Jumping demands during classical ballet class.

Ballet class represents a considerable portion of professional ballet training, yet the external training load demands associated with class-and particularly the jumping demands-have not been investigated. The purpose of this study was to measure the jumping demands of ballet class by sex and rank. Eleven female and eight male elite professional ballet dancers participated in 109 ballet classes taught by 12 different teachers. Jump counts and jump heights were measured during each class. A Poisson generalized linear mixed effects model was used to examine the differences in jump counts between sexes and ranks. Greater jump counts were observed during class in men than in women (153, 95% confidence intervals [CI] [137, 170] vs. 119, 95% CI [109, 131], p = 0.004) and in junior ranking dancers compared with senior ranking dancers (151, 95% CI [138, 165] vs. 121, 95% CI [108, 135], p = 0.006). Female junior and senior ranking dancers jumped at rates of 9.2 ± 2.6 and 8.6 ± 4.7 jumps·min-1 , respectively, while male junior and senior ranking dancers jumped at rates of 9.1 ± 2.6 and 8.7 ± 2.6 jumps·min-1 , respectively. Across all classes, 73% of jumps observed were below 50% of maximum double-legged countermovement jump height. Unlike rehearsals and performances, class offers dancers an opportunity to self-regulate load, and as such, are a useful session to manage jump load, and facilitate gradual return-to-dance pathways. Communication between health care and artistic staff is essential to facilitate load management during class.

Monitoring training, performance, biomarkers, and psychological state throughout a competitive season: a case study of a triathlete.

INTRODUCTION: Ironman triathletes undergo high workloads during competition preparation which can result in nonfunctional overreaching or overtraining syndrome if not matched with adequate recovery. PURPOSE: The purpose of this case study was to observe changes in physiological and psychological status over the course of a competitive season in a free-living triathlete. METHODS: The subject was a 41-year-old triathlete competing in three 113.1-km events. Over the course of a 40-week period, the participant arrived at the laboratory every 4 weeks and underwent body composition testing via air displacement plethysmography, a blood draw for analysis of various biomarkers, and a treadmill-based lactate threshold test. Workload during training and competitions was monitored via a wearable heart rate-monitoring device. RESULTS: Throughout the season, training volume remained high (12.5 ± 3.4 h/week) and body mass and fat-free mass (FFM) continuously decreased, while biomarkers including cortisol, testosterone, and markers of immunological status exhibited minor changes. Laboratory performance remained relatively consistent, while competition performance continually improved. Following the completion of the competitive period, training volume decreased, FFM remained below baseline levels, free cortisol increased, and both free and total testosterone decreased. CONCLUSIONS: Workload and recovery seem to have been properly managed throughout the season, evidenced by minimal fluctuations in endocrine and immunological markers. The reason for changes observed in testosterone, cortisol, and body composition following the last competition is unclear, though it may be attributed to changes in stressors and recovery practices outside of training. It is recommended that athletes follow a structured plan during the transition period into the offseason to ensure recovery of physiological state and to set up a productive offseason.

Design, Calibration and Morphological Characterization of a Flexible Sensor with Adjustable Chemical Sensitivity and Possible Applications to Sports Medicine.

Active life monitoring via chemosensitive sensors could hold promise for enhancing athlete monitoring, training optimization, and performance in athletes. The present work investigates a resistive flex sensor (RFS) in the guise of a chemical sensor. Its carbon 'texture' has shown to be sensitive to CO2, O2, and RH changes; moreover, different bending conditions can modulate its sensitivity and selectivity for these gases and vapors. A three-step feasibility study is presented including: design and fabrication of the electronic read-out and control; calibration of the sensors to CO2, O2 and RH; and a morphological study of the material when interacting with the gas and vapor molecules. The 0.1 mm-1 curvature performs best among the tested configurations. It shows a linear response curve for each gas, the ranges of concentrations are adequate, and the sensitivity is good for all gases. The curvature can be modulated during data acquisition to tailor the sensitivity and selectivity for a specific gas. In particular, good results have been obtained with a curvature of 0.1 mm-1. For O2 in the range of 20-70%, the sensor has a sensitivity of 0.7 mV/%. For CO2 in the range of 4-80%, the sensitivity is 3.7 mV/%, and for RH the sensitivity is 33 mV/%. Additionally, a working principle, based on observation via scanning electron microscopy, has been proposed to explain the chemical sensing potential of this sensor. Bending seems to enlarge the cracks present in the RFS coverage; this change accounts for the altered selectivity depending on the sensor's curvature. Further studies are needed to confirm result's reliability and the correctness of the interpretation.

