Foam Rolling Prescription: A Clinical Commentary
Bibliographic record
Abstract
Behm, DG, Alizadeh, S, Hadjizadeh Anvar, S, Mahmoud, MMI, Ramsay, E, Hanlon, C, and Cheatham, S. Foam rolling prescription: a clinical commentary. J Strength Cond Res 34(11): 3301-3308, 2020-Although the foam rolling and roller massage literature generally reports acute increases in range of motion (ROM) with either trivial or small performance improvements, there is little information regarding appropriate rolling prescription. The objective of this literature review was to appraise the evidence and provide the best prescriptive recommendations for rolling to improve ROM and performance. The recommendations represent studies with the greatest magnitude effect size increases in ROM and performance. A systematic search of the rolling-related literature found in PubMed, ScienceDirect, Web of Science, and Google Scholar was conducted using related terms such as foam rolling, roller massage, ROM, flexibility, performance, and others. From the measures within articles that monitored ROM (25), strength (41), jump (41), fatigue (67), and sprint (62) variables; regression correlations and predictive quadratic equations were formulated for number of rolling sets, repetition frequency, set duration, and rolling intensity. The analysis revealed the following conclusions. To achieve the greatest ROM, the regression equations predicted rolling prescriptions involving 1-3 sets of 2-4-second repetition duration (time for a single roll in one direction over the length of a body part) with a total rolling duration of 30-120-second per set. Based on the fewer performance measures, there were generally trivial to small magnitude decreases in strength and jump measures. In addition, there was insufficient evidence to generalize on the effects of rolling on fatigue and sprint measures. In summary, relatively small volumes of rolling can improve ROM with generally trivial to small effects on strength and jump performance.
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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 | 0.011 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.022 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".