No Effect of Topical Application of a Commercial Magnesium Gel on Exercise Recovery in Active Individuals
Bibliographic record
Abstract
Oral magnesium supplementation can reduce muscle soreness and muscle damage markers after unaccustomed exercise. However, the effectiveness of topical magnesium applications remains unclear. This randomized, double-blind, parallel-design study investigated whether a commercial magnesium gel could reduce the perception of muscle soreness and muscle damage markers following an acute bout of unaccustomed exercise. Healthy active participants (n = 35) performed a 40-min bout of downhill treadmill running. Magnesium (Mg) or placebo (Pla) gels were administered to each thigh 10 min before and immediately after exercise. Measurements were made before (Pre) and immediately (Post), 24 hr, and 48 hr after exercise. The primary outcome was muscle soreness using a 0-100 visual analog scale after a sit-to-stand maneuver. Secondary outcomes included peak isometric knee extensor strength, plasma creatine kinase, and serum interleukin-6. There were no differences between treatments on any outcome. Compared with Pre (Mg: 4 ± 5, Pla: 4 ± 5 a.u.), muscle soreness was higher post (Mg: 28 ± 20, Pla: 21 ± 14) and after 24 hr (Mg: 43 ± 27, Pla: 41 ± 26) and 48 hr (Mg: 42 ± 31, Pla: 32 ± 23) (all main effects, p < .0001). Interleukin-6 was higher Post (Mg: 1.8 ± 0.7, Pla: 1.6 ± 0.7 pg/ml) versus Pre (Mg: 1.1 ± 0.8, Pla: 1.1 ± 0.6; main effect, p = .006), and creatine kinase was higher Post (Mg: 111 ± 57, Pla: 121 ± 44 U/L), 24 hr (Mg: 216 ± 113, Pla: 228 ± 97), and 48 hr (Mg: 136 ± 45, Pla: 179 ± 85) versus Pre (Mg: 93 ± 54, Pla: 91 ± 30; all main effects, p < .001). Knee extensor strength was reduced after exercise (main effect, p = .04). The Mg gel did not reduce muscle soreness or muscle damage markers after unaccustomed exercise compared with Pla. The dose (0.53 mg/ml) or application protocol may have been insufficient to elicit a meaningful effect.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".