4.3 Article

A comparison of FELS and Tanner-Whitehouse II skeletal ages in male youth soccer players from the Middle East

Journal

AMERICAN JOURNAL OF HUMAN BIOLOGY
Volume -, Issue -, Pages -

Publisher

WILEY
DOI: 10.1002/ajhb.23941

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This study assessed the agreement between FELS and Tanner-Whitehouse II skeletal ages in male youth soccer players from the Middle East. The results showed a mean difference of 0.02 years between the two methods, with a range of -1.39 to 1.43 years for the limits of agreement. The findings suggest that there may be differences in skeletal age determination between FELS and Tanner-Whitehouse II methods in youth Middle Eastern athletes.
Objectives: To assess measurement agreement between FELS and Tanner-Whitehouse (TW) II skeletal ages in male youth soccer players from the Middle East. Methods: We examined agreement between FELS and TW-II skeletal ages using data collected between- and within-subjects (n = 1057 observations) for 409 male, full-time, academy student-athletes recruited as part of the Qatar Football Association national soccer development programme (chronological age range, 9.8 to 18 years; annual screening range, one to seven visits). The Bland-Altman method for repeated measurements estimated the limits of agreement describing the expected range of differences for 95% of pairs of future FELS and TW-II skeletal ages determined on similar individuals from the reference population. Results: The mean difference for TW-II versus FELS protocols was 0.02 years (95% confidence interval, -0.04 to 0.08 years) with lower and upper limits of agreement ranging from -1.39 years (95% confidence interval, -1.48 to -1.30 years) to 1.43 years (95% confidence interval, 1.34 to 1.52 years). Conclusion: Differences for 95% of pairs of future skeletal ages determined with FELS and TW-II methods in this population could be as high as similar to 3 years for some people that suggested protocols may not be interchangeable in youth Middle Eastern athletes. Justification of skeletal age protocol selection rests on knowledge of measurement bias and variability of expected growth estimations for rationalized application to a population of interest.

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