=Paper=
{{Paper
|id=Vol-3728/preface1
|storemode=property
|title=Preface to the 1st International Workshop on Algorithmic Behavior Change Support
|pdfUrl=https://ceur-ws.org/Vol-3728/preface1.pdf
|volume=Vol-3728
|authors=Nele Albers,Amal Abdulrahman,Deborah Richards,Caroline Figueroa,Bibhas Chakraborty,Ananya Bhattacharjee,Linwei He,Mark A. Neerincx,Joseph Jay Williams,Nezih Younsi,Tibor Bosse,Annemiek Linn,Crystal Smit,Willem-Paul Brinkman
|dblpUrl=https://dblp.org/rec/conf/persuasive/AlbersA0FCBHNWY24
}}
==Preface to the 1st International Workshop on Algorithmic Behavior Change Support==
https://ceur-ws.org/Vol-3728/preface1.pdf
Workshop on Algorithmic Behavior Change Support
Nele Albers1,∗ , Amal Abdulrahman1,2 , Deborah Richards2 , Caroline Figueroa1 ,
Bibhas Chakraborty3,4 , Ananya Bhattacharjee5 , Linwei He6 , Mark A. Neerincx1 ,
Joseph Jay Williams5 , Nezih Younsi7 , Tibor Bosse8 , Annemiek Linn9 , Crystal Smit10
and Willem-Paul Brinkman1
1
Delft University of Technology, Delft, Netherlands
2
Macquarie University, Sydney, Australia
3
National University of Singapore, Singapore, Singapore
4
Duke University, Durham, NC, United States
5
University of Toronto, Toronto, ON, Canada
6
Tilburg University, Tilburg, Netherlands
7
Sorbonne University, Paris, France
8
Radboud University, Nijmegen, Netherlands
9
University of Amsterdam, Amsterdam, Netherlands
10
Erasmus University Rotterdam, Rotterdam, Netherlands
Abstract
To increase the effectiveness of behavior change applications, a large variety of algorithms has been
developed to adapt what the applications offer, when, how, and with whom. Given the multitude of
challenges related to the concept of algorithmic behavior change support, its development, evaluation,
and impact on behavior change, this workshop aims to strengthen the community of people with diverse
backgrounds (e.g., computer science, psychology, human-computer interaction) and roles in behavior
change support (e.g., researcher, designer, practitioner). Combining keynotes of leading researchers with
sessions in which individual workshop participants present their work and discuss problems with the
audience, the workshop encouraged a lively exchange of ideas that benefits current and future research
on algorithmic behavior change support.
Keywords
Behavior change support systems, Persuasion, Persuasive technology, Algorithmic acceptance
1. Introduction
Health, sustainability, education - being potentially easy to use, available at all times, scalable,
and cost-effective, behavior change applications have a large potential and have thus been
developed for diverse domains (e.g., [1, 2, 3]). However, despite their potential, users commonly
In: Kiemute Oyibo, Wenzhen Xu, Elena Vlahu-Gjorgievska (eds.): The Adjunct Proceedings of the 19𝑡ℎ International
Conference on Persuasive Technology, April 10, 2024, Wollongong, Australia
∗
Corresponding author.
Envelope-Open N.Albers@tudelft.nl (N. Albers); W.P.Brinkman@tudelft.nl (W. Brinkman)
Orcid 0000-0002-0502-6176 (N. Albers); 0000-0001-5360-0833 (A. Abdulrahman); 0000-0002-7363-1511 (D. Richards);
0000-0003-0692-2244 (C. Figueroa); 0000-0002-7366-0478 (B. Chakraborty); 0000-0002-9116-3766 (A. Bhattacharjee);
0000-0002-6593-1661 (L. He); 0000-0002-8161-5722 (M. A. Neerincx); 0000-0002-9122-5242 (J. J. Williams);
0000-0003-4233-0406 (T. Bosse); 0000-0003-0912-3712 (A. Linn); 0000-0001-8485-7092 (W. Brinkman)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
do not adhere to these applications or abandon them entirely [4, 5, 6]. Thus, there appears to be
a mismatch between what the applications offer and what users need.
To create behavior change applications that do meet users’ needs, a variety of algorithms has
been developed to adapt what the applications offer, when, how, and with whom. For example,
Zhou et al. [7], Adams et al. [8], and Costa et al. [9] have adapted daily step goals and the choice
of activities for elderly people, Wang et al. [10] and Trella et al. [11] have developed algorithms
for adapting the timing of notifications for physical activity and oral self-care, Kaptein et al. [12]
and Bertolotti et al. [13] have adapted the use of Cialdini’s persuasion principles and different
message frames, and Piette et al. [14] and Forman et al. [15] have optimized the addition of
human support to eHealth applications for pain management and weight loss. These algorithms
thereby consider various elements from dynamic factors (e.g., user states derived from the
COM-B model [16], self-efficacy [13]) to more stable user characteristics (e.g., personality and
gender [17]) and employ various algorithmic techniques from reinforcement learning (e.g.,
[10]) to recommender systems (e.g., [18]). Placing the human at the center and combining
the strengths of humans and technology (i.e., augmented or hybrid intelligence) is often an
important design guideline, as is accounting for ethical and societal values.
This workshop brings together researchers, designers, developers, practitioners, and edu-
cators who are interested in the concept, development, evaluation, and impact of algorithmic
behavior change support. We explicitly invite participants from various backgrounds such as
artificial intelligence, human-computer interaction, psychology, medical practice, and ethics
of technology to contribute their perspectives and experiences. The broader objective of this
workshop is to strengthen the community of people working on adaptive support in behavior
change applications. To this end, the workshop aims to create a lively exchange of ideas that
benefits the individual workshop participants’ current and future research. Specifically, the
workshop’s aim is to a) learn about each other’s work, b) jointly work on problems of the
workshop participants, and c) establish a vision for future work on algorithmic behavior change
support.
