scholarly journals Modeling Optimal Cadence as a Function of Time during Maximal Sprint Exercises Can Improve Performance by Elite Track Cyclists

2021 ◽  
Vol 11 (24) ◽  
pp. 12105
Author(s):  
Anna Katharina Dunst ◽  
René Grüneberger ◽  
Hans-Christer Holmberg
Keyword(s):  
Linear Regression Analysis ◽  
Bicycle Ergometer ◽  
Mean Power ◽  
Exponential Equation ◽  
Force Velocity ◽  
The Time Domain ◽  
Mean Power Output ◽  
Induced Changes ◽  
Constant Slope ◽  
Optimal Cadence

In track cycling sprint events, optimal cadence PRopt is a dynamic aspect of fatigue. It is currently unclear what cadence is optimal for an athlete’s performance in sprint races and how it can be calculated. We examined fatigue-induced changes in optimal cadence during a maximal sprint using a mathematical approach. Nine elite track cyclists completed a 6-s high-frequency pedaling test and a 60-s isokinetic all-out sprint on a bicycle ergometer with continuous monitoring of crank force and cadence. Fatigue-free force-velocity (F/v) and power-velocity (P/v) profiles were derived from both tests. The development of fatigue during the 60-s sprint was assessed by fixing the slope of the fatigue-free F/v profile. Fatigue-induced alterations in PRopt were determined by non-linear regression analysis using a mono-exponential equation at constant slope. The study revealed that PRopt at any instant during a 60-s maximal sprint can be estimated accurately using a mono-exponential equation. In an isokinetic mode, a mean PRopt can be identified that enables the athlete to generate the highest mean power output over the course of the effort. Adding the time domain to the fatigue-free F/v and P/v profiles allows time-dependent cycling power to be modelled independent of cadence.

Maximum leg force and power output during short-term dynamic exercise

1981 ◽  
Vol 51 (5) ◽  
pp. 1175-1182 ◽  
Author(s):  
A. J. Sargeant ◽  
E. Hoinville ◽  
A. Young
Keyword(s):  
Power Output ◽  
Maximum Level ◽  
Bicycle Ergometer ◽  
Peak Force ◽  
Short Term ◽  
Mean Power ◽  
Mechanical Power Output ◽  
Rate Of Decline ◽  
Mean Power Output ◽  
The Relationship

Force exerted and power generated were measured during short-term exercise performed on a bicycle ergometer that had been modified by the addition of an electric motor driving the cranks at a chosen constant velocity. Five subjects made a series of 20-s maximum efforts at different crank velocities (range 23--171 rev/min). The forces exerted were continuously monitored with strain gauges bonded to the cranks. Peak force was exerted at approximately 90 degrees past top dead center in each revolution. During the 20-s effort peak force declined from the maximum level (PFmax) attained near the start of exercise, the rate of decline being velocity dependent. PFmax was found to be inversely and linearly related to crank velocity and when standardized for upper leg muscle (plus bone) volume (ULV) was given by PFmax (kgf/l ULV) = 27.51--0.125 crank velocity (rev/min). Integration of the force records with pedal velocity enabled power output to be calculated. Maximum power output was a parabolic function of crank velocity, the apex of the relationship indicating that the velocity for greatest power output was 110 rev/min. At this velocity our subjects achieved a maximum mean power output, averaged over a complete revolution, of 840 +/- 153 W (85 +/- 5 W/l ULV). This was compared with the calculated value for maximum mechanical power output from aerobic sources, which was 272 +/- 49 W (30 +/- 1 W/l ULV).

