Training-Intensity Distribution in Road Cyclists: Objective Versus Subjective Measures

Sanders, Dajo; Myers, Tony; Akubat, Ibrahim

doi:10.1123/ijspp.2016-0523

Cited by 39 publications

(61 citation statements)

References 25 publications

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“…Power was measured using a Rotor power meter, which is a single-sided power meter measuring power based on output from strain gauges in the left side crank arm. This systems was recently compared to a number of portable power meters during road cycling (SRM, Powertap, SRAM Quark, Stages powermeter) and showed a high concurrent validity (standard error estimate: 2 W; Bland Altman 95% Limits of Agreement:+ 6W and Intraclass Correlations: 1.00) ( Sanders et al, 2017 ). The power of the Rotor system was calibrated based on the power of the Lode cycling-ergometer.…”

Section: Methodsmentioning

confidence: 99%

Understanding the Physiological Requirements of the Mountain Bike Cross-Country Olympic Race Format

et al. 2018

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Objectives: To evaluate the physiological requirements imposed by the current mountain biking Cross-Country Olympic (XCO) format.Methods: Sixteen Cross-Country cyclists competing at national or international level participated in this study. All participants completed a simulated and a real official race on a cycling-accredited race track. Oxygen consumption (O2) and heart rate (HR) values expressed as %O2max and %HRmax, respectively, were divided into three physiological intensity zones. The first zone (Z1) was the physiological region below VT1, the second zone (Z2) corresponded to a region between VT1 and VT2, and the third zone (Z3) was located between VT2 and VO2max. For power output, an additional fourth zone was considered above maximal aerobic power (MAP).Results: When competing in the current XCO format, 37.0 ± 17.9% of the race is performed above the second ventilatory threshold at a mean intensity of 87% O2max and 25% of the race was spent above MAP. This contribution varied between laps, with a very high intensity during the first lap and more aerobic subsequent laps. The durations of most of the periods beyond MAP oscillated between 5 and 30 s. Between these short, repeated bursts, low-intensity periods of exercise were recorded.Conclusion: The current XCO race format is an acyclical and intermittent exercise comparable to high-intensity team sports. Moreover, our results highlight the relevance of O2 values when analyzing XCO performance, they should be combined with commonly used HR and/or power output data.

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Section: Methodsmentioning

confidence: 99%

Understanding the Physiological Requirements of the Mountain Bike Cross-Country Olympic Race Format

et al. 2018

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“…Of these training prescription variables, the way in which athletes distribute their training across the training intensity spectrum (i.e., training intensity distribution; TID) is considered to be a key determinant of training and performance adaptations. [1][2][3][4][5] Training intensity can be measured via external work rate (running speed or power output), 6,7 an internal physiological response (i.e., heart rate or VO2), 8,9 or by how the training is perceived (i.e., rating of perceived exertion; RPE). 10 To further guide training prescription and monitoring, training intensity zones have been employed, and while there is not yet a consensus on the most appropriate training zone model, models with up to 7 training zones have been described.…”

Section: Introductionmentioning

confidence: 99%

“…7,17,18,21 These differences between studies may relate to the duration or phase of the training season that was monitored, 1 the method of establishing training zone demarcations (i.e., incremental test protocol and method of threshold determination), 12 or the training intensity quantification method itself. 6 Indeed, comparisons between studies are problematic because some studies have employed RPE, 18 fractions of the velocity at VO2max, 22 heart rate, 7 running velocity, 17 or lactate, 14 or ventilatory thresholds 16 to demarcate the boundaries between training zones, and subsequently quantify the TID. Only one previous study 6 has investigated how the TID is influenced by an internal, external and perceptual measure of training intensity quantification.…”

Section: Introductionmentioning

confidence: 99%

“…6 Indeed, comparisons between studies are problematic because some studies have employed RPE, 18 fractions of the velocity at VO2max, 22 heart rate, 7 running velocity, 17 or lactate, 14 or ventilatory thresholds 16 to demarcate the boundaries between training zones, and subsequently quantify the TID. Only one previous study 6 has investigated how the TID is influenced by an internal, external and perceptual measure of training intensity quantification. Sanders et al 6 employed heart rate, power output and RPE demarcations to categorize the TID of fifteen well-trained road cyclists over a 10-…”

