Is Stryd critical power a meaningful parameter for runners?

Running critical power modeling: insights into physiological and neuromuscular performance factors

This study aims to determine the relationship between the critical power and the work above critical power (W') with physiological and neuromuscular performance factors. Twenty-one recreational runners, 11 men and 10 women, were tested on three separate occasions spaced by 48-72 hours. In the first testing session, the vertical load-velocity and horizontal force-velocity profiles were determined. In the second testing session, the maximal oxygen uptake, running economy, first and second ventilatory thresholds and maximal aerobic power were determined through a graded exercise test. In the third testing session, the critical power and W' were determined through two-time trials of 9- and 3-minutes. The critical power was significantly correlated with all physiological factors evaluated (r≥- 0.479; p≤0.028); meanwhile, none were with W' (r≤0.377; p≥0.092). On the other hand, W' was positively associated with specific neuromuscular performance factors of the vertical load-velocity (r=0.763; p<0.001; i.e., theoretical maximal vertical velocity) and horizontal force-velocity (r≥0.658; p≤0.001; i.e., theoretical maximal horizontal velocity and maximal power) profiles. The critical power modelling results in a feasible procedure to capture both physiological and neuromuscular performance determinants through the critical power and W' parameters, respectively.

Classification of Male Athletes Based on Critical Power

This study aimed to classify male athletes based on their performance levels derived from running critical power (CP) using the 9/3-minute Stryd CP test, enabling customized training strategies and goal setting. Twenty-four trained athletes underwent the 9/3-minute running CP test on a certified 400-m athletics track. Hierarchical cluster analysis using Ward's method categorized athletes based on CP into distinct performance tiers. Three clusters were identified with centroids of 3.87±0.12, 4.45±0.17, and 5.14±0.29 W/kg. Five performance tiers were defined through ordinary least square linear regression based on power (W/kg): Tier 1: Fair (2.9 to 3.6 W/kg), Tier 2: Tourist (3.6 to 4.2 W/kg), Tier 3: Regional (4.2 to 4.8 W/kg), Tier 4: National (4.8 to 5.5 W/kg), Tier 5: International (5.5 to 6.1 W/kg). Low semi-partial R-squared (SpR 2) values (0.02 to 0.05) indicated minimal homogeneity loss when merging clusters. R-squared (R 2) explained 89% to 96% of CP variance, emphasizing cluster analysis effectiveness. The linear regression model demonstrated a strong fit (r 2+=+0.997) with a significant intercept (3.22 W/kg), slope (0.63 W/kg/tier), and a low standard error of estimate (0.045 W/kg). This classification offers insights into male athlete performance levels based on CP, facilitating targeted training programs for varying performance levels.

Can We Predict Long-Duration Running Power Output? Validity of the Critical Power, Power Law, and Logarithmic Models

ABSTRACT

Ruiz-Alias, SA, Ñancupil-Andrade, AA, Pérez-Castilla, A, and García-Pinillos, F. Can we predict long-duration running power output? Validity of the critical power, power law, and logarithmic models. J Strength Cond Res 38(2): 306-310, 2024-Predicting long-distance running performance has always been a challenge for athletes and practitioners. To ease this task, different empirical models have been proposed to model the drop of the running work rate with the increase of time. Therefore, this study aims to determine the validity of different models (i.e., CP, power law, and Peronnet) to predict long-duration running power output (i.e., 30 and 60 minutes). In a 4-week training period, 15 highly trained athletes performed 7-time trials (i.e., 3, 4, 5, 10, 20, 30, and 60 minutes) in a randomized order. Then, their power-duration curves (PDCs) were defined through the work-time critical power model (CP work ), power-1/time (CP 1/time ), 2-parameter hyperbolic (CP 2hyp ), 3-parameter hyperbolic (CP 3hyp ), the undisclosed Stryd (CP stryd ), and Golden Cheetah (CP cheetah ) proprietary models, and the power law and Peronnet models using the 3 to 20 minutes time trials. These ones were extrapolated to the 30- and 60-minute power output and compared with the actual performance. The CP 2hyp , CP 3hyp , CP stryd , and CP cheetah provided valid 30- and 60-minute power output estimations (≤2.6%). The CP work and CP 1/time presented a large predicting error for 30 minutes (≥4.4%), which increased for 60 minutes (≥8.1%). The power law and Peronnet models progressively increased their predicting error at the longest duration (30 minutes: ≤-1.6%; 60 minutes: ≤-6.6%), which was conditioned by the endurance capability of the athletes. Therefore, athletes and practitioners are encouraged to applicate the aforementioned valid models to their PDC to estimate the 30-minute and 60-minute power output.

