The effect of self- even- and variable-pacing strategies on the physiological and perceptual response to cycling

Fatigue and Performance Rates as Decision-Making Criteria in Pacing Control During CrossFit®

We investigated fatigue and performance rates as decision-making criteria in pacing control during CrossFit®. Thirteen male regional-level competitors completed conditions of all-out (maximum physical work from beginning to end) and controlled-split (controlled physical work in the first two rounds but maximum work in the third round) pacing throughout the Fight Gone Bad workout separated by one week. We assessed benchmarks, countermovement jumps and ratings of fatigue after each round. Benchmarks were lower in round 1 (99 vs. 114, p < .001) but higher in rounds 2 (98 vs. 80, p < .001) and 3 (97 vs. 80, p < .001) for controlled-split compared with all-out pacing. Reductions in countermovement jumps were higher after rounds 1 (-12.6% vs. 1.6%, p < .001) and 2 (-12.7% vs. -4.0%, p = .014) but similar after round 3 (-13.2% vs. -11.3%, p = .571) for all-out compared with controlled-split pacing. Ratings of fatigue were higher after rounds 1 (7 vs. 5 a.u., p < .001) and 2 (8 vs. 7 a.u, p = .023) but similar after round 3 (9 vs. 9 a.u., p = .737) for all-out compared with controlled-split pacing. During all-out pacing, countermovement jump reductions after round 2 correlated with benchmark drops across rounds 1 and 2 (r = .78, p = .002) and rounds 1 and 3 (r = -.77, p = .002) and with benchmark workout changes between pacing strategies (r = -.58, p = .036), suggesting that the larger the countermovement jump reductions the higher the benchmark drops across rounds and workouts. Therefore, benchmarks, countermovement jumps and ratings of fatigue may assess exercise-induced fatigue as decision-making criteria to improve pacing strategy during workouts performed for as many repetitions as possible.

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.

Pacing and Positioning Strategies During an Elite Fixed-Gear Cycling Criterium

Fixed-gear cycling performance during criteriums predominantly involves the aerobic system. Whether pacing is another important factor for performance is unknown. The purpose of the present study was to explore pacing and/or positioning strategies of fixed-gear riders during criteriums. Race results of an international fixed-gear criterium were analyzed (20 laps for women and 28 laps for men; laps = 1,270 m). Statistics were conducted on individuals lap time and positioning during the finals. Race pattern in women (n = 35) and men (n = 53) revealed that the faster laps (P < 0.05) were in the middle and at the end of the race and the slower laps (P < 0.05) were at the end of the race (laps 17–18 for women and lap 26 for men). The final ranking was significantly correlated with the mean race position (Kendall's tau = 0.664 and 0.689 for women and men, respectively). A coefficient of variation >50% revealed an important positioning variability. The best riders are mostly amongst the first during the race. However, the others exhibited larger mean position variations during the first half of the race. Our results demonstrated variable pacing strategies during fixed-gear criteriums. Although some riders had economical drafting strategies during the first half of the race, riding placed ahead during the whole race seemed to be an essential performance factor.

Virtual cycling effort is dependent on power update rate

Cycling ergometer protocols are commonly integrated with a virtual reality environment (VRE), especially because of its static position that also allows multiple exercise experiments. Concerning VRE scenarios, visually delayed situations like the ones produced at excessive low update rates can also affect the sense of presence and physiological responses. However, the main interface between the subject and a cycling VRE is the power applied over the crank, and there are only a few experiments to evaluate the effect of delayed situations on this particular interface. Thus, this work aims to investigate the effects of the power update rate (PUR) over the subject`s performance on an avatar-based simulator during a drafting task. A custom cycling VRE was built, and 21 male recreational cyclists (175.9 ± 7.5 cm; 76.5 ± 13.9 kg) were tested at six different PUR levels from 100 to 3000 ms. As a result, PUR affects performance scores (virtual distance, efficiency, and heart rate, p < 0.01) at the given VRE conditions. The case-by-case analysis of the groups reveals that higher update rates always lead to a statistical equivalent or superior performance. Nevertheless, no parameter shows any group difference between 500 ms and lower PUR. These results suggest that virtual cycling protocols should consider PUR and other delay-related mechanisms as possible intervening factors over physiological responses and performance scores.

