Abstract The increased use of videoconferencing has led to the development of a new mental health concern known as Zoom Fatigue. This phenomenon is characterized by emotional exhaustion and impaired well-being resulting from prolonged gazing at computer screens and long hours of back-to-back video meetings. The purpose of this research is to examine the impact of Zoom Fatigue on cognitive performance across gender and develop an automated method to detect it. The research will accomplish three specific aims: (1) To examine participants’ behavior and cognitive performance during a two-hour live video meeting, analyzing the relationship between cognitive performance, fatigue, working memory capacity, and gender. Data collected from communication transcripts, chat messages, meeting behaviors, and nonverbal cues will be subsequently utilized to train machine learning algorithms to detect the presence of Zoom Fatigue. (2) To investigate the relationship between self-reported and physiological measures (heart rate, skin temperature, pulse rate and blood pressure) of Zoom Fatigue in males and females over time using a wearable device. The physiological data will also be used to improve the accuracy of predicting Zoom Fatigue in the next aim of creating a computational tool using statistical and machine learning methods. (3) To develop a preliminary statistical and machine learning-based computational tool to monitor and detect predictors of Zoom Fatigue. The proposed study is innovative in its focus on a new mental health concern, its interdisciplinary approach, and its comprehensive data sources. Combining these elements, this research will generate not only knowledge about the cognitive processes associated with Zoom Fatigue and its impact on anxiety but also develop an innovative tool to help identify and prevent the onset of the issue. Additionally, this proposal will provide research experiences to diverse students, including underrepresented minority and female students, allowing them to acquire skills such as data analysis used in modern scientific investigations. Ultimately, the results will inform the development of tools and strategies to mitigate the negative impact of Zoom Fatigue and enhance the overall experience of videoconferencing, thereby promoting mental well-being.

PROJECT NARRATIVE This exploratory, interdisciplinary research is relevant to mental health for two reasons. First, this may provide proof-of-concept evidence for the existence of an important mental health issue associated with prolonged exposure to video-based meetings and its association with working memory, physiological measures, cognitive performance, and anxiety of the participants. Secondly, this research will lead to the creation of a basic machine-learning-based prototype that will help in detecting Zoom Fatigue in live video-based groups.