Use of advanced digital technologies for real-time monitoring of elderly mental health

a study on AI and mobile applications

Abstract

Introduction: this research addresses the use of Artificial Intelligence (AI) and Big Data in the prevention of falls among the elderly, highlighting the effectiveness of these technologies to identify risks and personalize interventions. Given the growing aging population, the implementation of preventive technologies has become essential to promote the autonomy and safety of the elderly. The study investigated how AI and Big Data can contribute to the prevention of falls, answering questions about their applicability in geriatric contexts and identifying challenges and potentialities. Objective: to analyze the effectiveness and impacts of these technological tools on preventive health. Methodology: the Giftedean neoperspectivist paradigm was used, supported by the theories of Planned Action, Activity, Complex Adaptive Systems and Social Determinants of Health. Methodologically, the hypothetical-deductive method was used and a Bibliographic and Documentary Narrative Review was conducted, consulting databases such as Scopus, PubMed and Web of Science, with a total of 50 works analyzed. Results: The main findings point to the ability of AI and Big Data to monitor and adapt interventions in real time, providing more effective and personalized prevention. However, limitations regarding privacy, costs, and training of professionals were observed, in addition to gaps in longitudinal studies and in public health contexts. Conclusions: This research contributes by promoting the integration of these technologies in geriatric care and highlights their value for Science, society, and postgraduate studies, aiming at advances in the quality of life of elderly people.