Application of Deep Learning Techniques for Detecting Digital Burnout among IT Professionals: A Systematic Review

Abstract

Digital burnout is a Mental, Emotional and Physical stress caused by prolonged use of digital devices due to increasing work demands. Mostly, IT professionals face this vulnerability because they are totally dependent on the digital devices to complete their work. Digital burnout may lead to emotional exhaustion, reduced productivity, decreased job satisfaction, cognitive fatigue, and mental health concerns. To maintain as healthy environment in terms of well-being of employees and growth of organization, the early burnout detection becomes the necessity. The recent techniques like deep learning provide automatic analysis of complex data patterns from massive digital data. Deep learning models such as Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), can learn hidden patterns from app usage, screen activity, notification frequency, and behavioural logs without immense manual feature engineering. This systematic review mainly focuses on analysis of existing research on digital burnout detection, techno stress assessment, and behavioural analytics. To identify the findings, challenges, research gaps and future prospects to develop an intelligent burnout detection system, major databases were reviewed.