=Paper=
{{Paper
|id=Vol-3728/paper3
|storemode=property
|title=Algorithmic Support for Health Behavior Change: A Scoping Review Protocol
|pdfUrl=https://ceur-ws.org/Vol-3728/paper3.pdf
|volume=Vol-3728
|authors=Diederik Heijbroek,Nele Albers,Willem-Paul Brinkman
|dblpUrl=https://dblp.org/rec/conf/persuasive/HeijbroekAB24
}}
==Algorithmic Support for Health Behavior Change: A Scoping Review Protocol==
https://ceur-ws.org/Vol-3728/paper3.pdf
Algorithmic Support for Health Behavior Change: A
Scoping Review Protocol
Diederik Heijbroek1 , Nele Albers1,∗ and Willem-Paul Brinkman1
1
Delft University of Technology, Delft, Netherlands
Abstract
A wide variety of algorithms has been developed to provide effective support in eHealth applications
for behavior change. However, an overview of the types of algorithms is missing. We aim to provide
such an overview by conducting a scoping review of papers published in the Scopus database. We are
currently screening the 44 remaining papers based on their full texts and collecting information on the
characteristics of the algorithms themselves, what the algorithms optimize in an intervention, and the
domain in which the algorithms are employed. We also keep track of how the algorithms have been
evaluated. Our review will provide insights into what types of algorithms are currently used and how
they can be improved in the future.
Keywords
Behavior change support systems, Persuasion, Algorithmic support, Digital health, Scoping review
1. Introduction
Given that 18.5% of the disease burden in the Netherlands is caused by unhealthy behavior [1]
and one in three people would need to work in healthcare by 2060 to meet the needs of the aging
population [2], eHealth applications for behavior change have a large potential in supporting
people in changing behaviors such as physical inactivity [3], smoking [4], and unhealthy eating
[5]. However, these applications typically suffer from dropout and low levels of adherence
[6, 7, 8], indicating a discrepancy between the support provided by the applications and the
needs of users.
Various algorithms have been designed to address this discrepancy by adapting what these
applications offer (e.g., different physical activity suggestions [9]), how (e.g., using different
persuasive strategies such as commitment and authority [10]), when (e.g., optimizing the timing
of physical activity notifications [11]), and with whom (e.g., deciding when to add human
support [12]). The decisions these algorithms make can be based on theories such as the
Transtheoretical Model (e.g., [13]), expert knowledge (e.g., [14]), as well as offline and online
data (e.g., [10, 14]). Moreover, the algorithms can be forward- (e.g., [10]) or backward-directed
(e.g., [15]), include a positive feedback loop (e.g., [14]) or a negative one (e.g., [16]), consider
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 D.R.A.Heijbroek@student.tudelft.nl (D. Heijbroek); N.Albers@tudelft.nl (N. Albers); W.P.Brinkman@tudelft.nl
(W. Brinkman)
Orcid 0009-0002-2891-0076 (D. Heijbroek); 0000-0002-0502-6176 (N. Albers); 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
users’ future states (e.g., [10]) or the effects of repetitions (e.g., [17]), and balance exploration
and exploitation (e.g., [18]).
In light of this variety of algorithms, we seek to provide a review of algorithms for adaptive
health behavior change support. The focus thereby lies on the characteristics of these algo-
rithms as well as how their effectiveness has been evaluated (e.g., controlled experiments [10],
simulations [11]). To this end, we are conducting a scoping review using journal and conference
articles published in the Scopus database. The general goal of a scoping review is to ”identify
and map the available evidence” [19]. For example, scoping reviews can be used to examine
how research is conducted in a field or to identify important characteristics related to a concept
[19]. We expect that our scoping review will give us insights into the types of algorithms that
are currently developed to support health behavior change and how they can be improved.
2. Approach
We formulated a search query consisting of four components. Specifically, we wanted to obtain
papers about 1) digital interventions, 2) algorithms, 3) behavior change, and 4) health. The
resulting query (Table 1) led to 993 results in Scopus in March 2024.
Table 1
Search query components.
