Balance and Mobility Training With or Without Simultaneous Cognitive Training Reduces Attention Demand But Does Not Improve Obstacle Clearance in Older Adults
Bibliographic record
Abstract
The purpose of this study was to determine whether balance and mobility training (BMT) or balance and mobility plus cognitive training (BMT + C) would improve obstacle clearance and reaction time (RT); whether further improvements would be exposed in the BMT + C group relative to the BMT group; and whether possible improvements would be sustained at the follow-up. Healthy older adults were allocated to the BMT (n = 15; age: 70.2 ± 3.2), BMT + C (n = 14; age: 68.7 ± 5.5), or control group (n = 13; age: 66.7 ± 4.2). The BMT and BMT + C groups trained one-on-one, three times per week for 12 weeks on a balance obstacle course. The BMT + C group also completed cognitive training. Participants walked onto and over six obstacles of varying heights while completing no RT, simple RT, and choice RT tasks at baseline, posttraining, and at the 12-week follow-up. Both the BMT and BMT + C groups improved RT and maintained these improvements at the follow-up. No meaningful improvements in obstacle clearance emerged following training. Thus, dual-task balance training likely reduces attention demand.
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 | 0.000 | 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 | 0.000 | 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".