Journal of Sports Sciences

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Day 2. Free Communications – Sport and Performance To cite this article: (2015) Day 2. Free Communications – Sport and Performance, Journal of Sports Sciences, 33:sup1, s75-s77, DOI: 10.1080/02640414.2015.1110331 To link to this article:

Published online: 25 Nov 2015.

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Date: 27 January 2016, At: 11:38

Journal of Sports Sciences, 2015 Vol. 33, Supplement 1, s75–s77,

Day 2. Free Communications – Sport and Performance

D2.S2.3(1). The effects of hypohydration on cognitive function in physically active males KARAH DRING1*, SIMON COOPER1, RUTH JAMES1, ROBERT CORNEY2 AND LEWIS JAMES2

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Nottingham Trent University; 2Loughborough University *Corresponding author: [email protected] The effect of hypohydration on cognitive function remains equivocal, with confounders in previous studies such as the method used to attain hypohydration (Ganio et al., 2011, British Journal of Nutrition, 106, 1535–1543; Szinnai et al., 2005, American Journal of Physiology Regulatory, Integrative and Comparative Physiology, 289, R275–R280). Many studies have used a combination of exercise and heat exposure, making it difficult to distinguish the effects of hypohydration from the confounding effects of heat exposure and exercise, a limitation addressed by the present study. Following ethical approval, 20 physically active males (age: 24.1 ± 0.8 years, height: 1.80 ± 0.06 m, body mass: 74.1 ± 8.7 kg, VO2max: 47.4 ± 11.8 ml · kg−1 · min−1) completed a familiarisation session and two experimental trials, euhydrated (EUH) and hypohydrated (HYP), separated by 7 days. Baseline measurements of body mass, plasma volume and cognitive function (visual search test, Stroop test and Sternberg paradigm) were taken upon arrival (~4 pm). Participants completed intermittent cycling at 50% of predetermined WRmax (10 min cycling interspersed with 5 min rest) in an environmental chamber (35°C, 70% RH) until ~2% of initial body mass was lost. Participants consumed 175% BML plain water (EUH) in four aliquots or 200 ml plain water (HYP) and returned to the laboratory the following morning (~8 am) for all measures. Cognitive function data were analysed in R and all other data were analysed in SPSS using a trial × time interaction. Changes in body mass and plasma volume were greater on the HYP trial when compared to the REH trial (body mass: HYP −2.7 ± 0.4% BML, REH −0.6 ± 0.6% BML, P < 0.01; plasma volume: HYP −7.1 ± 5.5%, REH: 0.9 ± 3.3%, P < 0.01). Response times were slower the following morning when hypohydrated compared to when rehydrated on both baseline and complex levels of the visual search test (baseline: © 2015 Taylor & Francis

REH +1 ms, HYP +15 ms, P = 0.027; complex: REH −85 ms, HYP +99 ms, P = 0.003). However, there was no effect of hypohydration on response times on either the Stroop test or Sternberg paradigm (all P > 0.05). Furthermore, there were no effects of hypohydration on accuracy on any of the cognitive function tests (all P > 0.05). Overall, these findings suggest the effect of hypohydration on cognitive function is dependent upon the component examined. Specifically, hypohydration impaired the speed of visual processing (as assessed by the visual search test), but did not affect executive function (Stroop test) or working memory (Sternberg paradigm). These findings have implications for athletes who may experience hypohydration, given the implications of cognitive function for sporting performance.

D2.S2.3(2). The validity and reliability of an amateur boxing conditioning and fitness test EDWARD THOMSON*, KEVIN LAMB AND CERI NICHOLAS University of Chester *Corresponding author: [email protected] @ethomson_boxing Despite the notable physiological demands made of amateur boxers during competition, scientific appraisal of the sport has been scarce, and attempts to quantify its demands have suffered from inadequate measurement validity and reliability. As simulation protocols (of performance) offer viable frameworks for this intent and permit examination of intervention-based changes in performance, this study addressed the validity and reliability of the internal responses to a recently developed boxing conditioning and fitness test (BOXFIT). With institutional ethics approval, 28 male amateur boxers (mean ± SD; age 22.4 ± 3.5 years, body mass 67.7 ± 10.1 kg, stature 171 ± 9 cm) performed repeated trials of the BOXFIT separated by 4–7 days, which involved three 3-min rounds interspersed with 60 s rests, and included offensive punches (26 per min), defensive movements (12 per min), and boxing-specific locomotion (covering 35.8 m · min−1 @ 0.5 m · s−1). Measurements of heart rate (HR), oxygen uptake (VO2), post-

