Gait control: a specific subdomain of executive function?
Bibliographic record
Abstract
BACKGROUND: Few studies looked at the association between gait variability and executive subdomains (ESD). The aim of this study was to examine the association between ESD (i.e., information updating and monitoring) and stride time variability among healthy older adults. METHODS: Seventy-eight healthy older adults (mean age 69.9 ± 0.9 years, 59% women) were divided into 3 groups according to stride time variability (STV) tertiles while steady state walking. Coefficient of variation of stride time was used as a marker of STV. Scores on cognitive tests evaluating information updating and monitoring (Digit Span test), mental shifting (Trail Making Test part A and part B) and cognitive inhibition (Stroop Color Word test) were used as measures of ESD. RESULTS: The full adjusted and the stepwise backward logistic regression models showed that the highest tertile (i.e., the worst performance) of STV was only associated with lower Digit Span performance (Odds ratio = 0.78 with P = 0.020 and Odds ratio = 0.81 with P = 0.019). CONCLUSIONS: Information updating and monitoring are associated with STV in the sample of studied participants, suggesting that walking may be a complex motor task depending specifically of this subdomain of executive functions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".