Running-based team sport athletes, such as rugby league, soccer, and Australian rules football players, perform bouts of high-intensity efforts interspersed with periods of low-intensity activity during matches (11,28,30). The highly strenuous nature of match play results in players experiencing physical (5,22,25) and perceptual fatigue (12). In addition, players are required to train regularly at high exercise intensities to prepare for the physical and mental demands of competitions. On a weekly basis, coaches and conditioning staff are challenged to facilitate recovery from competition and then prepare players for subsequent matches, usually with a turnaround of 6–8 days. In this context, it is therefore essential to continually balance match- and training-related fatigue with sufficient recovery for optimal performance outcomes each week.
Short maximal-effort performance tests, in particular countermovement jump (CMJ) tests, have been extensively examined in team sports, such as soccer (1,26), rugby league (18,20,22), and Australian rules football (4,5,24), and are now often used to monitor neuromuscular fatigue (NMF). However, despite the similarities in neuromuscular function between CMJ and running (3), the time course of recovery between running performance and CMJ performance can differ (1). The large dependence on the stretch-shortening cycle of the muscle to produce force during a CMJ may confound the efficacy of using a CMJ test to determine the overall force-production capacity of the muscle in a fatigued state. Recently, short tests of peak power, derived from sprints lasting 3–6 seconds performed on cycle ergometers, have also been used to monitor NMF in team sport athletes (21,27). These novel investigations (21,27) suggest that there may be practical applications for cycle ergometers beyond cycling and across running-based team sports, such as soccer, rugby league, and Australian rules football. Team sport athletes from non-cycling sports could potentially use cycle ergometers for performance testing and fatigue monitoring.
Previously, peak power derived from short (3–6 seconds) sprints on cycle ergometers has demonstrated higher reliability in cyclists (9,19,23) than did CMJ measures commonly used to track NMF in team sport athletes (6). However, the interday reliability of short sprint tests on a cycle ergometer has not been investigated in running-based team sport athletes. Determining the reliability of power output measures is particularly important when performance tests are used to monitor progression and track performance longitudinally (17). In addition, quantifying the smallest worthwhile change (SWC) in running-based team sport athletes is required to monitor meaningful fatigue-induced reductions in cycle ergometer maximal power output for such populations. Therefore, the aim of this study was to investigate the reliability of a cycle ergometer to measure peak power during short maximal sprint cycle efforts in running-based team sport athletes, with the objective of quantifying the SWC in this population.
Experimental Approach to the Problem
Players performed a standardized sprint cycle protocol during 4 separate testing sessions, including an initial familiarization trial. Testing occurred during the preseason phase, and players completed each testing session on the same day of the week and at the same time of day, with each testing session separated by 7 days. The cycle ergometer sprint protocol consisted of a standardized warm-up, a maximal 6-second sprint cycle effort, 1-minute active recovery, and a second maximal 6-second sprint cycle effort. Players were asked to arrive for each testing session in a rested and hydrated state, and to refrain from caffeine intake 12 hours before testing. Throughout the testing period, players were required to complete a daily perceptual fatigue questionnaire, which was similar to that described by McLean et al. (20).
Fourteen professional male Australian rules football players (mean ± SD; age = 21.2 ± 3.2 years [range = 18–28 years]; mass = 84.4 ± 8.8 kg; height = 188.9 ± 6.5 cm) participated in this study after providing written informed consent. Players had participated in an average of 28.3 ± 47.6 (range = 0–141) regular season career matches in the Australian Football League (AFL) before the commencement of this study. The Australian Catholic University Human Research Ethics Committee granted ethical approval, and the study was conducted in accordance with the ethical guidelines set out by the Committee.
All cycle ergometer testing was performed on a factory-calibrated Wattbike cycle ergometer (Wattbike Pro, Nottingham, United Kingdom). The Wattbike Pro operates with both air-braked and magnetically braked systems. A lever regulates the flow of air through the flywheel to control air-braked resistance (“gear” levels 1–10), and a turn dial adjusts the magnetically braked resistance (“climb control” levels 1–7). Each subject was assigned 1 of 4 ergometers for data collection and performed their weekly testing on the same ergometer for each testing session. Players were familiar with the testing equipment, having used the Wattbike as part of their regular training program, and had previously performed short maximal sprint cycle efforts similar to those described in the testing protocol. As recommended by the manufacturer, all ergometer computers were cleared of memory and zeroed to restore their factory settings before testing. The validity and reliability of the Wattbike cycle ergometer has previously been reported during mechanical trials (14) and during human trials for both recreational and trained cyclists (7,8,14).