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.

Sprinting After Having Sprinted: Prior High-Intensity Stochastic Cycling Impairs the Winning Strike for Gold

Bunch riding in closed circuit cycling courses and some track cycling events are often typified by highly variable power output and a maximal sprint to the finish. How criterium style race demands affect final sprint performance however, is unclear. We studied the effects of 1 h variable power cycling on a subsequent maximal 30 s sprint in the laboratory. Nine well-trained male cyclists/triathletes (O_2peak 4.9 ± 0.4 L⋅min^-1; mean ± SD) performed two 1 h cycling trials in a randomized order with either a constant (CON) or variable (VAR) power output matched for mean power output. The VAR protocol comprised intervals of varying intensities (40–135% of maximal aerobic power) and durations (10 to 90 s). A 30 s maximal sprint was performed before and immediately after each 1 h cycling trial. When compared with CON, there was a greater reduction in peak (-5.1 ± 6.1%; mean ± 90% confidence limits) and mean (-5.9 ± 5.2%) power output during the 30 s sprint after the 1 h VAR cycle. Variable power cycling, commonly encountered during criterium and triathlon races can impair an optimal final sprint, potentially compromising race performance. Athletes, coaches, and staff should evaluate training (to improve repeat sprint-ability) and race-day strategies (minimize power variability) to optimize the final sprint.

Pacing Strategy of a Full Ironman Overall Female Winner on a Course With Major Elevation Changes

Pryor, JL, Adams, WM, Huggins, RA, Belval, LN, Pryor, RR, and Casa, DJ. Pacing strategy of a full Ironman overall female winner on a course with major elevation changes. J Strength Cond Res 32(11): 3080-3087, 2018-The purpose of this study was to use a mixed-methods design to describe the pacing strategy of the overall female winner of a 226.3-km Ironman triathlon. During the race, the triathlete wore a global positioning system and heart rate (HR)-enabled watch and rode a bike outfitted with a power and cadence meter. High-frequency (every km) analyses of mean values, mean absolute percent error (MAPE), and normalized graded running pace and power (accounting for changes in elevation) were calculated. During the bike, velocity, power, cadence, and HR averaged 35.6 km·h, 199 W, 84 rpm, and 155 b·min, respectively, with minimal variation except for velocity (measurement unit variation [MAPE]: 7.4 km·h [20.3%], 11.8 W [7.0%], 3.6 rpm [4.6%], 3 b·min [2.3%], respectively). During the run, velocity and HR averaged 13.8 km·h and 154 b·min, respectively, with velocity varying four-fold more than HR (MAPE: 4.8% vs. 1.2%). Accounting for elevation changes, power and running pace were less variable (raw [MAPE] vs. normalized [MAPE]: 199 [7.0%] vs. 204 W [2.7%]; 4:29 [4.8%] vs. 4:24 min·km [3.6%], respectively). Consistent with her planned pre-race pacing strategy, the triathlete minimized fluctuations in HR and watts during the bike and run, whereas velocity varied with changes in elevation. This case report provides observational evidence supporting the utility of a pacing strategy that allows for an oscillating velocity that sustains a consistent physiological effort in full Ironman races.

How much further for the sub-2-hour marathon?

The sub-2-hour marathon is a new barrier in endurance running performance, and it has been widely debated in the scientific community. In this review we present a mathematical model to estimate the possible year when a male could break through the sub-2-hour barrier, and also an estimation of when a female could break Paula Radcliffe’s marathon running record. Further, we present several aspects (ie, physiology, nationality, age, biomechanics, pacing, and drafting) that are associated with marathon running performance in elite runners and, finally, the possible characteristics of the male to break the sub-2-hour barrier. In summary, with the results of the developed equations, it is possible that a male athlete can break through the sub-2-hour barrier in the next decade (with Nike^® Breaking2 performance 1920–2018 [NBP]: y =0.0417x²–14.18x +3,128; year of 2026; without NBP 1920–2018: y =0.045x²–15.12x +3,194; year of 2027). This marathoner will possibly have a maximal oxygen uptake >85 mL∙kg⁻¹∙min⁻¹ and should perform the race at a pacing higher than 85% of maximal oxygen uptake. In addition, this runner should pay more attention to strength training, endurance strength, speed training, and focus on running training at an intensity above the anaerobic threshold. Most likely, this runner originates from East Africa (especially from Ethiopia) and will have an age of ~27 years. For the females, there is poor evidence regarding the physiological profile of the female marathoner who will break Radcliffe’s record, but the available literature suggests that it will not happen any time soon.

