Individual Responses to Pectoral Static Stretch Intensities
Bibliographic record
Abstract
Static stretching (SS) is well-established to increase range of motion (ROM) at the group level; however, considerable inter-individual variability exists in physiological responses to exercise interventions.To optimize individual exercise prescriptions, it is important to understand variability in responsiveness to SS and identify factors associated with meaningful changes.This study examined the incidence of individual responses to different intensities of pectoral SS on forward shoulder posture (FSP), pectoralis minor length (PML), horizontal abduction ROM (haROM), and pectoral stiffness.It also explored whether baseline values differed between responders and non-responders, and how these influenced the magnitude of change.Thirty participants (n=30) completed two experimental sessions consisting of a control condition (CON) and two pectoral SS intensities: moderate and maximal.Pectoralis major and minor stiffness, PML, FSP, and haROM were measured before and after each intervention.The CON involved 150s of rest, while the SS interventions consisted of three 30s standing doorway stretches.Responders were defined as individuals demonstrating changes at least two time the typical error in the desired direction calculated from CON.Following moderate intensity, 43% of participants experienced a meaningful increase in haROM, compared to 70% following maximal intensity.Similarly, 37% and 40% of participants demonstrated meaningful increases in PML after moderate and maximal SS, respectively.Response rates for pectoralis major and minor stiffness and FSP were negligible but exhibited substantial inter-individual variability.No differences were found in baseline values between responders and non-responders; however, a moderately positive relationship was observed between baseline haROM and change in haROM.Higher stretch intensity increased the likelihood of meaningful ROM improvements, yet substantial inter-individual variability was evident across outcomes.These findings underscore the importance of individualized tailoring of SS interventions to optimize clinical outcomes.
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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) | 0.000 | 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) | 0.002 | 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".