The ManageHF Just-in-Time Adaptive Mobile Application Interventions to Promote Self-Management and Improve Outcomes in Heart Failure: A Randomized Controlled Trial (Preprint)
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) is a major healthcare challenge in the United States, with approximately 900,000 older adults hospitalized annually. Gaps in self-management, including unrecognized worsening symptoms and failure to adhere to dietary sodium restriction, can reduce quality of life and precipitate hospital admissions. Existing mHealth approaches to HF self-management have produced mixed results, highlighting the need for innovative strategies to improve post-discharge outcomes in at-risk patients. OBJECTIVE: The ManageHF trial aimed to evaluate the effectiveness of two just-in-time adaptive interventions (JITAIs) delivered via a mobile application to enhance HF self-management. The interventions focused on symptom recognition and lower dietary sodium restriction, with the goal of reducing readmissions and improving HRQOL over a 12-week period. METHODS: The trial was a 2x2 factorial, double-blind, randomized controlled study conducted across several U.S. institutions. Participants recently hospitalized for acute HF were randomized into four groups: both interventions, either intervention alone, or an active control. The primary outcome was a composite measure assessing time to all-cause death, time to first HF readmission, and HRQOL changes, using the Minnesota Living with Heart Failure Questionnaire (MLHFQ). RESULTS: Recruitment was hindered by the COVID-19 pandemic, leading to the early discontinuation of the trial. Of 62 participants enrolled, 43 completed the study. Participants were diverse, with a mean age of 55 years; 32% were female, and 55% were Black or African American. Most had HF with reduced ejection fraction . However, due to the early termination and small sample size, the ability to detect statistically significant differences was limited. CONCLUSIONS: The ManageHF trial highlighted the potential of mobile health technology to support HF management, particularly in enhancing HRQOL. Future studies employing more effective recruitment and retention strategies are crucial for establishing the efficacy of these interventions with greater certainty.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".