Health coaching can be an effective intervention to support self-management of chronic conditions like diabetes, but there are not enough coaching practitioners to reach the growing population in need of support. Conversational technology, like chatbots, presents an opportunity to extend health coaching support to broader and more diverse populations. However, some have suggested that the human element is essential to health coaching and cannot be replicated with technology. In this research, we examine automated health coaching using a theory-grounded, wizard-of-oz chatbot, in comparison with text-based virtual coaching from human practitioners who start with the same protocol as the chatbot but have the freedom to embellish and adjust as needed. We found that even a scripted chatbot can create a coach-like experience for participants. While human coaches displayed advantages expressing empathy and using probing questions to tailor their support, they also encountered tremendous barriers and frustrations adapting to text-based virtual coaching. The chatbot coach had advantages in being persistent, as well as more consistently giving choices and options to foster client autonomy. We discuss implications for the design of virtual health coaching interventions.

Objective: To develop and test a visual analytics tool to help clinicians identify systematic and clinically meaningful patterns in patient-generated data (PGD) while decreasing perceived information overload. Methods: Participatory design was used to develop Glucolyzer, an interactive tool featuring hierarchical clustering and a heatmap visualization to help registered dietitians (RDs) identify associative patterns between blood glucose levels and per-meal macronutrient composition for individuals with type 2 diabetes (T2DM). Ten RDs participated in a within-subjects experiment to compare Glucolyzer to a static logbook format. For each representation, participants had 25 minutes to examine 1 month of diabetes self-monitoring data captured by an individual with T2DM and identify clinically meaningful patterns. We compared the quality and accuracy of the observations generated using each representation. Results: Participants generated 50% more observations when using Glucolyzer (98) than when using the logbook format (64) without any loss in accuracy (69% accuracy vs 62%, respectively, p = .17). Participants identified more observations that included ingredients other than carbohydrates using Glucolyzer (36% vs 16%, p = .027). Fewer RDs reported feelings of information overload using Glucolyzer compared to the logbook format. Study participants displayed variable acceptance of hierarchical clustering. Conclusions: Visual analytics have the potential to mitigate provider concerns about the volume of self-monitoring data. Glucolyzer helped dietitians identify meaningful patterns in self-monitoring data without incurring perceived information overload. Future studies should assess whether similar tools can support clinicians in personalizing behavioral interventions that improve patient outcomes.

The ubiquity of internet-based nutrition information sharing indicates an opportunity to use social computing platforms to promote nutrition literacy and healthy nutritional choices. We conducted a series of experiments with unpaid volunteers using an online Nutrition Knowledge Test. The test asked participants to examine pairs of photographed meals and identify meals higher in a specific macronutrient (e.g., carbohydrate). After each answer, participants received no feedback on the accuracy of their answers, viewed proportions of peers choosing each response, received correctness feedback from an expert dietitian with or without expert-generated explanations, or received correctness feedback with crowd-generated explanations. The results showed that neither viewing peer responses nor correctness feedback alone improved learning. However, correctness feedback with explanations (i.e., modeling) led to significant learning gains, with no significant difference between explanations generated by experts or peers. This suggests the importance of explanations in social computing-based casual learning about nutrition and the potential for scaling this approach via crowdsourcing.

OBJECTIVE: To investigate subjective experiences and patterns of engagement with a novel electronic tool for facilitating reflection and problem solving for individuals with type 2 diabetes, Mobile Diabetes Detective (MoDD). METHODS: In this qualitative study, researchers conducted semi-structured interviews with individuals from economically disadvantaged communities and ethnic minorities who are participating in a randomized controlled trial of MoDD. The transcripts of the interviews were analyzed using inductive thematic analysis; usage logs were analyzed to determine how actively the study participants used MoDD. RESULTS: Fifteen participants in the MoDD randomized controlled trial were recruited for the qualitative interviews. Usage log analysis showed that, on average, during the 4 weeks of the study, the study participants logged into MoDD twice per week, reported 120 blood glucose readings, and set two behavioral goals. The qualitative interviews suggested that individuals used MoDD to follow the steps of the problem-solving process, from identifying problematic blood glucose patterns, to exploring behavioral triggers contributing to these patterns, to selecting alternative behaviors, to implementing these behaviors while monitoring for improvements in glycemic control. DISCUSSION: This qualitative study suggested that informatics interventions for reflection and problem solving can provide structured scaffolding for facilitating these processes by guiding users through the different steps of the problem-solving process and by providing them with context-sensitive evidence and practice-based knowledge related to diabetes self-management on each of those steps. CONCLUSION: This qualitative study suggested that MoDD was perceived as a useful tool in engaging individuals in self-monitoring, reflection, and problem solving.

OBJECTIVE: To investigate how individuals with diabetes and diabetes educators reason about data collected through self-monitoring and to draw implications for the design of data-driven self-management technologies. MATERIALS AND METHODS: Ten individuals with diabetes (six type 1 and four type 2) and 2 experienced diabetes educators were presented with a set of self-monitoring data captured by an individual with type 2 diabetes. The set included digital images of meals and their textual descriptions, and blood glucose (BG) readings captured before and after these meals. The participants were asked to review a set of meals and associated BG readings, explain differences in postprandial BG levels for these meals, and predict postprandial BG levels for the same individual for a different set of meals. Researchers compared conclusions and predictions reached by the participants with those arrived at by quantitative analysis of the collected data. RESULTS: The participants used both macronutrient composition of meals, most notably the inclusion of carbohydrates, and names of dishes and ingredients to reason about changes in postprandial BG levels. Both individuals with diabetes and diabetes educators reported difficulties in generating predictions of postprandial BG; their predictions varied in their correlations with the actual captured readings from r = 0.008 to r = 0.75. CONCLUSION: Overall, the study showed that identifying trends in the data collected with self-monitoring is a complex process, and that conclusions reached by both individuals with diabetes and diabetes educators are not always reliable. This suggests the need for new ways to facilitate individuals' reasoning with informatics interventions.

Collaborative tagging mechanisms are integral to social computing applications in a variety of domains. Their expected benefits include simplified retrieval of digital content, as well as enhanced ability of a community to makes sense of the shared content. We examine the impact of collaborative tagging in context of nutrition management. In a controlled experiment we asked individuals to assess the nutritional value of meals based on photographic images and observed the impact of different types of tags and tagging mechanisms on individuals nutritional sensemaking. The results of the study show that tags enhance individuals' ability to remember the viewed meals. However, we found that some types of tags can be detrimental to sensemaking, rather than supporting it. These findings stress the importance of tagging vocabularies and suggest a need for expert moderation of community sensemaking.

In the recent years, the number of individuals engaged in self-care of chronic diseases has grown exponentially. Advances in computing technologies help individuals with chronic diseases collect unprecedented volumes of health-related data. However, engaging in reflective analysis of the collected data may be challenging for the untrained individuals. We present MAHI, a health monitoring application that assists newly diagnosed individuals with diabetes in acquiring and developing reflective thinking skills through social interaction with diabetes educators. The deployment study with twenty five newly diagnosed individuals with diabetes demonstrated that MAHI significantly contributed to individuals' achievement of their diabetes management goals (changing diet). More importantly, MAHI inspired individuals to adopt Internal Locus of Control, which often leads to persistent engagement in self-care and positive health outcomes.