Conflict Graphs and the Capacity of the Mean Power Scheme

2013 ◽  
pp. 278-290 ◽  
Author(s):  
Tigran Tonoyan
Keyword(s):  
Mean Power ◽  
Conflict Graphs ◽  
The Mean

The Influence of Training Phase on Error of Measurement in Jump Performance

2016 ◽  
Vol 11 (2) ◽  
pp. 235-239 ◽  
Author(s):  
Kristie-Lee Taylor ◽  
Will G. Hopkins ◽  
Dale W. Chapman ◽  
John B. Cronin
Keyword(s):  
Mixed Model ◽  
Training Phase ◽  
Confidence Limits ◽  
Mean Power ◽  
Random Factor ◽  
Jump Performance ◽  
Mean Error ◽  
Overload Training ◽  
The Mean ◽  
Error Of Measurement

The purpose of this study was to calculate the coefficients of variation in jump performance for individual participants in multiple trials over time to determine the extent to which there are real differences in the error of measurement between participants. The effect of training phase on measurement error was also investigated. Six subjects participated in a resistance-training intervention for 12 wk with mean power from a countermovement jump measured 6 d/wk. Using a mixed-model meta-analysis, differences between subjects, within-subject changes between training phases, and the mean error values during different phases of training were examined. Small, substantial factor differences of 1.11 were observed between subjects; however, the finding was unclear based on the width of the confidence limits. The mean error was clearly higher during overload training than baseline training, by a factor of ×/÷ 1.3 (confidence limits 1.0–1.6). The random factor representing the interaction between subjects and training phases revealed further substantial differences of ×/÷ 1.2 (1.1–1.3), indicating that on average, the error of measurement in some subjects changes more than in others when overload training is introduced. The results from this study provide the first indication that within-subject variability in performance is substantially different between training phases and, possibly, different between individuals. The implications of these findings for monitoring individuals and estimating sample size are discussed.

Carbohydrate Drink Use During 30 Minutes of Variable-Intensity Exercise Has No Effect on Exercise Performance in Premenarchal Girls

2021 ◽  
Vol 33 (2) ◽  
pp. 65-69
Author(s):  
C. Eric Heidorn ◽  
Brandon J. Dykstra ◽  
Cori A. Conner ◽  
Anthony D. Mahon
Keyword(s):  
Peak Power ◽  
Perceived Exertion ◽  
Rating Of Perceived Exertion ◽  
Fatigue Index ◽  
Mean Power ◽  
Variable Intensity ◽  
Performance Effects ◽  
Power Peak ◽  
The Mean ◽  
And Performance

Purpose: This study examined the physiological, perceptual, and performance effects of a 6% carbohydrate (CHO) drink during variable-intensity exercise (VIE) and a postexercise test in premenarchal girls. Methods: A total of 10 girls (10.4 [0.7] y) participated in the study. VO2peak was assessed, and the girls were familiarized with VIE and performance during the first visit. The trial order (CHO and placebo) was randomly assigned for subsequent visits. The drinks were given before VIE bouts and 1-minute performance (9 mL/kg total). Two 15-minute bouts of VIE were completed (10 repeated sequences of 20%, 55%, and 95% power at VO2peak and maximal sprints) before a 1-minute performance sprint. Results: The mean power, peak power, heart rate (HR), %HRpeak, and rating of perceived exertion during VIE did not differ between trials. However, the peak power decreased, and the rating of perceived exertion increased from the first to the second bout. During the 1-minute performance, there were no differences between the trial (CHO vs placebo) for HR (190 [9] vs 189 [9] bpm), %HRpeak (97.0% [3.2%] vs 96.6% [3.0%]), rating of perceived exertion (7.8 [2.3] vs 8.1 [1.9]), peak power (238 [70] vs 235 [60] W), fatigue index (54.7% [10.0%] vs 55.9% [12.8%]), or total work (9.4 [2.6] vs 9.4 [2.1] kJ). Conclusion: CHO supplementation did not alter physiological, perceptual, or performance responses during 30 minutes of VIE or postexercise sprint performance in premenarchal girls.

scholarly journals Relationship Between the Critical Power Test and a 20-min Functional Threshold Power Test in Cycling

