Multilevel models approach on longitudinal studies for disease data

Tantular, Bertho; Jaya, I Gede Nyoman Mindra; Zulhanif,

doi:10.1088/1742-6596/1025/1/012108

Cited by 1 publication

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“…To determine the relationship between age and performance, the CA (i.e., age in years and days as independent variable) and the furthest jump performance (performance in m as dependent variable) were examined using a mixed model regression analysis. This model takes the correlation of intra-individual datapoints into account—as repeated measures of the same person are correlated ( Tantular and Jaya, 2018 ) and was recently used in similar studies ( Abbott et al, 2021 ; Brustio et al, 2022 ). Then the trend line of performance development was calculated using the model.…”

Section: Methodsmentioning

confidence: 99%

Why age categories in youth sport should be eliminated: Insights from performance development of youth female long jumpers

et al. 2023

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Long-term sports participation and performance development are major issues in popular sports and talent development programs. This study aimed to provide longitudinal trends in youth female long jump performance development, participation, and relative age effects (RAEs), as longitudinal data for female athletes are missing. 51′894 season’s best results of female long jump athletes (n = 16′189) were acquired from the Swiss Athletics online database and analyzed within a range of 6–22 years of age. To examine longitudinal performance development and RAEs, data from athletes who participated in at least three seasons were selected (n = 41′253) and analyzed. Performance development was analyzed using age groups (AGs) and exact chronological age (CA) at competition. Differences between performances of birth quarters were analyzed using 83% confidence intervals (CIs) and smallest worthwhile change. Odds ratios (ORs) with 95% CI were used to quantify RAEs. With the traditional classification into age groups (AG), performances of athletes born between January and March (Q1) were significantly better than those born between October and December (Q4) from U8 to U17. Using exact CA resulted in similar performances in Q1 and Q4 until the U20 age category. The peak of participation was reached in the U12 category, and then decreased until the U23 category with a substantial drop at U17. Significant RAEs were observed from U8 to U19 and at U22. RAEs continuously decreased from U8 (large effect) to U14 (small effect). The present results show that differences in performance arise from the comparison of athletes in AGs. Thus, going beyond AGs and using exact CA, Q4 athletes could benefit from a realistic performance comparison, which promotes fair performance evaluation, un-biased talent development, realistic feedback, and long-term participation.

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Section: Methodsmentioning

confidence: 99%