Co-Designing Personal Health?: Multidisciplinary Benefits and Challenges in Informing Diabetes Self-Care Technologies

Amid Ayobi; Katarzyna Stawarz; Dmitri Katz; Paul Marshall; Taku Yamagata; Raul Santos-Rodriguez; Peter A Flach; Aisling Ann O'Kane

doi:10.1145/3479601

Co-Designing Personal Health? Multidisciplinary Benefits and Challenges in Informing Diabetes Self-Care Technologies

Amid Ayobi, Katarzyna Stawarz, Dmitri Katz, Paul Marshall, Taku Yamagata, Raul Santos-Rodriguez, Peter A Flach, Aisling Ann O'Kane

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

15 Citations (Scopus)

242 Downloads (Pure)

Abstract

Co-design is a widely applied design process with well-documented values, including mutual learning and collective creativity. However, the real-world challenges of conducting multidisciplinary co-design research to inform the design of self-care technologies are not well established. We provide a qualitative account of a multidisciplinary project that aimed to co-design machine learning applications for Type 1 Diabetes (T1D) self-management. Through interviews, we identify not only perceived social, technological and strategic benefits of co-design but also organisational, translational and pragmatic design challenges: participants with T1D experienced difficulties in co-designing systems that met their individual self-care needs as part of group activities; HCI and AI researchers described challenges resulting from applying co-design outcomes to data-driven ML work; and industry collaborators highlighted academic data sharing regulations as cross- organisational challenges that can impede co-design efforts. Based on this understanding, we discuss opportunities for supporting multidisciplinary collaborations and aligning individual health needs with collaborative co-design activities.

Original language	English
Title of host publication	Proceedings of the ACM on Human-Computer Interaction
Publisher	Association for Computing Machinery (ACM)
Pages	1-26
Number of pages	24
Volume	5
Edition	CSCW2
DOIs	https://doi.org/10.1145/3479601
Publication status	Published - 18 Oct 2021
Event	ACM Conference on Computer Supported Cooperative Work 2021 - Virtual Duration: 23 Oct 2021 → 27 Oct 2021 https://cscw.acm.org/2021/

Publication series

Name	Proceedings of the ACM on Human-Computer Interaction
Publisher	ACM
ISSN (Electronic)	2573-0142

Conference

Conference	ACM Conference on Computer Supported Cooperative Work 2021
Abbreviated title	CSCW 2021
Period	23/10/21 → 27/10/21
Internet address	https://cscw.acm.org/2021/

Bibliographical note

Funding Information:
For context we provide here an overview of the 18-month long co-design project. Its aim was to co-design ML-based decision support concepts and co-create suitable machine learning approaches. The co-design project was funded by a scheme with a focus on fostering collaborations between businesses and academic organisations. The project involved HCI researchers, AI researchers and industry collaborators. The industry collaborators worked at a T1D start-up. A shared objective was to co-design ML-based decision support concepts and cocreate suitable machine learning approaches. Fifteen participants with T1D (aged 24-69; 3-34 years since T1D diagnosis; 11 were male) were recruited. Participants received an Apple Watch, a 12-months Dexcom Continuous Glucose Monitor (CGM) subscription, and access to an app provided by the start-up for their participation.

Publisher Copyright:
© 2021 ACM.

Research Groups and Themes

Bristol Interaction Group

Keywords

co-design
participatory design
diabetes
t1d
personal health
self-care
self-management
HCI-AI
explainable artificial intelligence
machine learning

UN SDGs

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

Access to Document

10.1145/3479601

Full-text PDF (author’s accepted manuscript)
This is the accepted author manuscript (AAM). The final published version (version of record) is available online via ACM at 10.1145/3479601. Please refer to any applicable terms of use of the publisher.
Accepted author manuscript, 893 KB

