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Different methods of training load quantification and their relationship to injury and illness in elite Australian football
G Model JSAMS-1130; No. of Pages 5
ARTICLE IN PRESS Journal of Science and Medicine in Sport xxx (2015) xxx–xxx
Contents lists available at ScienceDirect
Journal of Science and Medicine in Sport journal homepage: www.elsevier.com/locate/jsams
Original research
Different methods of training load quantification and their relationship to injury and illness in elite Australian football Kristopher R. Veugelers a,∗ , Warren B. Young a , Brendan Fahrner b , Jack T. Harvey a a b
School of Health Sciences, University of Ballarat, Australia Richmond Football Club, Australia
a r t i c l e
i n f o
Article history: Received 9 February 2014 Received in revised form 29 December 2014 Accepted 14 January 2015 Available online xxx Keywords: Rating of perceived exertion Load monitoring Team sport Injury prevention
a b s t r a c t Objectives: To compare different methods of training load (TL) quantification and their relationship to injury and illness in elite Australian footballers. Design: Prospective cohort study. Methods: Forty-five elite Australian footballers (mean ± standard deviation: age = 23.4 ± 3.8 years) from one elite club participated in this 15 week pre-season study. TL was quantified every session for each individual using four different methods involving rating of perceived exertion (RPE). Two of these methods enabled the quantification of TL for all exercise modalities whilst two were applicable only to outdoor field activities. One- and two-weekly cumulative TL was investigated against injury and illness data using a logistic regression model where the low TL group was considered as the reference group. Results: A general trend existed across all TL methods which suggested lower odds of injury and illness in high TL groups. The one-week RPE (all) and one-week RPE x Duration (all) methods detected reduced odds of injury in high TL groups compared to low TL groups (p < 0.05, OR = 0.199–0.202). Similarly, the one-week RPE (field) method identified lower illness odds in the high TL groups (p < 0.05, OR = 0.083–0.182). Conclusions: Higher TL appeared to provide a protective effect against both injury and illness. The inclusion of duration in the quantification of TL via RPE did not improve the ability of RPE to predict change in odds of injury or illness. © 2015 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.
1. Introduction The ultimate goal in the physical preparation of the elite athlete is to prescribe a training load (TL) which is conducive to an increase in performance1 . A less than adequate TL will not result in the required level of physiological development, whereas excessive TL may predispose the athlete to a greater risk of injury and illness2 . The pre-season period is seen as vital in an athlete’s progression as it represents a time frame where fitness can be improved without the need to allow for recovery from competitive matches3 . An effective pre-season will ensure an athlete’s peak level of physical readiness coincides with the start of competition4 . Australian rules football (ARF) is a high-paced team sport which places great physiological, technical, tactical and psychological demands on players5 . These demands have increased substantially over the past decade6 . A comparison of the past 20 years of injury surveillance in the elite competition shows that soft tissue
∗ Corresponding author. E-mail address: [email protected] (K.R. Veugelers).
injuries are the most common type of injury in ARF, accounting for the greatest number of matches missed and having high rates of recurrence7 . Excessive TL and/or inadequate recovery may increase the risk of non-contact soft tissue injury8 , therefore the quantification and monitoring of TL is a vital component of injury prevention. Effective methods of TL quantification may enable the more accurate prescription of exercise and recovery, subsequently improving player health and fitness9 . While there is currently no gold standard measure of TL, one of the most popular methods involves a rating of perceived exertion (RPE) scale10 . This allows an individual to estimate the intensity required to perform a bout of physical work11 . The given value is then multiplied by the duration of the session in minutes to calculate a global TL score, termed session RPE (sRPE)12 . The relationship between sRPE-derived training loads and injury risk has been previously explored in team sport. For example, greater overall TL was associated with increased injury risk (r = 0.86, p < 0.05; r = 0.82, p < 0.01) in rugby league players13,14 . Specific loads attributed to field training were also found to have a significant relationship with non-contact soft tissue injury (r = 0.68, p < 0.05)14 .
http://dx.doi.org/10.1016/j.jsams.2015.01.001 1440-2440/© 2015 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.
Please cite this article in press as: Veugelers KR, et al. Different methods of training load quantification and their relationship to injury and illness in elite Australian football. J Sci Med Sport (2015), http://dx.doi.org/10.1016/j.jsams.2015.01.001
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2 Table 1 Methods of quantifying training load via RPE. TL quantification method
Applicable activities
RPEa RPEa RPE x Duration (min)a RPE x Duration (min)a
All Field All Field
a
Foster et al. [12].
