The pervasiveness of wearable devices and apps for sensing and automatically capturing aspects relating to people's health and wellbeing have caused a rise in self-logging of such data. However, little work has considered the challenges of introducing this data into clinical workflows.
In the Quantified Self project, we worked with clinical cardiologists and other clinical care providers to understand the opportunities and challenges posed by use of self-logged data in clinical practice. Our work found that whereas self-logged data holds immediate promise for assessing and managing patients' symptomatic burdens, substantial challenges are present for diagnostic use: Self-logged data are currently highly non-standardised, which in combination with the sheer volume of data that can be collected presents makes interpretation of the data highly time-demanding.
Self-logged data also often represents dimensions of patient well-being that are different from those typically measured or considered in clinical workflows, which presents another barrier to effective interpretation of this data.
