Motor Imagery Deficits in High-Functioning Older Adults and Its Impact on Fear of Falling and Falls
Bibliographic record
Abstract
BACKGROUND: Older adults at risk of falling or who have fear of falling (FoF) present a discrepancy between "imagined" and "performed" actions. Using the gait-related motor imagery paradigm, we investigated whether prediction accuracy in motor execution is associated with the onset of FoF and with prospective falls among older adults with FoF. METHODS: A cohort of 184 community-dwelling older adults was tested for imaginary and executed Timed Up and Go (TUG) tests at a fast pace at baseline. They were first asked to imagine performing TUG and estimate the time taken to complete it (iTUG) and then to perform the actual trial (aTUG); the difference between the 2 times was calculated. Prospective falls were monitored between baseline and 2-year follow-up of FoF assessment. RESULTS: At follow-up, 27 of 85 participants without FoF at baseline (31.8%) had developed FoF. Twenty-seven of 99 participants (27.2%) with FoF at baseline experienced falls. A significantly shorter iTUG duration, when compared with aTUG, was observed in those who developed FoF or experienced multiple prospective falls, indicating overestimation of their TUG performance. The adjusted logistic regression model showed that a greater ΔTUG (ie, tendency to overestimate) at baseline was associated with an increased risk of new-onset FoF among those without FoF at baseline and multiple prospective falls among those with FoF at baseline. CONCLUSIONS: Deficits in motor imagery (ie, overestimation of physical capabilities), reflecting impairment in motor planning, could provide an additional explanation of the high risk of FoF and recurrent falls among people with FoF.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".