Speed and Temporal-Distance Adaptations during Treadmill and Overground Walking Following Stroke
Bibliographic record
Abstract
OBJECTIVE: To compare the maximum gait speed of stroke subjects attained during treadmill and overground in stroke subjects and to identify the temporal-distance determinants of the maximal gait speed. METHODS: Ten individuals with hemiparetic gait deficits and whose walking speeds ranged between 0.24 m/s and 0.82 m/s participated. Five healthy age-matched controls were also tested to provide comparative data for the gait speed transfer between the 2 modes of locomotion. Following a brief habituation process to walking on the treadmill, subjects were tested while walking at comfortable and maximal speeds on the treadmill and overground, in a random order. Main Outcome Measure. Self-selected comfortable and maximum gait speed and temporal-distance factors were acquired using a 6-camera Vicontrade mark motion analysis system and compared between treadmill and overground walking at a similar speed. RESULTS: Overground walking resulted in higher maximal speeds (P < 0.001), greater stride lengths (P < 0.001), and a lower cadence (P < 0.02), as compared to tread-mill. The comfortable gait speed and the maximum stride length proved to be strong determinants for the maximal speed on both modes of locomotion (P < 0.01), but the maximum cadence was correlated to maximum speed only for overground locomotion (P < 0.05). CONCLUSIONS: Stroke subjects walked slower on the treadmill as compared to overground. They also used a different strategy to increase gait speed, relying mostly on increasing the stride length during treadmill ambulation.
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 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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".