Cycling performance is superior for time-to-exhaustion versus time-trial in endurance laboratory tests

Time-to-exhaustion (TTE) trials are used in a laboratory setting to measure endurance performance. However, there is some concern with their ecological validity compared with time-trials (TT). Consequently, we aimed to compare cycling performance in TTE and TT where the duration of the trials was matched. Seventeen trained male cyclists completed three TTE trials at 80, 100 and 105% of maximal aerobic power (MAP). On a subsequent visit they performed three TT over the same duration as the TTE. Participants were blinded to elapsed time, power output, cadence and heart rate (HR). Average TTE was 865 ± 345 s, 165 ± 98 s and 117 ± 45 s for the 80, 100 and 105% trials respectively. Average power output was higher for TTE (294 ± 44 W) compared to TT (282 ± 43 W) at 80% MAP (P < 0.01), but not at 100 and 105% MAP (P > 0.05). There was no difference in cadence, HR, or RPE for any trial (P > 0.05). Critical power (CP) was also higher when derived from TTE compared to TT (P < 0.01). It is concluded that TTE results in a higher average power output compared to TT at 80% MAP. When determining CP, TTE rather than TT protocols appear superior.

Laboratory predictors of uphill cycling performance in trained cyclists

This study aimed to assess the relationship between an uphill time-trial (TT) performance and both aerobic and anaerobic parameters obtained from laboratory tests. Fifteen cyclists performed a Wingate anaerobic test, a graded exercise test (GXT) and a field-based 20-min TT with 2.7% mean gradient. After a 5-week non-supervised training period, 10 of them performed a second TT for analysis of pacing reproducibility. Stepwise multiple regressions demonstrated that 91% of TT mean power output variation (W kg^-1) could be explained by peak oxygen uptake (ml kg^-1.min^-1) and the respiratory compensation point (W kg^-1), with standardised beta coefficients of 0.64 and 0.39, respectively. The agreement between mean power output and power at respiratory compensation point showed a bias ± random error of 16.2 ± 51.8 W or 5.7 ± 19.7%. One-way repeated-measures analysis of variance revealed a significant effect of the time interval (123.1 ± 8.7; 97.8 ± 1.2 and 94.0 ± 7.2% of mean power output, for epochs 0-2, 2-18 and 18-20 min, respectively; P < 0.001), characterising a positive pacing profile. This study indicates that an uphill, 20-min TT-type performance is correlated to aerobic physiological GXT variables and that cyclists adopt reproducible pacing strategies when they are tested 5 weeks apart (coefficients of variation of 6.3; 1 and 4%, for 0-2, 2-18 and 18-20 min, respectively).

Effects of power variation on cycle performance during simulated hilly time-trials

It has previously been shown that cyclists are unable to maintain a constant power output during cycle time-trials on hilly courses. The purpose of the present study is therefore to quantify these effects of power variation using a mathematical model of cycling performance. A hypothetical cyclist (body mass: 70 kg, bicycle mass: 10 kg) was studied using a mathematical model of cycling, which included the effects of acceleration. Performance was modelled over three hypothetical 40-km courses, comprising repeated 2.5-km sections of uphill and downhill with gradients of 1%, 3%, and 6%, respectively. Amplitude (5-15%) and distance (0.31-20.00 km) of variation were modelled over a range of mean power outputs (200-600 W) and compared to sustaining a constant power. Power variation was typically detrimental to performance; these effects were augmented as the amplitude of variation and severity of gradient increased. Varying power every 1.25 km was most detrimental to performance; at a mean power of 200 W, performance was impaired by 43.90 s (±15% variation, 6% gradient). However at the steepest gradients, the effect of power variation was relatively independent of the distance of variation. In contrast, varying power in parallel with changes in gradient improved performance by 188.89 s (±15% variation, 6% gradient) at 200 W. The present data demonstrate that during hilly time-trials, power variation that does not occur in parallel with changes in gradient is detrimental to performance, especially at steeper gradients. These adverse effects are substantially larger than those previously observed during flat, windless time-trials.