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Persistent link

COTADS: 8030 EPSRC via Southampton TAS_PP_00034 - COdesigning Trustworthy Autonomous Diabetes Systems
O'Kane, A. A. (Principal Investigator), Marshall, P. (Principal Investigator) & Ayobi, A. (Co-Principal Investigator)
1/05/21 → 30/06/22
Project: Research
ML4D: InnovateUK ML4D: Machine Learning for Enhanced Diabetes Self-Care
O'Kane, A. A. (Principal Investigator), Marshall, P. (Co-Investigator), Santos-Rodriguez, R. (Co-Investigator) & Flach, P. A. (Co-Investigator)
1/11/18 → 30/04/20
Project: Research

Towards Safe and Robust Reinforcement Learning: Leveraging Multiple Sources of Information
Yamagata, T. (Author), Santos-Rodriguez, R. (Supervisor), 10 Dec 2024
Student thesis: Doctoral Thesis › Doctor of Philosophy (PhD)

File

CSCW 2021 Honorary Mention
Ayobi, A. (Recipient), Stawarz, K. (Recipient), Katz, D. (Recipient), Marshall, P. (Recipient), Yamagata, T. (Recipient), Santos-Rodriguez, R. (Recipient), Flach, P. A. (Recipient) & O'Kane, A. A. (Recipient), 14 Oct 2021
Prize: Prizes, Medals, Awards and Grants

File

Cite this

Ayobi, A., Stawarz, K., Katz, D., Marshall, P., Yamagata, T., Santos-Rodriguez, R., Flach, P. A., & O'Kane, A. A. (2021). Co-Designing Personal Health? Multidisciplinary Benefits and Challenges in Informing Diabetes Self-Care Technologies. In Proceedings of the ACM on Human-Computer Interaction (CSCW2 ed., Vol. 5, pp. 1-26). Article 475 (Proceedings of the ACM on Human-Computer Interaction). Association for Computing Machinery (ACM). https://doi.org/10.1145/3479601

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abstract = "Co-design is a widely applied design process with well-documented values, including mutual learning and collective creativity. However, the real-world challenges of conducting multidisciplinary co-design research to inform the design of self-care technologies are not well established. We provide a qualitative account of a multidisciplinary project that aimed to co-design machine learning applications for Type 1 Diabetes (T1D) self-management. Through interviews, we identify not only perceived social, technological and strategic benefits of co-design but also organisational, translational and pragmatic design challenges: participants with T1D experienced difficulties in co-designing systems that met their individual self-care needs as part of group activities; HCI and AI researchers described challenges resulting from applying co-design outcomes to data-driven ML work; and industry collaborators highlighted academic data sharing regulations as cross- organisational challenges that can impede co-design efforts. Based on this understanding, we discuss opportunities for supporting multidisciplinary collaborations and aligning individual health needs with collaborative co-design activities.",

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author = "Amid Ayobi and Katarzyna Stawarz and Dmitri Katz and Paul Marshall and Taku Yamagata and Raul Santos-Rodriguez and Flach, {Peter A} and O'Kane, {Aisling Ann}",

note = "Funding Information: For context we provide here an overview of the 18-month long co-design project. Its aim was to co-design ML-based decision support concepts and co-create suitable machine learning approaches. The co-design project was funded by a scheme with a focus on fostering collaborations between businesses and academic organisations. The project involved HCI researchers, AI researchers and industry collaborators. The industry collaborators worked at a T1D start-up. A shared objective was to co-design ML-based decision support concepts and cocreate suitable machine learning approaches. Fifteen participants with T1D (aged 24-69; 3-34 years since T1D diagnosis; 11 were male) were recruited. Participants received an Apple Watch, a 12-months Dexcom Continuous Glucose Monitor (CGM) subscription, and access to an app provided by the start-up for their participation. Publisher Copyright: {\textcopyright} 2021 ACM.; ACM Conference on Computer Supported Cooperative Work 2021, CSCW 2021 ; Conference date: 23-10-2021 Through 27-10-2021",

