Higher intensity walking improves global cognition during inpatient rehabilitation: a secondary analysis of a randomized control trial
Bibliographic record
Abstract
Cognitive deficits are common poststroke. Cognitive rehabilitation is typically used to improve cognitive deficits. It is unknown whether higher doses of exercise to promote motor recovery influence cognitive outcomes. Our recent trial, Determining Optimal Post-Stroke Exercise (DOSE), shows more than double the steps and aerobic minutes can be achieved during inpatient rehabilitation versus usual care, and translates to improved long-term walking outcomes. Thus, the secondary analysis aim was to determine the effect of the DOSE protocol on cognitive outcomes over 1-year poststroke. The DOSE protocol progressively increased step number and aerobic minutes during inpatient stroke rehabilitation over 20 sessions. The Montreal Cognitive Assessment (MoCA), Digit Symbol Substitution Test (DSST), and Trail Making Test B were completed at baseline, post-intervention, and 6- and 12-months poststroke, administered using standardized guidelines. Using the DOSE data, we used mixed-effect spline regression to model participants’ trajectories of cognitive recovery, controlling for relevant covariates. Participants (Usual Care n = 25, DOSE n = 50) were 56.7(11.7) years old, and 27(10) days post stroke. For the MoCA, there were statistically significant Group × Trajectory(p = 0.019), and Group × ΔTrajectory (p = 0.018) interactions with a substantial clinically meaningful difference, from +5.44 points/month improvement of the DOSE group compared to +1.59 points/month improvement with Usual Care during the 4-week intervention. The DSST and Trails B improved over time but were not different between groups. Taking advantage of this early difference may lend support to continued efforts to increase intensity, during and after discharge from inpatient rehabilitation, to improve cognition. Clinical trial registration: www.clinicaltrials.gov , NCT01915368.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".