Factors Affecting Patient and Physician Engagement in Remote Health Care for Heart Failure: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Adult chronic heart failure mainly affects an elderly population with multiple comorbidities that often require frequent medical visits to prevent poor health outcomes. However, the heart failure disease process reduces their independence by reducing mobility, exercise tolerance, and cognitive decline. Remote care technologies can bridge the gap in care for these patients by allowing them to be followed up within the comfort of their home and encourage their self-care. However, patients, carers, and health care professionals need to engage with the technology for it to be useful. OBJECTIVE: This systematic review explores qualitative primary studies of remote care technologies used in heart failure, to determine the factors that affect user engagement with the technology. This is explored from the perspective of patients, carers, and health care professionals. METHODS: Relevant studies published between January 1, 1990, and September 19, 2020, were identified from EMBASE, Ovid MEDLINE, PubMed, Cochrane Library, and Scopus. These studies were then synthesized using thematic analysis. Relevant user experiences with remote care were extracted using line-by-line coding. These codes were summarized into secondary codes and core concepts, which were further merged into overarching themes that encapsulate user experience with remote care. RESULTS: The review included 47 studies, which led to the generation of 5 overarching themes that affect engagement: (1) "Convenience" relates to time saved by the intervention; (2) "Clinical Care" relates to perceived quality of care and health outcomes; (3) "Communication" involves feedback and interaction between patients, staff, and carers; (4) "Education" concerns the tailored information provided; and (5) "Ease of Use" relates to accessibility and technical barriers to engagement. Each theme was applied to each user base of patient, carer, and health care professional in a different manner. CONCLUSIONS: The 5 themes identified highlight aspects of remote care that facilitate engagement, and should be considered in both future design and trials evaluating these technologies.
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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 | 0.011 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".