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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.

Background This study aimed to evaluate acute and chronic exercise-induced changes in cell-free DNA (cfDNA) concentrations during a 7-day high-intensity interval training (HIIT) shock microcycle in trained endurance athletes. Thirty-five participants were randomly assigned to one of three groups: a HIIT-only group (HSM), a HIIT plus low-intensity training group (HSM + LIT), and a control group maintaining regular training. The intervention included 10 HIIT sessions (5 × 4 min at 90–95% maximum heart rate) over 7 days, with HSM + LIT completing an additional 30 min of low-intensity training after each session. Physiological exercise testing (PET) was conducted at baseline, 3-, 7-, and 14-days post-intervention. On days 2 and 7 during the intervention, HIIT sessions were supervised in both morning and afternoon, and venous blood samples were collected at rest, immediately post-exercise, and 30 min post-exercise to measure cfDNA for 90 and 222 bp fragments. Correlations between cfDNA and physiological exercise variables such as peak power output (PPO), running velocity at lactate threshold (LT), and VO₂ max were analyzed. Results cfDNA 90 (10.4-fold, p  < 0.001) and cfDNA 222 (12.4-fold, p  < 0.001) increased significantly after PET. In addition, cfDNA 90 (17.1-fold, p  < 0.001) and cfDNA 222 (20.2-fold, p  < 0.001) increased after HIIT, both remaining significantly elevated 30 min post-HIIT (both p  < 0.001). cfDNA 90 concentrations were higher in afternoon (22.4-fold) compared to morning HIIT sessions (17.2-fold, p  < 0.001). A significant interaction effect was found between group and measurement point for cfDNA 90 ( p  < 0.001) and cfDNA 222 ( p  < 0.001), with higher concentrations in HSM + LIT compared to HSM 30 min post-HIIT. cfDNA 90 showed moderate correlations with PPO ( r  = 0.48, p  < 0.001), LT ( r  = 0.36, p  < 0.001) and VO ₂max ( r  = 0.30, p  = 0.01). cfDNA 222 correlated moderately with VO ₂max ( r  = 0.34, p  = 0.001) and slightly with PPO ( r  = 0.21, p  = 0.05). No chronic changes in cfDNA were observed throughout the study period. Conclusions cfDNA is a reliable marker for detecting acute exercise-induced stress. However, the potential of cfDNA for detecting chronic adaptations in short-term, high-intensity interval training settings, such as a HIIT shock cycle, appears limited thus far. Trial registration clinicaltrials.gov, NCT05067426. Registered 05 October 2021—Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT05067426 . Graphical Abstract Key Points Acute increases in cfDNA concentrations were observed after physiological exercise testing and high-intensity interval training (HIIT). No chronic cfDNA changes were observed during and after a HIIT shock microcycle. Acute increases in cfDNA concentrations correlated with physiological exercise variables e.g. peak power output, running velocity at lactate threshold and VO 2max during acute exercise. Higher increases in cfDNA were found after HIIT compared to physiological exercise testing. Higher increases in cfDNA were found after HIIT in the afternoon compared to morning. Higher increases in cfDNA were observed in males compared to females.

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.

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.

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.

The objective of this paper was to determine the impact of EEG-biofeedback training on the motivation and efficiency of powerlifters during the bench press exercise in relation to the external load and the level of training. The study included 18 trained powerlifters who were divided into the intermediate (IG) and the advanced (AG) groups. EEG-biofeedback training was conducted every three days, lasting 27 minutes each time (5 × 3-minute intervals with recovery periods - lying on a bench - between them 4 × 3 minutes), and ended with a final EEG measurement in the second cycle of research. The repeated measures ANOVA showed intra-group differences due to external loading for the FAI (Frontal Alpha Asymmetry) obtained in the EEG both before and after biofeedback training. In AG group analysis revealed significant differences between 65%1RM and 35%1RM. In the IG group between 35%1RM and 50, 65 and 80%1RM. One of the major variables influencing the efficiency of strength training, including bench press workouts, is the level of training. The more successfully an athlete uses motivation when exercising, the better their training, which translates into greater results and a lower chance of injury.