2021 ◽  
Vol 11 ◽  
Author(s):  
Bettina Karsten ◽  
Luca Petrigna ◽  
Andreas Klose ◽  
Antonino Bianco ◽  
Nathan Townsend ◽  
...  
Keyword(s):  
Critical Power ◽  
Pearson Correlation ◽  
Time Trial ◽  
Peak Oxygen Consumption ◽  
Threshold Power ◽  
Strong Correlations ◽  
Mean Power ◽  
Power Test ◽  
Paired Samples ◽  
The Mean

To investigate the agreement between critical power (CP) and functional threshold power (FTP), 17 trained cyclists and triathletes (mean ± SD: age 31 ± 9 years, body mass 80 ± 10 kg, maximal aerobic power 350 ± 56 W, peak oxygen consumption 51 ± 10 mL⋅min–1⋅kg–1) performed a maximal incremental ramp test, a single-visit CP test and a 20-min time trial (TT) test in randomized order on three different days. CP was determined using a time-trial (TT) protocol of three durations (12, 7, and 3 min) interspersed by 30 min passive rest. FTP was calculated as 95% of 20-min mean power achieved during the TT. Differences between means were examined using magnitude-based inferences and a paired-samples t-test. Effect sizes are reported as Cohen’s d. Agreement between CP and FTP was assessed using the 95% limits of agreement (LoA) method and Pearson correlation coefficient. There was a 91.7% probability that CP (256 ± 50 W) was higher than FTP (249 ± 44 W). Indeed, CP was significantly higher compared to FTP (P = 0.041) which was associated with a trivial effect size (d = 0.04). The mean bias between CP and FTP was 7 ± 13 W and LoA were −19 to 33 W. Even though strong correlations exist between CP and FTP (r = 0.969; P < 0.001), the chance of meaningful differences in terms of performance (1% smallest worthwhile change), were greater than 90%. With relatively large ranges for LoA between variables, these values generally should not be used interchangeably. Caution should consequently be exercised when choosing between FTP and CP for the purposes of performance analysis.

Dependence of the mean power frequency of the electromyogram on muscle force and fibre type

1991 ◽  
Vol 142 (4) ◽  
pp. 457-465 ◽  
Author(s):  
B. GERDLE ◽  
K. HENRIKSSON-LARSÉN ◽  
R. LORENTZON ◽  
M.-L. WRETLING
Keyword(s):  
Fibre Type ◽  
Muscle Force ◽  
Mean Power ◽  
Mean Power Frequency ◽  
Power Frequency ◽  
The Mean

Digital meter for the mean power of a random signal

1983 ◽  
Vol 26 (6) ◽  
pp. 449-452
Author(s):  
G. Ya. Mirskii ◽  
S. U. Klimovich
Keyword(s):  
Random Signal ◽  
Mean Power ◽  
The Mean

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.

Determination of aerobic work and power on a rope-braked cycle ergometer by direct measurement

2006 ◽  
Vol 31 (4) ◽  
pp. 392-397 ◽  
Author(s):  
Rae S. Gordon ◽  
Kathryn L. Franklin ◽  
Julien S. Baker ◽  
Bruce Davies
Keyword(s):  
Direct Measurement ◽  
Power Estimation ◽  
Cycle Ergometer ◽  
Mean Power ◽  
Physiological Assessment ◽  
Brake Force ◽  
The Mean ◽  
The Difference ◽  
Work Done

The purpose of this study was to compare the power and work outputs of a cycle ergometer using the manufacturer’s guidelines, with calculations using direct flywheel velocity and brake torque. A further aim was to compare the values obtained with those supplied by the manufacturer. A group of 10 male participants were asked to pedal a Monark 824E ergometer at a constant cadence of 60 r/min for a period of 3 min against a resistive mass of 3 kg. The flywheel velocity was measured using a tachometer. The brake force was determined by measuring the tension in the rope on either side of the flywheel. The calculated mean power was 147.45 ± 6.5 W compared with the Monark value of 183 ± 3.7 W. The difference between the methods for power estimation was 18% and was statistically significant (p < 0.01). The mean work done by the participants during the 3 min period was found to be 26 460 ± 1145 J compared with the Monark value of 33 067 ± 648 J (p < 0.01). The Monark formulae currently used to determine the power and work done by a participant overestimates the actual values required to overcome the resistance. There findings have far-reaching implications in the physiological assessment of athletic, sedentary, and diseased populations.

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