mHealth Intervention Promoting Cardiovascular Health Among African-Americans: Recruitment and Baseline Characteristics of a Pilot Study
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) interventions are promising avenues to promote cardiovascular (CV) health among African-Americans (AAs) and culturally tailored technology-based interventions are emerging for this population. OBJECTIVE: The objectives of this study were to use a community-based participatory research (CBPR) approach to recruit AAs into a pilot intervention study of an innovative mHealth CV health promotion program and to characterize technology use patterns and eHealth literacy (EHL). METHODS: Community partners from five predominately AA churches in southeast Minnesota collaborated with our academic institution to recruit AA congregants into the pilot study. Field notes as well as communications between the study team and community partners were used to design the recruitment strategy and its implementation with a goal of enrolling 50 participants. At its core, the recruitment strategy included community kickoff events to detail the state-of-the-art nature of the mHealth intervention components, the utility of CV health assessments (physical examination, laboratory studies and surveys) and the participants' role in advancing our understanding of the efficacy of mHealth interventions among racial/ethnic minority groups. Detailed recruitment data were documented throughout the study. A self-administered, electronic survey measured sociodemographics, technology use and EHL (eHEALS scale). RESULTS: A total of 50 participants (70% women) from five AA churches were recruited over a one-month period. The majority (>90%) of participants reported using some form of mobile technology with all utilizing these technologies within their homes. Greater than half (60% [30/50]) reported being "very comfortable" with mobile technologies. Overall, participants had high EHL (84.8% [39/46] with eHEALS score ≥26) with no differences by sex. CONCLUSIONS: This study illustrates the feasibility and success of a CBPR approach in recruiting AAs into mHealth intervention research and contributes to the growing body of evidence that AAs have high EHL, are high-users of mobile technologies, and thus are likely to be receptive to mHealth interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".