Sequential pattern mining suggests wellbeing supportive behaviors

Abstract

Amidst the headlines about the attention economy and the possible impacts of screen time, research investigating the complex relationship between digital technology usage and wellbeing has gained urgency. Researchers generally use a combination of surveys and automatic tracking tools to gather time and frequency of technology use. However, the focus of data analysis has been on measuring duration and frequency of usage rather than exploring behavioral patterns, possibly better indicators of mood states or stress levels. We propose a methodology for detecting behavioural patterns from digital footprints using a sequence pattern mining algorithm, and using these as features for predicting mood. Results show that our method can be used to analyze the relationship between digital usage and mood, and predict the latter with an accuracy of 80%, significantly above the baseline (71.1%). This method provides another angle to investigate digital technology usage in wellbeing-related research.