Designing in the Dark

Abstract

The design of personal health informatics tools has traditionally been explored in self-monitoring and behavior change. There is an unmet opportunity to leverage selftracking of individuals and study diseases and health conditions to learn patterns across groups. An open research question, however, is how to design engaging self-tracking tools that also facilitate learning at scale. Furthermore, for conditions that are not well understood, a critical question is how to design such tools when it is unclear which data types are relevant to the disease. We outline the process of identifying design requirements for self-tracking endometriosis, a highly enigmatic and prevalent disease, through interviews (N=3), focus groups (N=27), surveys (N=741), and content analysis of an online endometriosis community (1500 posts, N=153 posters) and show value in triangulating across these methods. Finally, we discuss tensions inherent in designing self-tracking tools for individual use and population analysis, making suggestions for overcoming these tensions.

Patient Perspectives on Self-Management Technologies for Chronic Fatigue Syndrome

“…Second, our design recommendations are based on patients' needs and existing practices. However, it is important to recognise that our work does not incorporate the perspectives of clinicians, and these may be complementary in terms of understanding opportunities for self-management [48]. Finally, there may be opportunities to explore how technologies could support the collaboration that occurs between ME/CFS patients and their caregivers [55].…”

“…The studies we report in this paper shed light on the current technologically-mediated self-management practices of people living with ME/CFS, providing knowledge of how technologies should be designed to better support those practices. We build on the approach of McKillop et al [48] by triangulating multiple sources of data to understand patients' current approaches to managing an enigmatic disease. The data sources we utilize comprise surveys to identify priorities for self-management of ME/CFS (Study 1); online discussions of existing ME/CFS self-management practices and technology use (Study 2); and user evaluations of three smartphone apps that address some of these priorities (Study 3).…”

“…In this paper we focus on the design of self-management technologies for people living with Chronic Fatigue Syndrome, or Myalgic Encephalomyelitis (ME/CFS) 1 . ME/CFS is an example of an enigmatic disease [48] in that it has a range of symptoms, little clinical consensus on effective treatment [75], and a lack of symptom specificity. While ME/CFS is thought to affect 0.2-0.4% of the world's population [26,32,36], research indicates that up to 90% of cases go undiagnosed [32], suggesting that people with ME/CFS are substantially undercounted and undertreated [19].…”

Learning endometriosis phenotypes from patient-generated data

“…1), available for both iOS 28 and Android 29 based phones. The app was specifically designed to capture the patient experience of the disease, as well as to engage participants in self-tracking the condition over time 30,31 . App users were recruited through patient advocacy groups, and active recruitment efforts were sustained throughout the study period, leveraging a wide range of strategies including social media (Twitter, Facebook, Instagram, and Medium), emails, radio, news articles, celebrity endorsement through social media posts, blog posts, and scientific articles.…”

"This Girl is on Fire"

“…These conditions often lack a clear etiology, making them difficult to diagnose and treat effectively. Within HCI, CSCW, and Health Informatics, a body of work has begun to examine complex health concerns, such as fibromyalgia [9], endometriosis [29], irritable bowel syndrome [21], multiple sclerosis [32], rare diseases [24], and conditions that are often subject to misdiagnosis and treatment, such as prolonged symptoms of Lyme disease [28]. As part of this work, researchers have investigated online information seeking and behavior patterns [9], the extent to which support needs are met through OHC interactions [24], how competing viewpoints online affects individuals' understanding of their health condition [28], and how carers make decisions about which online health resources to use and trust [32].…”