König, L.

Newby, K.

Smit, E.S.

English

International Migration, Integration and Social Cohesion online publications

UvA-DARE is a service provided by the library of the University of Amsterdam (https://dare.uva.nl)UvA-DARE (Digital Academic Repository)Digital health and computer tailoringopportunities and challenges in moving the field forwardSmit, E.S.; Newby, K.; König, L.Publication date2022Document VersionFinal published versionPublished inThe European Health PsychologistLicenseCC BYLink to publicationCitation for published version (APA):Smit, E. S., Newby, K., & König, L. (2022). Digital health and computer tailoring: opportunitiesand challenges in moving the field forward. The European Health Psychologist, 22(4), 788-792. https://www.ehps.net/ehp/index.php/contents/article/view/3383General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s)and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an opencontent license (like Creative Commons).Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, pleaselet the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the materialinaccessible and/or remove it from the website. Please Ask the Library: https://uba.uva.nl/en/contact, or a letterto: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. Youwill be contacted as soon as possible.Download date:10 Mar 2023788   ehpvolume 22 issue 4 The European Health Psychologistehps.net/ehpIn present times, digital health interventions are pervasive. This is not surprising, as the Internet is accessible 24/7, available independent of a person’s location, and the most consulted medium when people need health related information (van de Belt et al., 2013). As described in another article published in the European Health Psychologist (Smit et al., 2019), we de ne Digital health as “the use of digital information and communication technologies to improve health and increase the chances of sustainable healthcare for all”. According to this de nition, digital health interventions include but are not limited to eHealth, mHealth, telemedicine as well as wearable devices. Computer-tailoring is an inherent part of many digital health interventions. This is achieved through the programmed delivery of intervention materials that are tailored – or adjusted – based on an assessment of the characteristics, beliefs and/or behaviour of each individual user (de Vries & Brug, 1999). In contradiction to generic forms of digital health communication (e.g., health information websites), computer-tailored interventions provide participants with personally relevant information. In line with what is described in the Elaboration Likelihood Model (Cacioppo & Petty, 1984), this information is consequently more likely to be read, to be used and engaged with, and to be processed in depth. This results in bene cial outcomes such as greater recall and enhanced initiation or continuation of the communicated health behaviour (change) (Cacioppo & Petty, 1984; Kreuter et al., 1999; Nikoloudakis et al., 2018; Ritterband et al., 2009). A convincing amount of evidence exists showing that computer-tailored digital health interventions can (cost-)effectively change health behaviour for the better (Cheung, Wijnen, & de Vries, 2017; Lustria et al., 2013; Schulz et al., 2014). While this has led to calls for wide-scale implementation of digital health interventions, the modest effect sizes obtained from studies of ef cacy remind us that there is still room for improvement. This also extends to applications targeting intermediaries (e.g., health care professionals, de Ruijter et al., 2018) and intermediate behaviours (e.g., smoking cessation support tool uptake, Gültzow et al., 2021). Furthermore, new technologies such as arti cial intelligence bring about new opportunities as well as challenges that need attention if we are to ultimately bring the  eld forward.With all this in mind, and to join forces in moving the digital health  eld forward, a new Special Interest Group (SIG) on the topic of Digital Health and Computer-Tailoring was launched during the 2019 annual conference of the EHPS (Smit et al., 2019). The mission of this SIG is “to advance digital health and computer-tailoring research and to provide a forum for EHPS members to discuss new evidence, underlying mechanisms and speci c components of digital health interventions that may lead to enhanced behavioural outcomes”. The guest-editing of this special issue in the of cial EHPS bulletin, i.e., the European Health Psychologist, is one of the steps we have taken Digital Health and Computer-Tailoring: Opportunities and Challenges in Moving the Field ForwardSmit, E.S.University of Amsterdam, The NetherlandsNewby, K.University of Hertfordshire, UKKönig, L.M.University of Bayreuth, GermanyOriginal ArticleSmit, Newby & König digital health and computer tailoring789   ehpvolume 22 issue 4 The European Health Psychologistehps.net/ehpsince the SIG’s launch in 2019, to provide such a forum.We are very proud of the  nal collection of articles included in this special issue, covering a wide range of aspects related to digital health and computer-tailoring. To elaborate, Villinger et al. (2021) present the results of a smartphone-based Ecological Momentary Assessment study that aimed to assess health as well as risk behaviours and COVID-19 related risk perception in a real-world setting, capturing daily variations and changes over time in the context of the COVID-19 pandemic, to understand how variations in risk