Can Dietary Supplements Support Muscle Function and Physical Activity? A Narrative Review
Bibliographic record
Abstract
Dietary supplementation is commonly used by athletes to gain muscle mass, enhance performance, and improve recovery. Most adults engage in insufficient physical activity. Yet healthy muscles are also critical for activities of daily living (ADLs), maintaining a good quality of life and positive ageing. There is growing interest in whether dietary supplementation is of value, particularly among subgroups such as the occasionally active, the ill and elderly, and peri- and menopausal women. By focusing on function, performance, mass and strength, ADLs, exercise-induced muscle damage and delayed onset muscle soreness, this review sought to examine muscle health through a nutritional lens. Further, to look at the potential benefits and harms of some commonly proposed dietary supplements in non-athlete adults, while exploring the emerging role of the gut-muscle axis. Inflammation appears central to cellular events. Several supplements were identified that, alone or in combination, may help optimise muscle health, particularly when combined with exercise or where a deficit may exist. Although supportive evidence is emerging, real-world clinical benefits remain to be substantiated. Though dietary supplements are generally safe, their regulation is less stringent than for medicines. Adherence to recommended dosage, seeking medical advice regarding possible side effects/interactions, and obtaining supplies from reliable sources are recommended.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".