;6:94–102. doi:10.1109/TSMC.1976.5409179
- Fitz-Clarke JR, Morton RH, Banister EW. Optimizing athletic performance by influence curves. J Appl Physiol. 1991;71(3):1151–1158. PubMed
- Foster C, Snyder A, Welsh R. Monitoring of training, warm up, and performance in athletes. In: Lehmann M, Foster C, Gastmann U, Keizer H, Steinacker JM, eds. Overload, Performance Incompetence and Regeneration in Sport. New York, NY: Kluwer Academic/ Plenum; 1999:43–51. doi:10.1007/978-0-585-34048-7_4
- Gabbett TJ. Influence of training and match intensity on injuries in rugby league. J Sports Sci. 2004a;22(5):409–417. PubMed doi:10. 1080/02640410310001641638
- Gabbett TJ. Reductions in pre-season training loads reduce training injury rates in rugby league players. Br J Sports Med. 2004b;38(6):743–749. PubMed doi:10.1136/bjsm.2003.008391
- Gabbett TJ. The training–injury prevention paradox: should athletes be training smarter and harder? Br J Sports Med. 2016;50(5):273–280. PubMed doi:10.1136/bjsports-2015-095788
- Gabbett TJ, Domrow N. Risk factors for injury in sub-elite rugby league players. Am J Sports Med. 2005;33(3):428–434. PubMed doi:10.1177/0363546504268407
- Gabbett TJ, Domrow N. Relationships between training load, injury, and fitness in sub-elite collision sport athletes. J Sports Sci. 2007;25(13):1507–1519. PubMed doi:10.1080/02640410701215066
- Gabbett TJ, Hulin BT, Blanch P, Whiteley R. High training workloads alone do not cause sports injuries: how you get there is the real issue. Br J Sports Med. 2016;50(8):444–445. PubMed doi:10.1136/ bjsports-2015-095567
- Gabbett TJ, Ullah S. Relationship between running loads and soft-tissue injury in elite team sport athletes. J Strength Cond Res. 2012;26(4):953–960. PubMed doi:10.1519/JSC.0b013e3182302023
- Gabbett TJ, Ullah S, Finch CF. Identifying risk factors for contact injury in professional rugby league players—application of a frailty model for recurrent injury. J Sci Med Sport. 2012;15(6):496–504. PubMed doi:10.1016/j.jsams.2012.03.017
- Hellard P, Avalos M, Lacoste L, Barale F, Chatard JC, Millet GP. Assessing the limitations of the Banister model in monitoring training. J Sports Sci. 2006;24(5):509–520. PubMed doi:10.1080/ 02640410500244697
- Hulin BT, Gabbett TJ, Blanch P, Chapman P, Bailey D, Orchard JW. Spikes in acute workload are associated with increased injury risk in elite cricket fast bowlers. Br J Sports Med. 2014;48(8):708–712. PubMed doi:10.1136/bjsports-2013-092524
- Hulin BT, Gabbett TJ, Caputi P, Lawson DW, Sampson JA. 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. Br J Sports Med. 2016;50(16):1008–1012. PubMed doi:10.1136/bjsports-2015-095364
- Hulin BT, Gabbett TJ, Lawson DW, Caputi P, Sampson JA. The acute:chronic workload ratio predicts injury: high chronic workload may decrease injury risk in elite rugby league players. Br J Sports Med. 2016;50(4):231–236. PubMed doi:10.1136/bjsports-2015-094817
- Jobson SA, Passfield L, Atkinson G, Barton G, Scarf P. The analysis and utilization of cycling training data. Sports Med. 2009;39(10):833– 844. PubMed doi:10.2165/11317840-000000000-00000
- Kempton T, Sirotic AC, Coutts AJ. An integrated analysis of match-related fatigue in professional rugby league. J Sports Sci. 2015;33(1):39–47. PubMed doi:10.1080/02640414.2014.921832
- Malone JJ, Lovell R, Varley MC, Coutts AJ. Unpacking the black box: applications and considerations for using GPS devices in sport. Int J Sports Physiol Perform. 2017;17(Suppl 2):S2-18–S2-26. http:// dx.doi.org/10.1123/ijspp.2016-0236
- Malone S, Roe M, Doran DA, Gabbett TJ, Collins KD. Aerobic fitness and playing experience protect against spikes in workload: the role of the acute:chronic workload ratio on injury risk in elite Gaelic football [published online ahead of print August 24, 2016]. Int J Sports Physiol Perform. PubMed doi:10.1123/ijspp.2016-0090
- Menaspà P. Are rolling averages a good way to assess training load for injury prevention? Br J Sports Med. 2017;51(7):618–619. PubMed doi:10.1136/bjsports-2016-096131
- Morton RH, Fitz-Clarke JR, Banister EW. Modeling human performance in running. J Appl Physiol. 1990;69(3):1171–1177. PubMed
- Mujika I, Busso T, Lacoste L, Barale F, Geyssant A, Chatard J-C. Modelled responses to training and taper in competitive swimmers. Med Sci Sports Exerc. 1996;28(2):251–258. PubMed doi:10.1097/00005768-199602000-00015
- Murray NB, Gabbett TJ, Townshend AD, Hulin BT, McLellan CP. Individual and combined effects of acute and chronic running loads on injury risk in elite Australian footballers [published online ahead of print July 15, 2016]. Scand J Med Sci Sports.. doi:10.1111/ sms.12719 PubMed
- Racinais S, Buchheit M, Bilsborough J, Bourdon PC, Cordy J, Coutts AJ. Physiological and performance responses to a training camp in the heat in professional Australian football players. Int J Sports Physiol Perform. 2014;9(4):598–603. PubMed doi:10.1123/ ijspp.2013-0284
- Soligard T, Schwellnus M, Alonso J, et al. How much is too much? (part 1) International Olympic Committee consensus statement on training and competition loads and the risk of injury. Br J Sports Med. 2016;50(17):1030–1041. PubMed doi:10.1136/bjsports-2016-096581
- Wallace LK, Slattery KM, Coutts AJ. A comparison of methods for quantifying training load: relationships between modelled and actual training responses. Eur J Appl Physiol. 2014;114(1):11–20. PubMed doi:10.1007/s00421-013-2745-1
- Williams S, West S, Cross MJ, Stokes KA. Better way to determine the acute:chronic workload ratio? [published online ahead of print September 20, 2016]. Br J Sports Med. doi:10.1136/bjsports-2016-096589 PubMed
- Windt J, Gabbett TJ, Ferris D, Khan KM. Training load–injury paradox: is greater preseason participation associated with lower in-season injury risk in elite rugby league players? Br J Sports Med. 2016; in press doi:10.1136/bjsports-2016-095973. PubMed
- Wood RE, Hayter S, Rowbottom D, Stewart I. Applying a mathematical model to training adaptation in a distance runner. Eur J Appl Physiol. 2005;94(3):310–316. PubMed doi:10.1007/ s00421-005-1319-2