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

Decoding Victory in Cycling's Grand Monuments: A Performance Analysis of Top-5 Versus Top-6-30 Finishers

This study aimed to examine the key performance metrics and durability differences between cyclists finishing in the top-5 and those ranked between 6th and 30th in the Five Monuments of Cycling (Milan-San Remo, Tour of Flanders, Paris-Roubaix, Liège-Bastogne-Liège, and Il Lombardia). Data from 64 professional male cyclists were analyzed. Cyclists were categorized into top-5 finishers (n = 14) and top-6-30 finishers (n = 50). Race data were extracted from .fit files, including power output (PO), mean maximal power (MMP) across multiple durations (5 s, 30 s, 1 min, 5 min, 10 min, and 20 min), and power decay after accumulated workloads (30, 40, 50, and 60 kJ kg-1). Top-5 finishers exhibited significantly higher MMP for 5-min (d = 0.7; p = 0.02), 10-min (d = 0.8; p = 0.01), and 20-min efforts (d = 1.0; p = 0.01) compared to top-6-30 finishers. No significant differences were found in shorter durations (p > 0.05). Power decay analysis revealed that top-6-30 cyclists experienced greater reductions in MMP at 60 kJ kg-1, particularly for 10-min (d = 1.3; p < 0.01) and 20-min efforts (d = 1.2; p < 0.01). No significant differences were observed in total energy expenditure or time spent in power zones between groups. Top-5 finishers showed higher durability, with less power decay compared to top-6-30 finishers in the Five Monuments. These findings highlight functional performance differences (e.g., MMP, power decay) associated with top-5 finishes, though physiological mechanisms remain speculative. This study extends prior research by focusing on elite finishers in the Five Monuments.

Impact of Menstrual Cycles or Combined Oral Contraception on Training Loads Assessed Using Latent Effort States in Female Elite Cyclists

To quantify the impact of the regular or irregular menstrual cycle (MC) or combined oral contraception (OC) on the time spent at the maximum effort exertion per training, assessed using latent effort states through a Hidden Markov chain Model (HMM). 6303 training sessions with heart rate (HR) and power output (PO) recorded every second were used to train HMM in order to determine latent effort states of 12 elite French cyclists followed up over 30 months. A total of 101 MC/OCs full cycles were analyzed. A calendar method was used to estimate regular MC phases (menstruation, estimated follicular phase, estimated luteal phase). Irregular MC was divided into menstruations/no menstruations and OC into break/active pill taking. Four latent effort states were identified: high, medium+, medium-, and low. Focused on high intensity-oriented training sessions, the proportion of time spent in high intensity effort state was significantly lower during menstruation (34.5%) compared to estimated follicular (65.2%, p = 0.0009) and luteal (55.4% p = 0.024) phases for regular MC, and during pills' break (43.7%) compared to active pill taking phase (62.6% p = 0.031) for OC cycles. During the high intensity-oriented training sessions, the proportion of time spent in high effort state is almost twice higher in mid-regular cycles whereas is lower during menstruation or pill's break in elite cyclists. These findings rely on repeated assessment of training loads in a real-world context, analyzed using novel machine learning techniques that objectively quantify both internal and external training loads in elite female cyclists.

Do Power Meter Data Depend on the Device on Which They Are Collected? Comparison of Eleven Different Recordings

This study evaluated the influence of cycle computers on the accuracy of power and cadence data. The research was divided into three phases: (1) a graded exercise test (GXT) at different constant loads to record power and cadence data; (2) a self-paced effort lasting 1 min to measure mean maximal power output (MMP); and (3) a short all-out effort. Eight cyclists completed the GXT, ten participated in the 1-min test, and thirty participated in the sprint effort. All participants pedaled on a controlled-resistance cycle ergometer, and the data were recorded using the ergometer itself and ten synchronized cycle computers of the same brand, configured to record at 1 Hz. The results showed minimal variations in power and cadence between devices during the GXT, suggesting adequate accuracy for constant efforts lasting a certain duration. However, in self-paced and high-intensity efforts (1-min and short all-out efforts), significant differences were observed between several devices, particularly in cadence and mean power, highlighting the relevance of device selection in these contexts. These findings suggest that, while variations in constant efforts may be negligible, in short-duration, high-intensity activities, the choice of device may be crucial for the accuracy and reliability of the data.

Antidoping 2.0: Is Adding Power-Output Data to the Antidoping Pool the Next Step? Experts' Viewpoint

BACKGROUND

Efforts are needed to improve antidoping procedures. The widespread use of power meters among cyclists could help in this regard. However, controversy exists on whether performance monitoring through power-output data could be of help for antidoping purposes.

PURPOSE

The objective of the present study was to provide insight into the feasibility and utility of implementing power-based performance monitoring in elite cycling. An expert panel of 15 applied sport scientists and professional cycling coaches were asked for their opinions and perspectives on incorporating power data into the antidoping risk-assessment process.

RESULTS

Two different viewpoints were identified from the responses provided by the experts. Some believed that power monitoring could be implemented as an antidoping tool, provided that several surmountable challenges are first addressed. These authors provided suggestions related to the potential practical implementation of such measures. Others, on the contrary, believed that power meters lack sufficient reliability and suggest that the professional cycling world presents conflicts of interest that make this intervention impossible to implement nowadays.

CONCLUSIONS

The debate around the utility of power-meter data in the antidoping fight has been ongoing for more than a decade. According to the opinions provided by the experts' panel, there is still no consensus on the real utility and practical implementation of this intervention.

The Ability of Stryd Footpod Metrics to Reflect Changes in Metabolic Power Between Running Shoe Types

It is unclear if running power (RP) estimated by the Stryd footpod device maintains its linear relationship to metabolic power (WMET) when switching between training and racing shoe types. This study determined if RP estimated by the Stryd footpod and its other spatiotemporal metrics reflect the improvement (decrease) in WMET when wearing high-performance racing shoes (HPRS; Nike AlphaFly Next%) compared to control training shoes (CTS; Nike Revolution 5). Fourteen well-trained runners completed two treadmill tests: Absolute Velocity Running Test (AVRT; 11.3-14.5 km·hr-1) and Relative Velocity Running Test (RVRT; 55-75% VO2MAX). WMET was determined with indirect calorimetry. RP was not significantly different between shoe types (p > 0.432) during the AVRT, but WMET was ~5% lower in HPRS (p < 0.001). During the RVRT, participants ran ~6% faster and at ~6% higher RP (both, p < 0.001) in HPRS for the same WMET (p = 0.869). Linear mixed models confirmed WMET was ~5% lower in HPRS for a given RP (p < 0.001). Still, RP and WMET were strongly related within shoe types (p < 0.001, conditional-R2 = 0.982, SEE = 2.57%). Form power ratio and ground contact time correlated with energetic cost (p < 0.011) but did not fully reflect the influence of shoe type. Therefore, runners should account for their shoe type when using RP to indicate WMET between training and racing.

Training Intensity Distribution of a 7-Day HIIT Shock Microcycle: Is Time in the "Red Zone" Crucial for Maximizing Endurance Performance? A Randomized Controlled Trial

BACKGROUND

Various studies have shown that the type of intensity measure affects training intensity distribution (TID) computation. These conclusions arise from studies presenting data from meso- and macrocycles, while microcycles, e.g., high-intensity interval training shock microcycles (HIIT-SM) have been neglected so far. Previous literature has suggested that the time spent in the high-intensity zone, i.e., zone 3 (Z3) or the "red zone", during HIIT may be important to achieve improvements in endurance performance parameters. Therefore, this randomized controlled trial aimed to compare the TID based on running velocity (TIDV), running power (TIDP) and heart rate (TIDHR) during a 7-day HIIT-SM. Twenty-nine endurance-trained participant were allocated to a HIIT-SM consisting of 10 HIIT sessions without (HSM, n = 9) or with (HSM + LIT, n = 9) additional low-intensity training or a control group (n = 11). Moreover, we explored relationships between time spent in Z3 determined by running velocity (Z3V), running power (Z3P), heart rate (Z3HR), oxygen uptake ( Z 3 V ˙ O 2 ) and changes in endurance performance.

