The Effect of Dietary Supplements on the Quality of Life of Retired Professional Football Players
Bibliographic record
Abstract
Professional football players may experience negative health consequences when they retire such as chronic pain, cognitive problems as well as other consequences of sports-related injuries. The purpose of this pilot study is to determine the effects of dietary supplementation with multiple nutrients on the quality of life of retired football players. Fifteen retired players received daily supplementation of fish oil with cholecalciferol, antioxidants, natural vitamins and minerals, polysaccharides and phytosterol-amino acid complex for 6 months. Using an open-labeled repeated measures design, volunteers completed self-report assessment measures at baseline, 1, 3 and 6 months. Outcome measures were CDC HRQOL-4, WHOQOL-BREF, POMS, MFQ and pain self-assessment. General health rating improvement on CDC HRQOL-4 from month 1 was sustained to month 6 (p<0.0001). Mental health days improved at 6 months (p<0.05). WHOQOL-BREF showed increased health satisfaction at all measurement points (p<0.05) and the Physical and Psychological Domain Scores at 6 months (p<0.05). MFQ General Rating of Memory improved at 3 and 6 months (p<0.05). Vigor scale in POMS was significant at 3 months (p<0.05). Decreased pain was noted only for the elbow at month 1 and the knee at month 3 (p<0.05). No adverse events were reported. Results of this study offer preliminary insight into using dietary supplements to support and optimize quality of life in retired football players. Further research using a placebo-controlled design is needed to characterize the potential benefit to physical and psychological well-being of multiple dietary supplementations for this cohort.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".