Abstract

User engagement, a central construct in information systems, is particularly important in digital health research, as desirable health outcomes require meaningful engagement with the system. Yet user engagement remains inconsistently defined and inadequately measured for healthcare applications. While engagement is widely conceptualized as multidimensional, spanning behavioral, cognitive, and affective dimensions, empirical work often equates engagement with healthcare applications as system use, relying on metrics such as logins or clicks. These indicators are limited because they do not capture whether users meaningfully interact with the prescribed tasks of a digital health intervention, nor do they explain how engagement translates into health outcomes. This misalignment has hindered construct validity, weakened cross-study comparability, and constrained the ability to link engagement with behavioral and clinical improvements. To address these challenges, we conducted two rounds of narrative reviews examining user engagement with digital health interventions to explore this phenomenon. Building on the results of our review and Burton-Jones and Grange (2013) theory of effective use, this study introduces the concept of intervention engagement, defined as goal-directed, prescription-based participation with intervention content. We extend this concept to digital health, proposing that effective use arises from the combination of system use and intervention engagement. Here, effective use refers to how well system usage aligns with task performance and the achievement of relevant outcomes, which subsequently drives improved health outcomes. The conceptual model provides a theoretical bridge between system use and outcome-focused participation, since system use alone is necessary but not sufficient. Only when users follow the prescribed tasks does their interaction become effective and drive outcomes in line with the intervention’s aims. To operationalize the construct, we propose a taxonomy of engagement measurement attributes that distinguishes between system-level activity (e.g., frequency, intensity) and intervention-level participation (e.g., task adherence, reflective processing). Additionally, it categorizes across behavioral, cognitive, and affective dimensions, along with antecedents (e.g., motivation) and consequences (e.g., satisfaction). This study makes several contributions. First, it introduces intervention engagement as a theoretically grounded construct that measures engagement and extends effective use into digital health. Second, it offers a conceptual model that clarifies the relationship between system use, intervention engagement, and effective use for healthcare applications, challenging the assumption that activity alone reflects meaningful participation in digital interventions. Third, it presents a comprehensive measurement process supported by a taxonomy of engagement attributes. This process enables researchers to select context-appropriate measures and provides practitioners with a tool for selecting valid, context-sensitive metrics that reflect system performance and intervention engagement effectiveness. Future research will validate this framework using two diverse case studies of digital health interventions that vary in duration, purpose, and population. These studies will refine the taxonomy of measures and empirically test the association between intervention engagement and health outcomes. By aligning measurement practices with intervention prescriptions, this work enhances conceptual clarity, improves methodological rigor, promotes generalizable, and outcome-oriented science of engagement in digital health.

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