Towards an interdisciplinary theory of engagement: the Dynamic EngagEment Process framework for Digital Health Interventions (DEEP – DHI)

Kerem Doğan et al.

Behaviour and Information Technology2026https://doi.org/10.1080/0144929x.2026.2619659article
AJG 2ABDC A
Weight
0.50

Abstract

Engagement is a construct that can provide insight into why Digital Health Interventions (DHIs) are effective for some people but not others, ultimately addressing challenges such as high dropout, non-adherence rates, and poor effectiveness. However, the concept of engagement remains theoretically ambiguous, resulting in inconsistent definitions, disparate research methods, and outcomes that are difficult to compare and build upon. This paper addresses this gap by applying theory triangulation to reconceptualize engagement in the context of DHIs. After analysing key characteristics of engagement with DHIs, we identified Postphenomenology, Social Practice Theory, and Social Cognitive Theory as relevant theoretical perspectives. We synthesised these theories to develop a new conceptual framework: the Dynamic EngagEment Process framework for Digital Health Interventions (DEEP-DHI). The DEEP-DHI framework provides a dynamic, domain-specific approach to understanding engagement with DHIs. It outlines engagement states and modes that reveal patterns in the engagement process. The framework emphasises the individual, environmental, and material factors that shape engagement and situates engagement with DHIs within a network of health practices and societal structures. By moving beyond static, usage-based definitions of engagement, the DEEP-DHI framework facilitates more coherent evaluation and design methods that examine engagement as an evolving process embedded in lived experience.

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https://doi.org/https://doi.org/10.1080/0144929x.2026.2619659

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@article{kerem2026,
  title        = {{Towards an interdisciplinary theory of engagement: the Dynamic EngagEment Process framework for Digital Health Interventions (DEEP – DHI)}},
  author       = {Kerem Doğan et al.},
  journal      = {Behaviour and Information Technology},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1080/0144929x.2026.2619659},
}

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0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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