Factors associated with change in aerobic capacity following an exercise program for individuals with stroke
Bibliographic record
Abstract
OBJECTIVE: Community exercise programs are effective in improving aerobic capacity after stroke, but individual variability in training responses has not previously been studied. This study examined explanatory factors for post-program responses in aerobic capacity (VO2peak) with respect to training parameters and participant characteristics. DESIGN: Secondary analysis of data from a prospective cohort study. SUBJECTS: Thirty-two participants, mean age 63 years (standard deviation (SD) 13), 29 months (SD 26) post-stroke. METHOD: Following a 6-month community exercise program, multivariate regression analysis was performed to assess for correlates with change in VO2peak with respect to training parameters (exercise duration and intensity) and participant characteristics (lower limb and balance impairment, aerobic and walking capacity). RESULTS: The mean improvement in VO2peak was 11% (SD 19), but individually, changes ranged from 32% decline to 56% improvement. Regression analysis revealed that only 2 variables, baseline aerobic capacity and balance impairment, accounted for the training-related change in VO2peak (R2 = 0.20, p = 0.045). CONCLUSIONS: There is considerable inter-individual variability in stroke exercise program responses in aerobic capacity. Lower baseline aerobic capacity and better balance were associated with greater training-related improvements in VO2peak. Identifying individuals who are more likely to respond to exercise programs may inform future work in establishing effective interventions suited for different subsets of the stroke population.
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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 | 0.004 |
| 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 | 0.001 |
| 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".