Influence of Torque and Cadence on Power Output Production in Cyclists

Mechanistic influence of the torque cadence relationship on power output during exhaustive all-out field tests in professional cyclists

Understanding the torque-cadence-power relationship can be important in assessing a cyclist's performance potential. This study explored these relationships in elite male cyclists (N = 17; age: 24.1 ± 3.8 years; body mass: 66.0 ± 4.8 kg, critical power (CP): 5.5 ± 0.3 W.kg-1) through sprint, torque, and CP tests conducted in fresh and after accumulated work. Testing protocols, performed during a pre-season training camp, included maximal efforts across varied gear ratios and durations (15 s, 3 min, and 12 min), under stable environmental conditions (15-20°C). Results revealed reduced power output, torque, and cadence after accumulated work compared to fresh conditions (p ≤ .001). Sprint-derived maximum torque (Tmax) was strongly correlated with torque intercepts for CP fresh (r = .558, p = .020) and after accumulated work (r = .556, p = .020). The cadence relationships demonstrated a large negative correlation between maximum cadence (Cmax) and optimum cadence (Copt) from the sprint test and the 15 s, 3 min and 12 min cadence recorded during CP after accumulated work (r = -0.541 to -0.634, p = 0.006 to 0.025). These findings highlight that accumulated work-induced reductions in work capacity (W') and CP values were accompanied by lower cadences across all effort durations.

Enhancing endurance performance predictions: the role of movement velocity in metabolic simulations demonstrated by cycling cadence

BACKGROUND

Mader's mathematical model, widely employed for endurance performance prediction, aims to accurately represent metabolic response to exercise. However, it traditionally overlooks dynamic changes in metabolic processes at varying movement velocities.

METHODS

This narrative review examined the effect of cycling cadence on its key input parameters, including oxygen demand per Watt ( CE V ˙ O2 ), resting oxygen uptake (V ˙ O 2Base ), maximal oxygen uptake (V ˙ O 2max ), and maximal blood lactate accumulation rate (vLamax). These findings were integrated into the model to assess cadence-induced variations in predicted power output at maximal aerobic power (MAP), maximal lactate steady state (MLSS), and peak fat oxidation (FATmax).

RESULTS

A U-shaped relationship was found between cadence and both CE V ˙ O2 andV ˙ O 2Base , whileV ˙ O 2max remained largely cadence-independent within typical cadences. vLamax exhibited a polynomial increase with cadence, attributed to changes in lactate elimination, suggesting cadence-independent maximal glycolytic flux. Incorporating these findings into Mader's model considering various scenarios revealed significant cadence-induced variations, with power output differences of up to > 100 W. Using cadence-dependent CE V ˙ O2 andV ˙ O 2Base while maintaining constantV ˙ O 2max and vLamax yielded polynomial power output-cadence relationships, with optimal cadences of 84 rpm at MAP, 80 rpm at MLSS, and 70 rpm at FATmax. Incorporating cadence-dependent vLamax produced implausible results, supporting cadence-independent maximal glycolytic flux. A hypothesized cadence-dependentV ˙ O 2max improved alignment between model predictions and empirical data.

CONCLUSION

Neglecting dynamic changes in metabolic processes across different movement velocities can lead to inaccurate modelling results. Incorporating cadence alongside established parameters enhances the precision of Mader's metabolic model for cycling performance prediction.

Submaximal force-velocity relationships during mountain ultramarathon: Data from the field

This study presents a novel method for evaluating the submaximal velocity-force (V(F)) relationship in mountain ultramarathon races using crowdsourced data from Strava.com. The dataset includes positional data from 408 participants of the 171-km UTMB® 2023 race (9,850-m D+). The race was divided into 100-m segments. The mean net propulsive force and velocity were computed for each segment to describe the submaximal V(F) relationship as a rational function of three parameters. F1: propulsive force at 1 m · s-1; V0: theoretical maximum velocity on flat terrain; C: curvature parameter (the lower C, the more linear the V(F) relationship). The V(F) profile parameters were found to be F1 = 1.80 ± 0.33 N · kg-1, V0 = 2.36 ± 0.42 m · s-1, and C = 0.66 ± 1.81, with good independence between the parameters within a group of homogeneous performance. The best athletes had the highest F1, V0, and C values. V(F) parameters were affected by fatigue during the race, with decreases of 20.9%, 32.0%, and 59.8% between the first and second parts of the race respectively. These findings suggest that the V(F) relationship is an interesting original approach for studying performance and fatigability during mountain ultra-endurance races.

Relative pedaling forces are low during cycling

We quantified and compared the mechanical force demands relative to the maximum dynamic force (MDF) of 11 cyclists when pedaling at different intensities (ventilatory threshold, maximum lactate steady state, respiratory compensation point, and maximal aerobic power), cadences (free, 40, 60 and 80 rpm), and all-out resisted sprints. Relative force demands (expressed as %MDF) progressively increased with higher intensities (p < 0.001) and lower cadences (p < 0.001). Notwithstanding, relative force demands were low (<54 % MDF) for all conditions, even during the so-called 'torque training'. These results might be useful when programming on-bike resistance training to improve torque production capacity.

In Situ Power-Cadence Relationship for 2-, 5-, and 20-Minute Duration: A Proof of Concept in Under-19 Cyclists

BACKGROUND

The force-velocity relationship suggests that maximal power (Pmax) can only be produced in optimal torque (Topt) and cadence (Copt). However, the cadence at which mean maximal power (MMP) is produced has never been studied. This study aimed to determine the individual MMP-cadence relationship from in situ data.