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Day 2. Free Communications – Sport and Performance

exercise blood lactate (BLa), ratings of perceived exertion (RPE), the frequency of boxing-specific actions and total punch accelerations were recorded during both trials to characterise the internal and external demands. Ten participants also engaged in bouts of competitive sparring (contested over the same durations and instructed to compete realistically) 5–9 days later to provide a source of validation data for these demands. The BOXFIT yielded mean and peak HR of 169 ± 11 and 188 ± 11 beats · min−1, respectively, VO2 of 41 ± 6 ml · kg · min−1 , BLa of 4.6 ± 1.3 mmol · L−1, RPE 6–8 across rounds and punch accelerations of 2737 ± 104 g. Typically, values for all measures increased (P < 0.01) from one round to the next (e.g., mean HR; ES [95% CI] = 0.64 [0.1 to 1.17], 0.28 [−0.80 to 0.25] for round one vs. two and two vs. three, respectively). Of these, the mean and peak HR, post-exercise BLa and RPE represented 96 ± 4%, 97 ± 4%, 49 ± 10% and 90 ± 13% of sparring values, respectively. The coefficient of variation for measurements was found to be 2–12%. The findings reflect that whilst the boxers experienced a high internal demand, the simulation protocol yielded responses typically lower than those observed during competitive sparring. Nonetheless, the responses to the BOXFIT were sufficiently reliable that, with slight modifications (i.e., alterations to its external load), applied sports scientists, coaches and boxers could adopt the simulation to appraise systematic changes or improve features of boxing performance.

D2.S2.3(3). Influence of team cohesion in sport in school-aged students: in relation to gender, age and type of sport MARIA ESPADA-MATEOS1,2* AND ENRIQUE FRADEJAS MEDRANO1 1

Universidad Camilo José Cela, Spain; 2Universidad Pontificia de Comillas, Spain *Corresponding author: [email protected] Team cohesion provides greater learning, more satisfaction in sport and with team mates, more productivity, better communications, more feelings of security and greater adherence to the sports practice (Eys, Loughead, Bray, and Carron, 2009, The Sport Psychologist, 23, 330–345). Therefore, the main purpose of this study was to analyse the state of team cohesion in school-aged students practising sports, as a function of gender, age and type of sport. The research used a descriptive quantitative methodology by means of a questionnaire. The sample consisted of 816 subjects (50.3% male and 49.7% female) of between 12 and 18 years of

age (mean 14.6, s = 1.9), who practise different individual and team sports in the Castilla-La Mancha region. Several aspects were taken into account for the statistical calculations: the population is infinite; thus for the population variance we used the most unfavourable supposition where “P” and “Q” are equal with 50% each; the confidence interval was set at 95.5%, with a margin of error of ±3.5%. The Psychological Characteristics related to Sports Performance questionnaire (Características Psicológicas relacionadas con el Rendimiento Deportivo (CPRD)) with Cronbach alpha of r = .85 was used. The results show that with regard to gender there were no significant differences in team cohesion in any of the items analysed, t(814) = −1.71, P > 0.05; t(814) = −1.66, P > 0.05, coinciding with previous studies (e.g., Paradis and Loughead, 2010, International Journal of Sports, 41, 1–20). With regard to age, the younger sports practitioners (12–13 years) revealed a lower level of cohesion than the older ones (14–15 years), F(2, 538.22) = 3.78, P < 0.05, again coinciding with other previous studies (e.g., Subramanyam, 2013, International Journal of Sports Sciences & Fitness, 3, 250–258). With respect to different sports, there were statistically significant differences between the students who practised volleyball, who showed a stronger team spirit, and those who practised tennis F(9, 326.19) = 4.53, P < 0.05, again in line with previous research (e.g., Halbrook et al., 2012, Journal of Sport Behavior, 35, 61–77). The results suggest the need to promote team cohesion in school-aged sports people as it has been shown that this type of training provides the young sports person with a greater degree of commitment to their team. The team cohesion variable is particularly relevant in team sports (or in individual sports when playing doubles or as a team).

D2.S2.3(4). Between- and within-race variance in elite short-track speed skating: a new approach to analyse group behaviour during competition MARCO J. KONINGS* AND FLORENTINA J. HETTINGA University of Essex *Corresponding author: [email protected] @MarcoKo4 Previous research indicated that short-track speed skaters seem to alter their pacing behaviour based on their opponents, especially during the initial stages of 1500 m races (Konings et al., 2015, International Journal of Sport Physiology and Performance, doi: 10.1123/ijspp.2015-0137). The aim of the present study was to gain more insight into the interaction of

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Day 2. Free Communications – Sport and Performance athletes competing within the same race. Therefore, within-race and between-race variances (σ2) were assessed in elite short-track speed skating competitions. We hypothesised to find a relatively low withinrace σ2 and high between-race σ2 in the initial race stages, indicating that athletes adjusted their own pacing behaviour to the group’s pace in the early stages of competition. With institutional ethics approval, lap times of elite 500 m, 1000 m and 1500 m short-track speed skating competitions for both males and females of the seasons 2012–2013 (n = 1141 races) and 2013– 2014 (n = 973 races) were collected. Within-race and between-race σ2 were determined for each lap and for the finishing time. Within-race σ2 is the variance in lap times of a particular lap that could be explained by the difference in lap times of individual athletes compared to the average lap time in that lap in that particular race. In contrast, the between-race σ2 is the variance in lap times in a particular lap that could be explained by the difference in the average lap time in that lap of particular races compared to the average lap time in that lap of all races. Between-race and within-race σ2 were expressed as percentage of the total variance for each particular lap or for the finishing time. For finishing times, the between-race σ2 could explain respectively 79.2%, 93.4% and 86.6% of the total variance for the 500 m, 1000 m and 1500 m. In the first five laps of the 1000 m and in laps 2–7 of the 1500 m, withinrace σ2 could explain

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