Which training load indicators are greater correlated with maturation and wellness variables in elite U14 soccer players?

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dc.authoridPrieto-González, Pablo/0000-0002-0668-4031;
dc.authorwosidPrieto-González, Pablo/T-9113-2018
dc.authorwosidGorouhi, Armin/HMP-7718-2023
dc.contributor.authorNobari, Hadi
dc.contributor.authorEken, Ozgur
dc.contributor.authorSingh, Utkarsh
dc.contributor.authorGorouhi, Armin
dc.contributor.authorBordon, Jose Carlos Ponce
dc.contributor.authorPrieto-Gonzalez, Pablo
dc.contributor.authorKurtoglu, Ahmet
dc.date.accessioned2024-08-04T20:55:57Z
dc.date.available2024-08-04T20:55:57Z
dc.date.issued2024
dc.departmentİnönü Üniversitesien_US
dc.description.abstractBackground Monitoring of training load is done to improve physical performance and minimize the incidence of injuries. The study examined the correlation between accumulated training load parameters based on periods with maturity (i.e., maturity offset and peak height velocity -PHV- and wellness variables -e.g., stress and sleep quality-). The second aim was to analyze the multi-linear regression between the above indicators. Methods Twenty elite young U14 soccer players (M=13.260.52 years, 95% CI [13.02, 13.51]) were evaluated over 26 weeks (early, mid, and end-season) to obtain stress, sleep quality, and measures of workload in the season (accumulated acute workload [AW], accumulated chronic workload [CW], accumulated acute: chronic workload ratio [ACWLR], accumulated training monotony [TM], accumulated training strain [TS]). Results The analysis revealed a moderate, statistically significant negative correlation between sleep quality and training monotony (r = -0.461, p<0.05). No significant correlations were observed between other variables (p>0.05). In the multi-linear regression analysis, maturity, PHV, sleep, and stress collectively accounted for variances of 17% in AW, 17.1% in CW, 11% in ACWLR, 21.3% in TM, and 22.6% in TS. However, individual regression coefficients for these predictors were not statistically significant (p>0.05), indicating limited predictive power. Conclusion The study highlights the impact of sleep quality on training monotony, underscoring the importance of managing training load to mitigate the risks of overtraining. The non-significant regression coefficients suggest the complexity of predicting training outcomes based on the assessed variables. These insights emphasize the need for a holistic approach in training load management and athlete wellness monitoring.en_US
dc.identifier.doi10.1186/s12887-024-04744-9
dc.identifier.issn1471-2431
dc.identifier.issue1en_US
dc.identifier.pmid38689258en_US
dc.identifier.scopus2-s2.0-85191732070en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1186/s12887-024-04744-9
dc.identifier.urihttps://hdl.handle.net/11616/101956
dc.identifier.volume24en_US
dc.identifier.wosWOS:001262484400001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherBmcen_US
dc.relation.ispartofBmc Pediatricsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFootballen_US
dc.subjectMaturationen_US
dc.subjectMonotonyen_US
dc.subjectPsychological statesen_US
dc.subjectRPEen_US
dc.subjectYouth sportsen_US
dc.titleWhich training load indicators are greater correlated with maturation and wellness variables in elite U14 soccer players?en_US
dc.typeArticleen_US

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