This highlights the possibility that TL from different training modalities may influence the risk of specific injury types. Research in this area specific to ARF is limited. Previously, no statistically significant relationships were reported to exist between TL and the incidence of injury or illness over one pre-season9 . However, this study may have been limited by a small sample size (n = 16) and low injury tally (n = 5). In contrast, ARF players who experienced a substantial increase in load from the previous week to the current week (>1250 arbitrary units) during the competitive season were 2.58 times more likely to sustain an injury (p = 0.002) in comparison to a reference group (<250 arbitrary units)15 . Furthermore, low absolute changes in load were associated with greater odds of injury in comparison to moderate absolute changes in load15 . This suggests that both insufficient and excessive loads may be risk factors for injury. There does not appear to be any research in elite ARF which investigates different methods of TL quantification involving RPE and their relationship to the occurrence of injury and illness. Therefore the purpose of this study was to compare a number of RPE-derived TL methods to assess which may be the best predictor of change in the odds of injury and illness in an elite ARF cohort. 2. Methods Forty-five elite ARF players (mean ± standard deviation: age = 23.4 ± 3.8 years; height = 188.0 ± 7.2 cm; body mass = 88.60 ± 6.9 kg, time spent on an AFL playing list = 4.4 ± 2.7 years) from one Australian Football League (AFL) club participated in this 15-week pre-season study. Each player provided informed consent and the research was approved by the University of Ballarat Human Ethics Committee. For every session, each player estimated the intensity of training approximately 30 min post-session using Foster’s modified RPE scale12 . All players had been familiarized with the RPE scale according to standard procedure12 . Rating of perceived exertion is strongly correlated with objective measures of exercise intensity such as heart rate, maximal oxygen uptake and blood lactate concentration17 . Training load scores were then calculated via four different methods using the given RPE value. Each method is detailed in Table 1. The sRPE method of quantifying TL via the multiplication of RPE and duration has been employed in previous research2,9,15,16 . The values recorded for the RPE and sRPE methods were split into two categories; “all” training sessions, which encompassed all training modalities, and “field” training sessions, which included only outdoor field-based activities such as running, skill and tactical development. The purpose of this was to compare “field” TL to occurrence of injury as club staff believed this form of training may be more likely to influence soft tissue injury risk than other training modalities such as resistance and cross training. Similar to previous research8 , injury to a player was defined as a non-contact event that occurred during a training session which resulted in missed or modified training due to the presence of at least one of the following soft tissue attributes: pain, tenderness, swelling and restricted range of motion. Training load is believed to have a stronger relationship with non-contact soft tissue injuries compared to impact injuries8 . Illness was defined as an event where
a player missed a training session due to a medical condition which was diagnosed by a club doctor. This was subsequently recorded by the fitness staff. For each training session that an individual player was involved in, their previous one- and two-weekly cumulative TL was calculated for each of the four different TL methods. One- and two-weekly, but not three- and four-weekly cumulative TL’s were reported to alter odds of injury in elite ARF players15 . The consideration of prior TL was deemed important due to the potential delayed effect it may have on injury risk18 . Players undertaking modified training due to an identified injury risk or rehabilitation from a previous injury were excluded from the analysis until they returned to full training for at least one week. There were too few injury and illness events to place the cumulative loads into multiple categories; therefore the basis of analysis for these data was a dichotomous median split into low and high TL groups for all methods. For the two outcomes of injury and illness, the association of each relevant cumulative TL group with the outcome measure was investigated with a bivariate logistic regression model fitted by the method of generalized estimating equations, with adjustment for intra-player cluster effects. Three error correlation structures were compared—independence (zero correlation—no cluster effects), and two repeated measures structures—exchangeable (correlation constant over time) and first order autoregressive (correlation diminishing with time). This statistical method was based on similar research investigating the effect of training and game loads on the odds of injury in elite ARF15 , with the addition of the examination of intra-player correlation. 3. Results A total of 5164 individual training sessions were analysed, 1899 of which were classified as “field” sessions. There were a low number of injuries (n = 13) and illnesses (n = 13). For the analysis of the cumulative TL measures and the outcome variables of injury and illness, the error structure which produced the best fitting models was independence, indicating no significant cluster effects. Therefore the results of ordinary logistic regression are reported, with each low TL group considered as the reference group (Tables 2 and 3). Inverse associations are apparent between all but one of the cumulative TL measures and both outcome measures, but not all relationships were statistically significant (p < 0.05). There was a statistically significant association between injury incidence and the one-week RPE (all) and one-week RPE x Duration (all) methods. For occurrence of illness, only the one-week RPE (field) method demonstrated a statistically significant association. 4. Discussion The purpose of this study was to compare different methods of TL quantification and their relationship to injury and illness in elite AF. A general pattern existed within all TL methods which suggest that the odds of injury were reduced for individuals in the high training TL compared to those in the low TL groups. This trend reached statistical significance in the one week RPE (all) method (OR = 0.202) and one-week RPE x Duration (all) method (OR = 0.199). It appears that methods which quantify all training sessions are better predictors of injury risk compared to field measures of TL. This may suggest that field-based measures of TL are limited by their failure to account for the stress from other forms of training such as resistance and cross-training, and that the cumulative load associated with these other modalities is likely to impact on injury risk. None of the two-week cumulative TL measures reached statistical significance, indicating that one-week cumulative TL was a better predictor of injury risk.
Please cite this article in press as: Veugelers KR, et al. Different methods of training load quantification and their relationship to injury and illness in elite Australian football. J Sci Med Sport (2015), http://dx.doi.org/10.1016/j.jsams.2015.01.001
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Table 2 Relationship between training load and occurrence of injury. TL method RPE × Duration(all) 1 week Low TL High TL 2 week Low TL High TL RPE × Duration(field) 1 week Low TL High TL 2 week Low TL High TL RPE (all) 1 week Low TL High TL 2 week Low TL High TL RPE (field) 1 week Low TL High TL 2 week Low TL High TL
Mean TL(AU)
Odds Ratioa
95% Confidence Limits
p value
2547.7 4261.3
1.000 0.199
0.044–0.911
0.037*
>6745
4728.6 8110.9
1.000 0.374
0.099–1.412
0.147
<1530 >1532
958.1 2176.5
1.000 1.000
0.322–3.105
1.000
<3011 >3020
1870.4 4183.1
1.000 0.834
0.254–2.737
0.764
<73 >73.5
51.8 90.2
1.000 0.202
0.044–0.921
0.039*
<140 >140.5
97.6 169.8
1.000 0.225
0.049–1.044
0.057
<26 >26.5
18.2 33.2
1.000 0.501
0.151–1.665
0.259
<51 >51.5
34.7 63.3
1.000 1.228
0.374–4.032
0.734
TL range(AU) <3518.5 >3519 <6742
*
Significant at the 0.05 level. a Relative odds of an injury occurring versus not occurring, where the low TL group is the reference group.
