Feasibility and potential cognitive impact of a cognitive-motor dual-task training program using a custom exergame in older adults: A pilot study
Bibliographic record
Abstract
Introduction Dual-task training may be relevant and efficient in the context of active aging. An issue in training programs lies in enhancing the adherence of participants. This can potentially be improved using games as support. We designed and developed a custom interactive exergame in this way. The objective of this pilot study was to explore the potential use of this exergame and the feasibility of our intervention, including the level of safety and adherence. The result’s trends on cognitive and motor capacities, as well as on the level of motivation for physical activity, fear of falling, and quality of life of participants, were also explored. Methods Older adults aged 65 years or older were recruited and realized 30 min of supervised training in groups of 4, 2–3 times a week for 12 weeks. Exercises consisted of incorporated cognitive and motor dual tasks, with an increased difficulty over the weeks. Our program’s safety, engagement, attendance, and completion levels were evaluated. Participants’ postural control in single-task and dual-task conditions, as well as their performances in mental inhibition, flexibility, working memory, mobility, and postural control, and their levels of motivation for physical activity, fear of falling, and quality of life were also assessed. We realized a per protocol statistical analysis with a p-value set at 0.05. Results Thirty-nine participants (aged 84.6 ± 8.5 years) were recruited. No adverse events, and 89% adherence, 88% attendance, and 87% completion rates were observed. A potentially significant effect of our exergame on working memory in single-task conditions and on the cognitive aspect of dual-task conditions was also observed. We observed no differences in other parameters. Discussion Our exergame seemed feasible and safe and was enjoyed by participants, mainly due to the gamification of our training program. Moreover, our exergame may be efficient for cognitive training in older adults, as well as for the maintenance of motor functions, motivation for physical activity, fear of falling, and quality of life levels. This constitutes the first step for our solution with interesting results that need to be further studied.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".