RESULTS

Both intervention groups revealed a polarized pattern for TIDV (HSM: Z1: 38 ± 17, Z2: 16 ± 17, Z3: 46 ± 2%; HSM + LIT: Z1: 59 ± 18, Z2: 14 ± 18, Z3: 27 ± 2%) and TIDP (Z1: 50 ± 8, Z2: 14 ± 11, Z3: 36 ± 7%; Z1: 62 ± 15, Z2: 12 ± 16, Z3: 26 ± 2%), while TIDHR (Z1: 48 ± 13, Z2: 26 ± 11, Z3: 26 ± 7%; Z1: 65 ± 17, Z2: 22 ± 18, Z3: 13 ± 4%) showed a pyramidal pattern. Time in Z3HR was significantly less compared to Z3V and Z3P in both intervention groups (all p < 0.01). There was a time x intensity measure interaction for time in Z3 across the 10 HIIT sessions for HSM + LIT (p < 0.001, pη2 = 0.30). Time in Z3V and Z3P within each single HIIT session remained stable over the training period for both intervention groups. Time in Z3HR declined in HSM from the first (47%) to the last (28%) session, which was more pronounced in HSM + LIT (45% to 16%). A moderate dose-response relationship was found for time in Z3V and changes in peak power output (rs = 0.52, p = 0.028) as well as time trial performance (rs = - 0.47, p = 0.049) with no such associations regarding time in Z3P, Z3HR, and Z 3 V ˙ O 2 .

CONCLUSION

The present study reveals that the type of intensity measure strongly affects TID computation during a HIIT-SM. As heart rate tends to underestimate the intensity during HIIT-SM, heart rate-based training decisions should be made cautiously. In addition, time in Z3V was most closely associated with changes in endurance performance. Thus, for evaluating a HIIT-SM, we suggest integrating a comprehensive set of intensity measures. Trial Registration Trial register: Clinicaltrials.gov, registration number: NCT05067426.

The Accuracy and Reliability of the Power Measurements of the TACX Neo 2T Smart Trainer and Its Agreement against the Garmin Vector 3 Pedals

The power output in cycling is one of the most important factors for athletes and coaches. The cycling community has several commercial gears that can be used. One of the most used is the TACX Neo 2T (TN2T) smart trainer. The objective of this study was to investigate the metrological proprieties of the TN2T (accuracy and reliability), as well as its agreement with the Garmin Vector 3 (GV3) pedals at different power stages. The sample consisted of ten regional-level cyclists with a mean age of 45.6 ± 6.4 years, who regularly participated in regional and national competitions. Residual relative differences were found between the two devices. Both devices showed good reliability with coefficients of variation and intraclass correlation coefficients ranging from 0.03% to 0.15% and from 0.731 to 0.968, respectively. Independent samples t-test comparison between devices showed no significant differences in all power stages (p > 0.05). Bland-Altman plots showed that more than 80% of the plots were within the 95% confidence intervals in all power stages. The present data showed that there were non-significant differences between the two devices at power stages between 100 W and 270 W, with a strong agreement. Therefore, they can be used simultaneously.

Classification of Male Athletes Based on Critical Power

This study aimed to classify male athletes based on their performance levels derived from running critical power (CP) using the 9/3-minute Stryd CP test, enabling customized training strategies and goal setting. Twenty-four trained athletes underwent the 9/3-minute running CP test on a certified 400-m athletics track. Hierarchical cluster analysis using Ward's method categorized athletes based on CP into distinct performance tiers. Three clusters were identified with centroids of 3.87±0.12, 4.45±0.17, and 5.14±0.29 W/kg. Five performance tiers were defined through ordinary least square linear regression based on power (W/kg): Tier 1: Fair (2.9 to 3.6 W/kg), Tier 2: Tourist (3.6 to 4.2 W/kg), Tier 3: Regional (4.2 to 4.8 W/kg), Tier 4: National (4.8 to 5.5 W/kg), Tier 5: International (5.5 to 6.1 W/kg). Low semi-partial R-squared (SpR 2) values (0.02 to 0.05) indicated minimal homogeneity loss when merging clusters. R-squared (R 2) explained 89% to 96% of CP variance, emphasizing cluster analysis effectiveness. The linear regression model demonstrated a strong fit (r 2+=+0.997) with a significant intercept (3.22 W/kg), slope (0.63 W/kg/tier), and a low standard error of estimate (0.045 W/kg). This classification offers insights into male athlete performance levels based on CP, facilitating targeted training programs for varying performance levels.

Inertial measurement units to estimate drag forces and power output during standardised wheelchair tennis coast-down and sprint tests

The purpose of this study was to describe and explore an inertial measurement unit-based method to analyse drag forces and external power loss in wheelchair tennis, using standardised coast-down and 10 m sprint tests. Drag forces and power output were explored among different wheelchair-athlete combinations and playing conditions (tyre pressure, court-surface). Eight highly trained wheelchair tennis players participated in this study. Three inertial measurement units (IMUs) were placed on the frame and axes of the wheels of their wheelchair. All players completed a set of three standardised coast-down trials and two 10 m sprints with different tyre pressures on hardcourt surface. One athlete completed additional tests on a clay/grass tennis-court. Coast-down based drag forces of 4.8-7.2 N and an external power loss of 9.6-14.4 W at a theoretical speed of 2 m/s were measured on hardcourt surface. A higher tyre pressure led to lower drag forces during coast-down tests on hardcourt surface (Fr (4) = 10.7, p = 0.03). For the single athlete, there was an external power loss of 10.4, 15.6 and 49.4 W, respectively, for the hardcourt, clay and grass. The current prediction of power output was implemented during coast-down testing; unfortunately, the power prediction during 10 m sprints was difficult to accomplish.

The Influence of High-Intensity Work on the Record Power Profile of Under-23, Pro Team, and World Tour Cyclists

BACKGROUND

Durability (ie, the ability to attenuate the decline in performance after accumulated work) has been identified as a performance determinant in elite cyclists. The aim of the present study was to compare durability in elite cyclists of various performance levels, particularly after high-intensity work, referred to as "high-intensity durability."

METHODS

Forty-nine (N = 49) male road cyclists were categorized as either under 23 years of age (U23) (N = 11), Pro Team (N = 13), or World Tour (N = 24). The participants' critical power (CP) was assessed during the preseason. Thereafter, the participants' maximum mean power (MMP) values were determined for efforts of different durations (from 5 s to 30 min) after different levels of accumulated work above CP (from 0 to 7.5 kJ·kg-1).

RESULTS

U23 cyclists showed a significant reduction of all relative MMP values for durations ≥1 minute after ≥5 kJ·kg-1 above CP compared with the "fresh" state (0 kJ·kg-1), whereas in Pro Team and World Tour cyclists, a significant reduction was not observed until 7.5 kJ·kg-1 above CP. In the "fresh" state, both Pro Team and particularly World Tour cyclists attained higher MMP values for efforts ≥10 minutes than U23 riders. However, more differences emerged with greater previous work levels, and indeed after 7.5 kJ·kg-1 above CP World Tour cyclists attained higher MMP values than both U23 and Pro Team cyclists for most efforts (≥30 s).

CONCLUSION

Pro Team and particularly World Tour cyclists tolerate greater levels of accumulated work at high intensity, which might support the importance of high-intensity durability for performance.

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).

