Influence of Animation- Versus Text-Based Delivery of a Web-Based Computer-Tailored Smoking Cessation Intervention on User Perceptions




digital health, eHealth, computer tailoring, smoking cessation, user experience, user engagement


Computer-tailored (CT) digital health interventions have shown to be effective in obtaining behaviour change. Yet, user perceptions of these interventions are often unsatisfactory. Traditional CT interventions rely mostly on text-based feedback messages. A way of presenting feedback messages in a more engaging manner may be the use of narrated animations instead of text. The goal of this study was to assess the effect of manipulating the mode of delivery (animation vs. text) in a smoking cessation intervention on user perceptions among smokers and non-smokers. Smokers and non-smokers (N = 181) were randomized into either the animation or text condition. Participants in the animation condition assessed the intervention as more effective (ηp2 = .035), more trustworthy (ηp2 = .048), more enjoyable (ηp2 = .022), more aesthetic (ηp2 = .233), and more engaging (ηp2 = .043) compared to participants in the text condition. Participants that received animations compared to text messages also reported to actively trust the intervention more (ηp2 = .039) and graded the intervention better (ηp2 = .056). These findings suggest that animation-based interventions are superior to text-based interventions with respect to user perceptions.


Benowitz, N. L. (2010). Nicotine addiction. New England Journal of Medicine, 362(24), 2295-2303.

Carr, N. G. (2011). The shallows: What the internet is doing to our brains (1st ed.). W.W. Norton.

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), Article: e230.

Cisco. (2019). Cisco visual networking index: Forecast and trends, 2017–2022.

Crutzen, R., Beekers, N., van Eenbergen, M., Becker, M., Jongen, L., & van Osch, L. (2014). E‐loyalty towards a cancer information website: Applying a theoretical framework. Psycho‐Oncology, 23(6), 685-691.

Crutzen, R., Cyr, D., & de Vries, N. K. (2011). Bringing loyalty to e-health: Theory validation using three internet-delivered interventions. Journal of Medical Internet Research, 13(3), Article: e73.

Crutzen, R., Cyr, D., & de Vries, N. K. (2012). The role of user control in adherence to and knowledge gained from a website: Randomized comparison between a tunneled version and a freedom-of-choice version. Journal of Medical Internet Research, 14(2), Article: e45.

Crutzen, R., de Nooijer, J., Brouwer, W., Oenema, A., Brug, J., & de Vries, N. K. (2009). A conceptual framework for understanding and improving adolescents’ exposure to internet-delivered interventions. Health Promotion International, 24(3), 277-284.

de Vries, H. (2017). An integrated approach for understanding health behavior; the I-change model as an example. Psychology and Behavioral Science International Journal, 2(2), 555-585.

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.

de Vries, H., Kremers, S., Smeets, T., Brug, J., & Eijmael, K. (2008). The effectiveness of tailored feedback and action plans in an intervention addressing multiple health behaviors. American Journal of Health Promotion, 22(6), 417-424.

Donkin, L., Christensen, H., Naismith, S. L., Neal, B., Hickie, I. B., & Glozier, N. (2011). A systematic review of the impact of adherence on the effectiveness of e-therapies. Journal of Medical Internet Research, 13(3), Article: e52.

Eysenbach, G. (2005). The law of attrition. Journal of Medical Internet Research, 7(1), Article: e11.

Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175-191.

Hughes, J. R., Keely, J., & Naud, S. (2004). Shape of the relapse curve and long‐term abstinence among untreated smokers. Addiction, 99(1), 29-38.

Kalogeropoulos, A., Cherubini, F., & Newman, N. (2016). The future of online news video. Reuters Institute for the Study of Journalism.

Kanera, I. M., Willems, R. A., Bolman, C. A., Mesters, I., Zambon, V., Gijsen, B. C., & Lechner, L. (2016). Use and appreciation of a tailored self-management eHealth intervention for early cancer survivors: Process evaluation of a randomized controlled trial. Journal of Medical Internet Research, 18(8), Article: e229.

Krebs, P., Prochaska, J. O., & Rossi, J. S. (2010). A meta-analysis of computer-tailored interventions for health behavior change. Preventive Medicine, 51(3), 214-221.

Liu, Z. (2005). Reading behavior in the digital environment: Changes in reading behavior over the past ten years. Journal of Documentation, 61(6), 700-712.

Mayer, R. E. (2009). Multimedia learning (2nd ed.). Cambridge University Press.

Mayer, R. E. (2018). Thirty years of research on online learning. Applied Cognitive Psychology, 33, 152-159.

Meppelink, C. S., van Weert, J. C. M., Haven, C. J., & Smit, E. G. (2015). The effectiveness of health animations in audiences with different health literacy levels: An experimental study. Journal of Medical Internet Research, 17(1), Article: e11.

Ministerie van Binnenlandse Zaken en Koninkrijksrelaties. (2018). Tabaks- en rookwarenwet.

Nationaal Expertisecentrum Tabaksontmoediging. (2018). Kerncijfers roken 2017. Trimbos-instituut.

Nunn, A., Crutzen, R., Haag, D., Chabot, C., Carson, A., Ogilvie, G., Shoveller, J., & Gilbert, M. (2017). Examining e-loyalty in a sexual health website: Cross-sectional study. JMIR Public Health and Surveillance, 3(4), Article: e75.

Perski, O. (2017). Study protocol: Development and psychometric evaluation of a self-report instrument to measure engagement with digital behaviour change interventions. Open Science Framework.

Perski, O., Blandford, A., Garnett, C., Crane, D., West, R., & Michie, S. (2019). A self-report measure of engagement with digital behavior change interventions (DBCIs): Development and psychometric evaluation of the “DBCI engagement scale”. Translational Behavioral Medicine, 10(1), 267-277.

