Persuasive System Design Does Matter: A Systematic Review of Adherence to Web-Based Interventions

By Saskia M. Kelders, R. Kok, Hans C. Ossebaard, J. V. van Gemert-Pijnen. Journal of Medical Internet Research, 2012

1,304 citations recorded by the source corpus.

Background Although web-based interventions for promoting health and health-related behavior can be effective, poor adherence is a common issue that needs to be addressed. Technology as a means to communicate the content in web-based interventions has been neglected in research. Indeed, technology is often seen as a black-box, a mere tool that has no effect or value and serves only as a vehicle to deliver intervention content. In this paper we examine technology from a holistic perspective. We see it as a vital and inseparable aspect of web-based interventions to help explain and understand adherence. Objective This study aims to review the literature on web-based health interventions to investigate whether intervention characteristics and persuasive design affect adherence to a web-based intervention. Methods We conducted a systematic review of studies into web-based health interventions. Per intervention, intervention characteristics, persuasive technology elements and adherence were coded. We performed a multiple regression analysis to investigate whether these variables could predict adherence. Results We included 101 articles on 83 interventions. The typical web-based intervention is meant to be used once a week, is modular in set-up, is updated once a week, lasts for 10 weeks, includes interaction with the system and a counselor and peers on the web, includes some persuasive technology elements, and about 50% of the participants adhere to the intervention. Regarding persuasive technology, we see that primary task support elements are most commonly employed (mean 2.9 out of a possible 7.0). Dialogue support and social support are less commonly employed (mean 1.5 and 1.2 out of a possible 7.0, respectively). When comparing the interventions of the different health care areas, we find significant differences in intended usage (p = .004), setup (p < .001), updates (p < .001), frequency of interaction with a counselor (p < .001), the system (p = .003) and peers (p = .017), duration (F = 6.068, p = .004), adherence (F = 4.833, p = .010) and the number of primary task support elements (F = 5.631, p = .005). Our final regression model explained 55% of the variance in adherence. In this model, a RCT study as opposed to an observational study, increased interaction with a counselor, more frequent intended usage, more frequent updates and more extensive employment of dialogue support significantly predicted better adherence. Conclusions Using intervention characteristics and persuasive technology elements, a substantial amount of variance in adherence can be explained. Although there are differences between health care areas on intervention characteristics, health care area per se does not predict adherence. Rather, the differences in technology and interaction predict adherence. The results of this study can be used to make an informed decision about how to design a web-based intervention to which patients are more likely to adhere.

Subfields: Applied Psychology, Family Practice, General Health Professions

View Paper: https://www.semanticscholar.org/paper/5642eb6d333ca0d9e3772ca007f020f6e533c46c

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Citations in the corpus

Integrating Human Support Into Behavioral Intervention Technologies: The Efficiency Model of Support (2017, background): “These programs often make use of persuasive technology such as text messages, interactive media, tailoring, and personalization even within the automated support (Kelders et al., 2012, 2015).”
A foundation for the study of behavior change support systems (2012, background): “The model can be used in a variety of settings ranging from evaluating software speciﬁcations in the early stages of systems development [53] to studying full-ﬂedged commercial applications [54] to analyzing research literature in a given problem domain [14, 55].”
A fully automated conversational agent for promoting mental well-being: A pilot RCT using mixed methods (2017, background): “The typical web-based intervention is usually meant to be used once a week (Kelders et al., 2012).”
Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis (2016, background): “Novelty, generated by regular content updates, has been found to positively influence engagement through preventing boredom [25, 26].”
Do Persuasive Technologies Persuade? - A Review of Empirical Studies (2014, result): “However, no noteworthy overlap with the present study was detected with regard to the reviewed papers (see [16,17]).”
Handbook of the psychology of self-forgiveness (2017, background): “If a sense of global inferiority is what explains why the experience of shame is sometimes debilitating, then it makes sense, conceptually and empirically, to examine the sense of global inferiority directly rather than to examine it indirectly through the concept of shame, which may or may not impl”
The Behavioral Intervention Technology Model: An Integrated Conceptual and Technological Framework for eHealth and mHealth Interventions (2014, background): “Its clear articulation also allows the evaluation of the value of these components, as evidenced by a meta-analysis that evaluated both the frequency of the use of these components, as well as their impact on adherence [10].”
Measuring Engagement in eHealth and mHealth Behavior Change Interventions: Viewpoint of Methodologies (2018, background): “Consequently, it is often confused with adherence, which refers to whether the intervention is used as intended by the developers [3,8,9].”
An Overview of Chatbot-Based Mobile Mental Health Apps: Insights From App Description and User Reviews (2022, positive): “Consistent with previous work on persuasive technology in MH [80-82], we found that daily check-ins, gamification, reminders, and self-monitoring were perceived as helpful features, although they were prescriptive in nature.”