Even Between-Lap Pacing Despite High Within-Lap Variation During Mountain Biking

Purpose: Given the paucity of research on pacing strategies during competitive events, this study examined changes in dynamic high-resolution performance parameters to analyze pacing profiles during a multiple-lap mountain-bike race over variable terrain. Methods: A global-positioning-system (GPS) unit (Garmin, Edge 305, USA) recorded velocity (m/s), distance (m), elevation (m), and heart rate at 1 Hz from 6 mountain-bike riders (mean ± SD age = 27.2 ± 5.0 y, stature = 176.8 ± 8.1 cm, mass = 76.3 ± 11.7 kg, VO2max = 55.1 ± 6.0 mL · kg–1 . min–1) competing in a multilap race. Lap-by-lap (interlap) pacing was analyzed using a 1-way ANOVA for mean time and mean velocity. Velocity data were averaged every 100 m and plotted against race distance and elevation to observe the presence of intralap variation. Results: There was no significant difference in lap times (P = .99) or lap velocity (P = .65) across the 5 laps. Within each lap, a high degree of oscillation in velocity was observed, which broadly reflected changes in terrain, but high-resolution data demonstrated additional nonmonotonic variation not related to terrain. Conclusion: Participants adopted an even pace strategy across the 5 laps despite rapid adjustments in velocity during each lap. While topographical and technical variations of the course accounted for some of the variability in velocity, the additional rapid adjustments in velocity may be associated with dynamic regulation of self-paced exercise

Metabolic Factors Limiting Performance in Marathon Runners

Each year in the past three decades has seen hundreds of thousands of runners register to run a major marathon. Of those who attempt to race over the marathon distance of 26 miles and 385 yards (42.195 kilometers), more than two-fifths experience severe and performance-limiting depletion of physiologic carbohydrate reserves (a phenomenon known as ‘hitting the wall’), and thousands drop out before reaching the finish lines (approximately 1–2% of those who start). Analyses of endurance physiology have often either used coarse approximations to suggest that human glycogen reserves are insufficient to fuel a marathon (making ‘hitting the wall’ seem inevitable), or implied that maximal glycogen loading is required in order to complete a marathon without ‘hitting the wall.’ The present computational study demonstrates that the energetic constraints on endurance runners are more subtle, and depend on several physiologic variables including the muscle mass distribution, liver and muscle glycogen densities, and running speed (exercise intensity as a fraction of aerobic capacity) of individual runners, in personalized but nevertheless quantifiable and predictable ways. The analytic approach presented here is used to estimate the distance at which runners will exhaust their glycogen stores as a function of running intensity. In so doing it also provides a basis for guidelines ensuring the safety and optimizing the performance of endurance runners, both by setting personally appropriate paces and by prescribing midrace fueling requirements for avoiding ‘the wall.’ The present analysis also sheds physiologically principled light on important standards in marathon running that until now have remained empirically defined: The qualifying times for the Boston Marathon

A 1-Year Study of Endurance Runners: Training, Laboratory Tests, and Field Tests

Purpose: To compare critical speed (CS) measured from a single-visit field test of the distance–time relationship with the “traditional” treadmill time-to-exhaustion multivisit protocol. Methods: Ten male distance runners completed treadmill and field tests to calculate CS and the maximum distance performed above CS (D′). The field test involved 3 runs on a single visit to an outdoor athletics track over 3600, 2400, and 1200 m. Two field-test protocols were evaluated using either a 30-min recovery or a 60-min recovery between runs. The treadmill test involved runs to exhaustion at 100%, 105%, and 110% of velocity at VO2max, with 24 h recovery between runs. Results: There was no difference in CS measured with the treadmill and 30-min- and 60-minrecovery field tests (P .05). A typical error of the estimate of 0.14 m/s (95% confidence limits 0.09–0.26 m/s) was seen for CS and 88 m (95% confidence limits 60–169 m) for D′. A coefficient of variation of 0.4% (95% confidence limits: 0.3–0.8%) was found for repeat tests of CS and 13% (95% confidence limits 10–27%) for D′. Conclusion: The single-visit method provides a useful alternative for assessing CS in the field

How Do Humans Control Physiological Strain during Strenuous Endurance Exercise?

Background: Methodology/principal Findings: Conclusions/significance: Distance running performance is a viable model of human locomotion.To evaluate the physiologic strain during competitions ranging from 5-100 km, we evaluated heart rate (HR) records of competitive runners (n = 211). We found evidence that: 1) physiologic strain (% of maximum HR (%HRmax)) increased in proportional manner relative to distance completed, and was regulated by variations in running pace; 2) the %HRmax achieved decreased with relative distance; 3) slower runners had similar %HRmax response within a racing distance compared to faster runners, and despite differences in pace, the profile of %HRmax during a race was very similar in runners of differing ability; and 4) in cases where there was a discontinuity in the running performance, there was evidence that physiologic effort was maintained for some time even after the pace had decreased.The overall results suggest that athletes are actively regulating their relative physiologic strain during competition, although there is evidence of poor regulation in the case of competitive failures.2.308 SJR (2008) Q1, 60/1774 Medicine (miscellaneous), 19/144 Biochemistry, genetics and molecular biology (miscellaneous), 15/175 Agricultural and biological sciences (miscellaneous)UE

Is Body Fat a Predictor of Race Time in Female Long-Distance Inline Skaters?

