01 - A Voice-Driven Artificial Intelligence for Automatic Medical Data Collection:  CardioCube

Jadczyk, Tom; Kiwic, Oskar; Khandwalla, Raj M.; Grabowski, Krzysztof; Rudawski, Sławomir; Magaczewski, Przemyslaw; Benyahia, Hafidha; McClain, Todd; Wojakowski, Wojciech; Henry, Timothy D.

doi:10.26226/morressier.5d19cfb157558b317a10dd75

Cited by 1 publication

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“…To our knowledge, this has been the first clinical pilot evaluation of a voice app used for helping patients manage an advanced chronic condition at home. So far, studies have only reported on accuracy and acceptability levels in a controlled lab environment, however these findings are still consistent with the results presented in this paper (15,16). This study indicates the level of acceptability and feasibility of a voice app for patient self-management by measuring engagement levels.…”

Section: Principal Findingssupporting

confidence: 90%

A Voice App Design for Heart Failure Self-Management: A Pilot Study

Barbaric

Munteanu

Ross

et al. 2022

Preprint

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There is a growing interest to investigate the feasibility of using voice user interfaces as a platform for digital therapeutics in chronic disease management. While mostly deployed as smartphone applications, some demographics struggle when using touch screens and often cannot complete tasks independently. This research aimed to evaluate how heart failure patients interacted with a voice app version of an already existing digital therapeutic, Medly , using a mixed-methods concurrent triangulation approach. The objective was to determine the acceptability and feasibility of the voice app by better understanding who this platform is be best suited for. Quantitative data included engagement levels and accuracy rates. Participants (n=20) used the voice app over a four week period and completed questionnaires and semi-structured interviews relating to acceptability, ease of use, and workload. The average engagement level was 73%, with a 14% decline between week one and four. The difference in engagement levels between the oldest and youngest demographic was the most significant, 84% and 43% respectively. The Medly voice app had an overall accuracy rate of 97.8% and was successful in sending data to the clinic. Users were accepting of the technology (ranking it in the 80 th percentile) and felt it did not require a lot of work (2.1 on a 7-point Likert scale). However, 13% of users were less inclined to use the voice app at the end of the study. The following themes and subthemes emerged: (1) feasibility of clinical integration: user adaptation to voice app’s conversational style, device unreliability, and (2) voice app acceptability: good device integration within household, users blamed themselves for voice app problems, and voice app missing desirable user features. The voice app proved to be most beneficial to those who: are older, have flexible schedules, are confident with using technology, and are experiencing other medical conditions.

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Section: Principal Findingssupporting

confidence: 90%