Meaningful Smart Health Data: A Design Guide for Transparent Data to Enhance Self-Reflection

Yujie Zhu, Kun Pyo Lee, Zhang Lie, Stephen Jia Wang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Since the characteristic of the Internet of Medical Things (IoMT) is featured with connectivity, distribution, and context-awareness (Rowland et al., 2015; Chin et al., 2019), it has become a common understanding of constant monitoring from surrounding “Ubiquitous” Environment. Concerning such sensing and monitoring situation for humans, devices, and the context, this study tackles a raising concern of tracking personal health data without an awareness (Sun et al., 2019). The challenge has two folds. On one hand, the amount of health data being collected and transferred continually through the network between different devices in any place and anywhere. Ideally, high convenience should be achieved for supporting reviewing and reflective thinking of end-users’ daily activities and vital health signals through data. However, quantified self-reflection hasn’t been achieved to facilitate human behavioural change to reach various healthy living goals. On the other hand, due to the vital role of visualized data that could effectively sufficient human requirements, the challenges of health data transparency (including data privacy, data security, and data visualization for various stakeholders etc.) are not only demonstrated by scholars but also lead to the negative user experience of connected devices. This study proposes a human-centred approach that aims at ensuring make sense of data transparency to enhance the IoMT experience. For instance, “trust and privacy” are critical issues for IoMT (Haghi Kashani et al., 2021), “access and storage” of health data are obscured and untraceable, “access control of data” (Calvillo-Arbizu et al., 2021), and the opacity of data process and abstraction (Hepworth, 2019) are reasons for losing trust, which users cannot know and modify the decision-making result of their data. The gulf between user expectations and understanding of this emerging technology leads to negative experiences. Despite the market has witnessed a breakthrough in the adoption of IoMT, evidence has shown a large percentage of abandonment (Clawson et al., 2015). User experience (UX) is the significant factor that influences long-term use (Hermsen et al., 2017). Zou et al. (2020) demonstrate the gap between the user’s perception and data interaction. Furthermore, how users make sense of their data is identified as one of the reasons for abandonment and treated as the key design challenge for IoMT stakeholders (Lazar et al., 2015; Ravichandran et al., 2017; Attig & Franke, 2020). As such, a review of existing data transparency and visualization related works for health data is conducted and summarized in this paper, which incorporates the following three aspects:1)The user perception of quantified health data.2)The interconnection of self-reflection and meaningful data representation through data visualization and physicalization.3)The design approach for tracking, accessing, and controlling data.Besides such a review, a ‘data transparency’ based guiding framework is also proposed to contribute and explore the relationship between UX design and health data, which has limited evidence been provided (Hepworth, 2019). Finally, the proposed framework intends to be validated from the user perception identification of existing products and design practice for utilizing the framework to address the data challenges of the IoMT system.
Original languageEnglish
Title of host publicationHuman Interaction & Emerging Technologies (IHIET 2022): Artificial Intelligence & Future Applications
PublisherAHFE International
Pages316
Number of pages322
Volume68
DOIs
Publication statusPublished - Dec 2022

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