Understanding Health Behavior Technology Engagement: Pathway to Measuring Digital Behavior Change Interventions (DBCI) (Preprint)

UNSTRUCTURED Researchers and practitioners of Digital Behavior Change Interventions (DBCI) use varying and oftentimes incongruent definitions of the term “engagement;” thus, leading to 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 explain why precision in the measurement of these engagement types is integral to ensuring intervention effectiveness. 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 digital behavior change intervention (DBCI) engagement, referred to as “Little e.” DBCI engagement is further bifurcated into two sub-classes: 1) user interactions with features of the intervention designed to encourage frequency of use (i.e., simple login, games, social interactions) and make the user experience appealing; and 2) user interactions with behavior change intervention components (i.e., behavior change techniques) which influence determinants of health behavior-- and subsequently, influence health behavior. Achievement of Big E, health behavior engagement, in an intervention delivered via digital means, is contingent upon Little e, DBCI engagement. 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 Big E, health behavior engagement. Big E, health behavior engagement, 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 create Little e, DBCI engagement, which in turn is designed to improve Big E, health behavior engagement. 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 supporting various health behaviors and outcomes; and can be utilized to identify gaps in intervention efficacy and effectiveness.