European journal of sport science最新文献

Aim of the study was to analyze the metabolic profile of badminton matches and training drills. Therefore, 11 male (23.2 ± 3.8 years, 182 ± 7 cm, 74.4 ± 8.4 kg) and five female (19.3 ± 1.5 years, 170 ± 6 cm, 62.6 ± 9.2 kg) elite badminton players participated in either a training match (T_M; n = 7) and/or three protocols of multifeeding drills (T₁₀, T_30, T_50; n = 13), that varied in interval and rest durations (10 s/10 s, 30 s/30 s, 50 s/50 s). Absolute and relative energetic costs (W_tot and E_tot) and contribution to oxidative (W_Oxid), phosphagen (W_PCr), and anaerobic glycolytic (W_La) metabolism were calculated by the three-component PCr-La-O₂-method based on an indirect calorimetric approach from oxygen consumption during exercise, post exercise, and net blood lactate concentration. A novel intermittent approach was used to consider replenishment of phosphocreatine during each resting phase. Results show that during T_M, E_tot was 676 ± 98J·kg⁻¹ min⁻¹, while metabolic pathways contributed by 56.9 ± 8.6% (W_Oxid), 42.7 ± 8.7% (W_PCr), and 0.4 ± 0.6% (W_La). In the multifeeding drills E_tot was comparable between T₁₀ (1020 ± 160J·kg⁻¹ min⁻¹) and T₃₀ (985 ± 173 J·kg⁻¹ min⁻¹) but higher in T₅₀ (1266 ± 194J·kg⁻¹ min⁻¹) (p < 0.001). Relative contribution of W_Oxid was lower in T₁₀ (47.3 ± 7.7%) but similar in T₃₀ (56.5 ± 6.2%) and T₅₀ (57.3 ± 6.0%) (p < 0.001). W_PCr was highest in T₁₀ (51.1 ± 8.3%) followed by T₃₀ (42.2 ± 6.9%) and lowest in T₅₀ (31.2 ± 7.7%) (p < 0.001). W_La was similar between T₁₀ (1.6 ± 1.0%) and T₃₀ (2.1 ± 1.0%) but higher in T₅₀ (11.6 ± 4.8%) (p < 0.001). Concludingly, metabolic costs in badminton are predominantly covered by oxidative and phosphagen energetic pathways. Metabolic profiles of the multifeeding drills differ depending on rally/interval duration, with increasing contribution of anaerobic glycolysis and decreasing phosphagen contribution in case of longer intervals.

It has been suggested that time at a high fraction (%) of maximal oxygen uptake (VO_2max) plays a decisive role for adaptations to interval training. Yet, no study has, to date, measured the % of VO_2max during all interval sessions throughout a prolonged training intervention and subsequently related it to the magnitude of training adaptations. Thus, the present study aimed to investigate the relationship between % of VO_2max achieved during an interval training intervention and changes in endurance performance and its physiological determinants in well-trained cyclists. Twenty-two cyclists (VO_2max 67.1 (6.4) mL·min⁻¹ ·kg⁻¹; males, n = 19; females, n = 3) underwent a 9-week interval training intervention, consisting 21 sessions of 5 × 8-min intervals conducted at their 40-min highest sustainable mean power output (PO). Oxygen uptake was measured during all interval sessions, and the relationship between % of VO_2max during work intervals and training adaptations were investigated using linear regression. A performance index was calculated from several performance measures. With higher % of VO_2max during work intervals, greater improvements were observed for maximal PO during the VO_2max test (R²_adjusted = 0.44, p = 0.009), PO at 4 mmol·L⁻¹ [blood lactate] (R²_adjusted = 0.25, p = 0.035), the performance index (R²_adjusted = 0.36, p = 0.013), and VO_2max (R²_adjusted = 0.54, p = 0.029). Other measures, such as % of maximal heart rate, were related to fewer outcome variables and exhibited poorer session-to-session repeatability compared to % of VO_2max. In conclusion, improvements in endurance measures were positively related to the % of VO_2max achieved during interval training. Percentage of VO_2max was the measure that best reflected the magnitude of training adaptations.

