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Aerobic exercise has many effects on brain function, particularly at the hippocampus. Exercise has been shown to increase the rate of adult neurogenesis within the dentate gyrus and decrease the density of perineuronal nets in area CA1. The relationship between the rate of neurogenesis and the density of perineuronal nets in CA1 is robust; however, these studies only ever examined these effects across longer time scales, with running manipulations of 4 weeks or longer. With such long periods of manipulation, the precise temporal nature of the relationship between running-induced neurogenesis and reduced perineuronal net density in CA1 is unknown. Here, we provided male and female mice with home cage access to running wheels for 0, 1, 2, or 4 weeks and quantified hippocampal neurogenesis and CA1 perineuronal net density. In doing so, we observed a 2-week delay period prior to the increase in neurogenesis, which coincided with the same delay prior to decreased CA1 perineuronal net density. These results highlight the closely linked temporal relationship between running-induced neurogenesis and decreased perineuronal net expression in CA1.

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Numerous cross-sectional studies have attempted to identify the muscle morphology required to achieve high sprint velocity. Our longitudinal study addressed an unanswered question of cross-sectional studies: whether hypertrophy of the individual trunk and thigh muscles induced by daily training (e.g., sprint, jump, and resistance training) is linked to an improvement in sprint performance within well-trained sprinters. Twenty-three collegiate male sprinters (100-m best time of 11.36 ± 0.44 s) completed their daily training for 1 year without our intervention. Before and after the observation period, the sprint velocities at 0-100 m, 0-10 m, and 50-60 m intervals were measured using timing gates. The volumes of 14 trunk and thigh muscles were measured using magnetic resonance imaging. Muscle volumes were normalized to the participants' body mass at each time point. Sprint velocities increased at the 0-100 m (p < 0.001), 0-10 m (p = 0.019), and 50-60 m (p = 0.018) intervals after the observation period. The relative volumes of the tensor fasciae latae, sartorius, biceps femoris long head, biceps femoris short head, semitendinosus, and iliacus were increased (all p < 0.050). Among the hypertrophied muscles, only the change in the relative volume of the semitendinosus was positively correlated with the change in sprint velocity at the 50-60 m interval (p = 0.018 and ρ = 0.591). These findings suggest that semitendinosus hypertrophy seems to be associated with sprint performance improvement within well-trained sprinters during the maximal velocity phase.

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The study aimed to investigate the effects of acute ingestion of saffron (SAF) on physiological (i.e., heart rate and blood lactate) and perceptual (i.e., ratings of perceived exertion [RPE] and feeling scale) measures in response to a repeated-sprint ability test (RSS) in healthy young males (N = 22; mean ± SD: age, 21.7 ± 1.24 yrs.). All participants completed two experimental trials with a one-week washout period using a double-blind, placebo-controlled, crossover design. In each session, the participants were randomly chosen to receive either a capsule of saffron (300 mg) (SAF session) or a capsule of lactose (PLB session) two hours before performing the RSS.No significant differences (p > 0.05) were found for heart rate, RPE, and feeling scale between the SAF or PLB sessions at pre- and post-RSS. There were no significant changes (p > 0.05) in peak time, total time, fatigue index, and blood lactate in either the SAF or PLB sessions. Acute SAF ingestion did not significantly improve RSS performance nor physiological and perceptual measures in active young males. Future trials should address the topic by using shortened/prolonged higher doses of SAF on biological, physical, physiological, and perceptual responses to acute and chronic exercise.

