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

Abstract

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.

Expectations to data: Perspectives of service providers and users of future health and wellness services

“…[68]). The findings also support the results of Mamykina et al [52], who suggest that discovering patterns regarding individual's activities and changes, for instance, in blood glucose levels using self-monitoring data can enhance selfmanagement of chronic diseases.…”

“…Sometimes, people have no specific goal when they start self-tracking but instead, they try to identify patterns from self-tracking data to enable them to identify how body responses to various stimuli [15]. Mamykina et al [52] have noted that self-monitoring data may provide a starting point for personal discovery of cause and effect in serious illnesses such as diabetes, which can lead to positive behavioural changes. Karkar et al [53] have even developed a selfexperimentation framework for testing health-related hypotheses with person-generated health and wellness data.…”

Designing in the Dark

“…In particular, research has focused on understanding progress towards achieving a particular personal goal through self-tracking, supporting users by providing data or knowledge that helps individuals self-monitor their progress [9,28,33,53,82]. Earlier models of personal informatics also helped to describe how people use self-tracking technologies to gain self-knowledge in the context of self-monitoring [59,66]. More recent human-computer interaction (HCI) research has focused on moving toward examining self-tracking in the context of self-discovery and self-experimentation [32,51,57,84], and personal informatics models have been expanded to include technology that is not necessarily associated with self-improvement [34,63].…”

“It’s Not Just Technology, It’s People”: Constructing a Conceptual Model of Shared Health Informatics for Tracking in Chronic Illness Management (Preprint)

“…Whooley et al [27] expand on the integration stage of Li et al's personal informatics model with a discussion of why people track and how they integrate their data. More recently, Mamykina et al [28] studied the process of "self-discovery" in a structured diabetes education program, showing how self-tracking can assist in scaffolding learning and reflection on cause-and-effect understanding of diabetes management. The personal informatics model describes an ideal process for tracking, but that process can break down when it encounters the realities of everyday life [29].…”

“…Although much research has focused on tracking for general health and wellness (e.g., [30][31][32]), people who live with a chronic illness have further tracking requirements central to illness management that provide a rich opportunity for understanding through research. Furthermore, research examining the lived experiences of self-tracking find that this act of tracking is often coordinated or influenced by communication with health experts (e.g., [24,28,33]), with peers (e.g., [34]), with friends and acquaintances (e.g., [35]), among family members (e.g., [36]), and with workplace colleagues and by workplace programs (e.g., [37]). …”