Influence of Growth, Maturation, and Sex on Maximal Power, Force, and Velocity During Overground Sprinting.

ABSTRACT: Sudlow, A, Galantine, P, Del Sordo, G, Raymond, J-J, Dalleau, G, Peyrot, N, and Duché, P. Influence of growth, maturation, and sex on maximal power, force, and velocity during overground sprinting. J Strength Cond Res 38(3): 491-500, 2024-In pediatric populations maximal anaerobic power, force, and velocity capabilities are influenced by changes in body dimensions and muscle function. The aim of this study was to investigate the influences of growth, maturation, and sex on short-term anaerobic performance. One hundred forty children pre-, mid-, and postpeak height velocity performed two 30-m sprints concurrently measured using a radar device. Maximal power (Pmax), force (F0), and velocity (v0) were calculated from sprint velocity-time data and normalized using sex-specific, multiplicative, allometric models containing body mass, fat-free mass (FFM), or height, and chronological age. Absolute values for Pmax, F0, and v0 were higher with increasing maturity (p < 0.01; d ≥ 0.96), and boys had greater outputs than girls (p < 0.01; d ≥ 1.19). When Pmax and v0 were scaled all maturity-related and sex-related differences were removed. When F0 was scaled using models excluding age, all maturity-related differences were removed except for the least mature group (p < 0.05; d ≥ 0.88) and boys maintained higher values than girls (p < 0.05; d ≥ 0.92). All maturity-related and sex-related differences were removed when F0 was scaled using models including age. Maturity-related and sex-related variance in Pmax and v0 can be entirely explained when FFM, height, and chronological age are accounted for. Regarding F0, there seems to be a threshold after which the inclusion of age is no longer necessary to account for maturity-related differences. In young prepubertal children, the inclusion of age likely accounts for deficits in neuromuscular capacities and motor skills, which body dimensions cannot account for. Practitioners should focus on eliciting neural adaptations and enhancing motor coordination in prepubertal children to improve anaerobic performance during overground sprinting.

Energy and Macronutrient Intakes in Young Athletes: A Systematic Review and Meta-analysis.

The aim of this study was to conduct a systematic review and meta-analysis of differences in energy and macronutrient intakes between young athletes and non-athletes, considering age, gender and sport characteristics. The study included original research articles that compared energy and macronutrient intakes of 8 to 18-year-old athletes to non-athletes. Mean difference (MD) meta-analyses were performed to quantify energy and macronutrient intake differences between athletes and non-athletes. Eighteen observational studies were included. Results revealed that the energy and carbohydrate consumption of athletes was higher than that of non-athletes (MD=4.65kcal/kg/d, p<0.01 and MD=1.65% of total energy intake, p<0.01, respectively). Subgroup analyses revealed a significant effect of total training time on the observed mean differences between athletes and non-athletes. As practice time increased, the differences between athletes and non-athletes increased for carbohydrate and decreased for protein. Sport type analysis revealed a higher protein intake by mixed sport athletes compared to endurance and power sports. Analyses also indicated an age effect: the older the athletes, the smaller the differences between athletes and non-athletes for energy intake. However, the methods used to match groups and estimate dietary intakes forced us to moderate the results. More rigorous research methods are needed to define the dietary intakes of athletes and non-athletes.

Which Factors Influence Running Gait in Children and Adolescents? A Narrative Review.

In recent years, running has dramatically increased in children and adolescents, creating a need for a better understanding of running gait in this population; however, research on this topic is still limited. During childhood and adolescence multiple factors exist that likely influence and shape a child's running mechanics and contribute to the high variability in running patterns. The aim of this narrative review was to gather together and assess the current evidence on the different factors that influence running gait throughout youth development. Factors were classified as organismic, environmental, or task-related. Age, body mass and composition, and leg length were the most researched factors, and all evidence was in favour of an impact on running gait. Sex, training, and footwear were also extensively researched; however, whereas the findings concerning footwear were all in support of an impact on running gait, those concerning sex and training were inconsistent. The remaining factors were moderately researched with the exception of strength, perceived exertion, and running history for which evidence was particularly limited. Nevertheless, all were in support of an impact on running gait. Running gait is multifactorial and many of the factors discussed are likely interdependent. Caution should therefore be taken when interpreting the effects of different factors in isolation.

A Comparison of an Alternative Weight-Grading Model Against Chronological Age Group Model for the Grouping of Schoolboy Male Rugby Players.

OBJECTIVES: Concerns regarding marked differences in the weights and body composition of young rugby players competing within the same age groups have led to the suggestion of alternative models for grouping young players. The aims of this study were (1) to compare variance in the body size and body composition of schoolboy rugby players (9 to 14 years), across weight- and age-grading models, and (2) to identify morphotypes for the weight model using Hattori's body composition chart. MATERIALS AND METHODS: Skinfold thickness measurements were used to assess body fat mass (BF), fat-free mass (FFM), body fat mass index (BFMI), and fat-free mass index (FFMI). Standardized measure of height and weight were taken for all participants. Data were grouped according to the age categories of the French Rugby Federation (U11: Under 11 years, U13: Under 13 years, and U15: Under 15 years), and to the weight categories (W30-44.9; W45-59.9; and W60-79.9) carried out from 25th and 75th weight percentile in each age category. Body mass index status (NW normal-weight versus OW/OB overweight/obese) was considered. Extreme morphotypes are characterized from BFMI and FFMI in the weight-grading model on Hattori's body composition chart. RESULTS: The dispersion of anthropometric characteristics decreased significantly for the weight model, except for height in all groups and BFMI for U13. Among NW, 3, 1.8, and 0% upgraded; 18.2, 68.7, and 45.5% downgraded; among OW, 50, 21.5, and 12.5%; and among OB, 91.3, 83.3, and 74.6% upgraded, respectively, in U11, U13, U15. FFMI/BFMI were correlated in U11 (r = 0.80, p < 0.001), U13 (r = 0.66, p < 0.001), and U15 (r = 0.77, p < 0.001). There was no significant correlation in W45-59.9 and low correlations in W30-44.9 (r = 0.25, p < 0.001) and W60-79.9 (r = 0.29, p < 0.001). Significant grading difference between the centroids (p < 0.05) and the distribution deviates from centroids of BFMI and FFMI (p < 0.0001) were noted between the two models. Thirteen players were located in adipo-slender, twenty-three in adipo-solid, twenty-two in lean-slender, and two located in the lean-solid morphotype in weight model. CONCLUSION: A weight-grading model should be considered to limit mismatches in anthropometric variables. However, variations of body composition also persisted for this model. Hattori's body composition chart allowed more detailed examination of morphological atypicalities among schoolboy rugby players.

A study comparing care between nurses and doctors in the French prehospital setting: the case of hypoglycemia.

INTRODUCTION: The objective of this study was to compare the quality of care between French nurses and physicians in the prehospital management of hypoglycemic patients. METHODS: Response times, concordance with medical protocols/recommendations, quality of medical records, and percentage of hospitalized patients were evaluated. RESULTS: A total of 33 patients were treated for hypoglycemia by the nurse group and 41 by the physician group. The groups were similar in terms of response rates (mean time of 00:08 ± 00:06 minutes for nurses and 00:10 ± 00:09 minutes for doctors). For 51 patients not requiring hospitalization, the proportion was similar in each group (47.1% and 52.9% for nurses and doctors, respectively). The nurse group showed significantly higher mean scores for concordance with recommendations (P < .001) and quality of medical records (P = .005). DISCUSSION: In the management of hypoglycemic patients, the quality of care of an emergency ambulance team composed of nurses was comparable to that of doctors.