Motor imagery of gait: a new way to detect mild cognitive impairment?
Bibliographic record
Abstract
OBJECTIVES: 1) To measure and compare the time required to perform (pTUG) and the time required to imagine (iTUG) the Timed Up & Go (TUG), and the time difference between these two tasks (i.e., TUG delta time) in older adults with cognitive decline (i.e., mild cognitive impairment (MCI) and mild-to-moderate Alzheimer disease and related disorders (ADRD)) and in cognitively healthy individuals (CHI); and 2) to examine any association between the TUG delta time and a cognitive status. METHODS: Sixty-six participants (24 CHI, 23 individuals with MCI, and 19 individuals with ADRD) were recruited in this cross-sectional study. The mean and standard deviation of the pTUG and iTUG completion times and the TUG delta time, as well as age, gender, and Mini-Mental State Examination (MMSE) scores were used as outcomes. Participants were separated into three groups based on the tertilization of TUG delta time: lowest (<13.6%; n = 22; best performance), intermediate (13.6-52.2%; n = 22), and highest tertile (>52.2%; n = 22, worst performance). RESULTS: Fewer CHI were in the group exhibiting the highest tertile of TUG delta time compared to individuals with lowest and intermediate TUG delta times (p = 0.013). Being in the highest tertile of the TUG delta time was associated with cognitive decline in the unadjusted model (p = 0.012 for MCI, and p = 0.021 for mild-to-moderate ADRD). In the multivariate models, this association remained significant only for individuals with MCI (p = 0.019 while adjusting for age and gender; p = 0.047 while adjusting for age, gender, and MMSE score; p = 0.012 for the stepwise backward model). CONCLUSIONS: Our results provide the first evidence that motor imagery of gait may be used as a biomarker of MCI in older adults.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".