Personal discovery in diabetes self-management: Discovering cause and effect using self-monitoring data.

OBJECTIVE: To outline new design directions for informatics solutions that facilitate personal discovery with self-monitoring data. We investigate this question in the context of chronic disease self-management with the focus on type 2 diabetes. MATERIALS AND METHODS: We conducted an observational qualitative study of discovery with personal data among adults attending a diabetes self-management education (DSME) program that utilized a discovery-based curriculum. The study included observations of class sessions, and interviews and focus groups with the educator and attendees of the program (n = 14). RESULTS: The main discovery in diabetes self-management evolved around discovering patterns of association between characteristics of individuals' activities and changes in their blood glucose levels that the participants referred to as "cause and effect". This discovery empowered individuals to actively engage in self-management and provided a desired flexibility in selection of personalized self-management strategies. We show that discovery of cause and effect involves four essential phases: (1) feature selection, (2) hypothesis generation, (3) feature evaluation, and (4) goal specification. Further, we identify opportunities to support discovery at each stage with informatics and data visualization solutions by providing assistance with: (1) active manipulation of collected data (e.g., grouping, filtering and side-by-side inspection), (2) hypotheses formulation (e.g., using natural language statements or constructing visual queries), (3) inference evaluation (e.g., through aggregation and visual comparison, and statistical analysis of associations), and (4) translation of discoveries into actionable goals (e.g., tailored selection from computable knowledge sources of effective diabetes self-management behaviors). DISCUSSION: The study suggests that discovery of cause and effect in diabetes can be a powerful approach to helping individuals to improve their self-management strategies, and that self-monitoring data can serve as a driving engine for personal discovery that may lead to sustainable behavior changes. CONCLUSIONS: Enabling personal discovery is a promising new approach to enhancing chronic disease self-management with informatics interventions.

A new approach to integrating patient-generated data with expert knowledge for personalized goal setting: A pilot study.

INTRODUCTION: Self-monitoring technologies produce patient-generated data that could be leveraged to personalize nutritional goal setting to improve population health; however, most computational approaches are limited when applied to individual-level personalization with sparse and irregular self-monitoring data. We applied informatics methods from expert suggestion systems to a challenging clinical problem: generating personalized nutrition goals from patient-generated diet and blood glucose data. MATERIALS AND METHODS: We applied qualitative process coding and decision tree modeling to understand how registered dietitians translate patient-generated data into recommendations for dietary self-management of diabetes (i.e., knowledge model). We encoded this process in a set of functions that take diet and blood glucose data as an input and output diet recommendations (i.e., inference engine). Dietitians assessed face validity. Using four patient datasets, we compared our inference engine's output to clinical narratives and gold standards developed by expert clinicians. RESULTS: To dietitians, the knowledge model represented how recommendations from patient data are made. Inference engine recommendations were 63 % consistent with the gold standard (range = 42 %-75 %) and 74 % consistent with narrative clinical observations (range = 63 %-83 %). DISCUSSION: Qualitative modeling and automating how dietitians reason over patient data resulted in a knowledge model representing clinical knowledge. However, our knowledge model was less consistent with gold standard than narrative clinical recommendations, raising questions about how best to evaluate approaches that integrate patient-generated data with expert knowledge. CONCLUSION: New informatics approaches that integrate data-driven methods with expert decision making for personalized goal setting, such as the knowledge base and inference engine presented here, demonstrate the potential to extend the reach of patient-generated data by synthesizing it with clinical knowledge. However, important questions remain about the strengths and weaknesses of computer algorithms developed to discern signal from patient-generated data compared to human experts.

Chemopreventive role of fruits and vegetables in oropharyngeal cancer.

Oropharyngeal cancer is associated with lifestyle factors, including tobacco use, dietary habits, and alcohol consumption. Oropharyngeal cancers are one of the 10 most common types of cancers worldwide, and it is estimated that oropharyngeal cancers will have affected 30,990 men and women in the United States with a total of 7,430 deaths in 2008. The National Cancer Institute defines chemoprevention as "the use of drugs, vitamins, or other agents to try to reduce the risk of, or delay the development or reccurrence of, cancer." Chemopreventive agents such as antioxidants are derived from dietary sources, including fruits and vegetables. This review addresses the chemopreventive role of dietary intake of fruits and vegetables in the development of oropharyngeal cancers. It focuses on the variability of the incidence of oropharyngeal cancers and possible reasons behind this phenomenon as it relates to dietary factors, specifically fruits and vegetables.

A visual analytics approach for pattern-recognition in patient-generated data.

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.

Medical Nutrition Therapy and Weight Loss Questions for the Evidence Analysis Library Prevention of Type 2 Diabetes Project: Systematic Reviews.

BACKGROUND: Eleven recommendations, based on systematic reviews, were developed for the Evidence Analysis Library's prevention of type 2 diabetes project. Two recommendations, medical nutrition therapy (MNT) and weight loss, were rated strong. OBJECTIVE: Present the basis of systematic reviews for MNT and weight loss recommendations. METHODS: Literature searches using Medline were conducted to identify studies that met eligibility criteria. The MNT literature search covered a time span of 1995 to 2012, the weight loss literature search covered 2008 to 2012 due to inclusion of a Cochrane Review meta-analysis of randomized controlled trials (RCTs) published in 2008. Eligibility criteria for inclusion of articles included original research using higher-quality study designs (ie, RCTs, case control, cohort, crossover, and nonrandomized trials) with participants aged >18 years and meeting prediabetes or metabolic syndrome diagnostic criteria. MNT was defined as individualized and delivered by a registered dietitian nutritionist or international equivalent and length of weight loss interventions was ≥3 months. MAIN OUTCOME MEASURES: Two-hour postprandial blood glucose level, glycated hemoglobin level, albumin-to-creatinine ratio (metabolic syndrome samples only), fasting blood glucose level, high-density lipoprotein cholesterol level, systolic and diastolic blood pressure, triglyceride levels, urinary albumin excretion rate (metabolic syndrome samples only), waist circumference (WC), and waist-to-hip ratio were evaluated. RESULTS: For MNT, 11 publications were included, with all 11 using an RCT study design and 10 including participants with prediabetes. A majority of publications reported significant improvements in glycemic outcomes, WC, and blood pressure. For weight loss, 28 publications were identified, with one meta-analysis (only included RCTs) and 20 publications using an RCT study design, with the meta-analysis and 10 RCTs including participants with prediabetes. A majority of publications reported significant improvements in glycemic outcomes, triglyceride level, WC, and blood pressure. CONCLUSIONS: Systematic reviews provided strong evidence that MNT and weight loss alter clinical parameters in ways that should reduce the risk of developing type 2 diabetes.

Data-driven health management: reasoning about personally generated data in diabetes with information technologies.

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.

Structured scaffolding for reflection and problem solving in diabetes self-management: qualitative study of mobile diabetes detective.

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.