Developmental Activities That Lead to Dropout and Investment in Sport

Background: Studies suggest that expert performance in sport is the result of long-term engagement in a highly specialized form of training termed deliberate practice. The relationship between accumulated deliberate practice and performance predicts that those who begin deliberate practice at a young age accumulate more practice hours over time and would, therefore, have a significant performance advantage. However, qualitative studies have shown that a large amount of sport-specific practice at a young age may lead to negative consequences, such as dropout, and is not necessarily the only path to expert performance in sport. Studies have yet to investigate the activity context, such as the amount of early sport participation, deliberate play and deliberate practice within which dropout occurs. Purpose: To determine whether the nature and amount of childhood-organized sport, deliberate play and deliberate practice participation influence athletes' subsequent decisions to drop out or invest in organized sport. It was hypothesized that young athletes who drop out will have sampled fewer sports, spent less time in deliberate play activities and spent more time in deliberate practice activities during childhood sport involvement. Participants: The parents of eight current, high-level, male, minor ice hockey players formed an active group. The parents of four high-level, male, minor ice hockey players who had recently withdrawn from competitive hockey formed a dropout group. Data collection: Parents completed a structured retrospective survey designed to assess their sons' involvement in organized sport, deliberate play and deliberate practice activities from ages 6 to 13. Data analysis: A complete data-set was available for ages 6 through 13, resulting in a longitudinal data-set spanning eight years. This eight-year range was divided into three levels of development corresponding to the players' progress through the youth ice hockey system. Level one encompassed ages 6–9, level two included ages 10–11 and level three covered ages 12–13. Descriptive statistics were used to report the ages at which the active and dropout players first engaged in select hockey activities. ANOVA with repeated measures across the three levels of development was used to compare the number of sports the active and dropout players were involved in outside of hockey, the number of hours spent in these sports, and involvement in various hockey-related activities. Findings: Results indicated that both the active and dropout players enjoyed a diverse and playful introduction to sport. Furthermore, the active and dropout players invested similar amounts of time in organized hockey games, organized hockey practices, specialized hockey training activities (e.g. hockey camps) and hockey play. However, analysis revealed that the dropout players began off-ice training at a younger age and invested significantly more hours/year in off-ice training at ages 12–13, indicating that engaging in off-ice training activities at a younger age may have negative implications for long-term ice hockey participation. Conclusion: These results are consistent with previous research that has found that early diversification does not hinder sport-specific skill development and it may, in fact, be preferable to early specialization. The active and dropout players differed in one important aspect of deliberate practice: off-ice training activities. The dropout players began off-ice training at a younger age, and participated in more off-ice training at ages 12 and 13 than their active counterparts. This indicates a form of early specialization and supports the postulate that early involvement in practice activities that are not enjoyable may ultimately undermine the intrinsic motivation to continue in sport. Youth sport programs should not focus on developing athletic fitness through intense and routine training, but rather on sport-specific practice, games and play activities that foster fun and enjoyment.

Computational Methods for Identifying Hypertrophic Cardiomyopathy using 12-lead ECG

Hypertrophic cardiomyopathy (HCM) is a cardiovascular disease where the heart muscle is partially thickened and blood flow is - potentially fatally - obstructed. It is one of the leading causes of sudden cardiac death in young people. Electrocardiography (ECG) and Echocardiography (Echo) are the standard tests for identifying HCM and other cardiac abnormalities. The American Heart Association has recommended using a pre-participation questionnaire for young athletes instead of ECG or Echo tests due to considerations of cost and time involved in interpreting the results of these tests by an expert cardiologist. Initially we set out to develop a classifier for automated prediction of young athletes’ heart conditions based on the answers to the questionnaire. Classification results and further in-depth analysis using computational and statistical methods indicated significant shortcomings of the questionnaire in predicting cardiac abnormalities. Automated methods for analyzing ECG signals can help reduce cost and save time in the pre-participation screening process by detecting HCM and other cardiac abnormalities. Therefore, the main goal of this dissertation work is to identify HCM through computational analysis of 12-lead ECG. ECG signals recorded on one or two leads have been analyzed in the past for classifying individual heartbeats into different types of arrhythmia as annotated primarily in the MIT-BIH database. In contrast, we classify complete sequences of 12-lead ECGs to assign patients into two groups: HCM vs. non-HCM. The challenges and issues we address include missing ECG waves in one or more leads and the dimensionality of a large feature-set. We address these by proposing imputation and feature-selection methods. We develop heartbeat-classifiers by employing Random Forests and Support Vector Machines, and propose a method to classify full 12-lead ECGs based on the proportion of heartbeats classified as HCM. The results from our experiments show that the classifiers developed using our methods perform well in identifying HCM. Thus the two contributions of this thesis are the utilization of computational and statistical methods for discovering shortcomings in a current screening procedure and the development of methods to identify HCM through computational analysis of 12-lead ECG signals.

ISSP Position Stand: To Sample or to Specialize? Seven Postulates About Youth Sport Activities that Lead to Continued Participation and Elite Performance

A comprehensive approach to sport expertise should consider the entire situation that is comprised of the person, the task, the environment, and the complex interplay of these components (Hackfort, 1986). Accordingly, the Developmental Model of Sport Participation (Côté, Baker, & Abernethy, 2007; Côté & Fraser-Thomas, 2007) provides a comprehensive framework for sport expertise that outlines different pathways of involvement in sport. In pathways one and two, early sampling serves as the foundation for both elite and recreational sport participation. Early sampling is based on two main elements of childhood sport participation: 1) involvement in various sports and 2) participation in deliberate play. In contrast, pathway three shows the course to elite performance through early specialization in one sport. Early specialization implies a focused involvement on one sport and a large number of deliberate practice activities with the goal of improving sport skills and performance during childhood. This paper proposes seven postulates regarding the role that sampling and deliberate play, as opposed to specialization and deliberate practice, can have during childhood in promoting continued participation and elite performance in sport.