Relationship between physical capacity and match performance in semiprofessional Australian rules football

This study investigated the relationship between physical performance and match performance in Australian Rules Football (ARF). Thirty-six semiprofessional ARF players participated in this study. Physical capacity was measured using a 3-km time trial. Match performance was measured throughout the 2013 season through 2 methods: direct game involvements (DGIs) per minute and a recording of coaches\u27 vote after the game. The main finding of the study was that 3-km time trial performance was a significant predictor of DGI per minute (p ≤ 0.05). In addition, the number of senior games played was also significant in predicting DGI per minute (p ≤ 0.05). Furthermore, the number of senior games significantly correlated with coaches\u27 votes (p ≤ 0.05). There were no significant relationships between 3-km time trial and coaches\u27 vote. The results highlight the importance of developing physical capacity in the preseason period; the players who were better performers in the 3-km time trial had a greater number of DGIs per minute. This information is important to consider in preseason planning to ensure sufficient time is dedicated to developing physical capacity in the training program, as it is directly associated with performance. In addition, this research also highlights the importance of playing experience in relation to team selection. Playing experience, as measured by the number of senior games played, had a significant relationship with both measures of match performance

Multi-Stage 20-m Shuttle Run Fitness Test, Maximal Oxygen Uptake and Velocity at Maximal Oxygen Uptake.

The multi-stage 20-m shuttle run fitness test (20mMSFT) is a popular field test which is widely used to measure aerobic fitness by predicting maximum oxygen uptake (VO2max) and performance. However, the velocity at which VO2max occurs (vVO2max) is a better indicator of performance than VO2max, and can be used to explain inter-individual differences in performance that VO2max cannot. It has been reported as a better predictor for running performance and it can be used to monitor athletes' training for predicting optimal training intensity. This study investigated the validity and suitability of predicting VO2max and vVO2max of adult subjects on the basis of the performance of the 20mMST. Forty eight (25 male and 23 female) physical education students performed, in random order, a laboratory based continuous horizontal treadmill test to determine VO2max, vVO2max and a 20mMST, with an interval of 3 days between each test. The results revealed significant correlations between the number of shuttles in the 20mMSFT and directly determined VO2max (r = 0.87, p<0.05) and vVO2max (r = 0.93, p<0.05). The equation for prediction of VO2max was y = 0.0276x + 27.504, whereas for vVO2max it was y = 0.0937x + 6.890. It can be concluded that the 20mMSFT can accurately predict VO2max and vVO2max and this field test can provide useful information regarding aerobic fitness of adults. The predicted vVO2max can be used in monitoring athletes, especially in determining optimal training intensity

Examining adherence to activity monitoring devices to improve physical activity in adults with cardiovascular disease: A systematic review

Background Activity monitoring devices are currently being used to facilitate and monitor physical activity. No prior review has examined adherence to the use of activity monitoring devices amongst adults with cardiovascular disease. Methods Literature from June 2012 to October 2017 was evaluated to examine the extent of adherence to any activity monitoring device used to collect objective physical activity data. Randomized control trials comparing usual care against the use of an activity monitoring device, in a community intervention for adults from any cardiovascular diagnostic group, were included. A systematic search of databases and clinical trials registers was conducted using Joanna Briggs Institute methodology. Results Of 10 eligible studies, two studies reported pedometer use and eight accelerometer use. Six studies addressed the primary outcome. Mean adherence was 59.1% (range 39.6% to 85.7%) at last follow-up. Studies lacked equal representation by gender (28.6% female) and age (range 42 to 82 years). Conclusion This review indicates that current research on activity monitoring devices may be overstated due to the variability in adherence. Results showed that physical activity tracking in women and in young adults have been understudied

Variation in body composition in professional soccer players: inter- and intra-seasonal changes and the effects of exposure time and player position

