An Examination of Group and Individual Response Rates to Ischemic Preconditioning for Sport Performance
Bibliographic record
Abstract
PURPOSE: Ischemic Preconditioning (IPC) has been shown to improve exercise performance; yet large variability in response exists between individuals and the mean changes reported between studies. It has been suggested that there are responders and non-responders to IPC and this is a common explanation for the observed variability. At present, existing studies that demonstrate individual responses to IPC lack an appropriate assessment of the within-subject variability of the exercise task, thereby preventing proper evaluation of response versus non-response to the stimulus. Thus, the purpose of this study was to use repeated control trials to measure within-subject variability to assess the existence of true responders to IPC. METHODS: In a randomized, crossover design, twelve recreational cyclists (7m/5f, 30yrs, 72kg, 175cm, 55ml.min-1.kg-1) completed six, 5km cycling time trials, each separated by one week. Three separate trials were performed with and without IPC to characterize the expected individual variability in performance with and without treatment. For each IPC trial, IPC was completed 15 minutes prior to exercise and consisted of 3x5-min cycles of bilateral occlusion and reperfusion to the upper thighs. RESULTS: Comparing baseline control to IPC, mean time to completion did not reach significance (5±8s or 1.0±1.8%, p=0.08), despite a 1% change commonly being recognized as the benchmark for a meaningful alteration in performance. Examination of individual participant data revealed 8 of 12 (68%) participants improved mean 5km TT performance following IPC (2.1±1.3%). If the individual’s mean IPC response is considered only as an improvement that exceeded one’s own percent coefficient of variation from the repeated controls (0.4±0.8%) then 7 (58%) and 5 (42%) would be classed as legitimate responders and non-responders. When the individual response or non-response to IPC was examined over the three repeated IPC trials, 81% and 87% of trials confirmed the effect, respectively. CONCLUSIONS: We present evidence that individual performance is affected at a magnitude that exceeds normal variability. This suggests the existence of participants who consistently respond to IPC exposure at a magnitude that exceeds chance.
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 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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".