The purpose of this study is to use a mixed-method approach to explore perceived barriers and facilitators of engagement in mHealth research among African Americans and, secondarily, obtain in-depth feedback on a smartphone app that designed to help African Americans quit smoking and spend less time being sedentary (i.e., HealthyCells). African Americans  interested in quitting smoking will complete a baseline survey and participate in three semi-structured interviews throughout a one-week demo of HealthyCells. The baseline survey will obtain information about the perceived barriers and facilitators of participation in mHealth research, and the in-depth interviews will delve deeper into participants’ survey responses. In-depth interviews will also deeply explore perceptions of the HealthyCells app, which will be used to improve the app for future intervention research. Finally, a portion of the sample will be invited back to review changes made to the HealthyCells app based on participant feedback.

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Alexander, A. C. (Principal Investigator)*, Kendzor, D. E. (Primary Mentor), Businelle, M. S. (Mentor), Cheney, M. (Mentor), McNeill, L. H. (Mentor), Cohn, A. M. (Mentor)

 

Sponsored by the National Institute on Minority Health and Health Disparities, Federal 

Grant/Contract Number: K01MD015295

2021-2026

The aims of the projects are to 1) develop an mHealth intervention app to specifically address the needs of ethnically diverse Black males within the first two years after diagnosis, and 2) evaluate the feasibility and acceptability of the mHealth intervention app.

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Ogunsanya, M. (Principal Investigator)*, Kendzor, D. E. (Primary Mentor)

 

Sponsored by the National Institute on Minority Health and Health Disparities, The University of Oklahoma (Institutional)

Grant/Contract Number: R25MD011564

2021-2023

Mobile technology has enormous potential for delivering highly innovative, dynamic smoking cessation interventions. Phone sensors, wearable technology, and real time data collection methods such as ecological momentary assessment (EMA) have made it possible to collect a wealth of environmental and physiological data such as location, heart rate, and mood. Environmental and situational cues such as craving and proximity to others smoking are highly predictive of lapse among those trying to quit, suggesting that lapse risk is characterized by immediate, dynamic influences. Emerging strategies such as just-in-time adaptive interventions (JITAI), aim to prevent smoking lapse using tailored support delivered via mobile technology in the moments when it is most needed. Although research has identified antecedents of smoking lapse based on observations from EMA data, studies have been unable to utilize the full spectrum of contextual and environmental data available with current technology. Given the importance of dynamic influences on lapse risk, there is a critical need for strategies that accurately identify moments of highest lapse risk to improve cessation interventions. Recent research has demonstrated the utility of machine learning to predict individual behavior. Machine learning is a robust data analytic strategy that can produce highly accurate predictive models from large datasets and can automatically adapt to new data in real time. The overall objective of this application is to use supervised machine learning methods to develop an automated algorithm to quantify smoking lapse risk at the individual level. This method will model individual smoking behavior dynamically, incorporating passively obtained, real time environmental data such as heart rate and activity level, in addition to self-reported mood, affect, and social context. Specifically, we aim to apply supervised machine learning methods to quantify personalized risk of smoking lapse.

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Hébert, E. T. (Principal Investigator)*, Businelle, M. S. (Primary Mentor), Kendzor, D. E. (Mentor)

 

Sponsored by the National Institute on Drug Abuse, Federal

Grant/Contract Number: K99DA046564/R00DA046564

2019-2023