Design, Development, and Usability Evaluation of a Voice App Experience for Heart Failure Management
Bibliographic record
Abstract
Abstract The use of digital therapeutics (DTx) in the prevention and management of medical conditions has increased through the years with an estimated 44 million people using one as part of their treatment plan in 2021, nearly double the amount from last year. DTx are commonly accessed through smartphone apps, but offering these treatments through an alternative input can improve the accessibility of these interventions. Voice apps are an emerging technology in the digital health field, and may be an appropriate alternative platform for some patients. This research aimed to identify the acceptability and feasibility of offering a voice app as an alternative input for a chronic disease self-management program. The objective of this project was to design, develop, and evaluate a voice app of an already existing smartphone-based heart failure self-management program, Medly , to be used as a case study. A voice app version of Medly was designed and developed through a user-centered design process. We conducted a usability study and semi-structured interviews with representative end users (n=8) at the Peter Munk Cardiac Clinic in Toronto General Hospital to better understand the user experience. A Medly voice app prototype was built using a software development kit in tandem with a cloud computing platform. Three out of the eight participants were successful in completing the usability session, while the rest of the participants were not due to various errors. Almost all (7 out of the 8) participants were satisfied with the voice app and felt confident using it. Half of the participants were unsure about using the voice app in the future, though. With these findings, design changes were made to better improve the user experience. With rapid advancements in voice user interfaces, we believe this technology will play an integral role when providing access to DTx for chronic disease management.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".