Table 3 Relationship between training load and occurrence of illness. TL method RPE × Duration (all) 1 week Low TL High TL 2 week Low TL High TL RPE × Duration(field) 1 week Low TL High TL 2 week Low TL High TL RPE (all) 1 week Low TL High TL 2 week Low TL High TL RPE (field) 1 week Low TL High TL 2 week Low TL High TL * a
TL range(AU)
Mean TL(AU)
Odds Ratioa
95% Confidence Limits
p value
<3518.5 >3519
2547.7 4261.3
1.000 0.299
0.082–1.088
0.067
<6742 >6745
4728.6 8110.9
1.000 0.800
0.214–2.982
0.739
<1530 >1532
958.1 2176.5
1.000 0.299
0.082–1.088
0.067
<3011 >3020
1870.4 4183.1
1.000 0.125
0.016–0.998
0.050
<73 >73.5
51.8 90.2
1.000 0.449
0.138–1.459
0.183
<140 >140.5
97.6 169.8
1.000 0.814
0.218–3.035
0.759
<26 >26.5
18.2 33.2
1.000 0.182
0.040–0.821
0.027*
<51 >51.5
34.7 63.3
1.000 0.292
0.060–1.405
0.125
Significant at the 0.05 level. Relative odds of an illness occurring versus not occurring, where the low TL group is the reference group.
The trend of lower odds of injury in high TL groups is in contrast to the majority of research13,14,19 . This could be explained using the theoretical U-curve relationship between workload and injury risk20 . Players within the high TL group may have been operating
within the optimum workload range, subsequently reducing their risk of injury. In contrast, greater injury risk in the low TL group may have been a consequence of the inadequate exposure of some players to sufficient workload.
Please cite this article in press as: Veugelers KR, et al. Different methods of training load quantification and their relationship to injury and illness in elite Australian football. J Sci Med Sport (2015), http://dx.doi.org/10.1016/j.jsams.2015.01.001
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It is now common for the prescription of training for elite team sport athletes to be based upon measures of workload derived from training and competition demands20 . Such practices are built upon the notion that limiting an athletes’ level of fatigue via the reduction of workload will result in the decreased likelihood of injury20 . However, reducing this level of workload past the lower threshold necessary to encourage physiological adaptation may potentially increase injury risk due to inadequate fitness20 . For example, the existence of both low and high thresholds of workload has been found in the sport of cricket, beyond which the risk of injury was likely to increase21 . A protective effect was discovered in fast bowlers who had an average of 2–5 days rest between bowling sessions, whereas bowlers who had less than 2 days or more than 5 days rest between sessions were at a significantly greater risk of injury21 . Furthermore, likelihood of injury for bowlers averaging less than 123 or more than 188 deliveries per week was significantly greater than bowlers who averaged between 123 and 188 deliveries21 . Such findings support the notion that athletes are more capable of tolerating a consistent level of TL22 . It is believed that large fluctuations in TL characterized by periods of imposed rest may actually have a detrimental effect on injury incidence22 . This is reinforced by the discovery that significant week-to-week variations in TL were associated with an increased number of injuries in elite ARF15 . It should be noted that the capacity to draw conclusions from this study concerning the relationship between TL and injury may be limited by the low number of injury events (n = 13). This could be partly accounted for by the chosen definition of injury. While it was believed that the isolation of non-contact soft tissue injuries may improve the likelihood of identifying a relationship between TL and injury, the exclusion of contact injuries undoubtedly decreased the overall tally. Another limitation of this definition is that it does not allow for the classification of subsequent injury. Such injuries encompass both multiple and recurrent injuries, including cases where an individual is re-injured before the complete recovery of a prior injury23 . While the rationale for monitoring one- and two-weekly loads has been previously established in elite ARF15 , it should be noted that the accumulation of TL over longer periods has been associated with increased injury risk in rugby league14 . Longitudinal monitoring of load and the implications for injury risk in elite ARF may therefore be a worthwhile topic for future investigation. As with the results between TL and injury, there appears to be a trend of lower odds of illness in the high TL groups across all methods. This indicates that individuals within the high TL groups were less likely to become ill in comparison to players in the low TL groups. Research investigating the relationship between TL and illness in team sport athletes appears sparse. This current study seems to be the first of its kind to highlight a link between low TL and illness in this cohort. Despite the discovery that spikes in TL may precede an illness event9 , there is a lack of statistically significant findings which contend that greater TL is related to higher rates of illness in elite AF. Similar to the association between TL and injury, there is scientific evidence which suggests the existence of a Ushaped relationship between physical activity level and disease resistance24 . This hypothetical relationship proposes that whilst regular amounts of moderate intensity physical activity may improve health, either insufficient or excessive doses of exercise may result in negative consequences24 . The effect of repeated and strenuous exercise on the health of elite athletes has been investigated previously. It has been found that the immune system may become temporarily weakened following prolonged endurance exercise, potentially resulting in a higher risk of infection25 . It is also believed that the suppression of the immune system in elite
athletes may increase the prevalence of upper respiratory tract infection, the symptoms of which are associated with decreased performance24 . In contrast, moderate levels of physical activity have been shown to encourage positive immunological responses, leading to fewer days of illness and reducing the likelihood of upper respiratory tract infections and the common cold26 . In particular, it has been established that exercise of an endurance nature can have a protective effect on the body due to a number of immunological responses, most notably the reduction in markers of inflammation24 . The long-term anti-inflammatory effects of exercise are believed to promote beneficial health outcomes and decrease the risk of cardiovascular disease, type 2 diabetes and various forms of cancer24 . It is conceivable, as with the association between TL and injury, that players in the high TL groups for the “field” methods were training within the optimal workload range to encourage a protective effect against illness. Conversely, individuals within the low TL groups may not have completed the required volume of exercise to achieve the same immunological benefits, subsequently exposing them to a greater risk of illness. Only field methods of TL were able to