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Despite their nonclinical origins, wearables are emerging as valuable tools in supporting the diagnosis of cardiovascular disease, one of the leading causes of death worldwide. Diagnostic data once only available via a cardiologist is now available to consumers simply by wearing a smartwatch, so understanding how smartwatches currently support diagnosis is important for healthcare providers and for the designers of increasingly sophisticated personal informatics technology. We conducted a qualitative study comprising interviews and analysis of posts on an online community of accounts of smartwatch assisted cardiac diagnosis. Our findings reveal how smartwatches bridge a current gap in clinical diagnostic modalities, facilitating a diagnostic journey instigated and shaped by the interplay of self-collected data, bodily self-awareness, and increasing clinical acceptance. These insights focus attention on the consequences of the democratisation of health data, with ethical and design implications for health providers, consumer electronic companies, and third-party application designers.
Self-management of type 1 diabetes (T1D) involves multiple factors, frequent anticipation of changes in blood glucose, and complex decision-making. ML-based blood glucose predictions (BGP) may be valuable in supporting T1D management. However, it may be difficult for people with T1D to integrate BGP into their decision-making due to prediction uncertainty and interpretation. In this study, we investigate the lived experience of people with T1D focusing on their needs and expectations in using apps that provide BGP. We designed MOON-T1D, an app that shows simulated BGP and conducted a five-day study using the Experience Sampling Method coupled with semi-structured interviews with 15 individuals with T1D who used MOON-T1D. A reflexive thematic analysis of our data revealed implications for the design and use of BGP, including the complex role of emotions and trust surrounding predictions, and ways in which BGP may ease or complicate T1D management.
eHealth has strong potential to advance HIV care in low- and middle-income countries. Given the sensitivity of HIV-related information and the risks associated with unintended HIV status disclosure, clients’ privacy perceptions towards eHealth applications should be examined to develop client-centered technologies. Through focus group discussions with antiretroviral therapy (ART) clients from Lighthouse Trust, Malawi’s public HIV care program, we explored perceptions of data security and privacy, including their understanding of data flow and their concerns about data confidentiality across several layers of data use. Our findings highlight the broad privacy concerns that affect ART clients’ day-to-day choices, clients’ trust in Malawi's health system, and their acceptance of, and familiarity with, point-of-care technologies used in HIV care. Based on our findings, we provide recommendations for building robust digital health systems in low- and middle-income countries with limited resources, nascent privacy regulations, and political will to take action to protect client data.
Learning personalized self-management routines is pivotal for people with type 1 diabetes (T1D), particularly early in diagnosis. Context-aware technologies, such as hybrid closed-loop (HCL) insulin pumps, are important tools for diabetes self-management. However, clinicians have observed that practices using these technologies involve significant individual differences. We conducted interviews with 20 adolescents and young adults who use HCL insulin pump systems for managing T1D, and we found that these individuals leverage both technological and non-technological means to maintain situational awareness about their condition. We discuss how these practices serve to infrastructure their self-management routines, including medical treatment, diet, and glucose measurement-monitoring routines. Our study provides insights into adolescents’ and young adults’ lived experiences of using HCL systems and related technology to manage diabetes, and contributes to a more nuanced understanding of how the HCI community can support the contextualized management of diabetes through technology design.