JMIR Publications

ABSTRACT

Background:

: Cardiovascular disease (CVD) disparities are a particularly devastating manifestation of health inequity. Despite advancements in prevention and treatment, CVD is still the leading cause of death in the US. Additionally, research indicates that African American (AA) and other ethnic-minority populations are affected by CVD at earlier ages than white Americans. Many factors contribute to this disparity, including prevalence of CVD risk factors, psychosocial and economic determinants of health (including education, wealth/income, literacy, numeracy neighborhood environment and others) and insufficient access to quality health and social services. Given that AAs are the fastest growing populations of smartphone owners and users, mobile health (mHealth) technologies offer unparalleled potential to prevent and/or improve self-management of chronic disease among this population. To address the unmet need for culturally tailored primordial prevention CVD-focused mHealth interventions, the MOYO app was co-created with involvement of young people from the priority community. The present study aims to evaluate the effectiveness of smartphone app designed to reduce CVD risk factors among urban-African American, ages 18 – 29 years old.

Objective:

MOYO will develop an mHealth approach to eliminating health disparities by 1)co-design of the MOYO app utilizing community-based participatory and agile design methods;2) integration of Electronic Health Record (EHR) data generated by traditional methods with data collected "in the wild" (i.e. fitness devices, mobile phone usage, local weather, pollution, and geographic location). 3)creation a cloud-based system to allow users to view and track their integrated data (i.e. traditional and in the wild data) over time and improve healthcare outcomes 4)provide educational and community outreach to empower participants to co-create applications for deployment within the system.

Methods:

The theoretical underpinning will combine the principles Community Based Participatory Research and the Agile Software Development framework. The primary outcome goals of the study will be 1) to determine the usability, acceptability and functionality of the MOYO app, and 2) to build cloud-based data collection infrastructure suitable for digital epidemiology in a disparity population. Changes in health-related parameters over a 24-week period as determined by both passive (e.g., physical activity levels, sleep duration, social networking) and active (e.g., use of mood measures, surveys, uploading of pictures of meals and blood pressure readings will be the secondary outcome. Participants will be recruited from a majority AA “large city” school district, two Historically Black Colleges or Universities, and one urban undergraduate college. Following baseline screening for inclusion (administered in person), participants will receive the beta version of the MOYO app. Participants will be monitored during a 24 weeks pilot period. Analyses of varying data including social network dynamics, standard metrics of activity, percentage of time away from a given radius of home, circadian rhythm metrics, and proxies for sleep will be performed. Together, with external variables, (such as weather, pollution and socioeconomic indicators such as food access), these metrics be will used to train machine learning frameworks to regress them on the self-reported quality of life indicators.

Results:

This 5-year study (2015 - 2020) is currently in the implementation phase. We believe that MOYO can build upon findings of classical epidemiology and longitudinal studies like the Jackson Heart Study by 1) adding greater granularity to our knowledge of the exposures and behaviors that affect health, disease and resilience and 2) creating a channel for outreach capable of launching interventions, clinical trials and enhancement of health literacy.

Conclusions:

The results of this pilot will provide valuable information about community co-creation of mHealth programs, efficacious design features, and essential infrastructure for digital epidemiology among young AA adults.