Alternative Countermovement-Jump Analysis to Quantify Acute Neuromuscular Fatigue

2015 ◽  
Vol 10 (1) ◽  
pp. 84-92 ◽  
Author(s):  
Rob Gathercole ◽  
Ben Sporer ◽  
Trent Stellingwerff ◽  
Gord Sleivert
Keyword(s):  
Intermittent Exercise ◽  
Current Approach ◽  
College Level ◽  
Countermovement Jump ◽  
Mean Power ◽  
Monitoring Method ◽  
Power Peak ◽  
Force Velocity ◽  
Induced Changes ◽  
Fatigue Detection

Purpose:To examine the reliability and magnitude of change after fatiguing exercise in the countermovement-jump (CMJ) test and determine its suitability for the assessment of fatigue-induced changes in neuromuscular (NM) function. A secondary aim was to examine the usefulness of a set of alternative CMJ variables (CMJ-ALT) related to CMJ mechanics.Methods:Eleven male college-level team-sport athletes performed 6 CMJ trials on 6 occasions. A total of 22 variables, 16 typical (CMJ-TYP) and 6 CMJ-ALT, were examined. CMJ reproducibility (coefficient of variation; CV) was examined on participants’ first 3 visits. The next 3 visits (at 0, 24, and 72 h postexercise) followed a fatiguing high-intensity intermittent-exercise running protocol. Meaningful differences in CMJ performance were examined through effect sizes (ES) and comparisons to interday CV.Results:Most CMJ variables exhibited intraday (n = 20) and interday (n = 21) CVs of <10%. ESs ranging from trivial to moderate were observed in 18 variables at 0 h (immediately postfatigue). Mean power, peak velocity, flight time, force at zero velocity, and area under the force–velocity trace showed changes greater than the CV in most individuals. At 24 h, most variables displayed trends toward a return to baseline. At 72 h, small increases were observed in time-related CMJ variables, with mean changes also greater than the CV.Conclusions:The CMJ test appears a suitable athlete-monitoring method for NM-fatigue detection. However, the current approach (ie, CMJ-TYP) may overlook a number of key fatigue-related changes, and so practitioners are advised to also adopt variables that reflect the NM strategy used.

scholarly journals Effects of Acute Hypoxia on Lactate Thresholds and High-Intensity Endurance Performance—A Pilot Study

2021 ◽  
Vol 18 (14) ◽  
pp. 7573
Author(s):  
Martin Faulhaber ◽  
Katharina Gröbner ◽  
Linda Rausch ◽  
Hannes Gatterer ◽  
Verena Menz
Keyword(s):  
Power Output ◽  
High Intensity ◽  
Acute Hypoxia ◽  
Endurance Performance ◽  
Cycle Ergometer ◽  
Normobaric Hypoxia ◽  
Simulated Altitude ◽  
Mean Power ◽  
Mean Power Output ◽  
Induced Changes

The present project compared acute hypoxia-induced changes in lactate thresholds (methods according to Mader, Dickhuth and Cheng) with changes in high-intensity endurance performance. Six healthy and well-trained volunteers conducted graded cycle ergometer tests in normoxia and in acute normobaric hypoxia (simulated altitude 3000 m) to determine power output at three lactate thresholds (PMader, PDickhuth, PCheng). Subsequently, participants performed two maximal 30-min cycling time trials in normoxia (test 1 for habituation) and one in normobaric hypoxia to determine mean power output (Pmean). PMader, PDickhuth and PCheng decreased significantly from normoxia to hypoxia by 18.9 ± 9.6%, 18.4 ± 7.3%, and 11.5 ± 6.0%, whereas Pmean decreased by only 8.3 ± 1.6%. Correlation analyses revealed strong and significant correlations between Pmean and PMader (r = 0.935), PDickhuth (r = 0.931) and PCheng (r = 0.977) in normoxia and partly weaker significant correlations between Pmean and PMader (r = 0.941), PDickhuth (r = 0.869) and PCheng (r = 0.887) in hypoxia. PMader and PCheng did not significantly differ from Pmean (p = 0.867 and p = 0.784) in normoxia, whereas this was only the case for PCheng (p = 0.284) in hypoxia. Although investigated in a small and select sample, the results suggest a cautious application of lactate thresholds for exercise intensity prescription in hypoxia.

