Effect of a digital health intervention on outpatients with heart failure: a randomized, controlled trial
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Bibliographic record
Abstract
Abstract Aims Heart failure (HF) is associated with high mortality and reduced quality of life (QoL). Interventions encouraging a healthy lifestyle and self-care can reduce morbidity and HF-related hospitalizations. We conducted a randomized controlled trial (RCT) to assess the impact of a digital health programme on QoL and clinical outcomes of patients. The programme included remote patient monitoring (RPM), self-care, HF education, and empowered positive lifestyle changes. Methods and results Patients (n = 175) received standard-of-care (SoC) at a HF outpatient clinic (control, n = 89) or SoC plus a digital health programme (intervention, n = 86) for 6 months, followed by a 6-month maintenance period. Compliance with RPM was 93% at 6 months. No significant between-group difference was found in the primary endpoint (health-related QoL), except in an exploratory subgroup of New York Heart Association class III patients, where the intervention group had a significantly smaller QoL decline (P = 0.023). For secondary endpoints, the intervention group had significantly greater improvements in self-care at 6 months (P < 0.001) and 12 months (P = 0.003), and in disease-specific knowledge at 12 months (P = 0.001). Several exploratory endpoints favoured the intervention, with significant improvements in triglycerides (P = 0.012), HbA1c (P = 0.014), and fasting glucose (P = 0.010). The TG/HDL cholesterol ratio and TG/glucose index improved significantly at both 6 and 12 months in between-group comparisons. Conclusion Although the digital programme did not improve health-related QoL, it led to benefits in other important outcomes such as self-care, disease-specific knowledge, and several key metabolic parameters.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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