Journal of Sports Science and Medicine最新文献

Body mass (BM) can be used to prescribe loads for some weightlifting derivatives, as an alternative to the one-repetition maximum (1RM). However, the effectiveness of this method has not been investigated in weightlifting overhead pressing derivatives. The primary aim of the study was to investigate the effect of loads determined with percentages of 1RM and BM on kinetic and kinematic characteristics in the behind-neck push jerk (BNPJ). Sixteen recreational male athletes were recruited and performed 3 repetitions of the BNPJ from 40% to 80% of their 1RM and BM, respectively. Two force plates were used to collect kinematic (peak velocity, mean velocity, phase time) and kinetic variables (peak force, mean force, peak power, mean power, and impulse) in the concentric phase. A two-way repeated measures analysis of variance assessed the interaction and the main effect (approaches and intensities) on the dependent variables. The level of significance was set at p ≤ 0.05. No significant interactions existed between the approaches and intensities in all variables. The main effect approach was not significant in all the kinematic variables, but significant intensity main effects were found. Significant approach and intensity main effects were found in all kinetic variables. All kinetic variables were greater in the 1RM-based approach compared to the BM-based approach. BM can serve as a practical alternative to 1RM for load prescription in the BNPJ when targeting kinematic characteristics. However, 1RM-based loading may be more suitable for maximizing kinetic outputs.

This study investigated how varying task constraints - pitch size and the presence of a goal - affect psychophysiological intensity, technical execution, and tactical decision-making in small-sided games (SSGs). The study also examined correlations between intensities and technical and tactical performance. Thirty-six regional-level male youth soccer players (aged 16.5 ± 0.5 years) participated in a four-week intervention using a non-controlled, repeated-measures design within a single cohort. Players completed 3v3 SSGs under six conditions combining three pitch sizes (75, 100, and 125 m² per player) and two task goals (ball possession vs. small goals). Each session included standardized warm-ups and three 4-minute bouts per condition. Psychophysiological responses were measured via Rating of Perceived Exertion (RPE) and heart rate monitoring (HRmean, % time in Zone 5). Technical actions (passes, receptions, dribbles, shots) and tactical decision-making (Passing Decision-Making index) were assessed through video-based analysis. Significant interactions (p < 0.001) between field size and scoring method were found for HR measures and passing. Ball possession games showed higher HRmean and HR Zone 5 across all field sizes compared to small-goal games (p < 0.001). However, even in small-goal games, HRmean and HR Zone 5 significantly increased with larger field sizes (p < 0.001). Small-goal games resulted in more successful dribbles (p < 0.001), with fewer successful passes on the smallest field. A moderate negative correlation was observed between HRmean and successful shots (r = -0.346, p = 0.039), and between time in HR Zone 5 and the passing decision-making index (r = -0.363, p = 0.029). The study suggests that both field size and scoring method significantly influence players' physiological responses, technical performance, and decision-making. Ball possession games and larger fields increase physical intensity and passing success, while smaller fields and small-goal games promote dribbling. However, higher physiological strain appears to negatively impact shooting effectiveness and decision-making quality, although these correlations are moderate and no definitive conclusions can be drawn or generalized.

This study investigated the effect of high-intensity interval training (HIIT) in normobaric hypoxia on aerobic performance in young biathlon athletes. In addition, the study aimed to assess the impact of training in hypoxia on the mechanisms of exercise-induced motor performance fatigue. In a randomized, controlled crossover study twelve athletes (age 15.7 ± 1.0 years) completed a HIIT in normobaric hypoxia (hypoxia training) (fraction of inspired oxygen, F_iO₂ = 15.2%) and normoxia (normoxia training) in a randomized order. The HIIT was performed 3 days/week for 6 weeks (3 weeks in hypoxia and 3 weeks in normoxia, with a 3 week wash-out period in between) and consisted of 5 x 4 minutes running (80% of peak oxygen uptake), separated by 3 minutes of active recovery and 4 x 1minute arm cranking (60% peak power), interspersed with a 2 minute rest. Peak oxygen uptake (V̇O_2peak), hypoxia-inducible factor 1 alpha (HIF1α), vascular endothelial growth factor (VEGF), pro-inflammatory cytokines, muscle damage biomarkers and total antioxidant status were analyzed before and after both training protocols (HT and NT). A significant effect of hypoxia on V̇O_2peak (ηp² = 0.321, p = 0.044) and hypoxia and training on V̇O₂LT and haemoglobin concentrations (ηp² = 0.689 p = 0.001) were observed. The V̇O_2peak was significantly higher post-HT compared to pre-HT (p < 0.01). A significant effect of oxygen conditions and training on the serum post-exercise VEGF (ηp² = 0.352, p = 0.033) and myoglobin concentrations (ηp² = 0.647 p = 0.001) was found. A significant effect of hypoxia was also observed for cytokines levels: interleukin-6 (ηp² = 0.324 p = 0.042), tumour necrosis factor alpha (ηp² = 0.474 p = 0.009) and transforming growth factor beta (ηp²= 0.410, p = 0.018) with a non-significant effect on antioxidant status. This study shows significant differences in the aerobic performance and biomarkers of muscle damage after exposure to hypoxia training. These findings highlight that HIIT in hypoxia is sufficient to enhance aerobic performance and may also reduce skeletal muscle susceptibility to fatigue in young biathletes.

