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Margot A. Rogers, Michael K. Drew, Renee Appaneal, Greg Lovell, Bronwen Lundy, David Hughes, Nicole Vlahovich, Gordon Waddington, and Louise M. Burke

The Low Energy Availability in Females Questionnaire (LEAF-Q) was validated to identify risk of the female athlete triad (triad) in female endurance athletes. This study explored the ability of the LEAF-Q to detect conditions related to low energy availability (LEA) in a mixed sport cohort of female athletes. Data included the LEAF-Q, SCOFF Questionnaire for disordered eating, dual-energy X-ray absorptiometry-derived body composition and bone mineral density, Mini International Neuropsychiatric Interview, blood pressure, and blood metabolic and reproductive hormones. Participants were grouped according to LEAF-Q score (≥8 or <8), and a comparison of means was undertaken. Sensitivity, specificity, and predictive values of the overall score and subscale scores were calculated in relation to the triad and biomarkers relevant to LEA. Fisher’s exact test explored differences in prevalence of these conditions between groups. Seventy-five athletes (18–32 years) participated. Mean LEAF-Q score was 8.0 ± 4.2 (55% scored ≥8). Injury and menstrual function subscale scores identified low bone mineral density (100% sensitivity, 95% confidence interval [15.8%, 100%]) and menstrual dysfunction (80.0% sensitivity, 95% confidence interval [28.4%, 99.5%]), respectively. The gastrointestinal subscale did not detect surrogate markers of LEA. LEAF-Q score cannot be used to classify athletes as “high risk” of conditions related to LEA, nor can it be used as a surrogate diagnostic tool for LEA given the low specificity identified. Our study supports its use as a screening tool to rule out risk of LEA-related conditions or to create selective low-risk groups that do not need management as there were generally high negative predictive values (range 76.5–100%) for conditions related to LEA.

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Bent R. Rønnestad, Joar Hansen, Thomas C. Bonne, and Carsten Lundby

Purpose: The present case report aimed to investigate the effects of exercise training in temperate ambient conditions while wearing a heat suit on hemoglobin mass (Hbmass). Methods: As part of their training regimens, 5 national-team members of endurance sports (3 males) performed ∼5 weekly heat suit exercise training sessions each lasting 50 minutes for a duration of ∼8 weeks. Two other male athletes acted as controls. After the initial 8-week period, 3 of the athletes continued for 2 to 4 months with ∼3 weekly heat sessions in an attempt to maintain acquired adaptations at a lower cost. Hbmass was assessed in duplicate before and after intervention and maintenance period based on automated carbon monoxide rebreathing. Results: Heat suit exercise training increased rectal temperature to a median value of 38.7°C (range 38.6°C–39.0°C), and during the initial ∼8 weeks of heat suit training, there was a median increase of 5% (range 1.4%–12.9%) in Hbmass, while the changes in the 2 control athletes were a decrease of 1.7% and an increase of 3.2%, respectively. Furthermore, during the maintenance period, the 3 athletes who continued with a reduced number of heat suit sessions experienced a change of 0.7%, 2.8%, and −1.1%, indicating that it is possible to maintain initial increases in Hbmass despite reducing the weekly number of heat suit sessions. Conclusions: The present case report illustrates that heat suit exercise training acutely raises rectal temperature and that following 8 weeks of such training Hbmass may increase in elite endurance athletes.

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Alex G. Shaw, Sungwon Chae, Danielle E. Levitt, Jonathan L. Nicholson, Jakob L. Vingren, and David W. Hill

Purpose: Many athletes report consuming alcohol the day before their event, which might negatively affect their performance. However, the effects of previous-day alcohol ingestion on performance are equivocal, in part, due to no standardization of alcohol dose in previous studies. The purpose of this study was to examine the impact of a standardized previous-day alcohol dose and its corresponding impact on morning-after muscular strength, muscular power, and muscular fatigue in a short-duration test and on performance of severe-intensity exercise. Methods: On 2 occasions, 12 recreationally active individuals reported to the Applied Physiology Laboratory in the evening and ingested a beverage containing either 1.09 g ethanol·kg−1 fat-free body mass (ALC condition) or water (PLA condition). The following morning, they completed a hangover symptom questionnaire, vertical jumps, isometric midthigh pulls, biceps curls, and a constant-power cycle ergometer test to exhaustion. The responses from ALC and PLA were compared using paired-means t tests. Results: Time to exhaustion in the cycle ergometer tests was less (P = .03) in the ALC condition (181 [39] s vs 203 [34] s; –11%, Cohen d = 0.61). There was no difference in performance in vertical jump test, isometric midthigh pulls, and biceps curls tests between the ALC and PLA conditions. Conclusions: Previous-day alcohol consumption significantly reduces morning-after performance of severe-intensity exercise. Practitioners should educate their athletes, especially those whose events rely on anaerobic capacity and/or a rapid response of the aerobic pathways, of the adverse effect of previous-day alcohol consumption on performance.