Section: Introductionmentioning

confidence: 99%

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Quantifying the Training-Intensity Distribution in Middle-Distance Runners: The Influence of Different Methods of Training-Intensity Quantification

Bellinger¹,

Arnold²,

Minahan³

2020

International Journal of Sports Physiology and Performance

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Purpose: To compare the training-intensity distribution (TID) across an 8-week training period in a group of highly trained middle-distance runners employing 3 different methods of training-intensity quantification. Methods: A total of 14 highly trained middle-distance runners performed an incremental treadmill test to exhaustion to determine the heart rate (HR) and running speed corresponding to the ventilatory thresholds (gas-exchange threshold and respiratory-compensation threshold), as well as fixed rating of perceived exertion (RPE) values, which were used to demarcate 3 training-intensity zones. During the following 8 weeks, the TID (total and percentage of time spent in each training zone) of all running training sessions (N = 695) was quantified using continuous running speed, HR monitoring, and RPE. Results: Compared with the running-speed-derived TID (zone 1, 79.9% [7.3%]; zone 2, 5.3% [4.9%]; and zone 3, 14.7% [7.3%]), HR-demarcated TID (zone 1, 79.6% [7.2%]; zone 2, 17.0% [6.3%]; and zone 3, 3.4% [2.0%]) resulted in a substantially higher training time in zone 2 (effect size ± 95% confidence interval: −1.64 ± 0.53; P < .001) and lower training time in zone 3 (−1.59 ± 0.51; P < .001). RPE-derived TID (zone 1, 39.6% [8.4%]; zone 2, 31.9% [8.7%]; and zone 3, 28.5% [11.6%]) reduced time in zone 1 compared with both HR (−5.64 ± 1.40; P < .001) and running speed (−5.69 ± 1.9; P < .001), whereas time in RPE training zones 2 and 3 was substantially higher than both HR- and running-speed-derived zones. Conclusion: The results show that the method of training-intensity quantification substantially affects computation of TID.

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“…These differences have implications for the evaluation of training characteristics and load quantification, which are currently performed through several laboratory and field methods [7,8]. Among the field methods, subjective assessments, such as ratings of perceived exertion, stand out due to their easy implementation [9,10]. Previous research has shown that such methods present moderate to substantial differences compared to heart rate monitoring [7,[11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Power Assessment in Road Cycling: A Narrative Review

et al. 2020

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Nowadays, the evaluation of physiological characteristics and training load quantification in road cycling is frequently performed through power meter data analyses, but the scientific evidence behind this tool is scarce and often contradictory. The aim of this paper is to review the literature related to power profiling, functional threshold testing, and performance assessment based on power meter data. A literature search was conducted following preferred reporting items for review statement (PRISMA) on the topic of {“cyclist” OR “cycling” AND “functional threshold” OR “power meter”}. The reviewed evidence provided important insights regarding power meter-based training: (a) functional threshold testing is closely related to laboratory markers of steady state; (b) the 20-min protocol represents the most researched option for functional threshold testing, although shorter durations may be used if verified on an individual basis; (c) power profiling obtained through the recovery of recorded power outputs allows the categorization and assessment of the cyclist’s fitness level; and (d) power meters represent an alternative to laboratory tests for the assessment of the relationship between power output and cadence. This review elucidates the increasing amount of studies related to power profiling, functional threshold testing, and performance assessment based on power meter data, highlighting the opportunity for the expanding knowledge that power meters have brought in the road cycling field.

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Training-Intensity Distribution in Road Cyclists: Objective Versus Subjective Measures

Cited by 39 publications

References 25 publications

Understanding the Physiological Requirements of the Mountain Bike Cross-Country Olympic Race Format

Understanding the Physiological Requirements of the Mountain Bike Cross-Country Olympic Race Format

Quantifying the Training-Intensity Distribution in Middle-Distance Runners: The Influence of Different Methods of Training-Intensity Quantification

Power Assessment in Road Cycling: A Narrative Review

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