Power Assessment in Road Cycling: A Narrative Review

Influence of Conventional Resistance Training Compared to Core Exercises on Road Cycling Power Output

Conventional strength training and core exercises are commonly prescribed to improve cycling performance. Although previous studies have explored the utility of strength training in various cycling populations, this intervention has never been compared to core exercises. Thirty-six trained road cyclists were divided into three groups of 12 participants that performed either no strength training, conventional strength training, or core exercises, in all cases together with their regular cycling training during a 12-week period. Peak power outputs (POs) across different durations (five seconds, 60 seconds, five minutes, and 20 minutes) were recorded before and after the intervention. The results of the present study showed higher increases in relative PO with conventional strength training when compared to core training and no strength training for all measured durations: five-second Δ = 1.25 W/kg vs 0.47 W/kg and -0.17 W/kg; 60-second (Δ = 0.51 W/kg vs 0.13 W/kg and 0.02 W/kg; five-minute Δ = 0.22 W/kg vs 0.06 W/kg and 0.05 W/kg; and 20-minute Δ = 0.22 W/kg vs 0.07 W/kg and 0.06 W/kg. According to the data obtained in this study, conventional strength training is superior to core exercises, and no strength training was performed by trained road cyclists. Accordingly, it is recommended that this population incorporates strength training during their regular weekly workouts.

Power Profile during Cycling in World Triathlon Series and Olympic Games

This study aimed to analyze the power profile (PP) during the cycling segment of international-level triathletes in the World Triathlon Series (WTS) and Olympics and to evaluate the influence of circuit type, race distance (Sprint or Olympic distance) and race dynamics on the development of the cycling leg and the final race position. Four male triathletes participated in the study. Twenty races were analyzed using geolocation technology and power-meter data to analyze PP, race dynamics, and course characteristics. Before the races, incremental tests of volitional exhaustion with gas analysis were performed to determine power intensity zones. Nonparametric Mann-Whitney U tests and correlation analyses were conducted to identify differences and relationships between various variables. A correlation between the time spent above maximal aerobic power (MAP) and dangerous curves per kilometer (r = 0.46; p < 0.05) and bike split result (BSR) (r = -0.50; p < 0.05) was observed. Also, moderate correlation was found between BSR and the final race position (r = 0.46; p < 0.01). No differences were found between sprint and Olympic distance races in any variable. Power output variability, influenced by technical circuit segments, remains the main characteristic in international short-distance races. The results of the present study suggest that the triathletes who are better adapted to intermittent high intensity efforts perform better cycling legs at international high-level races.

An Update Of The Allen & Coggan Equation To Predict 60-Min Power Output In Cyclists Of Different Performance Levels

The Allen & Coggan protocol suggests that 95% of the power output during a 20-min time trial is a valid surrogate for 60-min maximal power. The validity of this concept has not been studied previously in cyclists with different performance levels. As a result, we classified 120 cyclists in our study as recreationally trained, trained, well trained or professional, based on their maximal oxygen consumption. Participants performed a functional threshold power testing protocol based on a 20-min time trial and a 60-min time trial, separated by a 72-hour rest. Sixty-minute maximal power was successfully modeled with 20-min maximal power and performance group using 2/3 of the dataset (R2=0.77, 95% CrI [0.74, 0.79]) with different coefficients for each group: Professional: PO60min=PO20min × 0.96; well trained: PO60min=PO20min × 0.95; trained: PO60min=PO20min × 0.92 and recreationally trained: PO60min=PO20min × 0.88. The predictions of the original equation and our model were assessed using the remaining third of the data. The predictive performance of the updated equation was better (original: R2=0.51, mean absolute error=27 W, mean bias=-12 W; updated: R2=0.54, mean absolute error=25 W, mean bias=-7 W).

Using Wearable Sensors to Estimate Mechanical Power Output in Cyclical Sports Other than Cycling-A Review

More insight into in-field mechanical power in cyclical sports is useful for coaches, sport scientists, and athletes for various reasons. To estimate in-field mechanical power, the use of wearable sensors can be a convenient solution. However, as many model options and approaches for mechanical power estimation using wearable sensors exist, and the optimal combination differs between sports and depends on the intended aim, determining the best setup for a given sport can be challenging. This review aims to provide an overview and discussion of the present methods to estimate in-field mechanical power in different cyclical sports. Overall, in-field mechanical power estimation can be complex, such that methods are often simplified to improve feasibility. For example, for some sports, power meters exist that use the main propulsive force for mechanical power estimation. Another non-invasive method usable for in-field mechanical power estimation is the use of inertial measurement units (IMUs). These wearable sensors can either be used as stand-alone approach or in combination with force sensors. However, every method has consequences for interpretation of power values. Based on the findings of this review, recommendations for mechanical power measurement and interpretation in kayaking, rowing, wheelchair propulsion, speed skating, and cross-country skiing are done.

