Applying models of self-regulated learning to understand engagement with digital health interventions: a narrative review.

Digital health interventions (DHIs) are often burdened by poor user engagement and high drop-out rates, diminishing their potential public health impact. Identifying user-related factors predictive of engagement has therefore drawn significant research attention in recent years. Absent from this literature-yet implied by DHI design-is the notion that individuals who use DHIs have well-regulated learning capabilities that facilitate engagement with unguided intervention content. In this narrative review, we make the case that learning capacity can differ markedly across individuals, and that the requirements of self-guided learning for many DHIs do not guarantee that those who sign up for these interventions have good learning capabilities at the time of uptake. Drawing upon a rich body of theoretical work on self-regulated learning (SRL) in education research, we propose a user-as-learner perspective to delineate parameters and drivers of variable engagement with DHIs. Five prominent theoretical models of SRL were wholistically evaluated according to their relevance for digital health. Three key themes were drawn and applied to extend our current understanding of engagement with DHIs: (a) common drivers of engagement in SRL, (b) the temporal nature of engagement and its drivers, and (c) individuals may differ in learning capability. Integrating new perspectives from SRL models offered useful theoretical insights that could be leveraged to enhance engagement with intervention content throughout the DHI user journey. In an attempt to consolidate these differing-albeit complementary-perspectives, we develop an integrated model of engagement and provide an outline of future directions for research to extend the current understanding of engagement issues in self-guided DHIs.