Acute Hemodynamic Responses to Three Types of Hamstrings Stretching in Senior Athletes
Bibliographic record
Abstract
Although stretching is recommended for fitness and health, there is little research on the effects of different stretching routines on hemodynamic responses of senior adults. It is not clear whether stretching can be considered an aerobic exercise stimulus or may be contraindicated for the elderly. The purpose of this study was to compare the effect of three stretching techniques; contract/relax proprioceptive neuromuscular facilitation (PNF), passive straight-leg raise (SLR), and static sit-and-reach (SR) on heart rate (HR) and blood pressure (BP) in senior athletes (119 participants: 65.6 ± 7.6 yrs.). Systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP) and HR measurements were taken at baseline (after 5-minutes in a supine position), 45 and 90-seconds, during the stretch, and 2-minutes after stretching. Within each stretching group, (SLR, PNF, and SR) DBP, MAP and HR at pre-test and 2-min post-stretch were lower than at 45-s and 90-s during the stretch. SLR induced smaller increases in DBP and MAP than PNF and SR, whereas PNF elicited lower HR responses than SR. In conclusion, trained senior adult athletes experienced small to moderate magnitude increases of hemodynamic responses with SLR, SR and PNF stretching, which recovered to baseline values within 2-min after stretching. Furthermore, the passive SLR induced smaller increases in BP than PNF and SR, while PNF elicited lower HR responses than SR. These increases in hemodynamic responses (HR and BP) were not of a magnitude to be clinically significant, provide an aerobic exercise stimulus or warrant concerns for most senior athletes.
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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 | 0.000 | 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) | 0.001 | 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".