Quality evaluation of digital voice assistants for the management of mental health conditions

Vanessa Kai Lin Chua; Li Lian Wong; Kevin Yi-Lwern Yap; Vanessa Kai Lin Chua; Li Lian Wong; Kevin Yi-Lwern Yap

doi:10.3934/medsci.2022028

AIMS Medical Science

2022, Volume 9, Issue 4: 512-530. doi: 10.3934/medsci.2022028

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Research article

Quality evaluation of digital voice assistants for the management of mental health conditions

1.
Department of Pharmacy, Faculty of Science, National University of Singapore, Block S4A, Level 2, 18 Science Drive 4, Singapore 117543, Singapore
2.
Department of Pharmacy, Singapore General Hospital, SingHealth Tower, 10 Hospital Boulevard, Lobby A, Level 9, Singapore 168582, Singapore
3.
Department of Public Health, School of Psychology and Public Health, La Trobe University, Melbourne (Bundoora), Victoria 3086, Australia

Received: 24 July 2022 Revised: 03 November 2022 Accepted: 21 November 2022 Published: 27 December 2022

Background
Digital voice assistants (DVAs) are gaining increasing popularity as a tool for accessing online mental health information. However, the quality of information provided by DVAs is not known. This study seeks to evaluate the quality of DVA responses to mental health-related queries in relation to six quality domains: comprehension ability, relevance, comprehensiveness, accuracy, understandability and reliability.
Materials and methods
Four smartphone DVAs were evaluated: Apple Siri, Samsung Bixby, Google Assistant and Amazon Alexa. Sixty-six questions and answers on mental health conditions (depression, anxiety, obsessive-compulsive disorder (OCD) and bipolar disorder) were compiled from authoritative sources, clinical guidelines and public search trends. Three evaluators scored the DVAs from an in-house-developed evaluation rubric. Data were analyzed by using the Kruskal-Wallis and Wilcoxon rank sum tests.
Results
Across all questions, Google Assistant scored the highest (78.9%), while Alexa scored the lowest (64.5%). Siri (83.9%), Bixby (87.7%) and Google Assistant (87.4%) scored the best for questions on depression, while Alexa (72.3%) scored the best for OCD questions. Bixby scored the lowest for questions on general mental health (0%) and OCD (0%) compared to all other DVAs. In terms of the quality domains, Google Assistant scored significantly higher for comprehension ability compared to Siri (100% versus 88.9%, p < 0.001) and Bixby (100% versus 94.5%, p < 0.001). Moreover, Google Assistant also scored significantly higher than Siri (100% versus 66.7%, p < 0.001) and Alexa (100% versus 75.0%, p < 0.001) in terms of relevance. In contrast, Alexa scored the worst in terms of accuracy (75.0%), reliability (58.3%) and comprehensiveness (22.2%) compared to all other DVAs.
Conclusion
Overall, Google Assistant performed the best in terms of responding to the mental health-related queries, while Alexa performed the worst. While the comprehension abilities of the DVAs were good, the DVAs had differing performances in the other quality domains. The responses by DVAs should be supplemented with other information from authoritative sources, and users should seek the help and advice of a healthcare professional when managing their mental health conditions.
- digital voice assistants,
- mental health information,
- quality evaluation,
- depression,
- anxiety,
- obsessive-compulsive disorder,
- bipolar disorder
Citation: Vanessa Kai Lin Chua, Li Lian Wong, Kevin Yi-Lwern Yap. Quality evaluation of digital voice assistants for the management of mental health conditions[J]. AIMS Medical Science, 2022, 9(4): 512-530. doi: 10.3934/medsci.2022028

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Abstract

Background

Digital voice assistants (DVAs) are gaining increasing popularity as a tool for accessing online mental health information. However, the quality of information provided by DVAs is not known. This study seeks to evaluate the quality of DVA responses to mental health-related queries in relation to six quality domains: comprehension ability, relevance, comprehensiveness, accuracy, understandability and reliability.

Materials and methods

Four smartphone DVAs were evaluated: Apple Siri, Samsung Bixby, Google Assistant and Amazon Alexa. Sixty-six questions and answers on mental health conditions (depression, anxiety, obsessive-compulsive disorder (OCD) and bipolar disorder) were compiled from authoritative sources, clinical guidelines and public search trends. Three evaluators scored the DVAs from an in-house-developed evaluation rubric. Data were analyzed by using the Kruskal-Wallis and Wilcoxon rank sum tests.

Results

Across all questions, Google Assistant scored the highest (78.9%), while Alexa scored the lowest (64.5%). Siri (83.9%), Bixby (87.7%) and Google Assistant (87.4%) scored the best for questions on depression, while Alexa (72.3%) scored the best for OCD questions. Bixby scored the lowest for questions on general mental health (0%) and OCD (0%) compared to all other DVAs. In terms of the quality domains, Google Assistant scored significantly higher for comprehension ability compared to Siri (100% versus 88.9%, p < 0.001) and Bixby (100% versus 94.5%, p < 0.001). Moreover, Google Assistant also scored significantly higher than Siri (100% versus 66.7%, p < 0.001) and Alexa (100% versus 75.0%, p < 0.001) in terms of relevance. In contrast, Alexa scored the worst in terms of accuracy (75.0%), reliability (58.3%) and comprehensiveness (22.2%) compared to all other DVAs.

Conclusion

Overall, Google Assistant performed the best in terms of responding to the mental health-related queries, while Alexa performed the worst. While the comprehension abilities of the DVAs were good, the DVAs had differing performances in the other quality domains. The responses by DVAs should be supplemented with other information from authoritative sources, and users should seek the help and advice of a healthcare professional when managing their mental health conditions.

Acknowledgments

The authors would like to thank Mr. Christopher Chua and Mr. Luke Si Sheng Lim for loaning their Samsung Galaxy Note 9 and iPhone 6, respectively, for the evaluation of the digital voice assistants; Ms. Joy Qi En Chia and Mr, Luke Si Sheng Lim for pilot testing the evaluation rubric; and Mr. Sheng Kiat Lee and Ms. Alyssa Pua for their time and effort in conducting the actual evaluation with all of the digital voice assistants.

Conflict of interest

No competing financial interests exist. All authors have no conflict of interest with any of the evaluated apps or the companies in this study. This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.

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