The decline in gross efficiency in relation to cycling time-trial length

Development and validation of dynamic bioenergetic model for intermittent ergometer cycling

PURPOSE

The aim of this study was to develop and validate a bioenergetic model describing the dynamic behavior of the alactic, lactic, and aerobic metabolic energy supply systems as well as different sources of the total metabolic energy demand.

METHODS

The bioenergetic supply model consisted of terms for the alactic, lactic, and aerobic system metabolic rates while the demand model consisted of terms for the corresponding metabolic rates of principal cycling work, pulmonary ventilation, and accumulated metabolites. The bioenergetic model was formulated as a system of differential equations and model parameters were estimated by a non-linear grey-box approach, utilizing power output and aerobic metabolic rate (MRae) data from fourteen cyclists performing an experimental trial (P2) on a cycle ergometer. Validity was assessed by comparing model simulation and measurements on a similar follow-up experimental trial (P3).

RESULTS

The root mean square error between modelled and measured MRae was 61.9 ± 7.9 W and 79.2 ± 30.5 W for P2 and P3, respectively. The corresponding mean absolute percentage error was 8.6 ± 1.5% and 10.6 ± 3.3% for P2 and P3, respectively.

CONCLUSION

The validation of the model showed excellent overall agreement between measured and modeled MRae during intermittent cycling by well-trained male cyclist. However, the standard deviation was 38.5% of the average root mean square error for P3, indicating not as good reliability.

Competitive Cross-Country Skiers Have Longer Time to Exhaustion Than Recreational Cross-Country Skiers During Intermittent Work Intervals Normalized to Their Maximal Aerobic Power

PURPOSE

To investigate differences in time to exhaustion (TTE), O2 uptake (V˙O2), and accumulated O2 deficit (O2def) between competitive and recreational cross-country (XC) skiers during an intermittent-interval protocol standardized for maximal aerobic power (MAP).

METHODS

Twelve competitive (maximal V˙O2 [V˙O2max]=76.5±3.8 mL·kg-1·min-1) and 10 recreational (V˙O2max=63.5±6.3 mL·kg-1·min-1) male XC skiers participated. All tests were performed on a rollerski treadmill in the V2 ski-skating technique. To quantify MAP and maximal accumulated oxygen deficit (MAOD), the skiers performed a steady-state submaximal test followed by a 1000-m time trial. After a 60-minute break, TTE, V˙O2, and accumulated O2def were measured during an intermittent-interval protocol (40-s work and 20-s recovery), which was individually tailored to 120% and 60% of each subject's MAP.

RESULTS

During the 1000-m time trial, the competitive skiers had 21% (95% CI, 12%-30%) shorter finish time and 24% (95% CI, 14%-34%) higher MAP (all P < .01) than the recreational skiers. No difference was observed in relative exercise intensity (average power/MAP; P = .28), MAOD (P = .18), or fractional utilization of V˙O2max. During the intermittent-interval protocol, the competitive skiers had 34% (95% CI, 3%-65%) longer TTE (P = .03) and accumulated 61% (95% CI, 27%-95%) more O2def (P = .001) than the recreational skiers during work phases.

CONCLUSIONS

Competitive XC skiers have longer TTE and accumulate more O2def than recreational XC skiers during an intermittent-interval protocol at similar intensity relative to MAP. This implies that performance in intermittent endurance sports is related to the ability to repeatedly recharge fractions of MAOD.

The effect of exercise hyperpnea on gross efficiency and anaerobic capacity estimates during a 3-min cycle time trial

This study aimed to analyze the effect of exercise-induced hyperpnea on gross efficiency (GE) and anaerobic capacity estimates during a self-paced 3-min supramaximal cycle time trial (TT). Fourteen highly trained male cyclists performed 7 × 4-min submaximal stages, a 6-min passive rest, a 3-min TT, a 5-min passive rest, and a 6-min submaximal stage. Three models were based on the 7 × 4-min linear regression extrapolation method, using 1) the conventional model (7-Y_LIN); 2) the same 7-Y_LIN model but correcting for the additional ventilatory cost (i.e., hyperpnea) (7-Y_LIN-V-cor); and 3) accounting for linearly declining GE during the TT (7-Y_LIN-D). The other three models were based on GE from the last submaximal stage, using the conventional model (GE_LAST) and the same modifications as described for 7+Y_LIN, i.e., 1) GE_LAST, 2) GE_LAST-V-cor, and 3) GE_LAST-D. The GE_LAST model generated 18% higher values of anaerobic capacity than the 7-Y_LIN model (P < 0.05). During the TT, the hyperpnea-corrected model (i.e., 7-Y_LIN-V-cor or GE_LAST-V-cor) generated, compared with the respective conventional model (i.e., 7-Y_LIN or GE_LAST), ∼0.7 percentage points lower GE and ∼11% higher anaerobic capacity (all, P < 0.05). The post-TT GE was 1.9 percentage points lower (P < 0.001) and the 7-Y_LIN-D or GE_LAST-D model generated, compared with the respective conventional model, a lower GE (∼1.0 percentage points) and ∼17% higher anaerobic capacity during the TT (all, P < 0.05). In conclusion, the correction for a declining GE due to hyperpnea during a supramaximal TT resulted in an increased required total metabolic rate and anaerobic energy expenditure compared with the conventional models.NEW & NOTEWORTHY This study demonstrates that GE declines during a 3-min supramaximal cycle TT, which is possibly related to the hyperpneic response during supramaximal exercise. The finding from this study also provides novel insight into how the increased ventilatory energy cost from exercise-induced hyperpnea contributes to decreased GE, increased required total metabolic rate, and increased anaerobic energy expenditure during supramaximal exercise. Therefore, conventional linear models for estimating anaerobic capacity are likely to generate underestimated values.

