Purpose: Fran is one of the most popular CrossFit benchmark workouts used to control CrossFitters’ improvements. Detailed physiological characterization of Fran is needed for a more specific evaluation of CrossFitters’ training performance improvements. The aim of the study was to analyze the oxygen uptake () kinetics and characterize the energy system contributions and the degree of postexercise fatigue of the unbroken Fran. Methods: Twenty trained CrossFitters performed Fran at maximal exertion. and heart-rate kinetics were assessed at baseline and during and post-Fran. Blood lactate and glucose concentrations and muscular fatigue were measured at baseline and in the recovery period. Results: A marked increase in kinetics was observed at the beginning of Fran, remaining elevated until the end (: 49.2 [3.7] mL·kg⁻¹·min⁻¹, amplitude: 35.8 [5.2] mL·kg⁻¹·min⁻¹, time delay: 4.7 [2.5] s and time constant: 23.7 [11.1] s; mean [SD]). Aerobic, anaerobic lactic, and alactic pathways accounted for 62% (4%), 26% (4%), and 12% (2%) of energy contribution. Reduction in muscle function in jumping ability (jump height: 8% [6%], peak force: 6% [4%], and maximum velocity: 4% [2%]) and plank prone test (46% [20%]) was observed in the recovery period. Conclusions: The Fran unbroken workout is a high-intensity effort associated with an elevated metabolic response. This pattern of energy response highlights the primary contribution of aerobic energy metabolism, even during short and very intense CrossFit workouts, and that recovery can take >24 hours due to cumulative fatigue.

Physical fatigue is a serious threat to the health and safety of firefighters. Its effects include decreased cognitive abilities and a heightened risk of accidents. Subjective scales and, recently, on-body sensors have been used to monitor physical fatigue among firefighters and safety-sensitive professionals. Considering the capabilities (e.g., noninvasiveness and continuous monitoring) and limitations (e.g., assessed fatiguing tasks and models validation procedures) of current approaches, this study aimed to develop a physical fatigue prediction model combining cardiorespiratory and thermoregulatory measures and machine learning algorithms within a firefighters’ sample. Sensory data from heart rate, breathing rate and core temperature were recorded from 24 participants during an incremental running protocol. Various supervised machine learning algorithms were examined using 21 features extracted from the physiological variables and participants’ characteristics to estimate four physical fatigue conditions: low, moderate, heavy and severe. Results showed that the XGBoosted Trees algorithm achieved the best outcomes with an average accuracy of 82% and accuracies of 93% and 86% for recognising the low and severe levels. Furthermore, this study evaluated different methods to assess the models’ performance, concluding that the group cross-validation method presents the most practical results. Overall, this study highlights the advantages of using multiple physiological measures for enhancing physical fatigue modelling. It proposes a promising health and safety management tool and lays the foundation for future studies in field conditions.

We have examined the impact of CrossFit^® workout sessions on physical fitness, comparing the obtained outcomes with the recommendations of the American College of Sports Medicine. In addition, we provide suggestions to improve training monitoring, as well as practical applications for researchers, coaches and practitioners. CrossFit^® imposes high cardiorespiratory and metabolic demands, promoting improvements in circulatory capacity, oxidative metabolism and muscular endurance. Sustained elevations in heart rate contribute to cardiovascular conditioning, while a post-exercise hypotensive effect may help to reduce cardiovascular risks. Structured CrossFit^® programs have led to improvements in maximal strength and muscular endurance, with substantial increases in squat performance observed in both untrained and recreationally active individuals. In addition, CrossFit^® improves mental health through its motivating community. However, the high metabolic demands, increased creatine kinase levels and reduced performance in the countermovement jump reveal that muscle damage and neuromuscular fatigue can persist for up to 48 h. Balancing these intense sessions with adequate recovery is crucial, as improper management may lead to overtraining and compromise fitness gains. Future research should explore long-term cardiovascular adaptations, differences in gains and recovery between males and females and the application of real-time biomarker and artificial intelligence technologies to improve the training efficiency and safety. Machine learning algorithms could further personalize feedback, adapting to each individual’s biomechanics and physiological responses over time.

