Running power: lab based vs. portable devices measurements and its relationship with aerobic power

Running Critical Power and W´: Influence of the Environment, Timing and Time Trial Order

This study aimed to determine the influence of the testing environment (track vs. treadmill), time trial order (long-short vs. short-long), and timing (within-session vs. between-sessions) on the critical power (CP) and work over CP (W´), using the power metric in runners. Fifteen highly trained athletes performed three test sessions composed of two time trials of 9- and 3-min, separated by a 30-min rest period. One session was performed on a track, and two sessions on a treadmill, alternating the order of the time trials. The CP and W´ values determined on the track were significantly greater and lower than on the treadmill, respectively (p<0.001; CP≥89 W; W´≥3.7 kJ). Their degree of agreement was low (SEE CP>5%; W´>10%) and therefore was not interchangeable. There were no performance differences in the timing of the time trials (p=0.320). Lastly, performing the 9-min trial first resulted in a greater power output compared to when executed last (p<0.001; 4.9 W), although this resulted in similar CP and W´ values (Bias<5 and 10%, respectively). In conclusion, it is feasible to test CP and W´ in a single testing session, irrespective of the time trial order, although not interchangeably between track and treadmill.

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.

A Comparison of Critical Speed and Critical Power in Runners Using Stryd Running Power

PURPOSE

Although running traditionally relies on critical speed (CS) as an indicator of critical intensity, portable inertial measurement units offer a potential solution for estimating running mechanical power to assess critical power (CP) in runners. The purpose of this study was to determine whether CS and CP differ when assessed using the Stryd device, a portable inertial measurement unit, and if 2 running bouts are sufficient to determine CS and CP.

METHODS

On an outdoor running track, 10 trained runners (V˙O2max, 59.0 [4.2] mL·kg-1·min-1) performed 3 running time trials (TT) between 1200 and 4400 m on separate days. CS and CP were derived from 2-parameter hyperbolic speed-time and power-time models, respectively, using 2 (CS2TT and CP2TT) and 3 (CS3TT and CP3TT) TTs. Subsequently, runners performed constant-intensity running for 800 m at their calculated CS3TT and CP3TT.

RESULTS

Running at the calculated CS3TT speed (3.88 [0.44] m·s-1) elicited an average Stryd running power (271 [28] W) not different from the calculated CP3TT (270 [28]; P = .940; d = 0.02), with excellent agreement between the 2 values (intraclass correlation coefficient = .980). The CS2TT (3.97 [0.42] m·s-1) was not higher than CS3TT (3.89 [0.44] m·s-1; P = .178; d = 0.46); however, CP2TT (278 [29] W) was greater than CP3TT (P = .041; d = 0.75).

CONCLUSION

The running intensities at CS and CP were similar, supporting the use of running power (Stryd) as a metric of aerobic fitness and exercise prescription, and 2 trials provided a reasonable, albeit higher, estimate of CS and CP.

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.

Estimation of horizontal running power using foot-worn inertial measurement units

Feedback of power during running is a promising tool for training and determining pacing strategies. However, current power estimation methods show low validity and are not customized for running on different slopes. To address this issue, we developed three machine-learning models to estimate peak horizontal power for level, uphill, and downhill running using gait spatiotemporal parameters, accelerometer, and gyroscope signals extracted from foot-worn IMUs. The prediction was compared to reference horizontal power obtained during running on a treadmill with an embedded force plate. For each model, we trained an elastic net and a neural network and validated it with a dataset of 34 active adults across a range of speeds and slopes. For the uphill and level running, the concentric phase of the gait cycle was considered, and the neural network model led to the lowest error (median ± interquartile range) of 1.7% ± 12.5% and 3.2% ± 13.4%, respectively. The eccentric phase was considered relevant for downhill running, wherein the elastic net model provided the lowest error of 1.8% ± 14.1%. Results showed a similar performance across a range of different speed/slope running conditions. The findings highlighted the potential of using interpretable biomechanical features in machine learning models for the estimating horizontal power. The simplicity of the models makes them suitable for implementation on embedded systems with limited processing and energy storage capacity. The proposed method meets the requirements for applications needing accurate near real-time feedback and complements existing gait analysis algorithms based on foot-worn IMUs.

A "Step-Ramp-Step" Protocol to Identify Running Speed and Power Associated with the Maximal Metabolic Steady State

PURPOSE

A previously established Step-Ramp-Step (SRS) exercise protocol was able to accurately predict the work rate associated with the maximal metabolic steady state (MMSS) in cyclists. The purpose of this study was to determine whether a modified SRS protocol could predict the running speed and power associated with the MMSS.

METHODS

Fifteen (8 male; 7 female) runners (V̇O 2max 54.5 [6.5] mL·kg -1 ·min -1 ) were recruited for this investigation composed of four to five visits. In the first visit, runners performed a moderate intensity step (MOD), an incremental exercise test, and a heavy intensity step (HVY), on a motorized treadmill. This SRS protocol was used to predict the running speed and power associated with the MMSS (i.e., the SRS-MMSS), where running power was assessed by a wearable device (Stryd) attached to each runner's shoe. Subsequent visits were used to confirm the maximal lactate steady state (MLSS) as a proxy measure of the MMSS (i.e., the MLSS-MMSS) and to validate the SRS-MMSS speed and power estimates.

RESULTS

The estimated SRS-MMSS running speed (7.2 [0.6] mph) was significantly lower than confirmed running speed at MLSS-MMSS (7.5 [0.8] mph; bias = 3.6%, P = 0.005); however, the estimated SRS-MMSS running power (241 [35] W) was not different than the MLSS-MMSS confirmed running power (240 [37] W; bias = -0.6%; P = 0.435). V̇O 2 at SRS-MMSS (3.22 [0.49] L·min -1 ) was not different than respiratory compensation point (3.26 [0.58] L·min -1 ; P = 0.430). Similarly, V̇O 2 at MLSS-MMSS (3.30 [0.54] L·min -1 ) was not different than respiratory compensation point ( P = 0.438).

CONCLUSIONS

The SRS protocol allows MMSS, as measured by MLSS, to be accurately determined using running power (Stryd), but not speed, in a single laboratory visit.

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.

9/3-Minute Running Critical Power Test: Mechanical Threshold Location With Respect to Ventilatory Thresholds and Maximum Oxygen Uptake

PURPOSE

The critical power (CP) concept has been extended from cycling to the running field with the development of wearable monitoring tools. Particularly, the Stryd running power meter and its 9/3-minute CP test is very popular in the running community. Locating this mechanical threshold according to the physiological landmarks would help to define each boundary and intensity domain in the running field. Thus, this study aimed to determine the CP location concerning anaerobic threshold, respiratory compensation point (RCP), and maximum oxygen uptake (VO2max).

METHOD

A group of 15 high-caliber athletes performed the 9/3-minute Stryd CP test and a graded exercise test in 2 different testing sessions.

RESULTS

Anaerobic threshold, RCP, and CP were located at 73% (5.41%), 86.82% (3.85%), and 88.71% (5.84%) of VO2max, respectively, with a VO2max of 66.3 (7.20) mL/kg/min. No significant differences were obtained between CP and RCP in any of its units (ie, in watts per kilogram and milliliters per kilogram per minute; P ≥ .184).

CONCLUSIONS

CP and RCP represent the same boundary in high-caliber athletes. These results suggest that coaches and athletes can determine the metabolic perturbance threshold that CP and RCP represent in an easy and accessible way.