Development of a scale to measure expected concussion reporting behavior

Background Most concussion education aims to increase athlete self-report of concussive symptoms. Although the population burden of concussion is high, frequency with which this injury occurs on a given sports team in a given season is relatively low. This means that powering concussion education evaluation studies to measure change in post-injury symptom reporting behavior requires what is often a prohibitively large sample size. Thus, evaluation studies are typically powered to measure proximal cognitions. Expected reporting behavior, a cognition that reflects planned and reactive decision-making, is a theoretically indicated construct for inclusion in evaluation studies. However, previously no scales were available to measure this construct with demonstrated reliability and validity among youth athletes. The objective of this study was to develop and assess the validity of a brief single-factor scale to measure expected youth athlete concussion reporting behavior (CR-E) in a sample of youth athletes. Methods A mixed methods approach was used, including cognitive interviews with youth athletes, and quantitative item reduction and validation. Participants were youth athletes (aged 9–16) from the Seattle metropolitan and rural south-Georgia regions. After refining an initial pool of items using cognitive interviews with a diverse group of youth athletes (n = 20), a survey containing these items was administered to youth soccer and football players (n = 291). Item reduction statistics and sequential confirmatory factor analyses were used to reduce the initial scale using a randomly selected half of the sample. Then, a final confirmatory factor analysis and validation tests were applied to the other half of the sample of youth athletes. Predictive validation was conducted longitudinally in a separate sample of youth athletes (n = 155). Results Internal consistency was high (alpha = 0.89), model fit was excellent, validation tests were in the hypothesized directions, and the scale was feasible to use. Using the finalized 4-item scale, we observed that less than one-third of youth soccer and football athletes expect to “always” tell their coach about symptoms of a suspected concussion. Conclusions The CR-E measure should be included in future studies evaluating concussion education programming in youth athlete populations.

Developmental Training Model for the Sport Specialized Youth Athlete: A Dynamic Strategy for Individualizing Load-Response During Maturation

Context: Most available data on athletic development training models focus on adult or professional athletes, where increasing workload capacity and performance is a primary goal. Development pathways in youth athletes generally emphasize multisport participation rather than sport specialization to optimize motor skill acquisition and to minimize injury risk. Other models emphasize the need for accumulation of sport- and skill-specific hours to develop elite-level status. Despite recommendations against sport specialization, many youth athletes still specialize and need guidance on training and competition. Medical and sport professionals also recommend progressive, gradual increases in workloads to enhance resilience to the demands of high-level competition. There is no accepted model of risk stratification and return to play for training a specialized youth athlete through periods of injury and maturation. In this review, we present individualized training models for specialized youth athletes that (1) prioritize performance for healthy, resilient youth athletes and (2) are adaptable through vulnerable maturational periods and injury. Evidence Acquisition: Nonsystematic review with critical appraisal of existing literature. Study Design: Clinical review. Level of Evidence: Level 4. Results: A number of factors must be considered when developing training programs for young athletes: (1) the effect of sport specialization on athlete development and injury, (2) biological maturation, (3) motor and coordination deficits in specialized youth athletes, and (4) workload progressions and response to load. Conclusion: Load-sensitive athletes with multiple risk factors may need medical evaluation, frequent monitoring, and a program designed to restore local tissue and sport-specific capacity. Load-naive athletes, who are often skeletally immature, will likely benefit from serial monitoring and should train and compete with caution, while load-tolerant athletes may only need occasional monitoring and progress to optimum loads. Strength of Recommendation Taxonomy (SORT): B.

Youth Athlete Development Models: A Narrative Review

Context: Physical activity has shown to be beneficial for the overall physical and mental health of youth. There has been an increasing focus on youth sports moving from a recreational activity to becoming a launching pad for participation at elite levels. Several models of athlete development have emerged to guide specialized and nonspecialized athletes at an age-appropriate level, taking into consideration their physical and mental development. The purpose of this review is to summarize the current evidence and theoretical models regarding youth athlete development and discuss broader initiatives for sports participation and future directions for the field. Evidence Acquisition: An electronic databases search, including PubMed, Google Scholar, ScienceDirect, National Institutes of Health, UpToDate, and Springer was conducted. Articles from 1993 to 2021 were included. The search terms long term athlete development, LTAD model, youth physical development, youth athlete development, sports specialization, and pediatric athlete, among others, were used. Study Design: Narrative review. Level of Evidence: Levels 4 and 5. Results: Several models of youth athlete development are discussed in this article. More recent models have built on previous models to incorporate more age- and development-specific recommendations; however, no singular model could be identified as the gold standard for youth athlete development, especially given the lack of empirical data to support these models. Conclusion: Youth athlete development currently consists of several theoretical models, each with their own strengths and weaknesses, that can guide the training of young athletes to maximize their performance. Those involved in this process—physicians, athletic trainers, coaches, physical educators, and parents—should understand these various models and trial their various features to see what works best for their individual athlete with consideration given to factors such as their stage of development. Ultimately, more empirical data are required to definitively state which is the optimal approach.