Internal and External Load Profile during Beach Invasion Sports Match-Play by Electronic Performance and Tracking Systems: A Systematic Review.

Beach variants of popular sports like soccer and handball have grown in participation over the last decade. However, the characterization of the workload demands in beach sports remains limited compared to their indoor equivalents. This systematic review aimed to: (1) characterize internal and external loads during beach invasion sports match-play; (2) identify technologies and metrics used for monitoring; (3) compare the demands of indoor sports; and (4) explore differences by competition level, age, sex, and beach sport. Fifteen studies ultimately met the inclusion criteria. The locomotive volumes averaged 929 ± 269 m (average) and 16.5 ± 3.3 km/h (peak) alongside 368 ± 103 accelerations and 8 ± 4 jumps per session. The impacts approached 700 per session. The heart rates reached 166-192 beats per minute (maximal) eliciting 60-95% intensity. The player load was 12.5 ± 2.9 to 125 ± 30 units. Males showed 10-15% higher external but equivalent internal loads versus females. Earlier studies relied solely on a time-motion analysis, while recent works integrate electronic performance and tracking systems, enabling a more holistic quantification. However, substantial metric intensity zone variability persists. Beach sports entail intermittent high-intensity activity with a lower-intensity recovery. Unstable surface likely explains the heightened internal strain despite moderately lower running volumes than indoor sports. The continued integration of technology together with the standardization of workload intensity zones is needed to inform a beach-specific training prescription.

Pupillometry as a new window to player fatigue? A glimpse inside the eyes of a Euro Cup Women's Basketball team.

A rapidly emerging area of interest in high-pressure environments is that of pupillometry, where handheld quantitative infrared pupillometers (HQIPs) are able to track psycho-physiological fatigue in a fast, objective, valid, reliable, and non-invasive manner. However, the application of HQIPs in the context of athlete monitoring is yet to be determined. Therefore, the main aim of this pilot study was to examine the potential usefulness of a HQIP to monitor game-induced fatigue inside a professional female basketball setting by determining its (1) test-retest repeatability, (2) relationship with other biomarkers of game-induced fatigue, and (3) time-course from rested to fatigued states. A non-ophthalmologic practitioner performed a standardized Pupil Light Reflex (PLR) test using a medically graded HQIP among 9 professional female basketball players (2020-2021 Euro Cup) at baseline, 24-h pre-game (GD-1), 24-h post-game (GD+1) and 48-h post-game (GD+2). This was repeated over four subsequent games, equalling a total of 351 observations per eye. Two out of seven pupillometrics displayed good ICCs (0.95-0.99) (MinD and MaxD). Strong significant relationships were found between MaxD, MinD, and all registered biomarkers of game-induced fatigue (r = 0.69-0.82, p < 0.05), as well as between CV, MCV, and cognitive, lower-extremity muscle, and physiological fatigue markers (r = 0.74-0.76, p < 0.05). Three pupillometrics were able to detect a significant difference between rested and fatigued states. In particular, PC (right) (F = 5.173, η2 = 0.115 p = 0.028) and MCV (right) (F = 3.976, η2 = 0.090 p = 0.049) significantly decreased from baseline to GD+2, and LAT (left) (F = 4.023, η2 = 0.109 p = 0.009) significantly increased from GD-1 to GD+2. HQIPs have opened a new window of opportunity for monitoring game-induced fatigue in professional female basketball players. However, future research initiatives across larger and heterogenous samples, and longer investigation periods, are required to expand upon these preliminary findings.

Predictive analysis of ratings of perceived exertion in elite Gaelic football.

