Dietary supplement use in younger and older men exercising at gyms in Cape Town
Bibliographic record
Abstract
Objective: Compare dietary supplement use and associated factors between younger and older men exercising at gyms (Cape Town). Design: Cross-sectional comparative study (self-administered questionnaire). Setting: Younger (21–31 years) and older (≥ 45) men exercising at gyms (Cape Town). Subjects: 210 younger and 91 older men. Outcome measures: Supplement use (frequency, reason, effectiveness, information sources, label use) and gym exercise profile and goals. Results: 80.6% younger and 81.3% older men had used supplements in the past 6 months. Younger men were more likely to use energy drinks (50% vs. 29.7%; p = 0.014), protein bars (18.1% vs. 7.7%; p = 0.038), protein powders (50% vs. 8.8%; p < 0.001), amino acids (15.2% vs. 2.2%; p = 0.004), weight gainers (10.9 vs. 1.1%; p = 0.011), recovery drinks (13.8 vs. 6.6%; p = 0.026), creatine (34.3 vs. 4.4%; p < 0.001), glutamine (22.4 vs. 7.7%; p = 0.004), arginine (8.6 vs. 0%; p = 0.016), pre-train (11.9 vs. 3.3%; p = 0.04) and fat burner (11.4 vs. 0%; p = 0.004). Multi-vitamins, vitamin C and B vitamins were consumed by both groups. Younger men spent more hours/week exercising in a gym (6.1 ± 4.0 vs. 4.0 ± 1.7; p < 0.001) and doing strength exercises (4.0 ± 2.9 vs. 1.6 ± 1.2; p < 0.001). The main exercising goal was building muscle/strength (38.8%), to stay fit (21.8%) or to look good (15.5%) for younger men and to stay fit (41.6%) or healthy (41.6%) for older men (p < 0.001). Conclusions: Younger and older men exercising in select gyms in Cape Town use a variety of supplements. Supplement use and exercising by younger men seem to focus on muscle building/strength and fitness; that of older men on improvement of fitness and health.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".