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Background The Behaviour Change Technique Taxonomy v1 (BCTTv1) specifies the potentially active content of behaviour change interventions. Evaluation of BCTTv1 showed the need to extend it into a formal ontology, improve its labels and definitions, add BCTs and subdivide existing BCTs. We aimed to develop a Behaviour Change Technique Ontology (BCTO) that would meet these needs. Methods The BCTO was developed by: (1) collating and synthesising feedback from multiple sources; (2) extracting information from published studies and classification systems; (3) multiple iterations of reviewing and refining entities, and their labels, definitions and relationships; (4) refining the ontology via expert stakeholder review of its comprehensiveness and clarity; (5) testing whether researchers could reliably apply the ontology to identify BCTs in intervention reports; and (6) making it available online and creating a computer-readable version. Results Initially there were 282 proposed changes to BCTTv1. Following first-round review, 19 BCTs were split into two or more BCTs, 27 new BCTs were added and 26 BCTs were moved into a different group, giving 161 BCTs hierarchically organised into 12 logically defined higher-level groups in up to five hierarchical levels. Following expert stakeholder review, the refined ontology had 247 BCTs hierarchically organised into 20 higher-level groups. Independent annotations of intervention evaluation reports by researchers familiar and unfamiliar with the ontology resulted in good levels of inter-rater reliability (0.82 and 0.79, respectively). Following revision informed by this exercise, 34 BCTs were added, resulting in the first published version of the BCTO containing 281 BCTs organised into 20 higher-level groups over five hierarchical levels. Discussion The BCTO provides a standard terminology and comprehensive classification system for the content of behaviour change interventions that can be reliably used to describe interventions. The development and maintenance of an ontology is an iterative and ongoing process; no ontology is ever ‘finished’. The BCTO will continue to evolve and grow (e.g. new BCTs or improved definitions) as a result of user feedback and new available evidence.

Background: Investigating and enhancing the effectiveness of behaviour change interventions requires detailed and consistent specification of all aspects of interventions. We need to understand not only their content, that is the specific techniques, but also the source, mode, schedule, and style in which this content is delivered. Delivery style refers to the manner by which content is communicated to intervention participants. This paper reports the development of an ontology for specifying the style of delivery of interventions that depend on communication. This forms part of the Behaviour Change Intervention Ontology, which aims to cover all aspects of behaviour change intervention scenarios. Methods: The Style of Delivery Ontology was developed following methods for ontology development used in the Human Behaviour-Change Project, with seven key steps: 1) defining the scope of the ontology, 2) identifying key entities and developing their preliminary definitions by reviewing 100 behaviour change intervention evaluation reports and existing classification systems, 3) refining the ontology by piloting the ontology through annotations of 100 reports, 4) stakeholder review by eight behavioural science and public health experts, 5) inter-rater reliability testing through annotating 100 reports using the ontology, 6) specifying ontological relationships between entities, and 7) disseminating and maintaining the ontology. Results: The resulting ontology is a five-level hierarchical structure comprising 145 unique entities relevant to style of delivery. Key areas include communication processes, communication styles, and attributes of objects used in communication processes. Inter-rater reliability for annotating intervention evaluation reports was α=0.77 (good) for those familiar with the ontology and α=0.62 (acceptable) for those unfamiliar with it. Conclusions: The Style of Delivery Ontology can be used for both annotating and describing behaviour change interventions in a consistent and coherent manner, thereby improving evidence comparison, synthesis, replication, and implementation of effective interventions.

Background Using reports of randomised trials of smoking cessation interventions as a test case, this study aimed to develop and evaluate machine learning (ML) algorithms for extracting information from study reports and predicting outcomes as part of the Human Behaviour-Change Project. It is the first of two linked papers, with the second paper reporting on further development of a prediction system. Methods Researchers manually annotated 70 items of information (‘entities’) in 512 reports of randomised trials of smoking cessation interventions covering intervention content and delivery, population, setting, outcome and study methodology using the Behaviour Change Intervention Ontology. These entities were used to train ML algorithms to extract the information automatically. The information extraction ML algorithm involved a named-entity recognition system using the ‘FLAIR’ framework. The manually annotated intervention, population, setting and study entities were used to develop a deep-learning algorithm using multiple layers of long-short-term-memory (LSTM) components to predict smoking cessation outcomes. Results The F1 evaluation score, derived from the false positive and false negative rates (range 0-1), for the information extraction algorithm averaged 0.42 across different types of entity (SD=0.22, range 0.05-0.88) compared with an average human annotator’s score of 0.75 (SD=0.15, range 0.38-1.00). The algorithm for assigning entities to study arms (e.g., intervention or control) was not successful. This initial ML outcome prediction algorithm did not outperform prediction based just on the mean outcome value or a linear regression model. Conclusions While some success was achieved in using ML to extract information from reports of randomised trials of smoking cessation interventions, we identified major challenges that could be addressed by greater standardisation in the way that studies are reported. Outcome prediction from smoking cessation studies may benefit from development of novel algorithms, e.g., using ontological information to inform ML (as reported in the linked paper (1)).