Each cycle testing session commenced with a 2-minute warm-up on the Wattbike, whereby players performed a steady state “easy spin” at a self-selected cadence with the air- and magnetically braked resistances fixed at the lowest settings (level 1 and 1, respectively). Players then ceased pedaling and adjusted the air-braked and magnetically braked resistance settings to levels 10 and 4, respectively. On the tester's command, a 6-second maximal sprint was performed from a static starting position. Immediately following, a 1-minute active recovery “easy spin” at a self-selected cadence was performed with both resistance settings returned to level 1. After the active recovery, an additional 6-second maximal sprint was again performed from a static starting position with the same resistance settings as the previous sprint. Peak power was recorded on the cycle ergometer performance computer as the highest power output of the 2 sprint cycle efforts. Using pilot data collected from academy players in our laboratory, we found that during this protocol peak power was achieved in the second sprint on 81 occasions out of 115 trials (70.4%).
Players were required to remain in their seats for the duration of the testing protocol, and apart from verbal encouragement, no other instructions were provided. Seat and handlebar positions were determined for each subject before testing and remained consistent across all testing sessions. Players wore their own cycling shoes for testing. The sprint cycle protocol was designed to elicit absolute maximal power output and, at the same time, be practical and time-efficient in an elite team sport environment. Similar cycle ergometer peak power protocols have been described previously (9,19,23).
Players completed a perceptual fatigue questionnaire throughout the testing period on each scheduled training day (5 days per week), including testing days, at around 7 AM ± 1 hour. Perceptual fatigue questionnaires have previously been identified as useful tools to monitor adaptive responses to training in AFL players (12). The perceptual fatigue questionnaire required players to rate their muscle soreness, energy levels, leg heaviness, sleep quality, and mood state on a 5-point scale (scores 1–5, with 1 point increments). Scores were totaled to provide an overall well-being score, with higher scores indicating less fatigue.
Data were analyzed using SPSS Statistics software (version 20; SPSS, Inc., Chicago, IL, USA) and are expressed as means and SDs. Data were checked for normality using the Shapiro-Wilk test. To assess reliability, data were log transformed to reduce bias due to non-uniform error (15), and the typical error of measurement was calculated with an Excel spreadsheet (16). To calculate the coefficient of variation (CV), the typical error of an individual's repeated test score was expressed as a percentage of their mean test score (15). Typical error is presented as a CV% with upper and lower 90% confidence intervals (CIs). In addition, the intraclass correlation coefficient (ICC) with the 90% CI was calculated, as well as the SEM. The SWC was subsequently calculated as 2 × CV (15), while the minimum difference to be considered a “real” change was also calculated as SEM × 1.96 × √2 (29). To address the issue of varying individual interpretations of perceived ratings, perceptual well-being scores were converted to individual z-scores (20,21) according to the formula: (individual subject's score − individual subject's mean score)/individual subject's SD. Perceptual well-being z-scores were calculated from the data acquired throughout the entire testing period, which included scores collected on non-testing days, and are reported as means and SDs. A one-way repeated measures analysis of variance was used to identify significant differences in perceptual well-being z-scores, and a Tukey's post-hoc test was used to identify the source of these differences. Statistical significance was set at p ≤ 0.05.
Absolute peak power was 1502 ± 202; 1498 ± 191; and 1495 ± 210 W for trials 1, 2, and 3, respectively. Relative peak power was 17.1 ± 1.6; 17.1 ± 1.8; and 17.1 ± 2.0 W·kg−1 for trials 1, 2, and 3, respectively. Within-subject reliability statistics for absolute peak power measures during the familiarization trial (F) and experimental trials 1–3 are presented in Table 1. The percentage differences of each trial from the mean of 3 trials are displayed in Figure 1. The SWC and minimum difference for relative peak power was 6.0% and 6.9%, respectively, which equated to 1.03 and 1.18 W·kg−1, respectively. Mean perceptual well-being z-score was higher on day 4 (testing day) than any other day throughout the week (p = 1.000, 0.953, 0.016 and 0.020 for days 1, 2, 5 and 6, respectively, compared with day 4; Figure 2A). Furthermore, there were no significant differences in perceptual well-being scores across the 3 testing sessions (F = 2.208; p = 0.136; Figure 2B).
