Nutrition Supplementation Practices of Recreational Gym Users in Uganda’s Capital City: A Cross Sectional Study
Bibliographic record
Abstract
Abstract Background: This study focused on investigating the prevalence of nutritional supplement (NS) usage, establishing their source and the motivating factors for the usage of NS among Gym users in Kampala city, Uganda.Methods: The study employed a cross-sectional research design. Multistage random sampling techniques were used to select 45 gym users among the 5 divisions that constitute Kampala city. Data were collected using questionnaires and analyzed using SPSS Version 26, where means, SD, frequencies and percentages were obtained. Chi-square tests were used for categorical comparisons between variables. Results: The results showed that there were more male participants (62.2%) than female participants (37.8%). The majority (76.9%) of gym users obtained NS from retail stores such as pharmacies, (10.2%) from their sports coaches, (7.7%) nutritionists/dieticians, and (5.1%) from team mates. Non-professional gym users (62.3%) reported higher levels of energy drink consumption than professional gym users (26.7%). The consumption of vitamins, herbal products and proteins was also considerably high. We also identified coaches/trainers (30.8%) as the main source of information, followed by nutritionists/dieticians (23.1%) and online websites (20.5%). Most gym users strongly agreed that supplements increase endurance training, increase strength, and make one healthier.Conclusions: The prevalence of nutritional supplement usage among gym users was high, with energy drinks and herbal products being the most preferred supplements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".