Feasibility, Acceptability, and Preliminary Effectiveness of a Combined Digital Platform and Community Health Worker Intervention for Patients With Heart Failure: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Interventions focused on remote monitoring and social needs care have shown promise in improving clinical outcomes for patients with heart failure (HF). However, patient willingness to use technology as well as concerns about access in underresourced settings have limited digital platform implementation and adoption. There is little research in HF populations examining the effect of a combined digital and social needs care intervention that could enhance patient engagement in digital platform use while closing gaps in care related to social determinants of health. Here, we describe the protocol for a clinical trial of a digitally enabled community health worker intervention designed for patients with HF. OBJECTIVE: This study aims to describe the protocol for a randomized controlled trial assessing the acceptability, feasibility, and preliminary effectiveness of an intervention that combines remote monitoring with a digital platform and community health worker (CHW) social needs care for patients with HF who are transitioning from hospital to home. Given the elevated morbidity and mortality, identifying comprehensive and patient-centered interventions at the time of hospital care transitions that can improve clinical outcomes, impact cost, and augment the quality of care for this cohort is a priority. METHODS: This trial randomized adult inpatient participants (n=50) with a diagnosis of HF receiving care at a single academic health care institution to the 30-day intervention (digital platform+CHW pairing+usual care) or the 30-day control (CHW pairing+usual care) arms. All study participants completed baseline questionnaires and 30-day exit interviews and questionnaires. The primary outcomes will be acceptability, feasibility, and preliminary effectiveness. RESULTS: This clinical trial opened for enrollment in September 2022 and was completed in June 2023. Initial results are expected to be published in the spring of 2024, and analysis is currently underway. Feasibility outcome measures will include the use rates of the biometric sensor (average hours per day), the digital blood pressure monitor (average times per day), the weight scale (average times per day), and the completion of the symptoms questionnaire (average times per day). The acceptability outcome will be measured by the patients' response to the truthfulness of the statement that they would be willing to use the digital platform in the future (response options: very true, somewhat true, or not true). Preliminary effectiveness will be measured by tracking 30-day clinical outcomes (hospital readmissions, emergency room visits, and missed primary care and cardiology appointments). CONCLUSIONS: The results of this investigation are expected to contribute to our understanding of the use of digital interventions and the implementation of supportive home-based social needs care to enhance engagement and the potential effectiveness of clinically focused digital platforms. These results may inform the construction of a future multi-institutional trial designed to test the true effectiveness of this intervention in HF. TRIAL REGISTRATION: ClinicalTrials.gov NCT05130008; https://clinicaltrials.gov/study/NCT05130008. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/55687.
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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 | 0.078 | 0.070 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.063 | 0.012 |
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".