Dual-Task Training Program for Older Adults: Blending Gait, Visuomotor and Cognitive Training
Bibliographic record
Abstract
Objective: Physical and cognitive impairments are common with aging and often coexist. Changes in the level of physical and mental activity are prognostic for adverse health events and falls. Dual-task (DT) training programs that can improve mobility and cognition simultaneously can bring significant improvements in rehabilitation. The objective of this mixed methods exploratory RCT was to provide evidence for the feasibility and therapeutic value of a novel game-assisted DT exercise program in older adults. Methods: Twenty-two community dwelling participants, between the ages of 70–85 were randomized to either dual-task treadmill walking (DT-TR) or dual-task recumbent bicycle (DT-RC). Both groups viewed a standard LED computer monitor and performed a range of cognitive game tasks while walking or cycling; made possible with the use of a “hands-free”, miniature, inertial-based computer mouse. Participants performed their respective 1-h DT exercise program twice a week, for 12 weeks at a community fitness centre. Semi-structured interviews and qualitative analysis was conducted to evaluate the participant’s experiences with the exercise program. Quantitative analysis included measures of standing balance, gait function (spatiotemporal gait variable), visuomotor and executive cognitive function, tested under single and DT walking conditions. Results: Compliance was 100% for all 22 participants. Four themes captured the range of participant’s experiences and opinions: 1) reasons for participation, 2) difficulties with using the technologies, 3) engagement with the computer games, and 4) positive effects of the program. Both groups showed significant improvements in standing balance performance, visuomotor and visuospatial executive function. However, significant improvement in dual task gait function was observed only in the DT-TR group. Medium to large effect sizes were observed for most balance, spatiotemporal gait variables, and cognitive performance measure. Conclusion: With only minor difficulties with the technology being reported, the findings demonstrate feasible trial procedures and acceptable DT oriented training with a high compliance rate and positive outcomes. These findings support further research and development, and will direct the next phase of a full-scale RCT.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".