Figure - available from: European Journal of Applied Physiology
This content is subject to copyright. Terms and conditions apply.
Source publication
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-dur...
Citations
... 27 In this line, several studies on the power profile of high-level cyclists have been recently published. 28,29 Yet descriptive studies on training and physiological adaptations over several seasons are less common. In fact, a recent systematic review concluded that there is an urgent need for additional long-term studies based on the systematic monitoring of athletes from a young age. ...
Objective:
The sports-science literature lacks data on training and performance characteristics of international elite athletes over multiple seasons. The present case study provided general training characteristics and performance data of two male short-distance triathletes in the Junior, U23, and international Elite categories.
Methods:
General training and performance data of two male elite triathletes were described in swimming, cycling, and running segments from the 2015 to 2022 season. The training load was presented using the ECO model while the training intensity distribution (TID) was a triphasic model.
Results:
Both triathletes increased their performance throughout the seasons. Triathlete A increased his VO2max in cycling by 20.6%, in running by 16.7%. His power at VO2max and his speed at VO2max by 18.9% and 11.0%, respectively. Triathlete B improved his VO2max by 17.8% in cycling, by 16.1% in running and his power at VO2max by 24%, and his speed at VO2max by 14.3%. The triathletes trained on average 14-17 h a week. The TID model was polarized.
Conclusions:
To achieve the top international level, it is necessary to consider the following measures: training load progression; improvements in physiological variables; and participation in international events starting from youth categories.
... Research in professional-level amputee cycling [14,18] has shown an increase in kinematic symmetry at higher workloads. Furthermore, previous findings have noted enhanced power delivery at higher resistances in regular cycling [19][20][21] because of the workload requirements. For the unaffected side, a slight increase in force parameters for the orthosis conditions over NO was observed (Figures 15-17), which could be attributed to unilateral amputee cyclists employing their sound leg more at lower cadences [12,13]; these results are in line with sEMG results. ...
Leg prostheses specially adapted for cycling in patients with transtibial amputation can be advantageous for recreational practice; however, their required features are not fully understood. Therefore, we aimed to evaluate the efficiency of unilateral cycling with a transtibial prosthesis and the characteristics of different attachment positions (middle and tip of the foot) between the prosthetic foot and the pedal. The cycling practice was performed on an ergometer at 40 W and 60 W resistance levels while participants (n = 8) wore custom-made orthoses to simulate prosthesis conditions. Using surface electromyogram, motion tracking, and power meter pedals, biomechanical data were evaluated and compared with data obtained through regular cycling. The results showed that power delivery became more asymmetrical at lower workloads for both orthosis conditions, while hip flexion and muscle activity of the knee extensor muscles in the sound leg increased. While both pedal attachment positions showed altered hip and knee joint angles for the leg wearing the orthosis, the middle of the foot attachment presented more symmetric power delivery. In conclusion, the middle of the foot attachment position presented better symmetry between the intact and amputated limbs during cycling performed for rehabilitation or recreation.
... exercise classed as "severe") "with startling precision" (Poole et al. 2016). It is ubiquitous in cycling (Leo et al. 2022a), where it is implemented in popular online exercise analytics platforms; but it is also widely used for training prescription and performance prediction in other endurance sports, such as running (Kranenburg and Smith 1996;Nimmerichter et al. 2017), rowing (Hill et al. 2003), swimming (Wakayoshi et al. 1992;Petrigna et al. 2022) as well as walking and skating (Hill 1925). Additionally, it has been applied to intermittent sports, such as football, hockey and rugby (Okuno et al. 2011) in order to optimise the length of recovery needed between exercise bouts (Fukuda et al. 2011). ...
The power–duration relationship describes the time to exhaustion for exercise at different intensities. It is believed to be a “fundamental bioenergetic property of living systems” that this relationship is hyperbolic. Indeed, the hyperbolic (a.k.a. critical-power) model which formalises this belief is the dominant tool for describing and predicting high-intensity exercise performance, e.g. in cycling, running, rowing or swimming. However, the hyperbolic model is now the focus of a heated debate in the literature because it unrealistically represents efforts that are short (< 2 min) or long (> 15 min). We contribute to this debate by demonstrating that the power–duration relationship is more adequately represented by an alternative, power-law model. In particular, we show that the often-observed good fit of the hyperbolic model between 2 and 15 min should not be taken as proof that the power–duration relationship is hyperbolic. Rather, in this range, a hyperbolic function just happens to approximate a power law fairly well. We also prove mathematical results which suggest that the power-law model is a safer tool for pace selection than the hyperbolic model and that the former more naturally models fatigue than the latter.
... The model is used to analyze different types of cyclists on different tracks and calculate their optimal target power respectively. [7] ...
Speed and endurance are tested in the sport of road cycling, and competitors must appropriately distribute their physical strength based on the road's conditions. This study establishes a bicycle dynamics model based on the rider's motion state and power limitations, as well as the rider's constant power, using the Euler formula in order to obtain the rider's ideal riding strategy. The road conditions are divided into four categories by creating a three-dimensional space coordinate model of the track: uphill, downhill, sharp turns, and flat ground. The cyclist dynamics model is then meticulously optimized to forecast the best performance of various riders on various tracks. The predicted result is improved by the outcome and the reference power allocation scheme.
