Physiological intensity profile,exercise load and performance predictors of a 65-km mountain ultra-marathon |
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| Authors: | Alessandro Fornasiero Aldo Savoldelli Damiano Fruet Gennaro Boccia Barbara Pellegrini Federico Schena |
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| Institution: | 1. CeRiSM, Sport Mountain and Health Research Center, University of Verona, Rovereto, Italy;2. Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy;3. NeuroMuscular Function research group, School of Exercise and Sport Sciences, Department of Medical Sciences, University of Turin, Turin, Italy |
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| Abstract: | The aims of the study were to describe the physiological profile of a 65-km (4000-m cumulative elevation gain) running mountain ultra-marathon (MUM) and to identify predictors of MUM performance. Twenty-three amateur trail-runners performed anthropometric evaluations and an uphill graded exercise test (GXT) for VO2max, ventilatory thresholds (VTs), power outputs (PMax, PVTs) and heart rate response (HRmax, HR@VTs). Heart rate (HR) was monitored during the race and intensity was expressed as: Zone I (VT2) for exercise load calculation (training impulse, TRIMP). Mean race intensity was 77.1%±4.4% of HRmax distributed as: 85.7%±19.4% Zone I, 13.9%±18.6% Zone II, 0.4%±0.9% Zone III. Exercise load was 766±110 TRIMP units. Race time (11.8±1.6h) was negatively correlated with VO2max (r = ?0.66, P <0.001) and PMax (r = ?0.73, P <0.001), resulting these variables determinant in predicting MUM performance, whereas exercise thresholds did not improve performance prediction. Laboratory variables explained only 59% of race time variance, underlining the multi-factorial character of MUM performance. Our results support the idea that VT1 represents a boundary of tolerable intensity in this kind of events, where exercise load is extremely high. This information can be helpful in identifying optimal pacing strategies to complete such extremely demanding MUMs. |
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| Keywords: | Mountain ultra-marathon heart rate exercise intensity distribution training load thresholds |
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