Estimating Functional Threshold Power in Endurance Running from Shorter Time Trials Using a 6-Axis Inertial Measurement Sensor

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.

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.

Selective Effect of Different High-Intensity Running Protocols on Resistance Training Performance

ABSTRACT

Pérez-Castilla, A, García-Pinillos, F, Miras-Moreno, S, Ramirez-Campillo, R, García-Ramos, A, and Ruiz-Alias, SA. Selective effect of different high-intensity running protocols on resistance training performance. J Strength Cond Res XX(X): 000-000, 2022-This study aimed to explore the acute effect of 2 high-intensity running protocols (high-intensity interval training [HIIT] and sprint interval training [SIT]) on resistance training (RT) performance and their combined effect on the lower-body maximal neuromuscular capacities. Eighteen healthy subjects randomly completed 3 experimental protocols: only RT, HIIT + RT, and SIT + RT. Characteristics of the RT protocol include 3 back-squat sets of 10 repetitions or 20% velocity loss against 60% of 1 repetition maximum with 3 minutes of interset rest. Characteristics of the high-intensity running protocols include HIIT (4 intervals of 4 minutes at ∼110% of functional threshold power with 3 minutes of interinterval rest) and SIT (6 all-out sprints of 30 seconds with 4 minutes and 24 seconds of interinterval rest). The force-velocity relationship (maximal values of force [F0], velocity [v0], and power [Pmax]) was evaluated at the beginning and at the end of each experimental protocol. The number of back-squat repetitions (p = 0.006; effect size [ES] = -0.96), fastest velocity (p = 0.003; ES = -0.63), and average velocity (p = 0.001; ES = -0.73) were lower for the SIT + RT protocol compared with the RT protocol, but no significant differences were observed between the RT and HIIT + RT (p ≥T0.057; ES ≤.-0.46, except -0.82 for the number of back-squat repetitions) and HIIT + RT and SIT + RT (p ≥T0.091; ES .0-0.35) protocols. The 3 protocols induced comparable decreases in v0 and Pmax (F(2,34) 2,0.96; p ≥ 0.393), but F0 tended to decrease after the SIT + RT protocol and to increase after the RT and HIIT + RT protocols (F(2,34) = 4.37; p = 0.035). Compared with RT alone, the data suggest that SIT deteriorates RT quality and F0 capacity more than long-interval HIIT.

Influence of the Shod Condition on Running Power Output: An Analysis in Recreationally Active Endurance Runners

Several studies have already analysed power output in running or the relation between VO2max and power production as factors related to running economy; however, there are no studies assessing the difference in power output between shod and barefoot running. This study aims to identify the effect of footwear on the power output endurance runner. Forty-one endurance runners (16 female) were evaluated at shod and barefoot running over a one-session running protocol at their preferred comfortable velocity (11.71 ± 1.07 km·h−1). The mean power output (MPO) and normalized MPO (MPOnorm), form power, vertical oscillation, leg stiffness, running effectiveness and spatiotemporal parameters were obtained using the Stryd™ foot pod system. Additionally, footstrike patterns were measured using high-speed video at 240 Hz. No differences were noted in MPO (p = 0.582) and MPOnorm (p = 0.568), whereas significant differences were found in form power, in both absolute (p = 0.001) and relative values (p < 0.001), running effectiveness (p = 0.006), stiffness (p = 0.002) and vertical oscillation (p < 0.001). By running barefoot, lower values for contact time (p < 0.001) and step length (p = 0.003) were obtained with greater step frequency (p < 0.001), compared to shod running. The prevalence of footstrike pattern significantly differs between conditions, with 19.5% of runners showing a rearfoot strike, whereas no runners showed a rearfoot strike during barefoot running. Running barefoot showed greater running effectiveness in comparison with shod running, and was consistent with lower values in form power and lower vertical oscillation. From a practical perspective, the long-term effect of barefoot running drills might lead to increased running efficiency and leg stiffness in endurance runners, affecting running economy.

