LAUSR

BACKGROUND AND SIGNIFICANCE Data-driven interventions for health can help to personalize self-management of Type 2 Diabetes (T2D), but lack of sustained engagement with self-monitoring among disadvantaged populations may widen existing health disparities. Prior work developing approaches to increase motivation and engagement with self-monitoring holds promise, but little is known about applicability of these approaches to underserved populations. OBJECTIVE To explore how low-income, Latino adults with T2D respond to different design concepts for data-driven solutions in health that require self-monitoring, and what features resonate with them the most. MATERIAL AND METHODS We developed a set of mockups that incorporated different design features for promoting engagement with self-monitoring in T2D. We conducted focus groups to examine individuals' perceptions and attitudes towards mockups. Multiple interdisciplinary researchers analyzed data using directed content analysis. RESULTS We conducted 14 focus groups with 25 English- and Spanish-speaking adults with T2D. All participants reacted positively to external incentives. Social connectedness and healthcare expert feedback were also well liked because they enhanced current social practices and presented opportunities for learning. However, attitudes were more mixed towards goal setting, and very few participants responded positively to self-discovery and personalized decision support features. Instead, participants wished for personalized recommendations for meals and other health behaviors based on their personal health data. CONCLUSION This study suggests connections between individuals' degree of internal motivation and motivation for self-monitoring in health and their attitude towards designs of self-monitoring apps. We relate our findings to the self-determination continuum in self-determination theory (SDT) and propose it as a blueprint for aligning incentives for self-monitoring to different levels of motivation.