significantly differentiate the odds of illness between low and high TL groups. This may be the result of the different nature of activities included in “all” and “field” sessions. Whilst the positive implications of endurance exercise on immune function are relatively well known, the effect of resistance training (and other forms of training) is not fully understood24 . It is possible that immunological changes resulting from exercise are more prominent in endurance rather than resistance or cross training. Subsequently, the field methods may be sensitive enough to pick up subtle changes in the odds of illness between low and high TL groups due to the endurance-based nature of these sessions. To fully understand the protective mechanisms of physical activity, further research is required which investigates the nature of exercise that is most beneficial to improving immune function24 . However, the ability of only field methods of TL quantification to identify differences in the odds of injury between high and low TL groups in this study provides an interesting discussion point, highlighting the necessity for further exploration into the effect of different forms of exercise on the immune system. Any advances that are made may enable the development of more comprehensive measures to enhance individual training programs24 . Interestingly, the common method of quantifying TL via the product of RPE and session duration did not improve the ability of RPE to predict change in odds of injury or illness. This may have two possible implications requiring further investigation; First, that the intensity of exercise and not overall load is more likely to cause change in the odds of injury and illness. Or second, that the implementation of RPE in field settings is indicative of overall load rather than solely an estimate of exercise intensity.
5. Conclusion Players within high TL groups were at reduced risk of injury and illness compared to those in the low TL groups, potentially due to the protective effect resulting from the prescribed dose of exercise. RPE (all) was the best predictor of change in the odds of injury between low and high TL groups, whilst RPE (field) was most sensitive to variations in illness odds. The inclusion of duration in the quantification of TL did not improve the ability of RPE to predict change in either outcome measure.
Please cite this article in press as: Veugelers KR, et al. Different methods of training load quantification and their relationship to injury and illness in elite Australian football. J Sci Med Sport (2015), http://dx.doi.org/10.1016/j.jsams.2015.01.001
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6. Practical applications • One-week cumulative TL measures should be individually monitored as they are significantly related to change in odds of injury and illness. • Both excessive and inadequate exposure to TL should be monitored to ensure that athletes are at a reduced risk of injury and illness. • RPE alone may be used to effectively quantify TL and assess injury and illness risk in elite AF players. Acknowledgements No external financial support was received for this study. References 1. Meeusen R, Duclos M, Gleeson M et al. Prevention, diagnosis and treatment of the overtraining syndrome: ECSS position statement ‘task force’. Euro J Sports Sci 2006; 6(1):1–14. 2. Gabbett T, Domrow N. Relationships between training load, injury, and fitness in sub-elite collision sport athletes. J Sports Sci 2007; 25(13):1507–1519. 3. Gamble P. Periodization of training for team sports athletes. Strength Cond J 2006; 28(5):56–66. 4. 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; 16:550–555. 5. Young WB, Hepner J, Robbins DW. Movement demands in Australian Rules Football as indicators of muscle damage. J Strength Cond Res 2012; 26(2): 492–496. 6. Wiseby B, Pyne D, Rattray, B. Quantifying changes in AFL player demands using GPS tracking. http://sport.fitsense.com.au/downloads/AFL GPS Research Report 2012.pdf; 2012. 7. Orchard JW, Seward H, Orchard JJ. Results of 2 decades of injury surveillance and public release of data in the Australian Football League. Am J Sports Med 2013; 41(4):734–740. 8. Gabbett T, Ullah S. Relationship between running loads and soft-tissue injury in elite team sport athletes. J Strength Cond Res 2012; 26(4):953–960.
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9. Piggott B, Newton MJ, McGuigan MR. The relationship between training load and incidence of injury and illness over a pre-season at an Australian Football League club. J Aust Strength Cond 2009; 17(3):4–17. 10. Lambert MI, Borresen J. Measuring training load in sports. Int J Sports Physiol Perform 2010; 5:406–411. 11. Borg G. Perceived exertion and pain scales, Champaign, IL, Human Kinetics, 1998. 12. Foster C, Florhaug JA, Franklin J et al. A new approach to monitoring exercise training. J Strength Cond Res 2001; 15(1):109–115. 13. Gabbett T. Influence of training and match intensity on injuries in rugby league. J Sports Sci 2004; 22:409–417. 14. Gabbett T, Jenkins DG. Relationship between training load and injury in professional rugby league players. J Sci Med Sport 2011; 14:204–209. 15. Rogalski B, Dawson B, Heasman J et al. Training and game loads and injury risk in elite Australian footballers. J Sci Med Sport 2013; 16:499–503. 16. Montgomery PG, Hopkins WG. The effects of game and training loads on perceptual responses of muscle soreness in Australian football. Int J Sports Physiol Perform 2013; 8(3):312–318. 17. Herman L, Foster C, Maher MA et al. Validity and reliability of the session RPE method for monitoring exercise training intensity. S Afr J Sports Med 2006; 18(1):14–17. 18. Orchard JW, James T, Kountouris A. Fast bowlers in cricket demonstrate up to 3- to 4-week delay between high workloads and increased risk of injury. Am J Sports Med 2009; 37(6):1186–1192. 19. Anderson L, Triplett-McBride T, Foster C et al. Impact of training patterns on incidence of illness and injury during a women’s collegiate basketball season. J Strength Cond Res 2003; 17(4):734–738. 20. Gamble P. Reducing injury in elite sport—is simply restricting workloads really the answer? N Z J Sports Med 2013; 40(1):34–36. 21. Dennis R, Farhart R, Goumas C et al. Bowling workload and the risk of injury in elite cricket fast bowlers. J Sci Med Sport 2003; 6(3):359–367. 22. Innes M, Aughey R. Cricket Australia’s ‘rotation policy’ could be causing injuries. The Conversation. http://theconversation.com/cricket-australiasrotation-policy-could-be-causing-injuries-11759; 2003. 23. Finch CF, Cook J. Categorising sports injuries in epidemiological studies: the subsequent injury categorisation (SIC) model to address multiple, recurrent and exacerbation injuries. Br J Sports Med 2013. http://dx.doi.org/10.1136/bjsports-2012-091729. 24. Walsh NP, Gleeson M, Shephard RJ et al. Position statement part one: immune function and exercise. Exerc Immunol Rev 2011; 17:6–63. 25. Nieman DC. Exercise effects on systemic immunity. Immunol Cell Biol 2000; 78:496–501. 26. Nieman DC. Current perspective on exercise immunology. Curr Sports Med Rep 2003; 2(5):239–242.
