Discussion
In this study, we analysed the predictive ability of a laboratory test for race performance in XCM. The test comprised a traditional incremental cycle ergometer test and maximal efforts with durations typical for MTB.1 4 15 In comparison with the parameters of the traditional incremental test (IAT and PPO), the 1 min maximal effort showed stronger correlations with mountain bike races. Using these three variables in a multiple regression model, race performance can be predicted more precisely. The results demonstrate the importance of the combined role of aerobic (IAT) and high-intensity physiological demands in XCM. Referring to the aims of the study, it can be postulated that the comprehensive incremental test outperforms the traditional incremental test with regard to the predictive ability.
Comparison between the parameters of the comprehensive incremental test
The traditional laboratory variables of the incremental test showed large to very large correlations and could partly explain the race performance of the different validation XCM races with values between 45% and 50% (r= −0.67 to −0.71) for IAT and between 53% and 59% (r= −0.73 to −0.77) for PPO. Besides the traditional variables of an incremental test, this study also recorded the data of an athlete’s maximal average power output in three maximal efforts with various loading times (10 s, 1 min and 5 min). Interval durations and high-intensity intermittent loads are typical of MTB.1 4 15 The power output in the 1 min maximal effort outperforms the predictive ability of the traditional incremental test in all three races. Very large correlations with race times 1, 2 and 3 explaining between 68% and 72% (r= −0.82 to −0.85) of the race time variance were found. The 10 s all-out sprint showed inferior results. The predictive ability of the 5 min maximal effort was similar to the traditional parameters, but in the multiple regression model the 5 min maximal effort did not explain any further variance of the performance, indicating that the 5 min maximal effort could be excluded in the future to abbreviate the test protocol. The use of the 1 min maximal effort (1 min) and the traditional incremental test parameter IAT in a multiple regression model confirmed additional explanatory power to predict XCM performance (87% of race time 3 and 76% of z-time).
Comparison with previous studies
The traditional laboratory variables (scaled by body weight (W/kg)) were also analysed by other studies for the mountain bike discipline cross-country. A comparable correlation (r=−0.64) between race time and the athlete’s onset of lactate threshold28 was described by Prins et al
12 in eight elite mountain bikers. The relation between race time and the lactate threshold29 reported by Impellizzeri et al,13 who analysed 13 national-level athletes, was stronger (r=−0.86). Costa and De-Oliveira6 analysed six national-level athletes and found different correlations between lactate threshold30 and final race rank position in two XCO races (r=−0.32 and r=−0.78). Impellizzeri et al
11 analysed 12 international-level athletes and reported correlations between PPO and race time of r=−0.48. Costa and De-Oliveira6 found a very large correlation between PPO and final race rank position of two races (r=−0.88 and r=−0.88). The traditional parameters of the incremental test showed large variability between the studies, indicating the difficulty to predict race performance in MTB.
To address this, we combined traditional parameters of an incremental test and maximal efforts. In this regard, the incremental test at the beginning of the performance test influences the power output of the maximal efforts because athletes cannot completely recover during the recovery periods. Because of increasing fatigue during the performance test, the power outputs of the maximal efforts are expected to be smaller than those seen without preloading. Moreover, the power outputs of the maximal efforts might be influenced by familiarisation and experience with these tests.
Despite these limitations of comparing the predictive ability of the power outputs of the maximal efforts with those seen with other tests that focus on short-term and mid-term intervals, the predictive ability of the power outputs obtained in this study will now be compared with those of previously published studies on XCO. They also tried to improve the prediction of race performance by using alternative testing methods. Costa and De-Oliviera6 only found small and non-significant correlations between mean power output scaled by body weight over 30 s and the final race rank position in two XCO races (r=−0.12; r=−0.29). Inoue et al
2 tested 10 XCO riders who performed five repeated Wingate tests with 30 s recovery between the Wingate tests. The average power output in all five Wingate tests scaled by body weight (r=−0.63; P<0.05) and the peak power of all five Wingate tests scaled by body weight (r=−0.79; P<0.01) correlated significantly with race time. Miller et al
14 used a field-based test to predict the XCO performance of 11 regionally competitive athletes. They reported a large correlation between race time and intermittent power scaled by body weight of 20 intervals of 45 s work and 15 s recovery (r=0.89; P<0.001). This correlation is similar to those observed between power output in the 1 min maximal effort and race times in this study. However, smaller correlations were found by Prins et al
12 between performance times for 1 km time trials and race time. Participants needed 80–95 s to complete the 1 km time trial. Therefore, durations were similar to those of the 1 min maximal effort in this study. Each participant (n=8) performed a 1 km time trial from rest (r=0.29) after a 26 min (r=0.53) and a 52 min (r=0.59) laboratory test with variable fixed intensities to simulate an XCO race.
