RESUMEN
Classifying athletes based on estimates of biological maturation (i.e., bio-banding) as a supplement to traditional age grouping has been shown to be a potential tool for enriching player development in team sports; however, bio-banding has not yet been evaluated in ice hockey. The primary aim was to investigate player experiences and coaches' selection preferences in bio-banding versus age-banding in a group of 12-13-year-old (early growth spurt) male elite players (n = 69). We also examined the relationship between somatic maturity, expressed as a % predicted adult height (%PAH), and fitness performance. Bio-banding was assessed using a questionnaire and 29 coaches selected their top players in each game based on age or bio-bands. %PAH correlated with grip strength (r = .57, p>0.001) and jumping power (r = .63, p<0.001), but not with vertical jump height, sprint time or endurance. Players who played against more mature players in bio-bands than in age groups experienced higher demands, while players who played against less mature players were able to utilize their skills to a greater extent. Coaches generally favored later-than-average maturing players who performed better on performance tests and chronologically older players in bio-banding. We conclude that bio-banding in youth ice hockey has some promising effects and warrants further evaluation.
Asunto(s)
Rendimiento Atlético , Hockey , Humanos , Hockey/fisiología , Masculino , Adolescente , Proyectos Piloto , Rendimiento Atlético/fisiología , Niño , Atletas , Encuestas y Cuestionarios , PercepciónRESUMEN
OBJECTIVES: This study investigated the relationship between biological maturation and success in adolescence and adulthood in male Swedish ice hockey players. METHODS: Anthropometric records of players in certified ice hockey high schools between 1998 and 2017 were retrieved (n = 4787). The database was complemented with records of Swedish junior national teams (U16, U18, U20) and National Hockey League (NHL) appearances. Biological maturation was recorded as a percentage of adult height (%AH), and selection probabilities were estimated using a generalised linear mixed effects model. Biological age was determined by comparing players with age-matched growth reference values. Categories of %AH, standard deviation z-scores and biological age offset describing early, on-time and late maturation were created. RESULTS: A total of 217 players had played on the U16 national team (junior success), and 96 reached the NHL (adult success). The difference [95% confidence interval (CI)] in baseline %AH between players with junior versus adult success was - 0.75 (- 0.39, - 1.11). Looking at age-offset categories in junior success, 30% of players were early maturing and 19% of players were late maturing, showing a bias towards early maturation (p < 0.01). In contrast, more late-maturing players (40%) achieved adult success than early-maturing players (25%), and NHL players had significantly later maturation [%AH: - 0.48 (- 0.80, - 0.16)] than non-NHL players. CONCLUSION: This unique 20-year analysis shows that junior success in male ice hockey is positively related to early maturation, while adult success is inversely related to advanced maturation. Ice hockey organisations should implement maturation assessments to optimise the development of both late- and early-matured players.
Asunto(s)
Rendimiento Atlético , Estatura , Hockey , Humanos , Masculino , Suecia , Estudios Retrospectivos , Adolescente , Rendimiento Atlético/fisiología , Adulto Joven , Adulto , Factores de Edad , AntropometríaRESUMEN
OBJECTIVES: We examined the longitudinal and cross-sectional relationship between automated MRI-analysis and single-slice axial CT imaging for determining muscle size and muscle fat infiltration (MFI) of the anterior thigh. METHODS: Twenty-two patients completing sex-hormone treatment expected to result in muscle hypertrophy (n = 12) and atrophy (n = 10) underwent MRI scans using 2-point Dixon fat/water-separated sequences and CT scans using a system operating at 120 kV and a fixed flux of 100 mA. At baseline and 12 months after, automated volumetric MRI analysis of the anterior thigh was performed bilaterally, and fat-free muscle volume and MFI were computed. In addition, cross-sectional area (CSA) and radiological attenuation (RA) (as a marker of fat infiltration) were calculated from single slice axial CT-images using threshold-assisted planimetry. Linear regression models were used to convert units. RESULTS: There was a strong correlation between MRI-derived fat-free muscle volume and CT-derived CSA (R = 0.91), and between MRI-derived MFI and CT-derived RA (R = -0.81). The 95% limits of agreement were ±0.32 L for muscle volume and ±1.3% units for %MFI. The longitudinal change in muscle size and MFI was comparable across imaging modalities. CONCLUSIONS: Both automated MRI and single-slice CT-imaging can be used to reliably quantify anterior thigh muscle size and MFI. ADVANCES IN KNOWLEDGE: This is the first study examining the intermodal agreement between automated MRI analysis and CT-image assessment of muscle size and MFI in the anterior thigh muscles. Our results support that both CT- and MRI-derived measures of muscle size and MFI can be used in clinical settings.