This is the first study to examine the reliability of a cycle ergometer peak power test in running-based team sport athletes and adds to the work by Hopker et al. (14) by examining the reliability of peak power during short sprint efforts on the Wattbike cycle ergometer. Given the importance of ensuring athletes present for training and competition in a non-fatigued state, reliable tests are required to monitor fatigue and the readiness of athletes to train. The CV, ICC, and SEM values for peak power derived from 6-second maximal sprint efforts on a Wattbike cycle ergometer in elite Australian rules football players in this study are in line with reliability coefficients reported for other commercially available cycle ergometers (9,19,23). The findings of this study demonstrate that the Wattbike cycle ergometer offers a reliable method of quantifying maximal power output in athletes from non-cycling sports, which could potentially be applied to monitor training-induced improvements or fatigue-induced decrements in performance.
In this study, mean within-subject typical error for peak power during 6-second sprint cycle efforts on a Wattbike cycle ergometer was 3.0% (90% CI = 2.5–3.8%), mean ICC was 0.96 (90% CI = 0.91–0.98), and mean SEM was 39 W (2.6%, expressed as a percentage of the mean value). The results of the present study are comparable with those reported during short (3–6 seconds) maximal sprint tests on other cycle ergometers (9,19,23). Previously, the mean CV and ICC for peak power produced by active male participants during short (3–4 seconds) maximal sprint efforts on a modified Monark cycle ergometer using the inertial load cycling method was 3.3% and 0.99, respectively (19). Similarly, the mean CV, SEM and ICC range for peak power on an isokinetic SRM cycle ergometer during 6-second sprint cycle efforts in recreational and competitive male cyclists was 2.2%, 17 W and 0.96–0.99, respectively (9). The reliability of peak power on an air-braked front-access cycle ergometer during 6-second sprints in active university students was also similar (CV = 2.8%) (23). Interpretations should be made with caution due to differences in ergometers, participants and sprint cycle protocols employed. It should also be noted that measurement error is likely to arise when average force and angular velocity are used to calculate peak power during rapid acceleration on a cycle ergometer. While validity was not assessed, the results of this study should be interpreted within the limitations of potential measurement error.
Other studies have reported the reliability of peak power during 30-second sprint cycle efforts on the Wattbike cycle ergometer (mean: CV = 4.9%, ICC = 0.97, SEM = 52.7 W) (8), and the “gold standard” Monark mechanically braked cycle ergometer (mean: CV = 4.6% and ICC = 0.96) (2). Considering these findings (2,8), along with those of the present study and others (9,19,23), it is likely that shorter maximal cycle efforts of around 6 seconds elicit more reliable peak power results than those lasting 30 seconds. In addition, frequent administration of a maximal 30-second sprint cycle protocol is perhaps not ideal for monitoring fatigue and performance in running-based team sports due to the unpleasant short-term side effects (nausea, vomiting, light headedness, and headaches) that are often experienced during this protocol (13). Tests to monitor athletes in an elite sport environment should be quick and easy to administer, should be practical for use in a field setting, and should not be strenuous to the extent that training becomes compromised (10). A 6-second sprint cycle protocol to regularly monitor team-sport athletic populations may therefore be advantageous over a 30 seconds protocol.
To minimize the influence of fatigue, testing was scheduled to take place the day after a rest day, 48 hours after any on-field football training. The perceptual well-being scores suggest that subjects were in a sufficiently recovered state before each test. Throughout a typical training week, the players recorded the highest well-being scores (experienced the least amount of fatigue) on the day of testing (Figure 2A), which was consistent across the 3 weeks of data collection (Figure 2B). Additionally, the low SD of perceptual well-being scores on the day of testing indicates decreased variation of reported fatigue in comparison to the other training days.
