Search Results (25)

Objectives: The aim of this study was to investigate the association between the effect of respiratory muscle strength parameters obtained from endurance athletes and their aerobic capacity levels. Methods: A total of 70 endurance athletes (23 women and 47 men) voluntarily participated in the study. Respiratory muscle strength was measured using a digital spirometer. Maximum VO2 was evaluated using the cardiopulmonary exercise test system (Cosmed K5). Results: As a result of the study, it was determined that in female endurance athletes, Maximum Inspiratory Pressure (MIP) and Maximum Expiratory Pressure (MEP) values were related to the End-Tidal Carbon Dioxide Pressure (PETCO2) value at maximum load. In male endurance athletes, it was determined that MEP values were related to PETCO2 values at maximum load, End-Tidal Oxygen Pressure (PETO2) values at maximum load, MaxVO2 values, VO2 values at RCP, and VO2 values at Ventilatory Threshold (VT). In addition, it was determined that in male endurance athletes, the MIP value was related to the VCO2 value at Ventilatory Threshold (RCP) and the VTidal value at maximum load. The other sub-parameters of maximum oxygen consumption (VO2) examined were not found to be related to respiratory muscle strength. Conclusions: The present findings indicate that respiratory muscle strength may be associated with selected cardiopulmonary exercise variables in endurance athletes. However, because this was a cross-sectional study, no causal inference can be made. Prospective and intervention studies are required to clarify these associations. Full article

Background/Objectives: Additional load is associated with a significant increase in energy expenditure during soldiers’ movement. The level of energy expenditure during military tasks depends on the speed at which soldiers move. The aim of the study was to determine how the speed of movement and military load in the form of a 20-kg backpack affect the energy expenditure of special forces operators. Methods: The study included a group of 24 special forces operators. The energy expenditure of participants was measured using a portable Cosmed K5 gas analyzer operating in “breath-by-breath” mode. Energy expenditure was calculated based on VO₂ and VCO₂ data. Respiratory exchange ratio (RER) was recorded in parallel with VO₂ and VCO₂ and used to calculate the oxidation of energy substrates. The soldiers moved in 6-min intervals at the following speeds: 4.5 km/h, 6.5 km/h, 8.5 km/h, 10.5 km/h. First, the soldiers covered each speed without a load, and then with a 20 kg tactical military backpack. Results: The analysis showed that increasing speed and the use of an external load significantly increased all physiological and metabolic responses. Speed × load interactions were observed for some metabolic variables, whereas no such interactions were found for heart rate. Conclusions: Adding a 20 kg tactical backpack causes a significant increase in energy expenditure at all speed levels. The additional load resulted in an average increase of 10% in heart rate (%HRmax) and 20% in oxygen uptake (%VO₂max). A more than threefold increase in energy expenditure was recorded (14.77 kcal/min without load and 17.70 kcal/min with a backpack at a speed of 10.5 km/h vs. 5.01 kcal/min and 6.35 kcal/min at a speed of 4.5 km/h). Full article

An integrated physiological model would be useful for monitoring internal load in functional fitness, including formats like CrossFit and Hyrox. Traditional performance metrics often neglect internal strain, energy system engagement, and neuromuscular fatigue, central to these modalities. Oxygen uptake kinetics, metabolic profiling, heart rate and heart rate variability monitoring, and neuromuscular fatigue assessment can be employed for load monitoring. Breath-by-breath oxygen uptake analysis characterizes aerobic activation and recovery. Metabolic stress is estimated via indirect calorimetry and capillary blood lactate to quantify oxidative, glycolytic, and phosphagen contributions. Heart rate is tracked continuously to assess session intensity, while heart rate variability provides insights into autonomic recovery. Neuromuscular fatigue can be assessed via countermovement jump performance, offering sensitive measures of recovery and training tolerance. Portable tools such as the Cosmed K5™, Lactate Pro 2, heart rate sensors, and force platforms support real-time monitoring in training and competitions. Rather than advocating for the continuous use of advanced tools, the model promotes strategic integration of high-precision methods for research, and practical, low-cost alternatives (e.g., heart rate monitoring, session rating of perceived exertion, or jump analysis apps) for day-to-day coaching. This approach enables early detection of maladaptation, supports individualized training adjustments, and improves safety and performance outcomes. Ultimately, this framework bridges physiological science and real-world practice, providing value across both applied and research settings. Full article

