The AppCare-HF randomized clinical trial: a feasibility study of a novel self-care support mobile app for individuals with chronic heart failure
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Bibliographic record
Abstract
Abstract Aims We evaluated a self-care intervention with a novel mobile application (app) in chronic heart failure (HF) patients. To facilitate patient-centred care in HF management, we developed a self-care support mobile app to boost HF patients’ optimal self-care. Methods and results We conducted a multicentre, randomized, controlled study evaluating the feasibility of the self-care support mobile app designed for use by HF patients. The app consists of a self-monitoring assistant, education, and automated alerts of possible worsening HF. The intervention group received a tablet personal computer (PC) with the self-care support app installed, and the control group received a HF diary. All patients performed self-monitoring at home for 2 months. Their self-care behaviours were evaluated by the European Heart Failure Self-Care Behaviour Scale. We enrolled 24 outpatients with chronic HF (ages 31–78 years; 6 women, 18 men) who had a history of HF hospitalization. During the 2 month study period, the intervention group (n = 13) showed excellent adherence to the self-monitoring of each vital sign, with a median [interquartile range (IQR)] ratio of self-monitoring adherence for blood pressure, body weight, and body temperature at 100% (92–100%) and for oxygen saturation at 100% (91–100%). At 2 months, the intervention group’s self-care behaviour score was significantly improved compared with the control group (n = 11) [median (IQR): 16 (16–22) vs. 28 (20–36), P = 0.02], but the HF Knowledge Scale, the General Self-Efficacy Scale, and the Short Form-8 Health Survey scores did not differ between the groups. Conclusion The novel mobile app for HF is feasible.
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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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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