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Growth, maturation, and development dominate the daily lives of children and adolescents for approximately the first 2 decades of life. Growth and maturation are biological processes, while development is largely a behavioral process. The 3 processes occur simultaneously and interact. They can be influenced by physical activity and also can influence activity, performance, and fitness. Allowing for these potential interactions, 10 questions on growth and maturation that have relevance to physical activity, performance, and fitness are presented. The questions are not mutually exclusive and address several broadly defined topical areas: exercise and growth, body weight status (body mass index, adiposity rebound, \"unhealthy weight gain\"), movement proficiency (hypothesized barrier, role in obesity), individual differences, tracking, maturity-associated variation in performance, and corresponding variation in physical activity. Central to the discussion of each is the need for a biocultural approach recognizing the interactions of biology and behavior as potential influences on the variables of interest.

Background Predicted maturity offset and age at peak height velocity are increasingly used with youth athletes, although validation studies of the equations indicated major limitations. The equations have since been modified and simplified. Objective The objective of this study was to validate the new maturity offset prediction equations in independent longitudinal samples of boys and girls. Methods Two new equations for boys with chronological age and sitting height and chronological age and stature as predictors, and one equation for girls with chronological age and stature as predictors were evaluated in serial data from the Wrocław Growth Study, 193 boys (aged 8–18 years) and 198 girls (aged 8–16 years). Observed age at peak height velocity for each youth was estimated with the Preece–Baines Model 1. The original prediction equations were included for comparison. Predicted age at peak height velocity was the difference between chronological age at prediction and maturity offset. Results Predicted ages at peak height velocity with the new equations approximated observed ages at peak height velocity in average maturing boys near the time of peak height velocity; a corresponding window for average maturing girls was not apparent. Compared with observed age at peak height velocity, predicted ages at peak height velocity with the new and original equations were consistently later in early maturing youth and earlier in late maturing youth of both sexes. Predicted ages at peak height velocity with the new equations had reduced variation compared with the original equations and especially observed ages at peak height velocity. Intra-individual variation in predicted ages at peak height velocity with all equations was considerable. Conclusion The new equations are useful for average maturing boys close to the time of peak height velocity; there does not appear to be a clear window for average maturing girls. The new and original equations have major limitations with early and late maturing boys and girls.

Problems with accurate chronological age (CA) reporting occur on a more or less regular basis in youth sports. As a result, there is increasing discussion of age verification. Use of 'bone age' or skeletal age (SA) for the purpose of estimating or verifying CA has been used in medicolegal contexts for many years and also in youth sport competitions. This article reviews the concept of SA, and the three most commonly used methods of assessment. Variation in SA within CA groups among male soccer players and female artistic gymnasts is evaluated relative to the use of SA as a tool for verification of CA. Corresponding data for athletes in several other sports are also summarized. Among adolescent males, a significant number of athletes will be identified as older than a CA cutoff because of advanced skeletal maturation when they in fact have a valid CA. SA assessments of soccer players are comparable to MRI assessments of epiphyseal-diaphyseal union of the distal radius in under-17 soccer players. Both protocols indicate a relatively large number of false negatives among youth players aged 15-17 years. Among adolescent females, a significant number of age-eligible artistic gymnasts will be identified as younger than the CA cutoff because of later skeletal maturation when in fact they have a valid CA. There is also the possibility of false positives-identifying gymnasts as younger than the CA cutoff because of late skeletal maturation when they have a valid CA. The risk of false negatives and false positives implies that SA is not a valid indicator of CA. [PUBLICATION ABSTRACT]