Subject specific muscle synergies and mechanical output during cycling with arms or legs

Background

Upper (UL) and lower limb (LL) cycling is extensively used for several applications, especially for rehabilitation for which neuromuscular interactions between UL and LL have been shown. Nevertheless, the knowledge on the muscular coordination modality for UL is poorly investigated and it is still not known whether those mechanisms are similar or different to those of LL. The aim of this study was thus to put in evidence common coordination mechanism between UL and LL during cycling by investigating the mechanical output and the underlying muscle coordination using synergy analysis.

Methods

Twenty-five revolutions were analyzed for six non-experts' participants during sub-maximal cycling with UL or LL. Crank torque and muscle activity of eleven muscles UL or LL were recorded. Muscle synergies were extracted using nonnegative matrix factorization (NNMF) and group- and subject-specific analysis were conducted.

Results

Four synergies were extracted for both UL and LL. UL muscle coordination was organized around several mechanical functions (pushing, downing, and pulling) with a proportion of propulsive torque almost 80% of the total revolution while LL muscle coordination was organized around a main function (pushing) during the first half of the cycling revolution. LL muscle coordination was robust between participants while UL presented higher interindividual variability.

Discussion

We showed that a same principle of muscle coordination exists for UL during cycling but with more complex mechanical implications. This study also brings further results suggesting each individual has unique muscle signature.

Five-Minute Power-Based Test to Predict Maximal Oxygen Consumption in Road Cycling

PURPOSE

To examine the ability of a multivariate model to predict maximal oxygen consumption (VO2max) using performance data from a 5-minute maximal test (5MT).

METHODS

Forty-six road cyclists (age 38 [9] y, height 177 [9] cm, weight 71.4 [8.6] kg, VO2max 61.13 [9.05] mL/kg/min) completed a graded exercise test to assess VO2max and power output. After a 72-hour rest, they performed a test that included a 5-minute maximal bout. Performance variables in each test were modeled in 2 independent equations, using Bayesian general linear regressions to predict VO2max. Stepwise selection was then used to identify the minimal subset of parameters with the best predictive power for each model.

RESULTS

Five-minute relative power output was the best explanatory variable to predict VO2max in the model from the graded exercise test (R2 95% credibility interval, .81-.88) and when using data from the 5MT (R2 95% credibility interval, .61-.77). Accordingly, VO2max could be predicted with a 5MT using the equation VO2max = 16.6 + (8.87 × 5-min relative power output).

CONCLUSIONS

Road cycling VO2max can be predicted in cyclists through a single-variable equation that includes relative power obtained during a 5MT. Coaches, cyclists, and scientists may benefit from the reduction of laboratory assessments performed on athletes due to this finding.

Relationship between functional threshold power, ventilatory threshold and respiratory compensation point in road cycling

BACKGROUND

The purpose of this study was to assess the relationship between power output and relative power output at the functional threshold power, ventilatory threshold and respiratory compensation point in road cyclists.

METHODS

Forty-six road cyclists (age 38 ± 9 years; height 177 ± 9 cm; body mass 71.4 ± 8.6 kg; body mass index 22.7 ± 2.2 kg·m-1; fat mass 7.8 ± 4%, VO2max 61.1 ± 9.1 ml·min-1·kg-1) performed a graded exercise test in which power output and relative power output at the ventilatory landmarks were identified. Functional threshold power was established as 95% of the power output during a 20-minute test.

RESULTS

Power output and relative power output at the functional threshold power were higher than at the ventilatory threshold (p < 0.001). There were very large to near perfect correlations for power output (95% CI for r from 0.71 to 0.9) and relative power output (95% CI for r from 0.79 to 0.93) at the functional threshold power and respiratory compensation point. Mean bias in power ouput and relative power output measured at RCP compared with FTP was not significant (mean bias 95% CI from -7 to 10 W and - 0.1 to 0.1 W/kg, respectively).

CONCLUSIONS

Power output and relative power output at the functional threshold power are higher than at the ventilatory threshold. Power output and relative power output at the functional threshold power and respiratory compensation point are strongly related, but caution is required when using both concepts indistinctly.