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 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.

The problem of the automatic determination of the first and second ventilatory thresholds (VT1 and VT2) from cardiopulmonary exercise test (CPET) still leads to controversy. The reliability of the gold standard methodology (i.e. expert visual inspection) feeds into the debate and several authors call for more objective automatic methods to be used in the clinical practice. In this study, we present a framework based on a collaborative approach, where a web‐application was used to crowd‐source a large number (1245) of CPET data of individuals with different aerobic fitness. The resulting database was used to train and test an artificial intelligence (i.e. a convolutional neural network) algorithm. This automatic classifier is currently implemented in another web‐application and was used to detect the ventilatory thresholds in the available CPET. A total of 206 CPET were used to evaluate the accuracy of the estimations against the expert opinions. The neural network was able to detect the ventilatory thresholds with an average mean absolute error of 178 (198) mlO₂/min (11.1%, r = 0.97) and 144 (149) mlO₂/min (6.1%, r = 0.99), for VT1 and VT2 respectively. The performance of the neural network in detecting VT1 deteriorated in case of individuals with poor aerobic fitness. Our results suggest the potential for a collective intelligence system to outperform isolated experts in ventilatory thresholds detection. However, the inclusion of a larger number of VT1 examples certified by a community of experts will be likely needed before the abilities of this collective intelligence can be translated into the clinical use of CPET.

First and second ventilatory thresholds (VT₁ and VT₂) 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 VT₁ and VT₂, 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.

Exercising in natural environments (green exercise, GE) has been shown to offer significant physiological and psychological health benefits compared to urban or indoor environments. This study evaluated the restorative effects of a 1-h light-to-moderate intensity exercise session across three environments: natural (G), urban (U), and indoor (I). Using a randomized crossover design, 25 male participants (M = 26.3, SD = 4.3) completed a 1-h walk at 6 km/h in each setting. Psychological outcomes, including perceived restorativeness (PRS), restoration (ROS), emotional states, enjoyment, and behavioral intentions, were assessed with validated questionnaires. Physiological measures (cortisol, heart rate, heart rate variability) were collected pre- and post-intervention. Results showed that G environment consistently elicited greater relaxation, higher positive emotions, and lower negative emotions compared to U and I. Restoration outcomes (PRS, ROS), enjoyment and intentions to exercise were significantly higher in G, while perceived exertion was lower in G compared to I. Physiologically, cortisol levels, heart rate, and heart rate variability differed by environment, with G promoting a more favorable recovery profile than U and I. No interaction effects were observed for physiological measures, suggesting consistent recovery patterns over time. These findings highlight the restorative and stress-relieving potential of GE, emphasizing its role in enhancing mental well-being and supporting physical activity adherence. The study underscores the importance of natural environments as a resource for promoting health and well-being, while also identifying the need for further research to clarify the nuanced differences between urban and natural settings.