This study aimed to determine whether the fatigue induced by a mountain ultramarathon (MUM) led to changes in energy cost and kinematic during level and graded running. Pre- and post-race, 14 ultratrail runners ran on a level, uphill (5%) and downhill (5%) treadmill at 10 km · h(-1). Kinematic data were acquired using a photocell system. Post-race, the downhill energy cost increased by 13.1% (P < 0.001). No change was noted in level and uphill running. Duty factor and stride frequency were increased, whereas swing time, cycle time and stride length were decreased in all conditions (P < 0.05). Contact time was increased and the rate of force generation was decreased only in the uphill and downhill conditions (P < 0.05). Positive correlations were observed between performance time and the pre- to post-changes in the energy cost of level (r = 0.52, P = 0.04) and uphill running (r = 0.50, P = 0.04). MUM-induced fatigue resulted in physiological and spatiotemporal changes, though the response to fatigue varied considerably between running conditions. These changes resulted in a significant increment only in the downhill energy cost. Incorporating downhill locomotion in the training programmes of ultratrailers may help to improve performance-related physiological and biomechanical parameters.

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 VO 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 VO (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.

Background: Acute exercise leads to an immediate drop in blood pressure (BP), also called post-exercise hypotension (PEH). Exercise in hypoxia is related to additional vasodilation, potentially contributing to more profound PEH. Therefore, we investigated the impact of hypoxia versus normoxia on the magnitude of PEH. Second, we examined whether these changes in PEH relate to the BP-lowering effects of 12-week exercise training under hypoxia. Methods: In this prospective study, 21 healthy individuals (age 22.2 ± 3.0 years, 14 male) performed a 45-minute high-intensity running exercise on 2 different days in a random order, under hypoxia (fraction of inspired oxygen 14.5%) and normoxia (fraction of inspired oxygen 20.9%). BP was examined pre-exercise ( t = 0) and at t = 15, t = 30, t = 45, and t = 60 minutes post-exercise. Afterward, subjects took part in a 12-week hypoxic running exercise training program. Resting BP was measured before and after the 12-week training program. Results: Acute exercise induced a significant decrease in systolic BP (systolic blood pressure [SBP], P = .001), but not in diastolic BP (diastolic blood pressure [DBP], P = .113). No significant differences were observed in post-exercise BP between hypoxic and normoxic conditions (SBP, P = .324 and DBP, P = .204). Post-exercise changes in SBP, DBP, and mean arterial pressure significantly correlated to the 12-week exercise training-induced changes in SBP ( r = 0.557, P = .001), DBP ( r = 0.615, P < .001), and mean arterial pressure ( r = 0.458, P = .011). Conclusion: Our findings show that hypoxia does not alter the magnitude of PEH in healthy individuals, whilst PEH relates to the BP-lowering effects of exercise. These data highlight the strong link between acute and chronic changes in BP.

First and second ventilatory thresholds (VT 1 and VT 2 ) represent the boundaries of the moderate-heavy and heavy-severe exercise intensity. Currently, VTs are primarily detected visually from cardiopulmonary exercise test (CPET) data, beginning with an initial data screening followed by data processing and statistical analysis. Automated VT detection is a challenging task owing to the high signal to noise ratio typical of CPET data. Recurrent neural networks describe a machine learning form of Artificial Intelligence that can be used to uncover complex non-linear relationships between input and output variables. Here we proposed detection of VTs using a single neural network classifier, trained with a database of 228 laboratory CPET data. We tested the neural network performance against the judgement of 7 couples of board-certified exercise-physiologists on 25 CPET tests. The neural network achieved expert-level performances across the tasks (mean absolute error was 9.5% (r = 0.79) and 4.2% (r = 0.94) for VT 1 and VT 2 , respectively). Estimation errors are compatible with the typical error of the current gold standard visual methodology. The neural network demonstrated VT detecting and exercise intensity level classifying at a high competence level. Neural networks could potentially be embedded in CPET hardware/software to extend the reach of exercise physiologists beyond their laboratories.

Affected fast-phase, slow-phase HRR and HRV indices suggested delayed parasympathetic reactivation and sympathetic withdrawal after maximal exercise in hypoxia. However, a similar cardiac autonomic recovery was re-established within 5 min after exercise cessation. These findings have several implications in cardiac autonomic recovery interpretation and in HR assessment in response to high-intensity hypoxic exercise.

No abstract

We are thankful to Amedeo Setti (ProM Facility, Trentino Sviluppo) for developing the web-based applications and for managing the data collection and storage on the cluster of servers.We are thankful to the CARITRO Foundation for partially supporting this project and for establishing the Deep Learning Lab at the ProM Facility (Trentino Sviluppo). Appreciation is expressed to Filippo Degasperi for partially funding the Oxynet web-application development within the "Restitution Project". Author contributionAZ and AF conceived of the original idea and drafted the manuscript. AZ developed the theory and performed the computations. PR assisted AZ in the creation of the models and contributed to the interpretation of the results. A.F., V.M., L.P.T., D.A.L., F.Y.F., D.B., M.P., S.R.D. and L.M. supervised and carried out the experiments, contributed to sample preparation and results interpretation. All authors discussed the results and contributed to the final manuscript.

Measurement of oxygen uptake during exercise ( _ VO 2 ) is currently non-accessible to most individuals without expensive and invasive equipment. The goal of this pilot study was to estimate cycling _ VO 2 from easy-to-obtain inputs, such as heart rate, mechanical power output, cadence and respiratory frequency. To this end, a recurrent neural network was trained from laboratory cycling data to predict _ VO 2 values. Data were collected on 7 amateur cyclists during a graded exercise test, two arbitrary protocols (Prot-1 and -2) and an "all-out" Wingate test. In Trial-1, a neural network was trained with data from a graded exercise test, Prot-1 and Wingate, before being tested against Prot-2. In Trial-2, a neural network was trained using data from the graded exercise test, Prot-1 and 2, before being tested against the Wingate test. Two analytical models (Models 1 and 2) were used to compare the predictive performance of the neural network. Predictive performance of the neural network was high during both Trial-1 (MAE = 229(35) mlO 2 min -1 , r = 0.94) and Trial-2 (MAE = 304(150) mlO 2 min -1 , r = 0.89). As expected, the predictive ability of Models 1 and 2 deteriorated from Trial-1 to Trial-2. Results suggest that recurrent neural networks have the potential to predict the individual _ VO 2 response from easy-to-obtain inputs across a wide range of cycling intensities.