Is Running Power a Useful Metric? Quantifying Training Intensity and Aerobic Fitness Using Stryd Running Power Near the Maximal Lactate Steady State

We sought to determine the utility of Stryd, a commercially available inertial measurement unit, to quantify running intensity and aerobic fitness. Fifteen (eight male, seven female) runners (age = 30.2 [4.3] years; V·O2max = 54.5 [6.5] ml·kg-1·min-1) performed moderate- and heavy-intensity step transitions, an incremental exercise test, and constant-speed running trials to establish the maximal lactate steady state (MLSS). Stryd running power stability, sensitivity, and reliability were evaluated near the MLSS. Stryd running power was also compared to running speed, V·O2, and metabolic power measures to estimate running mechanical efficiency (EFF) and to determine the efficacy of using Stryd to delineate exercise intensities, quantify aerobic fitness, and estimate running economy (RE). Stryd running power was strongly associated with V·O2 (R2 = 0.84; p < 0.001) and running speed at the MLSS (R2 = 0.91; p < 0.001). Stryd running power measures were strongly correlated with RE at the MLSS when combined with metabolic data (R2 = 0.79; p < 0.001) but not in isolation from the metabolic data (R2 = 0.08; p = 0.313). Measures of running EFF near the MLSS were not different across intensities (~21%; p > 0.05). In conclusion, although Stryd could not quantify RE in isolation, it provided a stable, sensitive, and reliable metric that can estimate aerobic fitness, delineate exercise intensities, and approximate the metabolic requirements of running near the MLSS.

Anthropometric characteristics according to the role performed by World Tour road cyclists for their team

Certain anthropometric characteristics are required for athletes to successfully perform in elite endurance sports. The present study aims to analyse the anthropometric characteristics of professional cyclists according to their specialty. Anthropometric measurements were conducted of the body composition of 76 male professional road cyclists in line with International Society for Advancement of Kinanthropometry protocol. Fat mass did not differ (p > 0.05) between climbers, all-rounders and flat specialists, although the following anthropometric variables did differ according to the role played within the team (p < 0.05): Body mass (climbers: 63.8 ± 3.6, all-rounders: 68.8 ± 5.3, flat specialists: 74.5 ± 5.6 kg) skeletal body mass (climbers: 29.7 ± 1.6, all-rounders: 31.4 ± 1.9, flat specialists: 33.5 ± 2.4 kg); body surface area (climbers: 1.78 ± 0.07, all-rounders: 1.89 ± 0.10, flat specialists: 1.96 ± 0.1 m2); frontal area (climbers: 0.33 ± 0.01, all-rounders: 0.35 ± 0.02, flat specialists: 0.36 ± 0.02 m2). Anthropometric characteristics differ between world-class cyclists depending on their specialty. These differences could influence performance in relation to different types of road cycling competitions. The present study identified characteristics that could be used by coaches to evaluate their athletes in the context of elite or professional road cycling.HighlightsNormative reference values of a large sample of professional cyclists of the highest category are presented.Anthropometric characteristics differ between world-class cyclists depending on their specialty.Body mass, BMI, height and skeletal muscle mass are determining factors to determine the role of the cyclist.

Acute effects of fatigue on internal and external load variables determining cyclists' power profile

The aim of the present study was to determine whether fatigue affects internal and external load variables determining power profile in cyclists. Ten cyclists performed outdoor power profile tests (lasting 1-, 5 and 20-min) on two consecutive days, subject either to a fatigued condition or not. Fatigue was induced by undertaking an effort (10-min at 95% of average power output obtained in a 20-min effort followed by 1-min maximum effort) until the power output decreased by 20% compared to the 1-min power output. Fatigued condition decreased power output (p < 0.05, 1-min: 9.0 ± 3.8%; 5-min: 5.9 ± 2.5%; 20-min: 4.1 ± 1.9%) and cadence in all test durations, without differences in torque. Lactate decreased in longer efforts when a fatigue protocol had previously been conducted (e.g., 20-min: 8.6 ± 3.0 vs. 10.9 ± 2.7, p < 0.05). Regression models (r² ≥ 0.95, p < 0.001) indicated that a lower variation in load variables of 20-min in fatigued condition compared with the non-fatigued state resulted in a lower decrease in critical power after the fatigue protocol. The results suggest that fatigued condition on power was more evident in shorter efforts and seemed to rely more on a decrease in cadence than on torque.

Estimation of horizontal running power using foot-worn inertial measurement units

Feedback of power during running is a promising tool for training and determining pacing strategies. However, current power estimation methods show low validity and are not customized for running on different slopes. To address this issue, we developed three machine-learning models to estimate peak horizontal power for level, uphill, and downhill running using gait spatiotemporal parameters, accelerometer, and gyroscope signals extracted from foot-worn IMUs. The prediction was compared to reference horizontal power obtained during running on a treadmill with an embedded force plate. For each model, we trained an elastic net and a neural network and validated it with a dataset of 34 active adults across a range of speeds and slopes. For the uphill and level running, the concentric phase of the gait cycle was considered, and the neural network model led to the lowest error (median ± interquartile range) of 1.7% ± 12.5% and 3.2% ± 13.4%, respectively. The eccentric phase was considered relevant for downhill running, wherein the elastic net model provided the lowest error of 1.8% ± 14.1%. Results showed a similar performance across a range of different speed/slope running conditions. The findings highlighted the potential of using interpretable biomechanical features in machine learning models for the estimating horizontal power. The simplicity of the models makes them suitable for implementation on embedded systems with limited processing and energy storage capacity. The proposed method meets the requirements for applications needing accurate near real-time feedback and complements existing gait analysis algorithms based on foot-worn IMUs.

Modeling the Power-Duration Relationship in Professional Cyclists During the Giro d'Italia

ABSTRACT

Vinetti, G, Pollastri, L, Lanfranconi, F, Bruseghini, P, Taboni, A, and Ferretti, G. Modeling the power-duration relationship in professional cyclists during the Giro d'Italia. J Strength Cond Res 37(4): 866-871, 2023-Multistage road bicycle races allow the assessment of maximal mean power output (MMP) over a wide spectrum of durations. By modeling the resulting power-duration relationship, the critical power ( CP ) and the curvature constant ( W' ) can be calculated and, in the 3-parameter (3-p) model, also the maximal instantaneous power ( P0 ). Our aim is to test the 3-p model for the first time in this context and to compare it with the 2-parameter (2-p) model. A team of 9 male professional cyclists participated in the 2014 Giro d'Italia with a crank-based power meter. The maximal mean power output between 10 seconds and 10 minutes were fitted with 3-p, whereas those between 1 and 10 minutes with the 2- model. The level of significance was set at p < 0.05. 3-p yielded CP 357 ± 29 W, W' 13.3 ± 4.2 kJ, and P0 1,330 ± 251 W with a SEE of 10 ± 5 W, 3.0 ± 1.7 kJ, and 507 ± 528 W, respectively. 2-p yielded a CP and W' slightly higher (+4 ± 2 W) and lower (-2.3 ± 1.1 kJ), respectively ( p < 0.001 for both). Model predictions were within ±10 W of the 20-minute MMP of time-trial stages. In conclusion, during a single multistage racing event, the 3-p model accurately described the power-duration relationship over a wider MMP range without physiologically relevant differences in CP with respect to 2-p, potentially offering a noninvasive tool to evaluate competitive cyclists at the peak of training.

Training Periodization, Intensity Distribution, and Volume in Trained Cyclists: A Systematic Review

A well-planned periodized approach endeavors to allow road cyclists to achieve peak performance when their most important competitions are held.

PURPOSE

To identify the main characteristics of periodization models and physiological parameters of trained road cyclists as described by discernable training intensity distribution (TID), volume, and periodization models.

METHODS

The electronic databases Scopus, PubMed, and Web of Science were searched using a comprehensive list of relevant terms. Studies that investigated the effect of the periodization of training in cyclists and described training load (volume, TID) and periodization details were included in the systematic review.

RESULTS

Seven studies met the inclusion criteria. Block periodization (characterized by employment of highly concentrated training workload phases) ranged between 1- and 8-week blocks of high-, medium-, or low-intensity training. Training volume ranged from 8.75 to 11.68 h·wk-1 and both pyramidal and polarized TID were used. Traditional periodization (characterized by a first period of high-volume/low-intensity training, before reducing volume and increasing the proportion of high-intensity training) was characterized by a cyclic progressive increase in training load, the training volume ranged from 7.5 to 10.76 h·wk-1, and pyramidal TID was used. Block periodization improved maximum oxygen uptake (VO2max), peak aerobic power, lactate, and ventilatory thresholds, while traditional periodization improved VO2max, peak aerobic power, and lactate thresholds. In addition, a day-by-day programming approach improved VO2max and ventilatory thresholds.