perception relate to behaviours. The main  ndings of their study were that perceived likelihood of having contracted COVID-19 was signi cantly higher on days when participants had had more in-person social contacts and left their homes for multiple reasons. Furthermore, there was substantial variation in health-related behaviours, including eating healthy foods, unhealthy snacking, alcohol consumption, physical activity, sedentary behaviour, and overnight sleep not only between, but also within individuals and on a daily basis. The latter  nding of the study by Villinger et al (2021) relates to the framework presented in the second article of our special collection, authored by Marques and Guastaferro (2021). They argue that MOST – which stands for Multiphase Optimization Strategy – can provide a valuable contribution to the development of behavioral interventions. MOST is an engineering-inspired framework to support the development, optimization and evaluation of multicomponent behavioral interventions. The framework includes three phases: Preparation, Optimization and Evaluation. In particular, the authors argue for the integration of the optimization phase within the standard intervention development process in order to increase the likelihood that resultant interventions are effective, parsimonious, and able to be readily implemented. By putting emphasis on optimization, MOST values the empirical process of identifying an intervention that produces the best expected outcome obtainable given key constraints imposed by the need for affordability, scalability, or ef ciency. The MOST framework lends itself to the use of adaptive experimental research designs during the evaluation phase. These include for example, Just-In-Time-Adaptive-Interventions (JITAIs; Nahum-Shani et al., 2015) that are especially designed to consider daily  uctuations in health-related cognitions and behaviors, e.g., as described in the article by Villinger et al. (2021).This brings us to the third article included in this special issue, in which Wunsch et al. (2021) provide a conceptual overview of JITAI research and discuss the challenges and opportunities with a focus on physical activity interventions. In their position paper, the authors describe key advantages of JITAIs as constituted by the potential to 1) tailor interventions to individual users’ needs in real time to deliver support at the most promising moment, 2) adapt to input data, 3) be system-triggered, 4) deliver goal-oriented interventions, and 5) allow for customization depending on the users’ preferences. Because of these characteristics, JITAIs may increase engagement with and effectiveness of health behavior interventions. Nevertheless, the authors also argue that most existing JITAI research shows considerable methodological shortcomings, with the most prominent being that JITAIs are not described in a standardized fashion which complicates extracting information on effective components of the interventions to inform future research and practice. The authors conclude their work by stating that although JITAIs are a promising feature in mHealth applications, a sound theoretical basis is still lacking and interdisciplinary expert-panels are needed to re ne development, implementation, and evaluation of JITAIs and to keep pace with technological innovations – as described by Marques and Guastaferro (2021), MOST might be a framework that is helpful here.Smit, Newby & König digital health and computer tailoring790   ehpvolume 22 issue 4 The European Health Psychologistehps.net/ehpThe last paper in this special issue refers to technological developments as well, detailing how routinely collected data and novel self-assessment methods can be used in computer-tailoring to measure psychological constructs and address the key challenges of low levels of engagement and high attrition that are likely caused by the high perceived user burden when completing the long, theory-based self-report questionnaires needed for the individual assessment that forms the basis for computer-tailored feedback generation. Building upon novel technological possibilities, Short et al. (2021) describe several examples of how routinely collected data can be used as input for computer-tailoring, one being that it may be possible to deduce exercise habits using a smartphone by combining automatically collected data on behavior frequency (e.g., using accelerometers, GPS or movements between cell towers) with data on contextual cues (e.g., location, time of day, interactions with speci c people). They also describe several novel ways in with data can be purposively sampled in a less burdensome manner as compared to self-report questionnaires, one example being the adoption of game-based elements such as avatar selection to assess real as well as ideal user self-perceptions. The authors conclude their article by discussing the challenges one may encounter when using the proposed methods for routinely collecting data and/or self-assessment, and providing multiple recommendations for future research and practice, which are hoped to stimulate further momentum in this area.All in all, we have very much enjoyed putting together this special issue about digital health and computer-tailoring and hope it will provide food for thought and scholarly discussion, so that we, as a community, can ultimately move this exciting  eld forward by taking advantage of the (technical) opportunities and overcoming the challenges