This study aimed at: (1) Reporting COVID‐19 symptoms and duration in professional football players; (2) comparing players’ pulmonary function before and after COVID‐19; (3) comparing players’ metabolic power (P_met) before and after COVID‐19. Thirteen male players (Age: 23.9 ± 4.0 years, V̇O_2peak: 49.7 ± 4.0 mL/kg/min) underwent a medical screening and performed a running incremental step test and a spirometry test after COVID‐19. Spirometric data were compared with the ones collected at the beginning of the same season. Players’ mean P_met of the 10 matches played before COVID‐19 was compared with mean P_met of the 10 matches played after COVID‐19. Players completed a questionnaire on COVID‐19 symptoms and duration 6 months following the disease. COVID‐19 positivity lasted on average 15 ± 5 days. “General fatigue” and “muscle fatigue” symptoms were reported by all players during COVID‐19 and persisted for 77% (general fatigue) and 54% (muscle fatigue) of the players for 37 ± 28 and 38 ± 29 days after the disease, respectively. No significant changes in spirometric measurements were found after COVID‐19, even though some impairments at the individual level were observed. Conversely, a linear mixed‐effects model analysis showed a significant reduction of P_met (−4.1 ± 3.5%) following COVID‐19 (t = −2.686, p < 0.05). “General fatigue” and “muscle fatigue” symptoms may persist for several weeks following COVID‐19 in professional football players and should be considered for a safer return to sport. Players’ capacity to compete at high intensities might be compromised after COVID‐19.

Objectives: Fatigue is a prominent feature of long COVID (LC) and may be related to several pathophysiologic mechanisms, including immune hyperstimulation. Aerobic endurance exercise training may be a useful therapy, with appropriate attention to preventing post-exertional malaise. Methods: Fourteen participants completed a pilot study of aerobic exercise training (twenty 1.5 h sessions of over 10 weeks). Cardiorespiratory fitness, 6 min walk distance, quality of life, symptoms, 7-day physical activity, immunophenotype, and inflammatory biomarkers were measured before and after exercise training. Results: The participant characteristics at baseline were as follows: 53.5 ± 11.6 yrs, 53% f, BMI 32.5 ± 8.4, 42% ex-smokers, 15.1 ± 8.8 months since initial COVID-19 infection, low normal pulmonary function testing, $\stackrel{.}{\mathrm{V}}$O_2peak 19.3 ± 5.1 mL/kg/min, 87 ± 17% predicted. After exercise training, participants significantly increased their peak work rate (+16 ± 20 W, p = 0.010) and $\stackrel{.}{\mathrm{V}}$O_2peak (+1.55 ± 2.4 mL/kg/min, p = 0.030). Patients reported improvements in fatigue severity (−11%), depression (−42%), anxiety (−29%), and dyspnea level (−46%). There were no changes in 6MW distance or physical activity. The circulating number of CD3+, CD4+, CD19+, CD14++CD16, and CD16++CD14+ monocytes and CD56+ cells (assessed with flow cytometry) increased with acute exercise (rest to peak) and was not diminished or augmented by exercise training. Plasma concentrations of TNF-α, IL-6, IL-8, IL-10, INF-γ, and INF-λ were normal at study entry and not affected by training. Conclusions: Aerobic endurance exercise training in individuals with LC delivered beneficial effects on cardiorespiratory fitness, quality of life, anxiety, depression, and fatigue without detrimental effects on immunologic function.

Identification of the breathing cycle forms the basis of any breath-by-breath gas exchange analysis. Classically, the breathing cycle is defined as the time interval between the beginning of two consecutive inspiration phases. Based on this definition, several research groups have developed algorithms designed to estimate the volume and rate of gas transferred across the alveolar membrane (“alveolar gas exchange”); however, most algorithms require measurement of lung volume at the beginning of the i^th breath ( V_Li−1; i.e., the end-expiratory lung volume of the preceding i^th breath). The main limitation of these algorithms is that direct measurement of V_Li−1 is challenging and often unavailable. Two solutions avoid the requirement to measure V_Li−1 by redefining the breathing cycle. One method defines the breathing cycle as the time between two equal fractional concentrations of lung expired oxygen ( Fo₂) (or carbon dioxide; Fco₂), typically in the alveolar phase, whereas the other uses the time between equal values of the Fo₂/ Fn₂ (or Fco₂/ Fn₂) ratios [i.e., the ratio of fractional concentrations of lung expired O₂ (or CO₂) and nitrogen (N₂)]. Thus, these methods identify the breathing cycle by analyzing the gas fraction traces rather than the gas flow signal. In this review, we define the traditional approach and two alternative definitions of the human breathing cycle and present the rationale for redefining this term. We also explore the strengths and limitations of the available approaches and provide implications for future studies.

