Mobile Apps to Support Family Caregivers of People With Alzheimer Disease and Related Dementias in Managing Disruptive Behaviors: Qualitative Study With Users Embedded in a Scoping Review
Bibliographic record
Abstract
BACKGROUND: People with Alzheimer disease and related dementias often display disruptive behaviors (eg, aggression, wandering, and restlessness), which increase family caregivers' burden of care. However, there are few tools currently available to help these caregivers manage disruptive behaviors. Mobile apps could meet this need, but to date little is known about them. OBJECTIVE: The aims of our study were to identify existing mobile apps designed to support family caregivers of people with Alzheimer disease and related dementias in managing disruptive behaviors; explore whether family caregivers view these mobile apps as relevant to meeting their needs and as useful in managing disruptive behaviors; and document the types of mobile apps that are of interest and appeal to most family caregivers (with regard to format, ergonomics, and clarity). METHODS: A review of mobile apps initially conducted in February 2018 was updated in March 2019 with 2 platforms (App Store [Apple Inc.] and Google Play [Google]). The selected apps were first analyzed independently by 3 raters (2 students and 1 researcher) for each of the platforms. A focus group discussion was then held with 4 family caregivers to explore their perceptions of the apps according to their needs and interests. The content of the discussion was analyzed. RESULTS: Initially, 7 of 118 apps identified met the inclusion criteria. An eighth app, recommended by one of the knowledge users, was added later. Four family caregivers (women aged between 58 and 78 years) participated in the discussion. Participants expressed a preference for easy-to-understand apps that provide concrete intervention strategies. They reported being most inclined to use two apps, Dementia Advisor and DTA Behaviours. CONCLUSIONS: Few mobile apps on the market meet the needs of family caregivers in terms of content and usability. Our results could help to address this gap by identifying what family caregivers deem relevant in a mobile app to help them manage disruptive behaviors.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".