Digital intervention Algorithm Behavior change Health domain
digital health intervention recommender system* beavio* change physical activity
mHealth algorithm intervention obesity
eHealth machine learning health self management smoking
digital intervention deep learning health promotion sleep
mobile health reinfocement learning non-communicable disease
artificial intelligence mental health
cessation
health
Subsequently, we removed papers using the first three exclusion criteria presented in Table 2,
leading to 678 remaining papers. Next, papers were excluded based on their titles and abstracts
if they were review papers or did not mention a behavior change algorithm.
Table 2
Exclusion criteria.
1. Document type is not either an article or conference paper
2. Source type is not either a journal or conference proceeding
3. Language of the paper is not English
4. Not about a behavior change algorithm
5. No access to full text
6. Insufficient information about behavior change algorithm
The remaining 235 papers were screened based on their full texts. 29 of these papers were
excluded because we did not have access to the full texts, 76 because they did not describe a
behavior change algorithm, and 86 because they did not provide enough information about
a behavior change algorithm. Currently, we are examining the 44 remaining papers in more
detail. The primary goal is to characterize the algorithms based on their characteristics (e.g.,
based on online data, expert-devised rules). Moreover, we will investigate what the algorithms
are used for (e.g., reminder timing, intervention selection), the domain they are employed in
(e.g., mental health, smoking cessation), and how they have been evaluated.
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.
References
[1] Rijksinstituut voor Volksgezondheid en Milieu (RIVM), Volksgezondheid toekomst
verkenning 2018: Een gezond vooruitzicht. synthese, https://www.rivm.nl/publicaties/
volksgezondheid-toekomst-verkenning-2018-gezond-vooruitzicht-synthese, 2018.
[2] M. de Visser, A. Boot, G. Werner, A. van Riel, M. Gijsberts, Kiezen voor houdbare zorg.
mensen, middelen en maatschapelijk draagvlak, https://www.wrr.nl/publicaties/rapporten/
2021/09/15/kiezen-voor-houdbare-zorg, 2021.
[3] N. Albers, B. Hizli, B. L. Scheltinga, E. Meijer, W.-P. Brinkman, Setting physical activity
goals with a virtual coach: vicarious experiences, personalization and acceptance, Journal
of Medical Systems 47 (2023) 15. doi:10.1007/s10916- 022- 01899- 9 .
[4] E. Meijer, J. S. Korst, K. G. Oosting, E. Heemskerk, S. Hermsen, M. C. Willemsen, B. van den
Putte, N. H. Chavannes, J. Brown, “at least someone thinks i’m doing well”: a real-world
evaluation of the quit-smoking app stopcoach for lower socio-economic status smokers,
Addiction Science & Clinical Practice 16 (2021) 1–14. doi:10.1186/s13722- 021- 00255- 5 .
[5] L. Poppe, C. Van der Mispel, I. De Bourdeaudhuij, M. Verloigne, S. Shadid, G. Crombez,
Users’ thoughts and opinions about a self-regulation-based ehealth intervention targeting
physical activity and the intake of fruit and vegetables: a qualitative study, PLoS One 12
(2017) e0190020. doi:10.1371/journal.pone.0190020 .
[6] S. M. Kelders, L. E. Van Zyl, G. D. Ludden, The concept and components of engagement in
different domains applied to ehealth: a systematic scoping review, Frontiers in Psychology
11 (2020) 926. doi:10.3389/fpsyg.2020.00926 .
[7] R. Beun, W. Brinkman, S. Fitrianie, F. Griffioen-Both, C. H. G. Horsch, J. Lancee, S. Spruit,
Improving adherence in automated e-coaching - A case from insomnia therapy, in:
A. Meschtscherjakov, B. E. R. de Ruyter, V. Fuchsberger, M. Murer, M. Tscheligi (Eds.),
Persuasive Technology - 11th International Conference, PERSUASIVE 2016, Salzburg,
Austria, April 5-7, 2016, Proceedings, volume 9638 of Lecture Notes in Computer Science,
Springer, 2016, pp. 276–287. doi:10.1007/978- 3- 319- 31510- 2\_24 .