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Despite the well-documented benefits of sprint interval training (SIT) and plyometric training (PT) in improving the physical fitness of soccer players, it remains unclear which of these training methods is superior for enhancing players' aerobic and anaerobic performance. Therefore, this study aimed to compare the effects of SIT and PT on physical performance measures of male soccer players. Thirty male soccer players were randomly assigned to PT (n = 10), SIT (n = 10), and an active control group (CON, n = 10). Before and after the training period, participants underwent a battery of tests consisting of vertical jump, Wingate, linear sprint with and without ball dribbling, change of direction, ball kick, and the Yo-Yo intermittent recovery level 1 (Yo-Yo IR1) tests. Both groups exhibited similar improvements in maximal kicking distance (PT, effect size [ES] = 0.68; SIT, ES = 0.92) and measures of aerobic fitness including maximum oxygen uptake (PT, ES = 1.24; SIT, ES = 1.26) and first (PT, ES = 0.85; SIT, ES = 1.08) and second (PT, ES = 0.86; SIT, ES = 0.98) ventilatory thresholds. However, PT intervention resulted in greater changes in vertical jump (ES = 1.72 vs. 0.82, p = 0.001), anaerobic power (peak power, ES = 1.62 vs. 0.97, p = 0.009; mean power, ES = 1.15 vs. 1.20, p = 0.05), linear speed (20-m, ES = -1.58 vs. -0.98, p = 0.038; 20-m with ball, ES = -0.93 vs. 0.71, p = 0.038), and change of direction ability (ES = -2.56 vs. -2.71, p = 0.046) than SIT. In conclusion, both PT and SIT demonstrated effectiveness in enhancing aerobic performance among male soccer players. However, PT yielded superior improvements in anaerobic power, vertical jump, linear speed, and change of direction performance compared to SIT. These findings suggest that PT may offer additional benefits beyond aerobic conditioning.

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Sports injuries pose significant challenges in athlete welfare and team dynamics, particularly in high-intensity sports like soccer. This study used machine learning algorithms to assess non-contact injury risk in professional male soccer players from physiological and mechanical load variables. Twenty-five professional male soccer players with a first-time, non-contact muscle injury were included in this study. Recordings of external load (speed, distance, and acceleration/deceleration data) and internal load (heart rate) were obtained during all training sessions and official matches over a 4-year period. Machine learning model training and evaluation features were calculated for each of nine different metrics for a 28-day period prior to the injury and an equal-length baseline epoch. The acute surge in the values of each workload metric was quantified by the deviation of maximum values from the average, while the variations of cumulative workload over the last four weeks preceding injury were also calculated. Seven features were selected by the model as prominent estimators of injury incidence. Three of the features concerned acute load deviations (number of sprints, training load score-incorporating heart rate and muscle load- and time of heart rate at the 90-100% of maximum). The four cumulative load features were (total distance, high speed and sprint running distance and training load score). The accuracy of the muscle injury risk assessment model was 0.78, with a sensitivity of 0.73 and specificity of 0.85. Our model achieved high performance in injury risk detection using a limited number of training load variables. The inclusion, for the first time, of heart rate related variables in an injury risk assessment model highlights the importance of physiological overload as a contributor to muscle injuries in soccer. By identifying the important parameters, coaches may prevent muscle injuries by controlling surges of training load during training and competition.

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This study aimed to characterize muscle activity in male soccer players with a history of hamstring strain injuries (HSI) during accelerated sprinting. Thirteen patients each in the HSI group (history of HSI) and in the healthy group (with no history of HSI) were included. 26 male soccer players of which 13 with and 13 without HSI history were included in this study. Ten muscles were evaluated on electromyography activity during overground sprinting. The testing protocol consisted of a maximal sprint over a distance of 30 meters. One running stride was divided into the early stance phase, late stance phase, early swing phase, mid-swing phase, and late swing phase, and the average muscle activity per phase and the timing of the peak root-mean-square value appearance during each stride were calculated. Statistical analysis was performed using repeated-measures two-way ANOVA (group × phase), and multiple comparison tests were performed using the Bonferroni method when the interaction or main effect was significant. The statistical significance level was set at p < 0.05. Gluteus maximus (Gmax), gluteus medius (Gmed), and external oblique (EO) showed activity differences based on HSI history. Gmax was 30% lower, EO was 20% lower, and Gmed was 40% higher in HSI group. This study suggests that, despite previous findings that HSI is most likely during the late swing phase, the HSI group shows a higher injury risk in the early stance phase. This is due to differences in trunk and gluteal muscle activity between the late swing and early stance phases compared to the healthy group. In summary, HSI group had lower activity in the muscles contributing to trunk instability, especially EO and Gmax, before and after ground impact during accelerated sprinting, compared to Healthy.

