Knowledge is power: Issues of measuring training and performance in cycling

Passfield, Louis; Hopker, James G.; Jobson, Simon A.; Friel, Don J.; Zabala, Míkel

doi:10.1080/02640414.2016.1215504

Cited by 85 publications

(77 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The inclusion of power meters in cycling revolutionized both training and competition [1], and the same may soon be accomplished for runners. Traditionally, the use of this metric during running was restricted to laboratory settings, because it was difficult to estimate.…”

Section: Introductionmentioning

confidence: 99%

How Does Power During Running Change when Measured at Different Time Intervals?

et al. 2019

View full text Add to dashboard Cite

This study aimed to examine how the power output changes while running at a continuous comfortable velocity on a motorized treadmill by comparing running power averaged during different time intervals. Forty-nine endurance runners performed a running protocol on a treadmill at self-selected comfortable velocity. Power output (W) was estimated with the Stryd™ power meter, and it was examined over six recording intervals within the 3-min recording period: 0–10 s, 0–20 s, 0–30 s, 0–60 s, 0–120 s and 0–180 s. The ANOVAs showed no significant differences in the magnitude of the power output between the recording intervals (p=0.276, F=1.614, partial Eta 2 =0.155). An almost perfect association was also observed in the magnitude of the power output between the recording intervals (ICC≥0.999). Bland-Altman plots revealed no heteroscedasticity of error for the power output in any of the between-intervals comparisons (r 2<0.1), although longer recording intervals yield smaller systematic bias, random errors, and narrower limits of agreement for power output. The results show that power data during running, as measured through the Stryd™ system, is a stable metric with negligible differences, in practical terms, between shorter (i. e., 10, 20, 30, 60 or 120 s) and longer recording intervals (i. e., 180 s).

show abstract

Section: Introductionmentioning

confidence: 99%

How Does Power During Running Change when Measured at Different Time Intervals?

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The measurement of power is an important determinant of performance and is vital for evaluating individual differences in performances, monitoring the effectiveness of both training/ergogenic aids, whilst providing a true representation of the performance capabilities of both recreational and elite cyclists. 1 Cycling ergometers which enable the use of cyclists' own bicycles have been shown to produce reliable results predictive of competitive performance whilst replicating movement economy and enhancing ecological validity, key markers of performance in the transfer of power from a laboratory setting to the field. 2 Therefore, the ability of a cycling ergometer to consistently record reliable measures of power output with a high degree of precision is of significant importance.…”

Section: Introductionmentioning

confidence: 99%

Reliability of Power Settings of the Wahoo KICKR Power Trainer After 60 Hours of Use

Zadow¹,

Kitic²,

Wu³

et al. 2018

International Journal of Sports Physiology and Performance

View full text Add to dashboard Cite

Purpose: The purpose of this study was to assess the reliability of power output measurements of a Wahoo KICKR Power Trainer (KICKR) on two separate occasions, separated by fourteen months of regular use (~1 h per week). Methods: Using the KICKR to set power outputs, powers of 100-600W in increments of 50W were assessed at cadences of 80, 90 and 100rev.min -1 which were controlled and validated by a dynamic calibration rig (CALRIG). Results:A small ratio bias of 1.002 (95%rLoA: 0.992-1.011) was observed over 100-600W at

show abstract

“…We hypothesized that a recurrent neural network approach could be successfully used to accurately predict individual cycling _ VO 2 data from easy-to-collect inputs [21]. In fact, the mechanical power output and the pedalling cadence are both easily collectable by portable power-meters [7]). Indeed, heart rate and respiratory frequency are both measurable with chest belts [47,48] and have already been successfully used by Beltrame et.…”

Section: Discussionmentioning

confidence: 99%

“…However, another method we might use to directly estimate _ VO 2 is through its relationship with mechanical power output (P). Indeed, cycling exercise is a repetitive and easily testable activity in which the mechanical power output can be measured directly and reliably using a power meter [7] and even estimated using simple energetic relationships [8].…”

Section: Introductionmentioning

confidence: 99%

Estimating an individual’s oxygen uptake during cycling exercise with a recurrent neural network trained from easy-to-obtain inputs: A pilot study

et al. 2020

View full text Add to dashboard Cite

Measurement of oxygen uptake during exercise ( _ VO 2 ) is currently non-accessible to most individuals without expensive and invasive equipment. The goal of this pilot study was to estimate cycling _ VO 2 from easy-to-obtain inputs, such as heart rate, mechanical power output, cadence and respiratory frequency. To this end, a recurrent neural network was trained from laboratory cycling data to predict _ VO 2 values. Data were collected on 7 amateur cyclists during a graded exercise test, two arbitrary protocols (Prot-1 and -2) and an "all-out" Wingate test. In Trial-1, a neural network was trained with data from a graded exercise test, Prot-1 and Wingate, before being tested against Prot-2. In Trial-2, a neural network was trained using data from the graded exercise test, Prot-1 and 2, before being tested against the Wingate test. Two analytical models (Models 1 and 2) were used to compare the predictive performance of the neural network. Predictive performance of the neural network was high during both Trial-1 (MAE = 229(35) mlO 2 min -1 , r = 0.94) and Trial-2 (MAE = 304(150) mlO 2 min -1 , r = 0.89). As expected, the predictive ability of Models 1 and 2 deteriorated from Trial-1 to Trial-2. Results suggest that recurrent neural networks have the potential to predict the individual _ VO 2 response from easy-to-obtain inputs across a wide range of cycling intensities.

show abstract

Knowledge is power: Issues of measuring training and performance in cycling

Abstract: word count: 179 9Text only word count: 4307 10

Cited by 85 publications

References 46 publications

How Does Power During Running Change when Measured at Different Time Intervals?

How Does Power During Running Change when Measured at Different Time Intervals?

Reliability of Power Settings of the Wahoo KICKR Power Trainer After 60 Hours of Use

Estimating an individual’s oxygen uptake during cycling exercise with a recurrent neural network trained from easy-to-obtain inputs: A pilot study

Contact Info

Product

Resources

About