Mental imagery strategy and body position effects on imagining gait in healthy young and old adults: results from a cross-sectional study (P6.094)
Bibliographic record
Abstract
Objective: This study aims to 1) examine the effects of MI strategies and body positions while completing the iTUG, and 2) compare TUG performances (i.e.; times of iTUG, aTUG and difference between both of these conditions called TUG delta time) under different MI strategies and body positions. Background: Comparison between actual Timed Up and Go test (aTUG) and its imagined version (iTUG) is clinically efficient to assess age-related changes in gait control (highest level). However, modulations of iTUG performance by mental imagery (MI) strategies and body positions while imaging gait have not been evaluated in healthy adults. Design/Methods: A total of 60 healthy individuals (30 young 26.6±7.4 years, and 30 old 75.0±4.4 years) were recruited in this cross-sectional study. Times of the aTUG, the iTUG, the TUG delta time, as well as age, sex, body mass index, number of drugs taken daily and physical activity were used as outcomes. The strategies of gait MI (i.e.; ego versus allocentric representation) were recorded. iTUG was performed, in randomized order after aTUG completion, while sitting, standing and supine positions. Results: Allocentric MI strategy was associated with significant decrease in iTUG (P≤0.015), whereas supine position was associated with increased iTUG (P≤0.04). TUG delta time was lower while supine compared to other body positions (P<0.001), the lowest value was reported while using the egocentric representation. Older participants used the allocentric representation recurrently, regardless of body positions (86.7% versus 46.7%, P≤0.001). Multiple linear regressions showed a significant increase of iTUG time with age (P≤0.008), except in the model non-adjusted on MI strategies. Conclusions: The results showed significant effects of MI strategies and body position on iTUG and delta TUG. Supine position represents a precise position of real performance compared to other body positions. Also, aging effects while imagining gait characterized a change in representation from ego to allocentric representation were identified. Disclosure: Dr. Sekhon has nothing to disclose. Dr. Allali has nothing to disclose. Dr. Beauchet has nothing to disclose.
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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 | 0.001 | 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".