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Evaluation of Dietary Intakes and Supplement Use in Elite Paralympic Athletes

2018· article· en· W3176856485 on OpenAlexaffabout
Robyn F. Madden, Jane Shearer, Jill A. Parnell

Bibliographic record

VenueThe FASEB Journal · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsMount Royal UniversityUniversity of Calgary
Fundersnot available
KeywordsAthletesCalorieMedicineDietary Reference IntakeVitaminElite athletesPopulationPantothenic acidGerontologyAnimal sciencePhysical therapyEnvironmental healthNutrientBiologyEndocrinology

Abstract

fetched live from OpenAlex

The Paralympic Games are highly competitive and athletes need to maximize performance, one crucial aspect of which is nutrition. Dietary intakes and supplement use in high‐performance, athletes with physical disabilities remain largely unexplored and specialized recommendations are lacking. Consequently, these athletes may be at risk for nutritional deficiencies and supplement misuse. The aim of this study is to assess nutritional intakes, dietary supplementation patterns, and sources of dietary supplement information in Canadian athletes with physical disabilities. Male (n=18) and female (n=22) athletes were recruited from various Paralympic sports across Canada through sporting organizations, coaches, and social media. Males had significantly greater total energy intakes than females (male=2092 kcal/day; female=1602 kcal/day, p=0.013). Males had greater protein intakes based on body weight than females (p=0.0017), while fat and carbohydrate intakes were similar in both genders. Females consumed a significantly greater percentage of their total calories from sugar as compared to males (p=0.049). Athletes' intakes met or exceeded the majority of recommended daily allowances (RDA) for vitamins and minerals, with the exception of vitamin D, vitamin E, and magnesium. Males did not meet the RDA for folate and vitamin A, while females did not meet the RDA for iron and calcium. Athletes did not meet the AI for pantothenic acid and potassium. Females had significantly lower percent of the recommendations for calcium (p=0.031), selenium (p=0.016), and iron (p<0.001) as compared to males. Supplement use is high in this population; 100% of males and 91% of females reported supplement use within the past three months. Males most regularly used sports bars, protein powder, and sport drinks, while females most regularly used vitamin D, protein powder, and fatty acids. Significant differences were noted in branched chain amino acids, as males were more likely to use this supplement (p=0.008). The top three reasons for taking supplements were “stay healthy,” “increase energy,” and “medical.” Sources of dietary supplements were reported as dietician/nutritionist, medical physician (doctor), and athletic trainer. In conclusion, elite Paralympic athletes tend to consume diets that are adequate in most macro‐ and micronutrients with a high usage of dietary supplements. Future studies should evaluate impairment levels on energy and macronutrient intakes to work towards recommendations and to adequately educate and support these athletes. Support or Funding Information Funded by a Mount Royal University Innovation Grant (J.P.) and the National Science and Engineering Research Council of Canada (J.S.). This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score0.507

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.044
GPT teacher head0.295
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes2
Has abstractyes

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