Self-care Practices of Patients With Heart Failure Using Wearable Electronic Devices
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) is the fastest growing cardiovascular condition globally; associated management costs and hospitalizations place an immense burden on healthcare systems. Wearable electronic devices (WEDs) may be useful tools to enhance HF management and mitigate negative health outcomes. OBJECTIVE: We aimed to perform a systematic review to examine the potential of WEDs to support HF self-care in ambulatory patients at home. METHODS: Five databases were searched for studies published between 2007 and May 2022, including OVID MEDLINE, EMBASE (OVID), APA PsycINFO (OVID), Cochrane Central Register of Controlled Trials (OVID), and CINAHL Plus with Full Text (Ebsco). After 6210 duplicates were removed, 4045 records were screened and 6 were included for review (2 conference abstracts and 4 full-text citations). All studies used WEDs as 1 component of a larger intervention. RESULTS: Outcome measures included quality of life, physical activity, self-efficacy, self-care, functional status, time to readmission, social isolation, and mood. Studies were of moderate to high quality and mixed findings were reported. Enhanced exercise habits and motivational behavior to exercise, as well as decreased adverse symptoms of fatigue and dyspnea, were identified in 2 studies. However, improvements in exercise capacity and increased motivational behavior did not lead to exercise adherence in another 2 studies. CONCLUSIONS: The findings from this review suggest that WEDs may be a viable health behavior improvement strategy for patients with HF. However, studies of higher quality, with the primary intervention being a WED, and consistent outcome measures are needed to replicate the positive findings of studies identified in this review.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".