Explore the vast range of titles available.

The study investigated the relationship between external and internal training load measures in 25 male elite youth soccer players (age: 16.6 ± 0.9 years, VO 2max : 59 ± 4 ml/min/kg) over 3 months. External load (i.e., total distance, high metabolic power distance, high-speed running) was quantified using a local positioning system and related to subjective (RPE, sleep quality, drive (energy level)), biochemical (creatine kinase (CK), lactate dehydrogenase (LDH), C-reactive protein (CRP), urea, cortisol, transferrin), and neuromuscular (CMJ) markers. Single day workload (1DL), exponential 7-day workload (7DL), and the acute: chronic workload ratio (ACWR) were calculated. 1DL parameters were correlated (Spearman’s rho) with RPE (range r  = 0.24 to 0.43, p  < 0.01) and 1DL distance was negatively related to drive ( r  = − 0.28, p  < 0.001). LDH correlated positively with training load across all calculation methods (up to r  = 0.27, p  < 0.01). CK exhibited positive correlations to ACWR training load ( r  = 0.23 to 0.27, p  < 0.05), while transferrin (ACWR) and CRP (1DL) showed negative associations to training load ( r  = − 0.21 to − 0.28, p  < 0.05). CMJ eccentric mean force was negatively correlated with all ACWR training load variables ( r  = − 0.22 to − 0.25, p  < 0.01). In summary, subjective measures showed stronger and more consistent associations with training load than biomarkers or neuromuscular testing. Practitioners may confidently use well-structured questionnaires for load monitoring in elite youth soccer.

The search for monitoring tools that provide early indication of injury and illness could contribute to better player protection. The aim of the present study was to i) determine the feasibility of and adherence to our monitoring approach, and ii) identify variables associated with up-coming illness and injury. We incorporated a comprehensive set of monitoring tools consisting of external load and physical fitness data, questionnaires, blood, neuromuscular-, hamstring, hip abductor and hip adductor performance tests performed over a three-month period in elite under-18 academy soccer players. Twenty-five players (age: 16.6 ± 0.9 years, height: 178 ± 7 cm, weight: 74 ± 7 kg, VO2max: 59 ± 4 ml/min/kg) took part in the study. In addition to evaluating adherence to the monitoring approach, data were analyzed using a linear support vector machine (SVM) to predict illness and injuries. The approach was feasible, with no injuries or dropouts due to the monitoring process. Questionnaire adherence was high at the beginning and decreased steadily towards the end of the study. An SVM resulted in the best classification results for three classification tasks, i.e., illness prediction, illness determination and injury prediction. For injury prediction, one of four injuries present in the test data set was detected, with 96.3% of all data points (i.e., injuries and non-injuries) correctly detected. For both illness prediction and determination, there was only one illness in the test data set that was detected by the linear SVM. However, the model showed low precision for injury and illness prediction with a considerable number of false-positives. The results demonstrate the feasibility of a holistic monitoring approach with the possibility of predicting illness and injury. Additional data points are needed to improve the prediction models. In practical application, this may lead to overcautious recommendations on when players should be protected from injury and illness.

The search for monitoring tools that provide early indication of injury and illness could contribute to better player protection. The aim of the present study was to i) determine the feasibility of and adherence to our monitoring approach, and ii) identify variables associated with up-coming illness and injury. We incorporated a comprehensive set of monitoring tools consisting of external load and physical fitness data, questionnaires, blood, neuromuscular-, hamstring, hip abductor and hip adductor performance tests performed over a three-month period in elite under-18 academy soccer players. Twenty-five players (age: 16.6 ± 0.9 years, height: 178 ± 7 cm, weight: 74 ± 7 kg, VO2max: 59 ± 4 ml/min/kg) took part in the study. In addition to evaluating adherence to the monitoring approach, data were analyzed using a linear support vector machine (SVM) to predict illness and injuries. The approach was feasible, with no injuries or dropouts due to the monitoring process. Questionnaire adherence was high at the beginning and decreased steadily towards the end of the study. An SVM resulted in the best classification results for three classification tasks, i.e., illness prediction, illness determination and injury prediction. For injury prediction, one of four injuries present in the test data set was detected, with 96.3% of all data points (i.e., injuries and non-injuries) correctly detected. For both illness prediction and determination, there was only one illness in the test data set that was detected by the linear SVM. However, the model showed low precision for injury and illness prediction with a considerable number of false-positives. The results demonstrate the feasibility of a holistic monitoring approach with the possibility of predicting illness and injury. Additional data points are needed to improve the prediction models. In practical application, this may lead to overcautious recommendations on when players should be protected from injury and illness.

