Effects of Task-Specific Locomotor and Strength Training in Adults Who Were Ambulatory After Stroke: Results of the STEPS Randomized Clinical Trial
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: A phase II, single-blinded, randomized clinical trial was conducted to determine the effects of combined task-specific and lower-extremity (LE) strength training to improve walking ability after stroke. SUBJECTS: The participants were 80 adults who were ambulatory 4 months to 5 years after a unilateral stroke. METHOD: The exercise interventions consisted of body-weight-supported treadmill training (BWSTT), limb-loaded resistive leg cycling (CYCLE), LE muscle-specific progressive-resistive exercise (LE-EX), and upper-extremity ergometry (UE-EX). After baseline assessments, participants were randomly assigned to a combined exercise program that included an exercise pair. The exercise pairs were: BWSTT/UE-EX, CYCLE/UE-EX, BWSTT/CYCLE, and BWSTT/LE-EX. Exercise sessions were 4 times per week for 6 weeks (total of 24 sessions), with exercise type completed on alternate days. OUTCOMES: were self-selected walking speed, fast walking speed, and 6-minute walk distance measured before and after intervention and at a 6-month follow-up. RESULTS: The BWSTT/UE-EX group had significantly greater walking speed increases compared with the CYCLE/UE-EX group; both groups improved in distance walked. All BWSTT groups increased walking speed and distance whether BWSTT was combined with LE strength training or not. DISCUSSION AND CONCLUSION: After chronic stroke, task-specific training during treadmill walking with body-weight support is more effective in improving walking speed and maintaining these gains at 6 months than resisted leg cycling alone. Consistent with the overtraining literature, LE strength training alternated daily with BWSTT walking did not provide an added benefit to walking outcomes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".