User-Centered Prototype Design of a Health Care Robot for Treating Type 2 Diabetes in the Community Pharmacy: Development and Usability Study
Bibliographic record
Abstract
Background: Technology can be an effective tool for providing health services and disease self-management, especially in diabetes care. Technology tools for disease self-management include health-related applications for computers and smartphones as well as the use of robots. To provide a more effective continuity of care and to better understand and facilitate disease management in middle-aged and older adult patients with diabetes, robots can be used to improve the quality of care and supplement community health resources, such as community pharmacies. Objective: The aim of this study was to develop a health care robot prototype that can be integrated into current community pharmacies. Methods: Three user-centered approaches were used: (1) review of the literature on technology use among older adults, 2) reference to the seven key diabetes self-care behaviors by the American Association of Diabetes Educators (AADE), and (3) meeting with health care providers in the community. Field investigations and interviews were conducted at community pharmacies and diabetes health education centers to determine the appearance, interface, content, and function of the robot. Results: The results show that diabetes health care prototype robots can be established through user-centered design. The following important features were revealed: (1) perceived ease of use is considered a friendly operating interface; therefore, we used less than 3 buttons in an interface; (2) minimization of the interface between blue and yellow, which is unfriendly to older adults; (3) the health education mode was the most preferred mode with sound, image, and video presentation; (4) the most predilected functions are health education resources and health records, and that patient data can be easily collected through health education games and dialogue with robots; and (5) touching the screen is the most preferred operation mode. Conclusions: An evidence-based health care robot can be developed through user-centered design, an approach in which a model that connects medical needs to people with health conditions can be built, thereby facilitating the sustainable development of technology in the diabetes care field.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".