scholarly journals Growth hormone responses to repeated maximal cycle ergometer exercise at different pedaling rates

2002 ◽  
Vol 92 (2) ◽  
pp. 602-608 ◽  
Author(s):  
K. A. Stokes ◽  
M. E. Nevill ◽  
G. M. Hall ◽  
H. K. A. Lakomy
Keyword(s):  
Growth Hormone ◽  
Area Under The Curve ◽  
Cycle Ergometer ◽  
Mean Power ◽  
Ergometer Exercise ◽  
Hormone Responses ◽  
Sprint Cycling ◽  
Mean Power Output ◽  
Male Subjects ◽  
Serum Gh

The present study examined the growth hormone (GH) response to repeated bouts of maximal sprint cycling and the effect of cycling at different pedaling rates on postexercise serum GH concentrations. Ten male subjects completed two 30-s sprints, separated by 1 h of passive recovery on two occasions, against an applied resistance equal to 7.5% (fast trial) and 10% (slow trial) of their body mass, respectively. Blood samples were obtained at rest, between the two sprints, and for 1 h after the second sprint. Peak and mean pedal revolutions were greater in the fast than the slow trial, but there were no differences in peak or mean power output. Blood lactate and blood pH responses did not differ between trials or sprints. The first sprint in each trial elicited a serum GH response (fast: 40.8 ± 8.2 mU/l, slow: 20.8 ± 6.1 mU/l), and serum GH was still elevated 60 min after the first sprint. The second sprint in each trial did not elicit a serum GH response ( sprint 1 vs. sprint 2, P < 0.05). There was a trend for serum GH concentrations to be greater in the fast trial (mean GH area under the curve after sprint 1vs. after sprint 2: 1,697 ± 367 vs. 933 ± 306 min · mU−1 · l−1; P = 0.05). Repeated sprint cycling results in an attenuation of the GH response.

Carbohydrate loading failed to improve 100-km cycling performance in a placebo-controlled trial

2000 ◽  
Vol 88 (4) ◽  
pp. 1284-1290 ◽  
Author(s):  
Louise M. Burke ◽  
John A. Hawley ◽  
Elske J. Schabort ◽  
Alan St Clair Gibson ◽  
Iñigo Mujika ◽  
...  
Keyword(s):  
Body Mass ◽  
Muscle Glycogen ◽  
Blood Glucose Concentration ◽  
Controlled Trial ◽  
Liver Glycogen ◽  
Cycling Performance ◽  
Controlled Study ◽  
Mean Power ◽  
Road Racing ◽  
Mean Power Output

We evaluated the effect of carbohydrate (CHO) loading on cycling performance that was designed to be similar to the demands of competitive road racing. Seven well-trained cyclists performed two 100-km time trials (TTs) on separate occasions, 3 days after either a CHO-loading (9 g CHO ⋅ kg body mass− 1 ⋅ day− 1) or placebo-controlled moderate-CHO diet (6 g CHO ⋅ kg body mass− 1 ⋅ day− 1). A CHO breakfast (2 g CHO/kg body mass) was consumed 2 h before each TT, and a CHO drink (1 g CHO ⋅ kg.body mass− 1 ⋅ h− 1) was consumed during the TTs to optimize CHO availability. The 100-km TT was interspersed with four 4-km and five 1-km sprints. CHO loading significantly increased muscle glycogen concentrations (572 ± 107 vs. 485 ± 128 mmol/kg dry wt for CHO loading and placebo, respectively; P < 0.05). Total muscle glycogen utilization did not differ between trials, nor did time to complete the TTs (147.5 ± 10.0 and 149.1 ± 11.0 min; P = 0.4) or the mean power output during the TTs (259 ± 40 and 253 ± 40 W, P = 0.4). This placebo-controlled study shows that CHO loading did not improve performance of a 100-km cycling TT during which CHO was consumed. By preventing any fall in blood glucose concentration, CHO ingestion during exercise may offset any detrimental effects on performance of lower preexercise muscle and liver glycogen concentrations. Alternatively, part of the reported benefit of CHO loading on subsequent athletic performance could have resulted from a placebo effect.

