A Behaviourally Informed Approach to Reducing the Risk of Inadvertent Anti-doping Rule Violations from Supplement Use
Susan H. Backhouse
Abstract:For many reasons, athletes’ use of supplements is highly prevalent across sports and competitive levels, despite the risk of these products containing a substance on the World Anti-Doping Agency Prohibited List. Contravening anti-doping rules through supplement use could have serious consequences for competitive athletes (e.g., ineligibility from major competitions, loss of medals and funding) due to the principle of strict liability. Indiscriminate supplement use also poses a risk to athlete health. To reduce… Show more
“…To start, the majority of athletes participating in high level competitive sports need to comply with WADA regulations ( 28 ). Not complying may result in a positive doping test ( 4 ). Supplements often contain impurifications, that can be on the WADA (or in this case NCAA) prohibited substances list.…”
Section: Discussionmentioning
confidence: 99%
“…In the United States, the number of doping violations with sanctions associated with supplements ranges from 0 to 17% of total investigated cases per year and has averaged around 9% of all doping cases annually since 2006 ( 3 ). One of the risk mitigation strategies to ensure athletes use safe nutritional supplements free from doping-related substances is to use products that are third-party tested (TPT) ( 4 , 5 ). Despite the literature consistently mentioning the need for the use of TPT supplements ( 6 , 7 ), research shows that a substantial number of athletes report inconsistently using TPT supplements ( 8 , 9 ).…”
IntroductionThe 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.MethodsOutcomes 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.ResultsA 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).DiscussionThe 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.
“…To start, the majority of athletes participating in high level competitive sports need to comply with WADA regulations ( 28 ). Not complying may result in a positive doping test ( 4 ). Supplements often contain impurifications, that can be on the WADA (or in this case NCAA) prohibited substances list.…”
Section: Discussionmentioning
confidence: 99%
“…In the United States, the number of doping violations with sanctions associated with supplements ranges from 0 to 17% of total investigated cases per year and has averaged around 9% of all doping cases annually since 2006 ( 3 ). One of the risk mitigation strategies to ensure athletes use safe nutritional supplements free from doping-related substances is to use products that are third-party tested (TPT) ( 4 , 5 ). Despite the literature consistently mentioning the need for the use of TPT supplements ( 6 , 7 ), research shows that a substantial number of athletes report inconsistently using TPT supplements ( 8 , 9 ).…”
IntroductionThe 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.MethodsOutcomes 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.ResultsA 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).DiscussionThe 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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