Factors affecting gross efficiency in cycling

Durability and Underlying Physiological Factors: How Do They Change Throughout a Cycling Season in Semiprofessional Cyclists?

PURPOSE

To investigate how cycling time-trial (TT) performance changes over a cycling season, both in a "fresh" state and in a "fatigued" state (durability). Additionally, the aim was to explore whether these changes are related to changes in underlying physiological factors such as gross efficiency, energy expenditure (EE), and substrate oxidation (fat oxidation [FatOx] and carbohydrate oxidation [CarbOx]).

METHODS

Sixteen male semiprofessional cyclists visited the laboratory on 3 occasions during a cycling season (PRE, START, and IN) and underwent a performance test in both fresh and fatigued states (after 38.1 [4.9] kJ/kg), containing a submaximal warm-up for the measurement of gross efficiency, EE, FatOx, and CarbOx and a maximal TT of 1 (TT1min) and 10 minutes (TT10min). Results were compared across states (fresh vs fatigued) and periods (PRE, START, and IN).

RESULTS

The average power output (PO) in TT1min decreased (P < .05) from fresh to fatigued state across all observed periods, whereas there was no change in the PO in TT10min. Over the course of the season, the PO in TT1min in the fatigued state improved more compared with the PO in TT1min in the fresh state. Furthermore, while EE did not significantly change, there was an increase in FatOx and a decrease in CarbOx toward the fatigued state. These changes diminished during the cycling season (IN), indicating a greater contribution of CarbOx in the fatigued state.

CONCLUSIONS

TT1min performance is more sensitive to fatigue compared with TT10min. Also, during a cycling season, durability improves more when compared with fresh maximal POs, which is also observed in the changes in substrate oxidation.

What Does It Take to Become a Professional Cyclist? A Laboratory-Based Longitudinal Analysis in Competitive Young Riders

PURPOSE

Laboratory-based indicators are commonly used for performance assessment in young cyclists. However, evidence supporting the use of these indicators mostly comes from cross-sectional research, and their validity as predictors of potential future performance remains unclear. We aimed to assess the role of laboratory variables for predicting transition from U23 (under 23 y) to professional category in young cyclists.

METHODS

Sixty-five U23 male road cyclists (19.6 [1.5] y) were studied. Endurance (maximal graded test and simulated 8-min time trial [TT]), muscle strength/power (squat, lunge, and hip thrust), and body composition (assessed with dual-energy X-ray absorptiometry) indicators were determined. Participants were subsequently followed and categorized attending to whether they had transitioned ("Pro") or not ("Non-Pro") to the professional category during the study period.

RESULTS

The median follow-up period was 3 years. Pro cyclists (n = 16) showed significantly higher values than Non-Pro riders (n = 49) for ventilatory thresholds, peak power output, peak oxygen uptake, and TT performance (all P < .05, effect size > 0.69) and lower levels of fat mass and bone mineral content/density (P < .05, effect size > 0.63). However, no significant differences were found for muscle strength/power indicators (P > .05, effect size < 49). The most accurate individual predictor was TT performance (overall predictive value = 76% for a cutoff value of 5.6 W·kg-1). However, some variables that did not reach statistical significance in univariate analyses contributed significantly to a multivariate model (R2 = .79, overall predictive value = 94%).

CONCLUSIONS

Although different "classic" laboratory-based endurance indicators can predict the potential of reaching the professional category in U23 cyclists, a practical indicator such as 8-minute TT performance showed the highest prediction accuracy.

Effect of Gear Ratio and Cadence on Gross Efficiency and Pedal Force Effectiveness during Multistage Graded Cycling Test Using a Road Racing Bicycle

Gross efficiency (GE) and the index of pedal force effectiveness (IFE) are important factors that enhance cyclists' performance; however, the effects of changing pedal force (gear ratio) and cadence on these indices while riding on a road racing bicycle are poorly investigated. This study aimed to examine the effect of changing gear ratio or cadence on GE and IFE using a road racing bicycle. Nine male cyclists completed graded submaximal cycling tests (five stages of 4 min submaximal cycling sessions with 1 min passive rest intervals). The work rate of each stage was determined using two principles: changing gear ratio at a fixed cadence and changing cadence at a fixed gear ratio. We determined GE and IFE using respiratory variables and pedal reaction forces, respectively. Increasing the gear ratio improved GE, and was associated with the IFE. Although increasing the cadence slightly improved GE from the initial level, the increased values then mostly maintained. IFE was almost stable even when cadence increased. Moreover, no significant correlation was observed between the changes in GE and IFE accompanied by increasing cadence. Our data indicate that an increasing gear ratio, but not cadence, may affect GE and IFE while riding on a road racing bicycle.