Problems with accurate chronological age (CA) reporting occur on a more or less regular basis in youth sports. As a result, there is increasing discussion of age verification. Use of ‘bone age’ or skeletal age (SA) for the purpose of estimating or verifying CA has been used in medicolegal contexts for many years and also in youth sport competitions. This article reviews the concept of SA, and the three most commonly used methods of assessment. Variation in SA within CA groups among male soccer players and female artistic gymnasts is evaluated relative to the use of SA as a tool for verification of CA. Corresponding data for athletes in several other sports are also summarized. Among adolescent males, a significant number of athletes will be identified as older than a CA cutoff because of advanced skeletal maturation when they in fact have a valid CA. SA assessments of soccer players are comparable to MRI assessments of epiphyseal-diaphyseal union of the distal radius in under-17 soccer players. Both protocols indicate a relatively large number of false negatives among youth players aged 15–17 years. Among adolescent females, a significant number of age-eligible artistic gymnasts will be identified as younger than the CA cutoff because of later skeletal maturation when in fact they have a valid CA. There is also the possibility of false positives-identifying gymnasts as younger than the CA cutoff because of late skeletal maturation when they have a valid CA. The risk of false negatives and false positives implies that SA is not a valid indicator of CA.

Inter-individual differences in size, maturity status, function, and behavior among youth of the same chronological age (CA) have long been a concern in grouping for sport. Bio-banding is a recent attempt to accommodate maturity-associated variation among youth in sport. The historical basis of the concept of maturity-matching and its relevance to youth sport, and bio-banding as currently applied are reviewed. Maturity matching in sport has often been noted but has not been systematically applied. Bio-banding is a recent iteration of maturity matching for grouping youth athletes into ‘bands’ or groups based on characteristic(s) other than CA. The percentage of predicted young adult height at the time of observation is the estimate of maturity status of choice. Several applications of bio-banding in youth soccer have indicated positive responses from players and coaches. Bio-banding reduces, but does not eliminate, maturity-associated variation. The potential utility of bio-banding for appropriate training loads, injury prevention, and fitness assessment merits closer attention, specifically during the interval of pubertal growth. The currently used height prediction equation requires further evaluation.

The health, fitness and other advantages of youth sports participation are well recognised. However, there are considerable challenges for all stakeholders involved—especially youth athletes—in trying to maintain inclusive, sustainable and enjoyable participation and success for all levels of individual athletic achievement. In an effort to advance a more unified, evidence-informed approach to youth athlete development, the IOC critically evaluated the current state of science and practice of youth athlete development and presented recommendations for developing healthy, resilient and capable youth athletes, while providing opportunities for all levels of sport participation and success. The IOC further challenges all youth and other sport governing bodies to embrace and implement these recommended guiding principles.

Sustainable aviation fuels (SAF) can reduce aviation greenhouse gas emissions, yet their production scale-up to meet policy goals remains unexplored. Here, we describe the Global SAF Capacity Database to quantify global and European Union (EU) SAF capacity, comparing it to production capacity announcements. Despite announcements of 9.1 Mt year −1 (2.2 Mt year −1 in the EU) by 2024 and 38.9 Mt year −1 (9.3 Mt year −1 in the EU) by 2030, only 24% (26% in the EU) of the announced capacity was realized on time by 2024. Over 40% of the announced capacity for 2030 risks delays or cancellations. Using a diffusion model parametrized by announced capacity, realization rates, expected demand, and historical growth analogs, we calculate SAF potential scale-up to meet net-zero carbon emissions by 2050. Even if SAF follows the rapid scale-up of solar and wind energy, the global and EU capacity will fall short of respective targets by 42% and 18% in 2030, and 7% and 5% in 2050. Sustainable aviation fuels production scale-up is slow. By 2024, only 24% of the capacity planned to be in production had been realized and more than 40% of year 2030 plans risk delays. Even with a solar/wind-like rapid scale-up, global and EU SAF capacity will miss 2030 and 2050 policy targets.

