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New research paper on the reliability of voice assistants for the management of chronic conditions

Noncommunicable diseases (NCD) constitute a burden on public health. These are best controlled through self-management practices. An example is self-information. Fostering patients’ access to health-related information through efficient and accessible channels such as commercial voice assistants (VAs) may support patients’ ability to make health-related decisions and manage their chronic conditions.

Our study aimed to evaluate the reliability of the most common VAs – that is, Amazon Alexa, Apple Siri, or Google Assistant – in responding to questions about the management of the main NCD. We assessed the rate of error-free voice responses and classified their web source (i.e., Expert, Commercial, Crowdsourced, or Not stated).

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 in responding to questions about NCD management. However, the rate of expert sources differed across diseases. Thus, based on these results, we urge health organizations to collaborate more closely not only with Google, but also with Amazon, and Apple to allow their VAs to consistently provide reliable answers to health-related questions for the management of NCDs.

For further details on this research article by Caterina Bérubé, Prof. Dr. Elgar Fleisch, Zsolt Ferenc Kovacs, and Prof. Dr. Tobias Kowatsch, please watch the main author’s video abstract below and refer to the full research article. If this topic is of interest to you, you can learn more about our research into voice assistants on our dedicated project page.

Reference:

Bérubé, C., Kovacs Z.F., Fleisch E., Kowatsch T. (2021). Reliability of Commercial Voice Assistants’ Responses to Health-related Questions in Noncommunicable Disease Management: Factorial Experiment Assessing Response Rate and Source of Information. Journal of medical Internet research, 23 (12), e32161.

Visual Abstract

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