A Novel Approach to the Determination of Time- and Fatigue-Dependent Efficiency during Maximal Cycling Sprints

BACKGROUND

During maximal cycling sprints, efficiency (η) is determined by the fiber composition of the muscles activated and cadence-dependent power output. To date, due to methodological limitations, it has only been possible to calculate gross efficiency (i.e., the ratio of total mechanical to total metabolic work) in vivo without assessing the impact of cadence and changes during exercise. Eliminating the impact of cadence provides optimal efficiency (η_opt), which can be modeled as a function of time. Here, we explain this concept, demonstrate its calculation, and compare the values obtained to actual data. Furthermore, we hypothesize that the time course of maximal power output (P_max) reflects time-dependent changes in η_opt.

METHODS

Twelve elite track cyclists performed four maximal sprints (3, 8, 12, 60 s) and a maximal-pedaling test on a cycle ergometer. Crank force and cadence were monitored continuously to determine fatigue-free force-velocity profiles (F/v) and fatigue-induced changes in P_max. Respiratory gases were measured during and for 30 min post-exercise. Prior to and following each sprint, lactate in capillary blood was determined to calculate net blood lactate accumulation (ΔBLC). Lactic and alactic energy production were estimated from ΔBLC and the fast component of excess post-exercise oxygen consumption. Aerobic energy production was determined from oxygen uptake during exercise. Metabolic power (MP) was derived from total metabolic energy (W_TOT). η_opt was calculated as P_max divided by MP. Temporal changes in P_max, W_TOT, and η_opt were analyzed by non-linear regression.

RESULTS

All models showed excellent quality (R² > 0.982) and allowed accurate recalculation of time-specific power output and gross efficiency (R² > 0.986). The time-constant for P_max(t) (τ_P) was closely correlated with that of η_opt (τ_η; r = 0.998, p < 0.001). Estimating efficiency using τ_P for τ_η led to a 0.88 ± 0.35% error.

CONCLUSIONS

Although efficiency depends on pedal force and cadence, the latter influence can be eliminated by η_opt(t) using a mono-exponential equation whose time constant can be estimated from P_max(t).

The Impact of Pre-Exercise Carbohydrate Meal on the Effects of Yerba Mate Drink on Metabolism, Performance, and Antioxidant Status in Trained Male Cyclists

Introduction

The consumption of yerba mate (YM), a source of antioxidants, in a fasted state increases fatty acid oxidation (FAT_ox) during low–moderate-intensity exercise and improves performance in high-intensity exercise. However, the impact of a pre-exercise carbohydrate (CHO) meal on YM effects during exercise is unknown.

Objective

We investigated the effects of yerba mate drink (YMD) consumed in the fasted state (YMD-F) or after a CHO meal (YMD-CHO) on measurements of metabolism, performance, and blood oxidative stress markers in cycling exercise.

Methods

In a randomized, repeated-measures, crossover design, eight trained male cyclists ingested (i) YMD-CHO, (ii) YMD-F, or (iii) control-water and CHO meal (Control-CHO). The YMD (an infusion of 5 g of ultrarefined leaves in 250 mL of water) was taken for 7 days and 40 min before exercise. CHO meal (1 g/kg body mass) was consumed 60 min before exercise. The cycling protocol included a 40-min low-intensity (~ 53% V̇O_2peak) constant load test (CLT); a 20-min time trial (TT); and 4 × 10-s all-out sprints. Blood samples and respiratory gases were collected before, during, and/or after tests.

Results

During CLT, YMD-CHO increased FAT_ox ~ 13% vs. YMD-F (P = 0.041) and ~ 27% vs. Control-CHO (P < 0.001). During TT, YMD-CHO increased FAT_ox ~ 160% vs. YMD-F (P < 0.001) and ~ 150% vs. Control-CHO (P < 0.001). Power output during TT improved ~ 3% (P = 0.022) in YMD-CHO vs. Control-CHO and was strongly correlated with changes in serum total antioxidant capacity (r = −0.87) and oxidative stress index (r = 0.76) at post-exercise in YMD-CHO. Performance in sprints was not affected by YMD.

Conclusion

CHO intake did not negate the effect of YMD on FAT_ox or TT performance. Instead, a synergism between the two dietary strategies may be present.

Clinical Trial Registration NCT04642144. November 18, 2020. Retrospectively registered.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40798-022-00482-3.

Estimation of muscular metabolic power in two different cross-country sit-skiing sledges using inverse-dynamics simulation

The aim of this study was to estimate and compare the muscular metabolic power produced in the human body using musculoskeletal inverse-dynamics during cross-country sit-skiing. Two sitting positions were adapted for athletes with reduced trunk and hip muscle control, knee low with frontal trunk support (KL-fix), and knee high (KH). Five female national class able-bodied cross-country skiers performed submaximal and maximal exercise in both sitting positions, while recording 3-D kinematics, pole forces, electromyography and respiratory variables. Simulations were performed from these experimental results and muscular metabolic power was computed. The main part of the muscle metabolic power was produced in the upper limbs for both sitting positions, but KH produced more muscle metabolic power in lower limbs and trunk during maximal intensity. KH was also more efficient, utilizing less muscular metabolic power during submaximal intensities, relatively less power in the upper limbs and more power in the trunk, hip and lower limb muscles. This implies that sitting position KH is preferable for high power output when using able-bodied simulation models. This study showed the potential of using musculoskeletal simulations to improve the understanding of how different equipment design and muscles contribute to performance.

Anaerobic work capacity in cycling: the effect of computational method

Purpose

To compare the anaerobic work capacity (AnWC, i.e., attributable anaerobic mechanical work) assessed using four different approaches/models applied to time-trial (TT) cycle-ergometry exercise.