Performance tests that are used to monitor athletes and track changes over time should be highly reliable to detect meaningful changes over recurrent testing intervals (10). In this study, we found that cycle ergometer peak power, derived from short (6 seconds) sprint tests, had greater test-retest reliability than CMJ variables of peak power (CV = 3.5%, SEM [expressed as a percentage of the mean value] = 3.3%), peak force (CV = 3.5%, SEM = 3.6%), and flight time:contraction time (CV = 6.1%, SEM = 6.9%) (6), measures which have previously been used to monitor NMF in running-based team sport athletes (4,5,18,22,24,26). Therefore, in light of these results, cycle ergometer peak power is an adequately reliable performance measure that can be used to monitor and detect meaningful reductions in running-based team sport athletes, which may be indicative of NMF. In addition, the use of a cycle ergometer sprint test to monitor NMF may be more favorable than a CMJ test for coaches seeking to limit weight-bearing activity for team sport athletes.
Mendez-Villanueva et al. (23) suggested that good reliability can be obtained after at least one familiarization trial in participants unfamiliar with maximal sprint cycle efforts. While players in the present study were already accustomed to short maximal sprint efforts on a cycle ergometer, an initial familiarization trial was conducted to familiarize players with the novel testing protocol. There was good reliability between the familiarization trial and trial 1 (Table 1), suggesting that the players did not experience any learning effects with regard to the testing protocol throughout the study.
The 6-second cycle ergometer sprint test protocol described in this study is highly reliable in elite running-based team sport athletes, specifically AFL players. Hopkins (15) proposed that an observed change equal to about 1.5–2.0 times the typical error could realistically indicate that a “real” change has occurred, while the minimum difference to be considered a “real” change according to Weir (29) is determined by the formula SEM × 1.96 × √2. According to the results of the present study, an observed change of about 4.5–6.9% in Wattbike peak power, or 0.77–1.18 W·kg−1, could be considered a meaningful change in AFL players. Furthermore, decrements in cycle ergometer peak power beyond the SWC or minimum difference may be indicative of NMF in team sport athletes. Therefore, regular monitoring of cycle ergometer peak power, along with reliable measures of perceptual well-being, could potentially provide strength and conditioning coaches and applied sport scientists with valuable information on the preparedness of team sport athletes to train and compete. In summary, the cycle ergometer sprint test protocol described in this study is practical, time-efficient, and relevant for use in applied team sport environments, and can be implemented with minimal training disruption.
The authors would like to thank the players who participated in the study, and the football staff for their support and collaboration. The authors also acknowledge Tom Stanef for providing Wattbike validity data from his laboratory at the South Australian Sports Institute. The results of the present study do not constitute endorsement of the product by the authors or the journal. Financial assistance was not provided for this study.
1. Andersson H, Raastad T, Nilsson J, Paulsen G, Garthe I, Kadi F. Neuromuscular fatigue and recovery in elite female soccer: Effects of active recovery. Med Sci Sports Exerc 40: 372–380, 2008.
2. Astorino TA, Cottrell T. Reliability and validity of the velotron racermate cycle ergometer to measure anaerobic power. Int J Sports Med 33: 205–210, 2012.
3. Bosco C, Montanari G, Ribacchi R, Giovenali P, Latteri F, Iachelli G, Faina M, Colli R, Dal Monte A, La Rosa M, Cortili G, Saibene F. Relationship between the efficiency of muscular work during jumping and the energetics of running. Eur J Appl Physiol Occup Physiol 56: 138–143, 1987.
4. Cormack SJ, Mooney MG, Morgan W, McGuigan MR. Influence of neuromuscular fatigue on accelerometer load in elite Australian football
players. Int J Sports Physiol Perform 8: 373–378, 2013.
5. Cormack SJ, Newton RU, McGuigan MR. Neuromuscular and endocrine responses of elite players to an Australian rules football
match. Int J Sports Physiol Perform 3: 359–374, 2008.
6. Cormack SJ, Newton RU, McGuigan MR, Doyle TL. Reliability of measures obtained during single and repeated countermovement jumps. Int J Sports Physiol Perform 3: 131–144, 2008.
7. Driller MW, Argus CK, Bartram JC, Boniventura J, Martin DT, West NP, Halson SL. The reliability of a two-bout exercise test on a wattbike cycle ergometer. Int J Sports Physiol Perform 9: 340–345, 2014.