Aerobic and Anaerobic Power Distribution During Cross-Country Mountain Bike Racing

2020 ◽  
pp. 1-6
Author(s):  
Bernhard Prinz ◽  
Dieter Simon ◽  
Harald Tschan ◽  
Alfred Nimmerichter
Keyword(s):  
Power Output ◽  
Power Distribution ◽  
Anaerobic Power ◽  
Energy System ◽  
Mountain Bike ◽  
Mean Power ◽  
Anaerobic Energy ◽  
Cross Country ◽  
Mean Power Output ◽  
Aerobic And Anaerobic

Purpose: To determine aerobic and anaerobic demands of mountain bike cross-country racing. Methods: Twelve elite cyclists (7 males;  = 73.8 [2.6] mL·min-1·kg−1, maximal aerobic power [MAP] = 370 [26] W, 5.7 [0.4] W·kg−1, and 5 females;  = 67.3 [2.9] mL·min−1·kg−1, MAP = 261 [17] W, 5.0 [0.1] W·kg−1) participated over 4 seasons at several (119) international and national races and performed laboratory tests regularly to assess their aerobic and anaerobic performance. Power output, heart rate, and cadence were recorded throughout the races. Results: The mean race time was 79 (12) minutes performed at a mean power output of 3.8 (0.4) W·kg−1; 70% (7%) MAP (3.9 [0.4] W·kg−1 and 3.6 [0.4] W·kg−1 for males and females, respectively) with a cadence of 64 (5) rev·min−1 (including nonpedaling periods). Time spent in intensity zones 1 to 4 (below MAP) were 28% (4%), 18% (8%), 12% (2%), and 13% (3%), respectively; 30% (9%) was spent in zone 5 (above MAP). The number of efforts above MAP was 334 (84), which had a mean duration of 4.3 (1.1) seconds, separated by 10.9 (3) seconds with a mean power output of 7.3 (0.6) W·kg−1 (135% [9%] MAP). Conclusions: These findings highlight the importance of the anaerobic energy system and the interaction between anaerobic and aerobic energy systems. Therefore, the ability to perform numerous efforts above MAP and a high aerobic capacity are essential to be competitive in mountain bike cross-country.

scholarly journals Validity of the Velocomp PowerPod Compared With the Verve Cycling InfoCrank Power Meter

2019 ◽  
Vol 14 (10) ◽  
pp. 1382-1387 ◽  
Author(s):  
Paul F.J. Merkes ◽  
Paolo Menaspà ◽  
Chris R. Abbiss
Keyword(s):  
Power Output ◽  
Average Power ◽  
Dynamic Environment ◽  
Rolling Resistance ◽  
Mean Power ◽  
Power Meter ◽  
Test Study ◽  
Environment Study ◽  
Road Cyclists ◽  
Mean Power Output

Purpose: To determine the validity of the Velocomp PowerPod power meter in comparison with the Verve Cycling InfoCrank power meter. Methods: This research involved 2 separate studies. In study 1, 12 recreational male road cyclists completed 7 maximal cycling efforts of a known duration (2 times 5 s and 15, 30, 60, 240, and 600 s). In study 2, 4 elite male road cyclists completed 13 outdoor cycling sessions. In both studies, power output of cyclists was continuously measured using both the PowerPod and InfoCrank power meters. Maximal mean power output was calculated for durations of 1, 5, 15, 30, 60, 240, and 600 seconds plus the average power output in study 2. Results: Power output determined by the PowerPod was almost perfectly correlated with the InfoCrank (r > .996; P < .001) in both studies. Using a rolling resistance previously reported, power output was similar between power meters in study 1 (P = .989), but not in study 2 (P = .045). Rolling resistance estimated by the PowerPod was higher than what has been previously reported; this might have occurred because of errors in the subjective device setup. This overestimation of rolling resistance increased the power output readings. Conclusion: Accuracy of rolling resistance seems to be very important in determining power output using the PowerPod. When using a rolling resistance based on previous literature, the PowerPod showed high validity when compared with the InfoCrank in a controlled field test (study 1) but less so in a dynamic environment (study 2).

