Development of a screener to assess athlete risk behavior of not using third-party tested nutritional supplements.

Autor:	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.
Jazyk:	angličtina
Zdroj:	Frontiers in nutrition [Front Nutr] 2024 May 15; Vol. 11, pp. 1381731. Date of Electronic Publication: 2024 May 15 (Print Publication: 2024).
DOI:	10.3389/fnut.2024.1381731
Abstrakt:	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% (χ 2 (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 (χ 2 (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. Competing Interests: FW and CG received grants from external partners during the last 5 years that were not related to this project. FW received grants from: Arizona Parks & Trails, Pac-12 Health and Wellbeing Initiative, the Collegiate and Professional Sports Dietetic Organization, Friesland Campina N.V., Standard Process Inc., Kraft Heinz Company, FEMA, Unilever Corporation, and the Arizona Board of Regents. CG received grants from the National Institutes of Health and Beyond Meat. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2024 Wardenaar, Schott, Seltzer and Gardner.)
Databáze:	MEDLINE
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