[8] T. Greenhalgh, J. Wherton, C. Papoutsi, J. Lynch, G. Hughes, S. Hinder, N. Fahy, R. Procter,
S. Shaw, Beyond adoption: a new framework for theorizing and evaluating nonadoption,
abandonment, and challenges to the scale-up, spread, and sustainability of health and care
technologies, Journal of Medical Internet Research 19 (2017) e367. doi:10.2196/jmir.8775 .
[9] M. Rabbi, A. Pfammatter, M. Zhang, B. Spring, T. Choudhury, et al., Automated person-
alized feedback for physical activity and dietary behavior change with mobile phones:
a randomized controlled trial on adults, JMIR mHealth and uHealth 3 (2015) e4160.
doi:10.2196/mhealth.4160 .
[10] N. Albers, M. A. Neerincx, W.-P. Brinkman, Addressing people’s current and future states
in a reinforcement learning algorithm for persuading to quit smoking and to be physically
active, PloS One 17 (2022) e0277295. doi:10.1371/journal.pone.0277295 .
[11] S. Wang, C. Zhang, B. Kröse, H. van Hoof, Optimizing adaptive notifications in mo-
bile health interventions systems: reinforcement learning from a data-driven behavioral
simulator, Journal of Medical Systems 45 (2021) 102. doi:10.1007/s10916- 021- 01773- 0 .
[12] J. D. Piette, S. Newman, S. L. Krein, N. Marinec, J. Chen, D. A. Williams, S. N. Edmond,
M. Driscoll, K. M. LaChappelle, R. D. Kerns, M. Maly, H. M. Kim, K. B. Farris, D. M. Higgins,
E. Buta, A. A. Heapy, Patient-centered pain care using artificial intelligence and mobile
health tools: a randomized comparative effectiveness trial, JAMA Internal Medicine 182
(2022) 975–983. doi:10.1001/jamainternmed.2022.3178 .
[13] R. A. J. de Vries, Theory-Based and Tailor-Made: Motivational Messages for Behavior
Change Technology, Ph.D. thesis, University of Twente, Netherlands, 2018.
[14] S. Hors-Fraile, S. Malwade, F. Luna-Perejon, C. Amaya, A. Civit, F. Schneider, P. Bamidis,
S. Syed-Abdul, Y.-C. Li, H. De Vries, Opening the black box: Explaining the process of
basing a health recommender system on the i-change behavioral change model, IEEE
Access 7 (2019) 176525–176540. doi:10.1109/ACCESS.2019.2957696 .
[15] O. A. Blanson Henkemans, P. J. Van Der Boog, J. Lindenberg, C. A. Van Der Mast, M. A.
Neerincx, B. J. Zwetsloot-Schonk, An online lifestyle diary with a persuasive computer
assistant providing feedback on self-management, Technology and Health Care 17 (2009)
253–267. doi:10.3233/THC- 2009- 0545 .
[16] D. Hartanto, Computer-Based Social Anxiety Regulation in Virtual Reality Exposure
Therapy, Ph.D. thesis, Delft University of Technology, Netherlands, 2019.
[17] Y. Mintz, A. Aswani, P. Kaminsky, E. Flowers, Y. Fukuoka, Nonstationary bandits with
habituation and recovery dynamics, Operations Research 68 (2020) 1493–1516. URL:
https://doi.org/10.1287/opre.2019.1918.
[18] H. Ritschel, A. Seiderer, K. Janowski, S. Wagner, E. André, Adaptive linguistic style for
an assistive robotic health companion based on explicit human feedback, in: F. Makedon
(Ed.), Proceedings of the 12th ACM International Conference on PErvasive Technologies
Related to Assistive Environments, PETRA 2019, Island of Rhodes, Greece, June 5-7, 2019,
ACM, 2019, pp. 247–255. doi:10.1145/3316782.3316791 .
[19] Z. Munn, M. D. Peters, C. Stern, C. Tufanaru, A. McArthur, E. Aromataris, Systematic
review or scoping review? guidance for authors when choosing between a systematic
or scoping review approach, BMC Medical Research Methodology 18 (2018) 1–7. doi:10.
1186/s12874- 018- 0611- x .