Review and Analysis of Existing Mobile Phone Apps to Support Heart Failure Symptom Monitoring and Self-Care Management Using the Mobile Application Rating Scale (MARS)
Bibliographic record
Abstract
BACKGROUND: Heart failure is the most common cause of hospital readmissions among Medicare beneficiaries and these hospitalizations are often driven by exacerbations in common heart failure symptoms. Patient collaboration with health care providers and decision making is a core component of increasing symptom monitoring and decreasing hospital use. Mobile phone apps offer a potentially cost-effective solution for symptom monitoring and self-care management at the point of need. OBJECTIVE: The purpose of this review of commercially available apps was to identify and assess the functionalities of patient-facing mobile health apps targeted toward supporting heart failure symptom monitoring and self-care management. METHODS: We searched 3 Web-based mobile app stores using multiple terms and combinations (eg, "heart failure," "cardiology," "heart failure and self-management"). Apps meeting inclusion criteria were evaluated using the Mobile Application Rating Scale (MARS), IMS Institute for Healthcare Informatics functionality scores, and Heart Failure Society of America (HFSA) guidelines for nonpharmacologic management. Apps were downloaded and assessed independently by 2-4 reviewers, interclass correlations between reviewers were calculated, and consensus was met by discussion. RESULTS: Of 3636 potentially relevant apps searched, 34 met inclusion criteria. Most apps were excluded because they were unrelated to heart failure, not in English or Spanish, or were games. Interrater reliability between reviewers was high. AskMD app had the highest average MARS total (4.9/5). More than half of the apps (23/34, 68%) had acceptable MARS scores (>3.0). Heart Failure Health Storylines (4.6) and AskMD (4.5) had the highest scores for behavior change. Factoring MARS, functionality, and HFSA guideline scores, the highest performing apps included Heart Failure Health Storylines, Symple, ContinuousCare Health App, WebMD, and AskMD. Peer-reviewed publications were identified for only 3 of the 34 apps. CONCLUSIONS: This review suggests that few apps meet prespecified criteria for quality, content, or functionality, highlighting the need for further refinement and mapping to evidence-based guidelines and room for overall quality improvement in heart failure symptom monitoring and self-care related apps.
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".