Is This the Real Life, or Is This Just Laboratory? A Scoping Review of IMU-Based Running Gait Analysis

Inertial measurement units (IMUs) can be used to monitor running biomechanics in real-world settings, but IMUs are often used within a laboratory. The purpose of this scoping review was to describe how IMUs are used to record running biomechanics in both laboratory and real-world conditions. We included peer-reviewed journal articles that used IMUs to assess gait quality during running. We extracted data on running conditions (indoor/outdoor, surface, speed, and distance), device type and location, metrics, participants, and purpose and study design. A total of 231 studies were included. Most (72%) studies were conducted indoors; and in 67% of all studies, the analyzed distance was only one step or stride or <200 m. The most common device type and location combination was a triaxial accelerometer on the shank (18% of device and location combinations). The most common analyzed metric was vertical/axial magnitude, which was reported in 64% of all studies. Most studies (56%) included recreational runners. For the past 20 years, studies using IMUs to record running biomechanics have mainly been conducted indoors, on a treadmill, at prescribed speeds, and over small distances. We suggest that future studies should move out of the lab to less controlled and more real-world environments.

Reliability and validity of the Stryd Power Meter during different walking conditions

BACKGROUND

The Styrd Power Meter is gaining special interest for on-field gait analyses due to its low-cost and general availability. However, the reliability and validity of the Stryd during walking on positive slopes using different backpack loads have never been investigated.

RESEARCH QUESTION

Is the Stryd Power Meter reliable and valid for quantifying gait mechanics during walking on positive inclines and during level walking incorporating load carriage?

METHODS

Seventeen participants from a police force rescue team performed 8 submaximal walking trials for 5-min at 3.6 km·h^-1 during different positive slope (1%, 10% and 20%) and backpack load (0%, 10%, 20%, 30% and 40% of body mass) conditions. Two Stryd devices were utilized for reliability analyses. Validity of cadence and ground contact time (GCT) were analyzed against a gold standard device (Optojump).

RESULTS

The Stryd demonstrated acceptable reliability [mean bias: < 2.5%; effect size (ES): < 0.25; standard error of the mean: < 1.7%; r: > 0.76] for power, cadence, and GCT. Validity measures (mean bias: <0.8%; ES: <0.07; r: >0.96; Lin's Concordance Coefficient: 0.96; Mean Absolute Percent Error: <1%) for cadence were also found to be acceptable. The Stryd overestimated (P < 0.001; ES: >5.1) GCT in all the walking conditions. A significant systematic positive bias (P < 0.022; r = 0.56-0.76) was found in 7 conditions.

SIGNIFICANCE

The Stryd Power Meter appears to produce reliable measurements for power output, cadence and GCT. The Stryd produced valid measurements for cadence during walking on positive slopes and during level walking with a loaded backpack. However, the Stryd is not valid for measuring GCT during these walking conditions. This study adds novel data regarding the reliability and validity of this device and might be of particular interest for scientists, practitioners, and first responders seeking reliable devices to quantify gait mechanics during walking.

The Relationship between VO2max, Power Management, and Increased Running Speed: Towards Gait Pattern Recognition through Clustering Analysis

Triathlon has become increasingly popular in recent years. In this discipline, maximum oxygen consumption (VO₂max) is considered the gold standard for determining competition cardiovascular capacity. However, the emergence of wearable sensors (as Stryd) has drastically changed training and races, allowing for the more precise evaluation of athletes and study of many more potential determining variables. Thus, in order to discover factors associated with improved running efficiency, we studied which variables are correlated with increased speed. We then developed a methodology to identify associated running patterns that could allow each individual athlete to improve their performance. To achieve this, we developed a correlation matrix, implemented regression models, and created a heat map using hierarchical cluster analysis. This highlighted relationships between running patterns in groups of young triathlon athletes and several different variables. Among the most important conclusions, we found that high VO₂max did not seem to be significantly correlated with faster speed. However, faster individuals did have higher power per kg, horizontal power, stride length, and running effectiveness, and lower ground contact time and form power ratio. VO₂max appeared to strongly correlate with power per kg and this seemed to indicate that to run faster, athletes must also correctly manage their power.