To explore neuromuscular control during blood flow restriction (BFR) squat exercise using wavelet packet transform (WPT) combined with non-negative matrix factorization (NMF). Fifteen resistance-trained males completed four sets of squats at 40% arterial occlusion pressure. Countermovement jump (CMJ) height and reactive strength index modified (RSImod) alongside surface electromyographic activity from eight lower-limb muscles were assessed before after the exercise. CMJ height and RSImod significantly increased post-exercise (P < 0.001, Cohen's d = 0.45 and 0.34, respectively). Four muscle synergy modules were consistently identified, though primary muscle contributions shifted across movement phases. The tibialis anterior (TA) was the primary contributor in Synergy1, while the gastrocnemius lateralis (GL) dominated Synergy 2, accompanied by a significant increase in gluteus maximus (GM) weight (P = 0.032). In Synergy 3, the rectus femoris (RF), vastus lateralis (VL), and vastus medialis (VM) were predominant, with significant changes in GM and VM muscle weights (P = 0.013, 0.039). Synergy 4 was characterized by contributions from the semitendinosus (ST), biceps femoris (BF), and GM, with a significant increase in VL muscle weight (P = 0.024). WPT-NMF analysis revealed distinct time-frequency synergy modules in CMJ movements before and after BFR squat exercise. Significant changes in activation weights were observed within the 0-250 Hz range (P < 0.05). BFR squat exercise acutely enhances countermovement jump performance by refining muscle synergy and neuromuscular activation patterns, providing novel insights into neuromuscular control strategies.

We investigated the effects of 8-week aquatic moderate-intensity continuous training (aMICT) and aquatic high-intensity interval training (aHIIT) on body composition, aerobic fitness, arterial stiffness, and endothelial function in overweight and obese young adults (OOYA). Sixty-one OOYA were randomly assigned to aHIIT, aMICT, or Control group. aHIIT group underwent twelve 30-second exercise bouts with the intensity of 85-95% HR_max, with a 60-second rest between each bout. aMICT group underwent an uninterrupted exercise with the intensity of 70-75% HR_max for 30 minutes. Endothelial function was assessed using brachial artery flow-mediated dilation (FMD) and arterial stiffness was evaluated through pulse wave velocity (PWV) and ankle-brachial index (ABI). Results revealed that aHIIT but not aMICT decreased ABI and increased FMD and skeletal muscle mass, whereas only aMICT decreased resting heart rate. A positive correlation was found between the change in weight with the change in FMD (r = 0.527, p = 0.020) after aHIIT. Following subgroup analysis, a positive correlation between change in weight and change in FMD was also found in participants with increased skeletal muscle mass in aHIIT group (r = 0.665, p = 0.002). Moreover, the change in VO_2max was positively correlated with the change in FMD (r = 0.568, p < 0.001). In conclusion, both aHIIT and aMICT can raise aerobic capacity among OOYA. Importantly, aHIIT offers a time-efficient option to improve vascular function in OOYA, whereas aMICT may be preferable when the primary goal is to lower resting heart rate.

