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Development of a screener to assess athlete risk behavior of not using third-party tested nutritional supplements.
Wardenaar, Floris C; Schott, Kinta D; Seltzer, Ryan G N; Gardner, Christopher D.
Afiliación
  • Wardenaar FC; College of Health Solutions, Arizona State University, Phoenix, AZ, United States.
  • Schott KD; College of Health Solutions, Arizona State University, Phoenix, AZ, United States.
  • Seltzer RGN; College of Health Solutions, Arizona State University, Phoenix, AZ, United States.
  • Gardner CD; Department of Medicine, Stanford University, Palo Alto, CA, United States.
Front Nutr ; 11: 1381731, 2024.
Article en En | MEDLINE | ID: mdl-38812931
ABSTRACT

Introduction:

The aim of this cross-sectional study was to develop an algorithm to predict athletes use of third-party tested (TPT) supplements. Therefore, a nutritional supplement questionnaire was used with a section about self-reported TPT supplement use.

Methods:

Outcomes were randomly assigned to a training dataset to identify predictors using logistic regression models, or a cross-validation dataset. Training data were used to develop an algorithm with a score from 0 to 100 predicting use or non-use of TPT nutritional supplements.

Results:

A total of n = 410 NCAA Division I student-athletes (age 21.4 ± 1.6 years, 53% female, from >20 sports) were included. Then n = 320 were randomly selected, of which 34% (n = 109) of users consistently reported that all supplements they used were TPT. Analyses resulted in a 10-item algorithm associated with use or non-use of TPT. Risk quadrants provided the best fit for classifying low vs. high risk toward inconsistent TPT-use resulting in a cut-off ≥60% (χ2(4) = 61.26, P < 0.001), with reasonable AUC 0.78. There was a significant association for TPT use (yes/no) and risk behavior (low vs. high) defined from the algorithm (χ2(1)=58.6, P < 0.001). The algorithm had a high sensitivity, classifying 89% of non-TPT users correctly, while having a low specificity, classifying 49% of TPT-users correctly. This was confirmed by cross-validation (n = 34), reporting a high sensitivity (83%), despite a lower AUC (0.61).

Discussion:

The algorithm classifies high-risk inconsistent TPT-users with reasonable accuracy, but lacks the specificity to classify consistent users at low risk. This approach should be useful in identifying athletes that would benefit from additional counseling.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Nutr Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Nutr Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza