Patients' perceptions and experiences of cardiovascular disease and diabetes prevention programmes: A systematic review and framework synthesis using the Theoretical Domains Framework.

Background: This review provides a worked example of 'best fit' framework synthesis using the Theoretical Domains Framework (TDF) of health psychology theories as an a priori framework in the synthesis of qualitative evidence. Framework synthesis works best with 'policy urgent' questions.
Objective: The review question selected was: what are patients' experiences of prevention programmes for cardiovascular disease (CVD) and diabetes? The significance of these conditions is clear: CVD claims more deaths worldwide than any other; diabetes is a risk factor for CVD and leading cause of death.
Method: A systematic review and framework synthesis were conducted. This novel method for synthesizing qualitative evidence aims to make health psychology theory accessible to implementation science and advance the application of qualitative research findings in evidence-based healthcare.
Results: Findings from 14 original studies were coded deductively into the TDF and subsequently an inductive thematic analysis was conducted. Synthesized findings produced six themes relating to: knowledge, beliefs, cues to (in)action, social influences, role and identity, and context. A conceptual model was generated illustrating combinations of factors that produce cues to (in)action. This model demonstrated interrelationships between individual (beliefs and knowledge) and societal (social influences, role and identity, context) factors.
Conclusion: Several intervention points were highlighted where factors could be manipulated to produce favourable cues to action. However, a lack of transparency of behavioural components of published interventions needs to be corrected and further evaluations of acceptability in relation to patient experience are required. Further work is needed to test the comprehensiveness of the TDF as an a priori framework for 'policy urgent' questions using 'best fit' framework synthesis.
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