Is the Functional Threshold Power a Valid Metric to Estimate the Maximal Lactate Steady State in Cyclists?

ABSTRACT: Lillo-Beviá, JR, Courel-Ibáñez, J, Cerezuela-Espejo, V, Morán-Navarro, R, Martínez-Cava, A, and Pallarés, JG. Is the functional threshold power a valid metric to estimate the maximal lactate steady state in cyclists? J Strength Cond Res 36(1): 167-173, 2022-The aims of this study were to determine (a) the repeatability of a 20-minute time-trial (TT20), (b) the location of the TT20 in relation to the main physiological events of the aerobic-anaerobic transition, and (c) the predictive power of a list of correction factors and linear/multiple regression analysis applied to the TT20 result to estimate the individual maximal lactate steady state (MLSS). Under laboratory conditions, 11 trained male cyclists and triathletes (V̇o2max 59.7 ± 3.0 ml·kg-1·min-1) completed a maximal graded exercise test to record the power output associated with the first and second ventilatory thresholds and V̇o2max measured by indirect calorimetry, several 30 minutes constant tests to determine the MLSS, and 2 TT20 tests with a short warm-up. Very high repeatability of TT20 tests was confirmed (standard error of measurement of ±3 W and smallest detectable change of ±9 W). Validity results revealed that MLSS differed substantially from TT20 (bias = 26 ± 7 W). The maximal lactate steady state was then estimated from the traditional 95% factor (bias = 12 ± 7 W) and a novel individual correction factor (ICF% = MLSS/TT20), resulting in 91% (bias = 1 ± 6 W). Complementary linear (MLSS = 0.7488 × TT20 + 43.24; bias = 0 ± 5 W) and multiple regression analysis (bias = 0 ± 4 W) substantially improved the individual MLSS workload estimation. These findings suggest reconsidering the TT20 procedures and calculations to increase the effectiveness of the MLSS prediction.

A New Short Track Test to Estimate the V[Combining Dot Above]O2max and Maximal Aerobic Speed in Well-Trained Runners.

Pallarés, JG, Cerezuela-Espejo, V, Morán-Navarro, R, Martínez-Cava, A, Conesa, E, and Courel-Ibáñez, J. A new short track test to estimate the V[Combining Dot Above]O2max and maximal aerobic speed in well-trained runners. J Strength Cond Res 33(5): 1216-1221, 2019-This study was designed to validate a new short track test (Track(1:1)) to estimate running performance parameters maximal oxygen uptake (V[Combining Dot Above]O2max) and maximal aerobic speed (MAS), based on a laboratory treadmill protocol and gas exchange data analysis (Lab(1:1)). In addition, we compared the results with the University of Montreal Track Test (UMTT). Twenty-two well-trained male athletes (V[Combining Dot Above]O2max 60.3 ± 5.9 ml·kg·min; MAS ranged from 17.0 to 20.3 km·h) performed 4 testing protocols: 2 in laboratory (Lab(1:1)-pre and Lab(1:1)) and 2 in the field (UMTT and Track(1:1)). The Lab(1:1)-pre was designed to determine individuals' Vpeak and set initial speeds for the subsequent Lab(1:1) short ramp graded exercise testing protocol, starting at 13 km·h less than each athlete's Vpeak, with 1 km·h increments per minute until exhaustion. The Track(1:1) was a reproduction of the Lab(1:1) protocol in the field. A novel equation was yielded to estimate the V[Combining Dot Above]O2max from the Vpeak achieved in the Track(1:1). Results revealed that the UMTT significantly underestimated the Vpeak (-4.2%; bias = -0.8 km·h; p < 0.05), which notably altered the estimations (MAS: -2.6%, bias = -0.5 km·h; V[Combining Dot Above]O2max: 4.7%, bias = 2.9 ml·kg·min). In turn, data from Track(1:1) were very similar to the laboratory test and gas exchange methods (Vpeak: -0.6%, bias = <0.1 km·h; MAS: 0.3%, bias = <0.1 km·h; V[Combining Dot Above]O2max: 0.4%, bias = 0.2 ml·kg·min, p > 0.05). Thus, the current Track(1:1) test emerges as a better alternative than the UMTT to estimate maximal running performance parameters in well-trained and highly trained athletes on the field.

Training Load and Performance Impairments in Professional Cyclists During COVID-19 Lockdown.