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The dynamic strength index (DSI) is calculated as the ratio between countermovement jump (CMJ) peak force and isometric mid-thigh pull (IMTP) peak force and is said to inform whether ballistic or strength training is warranted for a given athlete. This study assessed the impact of an individualized in-season resistance training program, guided by DSI on basketball players' physical performance. Forty-three elite players (19.4 ± 2.9 years; 1.97 ± 0.08 cm; 89.1 ± 9.5 kg) were divided into an intervention group (IG) (27 players) and a control group (CG) (16 players). The IG was further split based on DSI into a ballistic group (DSI ≤ 0.90, 11 players) and a strength group (DSI > 0.90, 16 players). Over five weeks, participants underwent two weekly resistance sessions, with the IG following a DSI-based program and the CG a standard program. Performance was measured pre- and post-intervention through 20-m sprints, 505 change of direction test, CMJ, and IMTP. There were statistically significant improvements in the IG, notably in sprint times (η2 = 0.12-0.21, p < 0.05) and 505 test (η2 = 0.15-0.16, p < 0.05), predominantly in the strength group. The CG's performance was either unchanged or declined for different variables. Our results suggest that DSI-guided training effectively enhances basketball players' physical performance within a competitive season.

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Hamstring strain injuries (HSIs) are prevalent in sports involving high-speed running and most of the HSIs are biceps femoris long head (BFlh) injuries. The primary cause for HSIs during high-speed running remains controversial due to the lack of in vivo measurement of the BFlh muscle behavior during running. Therefore, the purpose of this study was to quantify the muscle-tendon unit (MTU) and fascicle behavior of BFlh during running. Seven college male sprinters (22.14 ± 1.8 years; 177.7 ± 2.5 cm; 70.57 ± 5.1 kg; personal bests in 100m: 11.1 ± 0.2 s) were tested on a motorized treadmill instrumented with two force plate for running at 4, 5, 6m/s. The ground reaction force (GRF), 3D lower limb kinematics, EMG, and ultrasound images of biceps femoris long head (BFlh) in the middle region were recorded simultaneously. BFlh fascicles undergo little length change (about 1 cm) in the late swing phase during running at three submaximal speeds. BFlh fascicle lengthening accounted for about 30% of MTU length change during the late swing phase. BFlh was most active during the late swing and early stance phases, ranging from 83%MVC at a running speed of 4 m/s to 116%MVC at 6 m/s. Muscle fascicles in the middle region of BFlh undergo relatively little lengthening relative to the MTU in the late swing phase during running in comparison to results from simulation studies. These results suggest that there is a decoupling between the fascicle in the middle region and MTU length changes during the late swing phase of running.

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Running jumps that depart the ground from two feet require momenta redirection upward from initial momenta that are primarily horizontal. It is not known how each leg generates backward and upward impulses from ground reaction forces to satisfy this mechanical objective when jumping to maximize height. We examined whole-body linear momentum control strategies during these two-foot running jumps by uncovering the roles of each leg in impulse generation. 3D motion capture and force plates were used to record 14 male basketball players performing two-foot running jumps towards an adjustable basketball hoop. Total ground contact phase started from the first leg ground contact and ended at takeoff and was divided into center of mass descent and ascent subphases. During the total ground contact phase, all participants generated significantly more upward impulse with the first leg and ten participants generated significantly more backward impulse with the first leg compared to the second leg. During the descent subphase, all participants generated significantly more upward and backward impulses with the first leg. During the ascent subphase, all but one participant generated significantly more backward impulse with the second leg. In addition to group-level statistics, participant-specific strategies were described. Overall, this study revealed the fundamental whole-body momentum control strategies used in two-foot running jumps and supports future research into optimal jump techniques and training interventions that respect the need to satisfy the mechanical objectives of the movement.

