Initial Translation of a Dementia Caregiver Intervention Into a Mobile Health Application
Bibliographic record
Abstract
Abstract Caring for a person with dementia is associated with negative outcomes. Few caregiver interventions have been implemented in community settings. Mobile technology is one method for reaching many caregivers. This project translated two empirically-supported interventions for dementia caregivers into a mobile health application. A team of clinical researchers and computer engineers developed an App called CARE-Well (Caregiver Assessment, Resources, and Education) over 6 months. The group worked closely to do the following: 1). translate interventional content to be compatible with a mobile platform; 2). create new materials; 3). determine App components that captured key intervention areas; 4). troubleshoot formatting, technology, and data security; and 5). educate each other about respective areas of expertise. We developed a beta version of the App that included: 1). assessment of caregiver stress and care recipient behavioral problems; 2). psychoeducation; 3). goal diary; 4). managing behavior problems; 5). online message forum; and 6). video library. Several challenges arose during the App development process, such as how to create navigation paths and goal lists based off users’ assessment responses, data storage and usage tracking, enlarging text, and how to ensure privacy and confidentiality in the online message forum. Our experience developing the CARE-Well App showed that translating behavioral interventions into mobile health applications is feasible and dependent upon regular communication among multidisciplinary team members. Next steps for the App include beta testing with dementia caregivers and conducting a pilot randomized trial to determine feasibility for a future trial and its effects on caregiver stress.
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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 | 0.010 | 0.019 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".