... The record power profile (RPP; ie, the highest power output [PO] that a cyclist can attain for a given duration during training or racing) is gaining popularity in recent years as a tool for monitoring endurance cycling performance. 1 This indicator has proven to be a reliable proxy of the maximal performance capacity of these athletes, showing a strong agreement-at least when data are obtained from both training sessions and competitions-with PO measures obtained under controlled testing conditions. 2,3 Moreover, the RPP can be used to distinguish cyclists of different competition categories (eg, World Tour vs ProTeam, professional vs U23). ...
Purpose:
The record power profile (RPP) has gained popularity as a method of monitoring endurance cycling performance. However, the expected variation of cyclists' performance between seasons remains unknown. We aimed to assess the between-seasons variability of peak performance (assessed through the RPP) in male professional cyclists.
Methods:
The study followed a longitudinal observational design. Sixty-one male professional cyclists (age 26 [5] y) with power output data from both training sessions and competitions were analyzed for a median of 4 consecutive seasons (range 2-12). The highest mean maximum power values attained for different durations (from 10 s to 30 min), as well as the resulting critical power, were determined for each season. Within-cyclist variability between seasons was assessed, and the upper threshold of expected changes (ie, twice the normal coefficient of variation) was determined.
Results:
All mean maximum power values showed an overall high agreement and low variability between seasons (intraclass correlation coefficient [ICC] = .76-.88 and coefficient of variation [CV] = 3.2%-5.9%), with the lowest variability observed for long efforts (>1 min). Critical power showed an ICC and CV of .79 (95% CI, .70-.85) and 3.3% (95% CI, 3.0%-3.7%), respectively. Upper thresholds of expected variation were <12% for short efforts (≤1 min) and <8% for long efforts.
Conclusions:
"Real-world" peak performance assessed through the RPP shows a low variability between seasons in male professional cyclists-especially for long efforts-with expected variation being around 6% and 3% for short (≤1 min) and long efforts, respectively, and with changes >12% and >8%, respectively, being infrequent for these effort durations.
... The duration of each effort is related to the specific demands of each cycling profile and moment (e.g., high-intensity bursts observed in flat stages vs. high relative power outputs for extended durations observed in long uphill sections) , and it is useful for quantifying performance (Quod et al., 2010). Repeated testing provides information on a wide physiological spectrum, providing better data on actual performance than a single measurement (Leo, Spragg, Podlogar, et al., 2021). The intensity distribution during cycling races reveals that cyclists perform at moderate intensities most of the time, but crucial actions during racing, as well as important phases of training sessions, will require efforts at maximal or supramaximal intensities (Mujika, 2017;van Erp & Sanders, 2020;. ...
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.
... Recently, it was observed that the under 23-yr category (U23) and professional cyclists had higher values than junior cyclists inVO 2max , W max , threshold power, and 8-min maximal power output, whereas the professional cyclists were higher than U23 on the abovementioned variables except forVO 2max (7). However, that study "only" included 51 cyclists and was lacking measurements of cycling performance at the boundaries of the power-duration continuum (8), namely, short sprints lasting 6-60 s and longer efforts of maximal mean power output like 30 min or longer. Furthermore, among the studies comparing different age categories (7,9,10), none of them have included female cyclists or investigated the pedaling characteristics. ...
... Critical power and work capacity above CP (W′) were calculated using a linear power, inverse of time CP model, where CP and W′ are represented as the intercept and the slope of the linear relationship (31), which have been highlighted to have a high mathematical accuracy (8). The durations used for CP and W′ calculation were 1, 12, and 30 min, as the 5-min maximal test in the present study was performed in a semifatigued state. ...
Aim:
This study investigated the development of power profiles and performance-related measures from the junior level (<19 years) via U23 (19-23 years) to senior level (>23 years) in 19 female and 100 male Norwegian national team cyclists.
Methods:
A total of 285 tests were performed in a 3-day laboratory-standardized testing regime. The tests included power profiles with shorter duration (6-60 s) and longer durations (12-30 min) together with performance-related measures: Critical power (CP), work capacity above CP (W'), power output at 4 and 2 mmol·L-1 [BLa-] (L4 and L2), maximal aerobic power (Wmax), and maximal oxygen uptake (VO2max), gross efficiency (GE), and pedalling efficiency.
Results:
Females and males evolve similarly when maturing from junior via U23 to senior categories (all p > 0.07), except for VO2max which increased in females (but not males) from junior to senior level (534 ± 436 ml·min-1, p = 0.013). In general, only performances of longer durations improved with age (12-min and 30-min, p = 0.028, and p = 0.042, respectively). Performance-related measures like Wmax, VO2max, CP, L4, L2, and pedalling efficiency in the fresh state improved with age (all p ≤ 0.025). Importantly, performance in the semi-fatigued state during a 5-min maximal test, was also improved with age (p = 0.045) despite a higher external energy expenditure prior to the test (p = 0.026).