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Integrating running gait coordination assessment into athlete monitoring systems could provide unique insight into training tolerance and fatigue-related gait alterations. This study investigated the impact of an overload training intervention and recovery on running gait coordination assessed by field-based self-testing. Fifteen trained distance runners were recruited to perform 1-week of light training (baseline), 2 weeks of heavy training (high intensity, duration, and frequency) designed to overload participants, and a 10-day light taper to allow recovery and adaptation. Field-based running assessments using ankle accelerometry and online short recovery and stress scale (SRSS) surveys were completed daily. Running performance was assessed after each training phase using a maximal effort multi-stage running test-to-exhaustion (RTE). Gait coordination was assessed using detrended fluctuation analysis (DFA) of a stride interval time series. Two participants withdrew during baseline training due to changed personal circumstances. Four participants withdrew during heavy training due to injury. The remaining nine participants completed heavy training and were included in the final analysis. Heavy training reduced DFA values (standardised mean difference (SMD) = -1.44 ± 0.90; p = 0.004), recovery (SMD = -1.83 ± 0.82; p less than 0.001), performance (SMD = -0.36 ± 0.32; p = 0.03), and increased stress (SMD = 1.78 ± 0.94; p = 0.001) compared to baseline. DFA values (p = 0.73), recovery (p = 0.77), and stress (p = 0.73) returned to baseline levels after tapering while performance trended towards improvement from baseline (SMD = 0.28 ± 0.37; p = 0.13). Reduced DFA values were associated with reduced performance (r2 = 0.55) and recovery (r2 = 0.55) and increased stress (r2 = 0.62). Field-based testing of running gait coordination is a promising method of monitoring training tolerance in running athletes during overload training.

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Low-cost, portable devices capable of accurate physiological measurements are attractive tools for coaches, athletes, and practitioners. The purpose of this study was primarily to establish the validity and reliability of Movesense HR+ ECG measurements compared to the criterion three-lead ECG, and secondarily, to test the industry leader Garmin HRM. Twenty-one healthy adults participated in running and cycling incremental test protocols to exhaustion, both with rest before and after. Movesense HR+ demonstrated consistent and accurate R-peak detection, with an overall sensitivity of 99.7% and precision of 99.6% compared to the criterion; Garmin HRM sensitivity and precision were 84.7% and 87.7%, respectively. Bland-Altman analysis compared to the criterion indicated mean differences (SD) in RR' intervals of 0.23 (22.3) ms for Movesense HR+ at rest and 0.38 (18.7) ms during the incremental test. The mean difference for Garmin HRM-Pro at rest was -8.5 (111.5) ms and 27.7 (128.7) ms for the incremental test. The incremental test correlation was very strong (r = 0.98) between Movesense HR+ and criterion, and moderate (r = 0.66) for Garmin HRM-Pro. This study developed a robust peak detection algorithm and data collection protocol for Movesense HR+ and established its validity and reliability for ECG measurement.

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There is limited validation for portable methods in evaluating high-speed running biomechanics, with inertial measurement unit (IMU) systems commonly used as wearables for this purpose. This study aimed to evaluate the validity of an IMU system in high-speed running compared to a 3D motion analysis system (MAS). One runner performed incremental treadmill running, from 12 to 18 km/h, on two separate days. Sagittal angles for the shank, knee, hip and pelvis were measured simultaneously with three IMUs and the MAS at the point of contact (POC), the timing when the foot initially hits the ground, as identified by IMU system acceleration, and compared to the POC identified via force plate. Agreement between the systems was evaluated using intra-class correlation coefficients, Pearson's r, Bland-Altman limits of agreements, root mean square error and paired t-tests. The IMU system reliably determined POC (which subsequently was used to calculate stride time) and measured hip flexion angle and anterior pelvic tilt accurately and consistently at POC. However, it displayed inaccuracy and inconsistency in measuring knee flexion and shank angles at POC. This information provides confidence that a portable IMU system can aid in establishing baseline running biomechanics for performance optimisation, and/or inform injury prevention programs.

