Co-interpreting PGHD

Siddharth Nair siddharthn@student.unimelb.edu.au

Abstract

BACKGROUND: Consumer self-tracking technologies have led to an exponentially-increasing volume of device data generated by their users, referred to as person-generated health data (PGHD). Proponents of self-tracking highlight the importance of incorporating this data in health and medical research, particularly chronic disease management. Despite any perceived usefulness, there remain several unresolved barriers during the process of self-tracking that prevents the optimal use of these devices and/or their data, leading to significant attrition rates. Some of the key challenges pertain to the act of data-interpretation, which remains a significant hurdle for self-trackers. Similarly, there are several tensions identified when attempting to share and use this data with clinicians. Studies have shown that making sense of this data remains a problem here as well, particularly when dealing with large volumes of multiple, temporal and complex datasets. Guidance around resolving all these challenges and tensions is still inadequate, particularly in participatory health informatics research literature.

METHODS: This research aims to study the current use of self-tracked health data for managing chronic disease. The primary objective is to explore the aspects of data-interpretation when self-tracking individuals with chronic disease make use of PGHD collaboratively with their clinicians, i.e. the process of co-interpretation. A mixed methods approach is used to investigate these questions, and four studies are proposed: (1) An initial exploratory survey that will ask self-tracking chronic disease patients both qualitative and quantitative questions regarding their use, sharing and interpretation of PGHD with clinicians, (2) A subsequent semi-structured interview to explore the prominent participant-reported survey themes/findings further, (3) A case study through semi-structured interviews with patient-clinician dyads using an industry platform for self-tracked data, (4) A subjective autoethnographic study in the N=1 mode of exploration advocated by self-quantifiers.

ANTICIPATED FINDINGS: Through these 4 studies, we hope to identify the information needs for the design of effective data-centric platforms, and develop guidelines for the optimal use of self-tracked data in participatory digital health practices that involve both patients and their clinicians, towards improved chronic disease management. The poster will present initial findings from some of these 4 studies, as snapshots of works-in-progress (2017-2018).
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