The aim of this study was to examine variations in measures of body composition in elite soccer players. Skinfolds and measures of body mass (BM) recorded on a monthly basis across an entire competitive season in a group of senior professional players (n = 26) were used to estimate percentage body fat (%BF) and provide fat-free body mass (FFBM) values. Mean values in players were compared between 6 specific positional roles (goalkeepers, central and lateral defenders/midfielders and center-forwards). In-season variations in measures were studied by comparing values at 5 separate points across the season. The effects of positional group (goalkeepers, defenders, midfielders, and forwards) and exposure time to play (participation time in training and matches) in relation to in-season variations were also examined. To investigate interseasonal changes, repeated measures were taken in players (n = 9) over 3 consecutive seasons. In relation to positional role, a difference in average %BF and BM values was observed (p < 0.001), with substantial differences observed in goalkeepers, lateral midfielders, and forwards. Across all players, there were significant in-season variations in %BF (between start- and mid-season and mid- and end-season, p < 0.001) and FFBM (between start- and mid-season and start- and end-season, p < 0.001), whereas BM remained unchanged. Further analysis of these fluctuations in %BF and FFBM at different points of the season showed that variations differed across the positional groups (p < 0.01), especially in defenders and midfielders. In contrast, no association was observed between measures and exposure time and no differences were reported across seasons. Practitioners should consider individual positional role when interpreting mean body composition data. They should also take into account positional groups when in-season variations in body composition are identified

An individualised approach to monitoring and prescribing training in elite youth football players

The concept of how training load affects performance is founded in the notion that training contributes to two specific outcomes, these are developed simultaneously by repeated bouts of training and act in conflict of each other; fitness and fatigue (Banister et al., 1975). The ability to understand these two components and how they interact with training load is commonly termed the “dose-response relationship” (Banister, 1991). The accurate quantification of training load, fitness and fatigue are therefore of paramount importance to coaches and practitioners looking to examine this relationship. In recent years, the advancement in technology has seen a rise in the number of methodologies used to assess training load and specific training outcomes. However, there is a general lack of evidence regarding the reliability, sensitivity and usefulness of these methods to help inform the training process. The aim of this thesis was therefore to improve the current understanding around the monitoring and prescription of training, with special reference to the relationship between training load, fitness and fatigue. Chapter 4 of this thesis looked to establish test re-test reliability. Variables selected for investigation were measures of subjective wellness; fatigue, muscle soreness, sleep quality, stress levels and mood state, assessments of physical performance; countermovement jump (CMJ), squat jump (SJ) and drop jump (DJ) and the assessment of tri-axial accelerometer data; PlayerLoadTM and individual component planes anterior-posterior (PLAP), mediolateral (PLML), and vertical (PLV), were collected during a sub-maximal shuttle run. The results from this investigation suggest that a short three minute sub-maximal shuttle run can be used as a reliable method to collect accelerometer data. Additionally, assessments of CMJ height, SJ height, DJ contact time (DJ-CT) and DJ reactive strength index (DJ-RSI) were all deemed to have good reliability. In contrast, this chapter highlighted the poor test re-test reliability of the subjective wellness questionnaire. Importantly, the minimum detectable change (MDC) was also calculated for all measures within this study to provide an estimate of measurement error and a threshold for changes that can be considered ‘real’. Chapter 5 assessed the sensitivity and reproducibility of these measures following a standardised training session. To assess sensitivity, the signal-to-noise (S: N) ratio was calculated by using the post training fatigue response (signal) and the MDC derived from Chapter 4 (noise). The fatigue response was considered reproducible if the S: N ratio was greater than one following two standardised training sessions. Three measures met the criteria to be considered both sensitive and reproducible; DJ-RSI, PLML and %PLV. All other measures did not meet the criteria. Subjective ratings of fatigue, muscle soreness and sleep quality did show a sensitive response on one occasion, however, this was not reproducible. This might be due to the categorical nature of the data, making detectable group changes hard to accomplish. The subjective wellness questionnaire was subsequently adapted to include three items; subjective fatigue, muscle soreness and sleep quality on a 10-point scale. The test re-test reliability of these three questions was established in Chapter 6, demonstrating that subjective fatigue and muscle soreness have good test re-test reliability. Chapter 6 was comprised of two studies looking to simultaneously establish the dose-response relationship between training load, measures of fatigue (Part I) and measures of fitness (Part II). In Part I training load was strategically altered on three occasions during a standardised training session in a randomised crossover design. In Part II training and match load was monitored over a 6-week training period with maximal aerobic speed (MAS) assessed pre and post. A key objective for both studies was to assess differences in the training load-fitness-fatigue relationship when using various training load measures, in particular differences between arbitrary and individualised speed thresholds. Results from Part I showed a large to very large relationship between training load and subjective fatigue, muscle soreness and DJ-RSI performance. No differences were found between arbitrary and individualised thresholds. In Part II however, individual external training load, assessed via time above MAS (t>MAS), showed a very large relationship with changes in aerobic fitness. This was in contrast to the unclear relationships with arbitrary thresholds. Taking the results from both studies into consideration it was concluded that t>MAS is a key measure of training load if the objective is to assess the relationship with both fitness and fatigue concurrently with one measure. Chapter 7 subsequently looked to validate the training load-fitness-fatigue relationships established in Chapter 6 via an intervention study. The aim was to develop a novel intervention that prescribed t>MAS, in order to improve aerobic fitness, based on the findings from Chapter 6. Additionally, the fatigue response following a standardised training session was assessed pre and post intervention to evaluate the effect the predicted improvements in aerobic fitness would have on measures of fatigue. Results from Chapter 7 indicate a highly predictable improvement in aerobic fitness from the training load completed during the study, validating the use of t>MAS as a monitoring and intervention tool. Furthermore, this improvement in aerobic fitness attenuated the fatigue response following a standardised training session. The final key finding was the very strong relationship between improvements in aerobic fitness and reductions in fatigue response. This further highlights the relationship between t>MAS, fitness and fatigue. In summary, this thesis has helped further current understanding on the monitoring and prescription of training load, with reference to fitness and fatigue. Firstly, a rigorous approach was used to identify fatigue monitoring measures that are reliable, sensitive and reproducible. Secondly, the relationship between training load, fatigue and fitness was clearly established. And finally, it has contributed new knowledge to the existing literature by establishing the efficacy of a novel MAS intervention to improve aerobic fitness and attenuate a fatigue response in elite youth football players

