Comfort and Attitudes Towards Robots Among Young, Middle-Aged, and Older Adults: A Cross-Sectional Study
Corresponding Author
Uba Backonja PhD, RN
Psi-at-Large & Beta Eta-at-Large, Assistant Professor
Nursing & Healthcare Leadership, University of Washington Tacoma, Tacoma, WA; Adjunct Assistant Professor, Department of Biomedical Informatics and Medical Education, University of Washington School of Medicine, Seattle, WA, USA
Correspondence
Dr. Uba Backonja, Nursing & Healthcare Leadership, University of Washington Tacoma, Box 358421, Tacoma, WA 98402-3100. E-mail: [email protected]
Search for more papers by this authorAmanda K. Hall PhD
Affiliate Assistant Professor
Department of Biomedical Informatics and Medical Education, University of Washington School of Medicine, Seattle, WA, USA
Search for more papers by this authorIan Painter PhD
Clinical Assistant Professor
Department of Health Services, University of Washington School of Public Health, Seattle, WA, USA
Search for more papers by this authorLaura Kneale MS
PhD Candidate
Department of Biomedical Informatics and Health Education, University of Washington School of Medicine, Seattle, WA, USA
Search for more papers by this authorAmanda Lazar PhD
Assistant Professor
College of Information Studies, University of Maryland, College Park, College Park, MD, USA
Search for more papers by this authorMaya Cakmak PhD
Assistant Professor
Computer Science and Engineering Department, University of Washington, Seattle, WA, USA
Search for more papers by this authorHilaire J. Thompson PhD, ARNP, AGACNP-BC, CNRN, FAAN
Psi-at-Large, Joanne Montgomery Endowed Professor
Department of Biobehavioral Nursing and Health Informatics, University of Washington School of Nursing, Seattle, WA, USA
Search for more papers by this authorGeorge Demiris PhD, FACMI
Alumni Endowed Professor of Nursing
Department of Biomedical Informatics and Medical Education, University of Washington School of Medicine, Seattle, WA; Professor and Vice Chair, Department of Biobehavioral Nursing and Health Informatics, University of Washington School of Nursing, Seattle, WA; and PIK (Penn Integrates Knowledge) University Professor, Department of Biobehavioral Health Sciences School of Nursing & Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Search for more papers by this authorCorresponding Author
Uba Backonja PhD, RN
Psi-at-Large & Beta Eta-at-Large, Assistant Professor
Nursing & Healthcare Leadership, University of Washington Tacoma, Tacoma, WA; Adjunct Assistant Professor, Department of Biomedical Informatics and Medical Education, University of Washington School of Medicine, Seattle, WA, USA
Correspondence
Dr. Uba Backonja, Nursing & Healthcare Leadership, University of Washington Tacoma, Box 358421, Tacoma, WA 98402-3100. E-mail: [email protected]
Search for more papers by this authorAmanda K. Hall PhD
Affiliate Assistant Professor
Department of Biomedical Informatics and Medical Education, University of Washington School of Medicine, Seattle, WA, USA
Search for more papers by this authorIan Painter PhD
Clinical Assistant Professor
Department of Health Services, University of Washington School of Public Health, Seattle, WA, USA
Search for more papers by this authorLaura Kneale MS
PhD Candidate
Department of Biomedical Informatics and Health Education, University of Washington School of Medicine, Seattle, WA, USA
Search for more papers by this authorAmanda Lazar PhD
Assistant Professor
College of Information Studies, University of Maryland, College Park, College Park, MD, USA
Search for more papers by this authorMaya Cakmak PhD
Assistant Professor
Computer Science and Engineering Department, University of Washington, Seattle, WA, USA
Search for more papers by this authorHilaire J. Thompson PhD, ARNP, AGACNP-BC, CNRN, FAAN
Psi-at-Large, Joanne Montgomery Endowed Professor
Department of Biobehavioral Nursing and Health Informatics, University of Washington School of Nursing, Seattle, WA, USA
Search for more papers by this authorGeorge Demiris PhD, FACMI
Alumni Endowed Professor of Nursing
Department of Biomedical Informatics and Medical Education, University of Washington School of Medicine, Seattle, WA; Professor and Vice Chair, Department of Biobehavioral Nursing and Health Informatics, University of Washington School of Nursing, Seattle, WA; and PIK (Penn Integrates Knowledge) University Professor, Department of Biobehavioral Health Sciences School of Nursing & Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Search for more papers by this authorAbstract
Purpose
To explore the social impact of, comfort with, and negative attitudes towards robots among young, middle-aged, and older adults in the United States.
Design
Descriptive, cross-sectional. Conducted in 2014–2015 in an urban area of the western United States using a purposive sample of adults 18 years of age or older.
Methods
Respondents completed a survey that included the Negative Attitudes Toward Robots Scale (NARS) and two questions taken or modified from the European Commission's Autonomous System 2015 Report. Analyses were conducted to compare perceptions and demographic factors by age groups (young adults:18–44, middle-aged adults: 45–64, and older adults: >65 years old).
Findings
Sample included 499 individuals (n = 322 age 18–44 years, n = 50 age 45–64 years, and n = 102 age 65–98 years). There were no significant differences between age groups for 9 of the 11 items regarding social impact of robots and comfort with robots. There were no significant differences by age groups for 9 of the 14 items in the NARS. Among those items with statistically significant differences, the mean scores indicate similar sentiments for each group.
Conclusions
Older, middle-aged, and younger adults had similar attitudes regarding the social impact of and comfort with robots; they also had similar negative attitudes towards robots. Findings dispel current perceptions that older adults are not as receptive to robots as other adults. This has implications for nurses who integrate supportive robots in their practice.
Clinical Relevance
Nurses working in clinical and community roles can use these findings when developing and implementing robotic solutions. Understanding attitudes towards robots can support how, where, and with whom robots can be used in nursing practice.
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