An Empirical Study of Older Adult’s Voice Assistant Use for Health Information Seeking

Author:

Brewer Robin1,Pierce Casey1,Upadhyay Pooja2,Park Leeseul1

Affiliation:

1. University of Michigan, Ann Arbor, Michigan, USA

2. Human-Computer Interaction Lab (HCIL), University of Maryland College Park, College Park, MD, USA

Abstract

Although voice assistants are increasingly being adopted by older adults, we lack empirical research on how they interact with these devices for health information seeking. Also, prior work shows how voice assistant responses can provide misleading or inaccurate information and be harmful particularly in health contexts. Because of increased health needs while aging, this paper studies older adult’s (ages 65+) health-related voice assistant interactions. Motivated by a lack of empirical evidence for how older adults approach information seeking with emerging technologies, we first conducted a survey of n = 201 older adults to understand how they engage voice assistants compared to a range of offline and digital sources for health information seeking. Findings show how voice assistants were used for confirmatory health queries, with users showing signs of distrust. As much prior work focuses on perceptions of voice assistant use, we conducted scenario-based interviews with n = 35 older adults to study health-related voice assistant behavior. In interviews, participants engaged with different health topics (flu, migraine, high blood pressure) and scenario types (symptom-driven, behavior-driven) using a voice assistant. Findings show how conversational and human-like expectations with voice assistants lead to information breakdowns between the older adult and voice assistant. This paper contributes a nuanced query-level analysis of older adults’ voice-based health information seeking behaviors. Further, data provide evidence for how query reformulation happens with complex topics in voice-based information seeking. We use our findings to discuss how voice interfaces can better support older adults’ health information seeking behaviors and expectations.

Publisher

Association for Computing Machinery (ACM)

Subject

Artificial Intelligence,Human-Computer Interaction

Reference78 articles.

1. 2017. Nearly Half of Americans use Digital Voice Assistants, Mostly on their Smartphones. Technical Report. Pew Research Center. https://www.pewresearch.org/fact-tank/2017/12/12/nearly-half-of-americans-use-digital-voice-assistants-mostly-on-their-smartphones/.

2. 2019. NHS Health Information Available Through Amazon’s Alexa. Technical Report. Department of Health and Social Care. https://www.gov.uk/government/news/nhs-health-information-available-through-amazon-s-alexa.

3. Should I Trust It When I Cannot See It?

4. "Siri Talks at You"

5. Deep evidential regression;Amini Alexander;arXiv preprint arXiv:1910.02600,2019

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