Meghan Reading Turchioe1, Elizabeth M Heitkemper2, Maichou Lor3, Marissa Burgermaster4, Lena Mamykina2. 1. Division of Health Informatics, Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, NY, United States. Electronic address: mjr2011@med.cornell.edu. 2. Department of Biomedical Informatics, Columbia University, New York, NY, United States. 3. School of Nursing, Columbia University, New York, NY, United States. 4. Department of Nutritional Sciences, College of Natural Sciences & Department of Population Health, Dell Medical School, The University of Texas at Austin, Austin, TX, United States.
Abstract
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.
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.
Authors: Lena Mamykina; Elizabeth M Heitkemper; Arlene M Smaldone; Rita Kukafka; Heather J Cole-Lewis; Patricia G Davidson; Elizabeth D Mynatt; Andrea Cassells; Jonathan N Tobin; George Hripcsak Journal: J Biomed Inform Date: 2017-09-30 Impact factor: 6.317
Authors: Lena Mamykina; Elizabeth M Heitkemper; Arlene M Smaldone; Rita Kukafka; Heather Cole-Lewis; Patricia G Davidson; Elizabeth D Mynatt; Jonathan N Tobin; Andrea Cassells; Carrie Goodman; George Hripcsak Journal: J Am Med Inform Assoc Date: 2016-01-14 Impact factor: 4.497
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