Quality of an Assistive Technology Web Application for Primary Care Physicians Serving Older Adults: Concurrent Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Older Latinos living in Puerto Rico experience significantly higher rates of functional disabilities (FDs; 1093/87,300, 27.8%) compared to older adults in the continental United States (755,685/57,913,200, 13.3%). While assistive technologies (ATs) can improve daily function and support aging in place, primary care physicians (PCPs), who are essential in addressing FDs resulting from chronic diseases, often lack knowledge about AT devices and services. The Mi Guía de Asistencia Tecnológica (MGAT; My Assistive Technology Guide) web application was empirically developed to address this gap by providing comprehensive information and videos about AT devices for older adults with functional difficulties in daily living activities. OBJECTIVE: This study aimed to assess the quality of MGAT among PCPs and describe their experiences using the app to increase access to AT for older Latinos. METHODS: A total of 10 PCPs participated in this usability project, receiving MGAT training before a 30-day implementation period. A concurrent mixed methods design was used, combining quantitative data from the User Version of the Mobile User Application Rating Scale (uMARS) and qualitative insights from semistructured individual interviews. The analysis included descriptive statistics and a directed content analysis. RESULTS: The MGAT received high overall objective quality ratings on uMARS (mean 4.06, SD 1.05). Among subdomains, information scored highest (mean 4.60, SD 0.51), followed by functionality (mean 4.20, SD 0.63), aesthetics (mean 4.00, SD 0.82), and engagement, which scored lowest (mean 3.34, SD 1.51). Subjective quality ratings were also favorable, with a mean score of 3.93 (SD 1.19), with recommending the app to others scoring the highest (mean 4.70, SD 0.48) and willingness to pay for the app the lowest (mean 3.11, SD 1.90). Perceived impact received the highest score across all domains (mean 4.82, SD 0.39), with behavior change scoring the highest (mean 5.82, SD 0) and awareness scoring the lowest (mean 4.60, SD 0.52). Qualitative findings revealed that PCPs found MGAT entertaining and interesting, but wanted more customization and interactive features to boost engagement. They appreciated its ease of use and navigation, but noted the need for a stable internet connection. While the design was visually appealing, improvements to the color scheme and element sizes were suggested. Participants valued the high-quality information relevant to older adults but desired more specialized content for medical professionals. They were likely to recommend MGAT, though cost opinions varied. Most importantly, MGAT increased awareness of patient needs, expanded AT knowledge, and positively influenced intentions to recommend AT, ultimately facilitating patient access to AT. CONCLUSIONS: The high-quality and usefulness ratings suggest MGAT could be an effective tool for PCPs in managing older adults' FDs. Future research should evaluate the effectiveness of MGAT in managing FDs among older adults.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".