Factors Affecting Gross Efficiency in Cycling

Koning, Jos J. de; Noordhof, Dionne A.; Lucía, Alejandro; Foster, Carl

doi:10.1055/s-0032-1306285

Cited by 31 publications

(39 citation statements)

References 32 publications

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“…Unfortunately, little data exists examining this relationship. Numerous methods have been proposed to measure exercise efficiency in athletic and clinical populations including mechanical efficiency, gross efficiency, delta efficiency, oxygen uptake efficiency slope, and others (5, 13, 22, 23, 26). While all of these methods have their strengths and weaknesses, each measure is largely dependent on a single point of observation or on the slope of the changing relationship between the oxygen uptake of sequential stages, and hence, the workload involved in exercise testing.…”

Section: Discussionmentioning

confidence: 99%

Aerobic and Strength Training in Concomitant Metabolic Syndrome and Type 2 Diabetes

Earnest

Johannsen

Swift

et al. 2014

Medicine &Amp; Science in Sports &Amp; Exercise

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Purpose Concomitant type 2 diabetes (T2D) and metabolic syndrome exacerbates mortality risk; yet, few studies have examined the effect of combining (AER+RES) aerobic (AER) and resistance (RES) training for individuals with T2D and metabolic syndrome. Methods We examined AER, RES, and AER+RES training (9-months) commensurate with physical activity guidelines in individuals with T2D (N=262, 63% female, 44% black). Primary outcomes were change in, and prevalence of, metabolic syndrome score at follow-up (mean, 95%CI). Secondary outcomes included maximal cardiorespiratory fitness (VO2peak and estimated METs from time-to-exhaustion (TTE), and exercise efficiency calculated as the slope of the line between ventilatory threshold, respiratory compensation, and maximal fitness. General linear models and bootstrapped Spearman correlations were used to examine changes in metabolic syndrome associated with training primary and secondary outcome variables. Results We observed a significant decrease in metabolic syndrome scores (P-for-trend, 0.003) for AER (−0.59, 95%CI, −1.00, −0.21) and AER+RES (−0.79, 95%CI, −1.40, −0.35), both being significant (P < 0.02) vs. Control (0.26, 95%CI, −0.58, 0.40) and RES (−0.13, 95%CI, −1.00, 0.24). This lead to a reduction in metabolic syndrome prevalence for the AER (56% vs. 43%) and AER+RES (55% vs. 46%) groups between baseline and follow-up. The observed decrease in metabolic syndrome was mediated by significant improvements in exercise efficiency for the AER and AER+RES training groups (P<0.05), which was more strongly related to TTE (25–30%; r= −0.38; 95% CI: −0.55, −0.19) than VO2peak (5–6%; r= −0.24; 95% CI: −0.45, −0.01). Conclusion Aerobic and AER+RES training significantly improves metabolic syndrome scores and prevalence in patients with T2D. These improvements appear to be associated with improved exercise efficiency and are more strongly related to improved TTE versus VO2peak.

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Section: Discussionmentioning

confidence: 99%

Aerobic and Strength Training in Concomitant Metabolic Syndrome and Type 2 Diabetes

Earnest

Johannsen

Swift

et al. 2014

Medicine &Amp; Science in Sports &Amp; Exercise

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“…Gross efficiency was calculated for the last minute of each increment, dividing mechanical power by oxidative metabolic power and accounting for power loss within the Monark transmission system (2). Metabolic power was derived from Vȯ 2 and respiratory exchange ratio (40 …”