PURPOSE: The COVID-19 outbreak has challenged professional athletes' training and competition routines in a way not seen before. This report aims to inform about the changes in training volume and intensity distribution and their effects on functional performance due to a 7-week home-confinement period in professional road cyclists from a Union Cycliste Internationale Pro Team. METHODS: A total of 18 male professional cyclists (mean [SD] age = 24.9 [2.8] y, body mass = 66.5 [5.6] kg, maximal aerobic power = 449 [39] W; 6.8 [0.6] W/kg) were monitored during the 10 weeks before the lockdown (outdoor cycling) and the 7-week lockdown (indoor cycling turbo trainer). Data from the mean maximal power output (in watts per kilogram) produced during the best 5-minute and best 20-minute records and the training intensity distributions (weekly volumes at power-based training zones) were collected from WKO5 software. RESULTS: Total training volume decreased 33.9% during the lockdown (P < .01). Weekly volumes by standardized zones (Z1 to Z6) declined between 25.8% and 52.2% (effect size from 0.83 to 1.57), except for Z2 (P = .38). There were large reductions in best 5-minute and best 20-minute performance (effect size > 1.36; P < .001) with losses between 1% and 19% in all the cyclists. CONCLUSIONS: Total indoor volumes of 12 hours per week, with 6 hours per week at low intensity (Z1 and Z2) and 2 hours per week at high intensity over the threshold (Z5 and Z6), were insufficient to maintain performance in elite road cyclists during the COVID-19 lockdown. Such performance declines should be considered to enable a safe and effective return to competition.

Are we ready to measure running power? Repeatability and concurrent validity of five commercial technologies.

Training prescription in running activities have benefited from power output (PW) data obtained by new technologies. Nevertheless, to date, the suitability of PW data provided by these tools is still uncertain. The present study aimed to: (i) analyze the repeatability of five commercially available technologies for running PW estimation, and (ii) examine the concurrent validity through the relationship between each technology PW and oxygen uptake (VO2). On two occasions (test-retest), twelve endurance-trained male athletes performed on a treadmill (indoor) and an athletic track (outdoor) three submaximal running protocols with manipulations in speed, body weight and slope. PW was simultaneously registered by the commercial technologies StrydApp, StrydWatch, RunScribe, GarminRP and PolarV, while VO2 was monitored by a metabolic cart. Test-retest data from the environments (indoor and outdoor) and conditions (speed, body weight and slope) were used for repeatability analysis, which included the standard error of measurement (SEM), coefficient of variation (CV) and intraclass correlation coefficient (ICC). A linear regression analysis and the standard error of estimate (SEE) were used to examine the relationship between PW and VO2. Stryd device was found as the most repeatable technology for all environments and conditions (SEM ≤ 12.5 W, CV ≤ 4.3%, ICC ≥ 0.980), besides the best concurrent validity to the VO2 (r ≥ 0.911, SEE ≤ 7.3%). On the contrary, although the PolarV, GarminRP and RunScribe technologies maintain a certain relationship with VO2, their low repeatability questions their suitability. The Stryd can be considered as the most recommended tool, among the analyzed, for PW measurement.

Running power meters and theoretical models based on laws of physics: Effects of environments and running conditions.

Training prescription and load monitoring in running activities have benefited from power output (PW) data offered by new technologies. Nevertheless, to date, the sensitivity of PW data provided by these tools is still not completely clear. The aim of this study was to analyze the level of agreement between the PW estimated by five commercial technologies and the two main internationally theoretical models based on laws of physics, in different environments and running conditions. Ten endurance-trained male athletes performed three submaximal running protocols on a treadmill (indoor) and an athletic track (outdoor), with changes in speed, body weight, and slope. PW was simultaneously registered by the commercial technologies Stryd (StrydApp and StrydWatch), RunScribe, GarminRP and PolarV, whereas theoretical power output (TPW) was calculated by the two mathematical models (TPW1 and TPW2). Statistics included, among others, the Pearson's correlation coefficient (r) and standard error of measurement (SEM). The PolarV, and above all Stryd, showed the closest agreement with the TPW1 (Stryd: r ≥ 0.947, SEM ≤ 11 W; PolarV: r ≥ 0.931, SEM ≤ 64 W) and TPW2 (Stryd: r ≥ 0.933, SEM ≤ 60 W; PolarV: r ≥ 0.932, SEM ≤ 24 W), both indoors and outdoors. On the other hand, the devices GarminRP (r ≤ 0.765, SEM ≥ 59 W) and RunScribe. (r ≤ 0.508, SEM ≥ 125 W) showed the lowest agreement with the TPW1 and TPW2 models for all conditions and environments analyzed. The closest agreement of the Stryd and PolarV technologies with the TPW1 and TPW2 models suggest these tools as the most sensitive, among those analyzed, for PW measurement when changing environments and running conditions.

