Literature DB >> 29912073

An Assessment of Running Power as a Training Metric for Elite and Recreational Runners.

Rachel L Aubry1, Geoff A Power2, Jamie F Burr1.   

Abstract

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(8): 2258-2264, 2018-Power, as a testing and training metric to quantify effort, is well accepted in cycling, but is not commonly used in running to quantify effort or performance. This study sought to investigate a novel training tool, the Stryd Running Power Meter, and the applicability of running power (and its individually calculated run mechanics) to be a useful surrogate of metabolic demand (V[Combining Dot Above]O2), across different running surfaces, within different caliber runners. Recreational (n = 13) and elite (n = 11) runners completed a test assessing V[Combining Dot Above]O2 at 3 different paces, while wearing a Stryd Power Meter on both an indoor treadmill and an outdoor track, to investigate relationships between estimated running power and metabolic demand. A weak but significant relationship was found between running power and V[Combining Dot Above]O2 considering all participants as a homogenous group (r = 0.29); however, when assessing each population individually, no significant relationship was found. Examination of the individual mechanical components of power revealed that a correlative decrease in V[Combining Dot Above]O2 representing improved efficiency was associated with decreased ground contact time (r = 0.56), vertical oscillation (r = 0.46), and cadence (r = 0.37) on the treadmill in the recreational group only. Although metabolic demand differed significantly between surfaces at most speeds, run power did not accurately reflect differences in metabolic cost between the 2 surfaces. Running power, calculated via the Stryd Power Meter, is not sufficiently accurate as a surrogate of metabolic demand, particularly in the elite population. However, in a recreational population, this training tool could be useful for feedback on several running dynamics known to influence running economy.

Entities:  

Mesh:

Year:  2018        PMID: 29912073     DOI: 10.1519/JSC.0000000000002650

Source DB:  PubMed          Journal:  J Strength Cond Res        ISSN: 1064-8011            Impact factor:   3.775


  8 in total

1.  Wearables for Running Gait Analysis: A Systematic Review.

Authors:  Rachel Mason; Liam T Pearson; Gillian Barry; Fraser Young; Oisin Lennon; Alan Godfrey; Samuel Stuart
Journal:  Sports Med       Date:  2022-10-15       Impact factor: 11.928

2.  A Systematic Review and Meta-Analysis of Crossover Studies Comparing Physiological, Perceptual and Performance Measures Between Treadmill and Overground Running.

Authors:  Jayme R Miller; Bas Van Hooren; Chris Bishop; Jonathan D Buckley; Richard W Willy; Joel T Fuller
Journal:  Sports Med       Date:  2019-05       Impact factor: 11.136

3.  Comparison of energy expenditure and substrate metabolism during overground and motorized treadmill running in Chinese middle-aged women.

Authors:  Shuo Li; Jing-Jing Xue; Ping Hong; Chao Song; Zi-Hong He
Journal:  Sci Rep       Date:  2020-02-04       Impact factor: 4.379

4.  Effects of Wearable Devices with Biofeedback on Biomechanical Performance of Running-A Systematic Review.

Authors:  Alexandra Giraldo-Pedroza; Winson Chiu-Chun Lee; Wing-Kai Lam; Robyn Coman; Gursel Alici
Journal:  Sensors (Basel)       Date:  2020-11-19       Impact factor: 3.576

5.  Foot Strike Angle Prediction and Pattern Classification Using LoadsolTM Wearable Sensors: A Comparison of Machine Learning Techniques.

Authors:  Stephanie R Moore; Christina Kranzinger; Julian Fritz; Thomas Stӧggl; Josef Krӧll; Hermann Schwameder
Journal:  Sensors (Basel)       Date:  2020-11-25       Impact factor: 3.576

6.  Artificial Intelligence in Elite Sports-A Narrative Review of Success Stories and Challenges.

Authors:  Fabian Hammes; Alexander Hagg; Alexander Asteroth; Daniel Link
Journal:  Front Sports Act Living       Date:  2022-07-11

Review 7.  Mechanical Power in Endurance Running: A Scoping Review on Sensors for Power Output Estimation during Running.

Authors:  Diego Jaén-Carrillo; Luis E Roche-Seruendo; Antonio Cartón-Llorente; Rodrigo Ramírez-Campillo; Felipe García-Pinillos
Journal:  Sensors (Basel)       Date:  2020-11-13       Impact factor: 3.576

Review 8.  Is This the Real Life, or Is This Just Laboratory? A Scoping Review of IMU-Based Running Gait Analysis.

Authors:  Lauren C Benson; Anu M Räisänen; Christian A Clermont; Reed Ferber
Journal:  Sensors (Basel)       Date:  2022-02-23       Impact factor: 3.576

  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.