Background Various studies have shown that the type of intensity measure affects training intensity distribution (TID) computation. These conclusions arise from studies presenting data from meso- and macrocycles, while microcycles, e.g., high-intensity interval training shock microcycles (HIIT-SM) have been neglected so far. Previous literature has suggested that the time spent in the high-intensity zone, i.e., zone 3 (Z3) or the “red zone”, during HIIT may be important to achieve improvements in endurance performance parameters. Therefore, this randomized controlled trial aimed to compare the TID based on running velocity (TID V ), running power (TID P ) and heart rate (TID HR ) during a 7-day HIIT-SM. Twenty-nine endurance-trained participant were allocated to a HIIT-SM consisting of 10 HIIT sessions without (HSM, n = 9) or with (HSM + LIT, n = 9) additional low-intensity training or a control group (n = 11). Moreover, we explored relationships between time spent in Z3 determined by running velocity (Z3 V ), running power (Z3 P ), heart rate (Z3 HR ), oxygen uptake ( Z 3 V ˙ O 2 ) and changes in endurance performance. Results Both intervention groups revealed a polarized pattern for TID V (HSM: Z1: 38 ± 17, Z2: 16 ± 17, Z3: 46 ± 2%; HSM + LIT: Z1: 59 ± 18, Z2: 14 ± 18, Z3: 27 ± 2%) and TID P (Z1: 50 ± 8, Z2: 14 ± 11, Z3: 36 ± 7%; Z1: 62 ± 15, Z2: 12 ± 16, Z3: 26 ± 2%), while TID HR (Z1: 48 ± 13, Z2: 26 ± 11, Z3: 26 ± 7%; Z1: 65 ± 17, Z2: 22 ± 18, Z3: 13 ± 4%) showed a pyramidal pattern. Time in Z3 HR was significantly less compared to Z3 V and Z3 P in both intervention groups (all p < 0.01). There was a time x intensity measure interaction for time in Z3 across the 10 HIIT sessions for HSM + LIT (p < 0.001, p η 2  = 0.30). Time in Z3 V and Z3 P within each single HIIT session remained stable over the training period for both intervention groups. Time in Z3 HR declined in HSM from the first (47%) to the last (28%) session, which was more pronounced in HSM + LIT (45% to 16%). A moderate dose–response relationship was found for time in Z3 V and changes in peak power output (r s  = 0.52, p = 0.028) as well as time trial performance (r s  = − 0.47, p = 0.049) with no such associations regarding time in Z3 P , Z3 HR , and Z 3 V ˙ O 2 . Conclusion The present study reveals that the type of intensity measure strongly affects TID computation during a HIIT-SM. As heart rate tends to underestimate the intensity during HIIT-SM, heart rate-based training decisions should be made cautiously. In addition, time in Z3 V was most closely associated with changes in endurance performance. Thus, for evaluating a HIIT-SM, we suggest integrating a comprehensive set of intensity measures. Trial Registration Trial register: Clinicaltrials.gov, registration number: NCT05067426. Key Points Using heart rate, running velocity and running power as measures of intensity leads to different patterns of training intensity distribution during a 7-day HIIT shock microcycle. A polarized pattern was observed for velocity and power, whereas a more pyramidal distribution was found for heart rate. Additional low-intensity training volume during a HIIT shock microcycle led to a more pronounced decline in time in zone 3 measured with heart rate compared to a shock microcycle with HIIT sessions, only. A moderate dose–response relationship was observed between time in zone 3 measured by velocity and changes in peak power output as well as time-trial performance. No such correlation was found between time in zone 3 measured by power, heart rate, oxygen uptake, and changes in endurance performance parameters.

PurposeTo identify the effects of a single 30 min partial lower leg external pneumatic compression (EPC) treatment compared to a static compression (SC) garment or a no treatment control (CTL) on markers of recovery and performance following a muscle damaging protocol.MethodsThirty healthy, active males (23 ± 3 years; 180.2 ± 9.0 cm; 81.6 ± 11.3 kg) performed 100 drop jumps from a 0.6 m box followed by a randomized, single 30 min treatment of either a partial lower leg EPC device worn below the knee and above the ankle (110 mmHg), SC garment (20–30 mmHg) covering the foot and calf just below the knee, or no treatment CTL, and then returned 24 and 48 h later. Participants were assessed for measures of muscle soreness, fatigue, hemodynamics, blood lactate, muscle thickness, circumferences, and performance assessments.ResultsThe drop jump protocol significantly increased muscle soreness (p < 0.001), fatigue (p < 0.001), blood flow (p < 0.001), hemoglobin (p < 0.001), and muscle oxygen saturation (SMO2; p < 0.001). Countermovement jump and squat jump testing completed after treatment with either EPC, SC, or CTL revealed no differences for jump height between any condition. However, EPC treatment maintained consistent braking force and propulsive power measures across all timepoints for countermovement jump testing. EPC and SC treatment also led to better maintenance of squat jump performance for average relative propulsive force and power variables at 24 and 48 h compared to CTL.ConclusionsA single 30 min partial leg EPC treatment may lead to more consistent jump performance following a damaging bout of exercise.