2. Organization
This was a one-day workshop that was primarily designed as an in-person event. It combined
sessions where participants presented their work and worked together on their individual
problems (e.g., provide feedback on an idea for an algorithm, serve as a focus group, or brainstorm
on a research question) with keynotes by leading researchers.
2.1. Accepted Papers
Researchers interested in attending the workshop were asked to submit a short position paper
of their work as well as a problem that they would like to work on together with the other
workshop participants. We received ten submissions by authors from diverse countries such
as Canada, India, and Australia. Each submission was reviewed by at least two reviewers and
the authors of four accepted papers presented their work and problem at the workshop. The
accepted papers broadly covered the personalization of eHealth applications for different types
of behavior change (e.g., sleep adherence, stress management, diabetes lifestyle change) and
algorithm design guidelines.
2.2. Keynotes
The workshop featured two keynotes, one online and one in-person:
• Nina Deliu is an Assistant Professor in Statistics at Sapienza University of Rome, Italy,
and a long-term visitor of the MRC–Biostatistics Unit of the University of Cambridge,
UK. Her research focuses on exploring and developing reinforcement learning and multi-
armed bandit algorithms for applications in behavioral sciences (e.g., education, mobile
health, clinical trials). Moreover, she investigates how we can perform valid inference
based on data that has been adaptively collected in such settings.
• Deborah Richards is a Professor with the School of Computing in the Faculty of Science
and Engineering at Macquarie University in Sydney, Australia. She is an expert on
intelligent virtual agents, which she has developed for various domains including health
and education.
2.3. Organizing Committee
The workshop was organized by a multidisciplinary team of researchers from several institutes,
with expertise in human-computer interaction, artificial intelligence, health communication,
statistics, and behavioral science:
• Nele Albers is a PhD student in Computer Science at Delft University of Technology.
She studies how adaptive algorithms can be used to tailor behavior change applications
to individuals and their state in time, especially using reinforcement learning.
• Amal Abdulrahman is a research fellow at Macquarie University and Delft University
of Technology. Her main research interest lies in exploring how technology can support
humans in achieving a better quality of life. For this, she has developed embodied and
text-based virtual agents that use techniques ranging from reinforcement learning to
argumentation.
• Deborah Richards did, besides giving a keynote, also help with organizing the workshop.
Deborah is a Professor at Macquarie University. Her research focuses on intelligent
systems, agent technologies, and virtual worlds to support human learning and well-
being.
• Caroline Figueroa’s research focuses on developing, testing, and disseminating person-
alized digital health tools for behavior change, and tailoring these tools to the needs of
underserved populations such as people from ethnic and racial minority backgrounds
and low-income individuals.
• Bibhas Chakraborty is an Associate Professor at the Duke-NUS Medical School. His
main research focus revolves around the development of novel statistical methods and
associated study designs aimed at advancing data-driven precision health, particularly in
settings with varying temporal factors.
• Ananya Bhattacharjee is a Computer Science PhD student at the University of Toronto,
Canada. His main research interest lies in developing and understanding technology that
can help people manage their psychological well-being. To this end, he has developed
several text message services, mobile applications, and websites.
• Linwei He is a PhD student at Tilburg University in the Netherlands with a background
in communication science and especially persuasive communication. Her research focus
lies on using conversational agents to accomplish long-term health behavior change, for
example in the context of quitting smoking.
• Mark Neerincx is Full Professor in Human-Centered Computing at the Delft University
of Technology, and Senior Research Scientist at TNO Perceptual and Cognitive Systems.
He is an expert on fundamental and applied research on human-computer interaction in
domains such as health, security, and defense.
• Joseph Jay Williams is an Assistant Professor in Computer Science at the University
of Toronto. His vision is to develop intelligent, adaptive systems that are continuously
enhancing and personalizing interventions to help people in contexts such as education
and mental health.
• Nezih Younsi is a PhD student at Sorbonne University in France. He is interested in
how algorithms such as reinforcement learning can be used to help people change their
behavior, specifically in the context of healthy eating.
• Tibor Bosse is Full Professor of Artificial Intelligence and Communication Science at
Radboud University. His main research interest is the social interaction between humans
and socially intelligent systems, applied to various domains such as social skills training
and behavior change.
• Annemiek Linn is an Associate Professor in Health Communication at the University of
Amsterdam. Her research lies at the intersection of technology and health communication.
She is specifically interested in developing healthcare technologies that place the patient
at the center.
• Crystal Smit is an Assistant Professor in Clinical Child and Family Studies at Eras-
mus University Rotterdam. In her research, she focuses on encouraging positive health
behaviors among young individuals, especially considering the impact of their social
networks.
• Willem-Paul Brinkman is an Associate Professor at Delft University of Technology. His
research focuses on human-computer interaction, human-centered artificial intelligence,
and behavior change support systems, specifically in eHealth.
3. Outcome
The workshop’s goal was to enable a fruitful exchange of ideas that benefits both the current
and future research of the workshop participants. More specifically, the workshop outcomes
were that a) participants gained insights into current research projects that their peers from
different disciplines are working on, b) participants contributed to addressing problems of other
workshop participants, and c) participants established a vision for future work on algorithmic
behavior change support.
Acknowledgments
This work is part of the multidisciplinary research project Perfect Fit, which is supported by
several funders organized by the Netherlands Organization for Scientific Research (NWO),
program Commit2Data - Big Data & Health (project number 628.011.211). Besides NWO, the
funders include the Netherlands Organisation for Health Research and Development (ZonMw),
Hartstichting, the Ministry of Health, Welfare and Sport (VWS), Health Holland, and the
Netherlands eScience Center.
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