Purpose: The aim of this study was to evaluate predictor variables of race time in female ultra-endurance inliners in the longest inline race in Europe. Methods: We investigated the association between anthropometric and training characteristics and race time for 16 female ultraendurance inline skaters, at the longest inline marathon in Europe, the ‘Inline One-eleven’ over 111 km in Switzerland, using bi- and multivariate analysis. Results: The mean (SD) race time was 289.7 (54.6) min. The bivariate analysis showed that body height (r=0.61), length of leg (r=0.61), number of weekly inline skating training sessions (r=-0.51)and duration of each training unit (r=0.61) were significantly correlated with race time. Stepwise multiple regressions revealed that body height, duration of each training unit, and age were the best variables to predict race time. Conclusion: Race time in ultra-endurance inline races such as the ‘Inline One-eleven’ over 111 km might be predicted by the following equation (r2 = 0.65): Race time (min) = -691.62 + 521.71 (body height, m) + 0.58 (duration of each training unit, min) + 1.78 (age, yrs) for female ultra-endurance inline skaters

High Resolution MEMS Accelerometers to Estimate VO2 and Compare Running Mechanics between Highly Trained Inter-Collegiate and Untrained Runners

BACKGROUND: The purposes of this study were to determine the validity and reliability of high resolution accelerometers (HRA) relative to VO(2) and speed, and compare putative differences in HRA signal between trained (T) and untrained (UT) runners during treadmill locomotion. METHODOLOGY: Runners performed 2 incremental VO(2max) trials while wearing HRA. RMS of high frequency signal from three axes (VT, ML, AP) and the Euclidean resultant (RES) were compared to VO(2) to determine validity and reliability. Additionally, axial rms relative to speed, and ratio of axial accelerations to RES were compared between T and UT to determine if differences in running mechanics could be identified between the two groups. PRINCIPAL FINDINGS: Regression of RES was strongly related to VO(2), but T was different than UT (r = 0.96 vs 0.92; p<.001) for walking and running. During walking, only the ratio of ML and AP to RES were different between groups. For running, nearly all acceleration parameters were lower for T than UT, the exception being ratio of VT to RES, which was higher in T than UT. All of these differences during running were despite higher VO(2), O(2) cost, and lower RER in T vs UT, which resulted in no significant difference in energy expenditure between groups. CONCLUSIONS/SIGNFICANCE: These results indicate that HRA can accurately and reliably estimate VO(2) during treadmill locomotion, but differences exist between T and UT that should be considered when estimating energy expenditure. Differences in running mechanics between T and UT were identified, yet the importance of these differences remains to be determined

The kinetics of lactate production and removal during whole-body exercise

<p>Abstract</p> <p>Background</p> <p>Based on a literature review, the current study aimed to construct mathematical models of lactate production and removal in both muscles and blood during steady state and at varying intensities during whole-body exercise. In order to experimentally test the models in dynamic situations, a cross-country skier performed laboratory tests while treadmill roller skiing, from where work rate, aerobic power and blood lactate concentration were measured. A two-compartment simulation model for blood lactate production and removal was constructed.</p> <p>Results</p> <p>The simulated and experimental data differed less than 0.5 mmol/L both during steady state and varying sub-maximal intensities. However, the simulation model for lactate removal after high exercise intensities seems to require further examination.</p> <p>Conclusions</p> <p>Overall, the simulation models of lactate production and removal provide useful insight into the parameters that affect blood lactate response, and specifically how blood lactate concentration during practical training and testing in dynamical situations should be interpreted.</p

Pulmonary oxygen uptake and muscle deoxygenation kinetics during recovery in trained and untrained male adolescents

Previous studies have demonstrated faster pulmonary oxygen uptake ( V ˙ O 2 ) kinetics in the trained state during the transition to and from moderate-intensity exercise in adults. Whilst a similar effect of training status has previously been observed during the on-transition in adolescents, whether this is also observed during recovery from exercise is presently unknown. The aim of the present study was therefore to examine V ˙ O 2 kinetics in trained and untrained male adolescents during recovery from moderate-intensity exercise. 15 trained (15 ± 0.8 years, V ˙ O 2max 54.9 ± 6.4 mL kg−1 min−1) and 8 untrained (15 ± 0.5 years, V ˙ O 2max 44.0 ± 4.6 mL kg−1 min−1) male adolescents performed two 6-min exercise off-transitions to 10 W from a preceding “baseline” of exercise at a workload equivalent to 80% lactate threshold; V ˙ O 2 (breath-by-breath) and muscle deoxyhaemoglobin (near-infrared spectroscopy) were measured continuously. The time constant of the fundamental phase of V ˙ O 2 off-kinetics was not different between trained and untrained (trained 27.8 ± 5.9 s vs. untrained 28.9 ± 7.6 s, P = 0.71). However, the time constant (trained 17.0 ± 7.5 s vs. untrained 32 ± 11 s, P < 0.01) and mean response time (trained 24.2 ± 9.2 s vs. untrained 34 ± 13 s, P = 0.05) of muscle deoxyhaemoglobin off-kinetics was faster in the trained subjects compared to the untrained subjects. V ˙ O 2 kinetics was unaffected by training status; the faster muscle deoxyhaemoglobin kinetics in the trained subjects thus indicates slower blood flow kinetics during recovery from exercise compared to the untrained subjects