Engagement in digital interventions.

Abstract

The notion of “engagement,” which plays an important role in various domains of psychology, is gaining increased currency as a concept that is critical to the success of digital interventions. However, engagement remains an ill-defined construct, with different fields generating their own domain-specific definitions. Moreover, given that digital interactions in real-world settings are characterized by multiple demands and choice alternatives competing for an individual’s effort and attention, they involve fast and often impulsive decision-making. Prior research seeking to uncover the mechanisms underlying engagement has nonetheless focused mainly on psychological factors and social influences and neglected to account for the role of neural mechanisms that shape individual choices. This article aims to integrate theories and empirical evidence across multiple domains to define engagement and discuss opportunities and challenges to promote effective engagement in digital interventions. We also propose the affect–integration–motivation and attention–context–translation (AIM–ACT) framework, which is based on a neurophysiological account of engagement, to shed new light on how in-the-moment engagement unfolds in response to a digital stimulus. Building on this framework, we provide recommendations for designing strategies to promote engagement in digital interventions and highlight directions for future research.

Factors Influencing Adherence to mHealth Apps for Prevention or Management of Noncommunicable Diseases: Systematic Review

“…Previous research underscores the necessity of mHealth apps that must be first accepted and used in an intended way to then achieve a desired health behavior change [ 3 , 19 ]. Correspondingly, previous research has identified factors affecting the uptake of and engagement with health and well-being smartphone apps [ 11 , 20 - 23 ]. Many of these strategies and factors, such as well-designed reminders, self-monitoring features, and embedded health professional support, have been applied across various health domains [ 11 , 20 , 21 ].…”

Operationalizing Engagement With an Interpretation Bias Smartphone App Intervention: Case Series

“…Although there are many definitions of engagement, most concur on its multifaceted and dynamic nature [ 23 , 25 ] suggesting that it subsumes the extent of intervention use (amount, frequency, and duration), as well as subjective experience (attention, interest, and affect) with the intervention [ 26 ]. Nahum-Shani et al [ 27 ] integrated theories of engagement across disciplines (ie, education, industrial or organizational psychology, and computer science) and suggested that engagement may be best thought of as “energy investment involving physical, affective, and cognitive energies directed toward a focal stimulus or task.” Recent examinations of engagement have indeed focused on a tripart model: behavioral (physical involvement with the intervention), cognitive (thinking about, attending to, and processing the intervention), and affective (emotional response to the intervention) [ 23 , 27 - 29 ]. These 3 domains are distinct; an individual can enjoy an intervention (affective) but not complete the suggested amount of use (behavioral), or they can complete intervention sessions (behavioral) and not make connections between the app and their life (cognitive).…”

“…These 3 domains are distinct; an individual can enjoy an intervention (affective) but not complete the suggested amount of use (behavioral), or they can complete intervention sessions (behavioral) and not make connections between the app and their life (cognitive). Nahum-Shani et al [ 27 ] asserted that engagement is a state that waxes and wanes because of a variety of internal and external factors [ 30 ] rather than a relatively stable construct [ 28 , 31 ].…”

“…In this study, we aimed to operationalize the model of engagement by Nahum-Shani et al [ 27 ] for a novel mental health app called HabitWorks . We developed HabitWorks to provide support during the critical transition between acute psychiatric care and outpatient treatment, a time of high risk for symptom deterioration, rehospitalization, and treatment disengagement [ 32 ].…”

“…Although we primarily focused on behavioral engagement for pattern categorization, we also explored indicators of affective and cognitive engagement. Research indicates that presenting only behavioral outcomes may be simplistic and fail to fully capture engagement as a construct [ 27 ]. Examining affective and cognitive engagement is crucial for developing a more thorough and nuanced understanding of the way people interact with apps.…”