CONCLUSIONS

No evidence is currently available favoring a specific periodization model during 8 to 12 weeks in trained road cyclists. However, few studies have examined seasonal impact of different periodization models in a systematic way.

The effect of severe intensity bouts on muscle oxygen saturation responses in trained cyclists

Near-infrared spectroscopy (NIRS) quantifies muscle oxygenation (SmO₂) during exercise. Muscle oxygenation response to self-paced, severe-intensity cycling remains unclear. Observing SmO₂ can provide cycling professionals with the ability to assess muscular response, helping optimize decision-making. We aimed to describe the effect of self-paced severe intensity bouts on SmO₂, measured noninvasively by a wearable NIRS sensor on the vastus lateralis (VL) muscle, and examine its reliability. We hypothesized a greater desaturation response with each bout, whereas, between trials, good reliability would be observed. Fourteen recreationally trained, and trained cyclists completed a ramp test to determine the power output (PO) at the respiratory compensation point (RCP). Athletes completed two subsequent visits of 50-minute sessions that included four severe-intensity bouts done at 5% above RCP PO. Muscle oxygenation in the VL was monitored using a wearable NIRS device. Measures included mean PO, heart-rate (HR), cadence, and SmO₂ at bout onset, during work (work SmO₂), and ΔSmO₂. The bouts were compared using a one-way repeated measures ANOVA. For significant differences, a Fisher's least square difference post-hoc analysis was used. A two-way repeated measures ANOVA was used using trial and bout as main factors. Intraclass correlations (ICC) were used to quantify relative reliability for mean work, and standard error of the measurement (SEM) was used to quantify absolute agreement of mean work SmO₂. Both PO and cadence showed no effect of bout or trial. Heart-rate at bout 2 (168 ± 8 bpm) and 4 (170 ± 7 bpm) were higher than bout 1 (160 ± 6 bpm). Onset SmO₂ (%) response significantly increased in the final two bouts of the session. Mean work SmO₂ increased across bouts, with the highest value displayed in bout 4 (36 ± 22%). ΔSmO₂ showed a smaller desaturation response during bout 4 (27 ± 10%) compared to bout 3 (31 ± 10%). Mean work SmO₂ ICC showed good reliability (ICC = 0.87), and SEM was 12% (CI 9-15%). We concluded that a non-invasive, affordable, wearable NIRS sensor demonstrated the heterogeneous muscle oxygenation response during severe intensity cycling bouts with good reliability in trained cyclists.

Predicting BMX Performance with Laboratory Measurements in Elite Riders

In Bicycle Motocross (BMX) performance is determined by the riders' sprint ability and power output. Therefore, descriptive and predictive performance assessments through laboratory and field tests are of interest. Twelve members of the Spanish BMX National Team performed 4 laboratory tests, including 1RM in squat, a Wingate test, a 5x6-s repeated sprint test (RST) and a force-velocity profile test. These tests were compared with the riders' field performance using the best lap of a simulated BMX competition consisting of 6 all-out laps on a standard BMX track. Pearson's correlation and linear regression analysis showed a significant association (p < 0.05) between the riders' field performance and the 1RM (r = 0.84; R² = 0.65), the peak power output in the RST (r = 0.87; R² = 0.78) and the peak power, the mean power and the lactate production in the Wingate test (r = 0.68-0.77; R² = 0.14-0.65), as well as maximal power from the force-velocity profile (r = 0.71; R² = 0.53). The laboratory tests included in this study can show information about BMX riders' performance, with the highest values for the peak power obtained in the repeated sprint test (78% of the variance in common).

Acute effects of a 60-min time trial on power-related parameters in trained endurance runners

Background

The advent of power meters for running has raised the interest of athletes and coaches in new ways of assessing changes in running performance. The aim of this study is to determine the changes in power-related variables during and after a strenuous endurance running time trial.

Methods

Twenty-one healthy male endurance runners, with a personal record of 37.2 ± 1.2 min in a 10-km race, completed a 1-h run on a motorized treadmill trying to cover as much distance as they could. Before and after the time trial the athletes were asked to perform a 3-min run at 12 km h⁻¹. Normalized mean power output, step frequency, form power and running effectiveness were calculated using the Stryd™ power meter. Heart rate (HR) and rating of perceived exertion (RPE) were monitored, and data averaged every 5 min.

Results

Despite high levels of exhaustion were reached during the time trial (HRpeak = 176.5 ± 9.8 bpm; RPE = 19.2 ± 0.8), the repeated measures ANOVA resulted in no significant differences (p ≥ 0.05), between each pair of periods for any of the power-related variables. The pairwise comparison (T test) between the non-fatigued and fatigued constant 3-min runs showed an increase in step frequency (p = 0.012) and a decrease in form power (p < 0.001) under fatigue conditions, with no meaningful changes in normalized mean power output and running effectiveness.

Conclusions

Trained athletes are able to maintain power output and running effectiveness during a high demanding extended run. However, they preferred to reduce the intensity of vertical impacts under fatigue conditions by increasing their step frequency.

Field-Derived Maximal Power Output in Cycling: An Accurate Indicator of Maximal Performance Capacity?

PURPOSE

To determine the validity of field-derived mean maximum power (MMP) values for monitoring maximal cycling endurance performance.

METHODS

Twenty-seven male professional cyclists performed 3 timed trials (TTs) of 1-, 5-, and 20-minute duration that were used as the gold standard reference. Field-based power output data (3336 files; 124 [25] per cyclist) were registered during the preparatory (60 d pre-TT, including training data only) and specific period of the season (60 d post-TT, including both training and competitions). Comparisons were made between TT performance (mean power output) and MMP values obtained for efforts of the same duration as TT (MMP of 1-, 5-, and 20-min duration). The authors also compared TT- and MMP-derived values of critical power (CP) and anaerobic work capacity.

RESULTS

A large correlation (P < .001, r > .65) was found between MMP and TT performance regardless of the effort duration or season period. However, considerable differences (P < .05, standard error of measurement [SEM] > 5%) were found between MMP and TT values for all effort durations in the preparatory period, as well as for the derived CP and anaerobic work capacity. Significant differences were also found between MMP and TT of 1 minute in the specific period, as well as for anaerobic work capacity, yet with no differences for MMP of 5- and 20-minute duration or the derived CP (P > .05, SEM < 5%).

CONCLUSION

MMP values (for efforts ≥5 min) and the associated CP obtained from both training sessions and competitions can be considered overall accurate indicators of the cyclist's maximal capabilities, but specific tests might be necessary for shorter efforts or when considering training sessions only.

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.

Impact of prior accumulated work and intensity on power output in elite/international level road cyclists—a pilot study

Background

This study aimed to investigate the impact of the intensity of prior accumulated work on the decline in power output in elite/international level road cyclists, comparing the effects of prior continuous moderate intensity versus intermittent high intensity cycling.

Methods

Nine elite/international level road cyclists (age 26.2 ± 4.0 years; body mass: 66.6 ± 5.5 kg; height: 176 ± 0.4 cm) conducted a 12-min field test (12 minfresh) during two consecutive training camps. Participants then performed both a 150-min moderate intensity continuous (MIC) work bout or a 150-min high intensity intermittent (HII) race simulation in randomized order, cross-over design. After each condition a 12-min field test (12 minfatigue) was completed.

Results

Absolute and relative 12 minfresh power output were not significantly different between training camps (p > 0.05). The 12 minfatigue power after HII was significantly lower than 12 minfatigue after MIC (∆ = 14 W; p = 0.014). Participants recorded more percentage time (%Time) in heart rate (HR) zone 3 (∆ = 9.2%; p = 0.003) and power output band between 5.0–7.9 W $$\cdot$$ ∙ kg−1 (∆ = 8.9%; p = 0.002) as well as higher total work (∆ = 237 kJ; p ≤ 0.001) during HII.