Please cite this article in press as: Veugelers KR, et al. Different methods of training load quantification and their relationship to injury and illness in elite Australian football. J Sci Med Sport (2015), http://dx.doi.org/10.1016/j.jsams.2015.01.001
ARTICLE IN PRESS Journal of Science and Medicine in Sport xxx (2015) xxx–xxx
Contents lists available at ScienceDirect
Journal of Science and Medicine in Sport journal homepage: www.elsevier.com/locate/jsams
Original research
Different methods of training load quantification and their relationship to injury and illness in elite Australian football Kristopher R. Veugelers a,∗ , Warren B. Young a , Brendan Fahrner b , Jack T. Harvey a a b
School of Health Sciences, University of Ballarat, Australia Richmond Football Club, Australia
a r t i c l e
i n f o
Article history: Received 9 February 2014 Received in revised form 29 December 2014 Accepted 14 January 2015 Available online xxx Keywords: Rating of perceived exertion Load monitoring Team sport Injury prevention
a b s t r a c t Objectives: To compare different methods of training load (TL) quantification and their relationship to injury and illness in elite Australian footballers. Design: Prospective cohort study. Methods: Forty-five elite Australian footballers (mean ± standard deviation: age = 23.4 ± 3.8 years) from one elite club participated in this 15 week pre-season study. TL was quantified every session for each individual using four different methods involving rating of perceived exertion (RPE). Two of these methods enabled the quantification of TL for all exercise modalities whilst two were applicable only to outdoor field activities. One- and two-weekly cumulative TL was investigated against injury and illness data using a logistic regression model where the low TL group was considered as the reference group. Results: A general trend existed across all TL methods which suggested lower odds of injury and illness in high TL groups. The one-week RPE (all) and one-week RPE x Duration (all) methods detected reduced odds of injury in high TL groups compared to low TL groups (p < 0.05, OR = 0.199–0.202). Similarly, the one-week RPE (field) method identified lower illness odds in the high TL groups (p < 0.05, OR = 0.083–0.182). Conclusions: Higher TL appeared to provide a protective effect against both injury and illness. The inclusion of duration in the quantification of TL via RPE did not improve the ability of RPE to predict change in odds of injury or illness. © 2015 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.
1. Introduction The ultimate goal in the physical preparation of the elite athlete is to prescribe a training load (TL) which is conducive to an increase in performance1 . A less than adequate TL will not result in the required level of physiological development, whereas excessive TL may predispose the athlete to a greater risk of injury and illness2 . The pre-season period is seen as vital in an athlete’s progression as it represents a time frame where fitness can be improved without the need to allow for recovery from competitive matches3 . An effective pre-season will ensure an athlete’s peak level of physical readiness coincides with the start of competition4 . Australian rules football (ARF) is a high-paced team sport which places great physiological, technical, tactical and psychological demands on players5 . These demands have increased substantially over the past decade6 . A comparison of the past 20 years of injury surveillance in the elite competition shows that soft tissue
∗ Corresponding author. E-mail address: [email protected] (K.R. Veugelers).
injuries are the most common type of injury in ARF, accounting for the greatest number of matches missed and having high rates of recurrence7 . Excessive TL and/or inadequate recovery may increase the risk of non-contact soft tissue injury8 , therefore the quantification and monitoring of TL is a vital component of injury prevention. Effective methods of TL quantification may enable the more accurate prescription of exercise and recovery, subsequently improving player health and fitness9 . While there is currently no gold standard measure of TL, one of the most popular methods involves a rating of perceived exertion (RPE) scale10 . This allows an individual to estimate the intensity required to perform a bout of physical work11 . The given value is then multiplied by the duration of the session in minutes to calculate a global TL score, termed session RPE (sRPE)12 . The relationship between sRPE-derived training loads and injury risk has been previously explored in team sport. For example, greater overall TL was associated with increased injury risk (r = 0.86, p < 0.05; r = 0.82, p < 0.01) in rugby league players13,14 . Specific loads attributed to field training were also found to have a significant relationship with non-contact soft tissue injury (r = 0.68, p < 0.05)14 .
http://dx.doi.org/10.1016/j.jsams.2015.01.001 1440-2440/© 2015 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.
