Methodological Flaws in Aubry, RL, Power, GA, and Burr, JF. An Assessment of Running Power as a Training Metric for Elite and Recreational Runners. J Strength Cond Res 32: 2258-2264, 2018

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.

Reliability and Validity of the SHFT Running Power Meter

The SHFT device is a novel running wearable consisting of two pods connected to your smartphone issuing several running metrics based on accelerometer and gyroscope technology. The purpose of this study was to investigate the reliability and validity of the power output (PO) metric produced by the SHFT device. To assess reliability, 12 men ran on an outdoor track at 10.5 km·h^-1 and 12 km·h^-1 on two consecutive days. To assess validity, oxygen uptake (VO₂) and SHFT data from eight men and seven women were collected during incremental submaximal running tests on an indoor treadmill on one to four separate days (34 tests in total). SHFT reliability on the outdoor track was strong with coefficients of variance (CV) of 1.8% and 2.4% for 10.5 and 12 km·h^-1, respectively. We observed a very strong linear relationship between PO and VO₂ (r² = 0.54) within subjects, and a very strong linear relationship within each subject within each treadmill test (r² = 0.80). We conclude that SHFT provides a reliable running power estimate and that a very strong relationship between SHFT-Power and metabolic rate exists, which places SHFT as one of the leading commercially available running power meters.

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

In recent years, different companies have developed devices that estimate "running power". The main objective of this paper is to evaluate the effect of running speed on aerobic and running powers measured using force plates and by different devices. The second objective is to evaluate the relationship between aerobic power and running powers measured using force plates and by different devices. We enrolled 11 subjects in the study, they performed 5-min running trials at 2.22, 2.78, 3.33, 3.89 and 4.44 m/s respectively on a force-measuring treadmill while we collected metabolic data. We calculated running power as the dot product of ground reaction force and velocity of the centre of mass and compared it to the running power estimates of three devices: Skillrun (Technogym), Stryd Summit Powermeter (Stryd) and Garmin HRM-Run (Garmin). We found statistically significant linear correlations with running powers measured by all devices and running speed. Although absolute running power measurements were different among devices, an increase of 1 m/s in running speed translated to an increase of 0.944 W/kg in running power (p < 0.001). We found statistically significant linear correlations with running powers measured by all devices and aerobic power, in particular: as aerobic power increases by 1 W/kg, running power increases by 0.218 W/kg for all devices (p < 0.001). For level treadmill running, across speeds, running power measured by commercially available devices reflects force-based measurements and it can be a valuable metric, providing quasi real-time feedback during training sessions and competitions. HighlightsWe evaluated the effect of running speed on aerobic and running powers measured using force plates and by different devices.We also compared the relationship between aerobic power and running powers measured using force plates and by different devices.We found statistically significant linear correlations with running powers measured by all devices and aerobic power, in particular: as aerobic power increases by 1 W/kg, running power increases by 0.218 W/kg for all devices.For level treadmill running, across speeds, running power measured by commercially available devices reflects force-based measurements and it can be a valuable metric, providing quasi real-time feedback during training sessions and competitions.

Mechanical Power in Endurance Running: A Scoping Review on Sensors for Power Output Estimation during Running

Mechanical power may act as a key indicator for physiological and mechanical changes during running. In this scoping review, we examine the current evidences about the use of power output (PW) during endurance running and the different commercially available wearable sensors to assess PW. The Boolean phrases endurance OR submaximal NOT sprint AND running OR runner AND power OR power meter, were searched in PubMed, MEDLINE, and SCOPUS. Nineteen studies were finally selected for analysis. The current evidence about critical power and both power-time and power-duration relationships in running allow to provide coaches and practitioners a new promising setting for PW quantification with the use of wearable sensors. Some studies have assessed the validity and reliability of different available wearables for both kinematics parameters and PW when running but running power meters need further research before a definitive conclusion regarding its validity and reliability.

Validity of the Stryd Power Meter in Measuring Running Parameters at Submaximal Speeds

This study assessed the Stryd running power meter validity at sub-maximal speeds (8 to 19 km/h). Six recreational runners performed an incremental indoor running test. Power output (PO), ground contact time (GCT) and leg spring stiffness (LSS) were compared to reference measures recorded by portable metabolic analyser, force platforms and motion capture system. A Bayesian framework was conducted for systems validity and comparisons. We observed strong and positive linear relationships between Stryd PO and oxygen consumption ( R 2 = 0.82 , B F 10 > 100 ), and between Stryd PO and external mechanical power ( R 2 = 0.88 , B F 10 > 100 ). Stryd power meter underestimated PO ( B F 10 > 100 ) whereas GCT and LSS values did not show any significant differences with the reference measures ( B F 10 = 0.008 , B F 10 = 0.007 , respectively). We conclude that the Stryd power meter provides valid measures of GCT and LSS but underestimates the absolute values of PO.

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 (P_W) data obtained by new technologies. Nevertheless, to date, the suitability of P_W data provided by these tools is still uncertain. The present study aimed to: (i) analyze the repeatability of five commercially available technologies for running P_W estimation, and (ii) examine the concurrent validity through the relationship between each technology P_W 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. P_W was simultaneously registered by the commercial technologies Stryd_App, Stryd_Watch, RunScribe, Garmin_RP and Polar_V, 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 P_W 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 Polar_V, Garmin_RP 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 P_W measurement.