THE ROLE OF DIGITAL COMPETENCY IN AI-DRIVEN HEALTH MONITORING FOR THE ELDERLY: A QUANTITATIVE STUDY ON SYSTEM EFFECTIVENESS

This study investigates how real-time health data processing and personalized AI-based recommendations affect the effectiveness of remote health monitoring systems for the elderly. It also examines the role of digital literacy in moderating the link between real-time health data collection and systems effectiveness. using a quantitative approach, data were gathered through an online survey with 385 participants, including elders, caregivers, and health professionals. Responses were measured on a 5-point Likert scale, and the sample was selected using purposive stratified sampling to ensure diversity. Reliability and validity were tested using Cronbach’s alpha, exploratory factor analysis, and multiple linear regression. Findings show that both real-time data processing and personalized recommendations significantly enhance system effectiveness. notably, digital literacy strengthens the positive impact of data processing on systems performance, underlining the importance of user skills in maximizing AI’s benefits for eldercare. The study adds to existing research by applying Cognitive Fit Theory, Socioemotional Selectivity Theory, and Digital Divide Theory to AI-driven health systems. It offers practical insights for developers, healthcare providers, and policymakers, emphasizing the need for user-centered design and digital inclusion. Overall, it highlights how aligning technology with user capability can improve outcomes in elderly care support more accessible, intelligent health solutions.