Determinants of Longitudinal Adherence in Smartphone-Based Self-Tracking for Chronic Health Conditions: Evidence from Axial Spondyloarthritis
AuthorJones, S; Hue, W; Kelly, R; Barnett, R; Henderson, V; Sengupta, R
Source TitleProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
PublisherAssociation for Computing Machinery
University of Melbourne Author/sKelly, Ryan
AffiliationComputing and Information Systems
Document TypeJournal Article
CitationsJones, S., Hue, W., Kelly, R., Barnett, R., Henderson, V. & Sengupta, R. (2021). Determinants of Longitudinal Adherence in Smartphone-Based Self-Tracking for Chronic Health Conditions: Evidence from Axial Spondyloarthritis. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Access StatusOpen Access
The use of interactive mobile and wearable technologies for understanding and managing health conditions is a growing area of interest for patients, health professionals and researchers. Self-tracking technologies such as smartphone apps and wearable devices for measuring symptoms and behaviours generate a wealth of patient-centric data with the potential to support clinical decision making. However, the utility of self-tracking technologies for providing insight into patients’ conditions is impacted by poor adherence with data logging. This paper explores factors associated with adherence in smartphone-based tracking, drawing on two studies of patients living with axial spondyloarthritis (axSpA), a chronic rheumatological condition. In Study 1, 184 axSpA patients used the uMotif health tracking smartphone app for a period of up to 593 days. In Study 2, 108 axSpA patients completed a survey about their experience of using self-tracking technologies. We identify six significant correlates of self-tracking adherence, providing insight into the determinants of tracking behaviour. Specifically, our data provides evidence that adherence correlates with the age of the user, the types of tracking devices that are being used (smartphone OS and physical activity tracker), preferences for types of data to record, the timing of interactions with a self-tracking app, and the reported symptom severity of the user. We discuss how these factors may have implications for those designing, deploying or using mobile and wearable tracking technologies to support monitoring and management of chronic diseases.
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