Human–Robot Collaboration in Elderly Care: Assessing the Impact of Social Robots on Mental Well-being and Adherence

Abstract

As populations age globally, socially assistive and companion robots are emerging as promising tools to support older adults’ mental well-being and adherence to health routines (medication, exercise, appointments). This article synthesizes theoretical frameworks, empirical results, and technological approaches to human–robot collaboration (HRC) in eldercare, with a special focus on mental health outcomes (depression, loneliness, cognitive stimulation) and behavioral adherence (medication, physical activity, therapy attendance). This article provides an extended literature review, propose standardized study designs and evaluation metrics, analyze ethical and deployment challenges, and outline a research and engineering roadmap for clinical translation and large-scale adoption. Evidence shows moderate but promising effects of social robots on loneliness, mood, and adherence when interventions are person-centered, multimodal, and integrated with human care teams; however, methodological heterogeneity, short intervention durations, and limited large-scale trials constrain definitive conclusions. Recommendations include standardized outcome sets, mixed-methods longitudinal trials, privacy-preserving cloud architectures for data sharing, and design principles rooted in ethics of care.