A user-centered perspective on engaging with digital health data

The number of patients with chronic conditions, the costs for modern treatments, and life expectancy have been rising. At the same time, physician shortages are anticipated. These developments put a burden on current health systems. Digital health technologies can make health care systems more efficient, more personalized, and contribute to reaching underserved populations. Essential for the success of digital health technologies is large-scale and rigorous digital health data that facilitates health promotion, prevention, early diagnosis, and management of diseases. Digital health data empowers individuals to make better-informed decisions about their health. However, current health technologies often fail to engage users to generate, share, and understand health data. In this thesis, from the perspective of Human-Computer Interaction, we explore users' needs when interacting with digital health data. We introduce the relevance of digital health data, describe our contributions from four papers, and discuss the implications of our findings for HCI and digital health. We present the Digital Health Data Engagement Model (DHD-EM) and practical implications in the form of gulfs and bridges. Our model comprises the four stages lapse, generate, share, and understand. In the lapse stage, we identify reasons for a lapse of traditional health care and a shift towards digital health. This potentially happens when the health needs of patients are not fulfilled by health providers and patients consult online health communities for informational and emotional support. Our qualitative analysis of such communities showed when and how sub-communities for specific diseases emerge. In the generate stage, we explore physical and mental needs during the generation of health data. In three studies, a survey, a qualitative field study, and a usability study, we investigated the generation of medical images from the user perspective. The results suggest that carefully considering user preferences, e.g., in relation to sensitive body parts, and adhering to design principles paves the way for easy-to-use and trustworthy applications. In the share stage, we investigate motivations to share health data. Common barriers to health data sharing are a lack of motivation and technical difficulties. From a citizen science perspective, we show that, in times of crises, collective motives are prevalent and present design implications for fostering participation. Lastly, in the understand stage, we describe how individuals make sense of health data. In online health communities, sense-making processes are mainly facilitated in long threads about specific diseases. In digital health apps, disease-related background information increases the trustworthiness in the diagnosis provided by the app. Based on our studies, we identify gulfs in users' experience when engaging with their health data. We map each of the gulfs to one stage of the DHD-EM. To overcome those gulfs, we provide bridges with concrete guidance to improve the design of technologies for emerging digital health areas, such as mobile health, wearables, and online health communities. Our findings increase the impact of digital health technologies by allowing for a more nuanced understanding of the specific stages of users' engagement with digital health data. We foster the agency of an empowered patient who wants to understand their health and participate in decision-making. Adhering to this user-centered perspective, we argue that the proposed model and practical implications improve users' motivation and ability to share digital health data