Main Mechanisms of Remote Monitoring Programs for Cardiac Rehabilitation and Secondary Prevention
Bibliographic record
Abstract
PURPOSE: The objective of this report was to identify the main mechanisms of home-based remote monitoring programs for cardiac rehabilitation (RM CR) and examine how these mechanisms vary by context. METHODS: This was a systematic review using realist synthesis. To be included, articles had to be published in English between 2010 and November 2020 and contain specific data related to mechanisms of effect of programs. MEDLINE All (1946-) via Ovid, Embase (1974-) via Ovid, APA PsycINFO (1806-), CINAHL via EBSCO, Scopus databases, and gray literature were searched. RESULTS: From 13 747 citations, 91 focused on cardiac conditions, with 23 reports including patients in CR. Effective RM CR programs more successfully adapted to different patient home settings and broader lives, incorporated individualized patient health data, and had content designed specifically for patients in cardiac rehabilitation. Relatively minor but common technical issues could significantly reduce perceived benefits. Patients and families were highly receptive to the programs and viewed themselves as fortunate to receive such services. The RM CR programs could be improved via incorporating more connectivity to other patients. No clear negative effects on perceived utility or outcomes occurred by patient age, ethnicity, or sex. Overall, the programs were seen to best suit highly motivated patients and consolidated rather than harmed existing relationships with health care professionals and teams. CONCLUSIONS: Remote monitoring CR programs are perceived by patients to be beneficial and attractive. Future RM CR programs should consider adaptability to different home settings, incorporate individualized health data, and contain content specific to patient needs.
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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 | 0.022 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".