Predicting Youth Athlete Sleep Quality and the Development of a Translational Tool to Inform Practitioner Decision Making

Background: Identifying key variables that predict sleep quality in youth athletes allows practitioners to monitor the most parsimonious set of variables that can improve athlete buy-in and compliance for athlete self-report measurement. Translating these findings into a decision-making tool could facilitate practitioner willingness to monitor sleep in athletes. Hypothesis: Key predictor variables, identified by feature reduction techniques, will lead to higher predictive accuracy in determining youth athletes with poor sleep quality. Study Design: Cross-sectional study. Level of Evidence: Level 3. Methods: A group (N = 115) of elite youth athletes completed questionnaires consisting of the Pittsburgh Sleep Quality Index and questions on sport participation, training, sleep environment, and sleep hygiene habits. A least absolute shrinkage and selection operator regression model was used for feature reduction and to select factors to train a feature-reduced sleep quality classification model. These were compared with a classification model utilizing the full feature set. Results: Sport type, training before 8 am, training hours per week, presleep computer usage, presleep texting or calling, prebedtime reading, and during-sleep time checks on digital devices were identified as variables of greatest influence on sleep quality and used for the reduced feature set modeling. The reduced feature set model performed better (area under the curve, 0.80; sensitivity, 0.57; specificity, 0.80) than the full feature set models in classifying youth athlete sleep quality. Conclusion: The findings of our study highlight that sleep quality of elite youth athletes is best predicted by specific sport participation, training, and sleep hygiene habits. Clinical Relevance: Education and interventions around the training and sleep hygiene factors that were identified to most influence the sleep quality of youth athletes could be prioritized to optimize their sleep characteristics. The developed sleep quality nomogram may be useful as a decision-making tool to improve sleep monitoring practice among practitioners.

Youth Athlete Development and Nutrition

Adolescence (ages 13–18 years) is a period of significant growth and physical development that includes changes in body composition, metabolic and hormonal fluctuations, maturation of organ systems, and establishment of nutrient deposits, which all may affect future health. In terms of nutrition, adolescence is also an important time in establishing an individual’s lifelong relationship with food, which is particularly important in terms of the connection between diet, exercise, and body image. The challenges of time management (e.g., school, training, work and social commitments) and periods of fluctuating emotions are also features of this period. In addition, an adolescent’s peers become increasingly powerful moderators of all behaviours, including eating. Adolescence is also a period of natural experimentation and this can extend to food choice. Adolescent experiences are not the same and individuals vary considerably in their behaviours. To ensure an adolescent athlete fulfils his/her potential, it is important that stakeholders involved in managing youth athletes emphasize eating patterns that align with and support sound physical, physiological and psychosocial development and are consistent with proven principles of sport nutrition.

To succeed as youth athlete: Is it the dead end in track and field?

It is reasonable to believe that a young athlete who succeeds in a World Youth Championship, will also be successful as a senior athlete. To determine the percentage of success of all World Youth Championship finalists who also became finalists in a subsequent senior World Championship, considering all athletes and events at World Athletics Championships. This study analyzed the eight male and eight female finalists of all the events conducted at the World Athletics World Youth Championship from 1999 to 2009, who also became finalists at the World Athletics World Championship from 2001 to 2011. Percentage of success was calculated for track and field events, for male, female, and both. For all the events, from 1759 finalists in a World Youth Championship only 83, representing 4.72%, were also finalists in a World Championship in 2001, 2003, 2005, 2007, 2009, or 2011. Of those 83 athletes, 45 were males and 38 were female. A low rate of success was found. These results were discussed including injuries, early specialization, biological maturation, and/or overtraining, as possible factors related to this low rate of success.