Feasibility and Acceptability of a Combined Digital Platform and Community Health Worker Intervention for Patients With Heart Failure: Single-Arm Pilot Study
Bibliographic record
Abstract
BACKGROUND: Heart failure (HF) is one of the leading causes of hospital admissions. Clinical (eg, complex comorbidities and low ejection fraction) and social needs factors (eg, access to transportation, food security, and housing security) have both contributed to hospitalizations, emphasizing the importance of increased clinical and social needs support at home. Digital platforms designed for remote monitoring of HF can improve clinical outcomes, but their effectiveness has been limited by patient barriers such as lack of familiarity with technology and unmet social care needs. To address these barriers, this study explored combining a digital platform with community health worker (CHW) social needs care for patients with HF. OBJECTIVE: We aim to determine the feasibility and acceptability of an intervention combining digital platform use and CHW social needs care for patients with HF. METHODS: Adults (aged ≥18 years) with HF receiving care at a single health care institution and with a history of hospital admission in the previous 12 months were enrolled in a single-arm pilot study from July to November 2021 (N=14). The 30-day intervention used a digital platform within a mobile app that included symptom questionnaire and educational videos connected to a biometric sensor (tracking heart rate, oxygenation, and steps taken), a digital weight scale, and a digital blood pressure monitor. All patients were paired with a CHW who had access to the digital platform data. A CHW provided routine phone calls to patients throughout the study period to discuss their biometric data and to address barriers to any social needs. Feasibility outcomes were patient use of the platform and engagement with the CHW. The acceptability outcome was patient willingness to use the intervention again. RESULTS: Participants (N=14) were 67.7 (SD 11.7) years old; 8 (57.1%) were women, and 7 (50%) were insured by Medicare. Participants wore the sensor for 82.2% (n=24.66) of study days with an average of 13.5 (SD 2.1) hours per day. Participants used the digital blood pressure monitor and digital weight scale for an average of 1.2 (SD 0.17) times per day and 1.1 (SD 0.12) times per day, respectively. All participants completed the symptom questionnaire on at least 71% (n=21.3) of study days; 11 (78.6%) participants had ≥3 CHW interactions, and 11 (78.6%) indicated that if given the opportunity, they would use the platform again in the future. Exit interviews found that despite some platform "glitches," participants generally found the remote monitoring platform to be "helpful" and "motivating." CONCLUSIONS: A novel intervention combining a digital platform with CHW social needs care for patients with HF was feasible and acceptable. The majority of participants were engaged throughout the study and indicated their willingness to use the intervention again. A future clinical trial is needed to determine the effectiveness of this intervention.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".