Background Performing multiple high-intensity interval training (HIIT) sessions in a compressed period of time (approximately 7–14 days) is called a HIIT shock microcycle (SM) and promises a rapid increase in endurance performance. However, the efficacy of HIIT-SM, as well as knowledge about optimal training volumes during a SM in the endurance-trained population have not been adequately investigated. This study aims to examine the effects of two different types of HIIT-SM (with or without additional low-intensity training (LIT)) compared to a control group (CG) on key endurance performance variables. Moreover, participants are closely monitored for stress, fatigue, recovery, and sleep before, during and after the intervention using innovative biomarkers, questionnaires, and wearable devices. Methods This is a study protocol of a randomized controlled trial that includes the results of a pilot participant. Thirty-six endurance trained athletes will be recruited and randomly assigned to either a HIIT-SM (HSM) group, HIIT-SM with additional LIT (HSM + LIT) group or a CG. All participants will be monitored before (9 days), during (7 days), and after (14 days) a 7-day intervention, for a total of 30 days. Participants in both intervention groups will complete 10 HIIT sessions over 7 consecutive days, with an additional 30 min of LIT in the HSM + LIT group. HIIT sessions consist of aerobic HIIT, i.e., 5 × 4 min at 90–95% of maximal heart rate interspersed by recovery periods of 2.5 min. To determine the effects of the intervention, physiological exercise testing, and a 5 km time trial will be conducted before and after the intervention. Results The feasibility study indicates good adherence and performance improvement of the pilot participant. Load monitoring tools, i.e., biomarkers and questionnaires showed increased values during the intervention period, indicating sensitive variables. Conclusion This study will be the first to examine the effects of different total training volumes of HIIT-SM, especially the combination of LIT and HIIT in the HSM + LIT group. In addition, different assessments to monitor the athletes' load during such an exhaustive training period will allow the identification of load monitoring tools such as innovative biomarkers, questionnaires, and wearable technology. Trial Registration : clinicaltrials.gov, NCT05067426. Registered 05 October 2021—Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT05067426 . Protocol Version Issue date: 1 Dec 2021. Original protocol. Authors: TLS, NH.

This study examined the acute effects of exercise testing on immunology markers, established blood-based biomarkers, and questionnaires in endurance athletes, with a focus on biological sex differences. Twenty-four healthy endurance-trained participants (16 men, age: 29.2± 7.6 years, maximal oxygen uptake ( ): 59.4 ± 7.5 ml · min · kg ; 8 women, age: 26.8 ± 6.1 years, : 52.9 ± 3.1 ml · min · kg ) completed an incremental submaximal exercise test and a ramp test. The study employed exploratory bioinformatics analysis: mixed ANOVA, k-means clustering, and uniform manifold approximation and projection, to assess the effects of exhaustive exercise on biomarkers and questionnaires. Significant increases in biomarkers (lymphocytes, platelets, procalcitonin, hemoglobin, hematocrit, red blood cells, cell-free DNA (cfDNA)) and fatigue were observed post-exercise. Furthermore, differences pre- to post-exercise were observed in cytokines, cfDNA, and other blood biomarkers between male and female participants. Three distinct groups of athletes with differing proportions of females (Cluster 1: 100% female, Cluster 2: 85% male, Cluster 3: 37.5% female and 65.5% male) were identified with k-means clustering. Specific biomarkers (e.g., interleukin-2 (IL-2), IL-10, and IL-13, as well as cfDNA) served as primary markers for each cluster, potentially informing individualized exercise responses. In conclusion, our study identified exercise-sensitive biomarkers and provides valuable insights into the relationships between biological sex and biomarker responses.

