Use of a mobile health application by adult non-congenital cardiac surgery patients: A feasibility study
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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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".