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Development of a Digital Patient Assistant for the Management of Cyclic Vomiting Syndrome: Patient-Centric Design Study

Development of a Digital Patient Assistant for the Management of Cyclic Vomiting Syndrome: Patient-Centric Design Study

This study aimed to develop for patients with CVS an artificial intelligence (AI)–powered digital patient assistant (DPA) that provides more than symptom tracking, using the iterative design principles of user design and user interface alongside the design thinking approach, which has been adopted successfully for the development of health care applications [15,22-24].

Gaurav Narang, Yaozhu J Chen, Nicole Wedel, Melody Wu, Michelle Luo, Ashish Atreja

JMIR Form Res 2024;8:e52251

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

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

Google showed the highest response rate and 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 observed Google Assistant provided the highest response rate, even outperforming the web search results. Apple Siri showed the lowest response rate. This specific advantage of Google Assistant is consistent with previous studies [27,29,32,38,39].

Caterina Bérubé, Zsolt Ferenc Kovacs, Elgar Fleisch, Tobias Kowatsch

J Med Internet Res 2021;23(12):e32161

Using Speech Data From Interactions With a Voice Assistant to Predict the Risk of Future Accidents for Older Drivers: Prospective Cohort Study

Using Speech Data From Interactions With a Voice Assistant to Predict the Risk of Future Accidents for Older Drivers: Prospective Cohort Study

In this study, we aimed to investigate the relationship between speech data and future driving experiences related to car accidents in healthy older adults by collecting speech data during interactions with a voice assistant with simulated tasks on smart speakers and smartphones. We hypothesized that these speech data could be used for predicting accident risk for older drivers.

Yasunori Yamada, Kaoru Shinkawa, Masatomo Kobayashi, Hironobu Takagi, Miyuki Nemoto, Kiyotaka Nemoto, Tetsuaki Arai

J Med Internet Res 2021;23(4):e27667