Abstract
Researchers and practitioners of digital behavior change interventions (DBCI) use varying and, often, incongruent definitions of the term “engagement,” thus leading to a lack of precision in DBCI measurement and evaluation. The objective of this paper is to propose discrete definitions for various types of user engagement and to explain why precision in the measurement of these engagement types is integral to ensuring the intervention is effective for health behavior modulation. Additionally, this paper presents a framework and practical steps for how engagement can be measured in practice and used to inform DBCI design and evaluation. The key purpose of a DBCI is to influence change in a target health behavior of a user, which may ultimately improve a health outcome. Using available literature and practice-based knowledge of DBCI, the framework conceptualizes two primary categories of engagement that must be measured in DBCI. The categories are health behavior engagement, referred to as “Big E,” and DBCI engagement, referred to as “Little e.” DBCI engagement is further bifurcated into two subclasses: (1) user interactions with features of the intervention designed to encourage frequency of use (ie, simple login, games, and social interactions) and make the user experience appealing, and (2) user interactions with behavior change intervention components (ie, behavior change techniques), which influence determinants of health behavior and subsequently influence health behavior. Achievement of Big E in an intervention delivered via digital means is contingent upon Little e. If users do not interact with DBCI features and enjoy the user experience, exposure to behavior change intervention components will be limited and less likely to influence the behavioral determinants that lead to health behavior engagement (Big E). Big E is also dependent upon the quality and relevance of the behavior change intervention components within the solution. Therefore, the combination of user interactions and behavior change intervention components creates Little e, which is, in turn, designed to improve Big E. The proposed framework includes a model to support measurement of DBCI that describes categories of engagement and details how features of Little e produce Big E. This framework can be applied to DBCI to support various health behaviors and outcomes and can be utilized to identify gaps in intervention efficacy and effectiveness.
Highlights
Globally, the creative integration of technology solutions to address health issues is growing [1]
Recent systematic reviews demonstrate that digital interventions supported by behavior science evidence hold the key to driving critical behavior change processes that lead to improved health behaviors and health outcomes [2]
This utilization has been generally referred to as “engagement” or “user-engagement.”. This term is largely recognizable, but at the same time, abstract and difficult to measure accurately in different settings and contexts. This measurement limitation can be mitigated by applying frameworks/models that guide the way engagement is considered, measured, and applied in analysis within an intervention
Summary
2 (page number not for citation purposes) question, “What level of user-engagement with the DBCI, and by which users, leads to a desired health?” This model should ideally delineate and identify unique, meaningful interaction sequences that represent digital features informed by specific behavior change theory–informed components of the intervention (eg, BCTs) [13]. Use of the understanding from the intervention mapping literature [20] to determine specific change processes that illustrated our best-informed assumptions about which specific BCTs drive factors influence performance of the behaviors This helped define and guide the digital product strategy where each behavior change process is represented by a set of user experience/user interaction (UI) and content features. There are two broad approaches to consider when analyzing such data: an a priori theory approach and a grounded theory approach
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