Socially Assistive Robots in Mental Healthcare: Principles and Conceptual Framework for User-Centered Design

Abstract

Socially assistive robots (SARs) have a strong potential to advance digital mental healthcare, if they become able to promote meaningful interactions based on the user-centered approach. This review provides a comprehensive overview of user-centered design principles for SARs in mental healthcare, focusing on contemporary topics and practical guidelines. Successful SARs should enhance user autonomy, competence, and emotional experiences as their core objectives. Effective SAR design may integrate adaptive decision-making based on multimodal interactions, adequate motivation such as reward systems or gamification, contextual design, and participatory design for the personalized care that aligns with user needs. For long-term interactions, careful role setting such as mimicking human relationships and trust building are also recommended for in-depth user care. Evaluation of SARs incorporates holistic, multidimensional criteria including self-reports, behavioral data, physiological signals, and qualitative examinations to measure treatment effectiveness, user experiences, and safety, which ideally lead to iterative evaluation and refinement processes. Finally, recent breakthroughs in large language models (LLMs) have the potential to significantly boost SAR autonomy, level of personalization, and user engagement, but they also raise ethical risks such as user overdependence and compromised user autonomy, which may be addressed through person-centered principles, rigorous ethical/regulatory oversight, and evidence-based validation. Future roadmaps for SARs in mental healthcare emphasize integrated guidelines, responsible AI governance, and continued interdisciplinary collaboration to ensure safe and effective therapeutic care.