The relationship between spatiotemporal gait parameters and cognitive function in healthy adults: protocol for a cross-sectional study
Bibliographic record
Abstract
Abstract Background Motor dysfunctions, such as slower walking speed, precede the occurrence of dementia and mild cognitive impairment, suggesting that walking parameters are effective biomarkers for detecting early sub-clinical cognitive risk. It is often also concurrent with self-complained cognitive dysfunction, called motoric cognitive risk (MCR) syndrome. Our preliminary study found several walking parameters, obtained by a three-dimensional motion capture system, to be correlated with computer-based assessments of various cognitive function modalities, although the sample size was small. The Cognitive-Gait (CoGait) Database Project, described in the current protocol, aims to establish a database of multi-dimensional walking and cognitive performance data, collected from a large sample of healthy participants, crucial for detecting early sub-clinical cognitive risk. Methods We will recruit healthy volunteers, 20 years or older, without any neurological musculoskeletal or psychiatric disorders. The estimated sample size is 450 participants, including a 10% attrition rate. Using computer-based cognitive assessments, participants will perform six tasks: (i) the simple reaction time task, (ii) Go/No-Go task, (iii) Stroop Color–Word Test, (iv) N-back test, (v) Trail Making Test, and (vi) digit span test. We will also conduct paper-based cognitive assessments such as the Mini-Mental State Examination, Montreal Cognitive Assessment, and the Geriatric Depression Scale-15 for assessing MCR. Gait will be measured through joint kinematics and global positioning in participants’ lower legs while walking at a comfortable and faster pace, using pants with an inertial measurement unit-based three-dimensional motion capture system. Finally, we will establish a prediction model for various cognitive performance modalities based on walking performance. Discussion This will be the first study to reveal the relationship between walking and cognitive performance using multi-dimensional data collected from a large sample of healthy adults, from the general population. Despite certain methodological limitations such as the accuracy of measurements, the CoGait database is expected to be the standard value for both walking and cognitive functions, supporting the evaluation of psychomotor function in early sub-clinical cognitive risk identification, including motoric-cognitive risk syndrome.
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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 | 0.024 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".