Abstract: An Exploratory Analysis of Social and Care Robotic Agent Adoption with the Oldest Old (Society for Social Work and Research 24th Annual Conference - Reducing Racial and Economic Inequality)

69P An Exploratory Analysis of Social and Care Robotic Agent Adoption with the Oldest Old

Thursday, January 16, 2020
Marquis BR Salon 6 (ML 2) (Marriott Marquis Washington DC)
* noted as presenting author
Taylor Patskanick, LCSW, MPH, Technical Associate, Massachusetts Institute of Technology, MA
Julie Miller, PhD, MSW, Research Associate, Massachusetts Institute of Technology, MA
Chaiwoo Lee, PhD, Research Scientist, Massachusetts Institute of Technology, MA
Maryam FakhrHosseini, PhD, Postdoctoral Research Associate, Massachusetts Institute of Technology, MA
Lisa D'Ambrosio, PhD, Research Scientist, Massachusetts Institute of Technology, MA
Joseph Coughlin, PhD, Director, Massachusetts Institute of Technology, MA
Background: This study examined attitudes toward and perceptions of emerging robotic technologies among a sample of adults aged 85 and older. Demographic shifts in the United States (U.S.) towards a “grayer” society due to declines in life expectancy among younger people and proliferation of the oldest old (those aged 85+) persist. The integration of robotic agents into the care of older adults, especially social and caregiving robots, represent massive potential for an aging population, both in improving older adults’ socioemotional wellbeing and relieving some of the burden of caregiving. There are several theoretical models used to understand differences in technology adoption; however, less has been done focusing on the barriers older adults face as adopters of social and care robots.

Methods: Data were collected from a sample of adults all aged 85 and older through an exploratory mixed methods study design. This sample is part of an ongoing bimonthly panel that meets to discuss the challenges of longevity. In this study, two separate workshops were convened in a year – one workshop to discuss caregiving (N=22) and another about new technologies, including social robots (N=20). Participants completed a questionnaire on the topic before the workshops. At each in-person meeting, participants were divided into focus groups. As this is an ongoing panel, the majority of participants who attend the caregiving workshop also attended the technology workshop.

Participants in each of these sessions ranged in age from 85 to 99. All lived independently in the Boston-metro area. Participants were overwhelmingly more affluent, educated, and racially-homogeneous relative to the larger U.S. over-85 population.

Results: Analyses revealed panelists report at baseline they are generally comfortable with, trusting of, and open to new technologies as well as interested in learning about new technologies that can help with caregiving (66.7%). Panelist attitudes toward social robots were highly varied as several participants expressed they were unwilling to interact with or own one. Barriers to adoption of social robot agents included concerns about security and privacy as well as ambiguity around maintaining independence. However, when the role of robotic agents was shifted into a care context (e.g., a robot could help with activity of daily living tasks, I could talk with it about health matters, or it could alert someone in the event of an emergency), participants responded more favorably towards adoption. In focus groups, participants highlighted robo-caregivers may be utilitarian, but are not substitutes for human interaction.

Implications: The field of social work and welfare must be prepared to understand the implications of social and care robotics for clinical practice, research, and policy. Older adults and caregivers may benefit from these technologies. This study highlights the barriers to adoption older users may experience. Social workers are well-positioned to address user concerns and close disparities in access to social and care robots. These findings suggest a need for further research on the barriers to adoption of social and care robots with larger, more diverse samples.