The growing elderly population in developed countries highlights the critical need for preventing frailty, which poses significant challenges to health systems due to increased risks of severe health issues. This paper introduces Senselife, a framework that provides explainable service recommendations specifically tailored for frailty prevention. It begins by outlining the medical context and challenges associated with aging, followed by an overview of existing recommender systems with similar objectives. We detail the integration of three key resources-ROR, RNA, and Data Laregion-within the Senselife framework to represent service supply. The paper explains how service supply is structured and the transformation of available data for use within our recommender engine. We introduce the concept of operational activities derived from the ROR and leverage the capabilities of LLMs to incorporate RNA data into Senselife. Additionally, we illustrate how these services are ultimately compiled into recommended service packages. Finally, the paper concludes by summarizing key findings and suggesting potential directions for future research.

Frailty presents a significant global health challenge for older adults, necessitating effective interventions to support healthy aging. Technology-based solutions, particularly recommender systems, hold promise in addressing frailty prevention. This paper presents Senselife platform for service recommendation dedicated for frailty prevention.We delve into the challenges associated with developing an engaging and user-friendly recommender system specifically tailored for frailty prevention. Key challenges we address include implementing effective data collection strategies and designing user-centered interfaces. Our proposed recommendation platform leverages self-evaluation to deliver personalized recommendations, with the goal of enhancing the functional capabilities of older adults. By aligning the available services within the elderly environment with the demands they face, our solution tackles the complexities of managing frailty in this population.Throughout this study, we elucidate the construction of our surveys, the main source of data for Senselife and the design considerations behind our user interfaces, highlighting our efforts in overcoming the unique challenges associated with systems dedicated to elderly usage.

Abstract Frailty is a clinical syndrome associated with ageing that characterizes an intermediate state between robust health and loss of autonomy. To preserve the abilities of older adults and prevent dependency, it is important to identify and evaluate their frailty. This approach is part of a dependency prevention strategy, based on a thorough understanding of their medical, social, and living environment. This understanding is usually acquired through significant data collection using standardized evaluation surveys. The obtained data is then analyzed to provide personalized recommendations for the beneficiaries’ lifestyles. Our article presents the concept of frailty and a personalized recommendation system aimed at helping citizens prevent frailty. This system uses an innovative self-assessment approach designed for older adults, without necessarily involving healthcare professionals.