MHEALTH BEHAVIOR CHANGE: WHO IS READY?

Background: Despite promising statistics of widespread mobile adoption and studies which detail preferences of potential mhealth users, little evidence exists about which users are likely to adopt and benefit from the technology being created.  Types of users most appropriate for mHealth as well as barriers and drivers for this technology are still not well understood. As mhealth expands, better understanding of potential users is essential to ensure the right content and technology is offered to the right user at the right time in order to move people forward on a behavior change continuum.    

Objective: As part of a project aimed at developing mobile support to improve the health and wellness of an employee workforce, focus group were conducted to understand potential users’ views about mHealth technology.  We explored users’ opinions and reactions to multiple mobile health technology devices, delivery approaches, and health care team interactions to gain a better understanding of the role technology may play in sustaining individuals’ interest in improving their health. 

Methods: Our team conducted 4 focus groups with employee/patient groups.  Prior to attending the group, participants completed a survey requesting demographic information, experience with mobile health technology, and self-rated wellness.  Focus group topics included participants’ experiences with mobile technology and with health behavior change, reactions to current mobile health technology, preferences for the type of health data to be collected, and views on privacy and data sharing.  A combination of deductive and emergent coding strategies were used to identify themes from the focus groups.

Results: The focus groups were comprised of 24 women and 6 men, with an average of 7 participants in each group.  Participants ranged from 25 to 64 years; 63% self-identified as white, 20% as African-American, and 13% as Latino. Most participants rated their health as fair, good, or very good, although 63% reported living with a chronic health problem.

Despite variation of participants’ reactions to mobile health as a technology to support behavior change, some patterns emerged. At the extremes were potential users who were enthusiastic about using mobile health technology for behavior change and those who were skeptical it could add value to what they were already doing. General reactions could be placed on a high/low preparedness continuum regarding their desire for health data and their attention to their current health habits.  In addition, contextual factors such as trust, functionality, integration and customization play a role in moving people along the continuum of wanting to track personalized health data and/or focusing on health behaviors. 

Conclusions: In order to engage participants to use technology to improve their health, we must be aware of the user’s baseline interest in making health changes and not only understand their base interest in utilizing technology as a tool to assist behavior change.  We must learn to effectively address barriers toward adoption and harness the drivers of adoption and engagement. Depending on where each individual is at the initial assessment, approaches should be tailored to move them along the preparedness continuum.