Please cite this article in press as: Veugelers KR, et al. Different methods of training load quantification and their relationship to injury and illness in elite Australian football. J Sci Med Sport (2015), http://dx.doi.org/10.1016/j.jsams.2015.01.001
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2 Table 1 Methods of quantifying training load via RPE. TL quantification method
Applicable activities
RPEa RPEa RPE x Duration (min)a RPE x Duration (min)a
All Field All Field
a
Foster et al. [12].
This highlights the possibility that TL from different training modalities may influence the risk of specific injury types. Research in this area specific to ARF is limited. Previously, no statistically significant relationships were reported to exist between TL and the incidence of injury or illness over one pre-season9 . However, this study may have been limited by a small sample size (n = 16) and low injury tally (n = 5). In contrast, ARF players who experienced a substantial increase in load from the previous week to the current week (>1250 arbitrary units) during the competitive season were 2.58 times more likely to sustain an injury (p = 0.002) in comparison to a reference group (<250 arbitrary units)15 . Furthermore, low absolute changes in load were associated with greater odds of injury in comparison to moderate absolute changes in load15 . This suggests that both insufficient and excessive loads may be risk factors for injury. There does not appear to be any research in elite ARF which investigates different methods of TL quantification involving RPE and their relationship to the occurrence of injury and illness. Therefore the purpose of this study was to compare a number of RPE-derived TL methods to assess which may be the best predictor of change in the odds of injury and illness in an elite ARF cohort. 2. Methods Forty-five elite ARF players (mean ± standard deviation: age = 23.4 ± 3.8 years; height = 188.0 ± 7.2 cm; body mass = 88.60 ± 6.9 kg, time spent on an AFL playing list = 4.4 ± 2.7 years) from one Australian Football League (AFL) club participated in this 15-week pre-season study. Each player provided informed consent and the research was approved by the University of Ballarat Human Ethics Committee. For every session, each player estimated the intensity of training approximately 30 min post-session using Foster’s modified RPE scale12 . All players had been familiarized with the RPE scale according to standard procedure12 . Rating of perceived exertion is strongly correlated with objective measures of exercise intensity such as heart rate, maximal oxygen uptake and blood lactate concentration17 . Training load scores were then calculated via four different methods using the given RPE value. Each method is detailed in Table 1. The sRPE method of quantifying TL via the multiplication of RPE and duration has been employed in previous research2,9,15,16 . The values recorded for the RPE and sRPE methods were split into two categories; “all” training sessions, which encompassed all training modalities, and “field” training sessions, which included only outdoor field-based activities such as running, skill and tactical development. The purpose of this was to compare “field” TL to occurrence of injury as club staff believed this form of training may be more likely to influence soft tissue injury risk than other training modalities such as resistance and cross training. Similar to previous research8 , injury to a player was defined as a non-contact event that occurred during a training session which resulted in missed or modified training due to the presence of at least one of the following soft tissue attributes: pain, tenderness, swelling and restricted range of motion. Training load is believed to have a stronger relationship with non-contact soft tissue injuries compared to impact injuries8 . Illness was defined as an event where
a player missed a training session due to a medical condition which was diagnosed by a club doctor. This was subsequently recorded by the fitness staff. For each training session that an individual player was involved in, their previous one- and two-weekly cumulative TL was calculated for each of the four different TL methods. One- and two-weekly, but not three- and four-weekly cumulative TL’s were reported to alter odds of injury in elite ARF players15 . The consideration of prior TL was deemed important due to the potential delayed effect it may have on injury risk18 . Players undertaking modified training due to an identified injury risk or rehabilitation from a previous injury were excluded from the analysis until they returned to full training for at least one week. There were too few injury and illness events to place the cumulative loads into multiple categories; therefore the basis of analysis for these data was a dichotomous median split into low and high TL groups for all methods. For the two outcomes of injury and illness, the association of each relevant cumulative TL group with the outcome measure was investigated with a bivariate logistic regression model fitted by the method of generalized estimating equations, with adjustment for intra-player cluster effects. Three error correlation structures were compared—independence (zero correlation—no cluster effects), and two repeated measures structures—exchangeable (correlation constant over time) and first order autoregressive (correlation diminishing with time). This statistical method was based on similar research investigating the effect of training and game loads on the odds of injury in elite ARF15 , with the addition of the examination of intra-player correlation. 3. Results A total of 5164 individual training sessions were analysed, 1899 of which were classified as “field” sessions. There were a low number of injuries (n = 13) and illnesses (n = 13). For the analysis of the cumulative TL measures and the outcome variables of injury and illness, the error structure which produced the best fitting models was independence, indicating no significant cluster effects. Therefore the results of ordinary logistic regression are reported, with each low TL group considered as the reference group (Tables 2 and 3). Inverse associations are apparent between all but one of the cumulative TL measures and both outcome measures, but not all relationships were statistically significant (p < 0.05). There was a statistically significant association between injury incidence and the one-week RPE (all) and one-week RPE x Duration (all) methods. For occurrence of illness, only the one-week RPE (field) method demonstrated a statistically significant association. 4. Discussion The purpose of this study was to compare different methods of TL quantification and their relationship to injury and illness in elite AF. A general pattern existed within all TL methods which suggest that the odds of injury were reduced for individuals in the high training TL compared to those in the low TL groups. This trend reached statistical significance in the one week RPE (all) method (OR = 0.202) and one-week RPE x Duration (all) method (OR = 0.199). It appears that methods which quantify all training sessions are better predictors of injury risk compared to field measures of TL. This may suggest that field-based measures of TL are limited by their failure to account for the stress from other forms of training such as resistance and cross-training, and that the cumulative load associated with these other modalities is likely to impact on injury risk. None of the two-week cumulative TL measures reached statistical significance, indicating that one-week cumulative TL was a better predictor of injury risk.
