How Do We Achieve Impactful Patient and Public Involvement and Engagement in Statistical Methodology Research? A Qualitative Research Protocol.

Poor mental health is increasing, affecting 42.5% of US adults, according to a 2022 survey []. People face barriers to accessing traditional care, such as a lack of service availability and high costs [,]. Digital mental health interventions (DMHIs) have been shown to improve mental health [-], with evidence highlighting their potential to help users achieve their mental health goals (eg, symptom reduction or the development of new coping skills) [,]. Engagement is considered a key mediator of outcomes [-]. However, DMHIs currently experience low engagement and high dropout rates [,-]. Among commercial DMHIs, the median retention rate was 3.9% after 15 days of use [], indicating a need to both understand and improve user engagement with these tools [,]. The challenge of low engagement with DMHIs is further complicated by a lack of consensus on what engagement means and a lack of understanding of how engagement is applied in their design [,,]. Engagement with digital interventions is a multidimensional concept often discussed as being composed of 3 constructs: cognitive (eg, effort and attention), affective (eg, emotions and values), and behavioral (eg, usage and actions) engagement [,,-]. However, no standard definition of engagement exists across research fields, such as behavioral sciences and human-computer interaction (HCI) [,,]. Existing models of engagement highlight the dynamic process of user engagement with technology that goes beyond discrete technology use [,-]. The foundational model of engagement by O’Brien and Toms [] presents the temporal stages of engagement, starting with the point of engagement, sustained engagement, end of engagement, and, sometimes, re-engagement. Each stage is characterized by attributes of user experience (eg, positive affect and sensory appeal), based on interviews with users of video games, educational applications, online shopping, and web search []. As an extension of this dynamic view, Yardley et al [] differentiated between the micro- and macro-level engagement. Microengagement, also known as “little e,” refers to discrete or momentary engagement with the digital intervention, particularly with the technology [,], including the user interface elements and the behavior change intervention components that are presented in the technology []. Macroengagement refers to the user’s engagement with the behavior change process that the DMHI is designed to support [,]. Together, these levels capture both discrete and longitudinal contexts of how users engage with digital systems. Our study examined how the micro and macro levels of engagement are applied in the design of DMHIs. Research on engagement has mainly focused on engagement rather than on understanding how the definitions are reflected in the practice of creating technologies [,,,,]. There are engagement models specific to DMHIs, but they tend to focus on measuring or predicting engagement outcomes of a single intervention [,]. For example, the Integrative Engagement Model of Digital Psychotherapy [] combines frameworks from behavioral sciences and HCI to define the phases of engagement with therapist-supported DMHIs. These models advance the understanding of engagement with DMHIs, but do not inform how researchers operationalize engagement. Lipschitz et al [] emphasized that research needs to advance theories of what drives DMHIs engagement and that this needs to be understood at different levels, in real-world settings, rather than only in clinical trials.