A Conceptual Artificial Intelligence Driven Framework for Early Health Risk Detection and Holistic Well Being Enhancement in the Information Technology Workforce

Abstract

The rapid growth of the Information Technology (IT) industry has significantly increased occupational health challenges among employees, including musculoskeletal disorders, digital eye strain, technostress, burnout, anxiety, sedentary lifestyle-related metabolic risks, and poor worklife balance. Traditional workplace wellness programs often address these issues in isolation and rely on reactive interventions after symptoms become clinically evident. This study proposes a conceptual Artificial Intelligence (AI)-driven framework for early health risk detection and holistic well-being enhancement among IT professionals. The framework integrates computer vision-based ergonomic assessment, wearable sensor technologies, predictive analytics, and mobile health applications to continuously monitor physical, psychological, and metabolic health indicators. AI algorithms are utilized to identify early signs of stress, fatigue, cardiovascular risks, and workplace-related disorders, enabling timely and personalized interventions. The proposed model is supported by the Job DemandsResources model, Biopsychosocial model, Health Belief Model, and Technology Acceptance Model to ensure both effectiveness and user acceptance. By shifting occupational healthcare from a reactive to a preventive and personalized approach, the framework has the potential to improve employee well-being, enhance productivity, reduce healthcare costs, and promote sustainable workforce management in the evolving digital workplace.