Background: The accuracy of measurement of cardiometabolic outcomes in terms of gaseous exchange and energy expenditure of individuals is crucial. The objective of this study was to compare the validity and reliability of the PNO$\overline{\mathrm{E}}$ in measuring cardiometabolic outcomes from the respiratory gaseous exchange of healthy individuals during treadmill walking exercise. Methods: A total of 21 healthy subjects (15 male and 6 female) aged 22.76 ± 3.85 years took part in this study. Oxygen uptake (VO₂), carbon dioxide production (VCO₂), respiratory exchange ratio (RER), metabolic equivalents (METs), tidal volume (VT), and energy expenditure (EE) were measured using the PNO$\overline{\mathrm{E}}$ and COSMED K5 portable systems during a twenty-eight-minute, four-stage incremental protocol, where speed increased from 1.7 mph to 4.2 mph with a 2% incline on a treadmill. Test–retest reliability was tested on separate days with trail repetition. Validity was evaluated by Bland–Altman plots, intraclass correlation coefficients (ICCs) and mean percentage difference. Results: ICCs showed that VCO₂ was in the good range (0.75–0.90). The ICC of the RER from stages 1 to 3 of the incremental protocol and the VT from stages 2 to 4 of the incremental protocol showed good to excellent reliability. No clear trend was seen for VO₂, VCO₂, and EE datapoints with variations in speed. Pearson’s correlation coefficients were moderately high (r = 0.60–0.79) between VO₂, VCO₂, RER, METs, VT, and EE measured by the PNO$\overline{\mathrm{E}}$ and K5 systems. All subjects, except for a few cases in VT, were within the upper and lower 95% confidence intervals of the acceptable range of the Bland–Altman plots. Conclusions: The PNO$\overline{\mathrm{E}}$ system is a valid and reliable measure of cardiometabolic outcomes and is comparable to the COSMED K5 system. Full article

Background: Extensive data exists on external load during training and competition, but a significant gap remains in understanding internal physiological load, particularly in protocols conducted in ecological settings. Given the scarcity of studies on the on-field cardiorespiratory profiles of national-level athletes, especially in Argentine soccer, this study aimed to identify the on-field cardiorespiratory fitness profile of ten highly trained youth field soccer players (13.6 ± 1.3 years old) from both the first league of the Argentine Football Association and members of the national team in their age group category in the current year. Methods: Each athlete performed an on-field cardiorespiratory exercise test (20-m Shuttle Run Test, 20-m SRT) with the COSMED K5 wearable metabolic system (COSMED, Rome, Italy) in dynamic micro-mixing chamber mode. The 20-m Shuttle Run Test involves running back and forth between two lines set 20 m apart, following the pace set by an audio signal. The test starts at a running velocity of 8.5 km·h⁻¹ and increases by 0.5 km·h⁻¹ each min. Results: Mean velocity at maximal oxygen uptake (v$\dot{\mathrm{V}}$O_2max) was 12.3 ± 0.7 km·h⁻¹. The maximal oxygen uptake ($\dot{\mathrm{V}}$O_2max) on-field was 67.1 ± 5.3 mL·kg⁻¹·min⁻¹. The $\dot{\mathrm{V}}$O₂ at the first and second ventilatory thresholds (VT1 and VT2) were identified at 67.0 ± 3.0% $\dot{\mathrm{V}}$O_2max (44.9 ± 3.3 mL·kg⁻¹·min⁻¹) and 84.7 ± 3.7% $\dot{\mathrm{V}}$O_2max (56.8 ± 3.8 mL·kg⁻¹·min⁻¹), respectively. Conclusions: This is a scarce on-field gas exchange assessment, conducted in an ecological context using a portable analyzer with highly trained national-level youth soccer players from the Argentine youth national team, which underlines their cardiorespiratory fitness, showcases their high-performance potential, offers valuable insights into a selective group of players, and provides a reference for larger-scale research on elite youth soccer and the long-term development of aerobic power and capacity. Full article