The aim of this study was to examine how physiological, locomotor, and mechanical load parameters contribute to variations in aerobic, anaerobic, and neuromuscular adaptations in male soccer players. A 12-week cohort study was conducted involving 41 male under-17 soccer players (16.4 ± 0.5 years old). All training sessions and matches were monitored using heart rate (HR) monitors, ratings of perceived exertion (RPE), and a global positioning system (GPS). The following variables were recorded daily: training impulse (TRIMP), session-RPE, total distance, high speed running (14.0 to 19.9 km/h, HSR), and very high speed running (>20 km/h, VHSR), and the number of accelerations and decelerations. Physical fitness was assessed twice - at baseline and after the 12-week intervention. The assessments included aerobic capacity via the Yo-Yo Intermittent Recovery Test (YYIRT), anaerobic capacity via the mean sprint time at Running-Based Anaerobic Sprint Test (RSAmean), muscle power using the Countermovement Jump (CMJ), and sprint performance measured in a 30-meter sprint. Simple linear regressions showed that both accumulated session-RPE (R² = 0.446, β = 0.668, p < 0.001) and accumulated TRIMP (R² = 0.417, β = 0.646, p < 0.001) were significant positive predictors of YYIRT delta, although explain less than half of variance. A multiple regression analysis revealed that accumulated VHSR significantly predicted RSAmean delta, indicating that higher VHSR values are associated with smaller and improved RSAmean (B = -0.003, p = 0.002), while HSR was not a significant predictor (p = 0.291). These findings suggest that internal load measures (session-RPE, TRIMP) are more strongly associated with aerobic adaptations, while specific external load metrics (e.g., VHSR) better explain RSA changes, highlighting the importance of modifying load monitoring strategies to the specific physiological adaptations targeted. Incorporating individualized load management based on these measures may help maximize performance improvements in practical contexts.

Resistance training plays a key role in enhancing muscular performance; however, the effects of different combinations of loading intensity and velocity loss (VL) thresholds on muscle oxygen saturation (SmO₂) dynamics during exercise remain insufficiently understood. This study aimed to investigate the influence of intensity (60% vs. 80% one-repetition maximum [1RM]) and VL (20% vs. 40%) on SmO₂ responses during the back squat exercise. Eighteen resistance-trained males (age: 20.06 ± 1.63 years; height: 176.78 ± 6.45 cm; body mass: 70.26 ± 9.56 kg) performed four back squat protocols - 60%1RM-VL20% (60-20), 60%1RM-VL40% (60-40), 80%1RM-VL20% (80-20), and 80%1RM-VL40% (80-40) - using a repeated-measures, counterbalanced design. Each protocol comprised three sets with 5-minute inter-set rest periods. SmO₂ of the vastus lateralis was continuously monitored to determine changes in its magnitude and slope during exercise and recovery phases. Results revealed no significant differences were observed in the magnitude of SmO₂ decline across conditions, with values ranging from 47.28% to 57.67% across all sets (p > .05). The SmO₂ decline slope was significantly steeper (more negative) in the 80-20 condition (-1.71 to -2.04 %·s^-1) compared to both 60-20 (-0.80 to -1.13 %·s^-1) and 60-40 (-0.53 to -1.00 %·s^-1) across all sets (p < .001). No significant differences were observed in SmO₂ recovery slope during rest (range: 0.36-0.61 %·s^-1; p > .05). The present study demonstrated that combining 60% 1RM with a 40% VL% threshold elicited the slowest SmO₂ decline rate, potentially delaying fatigue onset and allowing greater repetition volume. Although both training intensity and velocity loss thresholds influenced muscle oxygenation dynamics, the rate of SmO₂ desaturation was particularly sensitive to changes in VL% thresholds under the 80% 1RM. These findings underscore the importance of integrating training intensity, VL% magnitude, and oxygenation dynamics when designing individualized resistance training protocols.