Running Functional Threshold versus Critical Power: Same Concept but Different Values

The aims of this study were (i) to estimate the functional threshold power (FTP) and critical power (CP) from single shorter time trials (TTs) (i. e. 10, 20 and 30 minutes) and (ii) to assess their location in the power-duration curve. Fifteen highly trained athletes randomly performed ten TTs (i. e. 1, 2, 3, 4, 5, 10, 20, 30, 50 and 60 minutes). FTP was determined as the mean power output developed in the 60-min TT, while CP was estimated in the running power meter platform according to the manufacturer's recommendations. The linear regression analysis revealed an acceptable FTP estimate for the 10, 20 and 30-min TTs (SEE≤12.27 W) corresponding to a correction factor of 85, 90 and 95%, respectively. An acceptable CP estimate was only observed for the 20-min TT (SEE=6.67 W) corresponding to a correction factor of 95%. The CP was located at the 30-min power output (1.0 [-5.1 to 7.1] W), which was over FTP (14 [7.0 to 21] W). Therefore, athletes and practitioners concerned with determining FTP and CP through a feasible testing protocol are encouraged to perform a 20-min TT and apply a correction factor of 90 and 95%, respectively.

Running Critical Power: A Comparison Of Different Theoretical Models

This study aimed (i) to compare the critical power (CP) and work capacity over CP (W´) values reported by the different CP models available in current analysis software packages (Golden Cheetah and Stryd platform), (ii) to locate the CP values in the power-duration curve (PDC), and (iii) to determine the influence of the CP model used on the W´ balance. Fifteen trained athletes performed four time trials (i. e., 3, 5, 10, 20 minutes) to define their PDC through different CP models: work-time (CPwork), power-1/time (CP1/time), Morton hyperbolic (CPhyp), Stryd platform (CPstryd), and Bioenergetic Golden Cheetah (CPCheetah). Three additional time trials were performed: two to locate the CP values in the PDC (30 and 60 minutes), and one to test the validity of the W' balance model (4 minutes). Significant differences (p<0.001) were reported between models for the estimated parameters (CP, W´). CPcheetah was associated with the power output developed between 10 to 20 minutes, CP1/time, CPstryd CPwork and CPhyp. The W´ reported by the three-parameter CP models overestimated the actual 4 minutes time to exhaustion, with CPwork (0.48 [- 0.19 to 1.16] minutes); and CP1/time (0.40 [- 0.13 to 0.94] minutes) being the only valid models (p≥0.240).

Is Running Power a Useful Metric? Quantifying Training Intensity and Aerobic Fitness Using Stryd Running Power Near the Maximal Lactate Steady State

We sought to determine the utility of Stryd, a commercially available inertial measurement unit, to quantify running intensity and aerobic fitness. Fifteen (eight male, seven female) runners (age = 30.2 [4.3] years; V·O2max = 54.5 [6.5] ml·kg-1·min-1) performed moderate- and heavy-intensity step transitions, an incremental exercise test, and constant-speed running trials to establish the maximal lactate steady state (MLSS). Stryd running power stability, sensitivity, and reliability were evaluated near the MLSS. Stryd running power was also compared to running speed, V·O2, and metabolic power measures to estimate running mechanical efficiency (EFF) and to determine the efficacy of using Stryd to delineate exercise intensities, quantify aerobic fitness, and estimate running economy (RE). Stryd running power was strongly associated with V·O2 (R2 = 0.84; p < 0.001) and running speed at the MLSS (R2 = 0.91; p < 0.001). Stryd running power measures were strongly correlated with RE at the MLSS when combined with metabolic data (R2 = 0.79; p < 0.001) but not in isolation from the metabolic data (R2 = 0.08; p = 0.313). Measures of running EFF near the MLSS were not different across intensities (~21%; p > 0.05). In conclusion, although Stryd could not quantify RE in isolation, it provided a stable, sensitive, and reliable metric that can estimate aerobic fitness, delineate exercise intensities, and approximate the metabolic requirements of running near the MLSS.