Metabolic Flexibility and Mechanical Efficiency in Women Over-60

Purpose: Aging deteriorates metabolic flexibility (MF). Moreover, recent studies show that glycolysis is barely increased despite impoverished lipid metabolism, in addition to increased relevance of muscle power in older adults. This study aims to analyze MF, i.e., fat and carbohydrates oxidation rates (FATox and CHOox), and the point of maximal fat oxidation (MFO), in a group of active women over-60. It also aims to delve into the role of power production and mechanical efficiency regarding MF. This will help to decipher their metabolic behavior in response to increasing intensity.

Methods: Twenty-nine women (66.13 ± 5.62 years) performed a submaximal graded cycling test, increasing 10 W each 3-min15-s, from 30 W to the second ventilatory threshold (VT₂). Muscle power was adjusted with a Saris-H3 roller, together with a continuous gas analysis by indirect calorimetry (Cosmed K4b2). Pre and post-test blood lactate (BLa) samples were included. Frayn’s equations, MFO and CHOox_peak (mg/min/kg FFM) were considered for MF analysis (accounting for average VO₂ and VCO₂ in each last 60-s), whilst delta and gross efficiencies (DE%, GE%), and exercise economy (EC), were added for Mechanical Efficiency. Mean comparisons regarding intensities 60, 80 and 100% at VT₂, completed the study together with correlation analysis among the main variables.

Results: MFO and CHOox_peak were small (6.35 ± 3.59 and 72.79 ± 34.76 g/min/kgFFM respectively) for a reduced muscle power (78.21 ± 15.84 W). Notwithstanding, GE% and EC increased significantly (p < 0.01) with exercise intensity. Importantly, coefficients of variation were very large confirming heterogeneity. Whilst muscle power outcomes correlated significantly (p < 0.01) with MFO (r = 0.66) and age (r = −0.62), these latter failed to be associated. Only GE% correlated to CHOox_peak (r = −0.61, p < 0.01) regarding mechanical efficiency.

Conclusions: Despite being active, women over-60 confirmed impaired substrates switching in response to exercise, from both FAT and CHO pathways. This limits their power production affecting exercise capacity. Our data suggest that decreased power with age has a key role above age per se in this metabolic inflexibility. Vice versa, increasing power seems to protect from mitochondrial dysfunction with aging. New studies will confirm if this higher efficiency when coming close to VT₂, where GE is the more informative variable, might be a protective compensatory mechanism.

Integrated Physiological, Biomechanical, and Subjective Responses for the Selection of Assistive Level in Pedelec Cycling

In recent decade, pedelec has become one of the most popular transportation modes due to its effectiveness in reducing physical effort. The effects of using pedelec as an alternative mode of exercise were explored in previous studies. However, the effects of pedelec parameters were not quantified for the self-selected gear ratio, random riding speed, and varied road slopes, which restricted its application. Hence, this study aimed to evaluate the effects of gear ratio and assistive torque and to determine the optimum riding condition regarding physiological, biomechanical, and subjective responses of the rider. The riding tests consisted of simulated slope (1.0 vs. 2.5% grade), gear ratio (light vs. heavy), and assistive levels (0.5, 1, 1.5, and 2), and the tests were conducted in a randomized order. A total of 19 non-athletes completed the riding tests to evaluate physiological [metabolic equivalent of task (MET), heart rate, and gross efficiency (GE)], biomechanical [muscle activity (expressed as reference voluntary contraction, RVC) and power output], and subjective responses [rating of perceived exertion (RPE) and sense of comfort (SC)]. The test conditions induced moderate to vigorous intensities (3.7–7.4 METs, 58.5–80.3% of maximal heart rate, 11.1–29.5% of RVC rectus femoris activity, and 9.4–14.2 RPEs). The effects of gear ratio and assistive level on the physiological responses were significant. Riding with the heavy gear ratio showed advantages in METs and GE. For the optimum assistive level selection, low GE and limited improvement in subjective responses suggested the impact of low-power output conditions. Overall, for the health pedelec commuters, riding with 0.75 W/kg power output with 50 rpm cadence is recommended to obtain the moderate intensity (4.7 METs) and the advantages in GE and subjective feelings. Moreover, the findings can be applied to exercise intensity control and save battery energy effectively in varying riding conditions.

