These studies revealed that there are common clinical workflows for using self-tracked data, delineating a process of evaluating data usability while collaborating with the patient to ensure mutual understanding. This was followed by workshops with five clinicians in the co-design of a software-based tool for using self-tracked data within the management of chronic heart conditions. Interviews were conducted with 13 clinicians of a broad spectrum of clinical roles, including cardiology, general practice, and mental health. This research aimed to understand clinicians’ experiences with self-tracked data in their clinical workflows, such that future use of such data can be enabled through appropriate technology design and consideration of clinicians’ work practices. However, these self-tracked data are new to medicine, so it is unknown how clinicians might use such unfamiliar data. Realising this potential is vital as we enter an era of ageing population, chronic disease epidemics, and soaring healthcare costs. From heart rate and physical activity, to sleep and mood, these data have the potential to help clinicians diagnose disease, personalise treatments to individual patients, and avoid delivering unnecessary medical procedures. These devices have empowered a new kind of patient – the quantified patient – to collect data on diverse aspects of their own health. Fitbit and Apple Health are two popular consumer technologies amongst a growing plethora of health wearables and smartphone apps.

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