Please cite this article in press as: Veugelers KR, et al. Different methods of training load quantification and their relationship to injury and illness in elite Australian football. J Sci Med Sport (2015), http://dx.doi.org/10.1016/j.jsams.2015.01.001
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Table 2 Relationship between training load and occurrence of injury. TL method RPE × Duration(all) 1 week Low TL High TL 2 week Low TL High TL RPE × Duration(field) 1 week Low TL High TL 2 week Low TL High TL RPE (all) 1 week Low TL High TL 2 week Low TL High TL RPE (field) 1 week Low TL High TL 2 week Low TL High TL
Mean TL(AU)
Odds Ratioa
95% Confidence Limits
p value
2547.7 4261.3
1.000 0.199
0.044–0.911
0.037*
>6745
4728.6 8110.9
1.000 0.374
0.099–1.412
0.147
<1530 >1532
958.1 2176.5
1.000 1.000
0.322–3.105
1.000
<3011 >3020
1870.4 4183.1
1.000 0.834
0.254–2.737
0.764
<73 >73.5
51.8 90.2
1.000 0.202
0.044–0.921
0.039*
<140 >140.5
97.6 169.8
1.000 0.225
0.049–1.044
0.057
<26 >26.5
18.2 33.2
1.000 0.501
0.151–1.665
0.259
<51 >51.5
34.7 63.3
1.000 1.228
0.374–4.032
0.734
TL range(AU) <3518.5 >3519 <6742
*
Significant at the 0.05 level. a Relative odds of an injury occurring versus not occurring, where the low TL group is the reference group.
Table 3 Relationship between training load and occurrence of illness. TL method RPE × Duration (all) 1 week Low TL High TL 2 week Low TL High TL RPE × Duration(field) 1 week Low TL High TL 2 week Low TL High TL RPE (all) 1 week Low TL High TL 2 week Low TL High TL RPE (field) 1 week Low TL High TL 2 week Low TL High TL * a
TL range(AU)
Mean TL(AU)
Odds Ratioa
95% Confidence Limits
p value
<3518.5 >3519
2547.7 4261.3
1.000 0.299
0.082–1.088
0.067
<6742 >6745
4728.6 8110.9
1.000 0.800
0.214–2.982
0.739
<1530 >1532
958.1 2176.5
1.000 0.299
0.082–1.088
0.067
<3011 >3020
1870.4 4183.1
1.000 0.125
0.016–0.998
0.050
<73 >73.5
51.8 90.2
1.000 0.449
0.138–1.459
0.183
<140 >140.5
97.6 169.8
1.000 0.814
0.218–3.035
0.759
<26 >26.5
18.2 33.2
1.000 0.182
0.040–0.821
0.027*
<51 >51.5
34.7 63.3
1.000 0.292
0.060–1.405
0.125
Significant at the 0.05 level. Relative odds of an illness occurring versus not occurring, where the low TL group is the reference group.
The trend of lower odds of injury in high TL groups is in contrast to the majority of research13,14,19 . This could be explained using the theoretical U-curve relationship between workload and injury risk20 . Players within the high TL group may have been operating
within the optimum workload range, subsequently reducing their risk of injury. In contrast, greater injury risk in the low TL group may have been a consequence of the inadequate exposure of some players to sufficient workload.
Please cite this article in press as: Veugelers KR, et al. Different methods of training load quantification and their relationship to injury and illness in elite Australian football. J Sci Med Sport (2015), http://dx.doi.org/10.1016/j.jsams.2015.01.001
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It is now common for the prescription of training for elite team sport athletes to be based upon measures of workload derived from training and competition demands20 . Such practices are built upon the notion that limiting an athletes’ level of fatigue via the reduction of workload will result in the decreased likelihood of injury20 . However, reducing this level of workload past the lower threshold necessary to encourage physiological adaptation may potentially increase injury risk due to inadequate fitness20 . For example, the existence of both low and high thresholds of workload has been found in the sport of cricket, beyond which the risk of injury was likely to increase21 . A protective effect was discovered in fast bowlers who had an average of 2–5 days rest between bowling sessions, whereas bowlers who had less than 2 days or more than 5 days rest between sessions were at a significantly greater risk of injury21 . Furthermore, likelihood of injury for bowlers averaging less than 123 or more than 188 deliveries per week was significantly greater than bowlers who averaged between 123 and 188 deliveries21 . Such findings support the notion that athletes are more capable of tolerating a consistent level of TL22 . It is believed that large fluctuations in TL characterized by periods of imposed rest may actually have a detrimental effect on injury incidence22 . This is reinforced by the discovery that significant week-to-week variations in TL were associated with an increased number of injuries in elite ARF15 . It should be noted that the capacity to draw conclusions from this study concerning the relationship between TL and injury may be limited by the low number of injury events (n = 13). This could be partly accounted for by the chosen definition of injury. While it was believed that the isolation of non-contact soft tissue injuries may improve the likelihood of identifying a relationship between TL and injury, the exclusion of contact injuries undoubtedly decreased the overall tally. Another limitation of this definition is that it does not allow for the classification of subsequent injury. Such injuries encompass both multiple and recurrent injuries, including cases where an individual is re-injured before the complete recovery of a prior injury23 . While the rationale for monitoring one- and two-weekly loads has been previously established in elite ARF15 , it should be noted that the accumulation of TL over longer periods has been associated with increased injury risk in rugby league14 . Longitudinal monitoring of load and the implications for injury risk in elite ARF may therefore be a worthwhile topic for future investigation. As with the results between TL and injury, there appears to be a trend of lower odds of illness in the high TL groups across all methods. This indicates that individuals within the high TL groups were less likely to become ill in comparison to players in the low TL groups. Research investigating the relationship between TL and illness in team sport athletes appears sparse. This current study seems to be the first of its kind to highlight a link between low TL and illness in this cohort. Despite the discovery that spikes in TL may precede an illness event9 , there is a lack of statistically significant findings which contend that greater TL is related to higher rates of illness in elite AF. Similar to the association between TL and injury, there is scientific evidence which suggests the existence of a Ushaped relationship between physical activity level and disease resistance24 . This hypothetical relationship proposes that whilst regular amounts of moderate intensity physical activity may improve health, either insufficient or excessive doses of exercise may result in negative consequences24 . The effect of repeated and strenuous exercise on the health of elite athletes has been investigated previously. It has been found that the immune system may become temporarily weakened following prolonged endurance exercise, potentially resulting in a higher risk of infection25 . It is also believed that the suppression of the immune system in elite