we will encounter.AcknowledgementsWe would like to thank the following people, who are part of the organizing committee of our SIG, for their support in curating this special issue: Ann DeSmet, Laura Maenhout, Olga Perski, Alexis Ruffault and Camille Vansimaeys. We would also like to thank the reviewers of all of the included papers for their time and valuable feedback.ReferencesCacioppo, J. T., & Petty, R. E. (1984). The elaboration likelihood model of persuasion. Advances in Consumer Research, 11(1), 673–675.Cheung, K. L., Wijnen, B., & de Vries, H. (2017). A Review of the Theoretical Basis, Effects, and Cost Effectiveness of Online Smoking Cessation Interventions in the Netherlands: A Mixed-Methods Approach. Journal of Medical Internet Research, 19(6), e230.de Ruijter, D., Candel, M., Smit, E. S., de Vries, H., & Hoving, C. (2018). The effectiveness of a computer-tailored e-learning program for practice nurses to improve their adherence to smoking cessation counseling guidelines: randomized controlled trial. Journal of Medical Internet Research, 20(5), e9276.de Vries, H., & Brug, J. (1999). Computer-tailored interventions motivating people to adopt health promoting behaviours: introduction to a new approach. Patient Education and Counseling, 36(2), 99–105.Gültzow T, Smit ES, Crutzen R, Jolani S, Hoving C, Dirksen CD. Does an Explicit Value Clari cation Method with Computer-Tailored Advice Increase the Effectiveness of a Web-Based Decision Aid to Assist Smokers in Choosing Evidence-Based Cessation Assistance? Findings From a Randomized Controlled Trial. (Preprint). JMIR Preprints, 34246. Kreuter, M., Farrell, D., Olevitch, L., & Brennan, L. (1999). Tailoring Health Messages: Customizing Smit, Newby & König digital health and computer tailoring791   ehpvolume 22 issue 4 The European Health Psychologistehps.net/ehpCommunication with Computer Technology. Lawrence Erlbaum Associates.Lustria, M. L. A., Noar, S. M., Cortese, J., Van Stee, S. K., Glueckauf, R. L., & Lee, J. (2013). A Meta-Analysis of Web-Delivered Tailored Health Behavior Change Interventions. Journal of Health Communication, 18(9), 1039–1069.Marques, M. M., Gustaferro, K. (2021). Frameworks for the Development of Digital behavior Change Interventions: The What and How of the Multiphase Optimization Strategy (MOST). European Health Psychologist. Nahum-Shani, I., Smith, S. N., Spring, B. J., Collins, L. M., Witkiewitz, K., Tewari, A., & Murphy, S. A. (2018). Just-in-time adaptive interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Annals of Behavioral Medicine, 52(6), 446-462.Nikoloudakis, I. A., Crutzen, R., Rebar, A. L., Vandelanotte, C., Quester, P., Dry, M., et al. (2018). Can you elaborate on that? Addressing participants’ need for cognition in computertailored health behavior interventions. Health Psychology Review, 12(4), 437–452.Ritterband, L. M., Thorndike, F. P., Cox, D. J., Kovatchev, B. P., & Gonder-Frederick, L. A. (2009). A Behavior Change Model for Internet Interventions. Annals of Behavioral Medicine, 38(1), 18–27.Schulz, D. N., Smit, E. S., Stanczyk, N. E., Kremers, S. P. J., de Vries, H., & Evers, S. M. A. A. (2014). Economic evaluation of a web-based tailored lifestyle intervention for adults: Findings regarding cost-effectiveness and cost-utility from a randomized controlled trial. Journal of Medical Internet Research, 16(3), e91.Short, C. E., Smit, E. S., & Crutzen, R. (2021). Measuring psychological constructs in computer-tailored interventions: novel possibilities to reduce participant burden and increase engagement. European Health Psychologist. Smit, E. S., Short, C. E., Vandelanotte, C., & De Vries, H. (2019). Digital health & computer-tailoring: The launch of an EHPS special interest group. European Health Psychologist, 20(6), 568-572.van de Belt, T. H., Engelen, L. J. L. P. G., Berben, S. A. A., Teerenstra, S., Samson, M., & Schoonhoven, L. (2013). Internet and Social Media For Health-Related Information and Communication in Health Care: Preferences of the Dutch General Population. Journal of Medical Internet Research, 15, e220. Villinger, K., Wahl, D. R., Debbeler, L. J., Koller, J., Brünecke, I., Lages, N., Schupp, H. T., & Renner, B. (2021). Using Ecological Momentary Assessment to Study Variations in Daily Experiences and Behaviour during the COVID-19 Pandemic. European Health Psychologist. Wunsch, K., Eckert, T., Fiedler, J., & Woll, A. (2021). Just-in-time adaptive interventions in mobile physical activity interventions – A synthesis of frameworks and future directions. European Health Psychologist. Smit, Newby & König digital health and computer tailoring792   ehpvolume 22 issue 4 The European Health Psychologistehps.net/ehpEline SmitDepartment of Communication Science, Amsterdam School of Communication Research/ASCoR,University of Amsterdam, Amsterdam, The NetherlandsE.S.smit@uva.nlSmit, Newby & KönigLaura KönigFaculty of Life Sciences: Food, Nutrition and Health, University of Bayreuth, Bayreuth, Germanylaura.koenig@uni-bayreuth.deKatie NewbyDepartment of Psychology, Sport and Geography, School of Life and Medical Sciences,University of Hertfordshire, Hat eld, UKk.newby@herts.ac.ukdigital health and computer tailoring

Digital health and computer tailoring:opportunities and challenges in moving the field forward

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Digital health and computer tailoring:opportunities and challenges in moving the field forward

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