Exploring Engagement with Remote Measurement Technologies in Major Depressive Disorder

    Student thesis: Doctoral ThesisDoctor of Philosophy

    Abstract

    Background

    Remote Measurement Technologies (RMTs), including smartphone applications (apps) and wearable devices, offer the potential to revolutionise the monitoring and treatment of chronic health conditions. However, understanding user engagement remains a fundamental challenge in the field. The lack of standardisation, coupled with varied reporting of engagement estimates, is stunting the ability to draw meaningful conclusions. Major depressive disorder (MDD) is a chronic mental health condition with many features amenable to assessment via RMT. Given the variable treatment efficacy rates for MDD, individuals might particularly benefit from relapse prediction and targeted interventions. Work in this field will be accelerated by the development of multi-parametric RMT research platforms, of which the Remote Assessment of Disease and Relapse- Major Depressive Disorder (RADAR-MDD) study is currently the largest example. The RADAR-MDD study therefore provides a conduit to explore engagement with RMTs for MDD research.

    Aims

    The overall aim of this thesis was to provide an exploration of the conceptualisation, measurement, and promotion of engagement with RMTs for symptom tracking in MDD. Specifically, it aimed to synthesise the current state of engagement reporting in the literature, analyse why and how users engaged with the RADAR-MDD study, and investigate the promotion of engagement with RMTs via system components.

    Methods

    A systematic review with critical interpretive synthesis quantified the extent of engagement reporting in the literature and uncovered themes of the most common engagement measures used. A multisite longitudinal qualitative analysis incorporated 124 interviews from the three sites of the RADAR-MDD study to understand the subjective experience of long-term engagement with RMTs. A series of mixed effects regression models quantitatively examined the effect of research team contact on RMT data availability in RADAR-MDD participants. A fully remote, two-armed randomised-controlled trial (N=100) was conducted to investigate the effect of additional in-app components on objective (completion of in-app questionnaires) and subjective (system usability, utility, and emotional self-awareness) engagement with a multi-parametric RMT research system.

    Results

    Thirty five percent of articles in the systematic review included a definition and corresponding measure of engagement when reporting their results. Measures were wide-ranging and incongruent. An integrative framework demonstrated that engagement with RMTs can be measured both objectively and subjectively. Facilitators of long-term RMT engagement can be considered from both an experiential (i.e., research altruism, research team support) and a system-related (i.e., usability of the technologies, utility for clinical practice) standpoint. RADAR-MDD recorded 4,673 contacts to and from the research team throughout the two-year follow-up, averaging 0.73 contacts per participant per study month. Mixed effects regression models found no evidence of an association between user-initiated contacts and data availability. Findings from the randomised-controlled trial suggested that adapting the RMT system did not lead to higher levels of objective engagement. Though ratings of subjective engagement were slightly higher in the intervention group, these differences were not statistically significant.

    Conclusions

    This thesis contributes both a framework for conceptualising, measuring, and promoting engagement with RMTs and an insight into how users objectively and subjectively engage with a multi-parametric RMT study of MDD. These findings support an argument for considering RMTs as distinct from existing digital health spheres. They also provide a blueprint for measuring engagement in future work. Implications for future RMT research include the importance of research altruism, the inclusion of a research team in some form, and the use of an open-source, adaptable system. This work also provides important considerations for the implementation of RMTs into clinical practice as the field advances further. Continuing to establish a standardised evidence base for the understanding of engagement with RMTs will be the key to realising their potential in research and digital healthcare, and for the ultimate benefit of those with chronic health conditions.




    Date of Award1 Sept 2023
    Original languageEnglish
    Awarding Institution
    • King's College London
    SupervisorMatthew Hotopf (Supervisor) & Claire Henderson (Supervisor)

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