Indoor Cycling Energy Expenditure: Does Sequence Matter?

Although cycling class intensity can be modified by changing interval intensity sequencing, it has not been established whether the intensity order can alter physiological and perceptual responses. Therefore, this study aimed to determine the effects of interval intensity sequencing on energy expenditure (EE), physiological markers, and perceptual responses during indoor cycling. Healthy volunteers (10 males = 20.0 ± 0.8years; 8 females = 21.3 ± 2.7years) completed three randomly ordered interval bouts (mixed pyramid—MP, ascending intervals—AI, descending intervals—DI) including three 3-min work bouts at 50%, 75%, and 100% of peak power output (PPO) and three 3-min recovery periods at 25% PPO. Heart rate (HR) and oxygen consumption (VO₂) were expressed as percentages of maximal HR (%HR_max) and VO₂ (%VO_2max). EE was computed for both the work bout and for the 5-min recovery period. Session Rating of Perceived Exertion (sRPE) and Exercise Enjoyment Scale (EES) were recorded. No differences emerged for % HR_max (MP = 73.3 ± 6.1%; AI = 72.1 ± 4.9%; DI = 71.8 ± 4.5%), % VO_2max (MP = 51.8 ± 4.6%; AI = 51.4 ± 3.9%; DI = 51.3 ± 4.5%), EE (MP = 277.5 ± 39.9 kcal; AI = 275.8 ± 39.4 kcal; DI = 274.9 ± 42.1 kcal), EES (MP = 4.9 ± 1.0; AI = 5.3 ± 1.1; DI = 4.9 ± 0.9), and sRPE (MP = 4.9 ± 1.0; AI = 5.3 ± 1.1; DI = 4.9 ± 0.9). EE during recovery was significantly (p < 0.005) lower after DI (11.9 ± 3.2 kcal) with respect to MP (13.2 ± 2.5 kcal) and AI (13.3 ± 2.5 kcal). Although lower EE was observed during recovery in DI, interval intensity sequencing does not affect overall EE, physiological markers, and perceptual responses.

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.

Effect of Progressive Fatigue on Session RPE

Rating of perceived exertion (RPE) and session RPE (sRPE) are reliable tools for predicting exercise intensity and are alternatives to more technological and physiological measurements, such as blood lactate (HLa) concentration, oxygen consumption and heart rate (HR). As sRPE may also convey some insights into accumulated fatigue, the purpose of this study was to examine the effects of progressive fatigue in response to heavier-than-normal training on sRPE, with absolute training intensity held constant, and determine its validity as marker of fatigue. Twelve young adults performed eight interval workouts over a two-week period. The percentage of maximal HR (%HRmax), HLa, RPE and sRPE were measured for each session. The HLa/RPE ratio was calculated as an index of fatigue. Multilevel regression analysis showed significant differences for %HRmax (p = 0.004), HLa concentration (p = 0.0001), RPE (p < 0.0001), HLa/RPE ratio (p = 0.0002) and sRPE (p < 0.0001) across sessions. Non-linear regression analysis revealed a very large negative relationship between HLa/RPE ratio and sRPE (r = -0.70, p < 0.0001). These results support the hypothesis that sRPE is a sensitive tool that provides information on accumulated fatigue, in addition to training intensity. Exercise scientists without access to HLa measurements may now be able to gain insights into accumulated fatigue during periods of increased training by using sRPE.

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.

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.

Muscle Free Fatty-Acid Uptake Associates to Mechanical Efficiency During Exercise in Humans