Time to exhaustion during cycling is not well predicted by critical power calculations.

Three to 5 cycling tests to exhaustion allow prediction of time to exhaustion (TTE) at power output based on calculation of critical power (CP). We aimed to determine the accuracy of CP predictions of TTE at power outputs habitually endured by cyclists. Fourteen endurance-trained male cyclists underwent 4 randomized cycle-ergometer TTE tests at power outputs eliciting (i) mean Wingate anaerobic test (WAnTmean), (ii) maximal oxygen consumption, (iii) respiratory compensation threshold (VT2), and (iv) maximal lactate steady state (MLSS). Tests were conducted in duplicate with coefficient of variation of 5%-9%. Power outputs were 710 ± 63 W for WAnTmean, 366 ± 26 W for maximal oxygen consumption, 302 ± 31 W for VT2 and 247 ± 20 W for MLSS. Corresponding TTE were 00:29 ± 00:06, 03:23 ± 00:45, 11:29 ± 05:07, and 76:05 ± 13:53 min:s, respectively. Power output associated with CP was only 2% lower than MLSS (242 ± 19 vs. 247 ± 20 W; P < 0.001). The CP predictions overestimated TTE at WAnTmean (00:24 ± 00:10 mm:ss) and MLSS (04:41 ± 11:47 min:s), underestimated TTE at VT2 (-04:18 ± 03:20 mm:ss; P < 0.05), and correctly predicted TTE at maximal oxygen consumption. In summary, CP accurately predicts MLSS power output and TTE at maximal oxygen consumption. However, it should not be used to estimate time to exhaustion in trained cyclists at higher or lower power outputs (e.g., sprints and 40-km time trials). Novelty CP calculation enables to predict TTE at any cycling power output. We tested those predictions against measured TTE in a wide range of cycling power outputs. CP appropriately predicted TTE at maximal oxygen consumption intensity but err at higher and lower cycling power outputs.

The Relationship Between Lactate and Ventilatory Thresholds in Runners: Validity and Reliability of Exercise Test Performance Parameters.

The aims of this study were (1) to establish the best fit between ventilatory and lactate exercise performance parameters in running and (2) to explore novel alternatives to estimate the maximal aerobic speed (MAS) in well-trained runners. Twenty-two trained male athletes ( V ˙ O2max 60.2 ± 4.3 ml·kg·min-1) completed three maximal graded exercise tests (GXT): (1) a preliminary GXT to determine individuals' MAS; (2) two experimental GXT individually adjusted by MAS to record the speed associated to the main aerobic-anaerobic transition events measured by indirect calorimetry and capillary blood lactate (CBL). Athletes also performed several 30 min constant running tests to determine the maximal lactate steady state (MLSS). Reliability analysis revealed low CV (<3.1%), low bias (<0.5 km·h-1), and high correlation (ICC > 0.91) for all determinations except V-Slope (ICC = 0.84). Validity analysis showed that LT, LT+1.0, and LT+3.0 mMol·L-1 were solid predictors of VT1 (-0.3 km·h-1; bias = 1.2; ICC = 0.90; p = 0.57), MLSS (-0.2 km·h-1; bias = 1.2; ICC = 0.84; p = 0.74), and VT2 (<0.1 km·h-1; bias = 1.3; ICC = 0.82; p = 0.9l9), respectively. MLSS was identified as a different physiological event and a midpoint between VT1 (bias = -2.0 km·h-1) and VT2 (bias = 2.3 km·h-1). MAS was accurately estimated (SEM ± 0.3 km·h-1) from peak velocity (Vpeak) attained during GXT with the equation: MASEST (km·h-1) = Vpeak (km·h-1) * 0.8348 + 2.308. Current individualized GXT protocol based on individuals' MAS was solid to determine both maximal and submaximal physiological parameters. Lactate threshold tests can be a valid and reliable alternative to VT and MLSS to identify the workloads at the transition from aerobic to anaerobic metabolism in well-trained runners. In contrast with traditional assumption, the MLSS constituted a midpoint physiological event between VT1 and VT2 in runners. The Vpeak stands out as a powerful predictor of MAS.