Step Number and Aerobic Minute Exercise Prescription and Progression in Stroke: A Roadmap
Bibliographic record
Abstract
BACKGROUND: more than double the steps and aerobic minutes within a session can be achieved compared with usual care and translates to improved long-term walking outcomes. OBJECTIVE: We modeled data from this successful higher intensity multi-site RCT to develop targets for prescribing and progressing exercise for varying levels of walking impairment after stroke. METHODS: In twenty-five individuals in inpatient rehabilitation, twenty sessions were monitored for a total of 500 one-hour physical therapy sessions. For the 500 sessions, step number and aerobic minute progression were modeled using linear mixed effects regression. Using formulas from the linear mixed effects regression, targets were calculated. RESULTS: The model for step number included session number and baseline walking speed, and for aerobic minutes, session number and age. For steps, there was an increase of 73 steps per session. With baseline walking speed, for every 0.1 m/s increase, a corresponding increase of 302 steps was predicted. For aerobic minutes, there was an increase of .56 minutes of aerobic activity (ie, 34 seconds) per session. For every year increase in age, a decrease of .39 minutes (ie, 23 seconds) was predicted. CONCLUSIONS: Using data associated with better walking outcomes, we provide step number and aerobic minute targets that future studies can cross-validate. As walking speed and age are collected at admission, these models allow for uptake of routine measurement of therapeutic intensity.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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".