Spartan Daily, March 20, 2006

Volume 126, Issue 30https://scholarworks.sjsu.edu/spartandaily/10230/thumbnail.jp

Sports industry research North America: USA & Canada

The Sports Industry is a potential business that not only involves the game at the field. It includes different aspects like food & beverage, apparel, sponsorship, licensing, events, tourism, and infrastructure (ATKearney, 2011). In North America this industry is one of the most important in terms of creating a positive impact to the economy, increasing surprisingly fast the GDP of the United States and Canada. The United States and Canada are the world’s biggest sports nations that provide a wide range of sport facilities and infrastructure and hosts yearly enigmatic events in key cities like Boston, New York, Los Angeles, Vancouver and Toronto. For this reason, we identified that these countries are a strategic move for any sports-related company to keep growing within the Sports Industry. The current report aims to provide a comprehensive research about the Sports Industry in North America, describing and analyzing possible investment opportunities in these countries for the upcoming years. The document is structured to explain an I) Overview of The Sports Industry in the United States and Canada, including the main sports leagues, secondary sports, sport facilities and new technology and trends. Then, we will discuss about the II) Main Leagues in North America considering its main teams, athletes, events, and highlight sport cases. Finally, we will describe the III) Sports Media Industry in North America, explaining about the Print, TV, Radio, Online channels and current media trends

Management of the technical training process of athletes in cycling sports

In cyclic sports, the main indicator that characterizes adversarial activity is the average speed of passing distances. The presence of functional dependencies of speed factors on various indicators of sports activity can determine its dynamics. It allows to simulate the process of competitive activity, and according to the dynamics of speed, to determine the nature of a particular indicator. Cyclists and swimmers defined law of motion, the dependence of the athlete's instantaneous speed and its acceleration ontime, applied forces, resistance forces and forces of inertia, as well as on specific physical and morphological data. The presence of a mathematical model allows us to create an adaptive system for controlling the technical preparedness of athletes in cyclic sports