Nine cyclists (age: 26 ± 5 years, height: 168 ± 5 cm and mass 58.5 ± 4.5 kg) were observed using continuous glucose monitoring devices throughout a training camp. Interstitial glucose [iG] data were captured via the Abbott libre sense biosensor (Abbott Laboratories) and paired with the Supersapiens software (TT1 Products Inc.). [iG] data were split into time ranges, that is, overall (24-hourly), day-time (06:00–23:59), night-time (00:00–05:59) and exercise. [iG] data were stratified into percentage of time, below range ([TBR] < 70 mg/dl), in range ([TIR] 70–140 mg/dl) and above range ([TAR] ≥ 141 mg/dl). Differences in diurnal and nocturnal data were analysed via repeated measures analysis of variance and paired t-tests where appropriate. p-value of ≤0.05 was accepted as significant. Riders spent an average of 3 ± 1% TAR, 93 ± 2% TIR and 8 ± 3% TBR. Mean 24 h [iG] was 93 ± 2 mg/dl with a coefficient of variation (CV) of 18 ± 1%. Mean (day: 95 ± 3 vs. night: 86 ± 3 mg/dl and p < 0.001) and CV (day: 18 ± 1 vs. night: 9 ± 1% and p < 0.001) in [iG] were higher during the day-time hours. TAR was greater during the day (day: 3 ± 1 vs. night: 0 ± 0% and p < 0.001) but TBR and TIR were similar. Glucose levels below the clinical range may have implications for those without diabetes and warrants further investigation.

Selecting the right individuals for a sports team, organization, or military unit has a large influence on the achievements of the organization. However, the approaches commonly used for selection are either not reporting predictive performance or not explainable (i.e., black box models). In the present study, we introduce a novel approach to selection research, using various machine learning models. We examined 274 special forces recruits, of whom 196 dropped out, who performed a set of physical and psychological tests. On this data, we compared four machine learning models on their predictive performance, explainability, and stability. We found that a stable rule-based (SIRUS) model was most suitable for classifying dropouts from the special forces selection program. With an averaged area under the curve score of 0.70, this model had good predictive performance, while remaining explainable and stable. Furthermore, we found that both physical and psychological variables were related to dropout. More specifically, a higher score on the 2800 m time, need for connectedness, and skin folds was most strongly associated with dropping out. We discuss how researchers and practitioners can benefit from these insights in sport and performance contexts.

Professional tennis competition at the highest levels requires high physical, technical, tactical, and mental demands. Player retirement is a scenario that often occurs at the highest echelons of the game. The objective of this study was to descriptively determine which factors influence retirement of matches in tennis. Matches from the Women's Tennis Association (WTA) and Association of Tennis Professionals (ATP) tours played over 44 and 46 years were included in the study, respectively. The results showed an increasing trend in the incidence of retirements in both ATP and WTA events, especially in recent years. Factors associated with the match characteristics, such as the type of surface, the type of tournament, and the round of the draw, were shown to influence retirement. Variables associated with player characteristics, such as the age difference between players, did not show to influence retirement, whereas the ranking difference did. When comparing ATP and WTA matches, similar results were observed in which surfaces and tournaments had the highest or lowest incidence of retirements. On the other hand, as per the rounds, the results are not conclusive. The findings highlight the growing trend of match retirements in professional tennis and emphasize the relevant influence of match characteristics such as surface type, tournament type, and round of the draw. These insights can guide coaches, players, and tournament organizers in developing strategies to mitigate retirements and inform future research on enhancing player longevity and performance in tennis.

The study aimed to illustrate how contact (from match-event data) and head acceleration event (HAE) (from instrumented mouthguard [iMG]) data can be combined to inform match limits within rugby. Match-event data from one rugby union and rugby league season, including all competitive matches involving players from the English Premiership and Super League, were used. Playing exposure was summarised as full game equivalents (FGE; total minutes played/80). Expected contact and HAE exposures at arbitrary thresholds were estimated using match-event and iMG data. Generalised linear models were used to identify differences in contact and HAE exposure per FGE. For 30 FGEs, forwards had greater contact than backs in rugby union (n = 1272 vs. 618) and league (n = 1569 vs. 706). As HAE magnitude increased, the differences between positional groups decreased (e.g., rugby union; n = 34 and 22 HAE >40 g for forwards and backs playing 30 FGEs). Currently, only a relatively small proportion of rugby union (2.5%) and league (7.3%) players exceeded 25 FGEs. Estimating contact and HAEs per FGE allows policymakers to prospectively plan and model estimated overall and position-specific loads over a season and longer term. Reducing FGE limits by a small amount would currently only affect contact and HAE exposure for a small proportion of players who complete the most minutes. This may be beneficial for this cohort but is not an effective HAE and contact exposure reduction strategy at a population level, which requires individual player management. Given the positional differences, FGE limits should exist to manage appropriate HAE and contact exposure.