Six considerations when comparing with previous studies
A comparison of our findings with those of other studies may by hampered by several factors: (1) As described by Impellizzeri et al,11 small variations in correlation coefficients between studies might be explained by differences in the distinct sample size. The variability of the race times in the studies by Prins et al,12 Impellizzeri et al
13 and Impellizzeri et al
11 was similar and less pronounced than that of the race times in this study, indicating more homogeneous study populations in the previous investigations. Other studies did not report the ranges of the dependent variable. (2) In addition, the small sample sizes in the previous studies resulted in large CIs. For example, Prins et al
12 mentioned that the low correlations of the 1 km trials could have been the result of a type II error. (3) Moreover, athletes’ performance levels in this study, which were measured with the absolute PPO as suggested by De Pauw et al,21 were lower than those of international competitive XCO athletes in the study by Impellizzeri et al
11 (PPO=426 W) but higher than those of XCM athletes in the study by Wirnitzer and Kornexl.7 (4) In addition, different calculation methods of lactate threshold and (5) differences in the incremental test protocol could influence the correlations found in the different studies. Moreover, previous reports compared predictors with the (6) race performance in XCO races. These races are more intense and shorter in duration than XCM races.4 7 In reference to the popularity of XCM as a recreational and competitive sport, this study can therefore fill a diagnostic gap by allowing recommendations for XCM performance.
However, different race characteristics between XCM races also influence the correlations. In this study, correlation coefficients between race times and the afforded power in the 10 s all-out sprint and 5 min maximal effort showed variation between the races. IAT, PPO and the 1 min maximal effort are less dependent on race characteristics because the correlations found were constant over all three races. Even the fact that race 2 was a stage race does not influence the explanatory power of the results. The generalisability of IAT, PPO and 1 min maximal effort is therefore better than that of power in the 10 s all-out sprint and in the 5 min maximal effort.
The aforementioned studies have investigated the correlations between physiological-based measures and the endurance performance of only one2 11–14 and two6 races. The results of our study could demonstrate that the results of a single comparison between laboratory variables and a specific race performance in XCM overestimate the predictive value of variables when considering different races. This can be seen in the lower correlation coefficients between z-time and laboratory variables compared with correlation coefficients of the single races.
Multiple regression models
Due to the large sample size, multiple regression models for a more precise race prediction could be calculated with three predictors. Using backward calculation for race time 3 as well as the z-transformed race time, two significant models could be calculated with the same predictor variables. This new test protocol was able to explain up to 86.8% of the variance of a single XCM race. This value is slightly smaller than the explanatory power described for laboratory tests to predict a 10 km run (r²=0.889), half marathon (r²=0.924) and marathon (r²=0.899) with three independent variables.31 In contrast to a running marathon with a predefined distance of 42.2 km and no relevant differences in altitude or challenging running surfaces, XCM races differ remarkably with respect to length, altitude and technical demands. Those race characteristics could have been added to the calculation model. However, more validation races would have been necessary to obtain a valid model. Therefore, the calculated models between predictor variables and response variables are not as generalisable as the results of a laboratory test to predict the finish time of a running marathon. Calculating data across all three races reduced the explanation of variance for all three race times (r²=0.76). Consequently, the inclusion of different races into one model via z-transformed data results in a lower explanatory power but a higher external validity of the model.
Practical application and further research
This study verified the test protocol’s validity for trained, amateur XCM athletes. The additional explanatory power of the comprehensive protocol is possible through the analysis of aerobic variables and maximal efforts. In total, this protocol takes approximately 50 min, including warm-up and cool-down periods. This laboratory-based performance test can therefore be implemented into a clinical routine. However, this laboratory-based performance test is limited by smaller ecological validity compared with field-based performance tests.32
Apart from predicting race performance, this protocol can be used to create individual athlete profiles with regard to different physiological demands of XCM. An athlete’s profile can be compared with the results of other athletes, and individual strengths and weaknesses can be outlined and implemented into recommendations for training. In this regard, repeated tests allow a detailed control of the training process and are therefore valuable for athletes and coaches.
Further research is needed to evaluate the test reliability and training recommendations based on the results of the test. Moreover, the validity of the performance test and the study’s results should be analysed in further studies for the XCO discipline and for professional cyclists.