Abstract

Background Behaviour change interventions influence behaviour through causal processes called “mechanisms of action” (MoAs). Reports of such interventions and their evaluations often use inconsistent or ambiguous terminology, creating problems for searching, evidence synthesis and theory development. This inconsistency includes the reporting of MoAs. An ontology can help address these challenges by serving as a classification system that labels and defines MoAs and their relationships. The aim of this study was to develop an ontology of MoAs of behaviour change interventions. Methods To develop the MoA Ontology, we (1) defined the ontology’s scope; (2) identified, labelled and defined the ontology’s entities; (3) refined the ontology by annotating (i.e., coding) MoAs in intervention reports; (4) refined the ontology via stakeholder review of the ontology’s comprehensiveness and clarity; (5) tested whether researchers could reliably apply the ontology to annotate MoAs in intervention evaluation reports; (6) refined the relationships between entities; (7) reviewed the alignment of the MoA Ontology with other relevant ontologies, (8) reviewed the ontology’s alignment with the Theories and Techniques Tool; and (9) published a machine-readable version of the ontology. Results An MoA was defined as “a process that is causally active in the relationship between a behaviour change intervention scenario and its outcome behaviour”. We created an initial MoA Ontology with 261 entities through Steps 2-5. Inter-rater reliability for annotating study reports using these entities was α=0.68 (“acceptable”) for researchers familiar with the ontology and α=0.47 for researchers unfamiliar with it. As a result of additional revisions (Steps 6-8), 23 further entities were added to the ontology resulting in 284 entities organised in seven hierarchical levels. Conclusions The MoA Ontology extensively captures MoAs of behaviour change interventions. The ontology can serve as a controlled vocabulary for MoAs to consistently describe and synthesise evidence about MoAs across diverse sources.

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