athletes may increase the prevalence of upper respiratory tract infection, the symptoms of which are associated with decreased performance24 . In contrast, moderate levels of physical activity have been shown to encourage positive immunological responses, leading to fewer days of illness and reducing the likelihood of upper respiratory tract infections and the common cold26 . In particular, it has been established that exercise of an endurance nature can have a protective effect on the body due to a number of immunological responses, most notably the reduction in markers of inflammation24 . The long-term anti-inflammatory effects of exercise are believed to promote beneficial health outcomes and decrease the risk of cardiovascular disease, type 2 diabetes and various forms of cancer24 . It is conceivable, as with the association between TL and injury, that players in the high TL groups for the “field” methods were training within the optimal workload range to encourage a protective effect against illness. Conversely, individuals within the low TL groups may not have completed the required volume of exercise to achieve the same immunological benefits, subsequently exposing them to a greater risk of illness. Only field methods of TL were able to significantly differentiate the odds of illness between low and high TL groups. This may be the result of the different nature of activities included in “all” and “field” sessions. Whilst the positive implications of endurance exercise on immune function are relatively well known, the effect of resistance training (and other forms of training) is not fully understood24 . It is possible that immunological changes resulting from exercise are more prominent in endurance rather than resistance or cross training. Subsequently, the field methods may be sensitive enough to pick up subtle changes in the odds of illness between low and high TL groups due to the endurance-based nature of these sessions. To fully understand the protective mechanisms of physical activity, further research is required which investigates the nature of exercise that is most beneficial to improving immune function24 . However, the ability of only field methods of TL quantification to identify differences in the odds of injury between high and low TL groups in this study provides an interesting discussion point, highlighting the necessity for further exploration into the effect of different forms of exercise on the immune system. Any advances that are made may enable the development of more comprehensive measures to enhance individual training programs24 . Interestingly, the common method of quantifying TL via the product of RPE and session duration did not improve the ability of RPE to predict change in odds of injury or illness. This may have two possible implications requiring further investigation; First, that the intensity of exercise and not overall load is more likely to cause change in the odds of injury and illness. Or second, that the implementation of RPE in field settings is indicative of overall load rather than solely an estimate of exercise intensity.
5. Conclusion Players within high TL groups were at reduced risk of injury and illness compared to those in the low TL groups, potentially due to the protective effect resulting from the prescribed dose of exercise. RPE (all) was the best predictor of change in the odds of injury between low and high TL groups, whilst RPE (field) was most sensitive to variations in illness odds. The inclusion of duration in the quantification of TL did not improve the ability of RPE to predict change in either outcome measure.
Please cite this article in press as: Veugelers KR, et al. Different methods of training load quantification and their relationship to injury and illness in elite Australian football. J Sci Med Sport (2015), http://dx.doi.org/10.1016/j.jsams.2015.01.001
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6. Practical applications • One-week cumulative TL measures should be individually monitored as they are significantly related to change in odds of injury and illness. • Both excessive and inadequate exposure to TL should be monitored to ensure that athletes are at a reduced risk of injury and illness. • RPE alone may be used to effectively quantify TL and assess injury and illness risk in elite AF players. Acknowledgements No external financial support was received for this study. References 1. Meeusen R, Duclos M, Gleeson M et al. Prevention, diagnosis and treatment of the overtraining syndrome: ECSS position statement ‘task force’. Euro J Sports Sci 2006; 6(1):1–14. 2. Gabbett T, Domrow N. Relationships between training load, injury, and fitness in sub-elite collision sport athletes. J Sports Sci 2007; 25(13):1507–1519. 3. Gamble P. Periodization of training for team sports athletes. Strength Cond J 2006; 28(5):56–66. 4. 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; 16:550–555. 5. Young WB, Hepner J, Robbins DW. Movement demands in Australian Rules Football as indicators of muscle damage. J Strength Cond Res 2012; 26(2): 492–496. 6. Wiseby B, Pyne D, Rattray, B. Quantifying changes in AFL player demands using GPS tracking. http://sport.fitsense.com.au/downloads/AFL GPS Research Report 2012.pdf; 2012. 7. Orchard JW, Seward H, Orchard JJ. Results of 2 decades of injury surveillance and public release of data in the Australian Football League. Am J Sports Med 2013; 41(4):734–740. 8. Gabbett T, Ullah S. Relationship between running loads and soft-tissue injury in elite team sport athletes. J Strength Cond Res 2012; 26(4):953–960.
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Please cite this article in press as: Veugelers KR, et al. Different methods of training load quantification and their relationship to injury and illness in elite Australian football. J Sci Med Sport (2015), http://dx.doi.org/10.1016/j.jsams.2015.01.001