| Summary: | The 3D-study sought to explore depression and/or diabetes-specific distress (DSD) in people with Type 2 diabetes (T2DM), or ‘the 3-Ds’, to gain novel data and inform the design of a potential care pathway for this poorly identified and inadequately managed population. Firstly, a review of existing literature was performed to determine current understanding. Two systematic reviews with meta-analyses were performed: firstly, to determine the prevalence of DSD in people with T2DM; and secondly, to identify existing interventions that are successful in reducing DSD and physiological measures. A qualitative interview study was then conducted, with both patient and healthcare professional (HCP) participants, supplemented by field notes from development work, to elicit and explore the understanding, perceptions and experiences of the ‘3Ds’. The findings were then mapped against existing programmes of care, identifying areas lacking within current provision and informing the design of a proposed model of care. The 3D-model identified a lack of HCP understanding of ‘the 3-Ds’ and their interplay, and poor representation of holistic and patient-centred care, highlighting a fundamental need for training and education prior to any patient-level intervention. The model also identified a need for a change in how psychological concerns are identified, with a need for screening within meaningful discussion and adequate exploration of an individual’s wider-life circumstances. The model recognised a need for patient-choice, offering a stepped model of education and psychological support, utilising established programmes and building upon these with a specific ‘3Ds’ education programme. The model recommended a collaborative care design, with a sole care-coordinator to allow continuity of care, accessibility, and encourage shared-decision making and empowerment. The 3D-study formed the development phases of the Medical Research Council’s Framework for the development and evaluation of complex interventions, providing novel data to the field and laying the foundations for future work.
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