Dynamics of Perceived Exertion in Constant-Power Cycling: Time- and Workload-Dependent Thresholds

PURPOSE

The purpose of this study was to test the dynamics of perceived exertion shifts (PES) as a function of time and workload during constant-power cycling.

METHOD

Fifty-two participants assigned to 4 groups performed a cycling task at 4 different constant workloads corresponding to their individual rates of perceived exertion (RPEs = 13, 15, 17, and 19, respectively). PES ("increased"/"decreased" perceptions) without magnitude were reported when they occurred. PES "increased" percentages in different nonoverlapping temporal windows and for each workload were calculated to study the time- and workload-dependent relations, respectively.

RESULTS

A fluctuating PES dynamic characterized the cycling at RPE-13 and RPE-15. In contrast, a nonfluctuating PES dynamic characterized the cycling at RPE-17 and RPE-19. A time-dependent PES threshold, manifested as a switch from PES fluctuating to nonfluctuating dynamics, emerged in the RPE-15 condition near volitional exhaustion. A workload-dependent PES threshold occurred from RPE-15 to RPE-17.

CONCLUSIONS

Time- and workload-dependent thresholds were revealed studying the PES dynamics in constant cycling. Monitoring PES can complement or provide an alternative to the use of physiological measures for an accurate control of training workloads.

Did recent world record marathon runners employ optimal pacing strategies?

We apply statistical analysis of high frequency (1 km) split data for the most recent two world-record marathon runs: Run 1 (2:03:59, 28 September 2008) and Run 2 (2:03:38, 25 September 2011). Based on studies in the endurance cycling literature, we develop two principles to approximate 'optimal' pacing in the field marathon. By utilising GPS and weather data, we test, and then de-trend, for each athlete's field response to gradient and headwind on course, recovering standardised proxies for power-based pacing traces. The resultant traces were analysed to ascertain if either runner followed optimal pacing principles; and characterise any deviations from optimality. Whereas gradient was insignificant, headwind was a significant factor in running speed variability for both runners, with Runner 2 targeting the (optimal) parallel variation principle, whilst Runner 1 did not. After adjusting for these responses, neither runner followed the (optimal) 'even' power pacing principle, with Runner 2's macro-pacing strategy fitting a sinusoidal oscillator with exponentially expanding envelope whilst Runner 1 followed a U-shaped, quadratic form. The study suggests that: (a) better pacing strategy could provide elite marathon runners with an economical pathway to significant performance improvements at world-record level; and (b) the data and analysis herein is consistent with a complex-adaptive model of power regulation.

Cycling attributes that enhance running performance after the cycle section in triathlon

PURPOSE

To determine how cycling with a variable (triathlon-specific) power distribution affects subsequent running performance and quantify relationships between an individual cycling power profile and running ability after cycling.

METHODS

Twelve well-trained male triathletes (VO2peak 4.9 ± 0.5 L/min; mass 73.5 ± 7.7 kg; mean ± SD) undertook a cycle VO2peak and maximal aerobic power (MAP) test and a power profile involving 6 maximal efforts (6 s to 10 min). Each subject then performed 2 experimental 1-h cycle trials, both at a mean power of 65% MAP, at either variable power (VAR) ranging from 40% to 140% MAP or constant power (CON) followed by an outdoor 9.3-km time-trial run. Subjects also completed a control 9.3-km run with no preceding exercise.

RESULTS

The 9.3-km run time was 42 ± 37 s slower (mean ± 90% confidence limits [CL]) after VAR (35:32 ± 3:18 min:s, mean ± SD) compared with CON cycling (34:50 ± 2:49 min:s). This decrement after VAR appeared primarily in the first half of the run (35 ± 20 s; mean ± 90% CL). Higher blood lactate and rating of perceived exertion after 1 h VAR cycling were moderately correlated (r = .51-.55; ± ~.40) with a larger decrement in run performance. There were no clear associations between the power-profile test and decrement in run time after VAR compared with CON.

CONCLUSIONS

A highly variable power distribution in cycling is likely to impair 10-km triathlon run performance. Training to lower physiological and perceptual responses during cycling should limit the negative effects on triathlon running.