Monitoring physiological parameters is vital for tracking swimmers’ progress and performance. This study examines an elite male swimmer’s nutrition during his preparation for the 2024 Paris Olympics, considering his metabolic rate and body composition. His resting energy needs (2905 ± 407.99 kcal/day) were measured using indirect Cosmed K5, calorimetry, and body composition determined through skinfold measurements. Nutrition plans were developed using software, varying with his training intensity—providing 2910 ± 379 kcal/day on rest days, and 4238 ± 562 kcal/day on intense days. The analysis of the correlations between key variables revealed strong and diverse interactions among anthropometric, metabolic data, and energy substrates. Thus, weight exhibited a very strong positive correlation with lean mass (FFM), indicating that higher weight is associated with increased lean mass. Conversely, the moderate correlation between weight and body fat percentage suggests a weaker association. The amount of skin folds accurately reflects the body fat percentage. Ensuring that a high-energy dietary intake aligned with his actual needs throughout the season was crucial for sustaining performance. Experimenting with fueling and recovery tactics during smaller competitions enabled the athlete to meet energy and nutrient demands at the elite level. Full article

Background: Research into key performance factors in trail running, particularly in vertical kilometer (VK) races, is crucial for effective training and periodization. However, recent studies on metabolic and cardiorespiratory responses during VK races, especially using field tests, are limited. Objectives: Therefore, the aim of this study is to evaluate the metabolic and cardiorespiratory responses during a VK field test, identifying differences based on sex and performance level, as well as key performance factors and their deterioration due to fatigue. Fifteen trained trail runners (ten males and five females, 19 to 38 years old) perform a VK race. Methods: The global physiological response is evaluated using the portable gas analyzer Cosmed K5 and the local response using near-infrared spectroscopy technology. Results: In gender comparisons, the ANCOVA test shows significant differences (p < 0.05) in the ventilation, tidal volume, expiratory time-to-inspiratory time ratio, inspiratory flow rate, end-tidal CO₂ partial pressure, heart rate, oxygen pulse, and total hemoglobin. Additionally, the performance comparison reveals significant differences in the variables’ velocity, oxygen consumption, carbon dioxide production, ventilation, dead space-to-tidal volume ratio, total time of the breathing cycle, expiratory time-to-inspiratory time ratio, inspiratory duty cycle, expiratory fractions of CO₂, quadriceps saturation index, and VE/VCO₂ ratio. Finally, the correlation analysis shows oxygen consumption (r = −0.80 mean; r = −0.72 peak), carbon dioxide production (r = −0.91 mean; r = −0.75 peak), expiratory time-to-inspiratory time ratio (r = 0.68 peak), ventilation (r = −0.58 mean), and quadriceps saturation index (r = 0.54 mean; r = −0.76 coefficient of variation) as the key performance factors in the VK race. Conclusions: Overall, the physiological analysis indicates the importance of local muscular adaptations and respiratory system capacity in this type of short-duration race. Full article

Background: The biomechanical and physiological adaptations to resisted running have been well documented in sprinting; however, their impact at submaximal speeds, such as those typical of long-distance running, remains unclear. This study aimed to evaluate the impact of running with a weighted vest, loaded with 5% and 10% of body mass, on the physiological and mechanical variables of trained trail runners. Methods: Fifteen male trail runners completed an incremental protocol to exhaustion on a treadmill with 0%, 5%, and 10% of their body mass (BM), in random order, with one week of separation between the tests. The maximality of the test was confirmed by measuring lactate concentrations at the end of the test. Oxygen consumption ($\dot{\mathrm{V}}$O₂) and respiratory exchange ratio (RER) were recorded using a portable gas analyzer (Cosmed K5), and ventilatory thresholds 1 and 2 (VT1, VT2) were calculated individually. Running power was averaged for each speed stage using the Stryd device. Finally, the peak values and those associated with VT1 and VT2 for speed, power (absolute and normalized by body mass), $\dot{\mathrm{V}}$O₂, RER, and the cost of transport (CoT) were included in the analysis. Results: One-way repeated-measures ANOVA revealed a detrimental effect of the extra load on maximum speed and speed at ventilatory thresholds (p ≤ 0.003), with large effect sizes (0.34–0.62) and a nonlinear trend detected in post hoc analysis. Conclusions: Using running power to control the intensity of effort while carrying extra weight provides a more stable metric than speed, particularly at aerobic intensities. Future research in trail running should investigate the effects of weighted vests across various terrains and slopes. Full article