The purpose of the study was to evaluate predicted maturity offset (time before age at PHV) and age at PHV (chronological age [CA] minus maturity offset) in a longitudinal sample of 58 under-13 club level soccer players in central Portugal for whom ages at PHV were estimated with the SITAR model. Two maturity offset prediction equations were applied: the original equation which requires CA sitting height, estimated leg length, height and weight, and a modified equation which requires CA and height. Predicted maturity offset increased, on average, with CA at prediction throughout the age range considered, while variation in predicted maturity offset and ages at PHV within CA groups was considerably reduced compared to variation in observed ages at offset and at PHV. Predicted maturity offset and ages at PHV were consistently later than observed maturity offset and age at PHV among early maturing players, and earlier than observed in late maturing players. Both predicted offset and ages at PHV with the two equations were, on average, later than observed among players maturing on time. Intra-individual variation in predicted ages at PHV with each equation was considerable. The results for soccer players were consistent with similar studies in the general population and two recent longitudinal studies of soccer players. The results question the utility of predicted maturity offset and age at PHV as valid indicators of maturity timing and status.

Addressing abdominal obesity requires multifaceted strategies, with physical activity and diet playing a pivotal role. The objective of this study was to assess alterations in body composition, adipokine concentrations, insulin resistance parameters, and lipid metabolism in males with abdominal obesity following two distinct interventions: exercise alone and exercise combined with a specific diet. The study involved 44 males with abdominal obesity (average age 34.7 ± 5.5 years, waist circumference [WC] 110.3 ± 8.5), randomly assigned to three groups: an experimental group with aerobic-resistance exercise (EG, n  = 16), an experimental group with aerobic-resistance exercise combined with a high-protein, low-glycemic index carbohydrate diet (EDG, n  = 16), both interventions lasting 6 weeks, and a control group without interventions (CG, n  = 12). Body composition (body mass [BM], body fat percentage [BF%], fat-free mass [FFM], android body fat percentage [ANDR]), as well as biochemical blood analyses (asprosin [ASP], leptin [LEP], quantitative insulin sensitivity check index [QUICKI], and total cholesterol [TC]), were conducted at baseline and after 6 weeks of intervention. The impact of interventions on the analyzed variables among groups was assessed using mixed ANOVA tests with post-hoc comparisons. Effect size (ES) was also evaluated using 𝜂p 2 . Significant reductions in ASP concentration after intervention were observed in both EG ( p  = 0.04) and EDG ( p  = 0.01). However, post-hoc tests revealed a decrease in LEP only in the EDG group ( p  < 0.01). In EDG substantial decreases after 6 weeks of intervention were noted in BM ( p  < 0.01), BF% ( p  < 0.01), ANDR ( p  < 0.01) and TC ( p  < 0.01). The most notable increase in FFM was observed in the EDG group ( p  < 0.01). More favourable metabolic outcomes were confirmed in the group combining diet with exercise, where there was a notable reduction in ASP levels by 16% and LEP by 48% after 6 weeks of intervention, compared to the group undergoing exercise alone.

The contributions of height, weight and skeletal age (SA) to strength and motor performances of male soccer players 9–12 (n = 60) and 13–16 (n = 52) years were estimated. SA was assessed with the Fels method, and was expressed as the standardized residual of the regression of SA on chronological age CA (SAsr). Static strength (right + left grip), speed (5 m, 20 m sprints), acceleration (10 to 20 m), agility (figure-of-eight run), explosive strength (vertical jump) and endurance (yo–yo intermittent shuttle run, 13–16 years only) were measured. Hierarchical multiple regression was used. The interaction of SAsr with body size (height and height x weight interaction) explained most of the variance in strength in both age groups, 9–12 years (51.6%) and 13–16 years (56.7%), and in speed (31.4%, 38.7%), acceleration (39.6%), and explosive strength (32.6%) among players 13–16 years. In contrast, SAsr alone explained limited amounts of variance in strength, speed, acceleration and vertical jump among players 9–12 years (1.4–4.5%) and 13–16 years (0–0.5%). Results for agility varied with CA group, while SAsr per se was the primary contributor to endurance among players 13–16 years (18.5% of the variance). Although the influence of body size and skeletal maturity status on performances was significant, the explained variance differed among tasks and between CA groups, and suggested a role for other factors affecting performances of the soccer players.