Repository logo
  • English
  • Deutsch
Log In
or
  1. Home
  2. HSG CRIS
  3. HSG Publications
  4. Reliability of Commercial Voice Assistants’ Responses to Health-Related Questions in Noncommunicable Disease Management: Factorial Experiment Assessing Response Rate and Source of Information
 
  • Details

Reliability of Commercial Voice Assistants’ Responses to Health-Related Questions in Noncommunicable Disease Management: Factorial Experiment Assessing Response Rate and Source of Information

Journal
Journal of Medical Internet Research
ISSN
1438-8871
Type
journal article
Date Issued
2021-12-20
Author(s)
Bérubé, Caterina
Kovacs, Zsolt Ferenc
Fleisch, Elgar  
Kowatsch, Tobias  
DOI
https://doi.org/10.2196/32161
Abstract
Background: Noncommunicable diseases (NCDs) constitute a burden on public health. These are best controlled through self-management practices, such as self-information. Fostering patients’ access to health-related information through efficient and accessible channels, such as commercial voice assistants (VAs), may support the patients’ ability to make health-related decisions and manage their chronic conditions.
Objective: This study aims to evaluate the reliability of the most common VAs (ie, Amazon Alexa, Apple Siri, and Google Assistant) in responding to questions about management of the main NCD.
Methods: We generated health-related questions based on frequently asked questions from health organization, government, medical nonprofit, and other recognized health-related websites about conditions associated with Alzheimer’s disease (AD), lung cancer (LCA), chronic obstructive pulmonary disease, diabetes mellitus (DM), cardiovascular disease, chronic kidney disease (CKD), and cerebrovascular accident (CVA). We then validated them with practicing medical specialists, selecting the 10 most frequent ones. Given the low average frequency of the AD-related questions, we excluded such questions. This resulted in a pool of 60 questions. We submitted the selected questions to VAs in a 3×3×6 fractional factorial design experiment with 3 developers (ie, Amazon, Apple, and Google), 3 modalities (ie, voice only, voice and display, display only), and 6 diseases. We assessed the rate of error-free voice responses and classified the web sources based on previous research (ie, expert, commercial, crowdsourced, or not stated).
Results: Google showed the highest total response rate, followed by Amazon and Apple. Moreover, although Amazon and Apple showed a comparable response rate in both voice-and-display and voice-only modalities, Google showed a slightly higher response rate in voice only. The same pattern was observed for the rate of expert sources. When considering the response and expert source rate across diseases, we observed that although Google remained comparable, with a slight advantage for LCA and CKD, both Amazon and Apple showed the highest response rate for LCA. However, both Google and Apple showed most often expert sources for CVA, while Amazon did so for DM.
Conclusions: Google showed the highest response rate and the highest rate of expert sources, leading to the conclusion that Google Assistant would be the most reliable tool in responding to questions about NCD management. However, the rate of expert sources differed across diseases. We urge health organizations to collaborate with Google, Amazon, and Apple to allow their VAs to consistently provide reliable answers to health-related questions on NCD management across the different diseases.
Language
English
Keywords
voice assistants
conversational agents
health literacy
noncommunicable diseases
mobile phone
smart speaker
smart display
evaluation
protocol
assistant
agent
literacy
audio
health information
management
factorial
information source
HSG Classification
contribution to scientific community
HSG Profile Area
SoM - Business Innovation
Refereed
Yes
Publisher
JMIR Publications
Volume
23
Number
12
Official URL
https://www.jmir.org/2021/12/e32161
URL
https://www.alexandria.unisg.ch/handle/20.500.14171/109616
Subject(s)

computer science

information managemen...

health sciences

social sciences

behavioral science

Division(s)

ITEM - Institute of T...

Eprints ID
265381
File(s)
Loading...
Thumbnail Image

open.access

Name

Berube et al 2021 Commercial Voice Assistants Market Analysis.pdf

Size

174.82 KB

Format

Adobe PDF

Checksum (MD5)

231d956d5f074facd6bdb48e5b425d3e

here you can find instructions and news.

Built with DSpace-CRIS software - Extension maintained and optimized by 4Science

  • Privacy policy
  • End User Agreement
  • Send Feedback