“Know You (Self) Better”: N-of-1 Based Self-Examination Using Wearables And Self-Reported Behaviors For An Improved Well-Being

Igor Matias

Abstract: N-of-1 randomized trials help better understand the effects of an intervention, e.g., behavioral changes in a specific person, allowing for the correlation and causation between, for example, duration of sleep and the level of physical exercise the following day.
By leveraging the data originating from affordable wearables like Fitbit and smartphones, it is now possible to view the individual’s daily behaviors and vital signs in a minimally intrusive way. Collecting and combining this data with an N-of-1 approach makes it possible to understand better the interventions that benefit or detriment the individual’s well-being on an hourly, daily, or monthly level.
We introduce an N-of-1 method leveraging wearable collected behavioral data from three participants for up to 4 years to assess the correlations between sleep duration, physical activity, walking performance, resting heart rate, self-reported stress levels, traveling, relationship status, and many more intervention variables.
The study also aims to exemplify how wearable and self-reported data can help understand the changes most relevant to a specific individual, providing an alternative to the understanding based on an “in the lab” experiment, average population assessments; so far failing to provide personal insights into one’s well-being.
Finally, in this study, we also reflect on the corroboration of personal beliefs driving individual behaviors. Furthermore, we discuss the N-of-1 approach in our upcoming population study, focusing on monitoring the decline of memory and cognitive performance in older adults.

On Data Science Clinics, Université de Genève, 12 April 2021

https://datascience.unige.ch/recherche/data-science-clinics/12-april-2021 (Recording available)