International journal of sports physiology and performance最新文献

Purpose: The abundance of data in football presents both opportunities and challenges for decision making. Consequently, this review has 2 primary objectives: first, to provide practitioners with a concise overview of the characteristics of machine-learning (ML) analysis, and, second, to conduct a strengths, weaknesses, opportunities, and threats (SWOT) analysis regarding the implementation of ML techniques in professional football clubs. This review explains the difference between artificial intelligence and ML and the difference between ML and statistical analysis. Moreover, we summarize and explain the characteristics of ML learning approaches, such as supervised learning, unsupervised learning, and reinforcement learning. Finally, we present an example of a SWOT analysis that suggests some actions to be considered in applying ML techniques by medical and sport science staff working in football. Specifically, 4 dimensions are presented: the use of strengths to create opportunities and make the most of them, the use of strengths to avoid threats, working on weaknesses to take advantage of opportunities, and upgrading weaknesses to avoid threats.

Conclusion: ML analysis can be an invaluable tool for football clubs and sport-science and medical departments due to its ability to analyze vast amounts of data and extract meaningful insights. Moreover, ML can enhance performance by assessing the risk of injury, physiological parameters, and physical fitness, as well as optimizing training, recommending strategies based on opponent analysis, and identifying talent and assessing player suitability.

Purpose: Bone mineral measurements and their association with peak impact and sport-specific, persistent muscle loads were examined in 10 elite artistic swimmers age 15-19 years.

Methods: Bone mineral density (BMD) and bone mineral content (BMC) of total body, total body less head, spine, and dominant and nondominant limbs were assessed by dual-energy X-ray absorptiometry. Peak ground-reaction forces of 2 dry jumps (countermovement jump and frog jump) were measured on a force plate. Peak forces applied during in-water exercises (vertical scull, barracuda push, and kick pull) were measured.

Results: On average, artistic swimmers' total-body BMD (1.12 [0.08] g/cm2) was similar to values reported for young swimmers and nonathletic females, and total-body BMC (2359 [399] g) was higher than previously reported in race swimmers. Based on previously published reference curves, 9 out of 10 artistic swimmers had total-body less head BMD and BMC at or above the 90th percentile, with average to above average z scores and height-adjusted z scores for their age. Countermovement jump and frog jump exhibited moderate peak ground-reaction forces (2.61 [0.46] and 1.93 [0.42] N/kg, respectively). In water, greater force was exerted in kick pull (60.4 [4.8] N) compared with vertical skull (45.5 [6.4] N) and barracuda push (40.6 [4.8] N). Bone measurements were correlated with the peak ground-reaction forces exerted in both dry jumps (r = .61-.83, P ≤ .05) and the peak force output of the in-water exercises (r = .63-.80, P ≤ .05).

Conclusion: These results imply robust bone health among artistic swimmers, partially associated with the high muscle forces regularly applied during their sport-specific training that seem to counteract the low-impact nature of the sport.

Purpose: In world-class middle- and long-distance running races, a Wavelight signal has recently been used as a pacing guide for setting records. The aim of the present study was to compare performance and psychophysiological effects between light-guided, drafting, and nonassisted pacing conditions in distance runners.

Methods: Fifteen male middle- and long-distance runners of national and regional standard ran three 5000-m time trials in a counterbalanced order with the following pacing distribution: the first 4000 m and last 1000 m were covered at submaximal and maximal intensities, respectively. The 3 trials (conditions) were (1) self-paced, (2) guided by a light signal, and (3) guided by a cyclist in front (drafting condition). Pace, heart rate, rating of perceived exertion, and affective valence were recorded every 500 m.

Results: No statistically significant differences were found between pacing-light and self-paced conditions. Running time was shorter in the drafting versus self-paced condition in the final 500-m section (P = .031; d = 0.76). No differences were found between drafting and light conditions. Similarly, whereas 9 out of 10 significant differences in terms of lower heart rate, or rating of perceived exertion, or higher affective valence responses were found in the drafting versus self-paced condition (P = .004-.041; d = 0.63-1.39), only 4 were found across the tests in the drafting versus light condition (P = .005-.016; d = 0.66-0.84).

Conclusion: Light-guided pacing did not influence performance or psychophysiological responses in distance runners during a 5000-m test, but drafting produced a large effect.

Purpose: To correlate speed and heat scores with anthropometric variables and lower-limb strength and power in professional surfers.

