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

Abstract

Background For many people, tracking health indicators is central to managing a chronic illness. However, previous informatics research has largely viewed tracking as a solitary process that lacks the characteristics essential to tracking in support of chronic illness management. Objective To inform development of effective technologies that aid tracking of health indicators to support chronic illness management, this study aimed to construct a health informatics model that accurately describes the work and social context of that tracking work. Methods As part of a larger project, we conducted semistructured interviews with 40 adults concerning their chronic illness management practices, including tracking and communication. We also assembled transcripts of 30 publicly available videos of 24 adults discussing tracking processes for managing their own chronic illness. We used qualitative methods to analyze interviews and video transcripts through the lens of ongoing personal and health informatics research. Results We have described the people and work involved in tracking in support of chronic illness management and contributed a Conceptual Model of Shared Health Informatics (CoMSHI). Specifically, we identified the need for a health informatics model that (1) incorporates the ongoing nature of tracking work and (2) represents the social dimension of tracking for illness management. Our model depicts communication, information, collection, integration, reflection, and action work in the social context of the person with chronic illness, informal carers, health care providers, and community members. Conclusions The resulting CoMSHI yields a more detailed and nuanced viewpoint of tracking in support of chronic illness management and can inform technology design to improve tracking tools to support people in more confident and capable chronic illness management.

Self-Quantification Systems to Support Physical Activity: From Theory to Implementation Principles

“…From the same studies, some limits have been identified regarding the tools used: they talk about "unsuitable visualization and analytics tools" and "fragmented data scattered across multiple platforms". Vizer and colleagues similarly underline these inherent barriers to tools, and an article from Epstein even reports that some people find the commercial self-quantification tools useless [3,21].…”

“…In order to better characterize the quantified self experience users go through, some researchers have attempted to derive conceptual models. In 2019, Vizer et al have identified four main models related to the tracking process but we will ignore two of them as they focus on users who are already patients, hereby involving a specific clinical context which is out of scope for our study [3,22,23].…”

“…To start with the base of the aforementioned models, Li et al highlighted specific barriers for each stage of a self-quantification experience and how they can chain themselves together in a cascade of barriers [4]. This aspect has been further developed by Vizer and colleagues who identified precise relations between stages [3]. In a few words, this is about not using the right tool, not collecting the right data, sparse data sets, scattered and/or ineffective visualizations, and difficult organization.…”

“…On the scientific side, a major descriptive effort has been carried out since 2010 to define this quantified self movement, showing that the most popular objective is behavior change. Researchers agree that self-reflection and contextual factors are indispensable to achieve behavior change [3][4][5][6].…”

“…weather or schedule are parameters that influence the user's context. Consequently, a self-quantification system must be customizable and adaptable to the user's life to really help people change their behavior [3][4][5][6][7].…”