Ontologies are ways of representing aspects of the world in terms of uniquely defined classes of ‘entities’ and relationships between them. They are widely used in biological science, data science and commerce because they provide clarity, consistency, and the ability to link information and data from different sources. Ontologies offer great promise as representational systems in behavioural science and could revolutionise descriptions of studies and findings, and the expression of models and theories. This paper discusses issues that have been raised about using ontologies in behavioural science and how these can be addressed. The issues arise partly from the way that ontologies represent information, which can be perceived as reductionist or simplistic, and partly from issues to do with their implementation. However, despite the simplicity of their structure, ontologies can represent complex entities that change over time, as well as their inter-relationships and highly nuanced information about them. Nevertheless, ontologies are only one of many ways of representing information and it is important to recognise when other forms are more efficient. With regard to implementation, it is important to build ontologies with involvement from the communities who will be using them. Far from constraining intellectual creativity, ontologies that are broadly-based can facilitate expression of nuance, comparison of findings and integration of different approaches and theories. Maintaining and updating ontologies remain significant challenges but can be achieved through establishing and coordinating communities of practice.

Background: The Behaviour Change Technique Taxonomy v1 (BCTTv1) specifies the potentially active content of behaviour change interventions. Evaluation of BCTTv1 showed the need to extend it into a formal ontology, improve its labels and definitions, add BCTs and subdivide existing BCTs. We aimed to develop a Behaviour Change Technique Ontology (BCTO) that would meet these needs. Methods: The BCTO was developed by: (1) collating and synthesising feedback from multiple sources; (2) extracting information from published studies and classification systems; (3) multiple iterations of reviewing and refining entities, and their labels, definitions and relationships; (4) refining the ontology via expert stakeholder review of its comprehensiveness and clarity; (5) testing whether researchers could reliably apply the ontology to identify BCTs in intervention reports; and (6) making it available online and creating a machine-readable version. Results: Initially there were 282 proposed changes to BCTTv1. Following first-round review, 19 BCTs were split into two or more BCTs, 27 new BCTs were added and 26 BCTs were moved into a different group, giving 161 BCTs hierarchically organised into 12 logically defined higher-level groups in up to five hierarchical levels. Following expert stakeholder review, the refined ontology had 247 BCTs hierarchically organised into 20 higher-level groups. Independent annotations of intervention evaluation reports by researchers familiar and unfamiliar with the ontology resulted in good levels of inter-rater reliability (0.82 and 0.79, respectively). Following revision informed by this exercise, 34 BCTs were added, resulting in a final version of the BCTO containing 281 BCTs organised into 20 higher-level groups over five hierarchical levels. Discussion: The BCT Ontology provides a standard terminology and comprehensive classification system for the content of behaviour change interventions that can be reliably used to describe interventions.

Background: To build cumulative evidence about what works in behaviour change interventions, efforts have been made to develop classification systems for specifying the content of interventions. The Behaviour Change Techniques (BCT) Taxonomy v1 (BCTTv1) is one of the most widely used classifications of behaviour change techniques across a variety of behaviours. The BCTTv1 was intentionally named version 1 to allow for further revisions to the taxonomy. This study aimed to gather data to improve the BCTTv1 and provide recommendations for developing it into a more elaborated knowledge structure, an ontology. Methods: Feedback from users of BCTTv1 about limitations and proposed improvements was collected through the BCT website, user survey, researchers and experts involved in the Human Behaviour-Change Project, and a consultation. In addition, relevant published research reports and other classification systems of BCTs were analysed. These data were synthesised to produce recommendations to inform the development of an ontology of BCTs. Results: A total of 282 comments from six sources were reviewed and synthesised into four categories of suggestions: additional BCTs, amendments to labels and definitions of specific BCTs, amendments to the groupings, and general improvements. Feedback suggested some lack of clarity regarding understanding and identifying techniques from labels, definitions, and examples; distinctions and relations between BCTs; and knowing what they would look like in practice. Three recommendations to improve the BCTTv1 resulted from this analysis: to review the label and definition of each BCT, the 16 groupings of BCTs, and the examples illustrating BCTs. Conclusions : This review of feedback about BCTTv1 identified the need to improve the precision and knowledge structure of the current taxonomy. A BCT ontology would enable the specification of relationships between BCTs, more precise definitions, and allow better interoperability with other ontologies. This ontology will be developed as part of the Human Behaviour-Change Project.

