Toward Enhancing Diabetes Self-Management with Personalization Through Human Digital Twins for Behavior Change

Catarina Dias de Oliveira; Lorenzo James; Alireza Khanshan; Pieter Van Gorp

doi:10.1007/978-981-97-5035-1_49

Toward Enhancing Diabetes Self-Management with Personalization Through Human Digital Twins for Behavior Change

Catarina Dias de Oliveira (Corresponding author)
, Lorenzo James
, Alireza Khanshan
, Pieter Van Gorp

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

1 Citation (Scopus)

256 Downloads (Pure)

Abstract

Patients with type 2 diabetes need to adhere to a healthy lifestyle to prevent suffering from short-term and long-term escalations. A key aspect of treatment is to supplement educational content with techniques for behavior change. Unfortunately, introducing change and maintaining it is notoriously hard. We explore a novel approach to personalization in the context of Human Digital Twins (HDTs), that facilitates a bidirectional data flow between patients and their digital counterparts. While current mHealth tools show potential, there is room for improvement in incorporating the concept of such twins and considering the complexity of behavior change. We developed a novel architecture that enables mHealth tools, through the lens of the Transtheoretical Model of behavior change, to focus on multiple stages of change and personalize functionalities based on an HDT model that is trained more holistically. Our modular HDT is made of three subcomponents: a health literacy model, a daily activities model, and a model of the user’s player type. Our primary contribution is the demonstration of its relevance and feasibility toward the application of personalization for behavior change in patients with diabetes.

Original language	English
Title of host publication	Proceedings of Ninth International Congress on Information and Communication Technology
Subtitle of host publication	ICICT 2024, London, Volume 9
Editors	Xin-She Yang, R. Simon Sherratt, Nilanjan Dey, Amit Joshi
Publisher	Springer
Pages	623-634
Number of pages	12
ISBN (Electronic)	978-981-97-5035-1
ISBN (Print)	978-981-97-5034-4
DOIs	https://doi.org/10.1007/978-981-97-5035-1_49
Publication status	Published - 23 Oct 2024
Event	9th International Congress on Information and Communication Technology, ICICT 2024 - London, United Kingdom Duration: 19 Feb 2024 → 22 Feb 2024

Publication series

Name	Lecture Notes in Networks and Systems (LNNS)
Volume	1054
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	9th International Congress on Information and Communication Technology, ICICT 2024
Country/Territory	United Kingdom
City	London
Period	19/02/24 → 22/02/24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Behavior change
Diabetes
Education
Health literacy
Human digital twins
Personalization
Self-management

Access to Document

10.1007/978-981-97-5035-1_49

FinalAuthorVersionFinal author version , 1.2 MB
pdfFinal published version, 624 KBLicence: TAVERNE

Cite this

de Oliveira, C. D., James, L., Khanshan, A., & Van Gorp, P. (2024). Toward Enhancing Diabetes Self-Management with Personalization Through Human Digital Twins for Behavior Change. In X.-S. Yang, R. S. Sherratt, N. Dey, & A. Joshi (Eds.), Proceedings of Ninth International Congress on Information and Communication Technology: ICICT 2024, London, Volume 9 (pp. 623-634). (Lecture Notes in Networks and Systems (LNNS); Vol. 1054). Springer. https://doi.org/10.1007/978-981-97-5035-1_49

de Oliveira, Catarina Dias ; James, Lorenzo ; Khanshan, Alireza et al. / Toward Enhancing Diabetes Self-Management with Personalization Through Human Digital Twins for Behavior Change. Proceedings of Ninth International Congress on Information and Communication Technology: ICICT 2024, London, Volume 9. editor / Xin-She Yang ; R. Simon Sherratt ; Nilanjan Dey ; Amit Joshi. Springer, 2024. pp. 623-634 (Lecture Notes in Networks and Systems (LNNS)).

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de Oliveira, CD, James, L, Khanshan, A & Van Gorp, P 2024, Toward Enhancing Diabetes Self-Management with Personalization Through Human Digital Twins for Behavior Change. in X-S Yang, RS Sherratt, N Dey & A Joshi (eds), Proceedings of Ninth International Congress on Information and Communication Technology: ICICT 2024, London, Volume 9. Lecture Notes in Networks and Systems (LNNS), vol. 1054, Springer, pp. 623-634, 9th International Congress on Information and Communication Technology, ICICT 2024, London, United Kingdom, 19/02/24. https://doi.org/10.1007/978-981-97-5035-1_49

Toward Enhancing Diabetes Self-Management with Personalization Through Human Digital Twins for Behavior Change. / de Oliveira, Catarina Dias (Corresponding author); James, Lorenzo; Khanshan, Alireza et al.
Proceedings of Ninth International Congress on Information and Communication Technology: ICICT 2024, London, Volume 9. ed. / Xin-She Yang; R. Simon Sherratt; Nilanjan Dey; Amit Joshi. Springer, 2024. p. 623-634 (Lecture Notes in Networks and Systems (LNNS); Vol. 1054).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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N2 - Patients with type 2 diabetes need to adhere to a healthy lifestyle to prevent suffering from short-term and long-term escalations. A key aspect of treatment is to supplement educational content with techniques for behavior change. Unfortunately, introducing change and maintaining it is notoriously hard. We explore a novel approach to personalization in the context of Human Digital Twins (HDTs), that facilitates a bidirectional data flow between patients and their digital counterparts. While current mHealth tools show potential, there is room for improvement in incorporating the concept of such twins and considering the complexity of behavior change. We developed a novel architecture that enables mHealth tools, through the lens of the Transtheoretical Model of behavior change, to focus on multiple stages of change and personalize functionalities based on an HDT model that is trained more holistically. Our modular HDT is made of three subcomponents: a health literacy model, a daily activities model, and a model of the user’s player type. Our primary contribution is the demonstration of its relevance and feasibility toward the application of personalization for behavior change in patients with diabetes.

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de Oliveira CD, James L, Khanshan A, Van Gorp P. Toward Enhancing Diabetes Self-Management with Personalization Through Human Digital Twins for Behavior Change. In Yang XS, Sherratt RS, Dey N, Joshi A, editors, Proceedings of Ninth International Congress on Information and Communication Technology: ICICT 2024, London, Volume 9. Springer. 2024. p. 623-634. (Lecture Notes in Networks and Systems (LNNS)). doi: 10.1007/978-981-97-5035-1_49