Using a smart app method Maturo for precisely estimating maturation status for young basketball athletes
DOI:
https://doi.org/10.17398/1885-7019.21.401Keywords:
Artificial Intelligence, Monitoring, Growth and Maturation, Youth, BasketballAbstract
This study evaluates the effectiveness of Maturo, a smartphone application designed to estimate maturation status in youth basketball athletes. The sample comprised 41 basketball players (mean age ± SD: boys, 13.44 ± 1.32 years; girls, 13.21 ± 1.04 years) aged between 10 and 15 years. Maturo provides automated assessments of biological maturation indicators, including PHV speed, PHV age, maturation timing (early, on-time, late), and maturation status (prepubescent, pubescent, postpubescent). The validity of the Maturo application was assessed against traditional expert evaluation methods. Intraclass correlation coefficients (ICCs) and Pearson correlation coefficients revealed strong correlations between Maturo and expert assessments for PHV speed (ICC = 0.955, r = 0.96) and moderate correlations for PHV age (ICC = 0.673, r = 0.78). Bland-Altman analysis indicated minimal systematic error, confirming reliable estimations by Maturo. Additionally, Kappa coefficients demonstrated excellent agreement in classifying maturity status (κ = 0.95) and timing (κ = 0.87). Maturo's non-invasive, cost-effective, and user-friendly approach makes it valuable for routine monitoring of growth and maturation. The integration of Maturo assessments with other growth indicators could enhance the comprehensive understanding of youth athletes' development, thereby informing training programs and improving athlete safety and performance.
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