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Cited by 44Sports Performance and TrainingPhysical Education and Training StudiesHuman Health and Disease

Differences in Maturity and Anthropometric and Morphological Characteristics among Young Male Basketball and Soccer Players and Non-Players

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Toselli, Stefania, Campa, Francesco, Maietta Latessa, Pasqualino, Greco, Gianpiero, Loi, Alberto, Grigoletto, Alessia, Zaccagni, Luciana·2021·International Journal of Environmental Research and Public Health
DOI 10.3390/ijerph18083902PMC PMC8068181
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Abstract

Background: An aspect that influences sport performance is maturation status, since, within the same chronological age group, boys who have advanced maturation outperform their late maturing peers in tests of muscular strength, power, and endurance. Therefore, the aims of the present study were: (i) to investigate the differences in biological maturation and anthropometric and morphological characteristics among three groups of Italian adolescents, two of which were sportive (practicing basketball and football) and one non-sportive, and (ii) to identify the anthropometric and morphological predictors that best discriminate these three groups. Methods: Sixty-one basketball and 62 soccer players and 68 non-sportive youths were measured (mean age = 13.0 ± 1.1 y). Anthropometric characteristics were taken and body mass index, cormic index, body composition parameters, and somatotype were derived. An estimation of maturity status was carried out considering the years from peak height velocity (PHV). Two-way 3 × 3 ANOVAs was performed on all anthropometric characteristics to test the differences within sport groups and maturity status groups. Discriminant function analysis (stepwise criteria) was then applied to anthropometric and body composition variables to classify subjects into the three different sport categories. Results: Differences in anthropometric characteristics were detected among the three groups. For somatotype, differences among all of the considered groups were higher for endomorphy (p < 0.001; effect size = 0.13). Biological maturity influences the differences in the anthropometric characteristics and body composition among subjects of the same chronological age during adolescence. The variables that best discriminated the three groups were represented by body composition parameters, body proportions, and body build. Conclusions: This study confirms that boys who practice sport present healthier body composition parameters, with lower level of fat parameters. The assessment of maturity status is a fundamental factor in explaining anthropometric and body composition differences among peers in this period. Its comprehension may assist coaches and technical staff in optimizing competitive efficiency and monitoring the success of training regimes.

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Crossref · 10.3390/ijerph18083902

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  • Mixing different populations without including specific factors (e.g., age, maturity status, sex, physical activity level) as independent variables in the predictive models may question the validity of some BIA predictive equations.

    “Fourth, mixing different populations without including specific factors (e.g., age, maturity status, sex, physical activity level) as independent variables in the predictive models [30].”

    From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [30]

    The Discussion reports that maturity status and sport participation are associated with differences in body composition among adolescent boys, supporting the concern that populations can differ. It does not assess BIA predictive equations or show that omitting age, maturity, sex, or activity level as model variables undermines their validity. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • Development of predictive models without using criterion methods may question the validity of some BIA predictive equations.

    “Third, the development of predictive models without using criterion methods.”

    From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [30]

    The Methods (section 2.2; page unavailable) describe body-composition estimates calculated from skinfold equations, while the Results (section 3; page unavailable) describe predictors used to classify sports groups. Searches for BIA predictive equations and criterion methods found no passage assessing whether developing models without criterion methods calls their validity into question. (LLM, conf=0.74)

    Experimental — accuracy not yet measured

  • Predicting FFM and FM starting from hydration or density assumptions may question the validity of some BIA predictive equations.

    “Second, predicting FFM and FM starting from hydration or density assumptions.”

    From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [30]

    The Methods (section 2.2; page not provided) say FM and FFM were calculated using skinfold equations. Searches for hydration or density assumptions and BIA predictive equations found no passage addressing whether those assumptions affect the equations’ validity. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • Use of the two-component model instead of a multi-component model approach may question the validity of some BIA predictive equations.

    “First, the use of the two-component model instead of a multi-component model approach.”

    From High-standard predictive equations for estimating body composition using bioelectrical impedance analysis: a systematic review (2024) · cites this paper as [30]

    The Procedures section (2.2; page unavailable) says body composition was calculated using skinfold equations. Neither it nor the other retrieved passages addresses two- versus multi-component models or the validity of BIA predictive equations; searches for those terms found no relevant passage. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 1

  1. [20]Carter J.E.L. Heath B.H. Somatotyping. Development and Applications Cambridge University Press Cambridge, UK 1990

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Authors
Toselli, S., Campa, F., Maietta Latessa, P., Greco, G., Loi, A., Grigoletto, A., & Zaccagni, L.
Affiliations
  • University of Bologna (IT)
  • Department of Life Quality Studies, University of Bologna, 47921 Rimini, Italy
  • University of Bari Aldo Moro (IT)
  • University of Ferrara (IT)
Corresponding author
Greco, Gianpiero
ORCID
Venue
International Journal of Environmental Research and Public Health
Publisher
MDPI AG
Published
2021-04-08
Volume
18
Issue
8
Pages
3902
ISSN
1660-4601 (ISSN-L)
DOI
10.3390/ijerph18083902
PMC
PMC8068181
OpenAlex
W3141094347
Cited by
44
References
52
Citations by year
2026: 6 · 2025: 7 · 2024: 6 · 2023: 10 · 2022: 12 · 2021: 3
FWCI
7.63
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Environmental Research and Public Health (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, cc-by)
  • Padua Research Archive (University of Padova) (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) (submittedVersion, cc-by)
  • Institutional Research Information System University of Ferrara (University of Ferrara) (submittedVersion, cc-by)
Topics
  • Health Sciences › Medicine › Orthopedics and Sports Medicine › Sports Performance and Training
  • Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Physical Education and Training Studies
  • Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Human Health and Disease
Keywords
physiological maturity, peak height velocity, youth athletes, basketball players, soccer players, anthropometry, body composition, somatotype, body proportions, body build, endomorphy, fat mass, sports participation
MeSH terms
Adolescent, Anthropometry, Basketball, Body Height, Child, Humans, Italy, Male, Soccer

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