Methods

Fifteen male cyclists completed a 7 × 4-min submaximal protocol and a 3-min all-out TT (TTAO). Linear relationships between power output (PO) and submaximal metabolic rate were constructed to estimate TT-specific gross efficiency (GE) and AnWC, using either a measured resting metabolic rate as a Y-intercept (7 + YLIN) or no measured Y-intercept (7-YLIN). In addition, GE of the last submaximal bout (GELAST) was used to estimate AnWC, and critical power (CP) from TTAO (CP3´AO) was used to estimate mechanical work above CP (W’, i.e., “AnWC”).

Results

Average PO during TTAO was 5.43 ± 0.30 and CP was 4.48 ± 0.23 W∙kg−1. The TT-associated GE values were ~ 22.0% for both 7 + YLIN and 7-YLIN and ~ 21.1% for GELAST (both P < 0.001). The AnWC were 269 ± 60, 272 ± 55, 299 ± 61, and 196 ± 52 J∙kg−1 for the 7 + YLIN, 7-YLIN, GELAST, and CP3´AO models, respectively (7 + YLIN and 7-YLIN versus GELAST, both P < 0.001; 7 + YLIN, 7-YLIN, and GELAST versus CP3´AO, all P < 0.01). For the three pair-wise comparisons between 7 + YLIN, 7-YLIN, and GELAST, typical errors in AnWC values ranged from 7 to 11 J∙kg−1, whereas 7 + YLIN, 7-YLIN, and GELAST versus CP3´AO revealed typical errors of 55–59 J∙kg−1.

Conclusion

These findings demonstrate a substantial disagreement in AnWC between CP3´AO and the other models. The 7 + YLIN and 7-YLIN generated 10% lower AnWC values than the GELAST model, whereas 7 + YLIN and 7-YLIN generated similar values of AnWC.

Everesting: cycling the elevation of the tallest mountain on Earth

Purpose

With few cycling races on the calendar in 2020 due to COVID-19, Everesting became a popular challenge: you select one hill and cycle up and down it until you reach the accumulated elevation of Mt. Everest (8,848 m or 29,029ft). With an almost infinite number of different hills across the world, the question arises what the optimal hill for Everesting would be. Here, we address the biomechanics and energetics of up- and downhill cycling to determine the characteristics of this optimal hill.

Methods

During uphill cycling, the mechanical power output equals the power necessary to overcome air resistance, rolling resistance, and work against gravity, and for a fast Everesting time, one should maximize this latter term. To determine the optimal section length (i.e., number of repetitions), we applied the critical power concept and assumed that the U-turn associated with an additional repetition comes with a 6 s time penalty.

Results

To use most mechanical power to overcoming gravity, slopes of at least 12% are most suitable, especially since gross efficiency seems only minimally diminished on steeper slopes. Next, we found 24 repetitions to be optimal, yet this number slightly depends on the assumptions made. Finally, we discuss other factors (fueling, altitude, fatigue) not incorporated in the model but also affecting Everesting performances.

Conclusion

For a fast Everesting time, our model suggests to select a hill climb which preferably starts at (or close to) sea level, with a slope of 12–20% and length of 2–3 km.

Cross-Country Skiers With a Fast-Start Pacing Pattern Increase Time-Trial Performance by Use of a More Even Pacing Strategy

PURPOSE

To investigate whether skiers with a fast-start pacing pattern increase time-trial performance by use of a more even pacing strategy.

METHODS

Thirty-four skiers (∼17 y, 16 male) performed an individual 7.5 (3 × 2.5) km free-technique race on snow with a self-selected pacing strategy (day 1). Based on the starting pace the first ∼2 minutes (lap-1 first 600-m segment pace·7.5 km pace-1), subjects were ranked into 2 groups: an intervention group with the fastest start pace (INT, n = 17) and a control group with a more conservative pace (CON, n = 17). On day 2, INT were instructed to reduce their start pace based on their average laps-1-to-3 segment pace from day 1, while CON were instructed to maintain their day 1 strategy.

RESULTS

INT increased their time-trial performance more than CON from day 1 to day 2 (effect size; ES = 0.87, P < .05). From day 1 to day 2, INT slowed their start pace (mean ± 95% confidence interval; 7.7% ± 2.0%, ES = 2.00), with lowered heart rate (HR) (83% ± 2% to 81% ± 2% of HRmax) and 1 to 10 ratings of perceived exertion (5 ± 1 to 4 ± 1), but finished with a faster overall 7.5-km time (-1.9% ± 0.9%, ES = 0.99) (all P < .05). For CON, no change was found for starting pace (-0.7% ± 2.0%, P = .47), overall 7.5-km time (-0.2% ± 1.4%, ES = 0.02, P = .81), ratings of perceived exertion, or HR between days. No differences were found for end-ratings of perceived exertion (9 ± 1) or average HR between day 1 and 2 for either group.

CONCLUSION

Skiers with a pronounced fast-start pattern benefit by using a more even pacing strategy to optimize time-trial distance skiing performance.

The Dynamics of the Anaerobic Energy Contribution During a Simulated Mass-Start Competition While Roller-Ski Skating on a Treadmill

The purposes of this study were: 1) to investigate the anaerobic energy contribution during a simulated cross-country (XC) skiing mass-start competition while roller-ski skating on a treadmill; 2) to investigate the relationship between the recovery of the anaerobic energy reserves and performance; and 3) to compare the gross efficiency (GE) method and maximal accumulated oxygen deficit (MAOD) to determine the anaerobic contribution. Twelve male XC skiers performed two testing days while roller skiing on a treadmill. To collect submaximal data necessary for the GE and MAOD method, participants performed a resting metabolism measurement, followed by low-intensity warm up, 12 submaximal 4-min bouts, performed using three different skating sub-techniques (G2 on a 12% incline, G3 on 5% and G4 on 2%) on three submaximal intensities on day 1. On day 2, participants performed a 21-min simulated mass-start competition on varying terrain to determine the anaerobic energy contribution. The speed was fixed, but when participants were unable to keep up, a 30-s rest bout was included. Performance was established by the time to exhaustion (TTE) during a sprint at the end of the 21-min protocol. Skiers were ranked based on the number of rest bouts needed to finish the protocol and TTE. The highest GE of day 1 for each of the different inclines/sub-techniques was used to calculate the aerobic and anaerobic contribution during the simulated mass start using the GE method and two different MAOD approaches. About 85–90% of the required energy during the simulated mass-start competition (excluding downhill segments) came from the aerobic energy system and ~10–15% from the anaerobic energy systems. Moderate to large Spearman correlation coefficients were found between recovery of anaerobic energy reserves and performance rank (r_s = 0.58–0.71, p < 0.025). No significant difference in anaerobic work was found between methods/approaches (F_(1.2,8.5) = 3.2, p = 0.10), while clear individual differences existed. In conclusion, about 10–15% of the required energy during the periods of active propulsion of a 21-min simulated mass-start competition came from the anaerobic energy systems. Due to the intermittent nature of XC skiing, the recovery of anaerobic energy reserves seems highly important for performance. To assess the anaerobic contribution methods should not be used interchangeably.

