An Evidence-Based IT Program With Chatbot to Support Caregiving and Clinical Care for People With Dementia: The CareHeroes Development and Usability Pilot
Bibliographic record
Abstract
Background: There are numerous communication barriers between family caregivers and providers of people living with dementia, which can pose challenges to caregiving and clinical decision-making. To address these barriers, a new web and mobile-enabled app, called CareHeroes, was developed, which promotes the collection and secured sharing of clinical information between caregivers and providers. It also provides caregiver support and education. Objective: The primary study objective was to examine whether dementia caregivers would use CareHeroes as an adjunct to care and gather psychosocial data from those who used the app. Methods: This paper presents the implementation process used to integrate CareHeroes into clinical care at 2 memory clinics and preliminary outcome evaluation. Family caregivers receiving services at clinics were asked to use the app for a 12-month period to collect, track, and share clinical information with the care recipient's provider. They also used it to assess their own mental health symptoms. Psychosocial outcomes were assessed through telephone interviews and user data were collected by the app. Results: A total of 21 caregivers enrolled in the pilot study across the 2 memory clinics. Usage data indicated that caregivers used many of the features in the CareHeroes app, though the chatbot was the most frequently used feature. Outcome data indicated that caregivers' depression was lower at 3-month follow-up (t11=2.03, P=.03). Conclusions: Recruitment and retention of the pilot study were impacted by COVID-19 restrictions, and therefore more testing is needed with a larger sample to determine the potential impact of CareHeroes on caregivers' mental health. Despite this limitation, the pilot study demonstrated that integrating a new supportive app for caregivers as an adjunct to clinical dementia care is feasible. Implications for future technology intervention development, implementation planning, and testing for caregivers of people living with dementia are discussed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".