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

• Published in: Journal of medical Internet research 2021-12, Vol.23 (12), p.e32161-e32161
• Main Authors: Bérubé, Caterina, Kovacs, Zsolt Ferenc, Fleisch, Elgar, Kowatsch, Tobias
• Format: Article
• Language: English
• Subjects: Access to information; Accidents; Alzheimer's disease; Assistants; Cardiovascular diseases; Chronic illnesses; Chronic obstructive pulmonary disease; Crowdsourcing; Diabetes; Disease management; Factorial experiments; Health care industry; Health literacy; Humans; Information sources; Kidney diseases; Lung cancer; Noncommunicable diseases; Nonprofit organizations; Original Paper; Public health; Reliability; Reproducibility of Results; Response rates; Self-Management; Selfmanagement; Smartphones; Smoking cessation; Specialists; Stroke; Voice; Websites
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/32161

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. 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. 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). 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. 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 sou