Our study aimed to validate existing equations and develop the new NRG_CO equation to estimate resting energy expenditure (REE) in the Colombian population with moderate-to-high physical activity levels. Upon satisfying the inclusion criteria, a total of 86 (43F, 43M) healthy adults (mean [SD]: 27.5 [7.7] years; 67.0 [13.8] kg) were evaluated for anthropometric variables and REE by indirect calorimetry using wearable gas analyzers (COSMED K4 and K5). Significant positive correlations with REE were found for body mass (r = 0.65), body mass-to-waist (r = 0.58), arm flexed and tensed girth (r = 0.66), corrected thigh girth (r = 0.56), corrected calf girth (r = 0.61), and sum of breadths (∑3D, r = 0.59). As a novelty, this is the first time a significant correlation between REE and the sum of corrected girths (∑3CG, r = 0.63) is reported. Although existing equations such as Harris–Benedict (r = 0.63), Mifflin–St. Jeor (r = 0.67), and WHO (r = 0.64) showed moderate-to-high correlations with REE, the Bland-Altman analysis revealed significant bias (p < 0.05), indicating that these equations may not be valid for the Colombian population. Thus, participants were randomly distributed into either the equation development group (EDG, n = 71) or the validation group (VG, n = 15). A new model was created using body mass, sum of skinfolds (∑8S), corrected thigh, corrected calf, and age as predictors (r = 0.755, R² = 0.570, RMSE = 268.41 kcal). The new NRG_CO equation to estimate REE (kcal) is: 386.256 + (24.309 × BM) − (2.402 × ∑8S) − (21.346 × Corrected Thigh) + (38.629 × Corrected Calf) − (7.417 × Age). Additionally, a simpler model was identified through Bayesian analysis, including only body mass and ∑8S (r = 0.724, R² = 0.525, RMSE = 282.16 kcal). Although external validation is needed, our validation resulted in a moderate correlation and concordance (bias = 91.5 kcal) between measured and estimated REE values using the new NRG_CO equation. Full article

Lower limb exoskeletons have the potential to mitigate work-related musculoskeletal disorders; however, they often lack user-oriented control strategies. Human-in-the-loop (HITL) controls adapt an exoskeleton’s assistance in real time, to optimize the user–exoskeleton interaction. This study presents a HITL control for a knee exoskeleton using a CMA-ES algorithm to minimize the users’ physical effort, a parameter innovatively evaluated using the interaction torque with the exoskeleton (a muscular effort indicator) and metabolic cost. This work innovates by estimating the user’s metabolic cost within the HITL control through a machine-learning model. The regression model estimated the metabolic cost, in real time, with a root mean squared error of 0.66 W/kg and mean absolute percentage error of 26% (n = 5), making faster (10 s) and less noisy estimations than a respirometer (K5, Cosmed). The HITL reduced the user’s metabolic cost by 7.3% and 5.9% compared to the zero-torque and no-device conditions, respectively, and reduced the interaction torque by 32.3% compared to a zero-torque control (n = 1). The developed HITL control surpassed a non-exoskeleton and zero-torque condition regarding the user’s physical effort, even for a task such as slow walking. Furthermore, the user-specific control had a lower metabolic cost than the non-user-specific assistance. This proof-of-concept demonstrated the potential of HITL controls in assisted walking. Full article

Metabolic syndrome poses a significant health challenge worldwide, prompting the need for comprehensive strategies integrating physical activity monitoring and energy expenditure. Wearable sensor devices have been used both for energy intake and energy expenditure (EE) estimation. Traditionally, sensors are attached to the hip or wrist. The primary aim of this research is to investigate the use of an eyeglass-mounted wearable energy intake sensor (Automatic Ingestion Monitor v2, AIM-2) for simultaneous recognition of physical activity (PAR) and estimation of steady-state EE as compared to a traditional hip-worn device. Study data were collected from six participants performing six structured activities, with the reference EE measured using indirect calorimetry (COSMED K5) and reported as metabolic equivalents of tasks (METs). Next, a novel deep convolutional neural network-based multitasking model (Multitasking-CNN) was developed for PAR and EE estimation. The Multitasking-CNN was trained with a two-step progressive training approach for higher accuracy, where in the first step the model for PAR was trained, and in the second step the model was fine-tuned for EE estimation. Finally, the performance of Multitasking-CNN on AIM-2 attached to eyeglasses was compared to the ActiGraph GT9X (AG) attached to the right hip. On the AIM-2 data, Multitasking-CNN achieved a maximum of 95% testing accuracy of PAR, a minimum of 0.59 METs mean square error (MSE), and 11% mean absolute percentage error (MAPE) in EE estimation. Conversely, on AG data, the Multitasking-CNN model achieved a maximum of 82% testing accuracy in PAR, a minimum of 0.73 METs MSE, and 13% MAPE in EE estimation. These results suggest the feasibility of using an eyeglass-mounted sensor for both PAR and EE estimation. Full article

