Future Patient: Telerehabilitation of Patients With Heart Failure Empower Patients
Bibliographic record
Abstract
Background Heart failure is one of the most common cardiovascular diseases that causes mortality, and patients’ participation in rehabilitation programs is often low. During the COVID-19 pandemic, the Future Patient Telerehabilitation Program developed for patients with heart failure offered a new approach. The aim of the Future Patient program has been to increase the quality of life and educate patients to monitor any worsening of their symptoms. Patients used self-tracking devices for monitoring their physical activity, blood pressure, sleep, respiration, and pulse, with their data transmitted to a shared web platform (called the Heart Portal), which could be accessed by patients, their relatives, and health care professionals across sectors. Objective The aim of this paper is to determine whether the Future Patient Telerehabilitation Program has increased the quality of life of patients with heart failure and to empower them to manage their own disease. Methods A randomized controlled trial (n=140) was conducted. Data from the intervention group (n=70, 50%) on patient-reported outcomes were collected and analyzed using the Kansas City Cardiomyopathy Questionnaire and Spiegel Sleep Questionnaire. Semistructured interviews with 12 patients (n=6, 50% men and n=6, 50% women) were conducted and analyzed. Results The patients participating in the Future Patient program experienced a significant increase in clinical and social well-being as well as in quality of life. The patients participating in the program articulated their experiences in terms of the following themes: a sense of security and an increased sense of empowerment in managing their disease using the Heart Portal. The Heart Portal proved to be a valuable tool for remote monitoring and better communication with health care professionals across sectors. Conclusions Telerehabilitation of patients with heart failure can improve their quality of life and empower them to manage their own disease remotely.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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 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".