Background Large (48-g), isonitrogenous doses of rice and whey protein have previously been shown to stimulate similar adaptations to resistance training, but the impact of consuming smaller doses has yet to be compared. We evaluated the ability of 24-g doses of rice or whey protein concentrate to augment adaptations following 8 weeks of resistance training. Methods Healthy resistance-trained males ( n  = 24, 32.8 ± 6.7 years, 179.3 ± 8.5 cm, 87.4 ± 8.5 kg, 27.2 ± 1.9 kg/m 2 , 27.8 ± 6.0% fat) were randomly assigned and matched according to fat-free mass to consume 24-g doses of rice ( n  = 12, Growing Naturals, LLC) or whey (n = 12, NutraBio Labs, Inc.) protein concentrate for 8 weeks while completing a standardized resistance training program. Body composition (DXA), muscular strength (one-repetition maximum [1RM]) and endurance (repetitions to fatigue [RTF] at 80% 1RM) using bench press (BP) and leg press (LP) exercises along with anaerobic capacity (Wingate) were assessed before and after the intervention. Subjects were asked to maintain regular dietary habits and record dietary intake every 2 weeks. Outcomes were assessed using 2 × 2 mixed (group x time) factorial ANOVA with repeated measures on time and independent samples t-tests using the change scores from baseline. A p -value of 0.05 and 95% confidence intervals on the changes between groups were used to determine outcomes. Results No baseline differences ( p  > 0.05) were found for key body composition and performance outcomes. No changes ( p  > 0.05) in dietary status occurred within or between groups (34 ± 4 kcal/kg/day, 3.7 ± 0.77 g/kg/day, 1.31 ± 0.28 g/kg/day, 1.87 ± 0.23 g/kg/day) throughout the study for daily relative energy (34 ± 4 kcals/kg/day), carbohydrate (3.7 ± 0.77 g/kg/day), fat (1.31 ± 0.28 g/kg/day), and protein (1.87 ± 0.23 g/kg/day) intake. Significant main effects for time were revealed for body mass ( p  = 0.02), total body water ( p  = 0.01), lean mass ( p  = 0.008), fat-free mass ( p  = 0.007), BP 1RM ( p  = 0.02), BP volume ( p  = 0.04), and LP 1RM ( p  = 0.01). Changes between groups were similar for body mass (− 0.88, 2.03 kg, p  = 0.42), fat-free mass (− 0.68, 1.99 kg, p  = 0.32), lean mass (− 0.73, 1.91 kg, p  = 0.37), fat mass (− 0.48, 1.02 kg, p  = 0.46), and % fat (− 0.63, 0.71%, p  = 0.90). No significant between group differences were seen for BP 1RM (− 13.8, 7.1 kg, p  = 0.51), LP 1RM (− 38.8, 49.6 kg, p  = 0.80), BP RTF (− 2.02, 0.35 reps, p  = 0.16), LP RTF (− 1.7, 3.3 reps, p  = 0.50), and Wingate peak power (− 72.5, 53.4 watts, p  = 0.76) following the eight-week supplementation period. Conclusions Eight weeks of daily isonitrogenous 24-g doses of rice or whey protein in combination with an eight-week resistance training program led to similar changes in body composition and performance outcomes. Retroactively registered on as NCT04411173 .

Aerobic exercise and thermal stress instigate robust challenges to the immune system. Various attempts to modify or supplement the diet have been proposed to bolster the immune system responses. The purpose of this study was to identify the impact of yeast beta-glucan (Saccharomyces cerevisiae) supplementation on exercise-induced muscle damage and inflammation. Healthy, active men (29.6 ± 6.7 years, 178.1 ± 7.2 cm, 83.2 ± 11.2 kg, 49.6 ± 5.1 mL/kg/min, n = 16) and women (30.1 ± 8.9 years, 165.6 ± 4.1 cm, 66.7 ± 10.0 kg, 38.7 ± 5.8 mL/kg/min, n = 15) were randomly assigned in a double-blind and cross-over fashion to supplement for 13 days with either 250 mg/day of yeast beta-glucan (YBG) or a maltodextrin placebo (PLA). Participants arrived fasted and completed a bout of treadmill exercise at 55% peak aerobic capacity (VO2Peak) in a hot (37.2 ± 1.8 °C) and humid (45.2 ± 8.8%) environment. Prior to and 0, 2, and 72 h after completing exercise, changes in white blood cell counts, pro- and anti-inflammatory cytokines, markers of muscle damage, markers of muscle function, soreness, and profile of mood states (POMS) were assessed. In response to exercise and heat, both groups experienced significant increases in white blood cell counts, plasma creatine kinase and myoglobin, and soreness along with reductions in peak torque and total work with no between-group differences. Concentrations of serum pro-inflammatory cytokines in YBG were lower than PLA for macrophage inflammatory protein 1β (MIP-1β) (p = 0.044) and tended to be lower for interleukin 8 (IL-8) (p = 0.079), monocyte chemoattractment protein 1 (MCP-1) (p = 0.095), and tumor necrosis factor α (TNF-α) (p = 0.085). Paired samples t-tests using delta values between baseline and 72 h post-exercise revealed significant differences between groups for IL-8 (p = 0.044, 95% Confidence Interval (CI): (0.013, 0.938, d = −0.34), MCP-1 (p = 0.038, 95% CI: 0.087, 2.942, d = −0.33), and MIP-1β (p = 0.010, 95% CI: 0.13, 0.85, d = −0.33). POMS outcomes changed across time with anger scores in PLA exhibiting a sharper decline than YBG (p = 0.04). Vigor scores (p = 0.04) in YBG remained stable while scores in PLA were significantly reduced 72 h after exercise. In conclusion, a 13-day prophylactic period of supplementation with 250 mg of yeast-derived beta-glucans invoked favorable changes in cytokine markers of inflammation after completing a prolonged bout of heated treadmill exercise.