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Ayobi, A, Stawarz, K, Katz, D, Marshall, P , Yamagata, T , Santos-Rodriguez, R , Flach, PA & O'Kane, AA 2021, Co-Designing Personal Health? Multidisciplinary Benefits and Challenges in Informing Diabetes Self-Care Technologies. in Proceedings of the ACM on Human-Computer Interaction. CSCW2 edn, vol. 5, 475, Proceedings of the ACM on Human-Computer Interaction, Association for Computing Machinery (ACM), pp. 1-26, ACM Conference on Computer Supported Cooperative Work 2021, 23/10/21. https://doi.org/10.1145/3479601

Co-Designing Personal Health? Multidisciplinary Benefits and Challenges in Informing Diabetes Self-Care Technologies. / Ayobi, Amid; Stawarz, Katarzyna; Katz, Dmitri et al.
Proceedings of the ACM on Human-Computer Interaction. Vol. 5 CSCW2. ed. Association for Computing Machinery (ACM), 2021. p. 1-26 475 (Proceedings of the ACM on Human-Computer Interaction).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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AU - Katz, Dmitri

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AU - Santos-Rodriguez, Raul

AU - Flach, Peter A

AU - O'Kane, Aisling Ann

N1 - Funding Information: For context we provide here an overview of the 18-month long co-design project. Its aim was to co-design ML-based decision support concepts and co-create suitable machine learning approaches. The co-design project was funded by a scheme with a focus on fostering collaborations between businesses and academic organisations. The project involved HCI researchers, AI researchers and industry collaborators. The industry collaborators worked at a T1D start-up. A shared objective was to co-design ML-based decision support concepts and cocreate suitable machine learning approaches. Fifteen participants with T1D (aged 24-69; 3-34 years since T1D diagnosis; 11 were male) were recruited. Participants received an Apple Watch, a 12-months Dexcom Continuous Glucose Monitor (CGM) subscription, and access to an app provided by the start-up for their participation. Publisher Copyright: © 2021 ACM.

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AB - Co-design is a widely applied design process with well-documented values, including mutual learning and collective creativity. However, the real-world challenges of conducting multidisciplinary co-design research to inform the design of self-care technologies are not well established. We provide a qualitative account of a multidisciplinary project that aimed to co-design machine learning applications for Type 1 Diabetes (T1D) self-management. Through interviews, we identify not only perceived social, technological and strategic benefits of co-design but also organisational, translational and pragmatic design challenges: participants with T1D experienced difficulties in co-designing systems that met their individual self-care needs as part of group activities; HCI and AI researchers described challenges resulting from applying co-design outcomes to data-driven ML work; and industry collaborators highlighted academic data sharing regulations as cross- organisational challenges that can impede co-design efforts. Based on this understanding, we discuss opportunities for supporting multidisciplinary collaborations and aligning individual health needs with collaborative co-design activities.

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KW - participatory design

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KW - self-management

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KW - explainable artificial intelligence

KW - machine learning

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M3 - Conference Contribution (Conference Proceeding)

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Ayobi A, Stawarz K, Katz D, Marshall P , Yamagata T , Santos-Rodriguez R et al. Co-Designing Personal Health? Multidisciplinary Benefits and Challenges in Informing Diabetes Self-Care Technologies. In Proceedings of the ACM on Human-Computer Interaction. CSCW2 ed. Vol. 5. Association for Computing Machinery (ACM). 2021. p. 1-26. 475. (Proceedings of the ACM on Human-Computer Interaction). doi: 10.1145/3479601

Co-Designing Personal Health? Multidisciplinary Benefits and Challenges in Informing Diabetes Self-Care Technologies

Abstract

Publication series

Conference

Bibliographical note

Research Groups and Themes

Keywords

UN SDGs

Access to Document

Handle.net

Fingerprint

Projects

COTADS: 8030 EPSRC via Southampton TAS_PP_00034 - COdesigning Trustworthy Autonomous Diabetes Systems

ML4D: InnovateUK ML4D: Machine Learning for Enhanced Diabetes Self-Care

Student theses

Towards Safe and Robust Reinforcement Learning: Leveraging Multiple Sources of Information

Prizes

CSCW 2021 Honorary Mention

Cite this