EFFECTS OF LIFESTYLE ACTIVITY LEVEL ON ANTICIPATORY LOCOMOTOR ADJUSTMENTS FOR PEDESTRIAN CIRCUMVENTION
Bibliographic record
Abstract
Abstract Navigating public environments requires adjustments to one’s walking patterns to avoid stationary and moving obstacles. It is known that physical inactivity induces alterations in motor capacities, but the impact of inactivity on anticipatory locomotor adjustments (ALA) has not been studied. The purpose of the present study was to compare ALAs and related muscle co-contraction during a pedestrian circumvention task between active (AA) and inactive young adults (IA). Thirteen AA and thirteen IA were placed in a virtual environment simulating a public park. Participants circumvented virtual pedestrians walking towards them. Walking speed, onset of deviation, clearance, foot placement strategies and muscle co-contraction were analysed. IA exhibited slower walking speeds compared to the AA during circumvention condition but not during unobstructed walking condition. The distance at the onset of trajectory deviation was larger for IA. Both groups increased co-contraction for pedestrian circumvention at the ankle and left hip and IA displayed greater ankle co-contraction overall. No significant group differences were observed in minimum clearance. This study suggests that an inactive lifestyle influences ALAs by inducing a cautious behavior during pedestrian circumvention.
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) | 0.002 | 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".