This study aimed to compare the predictive accuracy of absolute and relative external load indices (ELI) across three machine learning models, and predict the rating of perceived exertion (RPE) of elite Gaelic football players using ELI, personal characteristics, wellness scores, and training workloads. ELI and related variables were collected from 49 elite Gaelic football players over three competitive seasons resulting in 1617 observations. ELI included total distance, high speed running distance (≥ 4.72 m · s-1), and number of accelerations and decelerations (n ± 3 m · s-2), expressed in both absolute and relative terms. Variables related to personal characteristics, wellness scores, and training workloads were also included. Data were analysed using decision tree, random forest (RF), and bootstrap aggregation (BS) models. The RF model had the highest predictive accuracy using absolute and relative ELI only, at 54.3% and 48.3%, respectively. Total and relative distance were the strongest predictors of RPE in the RF model, accounting for 38.8% and 27.9% of the normalised importance. The BS model had the highest accuracy at 67.0% and 65.2% for absolute and relative ELI when performed in conjunction with the related variables, respectively. The current models demonstrate potential to predict RPE and subsequently optimise training load in Gaelic football.

In-season autoregulation of one weekly strength training session maintains physical and external load match performance in professional male football players.

The aim of this study was to compare the effects of autoregulating strength training volume based on an objective (external load match performance) versus a subjective (self-selected) method in professional male football players. Sixteen players completed a 10-week strength training programme where the number of sets was regulated based on football match high-intensity running distance (HIR >19.8 km/h, AUTO, n = 7), or self-selected (SELF, n = 9). In addition to traditional physical performance assessments (30-m sprint, countermovement jump, leg-strength, and body composition), external load match performance was assessed with five matches in the beginning and in the end of the study period. Both groups performed ~ 1 weekly bout of ~ 6 sets in leg extensor exercises during the 10-week period, and maintained physical performance during the competitive season, with no group differences detected after the training period. Non-overlap of all pairs (NAP) analysis showed weak-to-moderate effects in external load match performance from before to after the study period, suggesting that players maintained or improved their performance. In conclusion, no group differences were observed, suggesting that both external load autoregulated and self-selected, low-volume in-season strength training maintained physical, and external load match performance in professional male football players.

The difference between several neuromuscular tests for monitoring resistance-training induced fatigue.

The purposes of this study were to investigate the acute effects of resistance training protocol on kinetic changes in squat jump (SJ), shortened isometric mid-thigh pull (IMTP), and isometric squat (ISQ) and to examine the relationship of dynamic maximum strength with performance changes over 48 hours in resistance-trained individuals. Participants completed performance tests at pre-, post-24 hours, and post-48 hours resistance training protocol (Baseline, Post24, and Post48). The training protocol consisted of 5 sets of 10 repetitions of back squat (BSQ) at 60% of 1 repetition maximum (1RM). SJ variables included jump height (JH), peak power (PP), and relative PP. For the IMTP and ISQ, isometric peak force (IPF), relative IPF, rate of force development at 250 milliseconds (RFD250), and impulse at 250 milliseconds (IMP250) were calculated. Significant decreases were observed from Baseline to Post24 (p = 0.023, Cohen's dz effect size [dz] = 1.00) and Post48 (p = 0.032, dz = 0.94) in SJ JH. IMTP IMP250 significantly decreased from Baseline to Post48 (p = 0.046, dz = 0.88). Significant negative correlation was found between relative 1RM BSQ and the changes from Baseline to Post48 in ISQ RFD250 (p = 0.046,r = -0.61). Acute performance decreases might remain until 48 hours after resistance training in explosive strength and impulse regardless of isometric testing type.

The development and validation of an open-source accelerometery algorithm for measuring jump height and frequency in ballet.

The aim was to determine the validity of an open-source algorithm for measuring jump height and frequency in ballet using a wearable accelerometer. Nine professional ballet dancers completed a routine ballet class whilst wearing an accelerometer positioned at the waist. Two investigators independently conducted time-motion analysis to identify time-points at which jumps occurred. Accelerometer data were cross-referenced with time-motion data to determine classification accuracy. To determine the validity of the measurement of jump height, five participants completed nine jetés, nine sautés and three double tour en l'air from a force plate. The jump height predicted by the accelerometer algorithm was compared to the force plate jump height to determine agreement. Across 1440 jumps observed in time-motion analysis, 1371 true positives, 34 false positives and 69 false negatives were identified by the algorithm, resulting in a sensitivity of 0.98, a precision of 0.95 and a miss rate of 0.05. For all jump types, mean absolute error was 2.6 cm and the repeated measures correlation coefficient was 0.97. Bias was 1.2 cm and 95% limits of agreement were -4.9 to 7.2 cm. The algorithm may be used to manage jump load, implement periodization strategies, or plan return-to-jump pathways for rehabilitating athletes.