The Aerobic and Anaerobic Contribution During Repeated 30-s Sprints in Elite Cyclists

Although the ability to sprint repeatedly is crucial in road cycling races, the changes in aerobic and anaerobic power when sprinting during prolonged cycling has not been investigated in competitive elite cyclists. Here, we used the gross efficiency (GE)-method to investigate: (1) the absolute and relative aerobic and anaerobic contributions during 3 × 30-s sprints included each hour during a 3-h low-intensity training (LIT)-session by 12 cyclists, and (2) how the energetic contribution during 4 × 30-s sprints is affected by a 14-d high-volume training camp with (SPR, n = 9) or without (CON, n = 9) inclusion of sprints in LIT-sessions. The aerobic power was calculated based on GE determined before, after sprints, or the average of the two, while the anaerobic power was calculated by subtracting the aerobic power from the total power output. When repeating 30-s sprints, the mean power output decreased with each sprint (p < 0.001, ES:0.6-1.1), with the majority being attributed to a decrease in mean anaerobic power (first vs. second sprint: -36 ± 15 W, p < 0.001, ES:0.7, first vs. third sprint: -58 ± 16 W, p < 0.001, ES:1.0). Aerobic power only decreased during the third sprint (first vs. third sprint: -17 ± 5 W, p < 0.001, ES:0.7, second vs. third sprint: 16 ± 5 W, p < 0.001, ES:0.8). Mean power output was largely maintained between sets (first set: 786 ± 30 W vs. second set: 783 ± 30 W, p = 0.917, ES:0.1, vs. third set: 771 ± 30 W, p = 0.070, ES:0.3). After a 14-d high-volume training camp, mean power output during the 4 × 30-s sprints increased on average 25 ± 14 W in SPR (p < 0.001, ES:0.2), which was 29 ± 20 W more than CON (p = 0.008, ES: 0.3). In SPR, mean anaerobic power and mean aerobic power increased by 15 ± 13 W (p = 0.026, ES:0.2) and by 9 ± 6 W (p = 0.004, ES:0.2), respectively, while both were unaltered in CON. In conclusion, moderate decreases in power within sets of repeated 30-s sprints are primarily due to a decrease in anaerobic power and to a lesser extent in aerobic power. However, the repeated sprint-ability (multiple sets) and corresponding energetic contribution are maintained during prolonged cycling in elite cyclists. Including a small number of sprints in LIT-sessions during a 14-d training camp improves sprint-ability mainly through improved anaerobic power.

Crossing the Golden Training Divide: The Science and Practice of Training World-Class 800- and 1500-m Runners

Despite an increasing amount of research devoted to middle-distance training (herein the 800 and 1500 m events), information regarding the training methodologies of world-class runners is limited. Therefore, the objective of this review was to integrate scientific and best practice literature and outline a novel framework for understanding the training and development of elite middle-distance performance. Herein, we describe how well-known training principles and fundamental training characteristics are applied by world-leading middle-distance coaches and athletes to meet the physiological and neuromuscular demands of 800 and 1500 m. Large diversities in physiological profiles and training emerge among middle-distance runners, justifying a categorization into types across a continuum (400–800 m types, 800 m specialists, 800–1500 m types, 1500 m specialists and 1500–5000 m types). Larger running volumes (120–170 vs. 50–120 km·week⁻¹ during the preparation period) and higher aerobic/anaerobic training distribution (90/10 vs. 60/40% of the annual running sessions below vs. at or above anaerobic threshold) distinguish 1500- and 800-m runners. Lactate tolerance and lactate production training are regularly included interval sessions by middle-distance runners, particularly among 800-m athletes. In addition, 800-m runners perform more strength, power and plyometric training than 1500-m runners. Although the literature is biased towards men and “long-distance thinking,” this review provides a point of departure for scientists and practitioners to further explore and quantify the training and development of elite 800- and 1500-m running performance and serves as a position statement for outlining current state-of-the-art middle-distance training recommendations.

Using Field Based Data to Model Sprint Track Cycling Performance

Cycling performance models are used to study rider and sport characteristics to better understand performance determinants and optimise competition outcomes. Performance requirements cover the demands of competition a cyclist may encounter, whilst rider attributes are physical, technical and psychological characteristics contributing to performance. Several current models of endurance-cycling enhance understanding of performance in road cycling and track endurance, relying on a supply and demand perspective. However, they have yet to be developed for sprint-cycling, with current athlete preparation, instead relying on measures of peak-power, speed and strength to assess performance and guide training. Peak-power models do not adequately explain the demands of actual competition in events over 15-60 s, let alone, in World-Championship sprint cycling events comprising several rounds to medal finals. Whilst there are no descriptive studies of track-sprint cycling events, we present data from physiological interventions using track cycling and repeated sprint exercise research in multiple sports, to elucidate the demands of performance requiring several maximal sprints over a competition. This review will show physiological and power meter data, illustrating the role of all energy pathways in sprint performance. This understanding highlights the need to focus on the capacity required for a given race and over an event, and therefore the recovery needed for each subsequent race, within and between races, and how optimal pacing can be used to enhance performance. We propose a shift in sprint-cyclist preparation away from training just for peak power, to a more comprehensive model of the actual event demands.

