Enhancing adherence: Evaluating interventions for Heart Failure management in older adults
Bibliographic record
Abstract
• Personalized and technology-driven interventions are crucial for improving HF management and outcomes. • Noncompliance with HF treatment can be attributed to a lack of clear communication from medical staff and insufficient out-of-hospital care. • Technology, particularly mobile health applications, offers scalable solutions to enhance patient engagement and adherence. Management of heart failure (HF) is challenging, particularly because of the high rates of medication non-adherence among older adults. This leads to increased hospital readmission and healthcare costs. To address these challenges, a combination of non-pharmacological strategies, such as lifestyle changes and targeted pharmacological interventions, is crucial for improving the outcomes and reducing the burden of HF. This scoping review aims to map out existing literature and highlight the interventions used for HF management to enhance adherence in the older adult population. This scoping review examined peer-reviewed studies from PubMed, CINAHL, SCOPUS, and the Cochrane Library databases The search yielded 511 articles, of which 13 were included in the final review. After examining all the studies, four key aspects emerged: increasing health literacy, utilizing mHealth applications, personalized cardiac rehabilitation, and scheduled mobile reminders. This study found that personalized care, technology-driven interventions and clear communication from healthcare professions are crucial for improving HF management and outcomes. Future studies should focus on broadening language inclusion and investigate the interplay between HF and comorbid conditions to enhance applicability of 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 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 | 0.039 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.019 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".