Unlocking Heart Health: Influence of Mobile Health on Treatment Adherence in Older Adults With Heart Failure
Bibliographic record
Abstract
Purpose: To map existing literature to understand the impact of mobile health (mHealth) interventions on older adult patients' self-efficacy in adhering to prescribed treatment plans for heart failure (HF). Method: The current scoping review examined peer-reviewed studies identified in PubMed, CINAHL, and SCOPUS, with data screening and extraction conducted independently by two reviewers to ensure accuracy and minimize bias. Results: The search yielded 731 articles, of which 16 were included in the final review. After examining all studies, three key themes were examined in detail: Medication Adherence , Self-Care Adherence , and Engagement With mHealth Services . Conclusion: mHealth apps have shown promising effects on medication and treatment adherence in patients with HF. However, modifications addressing the specific needs of older populations are necessary to effectively implement these tools. Comprehensive feasibility trials on a larger scale are essential for fully understanding the potential effectiveness and implementation requirements of these interventions.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".