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

Xue Feng1, 3, Xuefei Lu2, Zhuoran Li3, Mi Zhang3, Jiawei Li3, Dan Zhang3, *
1 School of Education, Yangtze University, Jingzhou 434023, P. R. China
2 Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, P. R. China
3 Department of Psychology, School of Social Sciences, Tsinghua University, Beijing 100084, P. R. China

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© 2020 Feng et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Department of Psychology, School of Social Sciences, Tsinghua University, Beijing 100084, Beijing, China; Tel: 86-10-62773687; E-mail:



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.

Keywords: Well-being, PERMA, Daily activities, Heart rate, Galvanic skin response, Skin temperature, Walking step.