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Purpose: This study examined the physiological effects of 12 weeks of detraining and retraining in a highly trained master triathlete (age 53.8 years). Methods: Variables associated with swimming, cycling, and running performance, including V ˙ O2max, peak power output (PPO), gross cycling efficiency (CE), running maximal aerobic velocity (MAV), running economy (RE), muscle strength, and body composition were assessed before the last race of the season (baseline), after 12 weeks of detraining, and after 12 weeks of retraining. Results: Detraining resulted in a 9.1% and 10.9% decrease in relative V ˙ O2max for cycling and running, respectively. PPO and MAV declined by 12.7% and 8.6%, respectively. After detraining, CE decreased by 6.2%, and RE was 22% higher than the baseline. The maximal strength capacity of the knee extensor muscles decreased by an average of 8.2%. Body fat percentage increased from 10.5% to 13.8%, while lean mass decreased by 2.2 kg. After retraining, almost all variables returned to baseline or even slightly increased, except RE and lean mass, which did not return to baseline. Conclusion: After 12 weeks of detraining, a lifelong master triathlete can regain his cardiorespiratory fitness (i.e., V ˙ O2max) with 12 weeks of progressive and structured retraining, but his running economy and lean mass remain slightly depressed. [ABSTRACT FROM AUTHOR]

Increases in power output and maximal oxygen consumption (VO2max) occur in response to sprint interval exercise (SIE), but common use of "all-out" intensities presents a barrier for many adults. Furthermore, lower-body SIE is not feasible for all adults. We compared physiological and perceptual responses to supramaximal, but "non-all-out" SIE between leg and arm cycling exercise. Twenty-four active adults (mean ± SD age: [25 ± 7] y; cycling VO2max: [39 ± 7] mL•kg-1•min-1) performed incremental exercise using leg (LCE) and arm cycle ergometry (ACE) to determine VO2max and maximal work capacity (Wmax). Subsequently, they performed four 20 s bouts of SIE at 130% Wmax on the LCE or ACE at cadence = 120-130 rev/min, with 2 min recovery between intervals. Gas exchange data, heart rate (HR), blood lactate concentration (BLa), rating of perceived exertion (RPE), and affective valence were acquired. Data showed significantly lower (p < 0.001) absolute mean ([1.24 ± 0.31] L•min-1 vs. [1.59 ± 0.34] L•min-1; d = 1.08) and peak VO2 ([1.79 ± 0.48] L•min-1 vs. [2.10 ± 0.44] L•min-1; d = 0.70) with ACE versus LCE. However, ACE elicited significantly higher (p < 0.001) relative mean ([62% ± 9%] VO2max vs. [57% ± 7%] VO2max, d ¼ 0.63) and peak VO2 ([88% ± 10%] VO2max vs. [75% ± 10%] VO2max, d = 1.33). Post-exercise BLa was significantly higher ([7.0 ± 1.7] mM vs. [5.7 ± 1.5] mM, p = 0.024, d = 0.83) for LCE versus ACE. There was no significant effect of modality on RPE or affective valence (p > 0.42), and lowest affective valence recorded (2.0 ± 1.8) was considered "good to fairly good". Data show that non "all-out" ACE elicits lower absolute but higher relative HR and VO2 compared to LCE. Less aversive perceptual responses could make this non-all-out modality feasible for inactive adults. [ABSTRACT FROM AUTHOR]

Purpose: Cardiopulmonary exercise testing (CPET) is considered the gold standard for assessing cardiorespiratory fitness. To ensure consistent performance of each test, it is necessary to adapt the power increase of the test protocol to the physical characteristics of each individual. This study aimed to use machine learning models to determine individualized ramp protocols based on non-exercise features. We hypothesized that machine learning models will predict peak oxygen uptake ( V ˙ O2peak) and peak power output (PPO) more accurately than conventional multiple linear regression (MLR). Methods: The cross-sectional study was conducted with 274 (♀168, ♂106) participants who performed CPET on a cycle ergometer. Machine learning models and multiple linear regression were used to predict V ˙ O2peak and PPO using non-exercise features. The accuracy of the models was compared using criteria such as root mean square error (RMSE). Shapley additive explanation (SHAP) was applied to determine the feature importance. Results: The most accurate machine learning model was the random forest (RMSE: 6.52 ml/kg/min [95% CI 5.21–8.17]) for V ˙ O2peak prediction and the gradient boosting regression (RMSE: 43watts [95% CI 35–52]) for PPO prediction. Compared to the MLR, the machine learning models reduced the RMSE by up to 28% and 22% for prediction of V ˙ O2peak and PPO, respectively. Furthermore, SHAP ranked body composition data such as skeletal muscle mass and extracellular water as the most impactful features. Conclusion: Machine learning models predict V ˙ O2peak and PPO more accurately than MLR and can be used to individualize CPET protocols. Features that provide information about the participant's body composition contribute most to the improvement of these predictions. Trial registration number: DRKS00031401 (6 March 2023, retrospectively registered). [ABSTRACT FROM AUTHOR]

Increases in power output and maximal oxygen consumption (V˙O2max) occur in response to sprint interval exercise (SIE), but common use of “all-out” intensities presents a barrier for many adults. Furthermore, lower-body SIE is not feasible for all adults. We compared physiological and perceptual responses to supramaximal, but “non-all-out” SIE between leg and arm cycling exercise. Twenty-four active adults (mean ​± ​SD age: [25 ​± ​7] y; cycling V˙O2max: [39 ​± ​7] mL·kg−1·min−1) performed incremental exercise using leg (LCE) and arm cycle ergometry (ACE) to determine V˙O2max and maximal work capacity (Wmax). Subsequently, they performed four 20 ​s bouts of SIE at 130% Wmax on the LCE or ACE at cadence ​= ​120–130 ​rev/min, with 2 ​min recovery between intervals. Gas exchange data, heart rate (HR), blood lactate concentration (BLa), rating of perceived exertion (RPE), and affective valence were acquired. Data showed significantly lower (p ​ ​0.42), and lowest affective valence recorded (2.0 ​± ​1.8) was considered “good to fairly good”. Data show that non “all-out” ACE elicits lower absolute but higher relative HR and V˙O2 compared to LCE. Less aversive perceptual responses could make this non-all-out modality feasible for inactive adults.

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The aim of the study was to verify changes in peak power output (PPO), acid-base balance, blood lactate (La−) accumulation, and oxygen uptake (VO2) whilst applying a 7.5% and 10% load of the participant’s body mass in a cycling sprint interval exercise (SIE) (two series consisting of 3 × 10 s efforts “all-out” separated by a 30 s active break). Twelve healthy, physically active men with maximal oxygen uptake (VO2max = 52.4 ± 7.8 mL∙kg−1∙min−1) were included in the study and performed two cross-over SIE sessions, with a load of 7.5% of the participant’s body mass (SIE7.5%) and 10% of the participant’s body mass applied (SIE10%). The physiological, mechanical, and perceptual responses were assessed during and after each session. After SIE10%, a 10.3% higher and 25.5% faster PPO time was obtained, with no significant differences in La−, a lower physiological cost (mean oxygen uptake and mean heart rate), faster restitution of VO2 and heart rate, and a lower rate of perceived exertion. Therefore, a 10% load of the participant’s body mass during 10 s effort induced greater physiological adaptations and mechanical responses, which may promote the use of a higher workload in sprint interval training to improve physical performance.