Conclusion

These findings reveal that the decline in power output is higher after HII compared to MIC cycling work bouts. This suggests that the quantification of total work and intensity should be used in conjunction to predict a distinctive decline in power output. Future research is required to better understand the mechanisms of endurance “durability” in elite/international level road cyclists.

The reproducibility of 20-min time-trial performance on a virtual cycling platform

This study aimed to analyse the reproducibility of mean power output during 20-min cycling time-trials, in a remote home-based setting, using the virtual-reality cycling software, Zwift. Forty-four cyclists (11 women, 33 men; 37 ± 8 years old, 180 ± 8 cm, 80.1 ± 13.2 kg) performed 3 x 20-min time-trials on Zwift, using their own setup. Intra-class correlation coefficient (ICC), coefficient of variation (CV) and typical error (TE) were calculated for the overall sample, split into 4 performance groups based on mean relative power output (25% quartiles) and sex. Mean ICC, TE and CV of mean power output between time-trials were 0.97 [0.95-0.98], 9.36 W [8.02-11.28 W], and 3.7% [3.2-4.5], respectively. Women and men had similar outcomes (ICC: 0.96 [0.89-0.99] vs 0.96 [0.92-0.98]; TE: 8.30 W [6.25-13.10] vs. 9.72 W [8.20-12.23]; CV: 3.8% [2.9-6.1] vs. 3.7% [3.1-4.7], respectively), although cyclists from the first quartile showed a lower CV in comparison to the overall sample (Q1: 2.6% [1.9-4.1] vs. overall: 3.7% [3.2-4.5]). Our results indicate that power output during 20-minute cycling time-trials on Zwift are reproducible and provide sports scientists, coaches and athletes, benchmark values for future interventions in a virtual-reality environment.

The Record Power Profile of Male Professional Cyclists: Normative Values Obtained From a Large Database

PURPOSE

To present normative data for the record power profile of male professional cyclists attending to team categories and riding typologies.

METHODS

Power output data registered from 4 professional teams during 8 years (N = 144 cyclists, 129,262 files, and 1062 total seasons [7 (5) per cyclist] corresponding to both training and competition sessions) were analyzed. Cyclists were categorized as ProTeam (n = 46) or WorldTour (n = 98) and as all-rounders (n = 65), time trialists (n = 11), climbers (n = 50), sprinters (n = 11), or general classification contenders (n = 7). The record power profile was computed as the highest maximum mean power (MMP) value attained for different durations (1 s to 240 min) in both relative (W·kg-1) and absolute units (W).

RESULTS

Significant differences between ProTeam and WorldTour were found for both relative (P = .002) and absolute MMP values (P = .006), with WT showing lower relative, but not absolute, MMP values at shorter durations (30-60 s). However, higher relative and absolute MMP values were recorded for very short- (1 s) and long-duration efforts (60 and 240 min for relative MMP values and ≥5 min for absolute ones). Differences were also found regarding cyclists' typologies for both relative and absolute MMP values (P < .001 for both), with sprinters presenting the highest relative and absolute MMP values for short-duration efforts (5-30 s) and general classification contenders presenting the highest relative MMP values for longer efforts (1-240 min).

CONCLUSIONS

The present results--obtained from the largest cohort of professional cyclists assessed to date-could be used to assess cyclists' capabilities and indicate that the record power profile can differ between cyclists' categories and typologies.

The Record Power Profile in Professional Female Cyclists: Normative Values Obtained From a Large Database

PURPOSE

To describe the record power profile of professional female cyclists and to assess potential differences based on the type of rider.

METHODS

Power output data (32,028 files containing both training and competition sessions recorded) in 44 female professional cyclists during 1-6 years were analyzed. Cyclists were categorized as all-rounders, time trialists, climbers, or sprinters. The record power profile was calculated using the mean maximal power output (MMP) values attained by each cyclist for different-effort durations (5 s to 60 min) expressed in relative (W·kg-1), as well as absolute, power output (W).

RESULTS

Participants' MMP averaged 15.3 (1.8) W·kg-1 for 5 seconds, 8.4 (0.8) W·kg-1 for 1 minute, 5.2 (0.5) W·kg-1 for 10 minutes, and 4.2 (0.4) W·kg-1 for 60 minutes. For short-duration efforts (5-30 s), sprinters attained the highest MMP results, with significantly higher relative (Hedges g = 1.40-2.31) or absolute (g = 4.48-8.06) values than the remainder of categories or climbers only, respectively. Time trialists attained the highest MMP for longer efforts, with higher relative values than both all-rounders and climbers when comparing efforts lasting 10 to 60 minutes (P < .05, g = 1.21-1.54).

CONCLUSIONS

In professional female cyclists, the record power profile substantially differs based on the specific category of the rider. These findings provide unique insights into the physical capacities of female professional cyclists, as well as a benchmark for coaches and scientists aiming to identify talent in female cycling.

Ambient Temperature and Field-Based Cycling Performance: Insights From Male and Female Professional Cyclists

PURPOSE

Ambient temperature affects endurance exercise performance. However, most research has been conducted in a laboratory-based setting, and whether there are sex-specific trends remains unclear. The present study aimed to analyze the influence of ambient temperature on cycling performance in male and female professional cyclists using field-based data collected during both training and racing.

METHODS

A total of 74 cyclists (48 male and 26 female; age 29 [5] y, 8 [5] y of experience in the professional category) were included in the analyses. We registered the participants' record power profile using data from both training and competitions over 8 years (2013-2020; 8 [5] seasons per cyclist). We analyzed their mean maximal power (MMP) values attained for efforts lasting 5 seconds, 30 seconds, 5 minutes, and 20 minutes at ambient temperatures ranging from <5°C to >35°C.

RESULTS

A significant influence of ambient temperature on MMP values was found in male and female cyclists (P < .001 for both), with no significant differences between sexes (P = .512). Cyclists attained the highest MMP values at temperate conditions (10-30°C in males and 5-25°C in females), whereas an impairment in performance was found at colder and hotter temperatures, particularly for the more extreme conditions (performance impairment at <5°C and >35°C of -18% to -9% and -16% to -9%, respectively).

CONCLUSIONS

Ambient temperature influences field-based cycling performance, following a reverse U-shaped relationship, with the highest MMP values attained in the range of ∼10°C to 25°C and with no major differences between sexes.

Validity of the Training-Load Concept

Training load (TL) is a widely used concept in training prescription and monitoring and is also recognized as as an important tool for avoiding athlete injury, illness, and overtraining. With the widespread adoption of wearable devices, TL metrics are used increasingly by researchers and practitioners worldwide. Conceptually, TL was proposed as a means to quantify a dose of training and used to predict its resulting training effect. However, TL has never been validated as a measure of training dose, and there is a risk that fundamental problems related to its calculation are preventing advances in training prescription and monitoring. Specifically, we highlight recent studies from our research groups where we compare the acute performance decrement measured following a session with its TL metrics. These studies suggest that most TL metrics are not consistent with their notional training dose and that the exercise duration confounds their calculation. These studies also show that total work done is not an appropriate way to compare training interventions that differ in duration and intensity. We encourage scientists and practitioners to critically evaluate the validity of current TL metrics and suggest that new TL metrics need to be developed.

The Record Power Profile of Male Professional Cyclists: Fatigue Matters

PURPOSE

The present study aimed to determine the influence of fatigue on the record power profile of professional male cyclists. We also assessed whether fatigue could differently affect cyclists of 2 competition categories.

METHODS

We analyzed the record power profile in 112 professional cyclists (n = 46 and n = 66 in the ProTeam [PT] and WorldTour [WT] category, respectively; age 29 [6] y, 8 [5] y experience in the professional category) during 2013-2021 (8 [5] seasons/cyclist). We analyzed their mean maximal power (MMP) values for efforts lasting 10 seconds to 120 minutes with no fatigue (after 0 kJ·kg-1) and with increasing levels of fatigue (after 15, 25, 35, and 45 kJ·kg-1).

