A Dyad-Based Mobile System to Support Older Adults and Informal Caregivers
Bibliographic record
Abstract
Despite a gradual rise in health care expenditures due to population aging between the Year 2020 and 2060 in several countries, the overall increase will contribute to additional GDP share (e.g., 1.3 percentage points in the EU). To potentially reduce costs, ongoing developments of mobile Health (mHealth) applications is essential which provide digital health care using mobile devices. However, there is no dyadic mHealth architecture (i.e., between caregivers and their older adults) exclusively utilizing smartphones, aimed at supporting older adults’ well-being through regular follow-ups for their caregivers. In response, we developed a novel dyad-based architecture, including mobile applications for caregivers to check on their older adults’ physical activities (e.g., walking). Our system is not only considered mobile but also highly accessible due to the pervasiveness of smartphones. This work in progress also included pilot usability study analysis for the user interface and experience design. Among the findings, participants mentioned that the proposed system strongly supports them in their caregiving for their older adults. This study will serve as a precursor for future usability studies that will include wearables.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".