8. Driller MW, Argus CK, Shing CM. The reliability of a 30 second sprint test on the wattbike cycle ergometer. Int J Sports Physiol Perform 8: 379–383, 2013.
9. Fernández-Peña E, Lucertini F, Ditroilo M. A maximal isokinetic exercise for EMG normalization in cycling. J Electromyogr Kinesiol 19: e162–e170, 2009.
10. Fowles JR. Technical issues in quantifying low-frequency fatigue in athletes. Int J Sports Physiol Perform 1: 169–171, 2006.
11. Gabbett TJ, Jenkins DG, Abernethy B. Physical demands of professional rugby league training and competition using microtechnology. J Sci Med Sport 15: 80–86, 2012.
12. Gastin PB, Meyer D, Robinson D. Perceptions of wellness to monitor adaptive responses to training and competition in elite Australian football
. J Strength Cond Res 27: 2518–2526, 2013.
13. Hachana Y, Attia A, Nassib A, Shephard RJ, Chelly MS. Test-retest reliability, criterion-related validity, and minimal detectable change of score on an abbreviated Wingate test for field sport participants. J Strength Cond Res 26: 1324–1330, 2012.
14. Hopker J, Myers S, Jobson SA, Bruce W, Passfield L. Validity and reliability of the Wattbike cycle ergometer. Int J Sports Med 31: 731–736, 2010.
15. Hopkins WG. Measures of reliability in sports medicine and science. Sports Med 30: 1–15, 2000.
16. Hopkins WG. Precision of measurement [Excel spreadsheet]. Available at: http://newstats.org/precision.html
. Accessed March 30, 2011.
17. Hopkins WG, Schabort EJ, Hawley JA. Reliability of power in physical performance tests. Sports Med 31: 211–234, 2001.
18. Johnston RD, Gibson NV, Twist C, Gabbett TJ, MacNay SA, MacFarlane NG. Physiological responses to an intensified period of rugby league competition. J Strength Cond Res 27: 643–654, 2013.
19. Martin JC, Wagner BM, Coyle EF. Inertial-load method determines maximal cycling power in a single exercise bout. Med Sci Sports Exerc 29: 1505–1512, 1997.
20. McLean BD, Coutts AJ, Kelly V, McGuigan MR, Cormack SJ. Neuromuscular, endocrine, and perceptual fatigue responses during different length between-match microcycles in professional rugby league players. Int J Sports Physiol Perform 5: 367–383, 2010.
21. McLean BD, Petrucelli C, Coyle EF. Maximal power output and perceptual fatigue responses during a division I female collegiate soccer season. J Strength Cond Res 26: 3189–3196, 2012.
22. McLellan CP, Lovell DI, Gass GC. Markers of postmatch fatigue in professional rugby league players. J Strength Cond Res 24: 1030–1039, 2011.
23. Mendez-Villanueva A, Bishop D, Hamer P. Reproducibility of a 6-s maximal cycling sprint test. J Sci Med Sport 10: 323–326, 2007.
24. Mooney MG, Cormack SJ, O'Brien BJ, Morgan WM, McGuigan M. Impact of neuromuscular fatigue on match exercise intensity and performance in elite Australian football
. J Strength Cond Res 27: 166–173, 2013.
25. Rampinini E, Bosio A, Ferraresi I, Petruolo A, Morelli A, Sassi A. Match-related fatigue in soccer players. Med Sci Sports Exerc 43: 2161–2170, 2011.
26. Thorlund JB, Aagaard P, Madsen K. Rapid muscle force capacity changes after soccer match play. Int J Sports Med 30: 273–278, 2009.
27. Wehbe G, Gabbett T, Dwyer D, McLellan C, Coad S. Neuromuscular fatigue monitoring in team sport athletes using a cycle ergometer test. Int J Sports Physiol Perform 10: 292–297, 2015.
28. Wehbe GM, Hartwig TB, Duncan CS. Movement analysis of Australian national league soccer players using global positioning system technology. J Strength Cond Res 28: 834–842, 2014.
29. Weir JP. Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. J Strength Cond Res 19: 231–240, 2005.
30. Wisbey B, Montgomery PG, Pyne DB, Rattray B. Quantifying movement demands of AFL football
using GPS tracking. J Sci Med Sport 13: 531–536, 2010.