RESULTS

A significant (P < .001) and progressive deterioration of all MMP values was observed from the lowest levels of fatigue assessed (ie, -1.6% to -3.0% decline after 15 kJ·kg-1, and -6.0% to -9.7% after 45 kJ·kg-1). Compared with WT, PT cyclists showed a greater decay of MMP values under fatigue conditions (P < .001), and these differences increased with accumulating levels of fatigue (decay of -1.8 to -2.9% [WT] with reference to 0 kJ·kg-1 vs -1.1% to -4.4% [PT] after 15 kJ·kg-1 and of -4.7% to -8.8% [WT] vs -7.6% to -11.6% [PT] after 45 kJ·kg-1). No consistent differences were found between WT and PT cyclists in MMP values assessed in nonfatigue conditions (after 0 kJ·kg-1), but WT cyclists attained significantly higher MMP values with accumulating levels of fatigue, particularly for long-duration efforts (≥5 min).

CONCLUSIONS

Our findings highlight the importance of considering fatigue when assessing the record power profile of endurance athletes and support the ability to attenuate fatigue-induced decline in MMP values as a determinant of endurance performance.

Power profiling and the power-duration relationship in cycling: a narrative review

Emerging trends in technological innovations, data analysis and practical applications have facilitated the measurement of cycling power output in the field, leading to improvements in training prescription, performance testing and race analysis. This review aimed to critically reflect on power profiling strategies in association with the power-duration relationship in cycling, to provide an updated view for applied researchers and practitioners. The authors elaborate on measuring power output followed by an outline of the methodological approaches to power profiling. Moreover, the deriving a power-duration relationship section presents existing concepts of power-duration models alongside exercise intensity domains. Combining laboratory and field testing discusses how traditional laboratory and field testing can be combined to inform and individualize the power profiling approach. Deriving the parameters of power-duration modelling suggests how these measures can be obtained from laboratory and field testing, including criteria for ensuring a high ecological validity (e.g. rider specialization, race demands). It is recommended that field testing should always be conducted in accordance with pre-established guidelines from the existing literature (e.g. set number of prediction trials, inter-trial recovery, road gradient and data analysis). It is also recommended to avoid single effort prediction trials, such as functional threshold power. Power-duration parameter estimates can be derived from the 2 parameter linear or non-linear critical power model: P(t) = W'/t + CP (W'-work capacity above CP; t-time). Structured field testing should be included to obtain an accurate fingerprint of a cyclist's power profile.

Cross-Sectional Differences in Race Demands Between Junior, Under 23, and Professional Road Cyclists

PURPOSE

To compare the race demands of junior (JUN), under 23 (U23), and professional (PRO) road cyclists.

METHODS

Thirty male cyclists, divided into 3 age-related categories (JUN, n = 10; U23, n = 10; and PRO, n = 10), participated in this study. Race data collected during the 2019 competitive season were retrospectively analyzed for race characteristics, external, and internal competition load.

RESULTS

Higher annual and per race duration, distance, elevation gain, Edward's training impulse, total work, and work per hour were observed in PRO versus U23 and JUN, and U23 versus JUN (P < .01). PRO and U23 recorded higher mean maximal power (RPOs) between 5 and 180 minutes compared with JUN (P < .01). Edward's training impulse per hour was higher in JUN than PRO and U23 (P < .01). Accordingly, JUN spent a higher percentage of racing time in high internal intensity zones compared with U23 and PRO, while these 2 categories spent more time at low internal intensity zones (P < .01).

CONCLUSIONS

JUN races were shorter and included less elevation gain per distance unit compared to U23 and PRO races, but more internally demanding. JUN produced less power output in the moderate-, heavy-, and severe-intensity exercise domains compared with U23 and PRO (RPOs: 5-180 min). U23 and PRO races presented similar work demands per hour and RPOs, but PRO races were longer than U23.

Caveats and Recommendations to Assess the Validity and Reliability of Cycling Power Meters: A Systematic Scoping Review

A large number of power meters have become commercially available during the last decades to provide power output (PO) measurement. Some of these power meters were evaluated for validity in the literature. This study aimed to perform a review of the available literature on the validity of cycling power meters. PubMed, SPORTDiscus, and Google Scholar have been explored with PRISMA methodology. A total of 74 studies have been extracted for the reviewing process. Validity is a general quality of the measurement determined by the assessment of different metrological properties: Accuracy, sensitivity, repeatability, reproducibility, and robustness. Accuracy was most often studied from the metrological property (74 studies). Reproducibility was the second most studied (40 studies) property. Finally, repeatability, sensitivity, and robustness were considerably less studied with only 7, 5, and 5 studies, respectively. The SRM power meter is the most used as a gold standard in the studies. Moreover, the number of participants was very different among them, from 0 (when using a calibration rig) to 56 participants. The PO tested was up to 1700 W, whereas the pedalling cadence ranged between 40 and 180 rpm, including submaximal and maximal exercises. Other exercise conditions were tested, such as torque, position, temperature, and vibrations. This review provides some caveats and recommendations when testing the validity of a cycling power meter, including all of the metrological properties (accuracy, sensitivity, repeatability, reproducibility, and robustness) and some exercise conditions (PO range, sprint, pedalling cadence, torque, position, participant, temperature, vibration, and field test).

Methodological Reconciliation of CP and MLSS and Their Agreement with the Maximal Metabolic Steady State

The critical power (CP) and maximal lactate steady state (MLSS) are operational surrogates of the maximal metabolic steady state (MMSS). However, their concordance and their agreement with MMSS remains variable likely due to methodological factors.

PURPOSE

To compare the concordance between CP and MLSS estimated by various models and criteria and their agreement with MMSS.

METHODS

After a ramp-test, ten recreationally active males performed four-to-five severe-intensity constant-power output (PO) trials to estimate CP, and three-to-four constant-PO trials to determine MLSS and identify MMSS. CP was computed using the 3-parameter hyperbolic (CP3-hyp), 2-parameter hyperbolic (CP2-hyp), linear (CPlin), and inverse of time (CP1/Tlim) models. In addition, the model with lowest combined parameter error identified the "best-fit" CP (CPbest-fit). MLSS was determined as an increase in blood lactate concentration ≤ 1 mM during constant-PO cycling from the 5th (MLSS10-30), 10th (MLSS10-30), 15th (MLSS15-30), 20th (MLSS20-30), or 25th (MLSS25-30) to 30th minute. MMSS was identified as the greatest PO associated with the highest submaximal steady state V[Combining Dot Above]O2 (MV[Combining Dot Above]O2ss).

RESULTS

Concordance between the various CP and MLSS estimates was greatest when MLSS was identified as MLSS15-30, MLSS20-30, and MLSS25-30. The PO at MV[Combining Dot Above]O2ss was 243 ± 43 W. Of the various CP models and MLSS criteria, CP2-hyp (244 ± 46 W) and CPlin (248 ± 46 W) and MLSS15-30 and MLSS20-30 (both 245 ± 46 W), respectively displayed, on average, the greatest agreement with MV[Combining Dot Above]O2ss. Nevertheless, all CP models and MLSS criteria demonstrated some degree of inaccuracies with respect to MV[Combining Dot Above]O2ss.

CONCLUSIONS

Differences between CP and MLSS can be reconciled with optimal methods of determination. When estimating MMSS, from CP the error margin of the model-estimate should be considered. For MLSS, MLSS15-30 and MLSS20-30 demonstrated the highest degree of accuracy.

