Supporting Dementia Caregiving With a Mobile Care Ecosystem: Development and Mixed Methods Study
Bibliographic record
Abstract
Background: Dementia presents substantial challenges for informal caregivers. A gap remains in technology-driven personalized support tailored to caregivers' needs. Objective: This study aimed to develop a theory-driven, multicomponent mobile app specifically designed for caregivers of individuals with dementia and test its usability among end users. Methods: We developed CareBuddy, a mobile care ecosystem based on the stress process model and user-centered design. The app includes personalized assessments and tailored solutions, an artificial intelligence-driven chatbot, GPS-based location monitoring, peer support, a helpline, telemedicine, health care provider integration, and caregiver self-care resources. Development was informed by interviews with caregivers and stakeholders, followed by a 2-phase pilot test involving 18 and 10 participants, respectively, to assess usability and acceptability. Results: In phase 1, the mean system usability scores increased from 65.4 (SD 11.8) in round 1 to 73.8 (SD 15.9) in round 3, exceeding the benchmark of 68. In phase 2, caregivers rated the app highly, with an overall mean score of 95.4 (SD 8.5) on the Mobile Health App Usability Questionnaire. The domains of ease of use (mean 24.1, SD 2.9), user interface and satisfaction (mean 40.3, SD 3.4), and usefulness (mean 31, SD 3.9) received high Mobile Health App Usability Questionnaire ratings. Participants valued the content focused on dementia management and caregiver well-being. Caregivers appreciated the interactive features: social networking portal, service directory, and conversational large language model. Feedback highlighted areas for improvement, including reducing textual overload and addressing navigational challenges. Conclusions: CareBuddy offers a multifaceted digital solution for dementia caregivers, with high usability and satisfaction. An ongoing trial is evaluating the app's effectiveness in improving caregiver outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".