Text-based Healthcare Chatbots Supporting Patient and Health Professional Teams: Preliminary Results of a Randomized Controlled Trial on Childhood Obesity
Open accessAbstract
Health professionals have limited resources and are not able to personally monitor and support patients in their everyday life.Against this background and due to the increasing number of self-service channels and digital health interventions, we investigate how text-based healthcare chatbots (THCB) can be designed to effectively support patients and health professionals in therapeutic settings beyond on-site consultations.We present an open source THCB system and how the THCP was designed for a childhood obesity intervention.Preliminary results with 15 patients indicate promising results with respect to intervention adherence (ca.13.000 conversational turns over the course of 4 months or ca. 8 per day and patient), scalability of the THCB approach (ca.99.5% of all conversational turns were THCB-driven) and over-average scores on perceived enjoyment and attachment bond between patient and THCB.Future work is discussed.
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The design of MAX was driven by an interdisciplinary effort that resulted in a conceptual model with intervention components informed by human behavior and experiential learning theories, findings from technology acceptance research, and prior experiences of the authors with conversational agents.
“The design of MAX was driven by an interdisciplinary effort that resulted in a conceptual model with intervention components informed by human behavior and experiential learning theories [61,65,66], findings from technology acceptance research [49,51,58,102], and prior experiences of the authors with conversational agents that support health care professionals and young adolescent patients [67,68]”
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Background
- Authors
- Kowatsch, T., Nißen, M. K., Shih, C.-H. I., Rüegger, D., Volland, D., Filler, A., Künzler, F., Barata, F., Hung, S., Büchter, D., Brogle, B., Heldt, K., Gindrat, P., Farpour-Lambert, N., & l'Allemand, D.
- ORCID
- Venue
- ETH Zurich
- Publisher
- ETH Zurich
- Published
- 2017
- OpenAlex
- W2783365884
- Cited by
- 74
- References
- 40
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 7 · 2023: 8 · 2022: 8 · 2021: 19 · 2020: 11 · 2019: 11 · 2018: 4
- FWCI
- 48.67
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- other-oa
- Locations
- Alexandria (UniSG) (University of St.Gallen) (acceptedVersion)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, other-oa)
- Alexandria (UniSG) (University of St.Gallen) (submittedVersion)
- ETH Zürich Research Collection
- Funders
- National Science Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (162724, 159289)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- childhood obesity, obesity intervention, patient support, healthcare teams, intervention adherence, perceived enjoyment, attachment bonds, digital health interventions, chatbots
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