The Application of User Event Log Data for Mental Health and Wellbeing Analysis

Abstract

Many digital interaction technologies, including web-based interventions,smartphone applications, and telephone helplines, can provide a basis forcapturing real time data of interactions between the user and the system. Suchdata is recorded in the form of log files, which records user events that rangefrom simple keystrokes on a computer, user activated sensor data orduration/frequency of phone calls. These interactions can provide rich datasetsamenable to user data analytics using machine learning and other analyticstechniques. This data analysis can highlight usage patterns and user behavioursbased on their interaction with the technology. User log data analysis can bedescriptive statistics (what users have done), predictive analytics (what eventswill happen) and prescriptive (what action to take given a predicted event oroutcome). This can also be thought of as spanning across different levels of useranalytics from hindsight, insight and foresight. Predictive analytics are used withlog data to provide predictions on future user behaviour based on early usagebehaviours. Event logs are objective regarding usage, but usage may notcorrelate with the level of the system’s user experience. Hence, ecologicalmomentary assessment (EMA) of the user experience can be used augment userlog data. Nevertheless, with the emergence of health applications and other appbasedhealth services, we consider how user event logs can be specifically usedwithin the mental health domain. This can provide beneficial insights into howusers interact with mental health e-services, which can provide an indication oftheir current and future mental state