Perski, O., Blandford, A., West, R., & Michie, S. (2017). Conceptualising engagement with digital behaviour change interventions: A systematic review using principles from critical interpretive synthesis. Translational Behavioral Medicine, 7(2), 254-267.

Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. In Communication and Persuasion (pp. 1-24). Springer.

Prochaska, J. O., & DiClemente, C. C. (1983). Stages and processes of self-change of smoking: Toward an integrative model of change. Journal of Consulting and Clinical Psychology, 51(3), 390.

Prochaska, J. O., & Velicer, W. F. (1997). The transtheoretical model of health behavior change. American Journal of Health Promotion, 12(1), 38-48.

Rijksinstituut voor Volksgezondheid en Milieu. (2018). Roken - cijfers & context - gevolgen. RIVM.

Ruiter, R. A. C., Kessels, L. T. E., Jansma, B. M., & Brug, J. (2006). Increased attention for computer-tailored health communications: An event-related potential study. Health Psychology, 25(3), 300-306.

Ryota, K., & Kep Kee, L. (2016). How has the internet reshaped human cognition? Neuroscientist, 22(5), 506-520.

Short, C. E., DeSmet, A., Woods, C., Williams, S. L., Maher, C., Middelweerd, A., Müller, A. M., Wark, P. A., Vandelanotte, C., Poppe, L., Hingle, M. D., & Crutzen, R. (2018). Measuring engagement in eHealth and mHealth behavior change interventions: Viewpoint of methodologies. Journal of Medical Internet Research, 20(11), Article: e292.

Short, C. E., Rebar, A. L., Plotnikoff, R. C., & Vandelanotte, C. (2015). Designing engaging online behaviour change interventions: A proposed model of user engagement. The European Health Psychologist, 17(1), 32-38.

Smit, E. S., Linn, A. J., & van Weert, J. C. M. (2015). Taking online computer-tailoring forward: The potential of tailoring the message frame and delivery mode of online health behaviour change interventions. The European Health Psychologist, 17(1), 25-31.

Soetens, K. C., Vandelanotte, C., de Vries, H., & Mummery, K. W. (2014). Using online computer tailoring to promote physical activity: A randomized trial of text, video, and combined intervention delivery modes. Journal of Health Communication, 19(12), 1377-1392.

Stanczyk, N. E., Bolman, C., Muris, J. W., & de Vries, H. (2011). Study protocol of a Dutch smoking cessation e-health program. BMC Public Health, 11(1), Article: 847.

Stanczyk, N. E., Bolman, C., van Adrichem, M., Candel, M., Muris, J., & de Vries, H. (2014). Comparison of text and video computer-tailored interventions for smoking cessation: Randomized controlled trial. Journal of medical Internet research, 16(3), Article: e69.

Stanczyk, N. E., Crutzen, R., Bolman, C., Muris, J., & de Vries, H. (2013). Influence of delivery strategy on message-processing mechanisms and future adherence to a Dutch computer-tailored smoking cessation intervention. Journal of Medical Internet Research, 15(2).

Stanczyk, N. E., de Vries, H., Candel, M. J., Muris, J. W., & Bolman, C. A. (2016). Effectiveness of video- versus text-based computer-tailored smoking cessation interventions among smokers after one year. Preventive Medicine, 82, 42-50.

Stanczyk, N. E., Smit, E. S., Schulz, D. N., de Vries, H., Bolman, C., Muris, J. W., & Evers, S. M. (2014). An economic evaluation of a video-and text-based computer-tailored intervention for smoking cessation: A cost-effectiveness and cost-utility analysis of a randomized controlled trial. PloS one, 9(10), Article: e110117.

Steiger, J. H. (2004). Beyond the F test: Effect size confidence intervals and tests of close fit in the analysis of variance and contrast analysis. Psychological Methods, 9(2), 164-186.

van der Heide, I., Wang, J., Droomers, M., Spreeuwenberg, P., Rademakers, J., & Uiters, E. (2013). The relationship between health, education, and health literacy: Results from the Dutch adult literacy and life skills survey. Journal of Health Communication, 18(sup1), 172-184.

van der Mispel, C., Poppe, L., Crombez, G., Verloigne, M., & De Bourdeaudhuij, I. (2017). A self-regulation-based eHealth intervention to promote a healthy lifestyle: Investigating user and website characteristics related to attrition. Journal of Medical Internet Research, 19(7), Article: e241.

van het Schip, C., Cheung, K. L., Vluggen, S., Hoving, C., Schaper, N. C., & de Vries, H. (2020). Spoken animated self-management video messages aimed at improving physical activity in people with Type 2 diabetes: Development and interview study. Journal of Medical Internet Research, 22(4), Article: e15397.

Vandelanotte, C., & Mummery, W. K. (2011). Qualitative and quantitative research into the development and feasibility of a video-tailored physical activity intervention. International Journal of Behavioral Nutrition and Physical Activity, 8(1), 1-11.

Walthouwer, M. J. L., Oenema, A., Lechner, L., & de Vries, H. (2015). Comparing a video and text version of a web-based computer-tailored intervention for obesity prevention: A randomized controlled trial. Journal of Medical Internet Research, 17(10), Article: e236.

World Health Organization. (2018). Classification of digital health interventions v1.0: A shared language to describe the uses of digital technology for health. World Health Organization.




How to Cite

Elling, J. M., & de Vries, H. (2021). Influence of Animation- Versus Text-Based Delivery of a Web-Based Computer-Tailored Smoking Cessation Intervention on User Perceptions. European Journal of Health Communication, 2(3), 1–23.



Original Research Paper