Ischemic Preconditioning Improves Handgrip Strength and Functional Capacity in Active Elderly Women
Bibliographic record
Abstract
Background: Aging decreases some capacities in older adults, sarcopenia being one of the common processes that occur and that interfered with strength capacity. The present study aimed to verify the acute effect of IPC on isometric handgrip strength and functional capacity in active elderly women. Methods: In a single-blind, placebo-controlled design, 16 active elderly women (68.1 ± 7.6 years) were randomly performed on three separate occasions a series of tests: (1) alone (control, CON); (2) after IPC (3 cycles of 5-min compression/5-min reperfusion at 15 mmHg above systolic blood pressure, IPC); and (3) after placebo compressions (SHAM). Testing included a handgrip isometric strength test (HIST) and three functional tests (FT): 30 s sit and stand up from a chair (30STS), get up and go time (TUG), and 6 min walk distance test (6MWT). Results: HIST significantly increased in IPC (29.3 ± 6.9 kgf) compared to CON (27.3 ± 7.1 kgf; 7.1% difference; p = 0.01), but not in SHAM (27.7 ± 7.9; 5.5%; p = 0.16). The 30STS increased in IPC (20.1 ± 4.1 repetitions) compared to SHAM (18.5 ± 3.5 repetitions; 8.7%; p = 0.01) and CON (18.5 ± 3.9 repetitions; 8.6%; p = 0.01). TUG was significantly lower in IPC (5.70 ± 1.35 s) compared to SHAM (6.14 ± 1.37 s; −7.2%; p = 0.01), but not CON (5.91 ± 1.45 s; −3.7%; p = 0.24). The 6MWT significantly increased in IPC (611.5 ± 93.8 m) compared to CON (546.1 ± 80.5 m; 12%; p = 0.02), but not in SHAM (598.7 ± 67.6 m; 2.1%; p = 0.85). Conclusions: These data suggest that IPC can promote acute improvements in handgrip strength and functional capacity in active elderly women.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".