Surface electromyogram power spectrum in human quadriceps muscle during incremental exercise

1993 ◽  
Vol 74 (6) ◽  
pp. 2704-2710 ◽  
Author(s):  
D. Gamet ◽  
J. Duchene ◽  
C. Garapon-Bar ◽  
F. Goubel
Keyword(s):  
Principal Component ◽  
Quadriceps Muscle ◽  
Bicycle Ergometer ◽  
Linear Increase ◽  
Total Power ◽  
Test Surface ◽  
Mean Power ◽  
Mean Power Frequency ◽  
Incremental Test ◽  
Power Frequency

Spectral electromyographic (EMG) changes in human quadriceps muscles were studied to reinvestigate discrepant results concerning mean power frequency (MPF) changes during dynamic exercise. An incremental test consisting of a quasi-linear increase in mechanical power on a bicycle ergometer (for 20–100% of maximal aerobic power) was performed by forty subjects. During this test, surface EMGs from the quadriceps muscles showed that EMG total power (PEMG) increased with a curvilinear pattern for every subject, whereas MPF kinetics varied from one subject to another. PEMG changes had the same shape, which would lead to disappointing results in terms of discrimination between subjects. The ability of normalized MPF kinetics to define significant clusters of subjects was tested using a principal component analysis. This analysis led to the projection of all experiments onto a plane and revealed a relevant grouping of MPF profiles. Differences in MPF kinetics between clusters are interpreted in terms of various possibilities of balance between physiological events leading to an increase or a decrease in MPF.

scholarly journals An Analysis of Variability in Power Output During Indoor and Outdoor Cycling Time Trials

2019 ◽  
Vol 14 (9) ◽  
pp. 1273-1279 ◽  
Author(s):  
Owen Jeffries ◽  
Mark Waldron ◽  
Stephen D. Patterson ◽  
Brook Galna
Keyword(s):  
Power Output ◽  
Time Trial ◽  
Mean Power ◽  
Portable Power ◽  
Output Variability ◽  
Competitive Cyclists ◽  
The Mean ◽  
Mean Power Output ◽  
Testing Protocols ◽  
Indoor And Outdoor

Purpose: Regulation of power output during cycling encompasses the integration of internal and external demands to maximize performance. However, relatively little is known about variation in power output in response to the external demands of outdoor cycling. The authors compared the mean power output and the magnitude of power-output variability and structure during a 20-min time trial performed indoors and outdoors. Methods: Twenty male competitive cyclists ( 60.4 [7.1] mL·kg−1·min−1) performed 2 randomized maximal 20-min time-trial tests: outdoors at a cycle-specific racing circuit and indoors on a laboratory-based electromagnetically braked training ergometer, 7 d apart. Power output was sampled at 1 Hz and collected on the same bike equipped with a portable power meter in both tests. Results: Twenty-minute time-trial performance indoor (280 [44] W) was not different from outdoor (284 [41] W) (P = .256), showing a strong correlation (r = .94; P < .001). Within-persons SD was greater outdoors (69 [21] W) than indoors (33 [10] W) (P < .001). Increased variability was observed across all frequencies in data from outdoor cycling compared with indoors (P < .001) except for the very slowest frequency bin (<0.0033 Hz, P = .930). Conclusions: The findings indicate a greater magnitude of variability in power output during cycling outdoors. This suggests that constraints imposed by the external environment lead to moderate- and high-frequency fluctuations in power output. Therefore, indoor testing protocols should be designed to reflect the external demands of cycling outdoors.

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