Public health is moving toward the goal of implementing evidence-based interventions. To accomplish this, there is a need to select, adapt, and evaluate intervention studies. Such selection relies, in part, on making judgments about the feasibility of possible interventions and determining whether comprehensive and multilevel evaluations are justified. There exist few published standards and guides to aid these judgments. This article describes the diverse types of feasibility studies conducted in the field of cancer prevention, using a group of recently funded grants from the National Cancer Institute. The grants were submitted in response to a request for applications proposing research to identify feasible interventions for increasing the utilization of the Cancer Information Service among underserved populations.

In this paper, we introduce the Intervention Mapping (IM) taxonomy of behaviour change methods and its potential to be developed into a coding taxonomy. That is, although IM and its taxonomy of behaviour change methods are not in fact new, because IM was originally developed as a tool for intervention development, this potential was not immediately apparent. Second, in explaining the IM taxonomy and defining the relevant constructs, we call attention to the existence of parameters for effectiveness of methods, and explicate the related distinction between theory-based methods and practical applications and the probability that poor translation of methods may lead to erroneous conclusions as to method-effectiveness. Third, we recommend a minimal set of intervention characteristics that may be reported when intervention descriptions and evaluations are published. Specifying these characteristics can greatly enhance the quality of our meta-analyses and other literature syntheses. In conclusion, the dynamics of behaviour change are such that any taxonomy of methods of behaviour change needs to acknowledge the importance of, and provide instruments for dealing with, three conditions for effectiveness for behaviour change methods. For a behaviour change method to be effective: (1) it must target a determinant that predicts behaviour; (2) it must be able to change that determinant; (3) it must be translated into a practical application in a way that preserves the parameters for effectiveness and fits with the target population, culture, and context. Thus, taxonomies of methods of behaviour change must distinguish the specific determinants that are targeted, practical, specific applications, and the theory-based methods they embody. In addition, taxonomies should acknowledge that the lists of behaviour change methods will be used by, and should be used by, intervention developers. Ideally, the taxonomy should be readily usable for this goal; but alternatively, it should be clear how the information in the taxonomy can be used in practice. The IM taxonomy satisfies these requirements, and it would be beneficial if other taxonomies would be extended to also meet these needs.

Background A fundamental challenge of implementation is identifying contextual determinants (i.e., barriers and facilitators) and determining which implementation strategies will address them. Numerous conceptual frameworks (e.g., the Consolidated Framework for Implementation Research; CFIR) have been developed to guide the identification of contextual determinants, and compilations of implementation strategies (e.g., the Expert Recommendations for Implementing Change compilation; ERIC) have been developed which can support selection and reporting of implementation strategies. The aim of this study was to identify which ERIC implementation strategies would best address specific CFIR-based contextual barriers. Methods Implementation researchers and practitioners were recruited to participate in an online series of tasks involving matching specific ERIC implementation strategies to specific implementation barriers. Participants were presented with brief descriptions of barriers based on CFIR construct definitions. They were asked to rank up to seven implementation strategies that would best address each barrier. Barriers were presented in a random order, and participants had the option to respond to the barrier or skip to another barrier. Participants were also asked about considerations that most influenced their choices. Results Four hundred thirty-five invitations were emailed and 169 (39%) individuals participated. Respondents had considerable heterogeneity in opinions regarding which ERIC strategies best addressed each CFIR barrier. Across the 39 CFIR barriers, an average of 47 different ERIC strategies (SD = 4.8, range 35 to 55) was endorsed at least once for each, as being one of seven strategies that would best address the barrier. A tool was developed that allows users to specify high-priority CFIR-based barriers and receive a prioritized list of strategies based on endorsements provided by participants. Conclusions The wide heterogeneity of endorsements obtained in this study’s task suggests that there are relatively few consistent relationships between CFIR-based barriers and ERIC implementation strategies. Despite this heterogeneity, a tool aggregating endorsements across multiple barriers can support taking a structured approach to consider a broad range of strategies given those barriers. This study’s results point to the need for a more detailed evaluation of the underlying determinants of barriers and how these determinants are addressed by strategies as part of the implementation planning process. Electronic supplementary material The online version of this article (10.1186/s13012-019-0892-4) contains supplementary material, which is available to authorized users.

Background: The ultimate impact of a health innovation depends not only on its effectiveness but also on its reach in the population and the extent to which it is implemented with high levels of completeness and fidelity. Implementation science has emerged as the potential solution to the failure to translate evidence from research into effective practice and policy evident in many fields. Implementation scientists have developed many frameworks, theories and models, which describe implementation determinants, processes, or outcomes; yet, there is little guidance about how these can inform the development or selection of implementation strategies (methods or techniques used to improve adoption, implementation, sustainment, and scale-up of interventions) ( 1 , 2 ). To move the implementation science field forward and to provide a practical tool to apply the knowledge in this field, we describe a systematic process for planning or selecting implementation strategies: Implementation Mapping. Methods: Implementation Mapping is based on Intervention Mapping (a six-step protocol that guides the design of multi-level health promotion interventions and implementation strategies) and expands on Intervention Mapping step 5. It includes insights from both the implementation science field and Intervention Mapping. Implementation Mapping involves five tasks: (1) conduct an implementation needs assessment and identify program adopters and implementers; (2) state adoption and implementation outcomes and performance objectives, identify determinants, and create matrices of change objectives; (3) choose theoretical methods (mechanisms of change) and select or design implementation strategies; (4) produce implementation protocols and materials; and (5) evaluate implementation outcomes. The tasks are iterative with the planner circling back to previous steps throughout this process to ensure all adopters and implementers, outcomes, determinants, and objectives are addressed. Discussion: Implementation Mapping provides a systematic process for developing strategies to improve the adoption, implementation, and maintenance of evidence-based interventions in real-world settings.

The field of implementation science was developed to better understand the factors that facilitate or impede implementation and generate evidence for implementation strategies. In this article, we briefly review progress in implementation science, and suggest five priorities for enhancing the impact of implementation strategies. Specifically, we suggest the need to: (1) enhance methods for designing and tailoring implementation strategies; (2) specify and test mechanisms of change; (3) conduct more effectiveness research on discrete, multi-faceted, and tailored implementation strategies; (4) increase economic evaluations of implementation strategies; and (5) improve the tracking and reporting of implementation strategies. We believe that pursuing these priorities will advance implementation science by helping us to understand when, where, why, and how implementation strategies improve implementation effectiveness and subsequent health outcomes.