Foam Rolling for Delayed-Onset Muscle Soreness and Recovery of Dynamic Performance Measures
Bibliographic record
Abstract
CONTEXT: After an intense bout of exercise, foam rolling is thought to alleviate muscle fatigue and soreness (ie, delayed-onset muscle soreness [DOMS]) and improve muscular performance. Potentially, foam rolling may be an effective therapeutic modality to reduce DOMS while enhancing the recovery of muscular performance. OBJECTIVE: To examine the effects of foam rolling as a recovery tool after an intense exercise protocol through assessment of pressure-pain threshold, sprint time, change-of-direction speed, power, and dynamic strength-endurance. DESIGN: Controlled laboratory study. SETTING: University laboratory. PATIENTS OR OTHER PARTICIPANTS: A total of 8 healthy, physically active males (age = 22.1 ± 2.5 years, height = 177.0 ± 7.5 cm, mass = 88.4 ± 11.4 kg) participated. INTERVENTION(S): Participants performed 2 conditions, separated by 4 weeks, involving 10 sets of 10 repetitions of back squats at 60% of their 1-repetition maximum, followed by either no foam rolling or 20 minutes of foam rolling immediately, 24, and 48 hours postexercise. MAIN OUTCOME MEASURE(S): Pressure-pain threshold, sprint speed (30-m sprint time), power (broad-jump distance), change-of-direction speed (T-test), and dynamic strength-endurance. RESULTS: Foam rolling substantially improved quadriceps muscle tenderness by a moderate to large amount in the days after fatigue (Cohen d range, 0.59 to 0.84). Substantial effects ranged from small to large in sprint time (Cohen d range, 0.68 to 0.77), power (Cohen d range, 0.48 to 0.87), and dynamic strength-endurance (Cohen d = 0.54). CONCLUSIONS: Foam rolling effectively reduced DOMS and associated decrements in most dynamic performance measures.
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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) | 0.000 | 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) | 0.003 | 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".