The Physiological, Neuromuscular, and Perceptual Response to Even- and Variable-Paced 10-km Cycling Time Trials

BACKGROUND

During self-paced (SP) time trials (TTs), cyclists show unconscious nonrandom variations in power output of up to 10% above and below average. It is unknown what the effects of variations in power output of this magnitude are on physiological, neuromuscular, and perceptual variables.

PURPOSE

To describe physiological, neuromuscular, and perceptual responses of 10-km TTs with an imposed even-paced (EP) and variable-paced (VP) workload.

METHODS

Healthy male, trained, task-habituated cyclists (N = 9) completed three 10-km TTs. First, an SP TT was completed, the mean workload from which was used as the mean workload of the EP and VP TTs. The EP was performed with an imposed even workload, while VP was performed with imposed variations in workload of ±10% of the mean. In EP and VP, cardiorespiratory, neuromuscular, and perceptual variables were measured.

RESULTS

Mean rating of perceived exertion was significantly lower in VP (6.13 [1.16]) compared with EP (6.75 [1.24]), P = .014. No mean differences were found for cardiorespiratory and almost all neuromuscular variables. However, differences were found at individual kilometers corresponding to power-output differences between pacing strategies.

CONCLUSION

Variations in power output during TTs of ±10%, simulating natural variations in power output that are present during SP TTs, evoke minor changes in cardiorespiratory and neuromuscular responses and mostly affect the perceptual response. Rating of perceived exertion is lower when simulating natural variations in power output, compared with EP cycling. The imposed variations in workload seem to provide a psychological rather than a physiological or neuromuscular advantage.

Fatigue development and perceived response during self-paced endurance exercise: state-of-the-art review

Performance in self-paced endurance exercises results from continuous fatigue symptom management. While it is suggested that perceived responses and neuromuscular fatigue development may determine variations in exercise intensity, it is uncertain how these fatigue components interact throughout the task. To address the fatigue development in self-paced endurance exercises, the following topics were addressed in the present review: (1) fatigue development during constant-load vs. self-paced endurance exercises; (2) central and peripheral fatigue and perceived exertion interconnections throughout the self-paced endurance exercises; and (3) future directions and recommendations. Based on the available literature, it is suggested (1) the work rate variations during a self-paced endurance exercise result in transitions between exercise intensity domains, directly impacting the end-exercise central and peripheral fatigue level when compared to constant-load exercise mode; (2) central and peripheral fatigue, as well as perceived exertion response contribute to exercise intensity regulation at the different stages of the trial. It seems that while neuromuscular fatigue development might be relevant at beginning of the trial, the perceived exertion might interfere in the remaining parts to achieve maximal values only at the finish line; (3) future studies should focus on the mechanisms underpinning fatigue components interactions throughout the task and its influence on exercise intensity variations.

Fatigue development and perceived response during self-paced endurance exercise: state-of-the-art review

Performance in self-paced endurance exercises results from continuous fatigue symptom management. While it is suggested that perceived responses and neuromuscular fatigue development may determine variations in exercise intensity, it is uncertain how these fatigue components interact throughout the task. To address the fatigue development in self-paced endurance exercises, the following topics were addressed in the present review: (1) fatigue development during constant-load vs. self-paced endurance exercises; (2) central and peripheral fatigue and perceived exertion interconnections throughout the self-paced endurance exercises; and (3) future directions and recommendations. Based on the available literature, it is suggested (1) the work rate variations during a self-paced endurance exercise result in transitions between exercise intensity domains, directly impacting the end-exercise central and peripheral fatigue level when compared to constant-load exercise mode; (2) central and peripheral fatigue, as well as perceived exertion response contribute to exercise intensity regulation at the different stages of the trial. It seems that while neuromuscular fatigue development might be relevant at beginning of the trial, the perceived exertion might interfere in the remaining parts to achieve maximal values only at the finish line; (3) future studies should focus on the mechanisms underpinning fatigue components interactions throughout the task and its influence on exercise intensity variations.

The Effect of Sodium Bicarbonate Supplementation on the Decline in Gross Efficiency During a 2000-m Cycling Time Trial

BACKGROUND

Gross efficiency (GE) declines during high-intensity exercise. Increasing extracellular buffer capacity might diminish the decline in GE and thereby improve performance.

PURPOSE

To examine if sodium bicarbonate (NaHCO3) supplementation diminishes the decline in GE during a 2000-m cycling time trial.

METHODS

Sixteen male cyclists and 16 female cyclists completed 4 testing sessions including a maximal incremental test, a familiarization trial, and two 2000-m GE tests. The 2000-m GE tests were performed after ingestion of either NaHCO3 supplements (0.3 g/kg body mass) or placebo supplements (amylum solani, magnesium stearate, and sunflower oil capsules). The GE tests were conducted using a double-blind, randomized, crossover design. Power output, gas exchange, and time to complete the 2000-m time trials were recorded. Capillary blood samples were analyzed for blood bicarbonate, pH, and lactate concentration. Data were analyzed using magnitude-based inference.

RESULTS

The decrement in GE found after the 2000-m time trial was possibly smaller in the male and female groups after NaHCO3 than with placebo ingestion, with the effect in both groups combined being unclear. The effect on performance was likely trivial for males (placebo 164.2 [5.0] s, NaHCO3 164.3 [5.0] s; Δ0.1; ±0.6%), unclear for females (placebo 178.6 [4.8] s, NaHCO3 178.0 [4.3] s; Δ-0.3; ±0.5%), and very likely trivial when effects were combined. Blood bicarbonate, pH, and lactate concentration were substantially elevated from rest to pretest after NaHCO3 ingestion.

