Investigating the Physiological Correlates of Daily Well-being: A PERMA Model-Based Study

Abstract

For decades, psychologists have studied the well-being and its importance in human prosperity. In the present study, a mobile sensing approach was employed to explore the physiological correlates of daily well-being experiences. 19 participants were recruited for a 30-day continuous physiological measurement using a smartwatch that collected their heart rates, galvanic skin responses, skin temperatures, and walking steps. They also reported their daily well-being experiences every day, on the five well-being dimensions of the well-established PERMA (Positive emotion, Engagement, Relationship, Meaning, Accomplishment) model. The daily activity data were categorized into four mental states: asleep, relaxed, high mental load, and high physical load. 344 valid samples of the participants' daily physiological data were obtained from the 19 participants. Using the daily physiological signals of these four states as features, both stepwise regression analyses and binary classification analyses revealed that the five well-being experiences were significantly predicted, with regression r-square values ranging from 0.052 to 0.157 and classification accuracies ranging from 55.8% to 61.3%. The findings provide evidence for the physiological basis of PERMA-based well-being.