Ocean salinity is a pivotal aspect of the ocean dynamic environment. Spaceborne L-band radiometers, like SMOS, Aquarius, and SMAP, offer a comprehensive approach to mapping out large-scale ocean salinity patterns. As China prepares for the launch of the Chinese Ocean Salinity and Soil Moisture Mission (COSM), it is essential to delve into the intricacies of radio frequency interference (RFI) detection, localization, and mitigation. The L-band, in particular, is highly susceptible to RFI. COSM will carry not one but two advanced instruments: a 2-D L-band aperture synthesis microwave radiometer (LASMR) and a 1-D L-C-K band microwave imager combined active and passive (MICAP). This article delves into the current state of RFI research, particularly in recent years, and introduces a fusion method that integrates MICAP and LASMR for more accurate RFI detection, localization, and mitigation. This fusion method relies on an algorithm that constructs localization and intensity objective functions based on the principle of least squares. By optimizing these functions, we can pinpoint the precise location and intensity of RFI. The resulting minimum mitigation residual offers a blueprint for achieving optimal RFI detection, localization, and mitigation. The experimental results, achieved in a controlled anechoic chamber, confirm that this fusion method—when weighted by variance—boosts detection accuracy, refines localization precision, and minimizes residual mitigation errors compared with standalone MICAP or LASMR techniques. Full article

Cardiorespiratory fitness (CRF) is considered an important indicator of health in children and adults. The main contribution of this paper is an analysis of cardiorespiratory fitness predictive models among a population of healthy and young women, using the non-exercise variables. The study was conducted on a group of 154 healthy women (aged 20.3 ± 1.2) from selected academic centers in Poland. The VO${}_2$max was measured using a Cosmed K4b2 portable analyzer during a 20 m shuttle test. In addition, selected anthropomotor parameters including body composition components were measured for each subject. The participants’ leisure-time physical activity was assessed using the Minnesota Leisure-Time Physical Activity Questionnaire. The Ridge regression was the most accurate model for estimating VO${}_2$max from anthropometric parameters. The most accurate model based on the level of leisure-time physical activity was calculated using stepwise regression for which the prediction error was at the level of 6.68 (mL·kg${}^{-1}·$min${}^{-1}$). The best model calculated from all non-exercise variables (age, anthropometric parameters, and leisure-time physical activity) had only two predictors: waist circumference and total physical activity, and had a prediction error equal to 6.20 (mL·kg${}^{-1}·$min${}^{-1}$). Full article

New and promising variables are being developed to analyze performance and fatigue in trail running, such as mechanical power, metabolic power, metabolic cost of transport and mechanical efficiency. The aim of this study was to analyze the behavior of these variables during a real vertical kilometer field test. Fifteen trained trail runners, eleven men (from 22 to 38 years old) and four women (from 19 to 35 years old) performed a vertical kilometer with a length of 4.64 km and 835 m positive slope. During the entire race, the runners were equipped with portable gas analyzers (Cosmed K5) to assess their cardiorespiratory and metabolic responses breath by breath. Significant differences were found between top-level runners versus low-level runners in the mean values of the variables of mechanical power, metabolic power and velocity. A repeated-measures ANOVA showed significant differences between the sections, the incline and the interactions between all the analyzed variables, in addition to differences depending on the level of the runner. The variable of mechanical power can be statistically significantly predicted from metabolic power and vertical net metabolic COT. An algebraic expression was obtained to calculate the value of metabolic power. Integrating the variables of mechanical power, vertical velocity and metabolic power into phone apps and smartwatches is a new opportunity to improve performance monitoring in trail running. Full article

The energy consumption measurement is important to carry out a correct risk evaluation of workers during green area management with the scope to achieve workstyle improvements. In contrast to sporting activities, few studies have been conducted on the assessment of physical fatigue by determining the functional parameters of the human body and oxygen consumption in this sector. This study aims to measure the energy cost of weed control using a wearable telemetry system Cosmed K4. For this purpose, twelve male workers, grouped into three groups of four workers each, were monitored during the work of weed control carried out with a brush cutter (by testing three different cutting heads). The monitoring period lasted 18 min including a 5-min rest period at the end of work. This study shows how the use of facilitating tools such as brush cutters contributes to getting low energy metabolism rate values, in tests performed equally on average to 119 Wm⁻². Full article