Section: Whole-body Oxygen Consumptionmentioning

confidence: 99%

Critical determinants of combined sprint and endurance performance: an integrative analysis from muscle fiber to the human body

et al. 2018

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Optimizing physical performance is a major goal in current physiology. However, basic understanding of combining high sprint and endurance performance is currently lacking. This study identifies critical determinants of combined sprint and endurance performance using multiple regression analyses of physiologic determinants at different biologic levels. Cyclists, including 6 international sprint, 8 team pursuit, and 14 road cyclists, completed a Wingate test and 15-km time trial to obtain sprint and endurance performance results, respectively. Performance was normalized to lean body mass 2/3 to eliminate the influence of body size. Performance determinants were obtained from whole-body oxygen consumption, blood sampling, knee-extensor maximal force, muscle oxygenation, wholemuscle morphology, and muscle fiber histochemistry of musculus vastus lateralis. Normalized sprint performance was explained by percentage of fast-type fibers and muscle volume (R 2 = 0.65; P < 0.001) and normalized endurance performance by performance oxygen consumption (Vȯ 2 ), mean corpuscular hemoglobin concentration, and muscle oxygenation (R 2 = 0.92; P < 0.001). Combined sprint and endurance performance was explained by gross efficiency, performance Vȯ 2 , and likely by muscle volume and fascicle length (P = 0.056; P = 0.059). High performance Vȯ 2 related to a high oxidative capacity, high capillarization 3 myoglobin, and small physiologic cross-sectional area (R 2 = 0.67; P < 0.001). Results suggest that fascicle length and capillarization are important targets for training to optimize sprint and endurance performance simultaneously.-Van der Zwaard, S., van der Laarse, W. J., Weide, G., Bloemers, F. W., Hofmijster, M. J., Levels, K., Noordhof, D. A., de Koning, J. J., de Ruiter, C. J., Jaspers, R. T. Critical determinants of combined sprint and endurance performance: an integrative analysis from muscle fiber to the human body. FASEB J. 32, 2110FASEB J. 32, -2123FASEB J. 32, (2018 Many sports require a combination of sprint and endurance performance. During the past decades, physiologic determinants of physical performance have been the subject of intensive investigation (e.g., in cycling, 1-11). Studies focused on determinants of either sprint (e.g., 8-18) or endurance performance (e.g., 1-7, 19-23), even though physical performance is rarely a dichotomous function of only sprint or only endurance. Generally, a limited number of whole-body determinants of sprint or endurance performance have been studied, although there are many physical performance determinants at different levels (i.e., molecular, cellular, whole-muscle, organ, and whole ABBREVIATIONS: 3D, 3-dimensional; CAF, capillaries around the fiber; CD, capillary density; C/F, capillary-to-fiber ratio; FCSA, fiber cross-sectional area; fVȯ 2max , fiber maximal oxygen consumption; [Hb], hemoglobin concentration; Hct, hematocrit; [HHbMb], deoxygenated hemoglobin and myoglobin concentration; iSDH activity, spatially integrated SDH activity, SDH activity 3 FCSA; KE, k...

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“…This mechanical variant of the anaerobic capacity can, in addition, be converted and expressed as an O 2 deficit (Noordhof et al, 2011). When using the GE approach, it is assumed that GE plateaus and remains constant during supramaximal exercise (de Koning et al, 2012). In previous studies on classic cross-country skiing, GE has been observed to be speed independent for diagonal stride but not for double poling (Andersson et al, 2017;Andersson and McGawley, 2018) which makes the traditional GE method more suitable for diagonal stride than double poling (Andersson et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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

Andersson

Noordhof

Lögdal

2020

Front. Sports Act. Living

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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% ofVO 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, η 2 = 0.51) for the anaerobic capacity estimates. The different methodologies resulted in significantly different anaerobic capacity values in DP (P < 0.001, η 2 = 0.74) and in DS (P = 0.016, η 2 = 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 O 2 eq·kg −1 ). Andersson et al. Estimating Anaerobic Energy ProductionConclusions: 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.

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Factors Affecting Gross Efficiency in Cycling

Cited by 31 publications

References 32 publications

Aerobic and Strength Training in Concomitant Metabolic Syndrome and Type 2 Diabetes

Aerobic and Strength Training in Concomitant Metabolic Syndrome and Type 2 Diabetes

Critical determinants of combined sprint and endurance performance: an integrative analysis from muscle fiber to the human body

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

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