Evaluating the Acceptability and Appropriateness of the Augmented Reality Home Assessment Tool (ARHAT): Qualitative Descriptive Study
Bibliographic record
Abstract
Background: The Augmented Reality Home Assessment Tool (ARHAT) is a mobile app developed to provide rapid, highly accurate assessments of the home environment. It uses 3D-capture technologies to help people identify and address functional limitations and environmental barriers. Objective: This study was conducted to gain stakeholder feedback on the acceptability and appropriateness of the ARHAT for identifying and addressing barriers within home environments. Methods: A qualitative descriptive study was conducted because it allows for variability when obtaining data and seeks to understand stakeholders' insights on an understudied phenomenon. Each stakeholder group (occupational therapists, housing professionals, and aging adult and caregiver "dyads") participated in a 60-minute, web-based focus group via a secure Zoom platform. Focus group data were analyzed by 2 trained qualitative research team members using a framework method for analysis. Results: A total of 19 stakeholders, aged from 18 to 85+ years, were included in the study. Of the occupational therapists (n=5, 26%), housing professionals (n=3, 16%), and dyads (n=11, 58%), a total of 32% (n=6) were male and 68% (n=13) were female, with most living in the Midwestern United States (n=10, 53%). The focus group data demonstrate the acceptability and appropriateness of the workflow, style, measurement tools, and impact of the ARHAT. All stakeholders stated that they could see the ARHAT being used at many different levels and by any population. Dyads specifically mentioned that the ARHAT would allow them to do forward planning and made them think of home modifications in a new light. Conclusions: Stakeholders found the ARHAT to be acceptable and appropriate for identifying and addressing functional limitations and barriers in the home environment. This study highlights the importance of considering the workflow, style, measurement tools, and potential impact of home assessment technology early in the developmental process.
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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 | 0.041 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".