Our purpose was to characterize the oxygen uptake kinetics (VO₂), energy systems contributions and total energy expenditure during a CrossFit^® benchmark workout performed in the extreme intensity domain. Fourteen highly trained male CrossFitters, aged 28.3 ± 5.4 years, with height 177.8 ± 9.4 cm, body mass 87.9 ± 10.5 kg and 5.6 ± 1.8 years of training experience, performed the Isabel workout at maximal exertion. Cardiorespiratory variables were measured at baseline, during exercise and the recovery period, with blood lactate and glucose concentrations, including the ratings of perceived exertion, measured pre- and post-workout. The Isabel workout was 117 ± 10 s in duration and the VO₂ peak was 47.2 ± 4.7 mL·kg⁻¹·min⁻¹, the primary component amplitude was 42.0 ± 6.0 mL·kg⁻¹·min⁻¹, the time delay was 4.3 ± 2.2 s and the time constant was 14.2 ± 6.0 s. The accumulated VO₂ (0.6 ± 0.1 vs. 4.8 ± 1.0 L·min⁻¹) value post-workout increased substantially when compared to baseline. Oxidative phosphorylation (40%), glycolytic (45%) and phosphagen (15%) pathways contributed to the 245 ± 25 kJ total energy expenditure. Despite the short ~2 min duration of the Isabel workout, the oxygen-dependent and oxygen-independent metabolism energy contributions to the total metabolic energy release were similar. The CrossFit^® Isabel requires maximal effort and the pattern of physiological demands identifies this as a highly intensive and effective workout for developing fitness and conditioning for sports.

Aim: To quantify the physiological demands and impact of muscle function t of the Fran workout, one of the most popular CrossFit benchmarks. Methods: Twenty experienced CrossFitters—16 male: 29 (6) years old and 4 female: 26 (5) years old— performed 3 rounds (with 30-s rests in between) of 21–21, 15–15, and 9–9 front squats to overhead press plus pull-up repetitions. Oxygen uptake and heart rate were measured at baseline, during the workout, and in the recovery period. Rating of perceived exertion, blood lactate, and glucose concentrations were assessed at rest, during the intervals, and in the recovery period. Muscular fatigue was also monitored at rest and at 5 minutes, 30 minutes, and 24 hours postexercise. Repeated-measures analysis of variance was performed to compare time points. Results: Aerobic (52%–29%) and anaerobic alactic (30%–23%) energy contributions decreased and the anaerobic lactic contribution increased (18%–48%) across the 3 rounds of the Fran workout. Countermovement jump height decreased by 8% (−12 to −3) mean change (95% CI), flight duration by 14% (−19 to −7), maximum velocity by 3% (−5 to −0.1), peak force 4% (−7 to −0.1), and physical performance (plank prone 47% [−54 to −38]) were observed. Conclusions: It appears that the Fran workout is a physically demanding activity that recruits energy from both aerobic and anaerobic systems. This severe-intensity workout evokes substantial postexercise fatigue and corresponding reduction in muscle function.

The current study aimed to longitudinally evaluate anthropometric, physiological, and biomechanical variables related to middle-distance performance during a 45-week swimming training season. Thirty-four swimmers (age: 12.07 ± 1.14 years) performed a maximum of 400 m front crawl at the beginning (T1) and finish of the first macrocycle (T2, 15 weeks) and the finish of the second (T3, 18 weeks) and third macrocycles (T4, 12 weeks). Time-related variables, stroke rate (SR), stroke length (SL), and stroke index (SI) were recorded during the test, and blood lactate ([La]) and glucose ([Glu]) concentrations were measured post-exercise. The time of the 400 m effort decreased after each macrocycle (T2 vs. T1, 7.8 ± 5.6%; T3 vs. T2, 3.7 ± 3.1%; T4 vs. T3, 3.8 ± 3.4%; p < 0.01). Four hundred meter speed changes between T1 and T2 were positively related to variations in [La], [Glu], SL, and SI (r = 0.36–0.60, p < 0.05). Changes between T2 and T3 were related to SI only (r = 0.5, p < 0.05), and modifications between T3 and T4 were associated with SL and SI variations (r = 0.34 and 0.65, p < 0.05). These results indicate that a well-structured year plan including three macrocycles leads to a significant age-group swimming performance improvement, mostly connected with an increase in technical proficiency.