Characteristics of Smart Health Ecosystems That Support Self-care Among People With Heart Failure: Scoping Review
Bibliographic record
Abstract
BACKGROUND People with heart failure are supported by healthcare providers who follow clinical guidelines, and they are also encouraged to participate in self-care behaviors. However, the management of heart failure is complex. Innovative solutions are required to support healthcare providers with decision-making and to support people with heart failure to sustain appropriate self-care behaviors. In recent years, more sophisticated technologies have emerged within healthcare practice. These technologies use data collection, intelligent data processing, and communication to enable new models of healthcare, such as smart health ecosystems, to assist diagnosis and treatment of conditions, support patients in managing a condition, and monitor patients to support disease prevention. Currently, there is little information about the behavioral and technical characteristics of smart health ecosystems for people with heart failure. OBJECTIVE We aimed to identify and describe the characteristics of smart health ecosystems that support self-care for people with heart failure. METHODS We conducted a scoping review using the Joanna Briggs Institute (JBI) methodology. Searches of MEDLINE, Embase, CINAHL, PsycINFO, IEEE Xplore, and ACM Digital Library databases were searched from January 2008 to September 2021. The search strategy focused on studies that described smart health ecosystems to support self-care among people with heart failure. Two reviewers screened studies at the title and abstract level, and full text then extracted relevant data from the included full texts. RESULTS After removing duplicates, 1543 articles were screened, and 34 articles were identified, representing 13 interventions. Articles represented study designs from different stages of the e-health development cycle; conceptual and planning (n=6), development and usability (n=14), pilot/feasibility (n=2), effectiveness testing (n=9), implementation (n=1), all phases (n=2). They collected data from the end user with sensors and questionnaires and used tailoring to provide personalized support. Interventions supported heart failure self-care behaviors using 34 different behavior change techniques (BCTs), which were facilitated by a combination of 8 intervention features; automated feedback, monitoring (integrated and manual input), presentation of data, education, reminders, communication with a healthcare provider and psychological support. Furthermore, features to support healthcare providers included the presentation of data, alarms, and alerts, communication with the end user, remote care plan modification, health record integration, and communication with other members of the care team. CONCLUSIONS This scoping review identified that there are few reports of smart health ecosystems to support self-care among people with heart failure, and those that have been reported do not provide comprehensive support across all domains of self-care. Further research on implementation and effectiveness is required. This review outlines the behavioral and technical components of the identified interventions; this information can be used as a starting point for designing and testing future smart health ecosystem interventions.
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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 | 0.010 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".