Use of a mobile health application by adult non-congenital cardiac surgery patients: A feasibility study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mobile Health (mHealth) technologies are becoming integral to our healthcare system. This study evaluated the feasibility (compliance, usability and user satisfaction) of a mHealth application (app) for delivering Enhanced Recovery Protocols (ERPs) information to Cardiac Surgery (CS) patients peri-operatively. This single centre, prospective cohort study involved patients undergoing CS. Patients received a mHealth app developed for the study at consent and for 6-8 weeks post-surgery. Patients completed system usability, patient satisfaction and quality of life surveys pre- and post-surgery. A total of 65 patients participated in the study (mean age of 64 years). The app achieved an overall utilization rate of 75% (68% vs 81% for <65 and ≥65 years respectively). Pre-surgery, the majority of patients found the app easy to use (94%), user-friendly (89%), and felt confident using the app (92%). The majority also found the app's educational information useful (90%) and easy to find (88%). 75% of patients reported that they would like to use the app frequently. This percentage decreased to 57% in the post-discharge survey. A lower percentage of patients ≥65 years indicated their preference for the app over printed information (51% vs 87%) and their recommendation for the app (84% vs 100% for >65 and <65 years respectively) in the post-surgery survey. MHealth technology is feasible for peri-operative CS patient education, including older adult patients. The majority of patients were satisfied with the app and would recommend using it over the use of printed materials.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it