METHOD

We analyzed 1 year of data from 14 under-19 cyclists and calculated the MMP for each cadence between 50 and 120 rpm for 2-, 5-, and 20-minute durations. The MMP-cadence relationship was fit with a second-order polynomial function. The goodness of fit (r2) and odd-day-even-day absolute and relative reliability were evaluated, respectively, for Pmax, Topt, and Copt.

RESULTS

The goodness of fit was very high for every duration studied. Topt and Pmax, but not Copt, were significantly higher for shorter durations. Pmax was significantly correlated only with Topt for the 3 durations (r2 = .63, .71, and .64 for 2, 5, and 20 min, respectively).

DISCUSSION

Evaluation of the MMP-cadence relationship from in situ data is feasible and reliable for 2-, 5-, and 20-minute durations. This profiling approach would enable better detection of the strengths and weaknesses of cyclists and make it possible to design more effective training interventions.

PRACTICAL APPLICATIONS

The analysis makes it possible to identify the torque versus cadence component that individually limits power production. Knowing the Copt for a given duration of maximal effort could help athletes choose the right gear ratio and regulate cadence during a race in order to maximize performance.

Understanding optimal cadence dynamics: a systematic analysis of the power-velocity relationship in track cyclists with increasing exercise intensity

Background: This study aimed to investigate the changes in force-velocity (F/v) and power-velocity (P/v) relationships with increasing work rate up to maximal oxygen uptake and to assess the resulting alterations in optimal cadence, particularly at characteristic metabolic states. Methods: Fourteen professional track cyclists (9 sprinters, 5 endurance athletes) performed submaximal incremental tests, high-intensity cycling trials, and maximal sprints at varied cadences (60, 90, 120 rpm) on an SRM bicycle ergometer. Linear and non-linear regression analyses were used to assess the relationship between heart rate, oxygen uptake (V.O2), blood lactate concentration and power output at each pedaling rate. Work rates linked to various cardiopulmonary and metabolic states, including lactate threshold (LT1), maximal fat combustion (FATmax), maximal lactate steady-state (MLSS) and maximal oxygen uptake (V.O2max), were determined using cadence-specific inverse functions. These data were used to calculate state-specific force-velocity (F/v) and power-velocity (P/v) profiles, from which state-specific optimal cadences were derived. Additionally, fatigue-free profiles were generated from sprint data to illustrate the entire F/v and P/v continuum. Results: HR, V.O2 demonstrated linear relationships, while BLC exhibited an exponential relationship with work rate, influenced by cadence (p < 0.05, η2 ≥ 0.655). Optimal cadence increased sigmoidally across all parameters, ranging from 66.18 ± 3.00 rpm at LT1, 76.01 ± 3.36 rpm at FATmax, 82.24 ± 2.59 rpm at MLSS, culminating at 84.49 ± 2.66 rpm at V.O2max (p < 0.01, η2 = 0.936). A fatigue-free optimal cadence of 135 ± 11 rpm was identified. Sprinters and endurance athletes showed no differences in optimal cadences, except for the fatigue-free optimum (p < 0.001, d = 2.215). Conclusion: Optimal cadence increases sigmoidally with exercise intensity up to maximal aerobic power, irrespective of the athlete's physical condition or discipline. Threshold-specific changes in optimal cadence suggest a shift in muscle fiber type recruitment toward faster types beyond these thresholds. Moreover, the results indicate the need to integrate movement velocity into Henneman's hierarchical size principle and the critical power curve. Consequently, intensity zones should be presented as a function of movement velocity rather than in absolute terms.

Physiological Characteristics of Competitive Male Junior Cyclists Transitioning to the Under-23 Level: A Retrospective Comparative Study

PURPOSE

The purpose of the current investigation was to retrospectively assess possible differences in physiological performance characteristics between junior cyclists signing a contract with an under-23 (U23) development team versus those failing to sign such a contract.

METHODS

Twenty-five male junior cyclists (age: 18.1 [0.7] y, stature: 181.9 [6.0] cm, body mass: 69.1 [7.9] kg, peak oxygen uptake: 71.3 [6.2] mL·min-1·kg-1) were assigned to this investigation. Between September and October of the last year in the junior category, each cyclist performed a ramp incremental exercise test to determine certain physiological performance characteristics. Subsequently, participants were divided in 2 groups: (1) those signing a contract with a U23 development team (JUNIORU23) and (2) those failing to sign such a contract (JUNIORNON-U23). Unpaired t tests were used to assess possible between-groups differences in physiological performance characteristics. The level of statistical significance was set at P < .05 two tailed.

RESULTS

No significant between-groups differences in submaximal (ie, gas exchange threshold, respiratory compensation point) and maximal physiological performance characteristics (ie, peak work rate, peak oxygen uptake) expressed in absolute values (ie, L·min-1, W) were observed (P > .05). However, significant between-groups differences were observed when physiological performance characteristics were expressed relative to the cyclists' body weights (P < .05).

CONCLUSIONS

The current investigation showed that junior cyclists stepping up to a U23 development team might be retrospectively differentiated from junior cyclists not stepping up based on certain physiological performance characteristics, which might inform practitioners and/or federations working with young cyclists during the long-term athletic development process.