Using mHealth to Improve Communication in Adult Day Services Around the Needs of People With Dementia: Mixed Methods Assessment of Acceptability and Feasibility
Bibliographic record
Abstract
BACKGROUND: Adult day services (ADS) provide community-based health care for older adults with complex chronic conditions but rely on outdated methods for communicating users' health information with providers. CareMOBI, a novel mobile health (mHealth) app, was developed to address the need for a technological platform to improve bidirectional information exchange and communication between the ADS setting and providers. OBJECTIVE: This study aims to examine the feasibility and acceptability of CareMOBI in the ADS setting. METHODS: A concurrent-triangulation mixed methods design was used, and participants were client-facing ADS staff members, including direct care workers (paid caregivers), nurses, and social workers. Interviews were conducted to describe barriers and facilitators to the adoption of the CareMOBI app. The acceptability of the app was measured using an adapted version of the Technology Acceptance Model questionnaire. Data were integrated into 4 themes as anchors of an informational matrix: ease of use, clinical value, fit within workflow, and likelihood of adoption. RESULTS: A mix of ADS staff (N=22) participated in the study. Participants reported high levels of acceptability across the 4 domains. Qualitative findings corroborated the questionnaire results; participants viewed the app as useful and were likely to implement CareMOBI in their practice. However, participants expressed a need for proper training and technical support throughout the implementation process. CONCLUSIONS: The CareMOBI app has the potential to improve care management in the ADS setting by promoting effective communication through an easy-to-use and portable method. While the integration of CareMOBI is acceptable and feasible, developing role-specific training modules and technical assistance programs is imperative for successful implementation within the ADS setting.
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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 | 0.085 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".