The Capability, Opportunity, Motivation-Behaviour (COM-B) model is used to understand and predict human behaviour, and develop interventions aimed at influencing behaviour. It has been presented in several different diagrams, varying in the specification of the causal links between capability, opportunity, motivation and behaviour. The differences arise from the different levels of elaboration of the model. For many purposes the simplest version as set out in the original paper describing the model is sufficient. This version represents causal influences from capability and opportunity to motivation and bidirectional influences between these and behaviour. However, in principle there can also be causal influences from motivation to capability and opportunity, and for greater precision the diagram can show capability and opportunity influencing the relationship between motivation and behaviour rather than behaviour directly. There may be occasions when it is useful to include one or more of these additional causal specifications in the COM-B model diagram.

Behavioural science as applied to human health and wellbeing is crucial for meeting the challenges facing humanity in the 21st century. Although only a small fraction of health-related research focuses on behaviour, it still represents a substantial body of evidence that is accumulating rapidly. The sheer number of publications presents a huge challenge for manual extraction of information from study reports for purposes of evidence synthesis. We assessed the extent of this challenge, focusing on estimating the number of published reports of effectiveness trials relating to behaviour, either as outcomes or as key determinants of health outcomes. We adopted a conservative search strategy using words and phrases relating to effectiveness or efficacy trials of interventions involving commonly researched health-related behaviours. We reviewed a sample of the papers identified using the search strategy to estimate the proportion that were in scope. Using the search strategy we found an estimated 6,793 papers published per year over the years 2018-2022 inclusive. Of these, 81% were estimated by manual inspection to be in scope, resulting in an estimated 5,502 per year or 106 papers per week. The true figure is likely to be higher because of behaviours that were not canvassed in the search and trials represent only a small fraction of papers seeking to describe and understand behavioural issues relating to health and wellbeing.

Ontologies aim to represent the world in terms of uniquely defined classes and their properties which are expressed as relationships with other classes. They are becoming widely used in science to improve clarity, searching, inference and the ability to link data from different sources. Ontological definitions are descriptions that represent the essential properties of classes of entities (which include objects, object attributes and processes) that distinguish them from other classes. Classes have unique IRIs (Internationalised Resource Identifiers) that can be used for searching; they also have labels which are words of phrases that people can use to identify the class in passages of text or tables. This article provides a brief guide to help with writing good ontological definitions. The standard format of such a definition of a class, A, is: ‘a B that C’ or its semantic equivalent, where A is the class being defined, B is a parent class and C describes a set of properties of A that distinguish it from other members of B.

Purpose: Increasing personal protective behaviours is critical for stopping the spread of respiratory viruses, including SARS-CoV-2: we need evidence to inform how to achieve this. We aimed to synthesise evidence on interventions to increase six personal protective behaviours (e.g. hand hygiene, face mask use) to limit the spread of respiratory viruses.Methods: We used best practice for rapid evidence reviews. We searched Ovid MEDLINE and Scopus. Studies conducted in adults or children with active or passive comparators were included. We extracted data from published intervention descriptions on study design, intervention content, delivery mode, population, setting, mechanism(s) of action, acceptability, practicability, effectiveness, affordability, spill-over effects and equity impact. Study quality was assessed with Cochrane’s risk of bias tool. A narrative synthesis and random-effects meta-analyses were conducted.Results: We identified 39 studies conducted across 15 countries. Interventions targeted hand hygiene (n=30) and/or face mask use (n=12) and used two- or three-arm study designs with passive comparators. Interventions were typically delivered face-to-face and included a median of three behaviour change techniques. The quality of included studies was low. Interventions to increase hand hygiene (k=6) had a medium, positive effect (d=0.62, 95% CI=0.43-0.80, p<.001, I2=81.2%). Interventions targeting face mask use (k=4) had mixed results, with an imprecise pooled estimate (OR=4.14, 95% CI=1.24-13.79, p<.001, I2=89.67%). Between-study heterogeneity was high.Conclusions: We found low-quality evidence for positive effects of hand hygiene interventions, with unclear results for face mask use interventions. There was a lack of evidence for interventions targeting most behaviours of interest within this review.

Ontologies are being used in many areas of science to improve clarity and communication of research methods, findings and theories. Many of these ontologies use an upper level ontology called Basic Formal Ontology (BFO) as their frame of reference. This article summarises Basic Formal Ontology and shows how it can provide a basis for development of an Addiction Ontology that encompasses all the things that addiction researchers, practitioners and policy makers want to refer to. BFO makes a fundamental distinction between what it calls continuants (e.g. objects and their characteristics) and occurrents (e.g. processes). Classifying addiction-related entities using this system enables important distinctions to be made that are frequently overlooked or confused in the literature due to inherent ambiguities in natural language expressions. The Addiction Ontology uses this framework to convey information about: people and populations and their characteristics (e.g. substance use disorder), products (e.g. heroin, tobacco-containing products), behaviours (e.g. cigarette smoking, alcohol consumption), interventions (e.g. detoxification, rehabilitation, legislation), research (e.g. measurement, theories, study designs), organisations (e.g. pharmaceutical industry, tobacco companies), and settings (e.g. hospital outpatient clinic, country).