CONCLUSIONS

NaHCO3 supplementation results in an unclear effect on the decrease in GE during high-intensity exercise and in a very likely trivial effect on performance.

The Anaerobic Capacity of Cross-Country Skiers: The Effect of Computational Method and Skiing Sub-technique

Anaerobic capacity is an important performance-determining variable of sprint cross-country skiing. Nevertheless, to date, no study has directly compared the anaerobic capacity, determined using the maximal accumulated oxygen deficit (MAOD) method and gross efficiency (GE) method, while using different skiing sub-techniques.

Purpose: To compare the anaerobic capacity assessed using two different MAOD approaches (including and excluding a measured y-intercept) and the GE method during double poling (DP) and diagonal stride (DS) cross-country skiing.

Methods: After an initial familiarization trial, 16 well-trained male cross-country skiers performed, in each sub-technique on separate occasions, a submaximal protocol consisting of eight 4-min bouts at intensities between ~47–78% of V.O_2peak followed by a 4-min roller-skiing time trial, with the order of sub-technique being randomized. Linear and polynomial speed-metabolic rate relationships were constructed for both sub-techniques, while using a measured y-intercept (8+Y_LIN and 8+Y_POL) or not (8–Y_LIN and 8–Y_POL), to determine the anaerobic capacity using the MAOD method. The average GE (GE_AVG) of all eight submaximal exercise bouts or the GE of the last submaximal exercise bout (GE_LAST) were used to calculate the anaerobic capacity using the GE method. Repeated measures ANOVA were used to test differences in anaerobic capacity between methods/approaches.

Results: A significant interaction was found between computational method and skiing sub-technique (P < 0.001, η² = 0.51) for the anaerobic capacity estimates. The different methodologies resulted in significantly different anaerobic capacity values in DP (P < 0.001, η² = 0.74) and in DS (P = 0.016, η² = 0.27). The 8-Y_POL model resulted in the smallest standard error of the estimate (SEE, 0.24 W·kg⁻¹) of the MAOD methods in DP, while the 8-Y_LIN resulted in a smaller SEE value than the 8+Y_LIN model (0.17 vs. 0.33 W·kg⁻¹) in DS. The 8-Y_LIN and GE_LAST resulted in the closest agreement in anaerobic capacity values in DS (typical error 2.1 mL O₂eq·kg⁻¹).

Conclusions: It is discouraged to use the same method to estimate the anaerobic capacity in DP and DS sub-techniques. In DP, a polynomial MAOD method (8-Y_POL) seems to be the preferred method, whereas the 8-Y_LIN, GE_AVG, and GE_LAST can all be used for DS, but not interchangeable, with GE_LAST being the least time-consuming method.

The Effect of 30-Second Sprints During Prolonged Exercise on Gross Efficiency, Electromyography, and Pedaling Technique in Elite Cyclists

BACKGROUND

Cycling competitions are often of long duration and include repeated high-intensity efforts.

PURPOSE

To investigate the effect of repeated maximal sprints during 4 hours of low-intensity cycling on gross efficiency (GE), electromyography patterns, and pedaling technique compared with work-matched low-intensity cycling in elite cyclists.

METHODS

Twelve elite, male cyclists performed 4 hours of cycling at 50% of maximal oxygen uptake either with 3 sets of 3 × 30-second maximal sprints (E&S) during the first 3 hours or a work-matched cycling without sprints (E) in a randomized order. Oxygen uptake, electromyography, and pedaling technique were recorded throughout the exercises.

RESULTS

GE was reduced from start to the end of exercise in both conditions (E&S: 19.0 [0.2] vs 18.1 [0.2], E: 19.1% [0.2%] vs 18.1% [0.2%], both P = .001), with no difference in change between conditions (condition × time interaction, P = .8). Integrated electromyography increased from start to end of exercise in m. vastus lateralis and m. vastus medialis (m. vastus medialis: 9.9 [2.4], m. vastus lateralis: 8.5 [4.0] mV, main effect of time: P < .001 and P = .03, respectively) and E&S increased less than E in m. vastus medialis (mean difference -3.3 [1.5] mV, main effect of condition: P = .03, interaction, P = .06). The mechanical effectiveness only decreased in E&S (E&S: -2.2 [0.7], effect size = 0.24 vs E: -1.3 [0.8] percentage points: P = .04 and P = .8, respectively). The mean power output during each set of 3 × 30-second sprints in E&S did not differ (P = .6).

CONCLUSIONS

GE decreases as a function of time during 4 hours of low-intensity cycling. However, the inclusion of maximal repeated sprinting does not affect the GE changes, and the ability to sprint is maintained throughout the entire session.

The Reliability of Measuring Gross Efficiency During High-Intensity Cycling Exercise

PURPOSE

To evaluate the reliability of calculating gross efficiency (GE) conventionally and using a back extrapolation (BE) method during high-intensity exercise (HIE).

METHODS

A total of 12 trained participants completed 2 HIE bouts (P1 = 4 min at 80% maximal aerobic power [MAP]; P2 = 4 min at 100%MAP). GE was calculated conventionally in the last 3 minutes of submaximal (50%MAP) cycling bouts performed before and after HIE (Pre50%MAP and Post50%MAP). To calculate GE using BE (BGE), a linear regression of GE submaximal values post-HIE were back extrapolated to the end of the HIE bout.

RESULTS

BGE was significantly correlated with Post50%MAP GE in P1 (r = .63; P = .01) and in P2 (r = .85; P = .002). Reliability data for P1 and P2 BGE demonstrate a mean coefficient of variation of 7.8% and 9.8% with limits of agreement of 4.3% and 4.5% in relative GE units, respectively. P2 BGE was significantly lower than P2 Post50%MAP GE (18.1% [1.6%] vs 20.3% [1.7%]; P = .01). Using a declining GE from the BE method, there was a 44% greater anaerobic contribution compared with assuming a constant GE during 4-minute HIE at 100%MAP.

