Analysis of Factors that Predict Positive Emotion and Well-Being Using Continuous Tracking

Abstract

The main objective of this work is to use quantified emotions and continuous evaluation to determine significant factors in predicting an individual’s overall well-being. In 2011, Martin Seligman introduced the concept of “PERMA” of factors that account for measuring an individual’s well-being. The PERMA theory separates overall well-being into five pillars of Positive Emotion, Engagement, Relationships, Meaning, and Accomplishment. Seligman’s work has led to the development of many questionnaires and scales incorporating this theory and has since become an industry standard for positive psychology. However, the origins of “PERMA” remain mostly unclear and little work has explored supporting these concepts with continuous tracking measures. The work in this present study was carried out by utilizing positive emotion and well-being tracking over a span of six-months, where positive emotion was evaluated in fifteen-minute intervals and well-being was evaluated daily. Additionally, a questionnaire was developed and prompted to the participant, and the tracked measures are evaluated as predictors for positive emotion and well-being. Many findings for well-being align closely with the theories in PERMA, with strong associations with accomplishment and relationships. Predictive factors that align with the participant’s well-being not included in PERMA include the influence of anticipation and specific physical and dietary components. Factors that influence positive emotion and daily exhaustion are analyzed and discussed as well. Distribution analysis of positive emotion emphasizes the impact of momentary, extremely happy moments for well-being. Future work should provide direction for studies to evaluate these findings and a new iteration of tracking measures.