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To reduce breast motion with a bra, we need to understand what drives the motion of the breasts, and what variables change as support increases. Quantifying breast-torso coordination and movement complexity across the gait cycle may offer deeper insights than previously reported discrete time lag. We aimed to compare breast-torso coordination and mutual influence across breast support conditions during running. Twelve female participants ran on a treadmill at 10 km h-1 with an encapsulation and compression sports bra, and in no bra. Nipple and torso position was recorded. Vector coding, granger causality and transfer entropy were calculated within gait cycles. In both bra conditions, a greater percentage of gait cycles was spent with the breast and torso in-phase (> 90%) compared to no bra running (~ 66%, p < 0.001), with most time spent in-phase in the encapsulation versus compression bra (p = 0.006). There was a main effect of breast support condition on Granger causality (p < 0.001), both from breast to torso and torso to breast. Transfer of information was highest from torso to breast, compared to breast to torso in all conditions. Overall, these results provide novel insight into the mutual and complex interaction between the breast and the torso while running in different bra conditions. The approaches presented allow for a greater understanding of bra support conditions than existing discrete measures, which may relate to comfort and performance. Therefore, measures of coupling, predictability and transfer of complexity should be employed in future work examining these features.

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Thermoregulation is argued to be an important factor influencing body breadth in hominins based on the relationship of surface area to body mass first proposed by Bergmann. Selection for a narrow thorax, and thus a narrow pelvis, increases body surface area relative to body mass, which could be beneficial in hot climates if it leads to a decrease in core body temperature. However, the relationship between pelvic breadth and thermoregulation in humans has not been established. Although previous work has shown that bi-iliac breadth is significantly positively associated with latitude in humans, we lack an understanding of whether this association is due to climate-related selection, neutral evolutionary processes, or other selective pressures. A missing piece of the puzzle is whether body breadth at the iliac blades is an important factor in thermoregulation. Here, we examine this in a mixed-sex sample of 28 adult runners who ran for one hour at 3.14 m s-1 in a variety of climatic conditions while their core body temperatures were measured using internal temperature sensors. The association of maximum core temperature with anthropometric and demographic variables such as age, sex, mass, body fat percentage, and bi-iliac breadth was analyzed using a linear mixed-effect model. Due to the small sample size, the model was also bootstrapped. We found that an increase in absolute bi-iliac breadth was significantly associated with an increase in maximum core temperature. Overall, this preliminary analysis suggests a link between variation in bi-iliac breadth and maximum core body temperature during running, but further investigation is needed.

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High intensity run counts-defined as the number of runs where a player reaches and maintains a speed above a certain threshold-are a popular football running statistic in sport science research. While the high intensity run number gives an insight into the volume or intensity of a player's work rate it does not give any indication about the effectiveness of their runs or whether or not they provided value to the team. To provide the missing context of value this research borrows the concept of value models from sports analytics which assign continuous values to each frame of optical tracking data. In this research the value model takes the form of goal-probability for the in-possession team. By aligning the value model with high intensity runs this research identifies positive correlations between speed and acceleration with high value runs, as well as a negative correlation between tortuosity (a measure of path curvature) and high value runs. There is also a correlation between the number of players making high intensity runs concurrently and the value generated by the team, suggesting a form of movement coordination. Finally positional differences are explored demonstrating that attacking players make more in-possession high intensity runs when goal probability is high, whereas defensive players make more out-of-possession high intensity runs while goal probability is high. By assigning value to high-intensity runs practitioners are able to add new layers of context to traditional sport science metrics and answer more nuanced questions.