Bilateral asymmetries in professional cyclists during a Grand Tour

BACKGROUND: Pedalling asymmetries are a topic of interest to cycling coaches and athletes due to a potential link with performance and injury prevention. OBJECTIVES: The aim of this study is to describe the bilateral asymmetry of professional cyclists during two editions of a Grand Tour. METHODS: Here we set out to determine the power balance (power produced by each lower limb) between stronger and weaker leg (dominant vs. non-dominant) of 12 UCI professional cyclists competing at two Giro d’Italia editions. Power data were recorded during competition stages. Further analysis considered power data clustered into individual intensity zones (from Z1 to Z7). RESULTS: Higher intensity elicited better power balance (lower asymmetry) regardless of the stage profile. Intensity distribution analysed according to the role of the cyclist was lower for climbers in Z2 (p= 0.006) and Z7 (p= 0.002) and higher in Z5 (p= 0.023) compared to team helpers. Power balance ranged from 0 to 9 % across the different athletes. CONCLUSIONS: Increase in power output improves power balance, especially in team helpers, and the lower power balance at lower exercise intensities, which are most of the race time, may elicit significant cumulative loading on a given leg of the cyclists, which requires further attention regarding risks of overuse injury.

Validating an Adjustment to the Intermittent Critical Power Model for Elite Cyclists-Modeling W' Balance During World Cup Team Pursuit Performances

PURPOSE

Modeling intermittent work capacity is an exciting development to the critical power model with many possible applications across elite sport. With the Skiba 2 model validated using subelite participants, an adjustment to the model's recovery rate has been proposed for use in elite cyclists (Bartram adjustment). The team pursuit provides an intermittent supramaximal event with which to validate the modeling of W' in this population.

METHODS

Team pursuit data of 6 elite cyclists competing for Australia at a Track World Cup were solved for end W' values using both the Skiba 2 model and the Bartram adjustment. Each model's success was evaluated by its ability to approximate end W' values of 0 kJ, as well as a count of races modeled to within a predetermined error threshold of ±1.840 kJ.

RESULTS

On average, using the Skiba 2 model found end W' values different from zero (P = .007; mean ± 95% confidence limit, -2.7 ± 2.0 kJ), with 3 out of 8 cases ending within the predetermined error threshold. Using the Bartram adjustment on average resulted in end W' values that were not different from zero (P = .626; mean ± 95% confidence limit, 0.5 ± 2.5 kJ), with 4 out of 8 cases falling within the predetermined error threshold.

CONCLUSIONS

On average, the Bartram adjustment was an improvement to modeling intermittent work capacity in elite cyclists, with the Skiba 2 model underestimating the rate of W' recovery. In the specific context of modeling team pursuit races, all models were too variable for effective use; hence, individual recovery rates should be explored beyond population-specific rates.

The Validity and Reliability of a Tire Pressure-Based Power Meter for Indoor Cycling

The purpose of this study was to evaluate the validity and reliability of a tire pressure sensor (TPS) cycling power meter against a gold standard (SRM) during indoor cycling. Twelve recreationally active participants completed eight trials of 90 s of cycling at different pedaling and gearing combinations on an indoor hybrid roller. Power output (PO) was simultaneously calculated via TPS and SRM. The analysis compared the paired 1 s PO and 1 min average PO per trial between devices. Agreement was assessed by correlation, linear regression, inferential statistics, effect size, and Bland–Altman LoA. Reliability was assessed by ICC and CV comparison. TPS showed near-perfect correlation with SRM in 1 s (r_s = 0.97, p < 0.001) and 1-min data (r_s = 0.99, p < 0.001). Differences in paired 1 s data were statistically significant (p = 0.04), but of a trivial magnitude (d = 0.05). There was no significant main effect for device (F(1,9) = 0.05, p = 0.83, ηp2 = 0.97) in 1 min data and no statistical differences between devices by trial in post hoc analysis (p < 0.01–0.98; d < 0.01–0.93). Bias and LoA were −0.21 ± 16.77 W for the 1 min data. Mean TPS bias ranged from 3.37% to 7.81% of the measured SRM mean PO per trial. Linear regression SEE was 7.55 W for 1 min TPS prediction of SRM. ICC_3,1 across trials was 0.96. No statistical difference (p = 0.09–0.11) in TPS CV (3.6–5.0%) and SRM CV (4.3–4.7%). The TPS is a valid and reliable power meter for estimating average indoor PO for time periods equal to or greater than 1 min and may have acceptable sensitivity to detect changes under less stringent criteria (±5%).

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.

Reproducibility of the Rotor 2INpower Crankset for Monitoring Cycling Power Output: A Comprehensive Analysis in Different Real-Context Situations

PURPOSE

To examine the reproducibility (intradevice and interdevice agreement) of the Rotor 2INpower device under a wide range of cycling conditions.

METHODS

Twelve highly trained male cyclists and triathletes completed 5 cycling tests, including graded exercise tests at different cadences (70-100 rpm), workloads (100-650 W), pedaling positions (seated and standing), and vibration conditions (20-40 Hz) and an 8-second maximal sprint (>1000 W). An intradevice analysis included a comparison between the power output registered by 3 units of Rotor 2INpower, whereas the power output provided by each one of these units and the gold-standard SRM crankset were compared for the interdevice analysis. Among others, statistical calculations included the standard error of measurement, expressed in absolute (in watts) and relative terms as the coefficient of variation (CV).

RESULTS

Except for the graded exercise test seated at 100 rpm/100 W (CV = 10.2%), the intradevice analysis showed an acceptable magnitude of error (CV ≤ 6.9%, standard error of measurement ≤ 12.3 W) between the 3 Rotor 2INpower. Similarly, these 3 units showed an acceptable agreement with the gold standard in all graded exercise test situations (CV ≤ 4.0%, standard error of measurement ≤ 13.1 W). On the other hand, both the intradevice and interdevice agreements proved to be slightly reduced under high cadences (intradevice: CV ≤ 10.2%; interdevice: CV ≤ 4.0%) and vibration (intradevice: CV ≤ 4.0%; interdevice: CV ≤ 3.6%), as well as during standing pedaling (intradevice: CV ≤ 4.1%; interdevice: CV ≤ 2.5%). Although within the limits of an acceptable agreement, measurement errors increased during the sprint tests (CV ≤ 7.4%).

CONCLUSIONS

Based on these results, the Rotor 2INpower could be considered a reproducible tool to monitor power output in most cycling situations.

A comparative analysis of critical power models in elite road cyclists

The aims of this study were to compare four different critical power model's ability to ascertain critical power and W' in elite road cyclists, while making comparison to power output at respiratory compensation point, work rate (J·sec-1) at Wmax, and the work done above critical power during the Wmax test in relation to the W'. Ten male, elite endurance cyclists (V̇O2max ​= ​71.9 ​± ​5.9 ​ml ​kg-1·min-1) all familiar with critical power testing, participated in 3 testing sessions comprising 1. 15-s isokinetic (130 ​rpm) sprint, 1-min time trial, a ramp test to exhaustion, 2-3. a 4-min and/or 10-min self-paced maximal time trial separated by at least 24-h but limited to a 3-week period. The main findings show that all critical power models provided different W' (F(1.061,8.486) ​= ​39.07, p ​= ​0.0002) and critical powers (F(1.022,8.179) ​= ​32.31, p ​= ​0.0004), while there was no difference between each model's critical power and power output at respiratory compensation point (F(1.155, 9.243) ​= ​2.72, p ​= ​0.131). Differences between models or comparisons with respiratory compensation point were deemed not clinically useful in the provision of training prescription or performance monitoring if the aim is to equal work rate at compensation point. There was also no post-hoc difference between work completed at Wmax (kJ) (p ​= ​0.890) and W' using the nonlinear-3 model. Further research is required to investigate the physiological markers of intensity associated with respiratory compensation point and critical power work rate and the bioenergetic contribution to W'.

The Use of Online Training Tools in Competition Cyclists During COVID-19 Confinement in Spain

COVID-19 confinement has supposed a challenge to the whole wide world, especially in athletes that have frustrated their expectations about training programs and competitions. Specifically, competition cyclists during confinement had lot of difficulties to train due to the need to train outside their homes. However, the increase of online training sessions, or virtual training tools could help to overcome training difficulties due to confinement although there are not studies that analyse the effects of using these types of tools in cyclists. This study aims to test how the use of online training tools in competition cyclists during confinement is related to training frequency and duration, and emotions. 329 cyclists filled an online questionnaire about feelings during confinement and the use of online training tools, before and during confinement. Results showed that the use of online training tools was associated with higher frequency and duration of trainings. Moreover, those who used these tools felt more positively about their future and preparation to compete, feeling more energy than those who did not used online tools. In addition, cyclists that started using these online tools (including virtual roller training) during confinement increased the frequency of training. In conclusion the use of online training tools for cycling could be appropriate to maintain training levels and energy and positive feelings about their future.

