Highly Active Middle School Athletes Demonstrate Poor Motor Skill Proficiency.

BACKGROUND: Fundamental movement skills (FMS) are critical components to lifelong participation in sports and physical activity. With the rise in early sports specialization, mastery of motor skills may be limited in youth athletes. The purpose of this study was to assess FMS proficiency in highly active middle school athletes and determine whether proficiency differed between specialization levels and sex. HYPOTHESIS: (1) Most athletes would fail to achieve proficiency in all domains of the Test of Gross Motor Development (TGMD-2), (2) highly specialized athletes would demonstrate lower proficiency in all domains of the TGMD-2, and (3) male athletes would demonstrate higher proficiency than female athletes. STUDY DESIGN: Cross-sectional. LEVEL OF EVIDENCE: Level 4. METHODS: A total of 91 athletes were recruited (44 male, 12.6 ± 0.9 years). Activity level was quantified using the Hospital for Special Surgery (HSS) Pediatric Functional Activity Brief Scale (Pedi-FABS), specialization level was determined using the Jayanthi Specialization Scale, and the TGMD-2 was used to assess FMS proficiency. Descriptive statistics were used to describe gross motor, locomotor, and object control percentile rank. A 1-way analysis of variance (ANOVA) was used to assess differences in percentile rank between low, moderate, and high specialization groups and independent samples t tests were used to compare sexes (α < 0.05). RESULTS: Mean Pedi-FABS score was 23.6 ± 4.9. In total, 24.2%, 38.5%, and 37.4% of athletes classified as low, moderate, and highly specialized, respectively. Mean percentile ranks were 56.2%, 64.7%, and 62.6% for locomotor, object control, and gross motor domains, respectively. No athlete achieved a percentile rank >99% in any domain of the TGMD-2, and there was no significant difference between specialization groups or sex. CONCLUSION: Despite high activity levels, no athlete demonstrated proficiency in any domain of the TGMD-2, and there was no difference in proficiency between specialization levels or by sex. CLINICAL RELEVANCE: Sport participation, regardless of level, does not ensure adequate mastery of FMS.

Intrinsic Evolution of Analog Circuits Using Field Programmable Gate Arrays.

Evolvable hardware is a field of study exploring the application of evolutionary algorithms to hardware systems during design, operation, or both. The work presented here focuses on the use of field programmable gate arrays (FPGAs), a type of dynamically reconfigurable hardware device typically used for electronic prototyping in conjunction with a newly created open-source platform for performing intrinsic analog evolvable hardware experiments. This work targets the reproduction of seminal field experiments that generated complex analog dynamics of unclocked FPGAs evolved through genetic manipulation of their binary circuit representation: the bitstream. Further, it demonstrates the intrinsic evolution of two nontrivial analog circuits with intriguing properties, amplitude maximization and pulse oscillation, as well as the robustness of evolved circuits to temperature variation and across-chip circuit translation.

Reinforcement Learning for Central Pattern Generation in Dynamical Recurrent Neural Networks.

Lifetime learning, or the change (or acquisition) of behaviors during a lifetime, based on experience, is a hallmark of living organisms. Multiple mechanisms may be involved, but biological neural circuits have repeatedly demonstrated a vital role in the learning process. These neural circuits are recurrent, dynamic, and non-linear and models of neural circuits employed in neuroscience and neuroethology tend to involve, accordingly, continuous-time, non-linear, and recurrently interconnected components. Currently, the main approach for finding configurations of dynamical recurrent neural networks that demonstrate behaviors of interest is using stochastic search techniques, such as evolutionary algorithms. In an evolutionary algorithm, these dynamic recurrent neural networks are evolved to perform the behavior over multiple generations, through selection, inheritance, and mutation, across a population of solutions. Although, these systems can be evolved to exhibit lifetime learning behavior, there are no explicit rules built into these dynamic recurrent neural networks that facilitate learning during their lifetime (e.g., reward signals). In this work, we examine a biologically plausible lifetime learning mechanism for dynamical recurrent neural networks. We focus on a recently proposed reinforcement learning mechanism inspired by neuromodulatory reward signals and ongoing fluctuations in synaptic strengths. Specifically, we extend one of the best-studied and most-commonly used dynamic recurrent neural networks to incorporate the reinforcement learning mechanism. First, we demonstrate that this extended dynamical system (model and learning mechanism) can autonomously learn to perform a central pattern generation task. Second, we compare the robustness and efficiency of the reinforcement learning rules in relation to two baseline models, a random walk and a hill-climbing walk through parameter space. Third, we systematically study the effect of the different meta-parameters of the learning mechanism on the behavioral learning performance. Finally, we report on preliminary results exploring the generality and scalability of this learning mechanism for dynamical neural networks as well as directions for future work.

A Preliminary Exploration of the Effects of a 6-week Interactive Video Dance Exercise Program in an Adult Population.

PURPOSE: The purpose of this study was to determine the effects of a 6-week interactive video dance game (IVDG) program on adult participants' cardiorespiratory status and body mass index (BMI). METHODS: Twenty-seven healthy adult participants attended IVDG sessions over a 6-week period. Participants completed pre- and post-testing consisting of a submaximal VO(2) treadmill test, assessment of resting heart rate (RHR) and blood pressure (BP), BMI, and general health questionnaires. Data were analyzed using descriptives, paired t-tests to assess pre-to post-testing differences, and one-way ANOVAs to analyze variables among select groups of participants. Questionnaire data was manually coded and assessed. RESULTS: Twenty participants attended at least 75% of available sessions and were used in data analysis. Mean BMI decreased significantly (from 26.96 kg/m(2) to 26.21 kg/m(2); 2.87%) and cardiorespiratory fitness measured by peak VO(2) increased significantly (from 20.63 ml/kg/min to 21.69 ml/kg/min; 5.14%). Most participants reported that the IVDG program was a good workout, and that they were encouraged to continue or start an exercise routine. Forty percent reported improvements in sleep, and nearly half stated they had or were considering purchasing a home version of a video dance game. CONCLUSIONS: Interactive video dance game is an effective and enjoyable exercise program for adults who wish to decrease their BMI and improve components of cardiorespiratory fitness.