Grip strength, a biomarker, can be measured at any age; however, its values vary daily for each individual, which impacts the assessment. Absolute test-retest reliability (i.e., minimal difference, MD) is commonly defined as the variation in absolute values of measurements taken by a single person or instrument on the same item under identical conditions. Nevertheless, the potential moderators of absolute repeatability in grip strength measurements have not yet been fully elucidated. We conducted a systematic review with meta-analysis to examine the influence of potential moderating factors on the absolute test-retest repeatability of grip strength measurements in healthy populations. PubMed, Scopus, and SPORTDiscus databases were searched up to January 2025 following the PRISMA guidelines, and 48 studies were included in this review. Age, test-retest interval, and device were used as potential moderating factors; however, sex and sports experience were excluded due to the limited number of published articles. We found considerable variation among studies reporting MD and percentage of MD to measured value (%MD) across each age group. The mean MD (%MD) values were 1.9 kg (25.4%) in young children (<7 years old), 2.5 kg (13.8%) in children (7-10 years old), 4.2 kg (17.1%) in adolescents (10-18 years old), 4.0 kg (11.6%) in young adults (18-35 years old), and 4.7 kg (16.7%) in older adults (>60 years old). Neither age [effect size [ES]: 0.015 (95% confidence interval [CI]: -0.004, 0.035; p = 0.113) for MD and ES: -0.025 (95% CI: -0.089, 0.039; p = 0.439) for %MD], test-retest interval [ES: 0.006 (95% CI: -0.002, 0.013; p = 0.143) for MD and ES: 0.022 (95% CI: -0.001, 0.046; p = 0.065) for %MD] nor handgrip device (p = 0.752 for MD and p = 0.334 for %MD) served as significant moderators of MD and %MD reliability. Due to the limited number of studies, sex and sports experience were excluded from the analysis; as a result, their impacts remain unknown.

Submaximal oxygen uptake (VO_2sub) scaled by ratio is commonly used to evaluate running economy (RE) to reflect metabolic consumption at a given submaximal-intensity velocity. However, this method is questionable due to its neglect of substrate-related issues and the inherent mathematical discrepancies in ratio scaling. This study aimed to investigate the validity of ratio-scaled VO_2sub as a measure of RE by comparing it with allometric-scaled energy cost (E_c, kcal/kg^b/min). Sixty-nine recreationally active college students underwent VO_2max tests and discontinuous submaximal running assessments at three %VO_2max intensities. A 1000-meter test assessed running performance. One-way repeated-measures ANOVA compared changes in VO_2sub or E_c with increasing running intensities. Regression analysis explored methods for metabolic data standardization. Pearson correlation coefficient evaluated the effectiveness of standardization and the correlations between sports performance and RE scaled by different measures. Magnitude-based inferences were used to assess sex differences and probabilities of RE at each running intensity. Both VO_2sub and E_c significantly increased with increasing intensities, suggesting that VO_2sub is a valid quantification of RE. Allometric scaling is more suitable than ratio scaling for removing the influence of body weight on both E_c and VO_2sub, with females showing better RE. Allometric-scaled E_c was sensitive in detecting correlations with performance, strongest at 65% VO_2max. While VO_2sub is a valid quantification of RE, allometric scaling, rather than ratio scaling, should be used to normalize the RE quantification before performing reliable interindividual comparisons. The 2/3 law can be considered as the exponent b value for body weight. Additionally, 65%VO_2max intensity is recommended as the submaximal testing intensity in the RE test. Nonetheless, more studies with diverse samples are needed to confirm the validity.

Soccer players are frequently categorized by playing positions, both in the scientific literature and in practice. However, the utility of this approach in evaluating physical match performance and optimizing physical training programs remains unclear. This study compares the effectiveness of categorizing soccer players by their playing position versus using unsupervised machine learning based on match-specific running performance. Match-specific running data were collected from 40 young elite male soccer players over two seasons. Thirty-one of these players completed a 20-meter sprint test and a maximal incremental treadmill test to measure maximal oxygen uptake. Players were categorized both by playing position and by subgroups derived through k-means clustering based on match-specific running performance. Differences in sprint capacity, endurance capacity, and match-specific running performance were compared between and within playing positions, as well as between and within clusters. The two categorization methods were further compared for variance within subgroups and standardized differences between subgroups for total distance (TD), low-intensity running (LIR), moderate-intensity running (MIR), high-intensity running (HIR), and sprint distance during matches. Match-specific running performance differed between playing positions, despite notable inter-individual differences in running intensities within playing positions. Clustering based on match-specific running performance revealed less variance within groups (TD: P = 0.049, LIR: P = 0.032, HIR: P = 0.033) and larger standardized differences between groups (LIR: P = 0.037, MIR: P = 0.041, HIR: P = 0.035, Sprint: P = 0.018) compared to grouping by playing position. Moreover, 20-meter sprint speed differed between the sprint and high intensity endurance clusters (25.22 vs 23.75 km/h, P = 0.012), but not between playing positions. Using unsupervised machine learning to categorize soccer players improves the identification of player groups with similar match-specific running performance, thereby supporting performance evaluation and contributing to the optimization of physical training.