Designing engaging online behaviour change interventions: A proposed model of user engagement

The potential of online behaviour change interventions for improving public health in both a primary and tertiary prevention setting is well recognised (Davies, Spence, Vandelanotte, Caperchione, & Mummery, 2012; Kuijpers, Groen, Aaronson, & van Harten, 2013). Due to this, and the growing popularity of the Internet, the last decade has seen a substantial increase in the number of online behaviour change interventions developed and evaluated. Several wellconducted systematic reviews and meta-analyses have synthesised the literature regarding this research, providing insight into the effectiveness of these interventions, as well as the factors associated with intervention success (e.g., Brouwer et al. , 2011; Davies et al. , 2012; Kuijpers et al. , 2013; Webb, Joseph, Yardley & Michie, 2010). In general, these reviews have shown that online interventions can be effective (albeit effect sizes are in general small) , and that effectiveness is mediated by factors related to health behaviour change (e.g., the use of behaviour change theory and the type and number of behaviour change techniques employed) as well as intervention characteristics related to user engagement in the intervention (e.g., whether intervention content is tailored to match individual characteristics, website interactivity, frequency of website updates and reminders and the use of supplementary delivery modes). Engagement in this context refers to a quality of user experience, characterised by increased attention, positive affect, sensory and intellectual satisfaction and mastery (O'Brien & Toms, 2008). Whilst there have been calls as early as 2009 (Ritterband & Tate, 2009) to consider determinants of user engagement when designing online interventions, very few studies have incorporated this into their conceptual framework. Rather, the development of online interventions has been guided predominantly by theories of behaviour change, which focus on the psychosocial determinants of behaviour (e.g., selfefficacy, intentions). Theories offering insight into how to foster user engagement in online interventions have been largely ignored, an oversight which may explain why issues with user engagement, such as low use of intervention features, few logins, and poor retention rates are consistently reported in the literature (Davies et al. , 2012; Kelders, Kok, Ossebaard, & Van Gemert-Pijnen, 2012). A notable exception is research investigating the efficacy of computer-tailored interventions. Computer-tailoring is a technique that utilises expert-system technology and individual assessments to provide individuals with customised health behaviour advice and feedback via an automated process (Kreuter, Farrell, & Olevitch, 2000). The Elaboration Likelihood Model (ELM; Petty, Barden, & Wheeler, 2009), an information processing theory, is often cited as the theoretical rationale for computertailoring (Kreuter et al. , 2000; Short, James, & Plotnikoff, 2013). According to the ELM, people process information elaborately (i.e. , in an active and deliberate manner) if they are motivated and have the resources (e.g., time), tendency and capabilities Designing engaging onl ine behaviour change interventions: A proposed model of user engagement original article designing engaging online behaviour change interventions Short et al.