Socially Assistive Robots in Elderly Care: A Mixed-Method Systematic Literature Review

Abstract

The world’s population is aging, and developed countries are engaged in developing a new aged-care paradigm to reduce spiraling healthcare costs. Assistive technologies like Socially Assistive Robots (SAR) are being considered as enablers to support the process of care giving or keep elderly at home longer. This article reports a mixed-method systematic review of SAR in elderly care and recognizes its impact on elderly well-being, integrating evidence from qualitative and quantitative studies. It follows the principles explained in Cochrane Handbook for Systematic Reviews of Interventions and classifies interventions, measures, and outcomes of field trials of SAR in elderly care. Eighty-six studies in 37 study groups have been included. The findings imply positive effects of SAR on elderly well-being. Ten significant recommendations are made to help avoid the current limitations of existing research and to improve future research and its applicability. This review revealed that SAR can potentially enhance elderly well-being and decrease the workload on caregivers. There is a need for rigorous research methodology, person-centered care, caregiver expectation model, multimodal interaction, multimodal data collection, and modeling of culturally diverse groups to facilitate acceptability of SAR.

Additional information

Notes on contributors

Reza Kachouie

Reza Kachouie is currently a Ph.D. student at Monash University. With a multidisciplinary background, in applied mathematics, engineering, and management, he has work experience in academia and industry. He has coauthored publications in management and social robotics. His research interests are dynamic capabilities, new product development, and social innovation.

Sima Sedighadeli

Sima Sedighadeli is working on her second master’s in Business Information Management and Systems at La Trobe Business School. She has completed her bachelor’s in Industrial Management and master’s in industrial Engineering. She has worked as human resource officer and research assistant. Her research interests are high-performance work systems, new service development, and socially assistive robotics.

Rajiv Khosla

Rajiv Khosla has a multidisciplinary background in management, engineering, and computer science. He has received several research and teaching excellence awards. He has edited five books and has more than 170 refereed publications. His research has made significant impact with about 30 films and documentaries and 230 reports in 14 countries.

Mei-Tai Chu

Mei-Tai Chu is currently a senior lecturer and program director of MBIMS program at La Trobe University. She has worked as a consultant and engagement manager for more than 40 Communities of Practices driven Knowledge Management implementation before joining academia. She is regularly published in good-quality journals in diverse areas.