CONCLUSION

HIE acutely reduced BGE at 100%MAP. A greater anaerobic contribution to exercise as well as excess postexercise oxygen consumption at 100%MAP may contribute to this decline in efficiency. The BE method may be a reliable and valid tool in both estimating GE during HIE and calculating aerobic and anaerobic contributions.

Cycling at Altitude: Lower Absolute Power Output as the Main Cause of Lower Gross Efficiency

BACKGROUND

Although cyclists often compete at altitude, the effect of altitude on gross efficiency (GE) remains inconclusive.

PURPOSE

To investigate the effect of altitude on GE at the same relative exercise intensity and at the same absolute power output (PO) and to determine the effect of altitude on the change in GE during high-intensity exercise.

METHODS

Twenty-one trained men performed 3 maximal incremental tests and 5 GE tests at sea level, 1500 m, and 2500 m of acute simulated altitude. The GE tests at altitude were performed once at the same relative exercise intensity and once at the same absolute PO as at sea level.

RESULTS

Altitude resulted in an unclear effect at 1500 m (-3.8%; ±3.3% [90% confidence limit]) and most likely negative effect at 2500 m (-6.3%; ±1.7%) on pre-GE, when determined at the same relative exercise intensity. When pre-GE was determined at the same absolute PO, unclear differences in GE were found (-1.5%; ±2.6% at 1500 m; -1.7%; ±2.4% at 2500 m). The effect of altitude on the decrease in GE during high-intensity exercise was unclear when determined at the same relative exercise intensity (-0.4%; ±2.8% at 1500 m; -0.7%; ±1.9% at 2500 m). When GE was determined at the same absolute PO, altitude resulted in a substantially smaller decrease in GE (2.8%; ±2.4% at 1500 m; 5.5%; ±2.9% at 2500 m).

CONCLUSION

The lower GE found at altitude when exercise is performed at the same relative exercise intensity is mainly caused by the lower PO at which cyclists exercise.

Self-Selected Pacing during a 24 h Track Cycling World Record

The present case study analyzed the pacing in a self-paced world record attempt during a 24 h track cycling event by the current world record holder. The cyclist completed 3767 laps on a 250 m long cycling track and covered a total distance of 941.873 km, breaking the existing world record by 37.99 km. The average cycling speed was 39.2 ± 1.9 km/h (range 35.5–42.8 km/h) and the power output measured was 214.5 ± 23.7 W (range 190.0–266.0 W) during the 24 h of cycling. We found a positive pacing result with negative correlations between cycling speed (r = −0.73, p < 0.001), power output (r = −0.66, p < 0.001), and laps per hour (r = −0.73, p < 0.001) and the covered distance. During the 24 h, we could identify four different phases: the first phase lasting from the start till the fourth hour with a relatively stable speed; the second phase from the fourth till the ninth hour, characterized by the largest decrease in cycling speed; the third phase from the ninth hour till the 22nd hour, showing relatively small changes in cycling speed; and the last phase from the 22nd hour till the end, presenting a final end spurt. The performance in the 24 h track cycling was 45.577 km better than in the 24 h road cycling, where the same athlete cycled slower but with higher power output. In summary, the current world-best ultracyclist covered more kilometers with less power output during the world record 24 h track cycling than during his world record 24 h road cycling. This was most probably due to the more favorable environmental conditions in the velodrome, which has no wind and stable temperatures.

A Comparison of Methodological Approaches to Measuring Cycling Mechanical Efficiency

BACKGROUND

Much is known about theoretical bases of different mechanical efficiency indices and effects of physiological and biomechanical factors to them. However, there are only a few studies available about practical bases and interactions between these efficiency indices, which were the aims of the present study.

METHODS

Fourteen physically active men (n = 12) and women (n = 2) participated in this study. From the incremental test, six different mechanical efficiency indices were calculated for cycling work: gross (GE) and net (NE) efficiencies, two work efficiencies (WE), and economy (T) at 150 W, and in addition delta efficiency (DE) using 3-5 observation points.

RESULTS

It was found that the efficiency indices can be divided into three groups by Spearman's rank correlation: GE, T, and NE in group I; DE and extrapolated WE in group II; and measured WE in group III. Furthermore, group II appeared to have poor reliability due to its dependence on a work-expended energy regression line, which accuracy is poorly measured by confidence interval.

CONCLUSION

As efficiency indices fall naturally into three classes that do not interact with each other, it means that they measure fundamentally different aspects of mechanical efficiency. Based on problems and imprecisions with other efficiency indices, GE, or group I, seems to be the best indicator for mechanical efficiency because of its consistency and unambiguity. Based on this methodological analysis, the baseline subtractions in efficiency indices are not encouraged.

Recovery of Cycling Gross Efficiency After Time-Trial Exercise

BACKGROUND

Research has shown that gross efficiency (GE) declines during high-intensity exercise, but the time course of recovery of GE after high-intensity exercise has not yet been investigated.

PURPOSE

To determine the time course of the recovery of GE after time trials (TTs) of different lengths.

METHODS

Nineteen trained male cyclists participated in this study. Before and after TTs of 2000 and 20,000 m, subjects performed submaximal exercise at 55% of the power output attained at maximal oxygen uptake (PVO₂max). The postmeasurement continued until 30 min after the end of the TT, during which GE was determined over 3-min intervals. The magnitude-based-inferences approach was used for statistical analysis.

RESULTS

GE decreased substantially during the 2000-m and 20,000-m TTs (-11.8% [3.6%] and -6.2% [4.0%], respectively). A most likely and very likely recovery of GE was found during the first half of the submaximal exercise bout performed after the 2000-m, with only a possible increase in GE during the first part of the submaximal exercise bout performed after the 20,000-m. After both distances, GE did not fully recover to the initial pre-TT values, as the difference between the pre-TT value and average GE value of minutes 26-29 was still most likely negative for both the 2000- and 20,000-m (-6.1% [2.8%] and -7.0% [4.5%], respectively).

