A User Experience Survey for a Mind-Motor Exercise Intervention Smartphone Application - HealtheBrain
Bibliographic record
Abstract
Square Stepping Exercise (SSE) was designed to improve mobility and balance in older adults. Preliminary evidence shows SSE may also improve cognitive functioning. SSE can be described as a visuospatial working memory task with a stepping response. There are over 200 stepping patterns that progress from beginner to advanced. SSE has been developed into a smartphone application (app) called HealtheBrain to extend the reach of SSE to rural and remote communities. PURPOSE: To determine if a mind-motor exercise app (HealtheBrain) is feasible and usable in an older adult population. METHODS: A questionnaire was developed to assess the design, usability and feasibility of HealtheBrain. Participants were recruited from previous exercise intervention studies involving older adults. The HealtheBrain app is available on the Apple Store and therefore ownership of an Apple device was required for participation. Participants were asked to use HealtheBrain for 2 to 3 weeks and then complete an online questionnaire about their experiences with the app. RESULTS: Of 83 participants contacted, 9 were eligible and have completed the study to date and 33 did not have an Apple device. About two-thirds of participants reported they were ‘satisfied’ or felt ‘neutral’ about the HealtheBrain app. The majority of participants liked the design features of the app: 89% responded the flow was sensible and 75% liked the colour and font. Participants found the app to be feasible - 75% indicated they would recommend and continue to use it. Usability of the app was varied; 50% of participants indicated that HealtheBrain was ‘very or somewhat helpful’ and 37% reported ‘don’t know’ regarding whether they felt the app could improve memory or balance. CONCLUSION: This study suggests that further development of the HealtheBrain app will be crucial to target older adults. Participants found the app to be feasible but struggled with its usability. A limitation with using technology in older adults is the lack of smartphone ownership. In future studies, it will be important to target younger individuals who are more likely to have smartphones and be at the critical age for intervening to prevent cognitive decline. Targeting remote and rural regions with limited programs may show increased engagement of HealtheBrain.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".