mHealth Intervention for Promoting Hypertension Self-management Among African American Patients Receiving Care at a Community Health Center: Formative Evaluation of the FAITH! Hypertension App
Bibliographic record
Abstract
BACKGROUND: African American individuals are at a higher risk of premature death from cardiovascular diseases than White American individuals, with disproportionate attributable risk from uncontrolled hypertension. Given their high use among African American individuals, mobile technologies, including smartphones, show promise in increasing reliable health information access. Culturally tailored mobile health (mHealth) interventions may promote hypertension self-management among this population. OBJECTIVE: This formative study aimed to assess the feasibility of integrating an innovative mHealth intervention into clinical and community settings to improve blood pressure (BP) control among African American patients. METHODS: A mixed methods study of African American patients with uncontrolled hypertension was conducted over 2 consecutive phases. In phase 1, patients and clinicians from 2 federally qualified health centers (FQHCs) in the Minneapolis-St Paul, Minnesota area, provided input through focus groups to refine an existing culturally tailored mHealth app (Fostering African-American Improvement in Total Health! [FAITH!] App) for promoting hypertension self-management among African American patients with uncontrolled hypertension (renamed as FAITH! Hypertension App). Phase 2 was a single-arm pre-post intervention pilot study assessing feasibility and patient satisfaction. Patients receiving care at an FQHC participated in a 10-week intervention using the FAITH! Hypertension App synchronized with a wireless BP monitor and community health worker (CHW) support to address social determinants of health-related social needs. The multimedia app consisted of a 10-module educational series focused on hypertension and cardiovascular risk factors with interactive self-assessments, medication and BP self-monitoring, and social networking. Primary outcomes were feasibility (app engagement and satisfaction) and preliminary efficacy (change in BP) at an immediate postintervention assessment. RESULTS: In phase 1, thirteen African American patients (n=9, 69% aged ≥50 years and n=10, 77% women) and 16 clinicians (n=11, 69% aged ≥50 years; n=14, 88% women; and n=10, 63% African American) participated in focus groups. Their feedback informed app modifications, including the addition of BP and medication tracking, BP self-care task reminders, and culturally sensitive contexts. In phase 2, sixteen African American patients were enrolled (mean age 52.6, SD 12.3 years; 12/16, 75% women). Overall, 38% (6/16) completed ≥50% of the 10 education modules, and 44% (7/16) completed the postintervention assessment. These patients rated the intervention a 9 (out of 10) on its helpfulness in hypertension self-management. Qualitative data revealed that they viewed the app as user-friendly, engaging, and informative, and CHWs were perceived as providing accountability and support. The mean systolic and diastolic BPs of the 7 patients decreased by 6.5 mm Hg (P=.15) and 2.8 mm Hg (P=.78), respectively, at the immediate postintervention assessment. CONCLUSIONS: A culturally tailored mHealth app reinforced by CHW support may improve hypertension self-management among underresourced African American individuals receiving care at FQHCs. A future randomized efficacy trial of this intervention is warranted. TRIAL REGISTRATION: ClinicalTrials.gov NCT04554147; https://clinicaltrials.gov/ct2/show/NCT04554147.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".