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Cited by 14Mobile Health and mHealth ApplicationsArtificial Intelligence in Healthcare and EducationUsability and User Interface Design

Usability and Overall Perception of a Health Bot for Nutrition-Related Questions for Patients Receiving Bariatric Care: Mixed Methods Study

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Beyeler, Marina, Légeret, Corinne, Kiwitz, Fabian, van der Horst, Klazine·2023·JMIR Human Factors
DOI 10.2196/47913PMC PMC10666014

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Abstract

Background: Currently, over 4000 bariatric procedures are performed annually in Switzerland. To improve outcomes, patients need to have good knowledge regarding postoperative nutrition. To potentially provide them with knowledge between dietetic consultations, a health bot (HB) was created. The HB can answer bariatric nutrition questions in writing based on artificial intelligence. Objective: This study aims to evaluate the usability and perception of the HB among patients receiving bariatric care. Methods: Patients before or after bariatric surgery tested the HB. A mixed methods approach was used, which consisted of a questionnaire and qualitative interviews before and after testing the HB. The dimensions usability of, usefulness of, satisfaction with, and ease of use of the HB, among others, were measured. Data were analyzed using R Studio (R Studio Inc) and Excel (Microsoft Corp). The interviews were transcribed and a summary inductive content analysis was performed. Results: A total of 12 patients (female: n=8, 67%; male: n=4, 33%) were included. The results showed excellent usability with a mean usability score of 87 (SD 12.5; range 57.5-100) out of 100. Other dimensions of acceptability included usefulness (mean 5.28, SD 2.02 out of 7), satisfaction (mean 5.75, SD 1.68 out of 7), and learnability (mean 6.26, SD 1.5 out of 7). The concept of the HB and availability of reliable nutrition information were perceived as desirable (mean 5.5, SD 1.64 out of 7). Weaknesses were identified in the response accuracy, limited knowledge, and design of the HB. Conclusions: The HB’s ease of use and usability were evaluated to be positive; response accuracy, topic selection, and design should be optimized in a next step. The perceptions of nutrition professionals and the impact on patient care and the nutrition knowledge of participants need to be examined in further studies.

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  • AI chatbots facilitate well-designed multimodal conversations that can effectively assist in patient intervention, postoperative support, and psychiatric care.

    “Previous studies have revealed that AI chatbots facilitate well-designed multimodal conversations that can effectively assist in a variety of areas such as patient intervention (8), postoperative support (9), and psychiatric care (10)”

    From Digital affordances of AI chatbots in nursing education: a systematic review of learning gains and gaps in the evidence (2026) · cites this paper as [8]

    The Methods and Discussion describe a nutrition health bot tested by patients before and after bariatric surgery, with participants reporting that it could help between consultations. But the Abstract Conclusions say its impact on patient care still needs study; the bot answered in writing, not through multimodal conversations. This source therefore supports potential postoperative support, not demonstrated effectiveness or the multimodal description. The indexed passages have no page numbers. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Beyeler, M., Légeret, C., Kiwitz, F., & van der Horst, K.
Affiliations
  • Bern University of Applied Sciences (CH)
  • University Children’s Hospital Basel (CH)
ORCID
Venue
JMIR Human Factors
Publisher
JMIR Publications Inc.
Published
2023-11-08
Volume
10
Pages
e47913
ISSN
2292-9495 (ISSN-L)
DOI
10.2196/47913
PMC
PMC10666014
OpenAlex
W4386393795
Cited by
14
References
11
Citations by year
2026: 6 · 2025: 6 · 2024: 2
FWCI
4.96
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR Human Factors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • ARBOR - Bern University of Applied Sciences Repository (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
Keywords
bariatric surgery, postoperative nutrition, nutrition counseling, AI chatbots, usability, patient perspectives, patient satisfaction, response accuracy, patient education
MeSH terms
Artificial Intelligence, Female, Humans, Male, Nutritional Status, Perception, Surveys and Questionnaires, Bariatric Surgery

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