Validity and Reliability of Polar Team Pro and Playermaker for Estimating Running Distance and Speed in Indoor and Outdoor Conditions.

Although global positioning systems and inertial measurement unit systems are often used to quantify physical variables in training, both types of systems need to be compared, considering their frequent use in measuring physical loads. Thus, the purpose of our study was to test the reliability and validity of speed and distance run measurements at different intensities in indoor and outdoor conditions made by Polar Team Pro and Playermaker. Four participants (age = 30.0 ± 5.1 years, body mass = 76.3 ± 5.3 kg, height = 1.79 ± 0.09 m), each wearing three Polar Team Pro and two Playermaker sensors, performed 100 m runs with different prescribed intensities (i.e., criterion measure) varying from 8 to 24 km h-1, in a straight line and/or rectangle under indoor and outdoor conditions. Both systems underestimated total distance; Playermaker underestimated speed, the extent of which increased as speed increased, while Polar Team Pro overestimated mean speed at 8 km h-1 for the straight-line condition. No differences emerged in mean speed estimated by Polar Team Pro at any intensities other than 20 km h-1, which was underestimated by 2%. The reliability of the sensors was good, given a coefficient of variation (CV) of <2% for all conditions except when measuring indoor conditions with Polar Team Pro (CV ≈ 10%). Intraclass correlations (ICCs) for consistency within the sensors varied from 0.47 to 0.99, and significantly lower ICCs were documented at 8, 10, and 12 km h-1. Both systems underestimated distance measured in indoor and outdoor conditions, and distance validity in different intensities seemed to worsen as speeds increased. Although Polar Team Pro demonstrated poor validity and reliability in indoor conditions, both systems exhibited good reliability between their sensors in outdoor conditions, whereas the reliability within their sensors varied with different speeds.

Unsupervised Gait Event Identification with a Single Wearable Accelerometer and/or Gyroscope: A Comparison of Methods across Running Speeds, Surfaces, and Foot Strike Patterns.

We evaluated 18 methods capable of identifying initial contact (IC) and terminal contact (TC) gait events during human running using data from a single wearable sensor on the shank or sacrum. We adapted or created code to automatically execute each method, then applied it to identify gait events from 74 runners across different foot strike angles, surfaces, and speeds. To quantify error, estimated gait events were compared to ground truth events from a time-synchronized force plate. Based on our findings, to identify gait events with a wearable on the shank, we recommend the Purcell or Fadillioglu method for IC (biases +17.4 and -24.3 ms; LOAs -96.8 to +131.6 and -137.0 to +88.4 ms) and the Purcell method for TC (bias +3.5 ms; LOAs -143.9 to +150.9 ms). To identify gait events with a wearable on the sacrum, we recommend the Auvinet or Reenalda method for IC (biases -30.4 and +29.0 ms; LOAs -149.2 to +88.5 and -83.3 to +141.3 ms) and the Auvinet method for TC (bias -2.8 ms; LOAs -152.7 to +147.2 ms). Finally, to identify the foot in contact with the ground when using a wearable on the sacrum, we recommend the Lee method (81.9% accuracy).

College Soccer Student-Athletes Demonstrate Differences in Self-Reported Athlete Health When Grouped by Match Volume.

CONTEXT: Physical changes following activity are well documented, but there is limited information about self-reported outcomes around competitive matches. High training volumes and poor recovery could predispose athletes to overuse injury. The purpose of this study was to identify the changes in daily athlete health measures before, during, and after the day of each match in high- and low-volume groups. DESIGN: Prospective cohort. METHODS: Fifty-five soccer athletes (age: 19.8 [1.2] y, 26 males, 29 females) provided daily measures of readiness, physical fatigue, mental stress, sleep quality, and soreness intensity match days, days 1 (D01) and 2 (D02) following matches, and standard practice days. Participants were grouped into high volume and low volume, based off the minutes played during the season. RESULTS: Soreness increased, readiness decreased, and fatigue increased on D01 compared with match days (P < .008) in the high-volume group. Between groups, the high-volume group demonstrated higher soreness on D01 and D02, lower readiness on D01 and D02, and lower fatigue on D01, compared with the low-volume group (P < .008). CONCLUSIONS: Soccer athletes demonstrate significant changes in self-reported athlete health variables around competitive matches. These changes are similar to physical outcomes, potentially indicating that the athlete health variables may be used to track athlete recovery from competition, potentially limiting the impact of overuse injuries.