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AbstractMuscle-tendon unit (MTU) morphology and physiology are likely major determinants of locomotor performance and therefore Darwinian fitness. However, the relationships between underlying traits, performance, and fitness are complicated by phenomena such as coadaptation, multiple solutions, and trade-offs. Here, we leverage a long-running artificial selection experiment in which mice have been bred for high levels of voluntary running to explore MTU adaptation, as well as the role of coadaptation, multiple solutions, and trade-offs, in the evolution of endurance running. We compared the morphological and contractile properties of the triceps surae complex, a major locomotor MTU, in four replicate selected lines to those of the triceps surae complex in four replicate control lines. All selected lines have lighter and shorter muscles, longer tendons, and faster muscle twitch times than all control lines. Absolute and normalized maximum shortening velocities and contractile endurance vary across selected lines. Selected lines have similar or lower absolute velocities and higher endurance than control lines. However, normalized shortening velocities are both higher and lower in selected lines than in control lines. These findings potentially show an interesting coadaptation between muscle and tendon morphology and muscle physiology, highlight multiple solutions for increasing endurance running performance, demonstrate that a trade-off between muscle speed and endurance can arise in response to selection, and suggest that a novel physiology may sometimes allow this trade-off to be circumvented.

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An acute bout of exercise in the moments after learning benefits the retention of new memories. This finding can be explained, at least partly, through a consolidation account: exercise provides a physiological state that is conducive to the early stabilisation of labile new memories, which supports their retention and subsequent retrieval. The modification of consolidation through non-invasive exercise interventions offers great applied potential. However, it remains poorly understood whether effects of exercise translate from the laboratory to naturalistic settings and whether the intensity of exercise determines the effect in memory. To this end, adult endurance runners were recruited as participants and completed two study sessions spaced two weeks apart. In each session, participants were presented with a list of words and asked to recall them on three occasions: (i) immediately following their presentation, (ii) after a 30-minute retention interval, and (iii) after 24 hours. Crucially, the 30-minute retention interval comprised our experimental manipulation: higher intensity exercise (running) in the first session and lower intensity exercise (walking) in the second, both completed in a naturalistic setting around participants' existing physical activity training programmes. Exertion was recorded through heart rate and rate of perceived exertion data. Alertness, mood, and arousal ratings were also collected before and after the 30-minute retention interval. Immediate memory for the two wordlists was matched, but participants retained significantly more words after 30 minutes and 24 hours when encoding was followed by higher than lower intensity exercise. Exertion data revealed that participants experienced vigorous and light exercise in the higher and lower intensity conditions, respectively. Significant improvements in alertness, mood, and arousal were observed following both exercise conditions, but especially in the higher intensity condition. These outcomes reveal that experiencing higher intensity physical activity in the field is conducive to declarative memory retention, possibly because it encourages consolidation.

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BACKGROUND: Accurately measuring energy expenditure during physical activity outside of the laboratory is challenging, especially on a large scale. Thigh-worn accelerometers have gained popularity due to the possibility to accurately detect physical activity types. The use of machine learning techniques for activity classification and energy expenditure prediction may improve accuracy over current methods. Here, we developed a novel composite energy expenditure estimation model by combining an activity classification model with a stride specific energy expenditure model for walking, running, and cycling. METHODS: We first trained a supervised deep learning activity classification model using pooled data from available adult accelerometer datasets. The composite energy expenditure model was then developed and validated using additional data based on a sample of 69 healthy adult participants (49% female; age = 25.2 ± 5.8 years) who completed a standardised activity protocol with indirect calorimetry as the reference measure. RESULTS: The activity classification model showed an overall accuracy of 99.7% across all five activity types during validation. The composite model for estimating energy expenditure achieved a mean absolute percentage error of 10.9%. For running, walking, and cycling, the composite model achieved a mean absolute percentage error of 6.6%, 7.9% and 16.1%, respectively. CONCLUSIONS: The integration of thigh-worn accelerometers with machine learning models provides a highly accurate method for classifying physical activity types and estimating energy expenditure. Our novel composite model approach improves the accuracy of energy expenditure measurements and supports better monitoring and assessment methods in non-laboratory settings.

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