THE USABILITY OF PHYSICAL ACTIVITY AND COGNITIVE TRAINING APPLICATIONS IN PEOPLE WITH MILD COGNITIVE IMPAIRMENT
Bibliographic record
Abstract
Evidence has shown that exercise, a healthier diet, and smoking cessation may protect the brain, but evidence is scarce on exercise combined with cognitive training and its benefits for people with mild cognitive impairment. The aim of this study is to identify key issues with utility, effectiveness, and appeal of specific electronic applications for people with mild cognitive impairment in order to guide the design and development of a mobile application which incorporates both physical and cognitive activities, which may improve, impede or prevent cognitive decline. Sixteen participants, 65 and older, with mild cognitive impairment, assessed using the Montreal Cognitive Assessment tool, with a range of 19–25 were recruited. To assess the participants’ ability to consent to the study, the MacArthur Competence Assessment Tool for Clinical Research was used. Participants were observed playing a physical activity application and cognitive training application via a tablet, on two separate occasions. A Usability Observation form was used to obtain data on facial features as well as verbal and body language while playing. A survey and focus group sessions were held to get feedback from the participants. The majority of the participants were able to use the tablets and play the physical activity and cognitive training applications. However, some of the applications had levels that were more difficult for some of the participants, but a few said parts of the applications were too easy. The investigators noted that a ‘tablet stand’ would have highly enhanced participation during the use of physical activity applications.
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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".