Intra-unit reliability and movement variability of submission grappling external load as measured by torso mounted accelerometery.

Submission grappling consists of skills and movements used in combat sports to physically control opponents whilst trying to apply choke holds and joint locks. There is currently no accepted method of monitoring external load in grappling-based sports due to the absence of key variables such as distance, velocity or time. The primary aim of this study was to determine whether PlayerLoad is a reliable variable for measuring external load of submission grappling movements, with a secondary aim of determining the between repetition variance of submission grappling movements. 7 experienced submission grapplers were recruited. Each wore a torso mounted Catapult® Optimeye S5 microelectromechanical systems (MEMS) device and completed 5 repetitions of each of the following: 4 submission techniques; 5 transition techniques; 2 guard pass techniques; 2 takedown techniques. Accumulated PlayerLoad (PLdACC) was recorded as a marker of absolute load, with accumulated PlayerLoad per minute (PLdACC∙min-1) representing relative load. Reliability of each was assessed using intraclass correlation coefficient (ICC(3,1)) (≥ 0.70). Between repetition movement variation was assessed via coefficient of variation with 95% confidence intervals (CV, 95%CI) (acceptable ≤ 15%, good ≤ 10%). PLdACC ICC(3,1) range = 0.78-0.98, with CV range = 9-22%. PLdACC∙min-1 ICC(3,1) range = 0.83-0.98, with CV range = 11-19%. Though several variables displayed CV > 15%, all had 95%CI lower boundaries ≤ 15%. Whilst PlayerLoad was found to be a reliable measure for submission grappling, relatively high CVs across most techniques examined suggest PlayerLoad may not be appropriate for measuring changes in external load for individual movements in submission grappling. However, it may prove a useful tool for monitoring the external load of full, grappling-based, training sessions within an individual.

Menstrual cycle affects iron homeostasis and hepcidin following interval running exercise in endurance-trained women.

PURPOSE: Menstrual cycle phase affects resting hepcidin levels, but such effects on the hepcidin response to exercise are still unclear. Thus, we investigated the hepcidin response to running during three different menstrual cycle phases. METHODS: Twenty-one endurance-trained eumenorrheic women performed three identical interval running protocols during the early-follicular phase (EFP), late-follicular phase (LFP), and mid-luteal phase (MLP). The protocol consisted of 8 × 3 min bouts at 85% of the maximal aerobic speed, with 90-s recovery. Blood samples were collected pre-exercise and at 0 h, 3 h and 24 h post-exercise. RESULTS: Data presented as mean ± SD. Ferritin were lower in the EFP than the LFP (34.82 ± 16.44 vs 40.90 ± 23.91 ng/ml, p = 0.003), while iron and transferrin saturation were lower during the EFP (58.04 ± 19.70 µg/dl, 14.71 ± 5.47%) compared to the LFP (88.67 ± 36.38 µg/dl, 22.22 ± 9.54%; p < 0.001) and the MLP (80.20 ± 42.05 µg/dl, 19.87 ± 10.37%; p = 0.024 and p = 0.045, respectively). Hepcidin was not affected by menstrual cycle (p = 0.052) or menstrual cycle*time interaction (p = 0.075). However, when comparing hepcidin at 3 h post-exercise, a moderate and meaningful effect size showed that hepcidin was higher in the LFP compared to the EFP (3.01 ± 4.16 vs 1.26 ± 1.25 nMol/l; d = 0.57, CI = 0.07-1.08). No effect of time on hepcidin during the EFP was found either (p = 0.426). CONCLUSION: The decrease in iron, ferritin and TSAT levels during the EFP may mislead the determination of iron status in eumenorrheic athletes. However, although the hepcidin response to exercise appears to be reduced in the EFP, it shows no clear differences between the phases of the menstrual cycle (clinicaltrials.gov: NCT04458662).

