Abstract

BackgroundMany complex intervention trials fail to show an intervention effect. Although this may be due to genuine ineffectiveness, it may also be the result of sub-optimal intervention design, implementation failure or a combination of these. Given current financial constraints and the pressure to reduce waste and increase value in health services research, pre-trial strategies are needed to reduce the likelihood of design or implementation failure and to maximise the intervention’s potential for effectiveness. In this scoping review, we aimed to identify and synthesise the available evidence relating to the strategies and methods used to ‘optimise’ complex interventions at the pre-trial stage.MethodsWe searched MEDLINE, CINAHL, AMED, PsycINFO and ProQuest Nursing & Allied Health Source for papers published between January 2000 and March 2015. We included intervention development and optimisation studies that explored potential intervention weaknesses and limitations before moving to a definitive randomised controlled trial (RCT). Two reviewers independently applied selection criteria and systematically extracted information relating to the following: study characteristics; intervention under development; framework used to guide the development process; areas of focus of the optimisation process, methods used and outcomes of the optimisation process. Data were tabulated and summarised in a narrative format.ResultsWe screened 3968 titles and 231 abstracts for eligibility. Eighty-nine full-text papers were retrieved; 27 studies met our selection criteria. Optimisation strategies were used for a range of reasons: to explore the feasibility and acceptability of the intervention to patients and healthcare professionals; to estimate the effectiveness and cost-effectiveness of different combinations of intervention components; and to identify potential barriers to implementation. Methods varied widely across studies, from interviews and focus groups to economic modelling and probability analysis.ConclusionsThe review identifies a range of optimisation strategies currently used. Although a preliminary classification of these strategies can be proposed, a series of questions remain as to which methods to use for different interventions and how to determine when the intervention is ready or ‘optimised enough’ to be tested in a RCT. Future research should explore potential answers to the questions raised, to guide researchers in the development and evaluation of more effective interventions.

Highlights

  • Many complex intervention trials fail to show an intervention effect

  • We focus on probably the least explored and understood process related to the development of complex interventions: the optimisation of the intervention under development prior to a full-scale randomised controlled trial (RCT)

  • For the purpose of this review, we have defined optimisation as a process aimed to evaluate or test intervention components and/or drafted interventions in order to identify what works and what does not work within the intervention under design

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Summary

Introduction

Many complex intervention trials fail to show an intervention effect. this may be due to genuine ineffectiveness, it may be the result of sub-optimal intervention design, implementation failure or a combination of these. Interactions between intervention components and their effects on outcomes are not always linear or obvious, and they are influenced by several factors [2] These include, for example, the number of interacting components, the intensity of behaviour change required by those delivering or receiving the intervention, the number of groups or organisational levels targeted by the intervention and the complexity of outcomes, as well as the context in which interventions are implemented [3,4,5]. This results in considerable challenges to the evaluation of CHIs, which in turn requires substantial resources. In the case of an intervention that does not influence the outcomes as expected, trials often fail to detect or report whether the lack of intervention effect is due to sub-optimal intervention design, implementation failure or genuine ineffectiveness [7, 8]

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