Nimmerichter, A, Novak, N, Triska, C, Prinz, B, and Breese, BC. Validity of treadmill-derived critical speed on predicting 5,000-meter track-running performance. J Strength Cond Res 31(3): 706-714, 2017-To evaluate 3 models of critical speed (CS) for the prediction of 5,000-m running performance, 16 trained athletes completed an incremental test on a treadmill to determine maximal aerobic speed (MAS) and 3 randomly ordered runs to exhaustion at the [INCREMENT]70% intensity, at 110% and 98% of MAS. Critical speed and the distance covered above CS (D') were calculated using the hyperbolic speed-time (HYP), the linear distance-time (LIN), and the linear speed inverse-time model (INV). Five thousand meter performance was determined on a 400-m running track. Individual predictions of 5,000-m running time (t = [5,000-D']/CS) and speed (s = D'/t + CS) were calculated across the 3 models in addition to multiple regression analyses. Prediction accuracy was assessed with the standard error of estimate (SEE) from linear regression analysis and the mean difference expressed in units of measurement and coefficient of variation (%). Five thousand meter running performance (speed: 4.29 ± 0.39 m·s; time: 1,176 ± 117 seconds) was significantly better than the predictions from all 3 models (p < 0.0001). The mean difference was 65-105 seconds (5.7-9.4%) for time and -0.22 to -0.34 m·s (-5.0 to -7.5%) for speed. Predictions from multiple regression analyses with CS and D' as predictor variables were not significantly different from actual running performance (-1.0 to 1.1%). The SEE across all models and predictions was approximately 65 seconds or 0.20 m·s and is therefore considered as moderate. The results of this study have shown the importance of aerobic and anaerobic energy system contribution to predict 5,000-m running performance. Using estimates of CS and D' is valuable for predicting performance over race distances of 5,000 m.

Purpose: To determine aerobic and anaerobic demands of mountain bike cross-country racing. Methods: Twelve elite cyclists (7 males;  = 73.8 [2.6] mL·min-1·kg−1, maximal aerobic power [MAP] = 370 [26] W, 5.7 [0.4] W·kg−1, and 5 females;  = 67.3 [2.9] mL·min−1·kg−1, MAP = 261 [17] W, 5.0 [0.1] W·kg−1) participated over 4 seasons at several (119) international and national races and performed laboratory tests regularly to assess their aerobic and anaerobic performance. Power output, heart rate, and cadence were recorded throughout the races. Results: The mean race time was 79 (12) minutes performed at a mean power output of 3.8 (0.4) W·kg−1; 70% (7%) MAP (3.9 [0.4] W·kg−1 and 3.6 [0.4] W·kg−1 for males and females, respectively) with a cadence of 64 (5) rev·min−1 (including nonpedaling periods). Time spent in intensity zones 1 to 4 (below MAP) were 28% (4%), 18% (8%), 12% (2%), and 13% (3%), respectively; 30% (9%) was spent in zone 5 (above MAP). The number of efforts above MAP was 334 (84), which had a mean duration of 4.3 (1.1) seconds, separated by 10.9 (3) seconds with a mean power output of 7.3 (0.6) W·kg−1 (135% [9%] MAP). Conclusions: These findings highlight the importance of the anaerobic energy system and the interaction between anaerobic and aerobic energy systems. Therefore, the ability to perform numerous efforts above MAP and a high aerobic capacity are essential to be competitive in mountain bike cross-country.

The purpose of this study was to assess the reliability of critical power (CP) and the total amount of work accomplished above CP (W´) across repeated tests using ecologically valid maximal effort time-trials (TT) under laboratory conditions. After an initial incremental exercise test, ten well-trained male triathletes (age: 28.5 ± 4.7 years; body mass: 73.3 ± 7.9 kg; height: 1.80 ± 0.07 m; maximal aerobic power [MAP]: 329 ± 41 W) performed three testing sessions (Familiarization, Test I and Test II) each comprising three TT (12, 7, and 3 min with a passive recovery of 60 min between trials). CP and W´ were determined using a linear regression of power vs. the inverse of time (1/t) (P = W´ ∙ 1/t + CP). A repeated-measures ANOVA was used to detect differences in CP and W´ and reliability was assessed using the intra-class correlation coefficient (ICC) and the coefficient of variation (CoV). CP and W´ values were not significantly different between repeated tests (P = 0.171 and P = 0.078 for CP and W´, respectively). The ICC between Familiarization and Test I was r = 0.86 (CP) and r = 0.58 (W´) and between Tests I and II it was r = 0.94 (CP) and r = 0.95 (W´). The CoV notably decreased from 4.1% to 2.6% and from 25.3% to 8.2% for CP and W´, respectively. Despite the non-significant differences for both parameter estimates between Familiarization, Test I, and Test II, ICC and CoV values improved notably after the familiarization trial. Our novel findings indicate that for both, CP and W´ a familiarization trial increased reliability. It is therefore advisable to familiarize well-trained athletes when determining the power-duration relationship using TT under laboratory conditions.

Purpose: To evaluate the predictive validity of critical power (CP) and the work above CP (W′) on cycling performance (mean power during a 20-min time trial; TT20). Methods: On 3 separate days, 10 male cyclists completed a TT20 and 3 CP and W′ prediction trials of 1, 4, and 10 min and 2, 7, and 12 min in field conditions. CP and W′ were modeled across combinations of these prediction trials with the hyperbolic, linear work/time, and linear power inverse-time (INV) models. The agreement and the uncertainty between the predicted and actual TT20 were assessed with 95% limits of agreement and a probabilistic approach, respectively. Results: Differences between the predicted and actual TT20 were “trivial” for most of the models if the 1-min trial was not included. Including the 1-min trial in the INV and linear work/time models “possibly” to “very likely” overestimated TT20. The INV model provided the smallest total error (ie, best individual fit; 6%) for all cyclists (305 [33] W; 19.6 [3.6] kJ). TT20 predicted from the best individual fit-derived CP, and W′ was strongly correlated with actual TT20 (317 [33] W; r = .975; P < .001). The bias and 95% limits of agreement were 4 (7) W (−11 to 19 W). Conclusions: Field-derived CP and W′ accurately predicted cycling performance in the field. The INV model was most accurate to predict TT20 (1.3% [2.4%]). Adding a 1-min-prediction trial resulted in large total errors, so it should not be included in the models.

To assess the validity and reliability of the Garmin Vector against the SRM power meter, 6 cyclists completed 3 continuous trials at power outputs from 100-300 W at 50-90 rev·min and a 5-min time trial in laboratory and field conditions. In field conditions only, a 30-s sprint was performed. Data were compared with paired samples t-tests, with the 95% limits of agreement (LoA) and the typical error. Reliability was calculated as the coefficient of variation (CV). There was no significant difference between the devices in power output in laboratory (p=0.245) and field conditions (p=0.312). 1-s peak power was significantly different between the devices (p=0.043). The LoA were ~1.0±5.0 W and ~0.5±0.5 rev·min in both conditions. The LoA during the 30-s sprint was 6.3±38.9 W and for 1-s peak power it was 18.8±17.1 W. The typical error for power output was 2.9%, while during sprint cycling it was 7.4% for 30-s and 2.7% for 1-s peak power. For cadence, the typical error was below 1.0%. The mean CVs were ~1.0% and ~3.0% for the SRM and Garmin, respectively. These findings suggest, that the Garmin Vector is a valid alternative for training. However, during sprint cycling there is lower agreement with the SRM power meter. Both devices provide good reliability (CV<3.0%).