A Voice App Design for Heart Failure Self-Management: A Pilot Study
Bibliographic record
Abstract
Abstract There is a growing interest to investigate the feasibility of using voice user interfaces as a platform for digital therapeutics in chronic disease management. While mostly deployed as smartphone applications, some demographics struggle when using touch screens and often cannot complete tasks independently. This research aimed to evaluate how heart failure patients interacted with a voice app version of an already existing digital therapeutic, Medly , using a mixed-methods concurrent triangulation approach. The objective was to determine the acceptability and feasibility of the voice app by better understanding who this platform is be best suited for. Quantitative data included engagement levels and accuracy rates. Participants (n=20) used the voice app over a four week period and completed questionnaires and semi-structured interviews relating to acceptability, ease of use, and workload. The average engagement level was 73%, with a 14% decline between week one and four. The difference in engagement levels between the oldest and youngest demographic was the most significant, 84% and 43% respectively. The Medly voice app had an overall accuracy rate of 97.8% and was successful in sending data to the clinic. Users were accepting of the technology (ranking it in the 80 th percentile) and felt it did not require a lot of work (2.1 on a 7-point Likert scale). However, 13% of users were less inclined to use the voice app at the end of the study. The following themes and subthemes emerged: (1) feasibility of clinical integration: user adaptation to voice app’s conversational style, device unreliability, and (2) voice app acceptability: good device integration within household, users blamed themselves for voice app problems, and voice app missing desirable user features. The voice app proved to be most beneficial to those who: are older, have flexible schedules, are confident with using technology, and are experiencing other medical conditions.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".