Journal Article
Research Support, Non-U.S. Gov't
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Assessing Mobile Health Capacity and Task Shifting Strategies to Improve Hypertension Among Ghanaian Stroke Survivors.

BACKGROUND: There has been a tremendous surge in stroke prevalence in sub-Saharan Africa. Hypertension (HTN), the most potent, modifiable risk factor for stroke, is a particular challenge in sub-Saharan Africa. Culturally sensitive, efficacious HTN control programs that are timely and sustainable are needed, especially among stroke survivors. Mobile health (mHealth) technology and task-shifting offer promising approaches to address this need.

METHODS: Using a concurrent triangulation design, we collected data from stroke survivors, caregivers, community leaders, clinicians and hospital personnel to explore the barriers, facilitators and perceptions toward mHealth related to HTN management among poststroke survivors in Ghana. Exploration included perceptions of a nurse-led navigational model to facilitate care delivery and willingness of stroke survivors and caregivers to use mHealth technology.

RESULTS: Two hundred stroke survivors completed study surveys while focus groups (n = 4) were conducted with stroke survivors, caregivers and community leaders (n = 28). Key informant interviews were completed with clinicians and hospital personnel (n = 10). A total of 93% of survey respondents had HTN (60% uncontrolled). Findings support mHealth strategies for poststroke care delivery and HTN management and for task-shifting through a nurse-led model. Of survey and focus group participants, 76% and 78.6%, respectively, have access to mobile phones and 90% express comfort in using mobile phones and conveyed assurance that task-shifting through a nurse-led model could facilitate management of HTN. Findings also identified barriers to care delivery and medication adherence across all levels of the social ecological model.

CONCLUSIONS: Participants strongly supported enhanced care delivery through mobile health and were receptive toward a nurse-led navigational model.

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