Engaging Gatekeeper-Stakeholders in Development of a Mobile Health Intervention to Improve Medication Adherence Among African American and Pacific Islander Elderly Patients With Hypertension
Bibliographic record
Abstract
BACKGROUND: Approximately 70 million people in the United States have hypertension. Although antihypertensive therapy can reduce the morbidity and mortality associated with hypertension, often patients do not take their medication as prescribed. OBJECTIVE: The goal of this study was to better understand issues affecting the acceptability and usability of mobile health technology (mHealth) to improve medication adherence for elderly African American and Native Hawaiian and Pacific Islander patients with hypertension. METHODS: In-depth interviews were conducted with 20 gatekeeper-stakeholders using targeted open-ended questions. Interviews were deidentified, transcribed, organized, and coded manually by two independent coders. Analysis of patient interviews used largely a deductive approach because the targeted open-ended interview questions were designed to explore issues specific to the design and acceptability of a mHealth intervention for seniors. RESULTS: A number of similar themes regarding elements of a successful intervention emerged from our two groups of African American and Native Hawaiian and Pacific Islander gatekeeper-stakeholders. First was the need to teach participants both about the importance of adherence to antihypertensive medications. Second, was the use of mobile phones for messaging and patients need to be able to access ongoing technical support. Third, messaging needs to be short and simple, but personalized, and to come from someone the participant trusts and with whom they have a connection. There were some differences between groups. For instance, there was a strong sentiment among the African American group that the church be involved and that the intervention begin with group workshops, whereas the Native Hawaiian and Pacific Islander group seemed to believe that the teaching could occur on a one-to-one basis with the health care provider. CONCLUSIONS: Information from our gatekeeper-stakeholder (key informant) interviews suggests that the design of a mHealth intervention to improve adherence to antihypertensives among the elderly could be very similar for African Americans and Native Hawaiian and Pacific Islanders. The main difference might be in the way in which the program is initiated (possibly through church-based workshops for African Americans and by individual providers for Native Hawaiian and Pacific Islanders). Another difference might be who sends the messages with African Americans wanting someone outside the health care system, but Native Hawaiian and Pacific Islanders preferring a provider.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".