Mobile Health Assisted Self Monitoring in Heart Failure Patients To Ensure Quality of Life: A Scoping Review
Bibliographic record
Abstract
Background: Heart failure leads to reduced quality of life, high hospitalization rates, mortality rates, and treatment costs where out-of-hospital self-monitoring can help with management and prevention of hospitalization and digital apps can help with this. The purpose of this review was getting all information of the studies regarding self-monitoring assisted by mobile health or digital application for heart failure patients.Subjects and Method: Using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols for a Scoping Review (PRISMA-ScR), literature searches were developed by searching the databases: PubMed.gov, ScienceDirect, SpringerLink, Google Scholar. Following the eligibility criteria, articles that included were analyzed to get the result.Results: Mostly the studies were conducted in 2016 and 2017 (20% each, n=5), less studies in 2018, 2019, 2020, and increased again in 2021 (16%, n=4). The studies were done 57% in USA (n=13), both Australia and Canada were 9% (n=2), and other countries. The designs of the studies were mostly RCT (74%, n=17). Sample size was variative mostly less than 50 participants (39%, n=9). There were 65% of the studies measured the daily body weight (n=15), others used vital signs 57%, medication adherence 39%, and other items. QoL was the most in the outcome (61%, n=14). Main findings mostly showed positive impact on self-monitoring with digital application.Conclusion: Using mobile apps for heart failure patients’ self-monitoring created the positive impacts in the expected outcomes, mostly for quality of life.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".