Athlete monitoring practices in elite sport in the United Kingdom.

Athlete monitoring systems (AMS) aid performance optimisation and support illness/injury prevention. Nonetheless, limited information exists on how AMS are employed across elite sports in the United Kingdom. This study explored how athlete monitoring (AM) data, in particular athlete self-report measures, were collected, analysed and disseminated within elite sports. Thirty elite sports practitioners representing 599 athletes responded to a survey on their AM methodologies. The majority, 83%, (n = 25) utilised an AMS, and a further 84% (n = 21) stated the collection of their AMS data was underpinned by a scientific rationale. Athlete self-report measures (ASRM) were the most commonly employed tool, with muscle soreness, sleep and energy levels amongst the most frequently collected measures. The ubiquitous use of custom single-item ASRM resulted in considerable variability in the questionnaires employed, thus potentially impacting questionnaire validity. Feedback processes were largely felt to be ineffective, with 44% (n = 11) respondents indicating that athletes did not receive sufficient feedback. Some respondents indicated that AMS data was never discussed with athletes and/or coaches. Overall, significant disparities exist in the use of athlete monitoring systems between research and elite sports practice, and the athlete, coach and practitioner experience of monitoring risks being poor if these disparities are not addressed.

External match load and the influence of contextual factors in elite futsal.

Quantifying external load during futsal competition can provide objective data for the management of the athlete's performance and late-stage rehabilitation. This study aimed to report the match external load collected via wearable technology according to time periods (i.e., halves) and contextual factors (i.e., team's ranking, match result, and location) in elite futsal. Nine professional male players used a GPS-accelerometer unit during all games of the 2019-2020 season. Player load (PL), PL·min-1, high-intensity acceleration (ACCHI), deceleration (DECHI), explosive movements (EXPL-MOV), and change of direction (CODHI) data were collected. On average, players displayed values of: total PL 3868 ± 594 a.u; PL·min-1: 10.8 ± 0.8 a.u; number of ACCHI: 73.3 ± 13.8, DECHI: 68.6 ± 18.8, EXPL-MOV: 1165 ± 188 and CODHI: 173 ± 29.1. A moderate and significant decrease was found in the 2nd half for total PL (p = 0.03; ES = 0.52), PL·min-1 (p = 0.001; ES = 1.16), DECHI (p = 0.001; ES = 0.83), and EXPL-MOV (p = 0.017; ES = 0.58) compared to the 1st half. Small and nonsignificant differences were found between contextual factors. In summary, this study indicates that futsal players are exposed to high-intensity mechanical external loads, and perform a great number of ACCHI, DECHI, EXPL-MOV and CODHI, without being influenced by the team ranking, result and match location. Coaches and sports scientists are advised to implement speed-power, DEC, and COD activities in the training sessions, and may use these reference values to design specific training and return-to-play plans.

Relationship between pre-training wellness scores and internal and external training loads in a Division I women's lacrosse team.

This study examined the relationship between pre-training wellness scores and internal and external training load outputs during the preparation phase of a Division I women's collegiate lacrosse team. Athletes (n = 27) logged daily wellness (sleep, energy, stress, and how their muscles felt), session ratings of perceived exertion, and training metrics for 13 weeks. Load metrics were collected with microtechnology units. Repeated measures mixed linear models were constructed to evaluate the relationships between wellness and training load. The largest effects observed indicated: improved sleep was predictive of increased total distance (by as much as 310.5 m, 95%CI 36.7-584.3), high-intensity distance (by as much as 72.7 m, 95%CI 17.2-128.3), and Athlete Load (by as much as 6.8AU, 95%CI 1.8-11.8); more energy was predictive of increased total distance (by as much as 323.6 m, 95%CI 52.2-594.9) and Athlete Load (by as much as 5.6AU. 95%CI 0.6-10.5). For each one-unit increase in overall wellness score, total distance (3.5 m, 95%CI 1.2-15.1) and Athlete Load (0.2AU, 95%CI 0.0-0.3) increased. Pre-training wellness scores and sub-scores are predictive of external load training output. Monitoring athlete wellness systematically can help coaches with decision-making relevant to training and game performance.

Is machine learning and automatic classification of swimming data what unlocks the power of inertial measurement units in swimming?