Purpose: This study aimed at comparing the predictive accuracy of the power law (PL), 2-parameter hyperbolic (HYP) and linear (LIN) models on elite 1-h track running performance, and evaluating pacing profile and running pattern of the men’s best two 1-h track running performances of all times. Methods: The individual running speed–distance profile was obtained for nine male elite runners using the three models. Different combinations of personal bests times (3000 m-marathon) were used to predict performance. The level of absolute agreement between predicted and actual performance was evaluated using intraclass correlation coefficient (ICC), paired t test and Bland–Altman analysis. A video analysis was performed to assess pacing profile and running pattern. Results: Regardless of the predictors used, no significant differences (p > 0.05) between predicted and actual performances were observed for the PL model. A good agreement was found for the HYP and LIN models only when the half-marathon was the longest event predictor used (ICC = 0.718–0.737, p < 0.05). Critical speed (CS) was highly dependent on the predictors used. Unlike CS, PL_V20 (i.e., the running speed corresponding to a 20-min performance estimated using the PL model) was associated with 1-h track running performances (r = 0.722–0.807, p < 0.05). An even pacing profile with minimal changes of step length and frequency was observed. Conclusions: The PL model may offer the more realistic 1-h track running performance prediction among the models investigated. An even pacing might be the best strategy for succeeding in such running events.

Long COVID patients present with a myriad of symptoms that can include fatigue, exercise intolerance and post exertional malaise (PEM). Long COVID has been compared to other post viral syndromes, including myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), where a reduction in day 2 cardiopulmonary exercise test (CPET) performance of a two-day CPET protocol is suggested to be a result of PEM. We investigated cardiopulmonary and perceptual responses to a two-day CPET protocol in Long COVID patients. 15 Long COVID patients [n=7 females; mean (SD) age: 53(11) yr; BMI = 32.2(8.5) kg/m²] performed a pulmonary function test and two ramp-incremental CPETs separated by 24 hr. CPET variables included gas exchange threshold (GET), peak oxygen uptake (V̇O_2peak) and peak work rate (WR_peak). Ratings of perceived dyspnoea and leg effort were recorded at peak exercise using the modified 0-10 Borg Scale. PEM (past six months) was assessed using the modified DePaul Symptom Questionnaire (mDSQ). One-sample t-tests were used to test significance of mean difference between days (p<0.05). mDSQ revealed PEM in 80 % of patients. Lung function was normal. Responses to day 1 CPET were consistent with the presence of aerobic deconditioning in 40 % of patients (V̇O_2peak 0.05). PEM symptoms in Long COVID patients, in the absence of differences in two-day CPET responses separated by 24 hours, suggests that PEM is not due to impaired recovery of exercise capacity between days.

Purpose: It is well established that mental fatigue impairs performance during lab-based endurance tests lasting less than 45 min. However, the effects of mental fatigue on longer duration endurance events and in field settings are unknown. The aim of this study was to investigate the effect of mental fatigue on performance during a half-marathon race. Methods: Forty-six male amateur runners (means ± SD: age 43.8 ± 8.6 years, $$\dot V{O_{2peak}}$$V ˙ O 2 p e a k 46.0 ± 4.1 ml/kg/min) completed a half-marathon after being randomly allocated to performing a 50-min mentally fatiguing task (mental fatigue group) or reading magazines for 50 min (control group). Running speed, heart rate, and perceived effort were measured during the race. Results: Runners in the mental fatigue group completed the half-marathon approximately 4 min slower (106.2 ± 12.4 min) than those in the control group (102.4 ± 10.2 min), but this difference was not statistically significant (Cohen’s d = 0.333; p = 0.265). However, equivalence was not established [t(40.88) = 0.239, p = 0.594] and equivalence testing analysis excluded a beneficial effect of mental fatigue on half-marathon performance. Conclusion: Due to its posttest-only design and the achievable sample size, the study did not have enough power to provide evidence that the observed 4-min increase in half-marathon time is statistically significant. However, equivalence testing suggests that mental fatigue has no beneficial effect on half-marathon performance in male amateur runners, and a harmful effect cannot be excluded. Overall, it seems prudent for endurance athletes to avoid mentally fatiguing tasks before competitions.