Socially Assistive Robots in Mental Healthcare
Principles and Conceptual Framework for User-Centered Design
Bibliographic Data
| ID | 21199762 |
|---|---|
| Authors | Han Wool Jung (0000-0001-8294-5451, Yonsei University), Jin Young Park (0000-0002-5351-9549, Yonsei University, corresponding author), Todd Holoubek (Yonsei University), Woo Jung Kim (0000-0002-4963-4819, Yonsei University), Jaesub Park (0000-0003-2597-2204, Yonsei University) |
| Year | 2025 |
| Volume | 17 |
| Issue | 11 |
| Pages | 2827-2851 |
| Publication date | 2025-11-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Social Robotics (JOURNAL) |
| Journal identifiers | ISSN: 1875-4791 • E-ISSN: 1875-4805 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s12369-025-01323-5 |
| OpenAlex | W4414330458 |
| Language | EN |
| References cited | 186 |
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
Citizen journalism · Conceptual design · Conceptual framework · Human–robot interaction · Mechatronics · Mental health · Participatory design · Robot · User experience design · Context-Aware Activity Recognition Systems · Digital Mental Health Interventions · Social Robot Interaction and HRI
Participatory design and "democratizing innovation"
Socially Assistive Robotics
An Empirical Evaluation of the System Usability Scale
Trust in Automation
Understanding and Promoting Effective Engagement With Digital Behavior Change Interventions
The Person-Based Approach to Intervention Development
The growing field of digital psychiatry
The Uncanny Valley [From the Field]
Just-in-Time Adaptive Interventions (Jitais) in Mobile Health
Barriers to and Facilitators of User Engagement With Digital Mental Health Interventions
Participatory design
Your Robot Therapist Will See You Now
Gamification in theory and action
A Mass-Produced Sociable Humanoid Robot
Is it all a game? Understanding the principles of gamification
A systematic review of gamification in e-Health
A meta-analysis on the effectiveness of anthropomorphism in human-robot interaction
Transdiagnostic approaches to mental health problems
User Acceptance of Information Technology
Ecological Momentary Assessment
Toward Personalized Affect-Aware Socially Assistive Robot Tutors for Long-Term Interventions with Children with Autism
Satisfied or Frustrated? A Qualitative Analysis of Need Satisfying and Need Frustrating Experiences of Engaging With Digital Health Technology in Chronic Care
Impact of Gamification on Motivation and Academic Performance
Do Anthropomorphic Chatbots Increase Counseling Satisfaction and Reuse Intention? The Moderated Mediation of Social Rapport and Social Anxiety
Do you feel safe with your robot? Factors influencing perceived safety in human-robot interaction based on subjective and objective measures
Social robots and gamification for technology supported learning
Effects of Explainable Artificial Intelligence on trust and human behavior in a high-risk decision task
User Experience Methods in Research and Practice
A Systematic Literature Review of Decision-Making and Control Systems for Autonomous and Social Robots
Differential Outcomes Training of Visuospatial Memory
A Taxonomy of Factors Influencing Perceived Safety in Human–Robot Interaction
Building Long-Term Human–Robot Relationships
A Holistic Approach to Behavior Adaptation for Socially Assistive Robots
Integrating Social Assistive Robots, IoT, Virtual Communities and Smart Objects to Assist at-Home Independently Living Elders
Artificial intelligence and counseling
Social Robots for Supporting Post-traumatic Stress Disorder Diagnosis and Treatment
Waiting for a digital therapist
Considerations for Designing Context-Aware Mobile Apps for Mental Health Interventions
Too human and not human enough
The ethical issues of social assistive robotics
Reward, distraction, and the overjustification effect
Using Socially Assistive Robots in Speech-Language Therapy for Children with Language Impairments
Context-Enhanced Human-Robot Interaction
Assistive Robots for the Social Management of Health
An Ethical Evaluation of Human–Robot Relationships
A Taxonomy to Structure and Analyze Human–Robot Interaction
Mutual Shaping in the Design of Socially Assistive Robots
Evaluating the Engagement with Social Robots
Learning and Personalizing Socially Assistive Robot Behaviors to Aid with Activities of Daily Living
Humanization of robots
Reward-driven distraction
| Citation velocity | historical |
|---|---|
| Highly cited | No |