Can we predict long-duration running power output? A matter of selecting the appropriate predicting trials and empirical model

When facing a long-distance race, athletes and practitioners could develop an efficient pacing strategy and training paces if an accurate performance estimate of the target distance is achieved. Therefore, this study aims to determine the validity of different empirical models (i.e. critical power [CP], Power law and Peronnet) to predict long-duration power output (i.e. 60 min) when using two or three time trial configurations. In a 5-week training period, fifteen highly trained athletes performed nine-time trials (i.e. 1, 2, 3, 4, 5, 10, 20, 30, and 60 min) in a randomized order. Their power-duration curves were defined through the work-time (CPwork), power-1/time (CP1/time), two-parameter hyperbolic (CP2hyp), three-parameter hyperbolic (CP3hyp) CP models using different two- and three-time trial configurations. The undisclosed proprietary CP models of the Stryd (CPstryd) and Golden Cheetah training software (CPcheetah) were also computed as well as the non-asymptotic Power law and Peronnet models. These were extrapolated to the 60-min power output and compared to the actual performance. The shortest valid configuration (95% confidence interval < 12 W) for CPwork and CP1/time was 3-30 min (Bias: 8.3 [4.9 to 11.7] W), for CPstryd was 10-30 min (Bias: 4.2 [- 1.0 to 9.4] W), for CP2hyp, CP3hyp and CPcheetah was 3-5-30 min (Bias < 5.7 W), for Power law was 1-3-10 min (- 1.0 [- 11.9 to 9.9] W), and for Peronnet was 4-20 min (- 3.0 [- 10.2 to 4.3] W). All the empirical models provided valid estimates when the two or three predicting trial configurations selected attended each model fitting needs.

Prediction of Half-Marathon Power Target using the 9/3-Minute Running Critical Power Test

Running power output allows for controlling variables that have been previously overlooked by relying solely on speed, such as surface, gradient and weight. The ability to measure this external load variable now enables the analysis of concepts that have predominantly been studied in cycling, such as the Critical Power (CP), in the context of running. This study aims to predict the CP target at which trained athletes run a half-marathon and determine whether races of this distance can serve as a valid alternative to update the CP record. A group of nine trained athletes performed the 9/3-minute Stryd CP test and participated in a half-marathon race in two separate testing sessions conducted in the field. The average power during a half-marathon race is a valid alternative method for determining the CP in trained athletes, as evidenced by the agreement (95% CI: -0.11 to 0.37 W/kg) and trivial systematic bias (0.13 W/kg) between methods. The linear regression model half-marathon power = 0.97 + 0.75·CP (W/kg) showed low standard error of estimate (0.29 W/kg) and significant large association between methods (r = 0.88; p = 0.002). Coaches and athletes should be aware that the CP target for a half-marathon race is 97.3% of the CP determined by the 9/3-minute Stryd CP test.

Effects of the Nike ZoomX Vaporfly Next% 2 shoe on long-interval training performance, kinematics, neuromuscular parameters, running power and fatigue

We analysed the effects of the Nike ZoomX Vaporfly (VPF) on long-interval training performance, kinematic parameters, running power and fatigue compared to a traditional running shoe. Twelve well-trained men (mean ± SD: 32.91 ± 7.50 years; 69.29 ± 7.55 kg and 172.73 ± 5.97 cm) performed two long-interval training sessions (5 × 1000 m with 90s recovery period) 7 days apart, with the VPF shoe or a traditional running shoe (CON) in random order. The countermovement jump (CMJ) height was measured before and after the training sessions and heart rate, spatiotemporal parameters, running power and leg stiffness was measured during training sessions. Running-related pain was assessed prior and post-24 h of each training session. Long-interval training performance improved 2.4% using the VPF shoe compared to CON (p = 0.009; ES = 0.482). Step length, contact time and leg stiffness were higher (p < 0.05; ES = 0.51, ES = 0.677, ES = 0.356) while flight time was lower (p < 0.001; ES = 0.756) when using VPF. Running power decreased in a similar way in both conditions throughout the training session. Vertical power was significantly higher in the VPF condition (p = 0.023, ES = 0.388). CMJ height decreased in both conditions after training (4.7 vs. 7.2%, for the VPF and control, respectively, p < 0.001; ES = 0.573). Finally, the perceived muscle pain was influenced by the shoe model condition (chi-square 5.042, P = 0.025). VPF shoes improved the long-interval training performance with similar running power, heart rate and neuromuscular fatigue, and reduced subjective perceived muscle pain compared to regular training shoes. HighlightsVPF shoe may improve long-interval training performance in trained runners with the same running power and heart rate.Lower subjective perceived muscle pain is found with VPF compared to the regular training shoes.This type of footwear may be used in high-intensity training sessions aiming to increase the training volume at higher intensities with lower associated fatigue.