The Interplay Between Walking Speed, Economy, and Stability After Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Energy minimization is thought to underlie the naturally selected, preferred walking speed; however, people post-stroke walk slower than their most economical speed, presumably to optimize other objectives, such as stability. The purpose of this study was to examine the interplay between walking speed, economy, and stability. METHODS: Seven individuals with chronic hemiparesis walked on a treadmill at 1 of 3 randomized speeds: slow, preferred, and fast. Concurrent measurements of speed-induced changes in walking economy (ie, the energy needed to move 1 kg of bodyweight 1 ml O 2 /kg/m) and stability were made. Stability was quantified as the regularity and divergence of the mediolateral motion of the pelvic center of mass (pCoM) during walking, as well as pCoM motion relative to the base of support. RESULTS: Slower walking speeds were more stable (ie, pCoM motion was 10% ± 5% more regular and 26% ± 16% less divergent) but 12% ± 5% less economical. Conversely, faster walking speeds were 9% ± 8% more economical, but also less stable (ie, pCoM motion was 17% ± 5% more irregular). Individuals with slower walking speeds had an enhanced energetic benefit when walking faster ( rs = 0.96, P < 0.001). Individuals with greater neuromotor impairment had an enhanced stability benefit when walking slower ( rs = 0.86, P = 0.01). DISCUSSION AND CONCLUSIONS: People post-stroke appear to prefer walking speeds that are faster than their most stable speed but slower than their most economical speed. The preferred walking speed after stroke appears to balance stability and economy. To encourage faster and more economical walking, deficits in the stable control of the mediolateral motion of the pCoM may need to be addressed.Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content 1, http://links.lww.com/JNPT/A416 ).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".