MOTOR IMAGERY OF GAIT WITH AGING: MENTAL IMAGERY STRATEGY AND BODY POSITION MATTER
Bibliographic record
Abstract
The imagined version of Timed Up and Go test (iTUG) is an efficient clinical way to assess age-related changes in highest levels of gait control. This study aims 1) to examine the effects of the MI strategy (i.e.; egocentric versus allocentric representation) and the body positions (standing, sitting, lying down) for the time needed to complete the iTUG, and 2) to compare TUG performances under different MI strategies and body positions in healthy young and older adults. A total of 60 healthy individuals (30 young participants 26.6 ± 7.4 years with 48.3% women, and 30 old participants 75.0 ± 4.4 with 40.0% women) were recruited in this cross-sectional study. Times of the pTUG and iTUG and the TUG delta time, used as outcomes. The strategies of gait MI (i.e.; ego versus allocentric representation) were recorded. Older participants used more frequently the allocentric representation compared to young adults, regardless the body position (P≤0.001). Multiple linear regressions showed a significant increase of iTUG time with age (P≤0.008), except in model non-adjusted on MI strategy. Allocentric MI strategy was associated with significant decrease in iTUG (P≤0.015), whereas lying down position was associated with increase in iTUG (P≤0.04). The results showed an aging effect while imagining gait characterized by a change in representation from ego to allocentric representation. Furthermore, lying down position represents the more accurate position of the real performance in comparison to other positions.
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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 | 0.000 | 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".