Facial expression of health professionals during online psychotherapy: video analyses using automated facial coding software

Abstract

Background: Online psychotherapy is extensively practiced given the shortage of doctors and in consideration of the COVID-19 pandemic. This study aimed to evaluate the applicability of video analyses using an automated facial coding software to assess the empathetic facial expressions of health professionals during online psychotherapy. Methods: University lecturers specializing in clinical psychology or medicine (n = 4) and university students (n = 4) participated as health professionals in the demonstrations of online psychotherapy. A simulated patient appeared to be suffering from insomnia and fatigue caused by care burden in the first medical examination. Psychotherapy videos were recorded by an online conference system and analyzed using an automated facial coding software called “Face Reader.” Results: The “Face Reader” could detect all facial action units of the health professionals except for their line of sight. The facial expression scores for neutral, anger, and disgust were significantly higher among university lecturers than university students. Conclusions: These results show that video analyses using an automated facial coding software can detect the facial expressions of health professionals. The concentrated expression of what appears to be anger or disgust is probably the typical facial expression among health professionals during online psychotherapy.