CONCLUSIONS

It is impossible to fully recover GE after TTs of 2000- or 20,000-m during 30 min of submaximal cycling exercise performed at an intensity of 55% PVO₂max.

A Comparison between Different Methods of Estimating Anaerobic Energy Production

Purpose: The present study aimed to compare four methods of estimating anaerobic energy production during supramaximal exercise.

Methods: Twenty-one junior cross-country skiers competing at a national and/or international level were tested on a treadmill during uphill (7°) diagonal-stride (DS) roller-skiing. After a 4-minute warm-up, a 4 × 4-min continuous submaximal protocol was performed followed by a 600-m time trial (TT). For the maximal accumulated O₂ deficit (MAOD) method the V.O₂-speed regression relationship was used to estimate the V.O₂ demand during the TT, either including (4+Y, method 1) or excluding (4-Y, method 2) a fixed Y-intercept for baseline V.O₂. The gross efficiency (GE) method (method 3) involved calculating metabolic rate during the TT by dividing power output by submaximal GE, which was then converted to a V.O₂ demand. An alternative method based on submaximal energy cost (EC, method 4) was also used to estimate V.O₂ demand during the TT.

Results: The GE/EC remained constant across the submaximal stages and the supramaximal TT was performed in 185 ± 24 s. The GE and EC methods produced identical V.O₂ demands and O₂ deficits. The V.O₂ demand was ~3% lower for the 4+Y method compared with the 4-Y and GE/EC methods, with corresponding O₂ deficits of 56 ± 10, 62 ± 10, and 63 ± 10 mL·kg⁻¹, respectively (P < 0.05 for 4+Y vs. 4-Y and GE/EC). The mean differences between the estimated O₂ deficits were −6 ± 5 mL·kg⁻¹ (4+Y vs. 4-Y, P < 0.05), −7 ± 1 mL·kg⁻¹ (4+Y vs. GE/EC, P < 0.05) and −1 ± 5 mL·kg⁻¹ (4-Y vs. GE/EC), with respective typical errors of 5.3, 1.9, and 6.0%. The mean difference between the O₂ deficit estimated with GE/EC based on the average of four submaximal stages compared with the last stage was 1 ± 2 mL·kg⁻¹, with a typical error of 3.2%.

Conclusions: These findings demonstrate a disagreement in the O₂ deficits estimated using current methods. In addition, the findings suggest that a valid estimate of the O₂ deficit may be possible using data from only one submaximal stage in combination with the GE/EC method.

Effect of caffeine ingestion on anaerobic capacity quantified by different methods

We investigated whether caffeine ingestion before submaximal exercise bouts would affect supramaximal oxygen demand and maximal accumulated oxygen deficit (MAOD), and if caffeine-induced improvement on the anaerobic capacity (AC) could be detected by different methods. Nine men took part in several submaximal and supramaximal exercise bouts one hour after ingesting caffeine (5 mg·kg-1) or placebo. The AC was estimated by MAOD, alternative MAOD, critical power, and gross efficiency methods. Caffeine had no effect on exercise endurance during the supramaximal bout (caffeine: 131.3 ± 21.9 and placebo: 130.8 ± 20.8 s, P = 0.80). Caffeine ingestion before submaximal trials did not affect supramaximal oxygen demand and MAOD compared to placebo (7.88 ± 1.56 L and 65.80 ± 16.06 kJ vs. 7.89 ± 1.30 L and 62.85 ± 13.67 kJ, P = 0.99). Additionally, MAOD was similar between caffeine and placebo when supramaximal oxygen demand was estimated without caffeine effects during submaximal bouts (67.02 ± 16.36 and 62.85 ± 13.67 kJ, P = 0.41) or when estimated by alternative MAOD (56.61 ± 8.49 and 56.87 ± 9.76 kJ, P = 0.91). The AC estimated by gross efficiency was also similar between caffeine and placebo (21.80 ± 3.09 and 20.94 ± 2.67 kJ, P = 0.15), but was lower in caffeine when estimated by critical power method (16.2 ± 2.6 vs. 19.3 ± 3.5 kJ, P = 0.03). In conclusion, caffeine ingestion before submaximal bouts did not affect supramaximal oxygen demand and consequently MAOD. Otherwise, caffeine seems to have no clear positive effect on AC.

Anaerobic work calculated in cycling time trials of different length

Previous research showed that gross efficiency (GE) declines during exercise and therefore influences the expenditure of anaerobic and aerobic resources.

PURPOSE

To calculate the anaerobic work produced during cycling time trials of different length, with and without a GE correction.

METHODS

Anaerobic work was calculated in 18 trained competitive cyclists during 4 time trials (500, 1000, 2000, and 4000-m). Two additional time trials (1000 and 4000 m) that were stopped at 50% of the corresponding "full" time trial were performed to study the rate of the decline in GE.

RESULTS

Correcting for a declining GE during time-trial exercise resulted in a significant (P<.001) increase in anaerobically attributable work of 30%, with a 95% confidence interval of [25%, 36%]. A significant interaction effect between calculation method (constant GE, declining GE) and distance (500, 1000, 2000, 4000 m) was found (P<.001). Further analysis revealed that the constant-GE calculation method was different from the declining method for all distances and that anaerobic work calculated assuming a constant GE did not result in equal values for anaerobic work calculated over different time-trial distances (P<.001). However, correcting for a declining GE resulted in a constant value for anaerobically attributable work (P=.18).

CONCLUSIONS

Anaerobic work calculated during short time trials (<4000 m) with a correction for a declining GE is increased by 30% [25%, 36%] and may represent anaerobic energy contributions during high-intensity exercise better than calculating anaerobic work assuming a constant GE.