Impact of 10-Minute Interval Roller Massage on Performance and Active Range of Motion
Bibliographic record
Abstract
Hodgson, DD, Quigley, PJ, Whitten, JHD, Reid, JC, and Behm, DG. Impact of 10-minute interval roller massage on performance and active range of motion. J Strength Cond Res 33(6): 1512-1523, 2019-Roller massage (RM) has been shown to increase range of motion (ROM) without subsequent performance deficits. However, prolonged static stretching (SS) can induce performance impairments. The objective of this study was to examine the effects of combining SS and RM with and without subsequent RM on ROM and neuromuscular performance. Subjects (n = 12) participated in 5 sessions: (a) SS only (SS_rest), (b) SS + RM (SS + RM_rest), (c) SS with RM at 10 and 20 minutes after stretch (SS_RM), (d) SS + RM with RM at 10 and 20 minutes after stretch (SS + RM_RM), and (e) control. For the SS conditions, the quadriceps and hamstrings received passive SS for 2 × 30 seconds each. For the SS + RM conditions, SS was applied to the quadriceps and hamstrings for 30 seconds each, and RM was performed for 30 seconds per muscle. SS_RM and SS + RM_RM conditions received an additional 30-second RM at 10 and 20 minutes after warm-up, whereas sessions without additional RM rested for the same duration. Testing measures included hip flexion (HF) and knee flexion (KF) active and passive ROM, hurdle jump height and contact time, countermovement jump height, and maximal voluntary isometric contraction force. Initial KF and HF ROM improvements provided by SS_RM and SS + RM_RM were sustained up to 30 minutes after intervention. Furthermore, SS_RM exhibited greater ROM compared with sessions lacking additional RM in active and passive HF as well as active and passive KF. Similarly, SS + RM_RM elicited greater KF and HF ROM improvements than SS_rest. In conclusion, active KF and HF ROM improvements were prolonged by additional RM, whereas neuromuscular performance remained relatively unaffected.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".