Bio-Experiential Technology to Support Persons With Dementia and Care Partners at Home (TEND): Protocol for an Intervention Development Study
Bibliographic record
Abstract
BACKGROUND: Alzheimer disease and related dementias are debilitating and incurable diseases. Persons with dementia and their informal caregivers (ie, dyads) experience high rates of emotional distress and negative health outcomes. Several barriers prevent dyads from engaging in psychosocial care including cost, transportation, and a lack of treatments that target later stages of dementia and target the dyad together. Technologically informed treatment and serious gaming have been shown to be feasible and effective among persons living with dementia and their care partners. To increase access, there is a need for technologically informed psychosocial interventions which target the dyad, together in the home. OBJECTIVE: This study aims to develop the toolkit for experiential well-being in dementia, a dyadic, "bio-experiential" intervention for persons with dementia and their caregivers. Per our conceptual model, the toolkit for experiential well-being in dementia platform aims to target sustained attention, positive emotions, and active engagement among dyads. In this paper, we outline the protocol and conceptual model for intervention development and partnership with design and development experts. METHODS: We followed the National Institutes of Health (NIH) stage model (stage 1A) and supplemented the model with principles of user-centered design. The first step includes understanding user needs, goals, and strengths. We met this step by engaging in methodology and definition synthesis and conducting focus groups with dementia care providers (N=10) and persons with dementia and caregivers (N=11). Step 2 includes developing and refining the prototype. We will meet this step by engaging dyads in up to 20 iterations of platform β testing workshops. Step 3 includes observing user interactions with the prototype. We will meet this step by releasing the platform for feasibility testing. RESULTS: Key takeaways from the focus groups include balancing individualization and the dyadic relationship and avoiding confusing stimuli. As of September 2023, we have completed focus groups with providers, persons with dementia, and their caregivers. Additionally, we have conducted 4 iterations of β testing workshops with dyads. Feedback from focus groups informed the β testing workshops; data have not yet been formally analyzed and will be reported in future publications. CONCLUSIONS: Technological interventions, particularly "bio-experiential" technology, can be used in dementia care to support emotional health among persons with a diagnosis and caregivers. Here, we outline a collaborative intervention development process of bio-experiential technology through a research, design, and development partnership. Next, we are planning to test the platform's feasibility as well as its impact on clinical outcomes and mechanisms of action. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/52799.
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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 | 0.038 | 0.032 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.064 | 0.012 |
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".