Intrinsic factors related to muscle metabolism may explain the differences in mechanical efficiency (ME) during exercise. Therefore, this study aimed to investigate the relationship between muscle metabolism and ME. Totally 17 healthy recreationally active male participants were recruited and divided into efficient (EF; n = 8) and inefficient (IE; n = 9) groups, which were matched for age (mean ± SD 24 ± 2 vs. 23 ± 2 years), BMI (23 ± 1 vs. 23 ± 2 kg m-2), physical activity levels (3.4 ± 1.0 vs. 4.1 ± 1.0 sessions/week), and V ˙ O2peak (53 ± 3 vs. 52 ± 3 mL kg-1 min-1), respectively, but differed for ME at 45% of V ˙ O2peak intensity during submaximal bicycle ergometer test (EF 20.5 ± 3.5 vs. IE 15.4 ± 0.8%, P < 0.001). Using positron emission tomography, muscle blood flow (BF) and uptakes of oxygen (m V ˙ O2), fatty acids (FAU) and glucose (GU) were measured during dynamic submaximal knee-extension exercise. Workload-normalized BF (EF 35 ± 14 vs. IE 34 ± 11 mL 100 g-1 min-1, P = 0.896), m V ˙ O2 (EF 4.1 ± 1.2 vs. IE 3.9 ± 1.2 mL 100 g-1 min-1, P = 0.808), and GU (EF 3.1 ± 1.8 vs. IE 2.6 ± 2.3 μmol 100 g-1 min-1, P = 0.641) as well as the delivery of oxygen, glucose, and FAU, as well as respiratory quotient were not different between the groups. However, FAU was significantly higher in EF than IE (3.1 ± 1.7 vs. 1.7 ± 0.6 μmol 100 g-1 min-1, P = 0.047) and it also correlated with ME (r = 0.56, P = 0.024) in the entire study group. EF group also demonstrated higher use of plasma FAU than IE, but no differences in use of plasma glucose and intramuscular energy sources were observed between the groups. These findings suggest that the effective use of plasma FAU is an important determinant of ME during exercise.

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.

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

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, whole-muscle morphology, and muscle fiber histochemistry of musculus vastus lateralis. Normalized sprint performance was explained by percentage of fast-type fibers and muscle volume ( R² = 0.65; P < 0.001) and normalized endurance performance by performance oxygen consumption ( V̇o₂), mean corpuscular hemoglobin concentration, and muscle oxygenation ( R² = 0.92; P < 0.001). Combined sprint and endurance performance was explained by gross efficiency, performance V̇o₂, and likely by muscle volume and fascicle length ( P = 0.056; P = 0.059). High performance V̇o₂ related to a high oxidative capacity, high capillarization × myoglobin, and small physiologic cross-sectional area ( R² = 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.

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.

Aerobic and strength training in concomitant metabolic syndrome and type 2 diabetes

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 and 95% confidence interval [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 to -0.21) and AER + RES (-0.79, 95% CI = -1.40 to -0.35), both being significant (P ≤ 0.02) versus control (0.26, 95% CI = -0.58 to 0.40) and RES (-0.13, 95% CI = -1.00 to 0.24). This led 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 to -0.19) than VO2peak (5%-6%; r = -0.24, 95% CI = -0.45 to -0.01).

CONCLUSIONS

AER and AER + RES training significantly improved 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.

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.

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

PURPOSE

To evaluate whether gross efficiency (GE), determined during submaximal cycling, is lower after time trials and if the magnitude of the decrease differs in relation to race distance. Secondary purposes were to study the rate of the decline in GE and whether changes in muscle-fiber recruitment could explain the decline.

METHODS

Cyclists completed 9 GE tests consisting of submaximal exercise performed before and after time trials of different length (500 m, 1000 m, 2000 m, 4000 m, 15,000 m, and 40,000 m). In addition, subjects performed time trials as if they were a 1000-m, 4000-m, or 40,000-m time trial during which they were stopped at 50% of the final time of the preceding "full" time trial. Power output, gas exchange, and EMG were measured continuously throughout the GE tests.

RESULTS

A significant interaction effect between distance and time was found for GE (P = .001). GE was significantly lower immediately after the time trials than before (P < .05), and the decline in GE differed between distances (P < .001). GE seemed to decline linearly during the relatively short trials, while it declined more hyperbolically during the 40,000-m. A significant effect of time (P = .04) on mean EMG amplitude was found. However, post hoc comparisons showed no significant differences in mean EMG amplitude between the different time points (before and after the time trials).

CONCLUSION

GE decreases during time-trial exercise. Unfortunately, the cause of the decrease remains uncertain. Future modeling studies should consider using a declining instead of a constant GE. In sport situations, the declining GE has to be taken into account when selecting a pacing strategy.

Determining Anaerobic Capacity in Sporting Activities

Anaerobic capacity/anaerobically attributable power is an important parameter for athletic performance, not only for short high-intensity activities but also for breakaway efforts and end spurts during endurance events. Unlike aerobic capacity, anaerobic capacity cannot be easily quantified. The 3 most commonly used methodologies to quantify anaerobic capacity are the maximal accumulated oxygen deficit method, the critical power concept, and the gross efficiency method. This review describes these methods, evaluates if they result in similar estimates of anaerobic capacity, and highlights how anaerobic capacity is used during sporting activities. All 3 methods have their own strengths and weaknesses and result in more or less similar estimates of anaerobic capacity but cannot be used interchangeably. The method of choice depends on the research question or practical goal.