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Sebastian Sitko, Rafel Cirer-Sastre, Francisco Corbi, and Isaac López-Laval

Purpose: To examine the ability of a multivariate model to predict maximal oxygen consumption (VO2max) using performance data from a 5-minute maximal test (5MT). Methods: Forty-six road cyclists (age 38 [9] y, height 177 [9] cm, weight 71.4 [8.6] kg, VO2max 61.13 [9.05] mL/kg/min) completed a graded exercise test to assess VO2max and power output. After a 72-hour rest, they performed a test that included a 5-minute maximal bout. Performance variables in each test were modeled in 2 independent equations, using Bayesian general linear regressions to predict VO2max. Stepwise selection was then used to identify the minimal subset of parameters with the best predictive power for each model. Results: Five-minute relative power output was the best explanatory variable to predict VO2max in the model from the graded exercise test (R 2 95% credibility interval, .81–.88) and when using data from the 5MT (R 2 95% credibility interval, .61–.77). Accordingly, VO2max could be predicted with a 5MT using the equation VO2max = 16.6 + (8.87 × 5-min relative power output). Conclusions: Road cycling VO2max can be predicted in cyclists through a single-variable equation that includes relative power obtained during a 5MT. Coaches, cyclists, and scientists may benefit from the reduction of laboratory assessments performed on athletes due to this finding.

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Jasmien Dumortier, An Mariman, Jan Boone, Liesbeth Delesie, Els Tobback, Dirk Vogelaers, and Jan G. Bourgois

Purpose: This study aimed to determine the influencing factors of potential differences in sleep architecture between elite (EG) and nonelite (NEG) female artistic gymnasts. Methods: Twelve EG (15.1 [1.5] y old) and 10 NEG (15.3 [1.8] y old) underwent a nocturnal polysomnography after a regular training day (5.8 [0.8] h vs 2.6 [0.7] h), and, on a separate test day, they performed an incremental treadmill test after a rest day in order to determine physical fitness status. A multiple linear regression assessed the predictive value of training and fitness parameters toward the different sleep phases. Total sleep time and sleep efficiency (proportion of time effectively asleep to time in bed), as well as percentage of nonrapid eye movement sleep phase 1 (NREM1) and 2 (NREM2), slow wave sleep (SWS), and rapid eye movement sleep (REM), during a single night were compared between EG and NEG using an independent-samples t test. Results: Peak oxygen uptake influenced NREM1 (β = 1.035, P = .033), while amount of weekly training hours predicted SWS (β = 1.897, P = .032). No differences were documented between EG and NEG in total sleep time and sleep efficiency. SWS was higher in EG (36.9% [11.4%]) compared with NEG (25.9% [8.3%], P = .020), compensated by a lower proportion of NREM2 (38.7% [10.2%] vs 48.4% [6.5%], P = .017), without differences in NREM1 and REM. Conclusions: The proportion of SWS was only predicted by weekly training hours and not by training hours the day of the polysomnography or physical fitness, while NREM1 was linked with fitness level. Sleep efficiency did not differ between EG and NEG, but in EG, more SWS and less NREM2 were identified.

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Sam McCormack, Ben Jones, Sean Scantlebury, Neil Collins, Cameron Owen, and Kevin Till

Purpose: To compare the physical qualities between academy and international youth rugby league (RL) players using principal component analysis. Methods: Six hundred fifty-four males (age = 16.7 [1.4] y; height = 178.4 [13.3] cm; body mass = 82.2 [14.5] kg) from 11 English RL academies participated in this study. Participants completed anthropometric, power (countermovement jump), strength (isometric midthigh pull; IMTP), speed (10 and 40 m speed), and aerobic endurance (prone Yo-Yo IR1) assessments. Principal component analysis was conducted on all physical quality measures. A 1-way analysis of variance with effect sizes was performed on 2 principal components (PCs) to identify differences between academy and international backs, forwards, and pivots at under 16 and 18 age groups. Results: Physical quality measures were reduced to 2 PCs explaining 69.4% of variance. The first PC (35.3%) was influenced by maximum and 10-m momentum, absolute IMTP, and body mass. Ten and forty-meter speed, body mass and fat, prone Yo-Yo, IMTP relative, maximum speed, and countermovement jump contributed to PC2 (34.1%). Significant differences (P < .05, effect size = −1.83) were identified between U18 academy and international backs within PC1. Conclusion: Running momentum, absolute IMTP, and body mass contributed to PC1, while numerous qualities influenced PC2. The physical qualities of academy and international youth RL players are similar, excluding U18 backs. Principal component analysis can reduce the dimensionality of a data set and help identify overall differences between playing levels. Findings suggest that RL practitioners should measure multiple physical qualities when assessing physical performance.