We examined the effect of prolonged tennis playing on lower limb muscles' activity during the execution of first and second tennis serves. Ten male competitive tennis players executed five first and second serves before (pretest) and after (posttest) a 3-h tennis match. Surface electromyographic (EMG) activity of four lower limb muscles (vastus lateralis, rectus femoris, gastrocnemius lateralis, and soleus muscles) on each leg was recorded along with maximum ball velocity measured by a radar gun and peak vertical forces recorded by a force platform. For the vastus lateralis, gastrocnemius lateralis, and soleus muscles of the left leg as well as the vastus lateralis muscle of the right leg, EMG amplitude decreased from pre- to posttests (p ≤ 0.033). These reductions in the EMG signal were generally more pronounced in the first serve (i.e., ranging from −10% to −40%) compared to the second serve (0% to −25%). Maximum ball velocity for both first (159 ± 12 vs. 154 ± 12 km/h) and second (126 ± 20 vs. 125 ± 15 km/h) serves remained unchanged from pre- to posttests (p = 0.638) Similarly, peak vertical forces did not differ between pretest and posttest for both first (1.78 ± 0.30 vs. 1.72 ± 0.29 body weight) and second (1.62 ± 0.25 vs. 1.75 ± 0.23 body weight) serves (p = 0.730). In conclusion, a 3-h tennis match led to decreased activation levels in various leg muscles during serves, particularly in first serves compared to second serves. Despite consistent maximum ball velocity and peak vertical forces, these reductions in EMG signals suggest that skilled tennis players may adopt compensatory strategies after prolonged play.

Predictive equations are widely employed for estimating the volume of oxygen consumption (VO₂) while walking, which is ultimately employed to determine energy expenditure and tailor exercise prescription. This study aimed to test the agreement between the measured VO₂ and estimated VO₂ during a walking protocol on a treadmill at 3.5, 4.5, 5.5, and 6.5 km/h. Thirty-eight young adults (50% women) participated in this cross-sectional study. The Omnical (Maastricht Instruments, Maastricht, The Netherlands) and K5 (Cosmed, Rome, Italy) metabolic systems were used to measure VO₂. To determine the predictive equations, a comprehensive literature search was conducted using the MEDLINE database from May 2022 to July 2023. Seven predictive equations were found and included for estimating VO₂ values. We calculated the mean bias (mean difference between measured VO₂ and estimated VO₂) obtained at each speed using one-sample t-tests. We compared the VO₂ measured and estimated values using repeated measures analysis of variance and the Bland–Altman method. One-sample t-tests showed that all score errors were different from zero (ranging from 1.1 to 5.4 mL/kg/min). Thus, no predictive equation estimated similar VO₂ values in comparison with the Omnical and K5 metabolic systems at all intensities. However, the Weyand equation showed the lowest bias across all intensities (score error of 1.1 mL/kg/min). This study showed a lack of agreement between the Omnical and K5 systems compared to diverse predictive equations specially designed to estimate VO₂ during walking. Nevertheless, based on our results, the Weyand equation should be the preferred option.

The study explores the validity of the nonlinear index alpha 1 of detrended fluctuation analysis (DFAa1) of heart rate (HR) variability for exercise prescription in prolonged constant load running bouts of different intensities. 21 trained endurance athletes (9 w and 12 m) performed a ramp test for ventilatory threshold (vVT1 and vVT2) and DFAa1-based (vDFAa1-1 at 0.75 and vDFAa1-2 at 0.5) running speed detection as well as two 20-min running bouts at vDFAa1-1 and vDFAa1-2 (20-vDFAa1-1 and 20-vDFAa1-2), in which HR, oxygen consumption (VO₂), respiratory frequency (RF), DFAa1, and blood lactate concentration [La-] were assessed. 20-vDFAa1-2 could not be finished by all participants (finisher group (FG), n = 15 versus exhaustion group (EG), n = 6). Despite similar mean external loads of vDFAa1-1 (10.6 ± 1.9 km/h) and vDFAa1-2 (13.1 ± 2.4 km/h) for all participants compared to vVT1 (10.8 ± 1.7 km/h) and vVT2 (13.2 ± 1.9 km/h), considerable differences were present for 20-vDFAa1-2 in EG (15.2 ± 2.4 km/h). 20-vDFAa1-1 and 20-DFAa1-2 yielded significant differences in FG for HR (76.2 ± 5.7 vs. 86.4 ± 5.9 %HR_PEAK), VO₂ (62.1 ± 5.0 vs. 77.5 ± 8.6 %VO_2PEAK), RF (40.6 ± 11.3 vs. 46.1 ± 9.8 bpm), DFA-a1 (0.86 ± 0.23 vs. 0.60 ± 0.15), and [La-] (1.41 ± 0.45 vs. 3.34 ± 2.24 mmol/L). Regarding alterations during 20-vDFAa1-1, all parameters showed small changes for all participants, while during 20-vDFAa1-2 RF and DFAa1 showed substantial alterations in FG (RF: 15.6% and DFAa1: −12.8%) and more pronounced in EG (RF: 20.1% and DFAa1: −35.9%). DFAa1-based exercise prescription from incremental testing could be useful for most participants in prolonged running bouts, at least in the moderate to heavy intensity domain. In addition, an individually different increased risk of overloading may occur in the heavy to severe exercise domains and should be further elucidated in the light of durability and decoupling assessment.