Patients’ experience using an app for home remote monitoring of heart failure for a university hospital in Quebec, Canada
Bibliographic record
Abstract
Abstract Background The use of mobile health apps for remote monitoring has considerable potential for patient care. However, more research is needed to gather patients’ experience with their use. This study evaluates heart failure patients’ experience of the effects of remote monitoring on the management of heart failure (HF) using a smartphone app, the challenges faced in such use, and patients’ suggestions for improving the efficacy of this device. Method A qualitative study was conducted with all patients who had used the TakeCare™ app for at least three months during the exploratory phase of the Continuum project. Patients were recruited before COVID-19 began, and they used the app during this period. The app allows patients to submit their daily vital signs and answer questions about their symptoms to help the clinical professionals adjust their treatment plan as needed. Individual interviews were conducted by telephone or videoconference using a semi-structured individual interview guide. All interviews were recorded, transcribed and analyzed using the thematic analysis method. Results Of the 29 patients invited, 5 had since died, 10 did not respond, two gave positive feedback on the app but declined the interview, and 12 patients agreed to participate in an individual interview. The participants all liked the app’s ease of use. They particularly praised the contribution it made to monitoring their disease, both by themselves and by clinicians. The app also allowed them to stay connected to their clinical team and have better access to healthcare professionals. Patients suggested, as an improvement, that the feedback and communication features should be personalized to make them more individualized and interactive. Conclusion The TakeCare™ smartphone app was well received by patients with heart failure. Its use could facilitate better remote monitoring of heart conditions in the home as well as improve access to clinical teams.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".