Researchers have heralded the power of inertial sensors as a reliable swimmer-centric monitoring technology, however, regular uptake of this technology has not become common practice. Twenty-six elite swimmers participated in this study. An IMU (100Hz/500Hz) sensor was secured in the participant's third lumbar vertebrae. Features were extracted from swimming data using two techniques: a novel intrastroke cycle segmentation technique and conventional sliding window technique. Six supervised machine learning models were assessed on stroke prediction performance. Models trained using both feature extraction methods demonstrated high performance (≥ 0.99 weighted average precision, recall, F1-score, area under ROC curve and accuracy), low computational training times (< 3 seconds - bar XGB and when hyperparameters were tuned) and low computational prediction times (< 1 second). Significant differences were observed in weighted average stroke prediction F1-score (p = 0.0294) when using different feature extraction methods and model computational training time (p = 0.0007), and prediction time (p = 0.0026) when implementing hyperparameter tuning. Automatic swimming stroke classification offers benefits to observational coding and notational analysis, and opportunities for automated workload and performance monitoring in swimming. This stroke classification algorithm could be the key that unlocks the power of IMUs as a biofeedback tool in swimming.

Monitoring External Training Loads and Neuromuscular Performance for Division I Basketball Players over the Preseason.

Limited research has paralleled concomitant changes in external training load (eTL) and countermovement jump (CMJ) performance. Therefore, this investigation characterized eTL and CMJ performance changes across preseason training in Division 1 male collegiate basketball athletes, while examining the influence of position (Guard vs. Forward/Center) and scholarship status (Scholarship = S vs. Walk-on = WO). During 22 practices, eTL was monitored in 14 male athletes, with weekly CMJs performed to quantify neuromuscular performance (Jump Height [JH], Flight Time:Contraction Time [FT:CT], Reactive Strength Index Modified [RSIMod ]). PlayerLoad per minute was significantly higher during W1 and W2 (5.4 ± 1.3au and 5.3 ± 1.2au, respectively; p < 0.05) compared to subsequent weeks, but no additional differences in eTL parameters across time were observed. Scholarship athletes displayed greater PlayerLoad (S = 777.1 ± 35.6, WO = 530.1 ± 56.20; Inertial Movement Analysis (IMA) IMA_High (S = 70.9 ± 15.2, WO = 41.3 ± 15.2); IMA_Medium (S = 159.9 ± 30.7, WO = 92.7 ± 30.6); and IMA_Low (S = 700.6 ± 105.1, WO = 405 ± 105.0;) (p < 0.05), with no observed differences in eTL by position. Moderate decreases in FT:CT and RSIMod paralleled increased eTL. Significant increases in practice intensity (W1 and W2) did not impact CMJ performance, suggesting athletes could cope with the prescribed training loads. However, moderate perturbations in FT:CT and RSIMod paralleled the weeks with intensified training. Cumulatively, scholarship status appears to influence eTL while player position does not.

Effects of total sleep deprivation on endurance cycling performance and heart rate indices used for monitoring athlete readiness.

This study investigated effects of total sleep deprivation on self-paced endurance performance, and heart rate (HR) indices of athletes' "readiness to perform". Endurance athletes (n = 13) completed a crossover experiment comprising a normal sleep (NS) and sleep deprivation (SD) condition. Each required completion of an endurance time-trial (TT) on consecutive days (D1, D2) separated by normal sleep or total sleep deprivation. Finishing time, perceived exertion (RPE), mood, psychomotor vigilance (PVT), and HR responses were assessed. Time on D2 of SD was 10% slower than D2 of NS (64 ± 7 vs 59 ± 4 min, P < 0.01), and 11% slower than D1 of SD (58 ± 5 min, P < 0.01). Subjective to objective (RPE:mean HR) intensity ratio was higher on D2 of SD compared with D2 of NS and D1 of SD (P < 0.01). Mood disturbance and PVT mean response time increased on D2 of SD compared with D2 of NS and D1 of SD. Anaerobic threshold and change in TT time were correlated (R = -0.73, P < 0.01). Sleep helps to optimise endurance performance. Subjective to objective intensity ratios appear sensitive to effects of sleep on athletes' readiness. Research examining more subtle sleep manipulation is required.