Does Cognitive Training Improve Mobility, Enhance Cognition, and Promote Neural Activation?
Bibliographic record
Abstract
A close inter-relationship between mobility and cognition is reported in older adults, with improvements in gait performance noticeable after cognitive remediation in frail individuals. The aim of this study was to evaluate the efficacy of computerized cognitive training (CCT) on mobility in healthy, independently living older adults, and to determine whether CCT is associated with changes in neural activation for mobility-related brain processes. Using a randomized single-blind control design, sixty-three non-demented adults age 60 y and older (mean age = 67 y; 76% female, mean Montreal Cognitive Assessment [MoCA] score = 27) were recruited from a local Senior Activity Center. Participants were randomly assigned to either a 2-month CCT program (8 weeks, 3x/week, 40 min/session) or a wait-list control group. Primary outcome was self-selected gait speed during single- and dual-task walking. Secondary outcome was executive function on Trail Making Test (TMT), Part B. Neural activity was assessed via electroencephalography/event-related potentials (EEG/ERPs) targeting lower-limb performance. Results from a linear mixed effect model, adjusted for baseline MoCA score, age, gender, and study completion revealed that compared to controls, CCT improved gait speed during the dual-task ( p = 0.008) but not during the single-task walking condition ( p = 0.057). CCT also improved executive function ( p = 0.024). Further, shorter foot reaction time responses ( p = 0.019) were found with enhanced neural activation over sensorimotor areas, with shorter ERP latencies during the P2 component ( p = 0.008) and enhanced motor responses ( p = 0.009) also evident in the CCT group after the intervention. Overall, the electrophysiological findings suggest possible neural adaptations that could explain improvements in mobility and executive functions associated with CCT in healthy older adults.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".