Methods: A total of 19 men participated in simulated competitions on different days. All surfed waves were scored, and each athlete's best 2 were used for their total heat score. Speed values were extracted by global positioning system and adjusted by Z score. Squat jump, countermovement jump, and drop jump were executed. Anthropometric variables and 1-repetition maximum (1RM) in the half squat were measured. Pearson product-moment correlation was used to analyze the relationships.

Results: Height had a significant (P < .05) inverse association with speed indicators (r = -.36 to - .68), and body mass index had a moderate association with maximum wave speed of the highest score. Significant correlations with moderate to large magnitudes were found between maximum speed and vertical jumps (r = .46 to .56), average speed and vertical jumps (r = .48 to .59), and both maximum and average speed with 1RM (r = .52-.53). Athletes' best score and total heat score showed moderate to large associations with vertical jumps and 1RM (r = .48-.64), whereas second scores were correlated with the reactive strength index of the drop jump (r = .48) and 1RM (r = .51).

Conclusions: Shorter surfers with lower center of gravity and those with superior lower-limb strength and power achieved greater speed and higher scores. Accordingly, surf coaches may consider prescribing dynamic strength and balance training based on an athlete's profile to improve performance.

Purpose: To compare the acute effects on mechanical, metabolic, neuromuscular, and muscle contractile responses to different velocity-loss (VL) thresholds (20% and 40%) under distinct blood-flow conditions (free [FF] vs restricted [BFR]) in full squat (SQ).

Methods: Twenty strength-trained men performed 4 SQ protocols with 60% 1-repetition maximum that differed in the VL within the set and in the blood-flow condition (FF20: FF with 20% VL; FF40: FF with 40% VL; BFR20: BFR with 20% VL; and BFR40: BFR with 40% VL). The level of BFR was 50% of the arterial occlusion pressure. Before and after the SQ protocols, the following tests were performed: (1) tensiomyography, (2) blood lactate, (3) countermovement jump, (4) maximal voluntary isometric SQ contraction, and (5) performance with the load that elicited a 1 m·s-1 at baseline measurements in SQ.

Results: No "BFR × VL" interactions were observed. BFR protocols resulted in fewer repetitions and lower increases in lactate concentration than FF protocols. The 40% VL protocols completed more repetitions but resulted in lower mechanical performance and electromyography median frequency during the exercise than the 20% VL protocols. At postexercise, the 40% VL protocols also experienced greater blood lactate concentrations, higher alterations in tensiomyography-derived variables, and accentuated impairments in SQ and countermovement-jump performances. The 20% VL protocols showed an increased electromyography median frequency at postexercise maximal voluntary isometric contraction.

Conclusions: Despite BFR-accelerated fatigue development during exercise, a given VL magnitude induced similar impairments in the distinct performance indicators assessed, regardless of the blood-flow condition.

Purpose: Identifying the determinants of performance is fundamental to talent identification and individualizing training prescription. Consequently, the aim of this study was to determine whether estimated muscle typology is associated with the key mechanical characteristics of track sprint cycling.

Methods: Sixteen world-class and elite track cyclists (n = 7 female) completed a laboratory session wherein torque-cadence and power-cadence profiles were constructed to determine maximal power output (Pmax), optimal cadence (Fopt), and maximal cadence (Fmax), and fatigue rate per pedal stroke was determined during a 15-second maximal sprint at Fopt. Muscle typology was estimated by measuring carnosine content via proton magnetic resonance spectroscopy in the gastrocnemius and soleus.

Results: Using partial correlation analysis to account for sex, greater muscle carnosine content (ie, greater estimated proportion of type II fibers) was associated with a greater Pmax (r = .68, P = .007), Fmax (r = .77, P = .0014), Fopt (r = .61, P = .0196), and absolute fatigue rate (W·stroke-1; r = -.55, P = .0418) but not relative fatigue rate (%peak power·stroke-1; r = -.33, P = .246).

Conclusions: The findings from this study substantiate the mechanical differences in muscle-fiber types derived from single muscle-fiber studies and highlight the importance of estimated muscle typology for sprint cycling performance.

Normal science is about assessing what is already known about a topic, about what needs to be known, and careful methods to collect the data required to answer the scientific question. It is the mainstay of scientific progress. But sometimes, luck or chance (eg, serendipity) plays a significant role in scientific process. We trace career experiences in our lives to show how serendipity, the gift that Mother Nature gives to science, has a nontrivial role in scientific progress and professional careers. This gift often encourages us to change the focus of experiments or to look for the answers to our questions in ways that were not immediately obvious.