Conclusions:
Junior cyclists show highly developed sprint-abilities, and the primary improvements of absolute power outputs and performance-related measures are seen for durations >60 s when maturing to U23 and senior categories. However, the durability, i.e., the capacity to maintain performance in a semi-fatigued state is improved with age.
... That is because smart e-bikes should be designed based on biomechanical information that embraces human metabolic activity and body fatigue. It is necessary to investigate the factors that influence rider fatigue [3][4][5]. ...
In this study, a switch controller manages the power‐sharing between the battery and human mode to improve the rider's metabolism and manage the battery SOC. The main idea is to optimize this power source switching element for changing the status to reach a trade‐off between lack of tiredness and keeping the SOC high. Calorie burning is closely related to the rider's physical characteristics. In this paper, these parameters are investigated to calculate calorie burning. When the electric‐powered mode is activated, the SOC level comes down. When the human‐powered mode is activated, the human power source provides energy. The model converts the bicycle speed into the rider's heart rate and then changes it into burned calories based on some equations. These equations are obtained by poly fitting after experiments. This optimization causes 33.5% and 50% burning calorie reduction in Cleaveland and Portuguese driving cycles. Also, in the Portuguese driving cycle, the battery usage percentage decreases 39.56% from to 20.54% after optimization; therefore, the burning calorie decreases 265.84 Kcal to 176.83 Kcal.
... Similarly, the critical power (CP) model has been used to model performance over short, medium and long durations reflecting exercise domains (heavy, severe, extreme) [32][33][34]. An advantage of this model is it allows the determination of a critical power for durations between 2-15 minutes [35], and also an estimate of finite high intensity energy referred to as W' and measured in kilojoules [32]. ...
... The APR and CP models may provide a useful means of predicting the power-duration relationship in the extreme exercise intensity domain, such as during sprint track cycling events Leo et al. [35]. It is questionable whether it is necessary to run multiple trials to predict performance given the ease of measuring actual performance [36], modelling the determinants from actual performance [7,37], and measuring performance with various sensors, especially power output, during competition [38]. ...
Current convention place peak power as the main determinant of sprint cycling performance. This study challenges that notion and compares two common durations of sprint cycling performance with not only peak power, but power out to 20-min. There is also a belief where maximal efforts of longer durations will be detrimental to sprint cycling performance. 56 data sets from 27 cyclists (21 male, 6 female) provided maximal power for durations from 1-s to 20-min. Peak power values are compared to assess the strength of correlation (R^2), and any relationship (slope) across every level. R^2 between 15-s-30-s power and durations from 1-s to 20-min remained high (R^2 = 0.83). Despite current assumptions around 1-s power, our data shows this relationship is stronger around competition durations, and 1-s power also still shared strong relationships with longer durations out to 20-min. Slopes for relationships at shorter durations were closer to a 1:1 relationship than longer durations, but closer to long-duration slopes than to a 1:1 line. The present analyses contradicts both well-accepted hypotheses that peak power is the main driver of sprint cycling performance and that maximal efforts of longer durations out to 20-min will hinder sprint cycling. This study shows the importance and potential of training durations from 1-s to 20-min over a preparation period to improve competition sprint cycling performance.
... Exercise intensity is one of many factors underpinning the levels of central and peripheral fatigue after a whole-body exercise [7][8][9]. Exercise intensity is commonly divided into three exercise-intensity domains: moderate-, heavy-, and severe-intensity domains [10,11]. The moderate-intensity domain is characterized by exercise intensities below the gas exchange threshold (GET) [12]. ...
Objectives. — Whether performance fatigability and its determinants (i.e., peripheral and central fatigue) after cycling exercise performed at extreme- and severe-intensity domains are
of similar magnitude is unknown. In the current study, we investigated the levels of performance fatigability and peripheral and central fatigue in nine young female after a cycling
exercise performed until the limit of tolerance at extreme- (i.e., 140% of peak power output) and severe-intensity domains (80% of the difference between gas exchange threshold and peak
power output). Equipment and methods. — The level of maximal voluntary isometric contraction (MVC), potentiated quadriceps twitch force evoked by single pulse (Q tw ), and voluntary activation (VA) were measured pre- and post-exercise. Results. — The MVC (a marker of performance fatigability) decreased from pre- to post-exercise (P < 0.05) in the extreme- (−10 ± 8%) and severe-intensity (−10 ± 10%) exercises. The Q tw (a marker of peripheral fatigue) reduced similarly from pre- to post-exercise (P < 0.05) in the extreme- (−18 ± 15%) and severe-intensity (−10 ± 17%) exercises. The VA (a marker of central fatigue) did not reduce from pre- to post-exercise in either severe- (2 ± 7%) or extreme-intensity (−2 ± 7%) exercises (P > 0.05). Conclusions. — These findings suggest a similar amount of performance fatigability and peripheral fatigue after severe- and extreme-intensity cycling exercises in young female, which is in
accordance with the concept that exercises performed above critical power until task failure attain a common level of peripheral fatigue regardless of exercise intensity.
