AUTHOR=Wardenaar Floris C. , Schott Kinta D. , Seltzer Ryan G. N. , Gardner Christopher D. 

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

JOURNAL=Frontiers in Nutrition

VOLUME=11

YEAR=2024

URL=https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2024.1381731

DOI=10.3389/fnut.2024.1381731

ISSN=2296-861X

ABSTRACT=<sec><title>Introduction</title><p>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.</p></sec><sec><title>Methods</title><p>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.</p></sec><sec><title>Results</title><p>A total of <italic>n</italic> = 410 NCAA Division I student-athletes (age: 21.4 ± 1.6 years, 53% female, from &gt;20 sports) were included. Then <italic>n</italic> = 320 were randomly selected, of which 34% (<italic>n</italic> = 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<sup>2</sup>(4) = 61.26, <italic>P</italic> &lt; 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<sup>2</sup>(1)=58.6, <italic>P</italic> &lt; 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 (<italic>n</italic> = 34), reporting a high sensitivity (83%), despite a lower AUC (0.61).</p></sec><sec><title>Discussion</title><p>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.</p></sec>