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.

Power Profiling in U23 Professional Cyclists During a Competitive Season

PURPOSE

The aim of this study was to investigate changes in the power profile of U23 professional cyclists during a competitive season based on maximal mean power output (MMP) and derived critical power (CP) and work capacity above CP (W') obtained during training and racing.

METHODS

A total of 13 highly trained U23 professional cyclists (age = 21.1 [1.2] y, maximum oxygen consumption = 73.8 [1.9] mL·kg-1·min-1) participated in this study. The cycling season was split into pre-season and in-season. In-season was divided into early-, mid-, and late-season periods. During pre-season, a CP test was completed to derive CPtest and W'test. In addition, 2-, 5-, and 12-minute MMP during in-season were used to derive CPfield and W'field.

RESULTS

There were no significant differences in absolute 2-, 5-, and 12-minute MMP, CPfield, and W'field between in-season periods. Due to changes in body mass, relative 12-minute MMP was higher in late-season compared with early-season (P = .025), whereas relative CPfield was higher in mid- and late-season (P = .031 and P = .038, respectively) compared with early-season. There was a strong correlation (r = .77-.83) between CPtest and CPfield in early- and mid-season but not late-season. Bland-Altman plots and standard error of estimates showed good agreement between CPtest and in-season CPfield but not between W'test and W'field.

CONCLUSION

These findings reveal that the power profile remains unchanged throughout the in-season, except for relative 12-minute MMP and CPfield in late-season. One pre-season and one in-season CP test are recommended to evaluate in-season CPfield and W'field.

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

Wearable technology has allowed for the real-time assessment of mechanical work employed in several sporting activities. Through novel power metrics, Functional Threshold Power have shown a reliable indicator of training intensities. This study aims to determine the relationship between mean power output (MPO) values obtained during three submaximal running time trials (i.e., 10 min, 20 min, and 30 min) and the functional threshold power (FTP). Twenty-two recreationally trained male endurance runners completed four submaximal running time trials of 10, 20, 30, and 60 min, trying to cover the longest possible distance on a motorized treadmill. Absolute MPO (W), normalized MPO (W/kg) and standard deviation (SD) were calculated for each time trial with a power meter device attached to the shoelaces. All simplified FTP trials analyzed (i.e., FTP10, FTP20, and FTP30) showed a significant association with the calculated FTP (p < 0.001) for both MPO and normalized MPO, whereas stronger correlations were found with longer time trials. Individual correction factors (ICF% = FTP60/FTPn) of ~90% for FTP10, ~94% for FTP20, and ~96% for FTP30 were obtained. The present study procures important practical applications for coaches and athletes as it provides a more accurate estimation of FTP in endurance running through less fatiguing, reproducible tests.

Absolute Reliability and Concurrent Validity of the Stryd System for the Assessment of Running Stride Kinematics at Different Velocities

ABSTRACT

García-Pinillos, F, Roche-Seruendo, LE, Marcen-Cinca, N, Marco-Contreras, LA, and Latorre-Román, PA. Absolute reliability and concurrent validity of the Stryd system for the assessment of running stride kinematics at different velocities. J Strength Cond Res 35(1): 78-84, 2021-This study aimed to determine the absolute reliability and to evaluate the concurrent validity of the Stryd system for measuring spatiotemporal variables during running at different velocities (8-20 km·h-1) by comparing data with another widely used device (the OptoGait system). Eighteen trained male endurance runners performed an incremental running test (8-20 km·h-1 with 3-minute stages) on a treadmill. Spatiotemporal parameters (contact time [CT], flight time [FT], step length [SL], and step frequency [SF]) were measured using 2 different devices (Stryd and OptoGait systems). The Stryd system showed a coefficient of variation (CV) <3%, except for FT (3.7-11.6%). The OptoGait achieved CV <4%, except for FT (6.0-30.6%). Pearson correlation analysis showed large correlations for CT and FT, and almost perfect for SL and SF over the entire protocol. The intraclass correlation coefficients partially support those results. Paired t-tests showed that CT was underestimated (p < 0.05, effect size [ES] > 0.7; ∼4-8%), FT overestimated (p < 0.05, ES > 0.7; ∼7-65%), whereas SL and SF were very similar between systems (ES < 0.1, with differences <1%). The Stryd is a practical portable device that is reliable for measuring CT, FT, SL, and SF during running. It provides accurate SL and SF measures but underestimates CT (0.5-8%) and overestimates FT (3-67%) compared with a photocell-based system.

Training Characteristics and Power Profile of Professional U23 Cyclists throughout a Competitive Season

BACKGROUND

The purpose of this study was to investigate differences in the power profile derived from training and racing, the training characteristics across a competitive season and the relationships between training and power profile in U23 professional cyclists.

METHODS

Thirty male U23 professional cyclists (age, 20.0 ± 1.0 years; weight, 68.9 ± 6.9 kg; V˙O2max, 73.7 ± 2.5 mL·kg-1·min-1) participated in this study. The cycling season was split into pre-, early-, mid- and late-season periods. Power data 2, 5, 12 min mean maximum power (MMP), critical power (CP) and training characteristics (Hours, Total Work, eTRIMP, Work·h-1, eTRIMP·h-1, TimeVT2) were recorded for each period. Power profiles derived exclusively from either training or racing data and training characteristics were compared between periods. The relationships between the changes in training characteristics and changes in the power profile were also investigated.

RESULTS

The absolute and relative power profiles were higher during racing than training at all periods (p ≤ 0.001-0.020). Training characteristics were significantly different between periods, with the lowest values in pre-season followed by late-season (p ≤ 0.001-0.040). Changes in the power profile between early- and mid-season significantly correlated with the changes in training characteristics (p < 0.05, r = -0.59 to 0.45).

CONCLUSION

These findings reveal that a higher power profile was recorded during racing than training. In addition, training characteristics were lowest in pre-season followed by late-season. Changes in training characteristics correlated with changes in the power profile in early- and mid-season, but not in late-season. Practitioners should consider the influence of racing on the derived power profile and adequately balance training programs throughout a competitive season.

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

Mechanical power may act as a key indicator for physiological and mechanical changes during running. In this scoping review, we examine the current evidences about the use of power output (PW) during endurance running and the different commercially available wearable sensors to assess PW. The Boolean phrases endurance OR submaximal NOT sprint AND running OR runner AND power OR power meter, were searched in PubMed, MEDLINE, and SCOPUS. Nineteen studies were finally selected for analysis. The current evidence about critical power and both power-time and power-duration relationships in running allow to provide coaches and practitioners a new promising setting for PW quantification with the use of wearable sensors. Some studies have assessed the validity and reliability of different available wearables for both kinematics parameters and PW when running but running power meters need further research before a definitive conclusion regarding its validity and reliability.

New Procedure for the Kinematic and Power Analysis of Cyclists in Indoor Training

In this research, the performance and movements of amateur and professional cyclists were analyzed. For this, reflective markers have been used on different parts of the body of the participants in conjunction with sports cameras and a mobile power meter. The trajectories of the markers have been obtained with the software Kinovea and subsequently analyzed using error ellipses. It is demonstrated that the error ellipses help determine movement patterns in the knees, back, and hip. The covariance of the error ellipses can be indicative of the alignment and symmetry of the frontal movement of the knees. In addition, it allows verifying the alignment of the spine and the symmetry of the hip. Finally, it is shown that it is necessary to consider the uncertainty of the power devices since it considerably affects the evaluation of the cyclists’ performance. Devices with high uncertainty will demand a greater effort from the cyclist to meet the power required in the endurance test developed. The statistical magnitudes considered help to analyze power and evaluate the cyclists’ performance.