Abstract Background Melanoma incidence has quadrupled since 1970 and melanoma is now the second most common cancer in individuals under 50. Targeted immunotherapies for melanoma now potentially enable long-term remission even in advanced melanoma, but these melanoma survivors require ongoing surveillance, with implications for NHS resources and significant social and psychological consequences for patients. Total skin self-examination (TSSE) can detect recurrence earlier and improve clinical outcomes but is underperformed in the UK. To support survivors, the Achieving Self-directed Integrated Cancer Aftercare (ASICA) intervention was developed to prompt and improve TSSE performance, with subsequent reporting of concerns and submission of skin photos to a Dermatology Nurse Practitioner (DNP). ASICA was delivered as a randomized pilot trial. Methods This paper reports on process evaluation. Data on participants’ demographics and the concerns they reported during the trial were tabulated and displayed using Microsoft Excel and SPSS. We explored which participants used ASICA, and how frequently, to report any skin concerns. We also determined how the interactions had worked in terms of quality of skin photographs submitted, clinical assessments made by the DNP, and the assessments and decisions made for each concern. Finally, we explored significant events occurring during the trial. Data on participants’ demographics and the concerns they reported during the trial were tabulated and displayed using SPSS. A semi-structured interview was undertaken with the DNP to gain perspective on the range of concerns presented and how they were resolved. Results Of 121 recruited melanoma patients receiving ASICA for 12 months, 69 participants submitted a total of 123 reports detailing 189 separate skin-related concerns and including 188 skin photographs. Where participants fully complied with follow-up by the DNP, concerns were usually resolved remotely, but 19 (10.1%) were seen at a secondary care clinic and 14 (7.4%) referred to their GP. 49 (25.9%) of concerns were not completely resolved due to partial non-compliance with DNP follow-up. Conclusion Melanoma patients randomized to the ASICA intervention were able to report skin-related concerns that could be resolved remotely through interaction with a DNP. Feasibility issues highlighted by ASICA will support further development and optimization of this digital tool. Trial registration Clinical Trials.gov , NCT03328247 . Registered on 1 November 2017

BackgroundMelanoma is common with increasing incidence. Guidelines recommend monthly total skin self-examinations (TSSEs) by survivors to detect recurrent and new primary melanomas. TSSE is underperformed despite evidence of benefit. ObjectiveThis study compares the effect on psychological well-being and TSSE practice of a self-directed digital intervention with treatment as usual in patients treated for a first stage 0 to IIC primary cutaneous melanoma within the preceding 60 months. MethodsThis randomized clinical trial was conducted at 2 UK National Health Service hospitals (Aberdeen Royal Infirmary, Grampian, and Addenbrooke’s, Cambridge). Adults (≥18 years) diagnosed with a first 0 to IIC primary cutaneous melanoma were randomized to receive Achieving Self-directed Integrated Cancer Aftercare (ASICA), a tablet-based intervention prompting and supporting TSSE in survivors of melanoma, or to usual care. The hypothesis was that ASICA would increase TSSE practice in users affected by melanoma and compared with controls without affecting psychological well-being. The main primary outcomes were melanoma worry (Melanoma Worry Scale), anxiety and depression (Hospital Anxiety and Depression Scale), and quality of life (EQ-5D-5L) as well as secondary outcomes collected using postal questionnaires 3, 6, and 12 months following randomization. ResultsA total of 240 recruits were randomized (1:1) into the ASICA (n=121, 50.4%) or control (n=119, 49.6%) groups. There were no significant differences between groups for melanoma worry at 12 months (mean difference: 0.12, 95% CI −0.6 to 0.84; P=.74), 3 months (0.23, 95% CI −0.31 to 0.78; P=.40), or 6 months (−0.1, 95% CI −0.7 to 0.51; P=.76). The ASICA group had lower anxiety scores at 12 months (−0.54, 95% CI −1.31 to 0.230; P=.17), 3 months (−0.13, 95% CI −0.79 to 0.54; P=.71), and significantly at 6 months (−1.00, 95% CI −1.74 to −0.26; P=.009). Depression scores were similar, being lower at 12 months (−0.44, 95% CI −1.11 to 0.23; P=.20) and 3 months (−0.24, 95% CI −0.84 to 0.35; P=.42) but only significantly lower at 6 months (−0.77, 95% CI −1.41 to −0.12; P=.02). The ASICA group had significantly higher quality of life scores at 12 months (0.044, 95% CI 0.003-0.085; P=.04) and 6 months (0.070, 95% CI 0.032-0.107; P5) TSSEs during the study year and significantly higher levels of self-efficacy in conducting TSSE. They also reported significantly higher levels of planning and intention to perform TSSE in the future. ConclusionsUsing ASICA for 12 months does not increase melanoma worry, can reduce anxiety and depression, and may improve quality of life. ASICA has the potential to improve the well-being and vigilance of survivors of melanoma and enable the benefits of regular TSSE. Trial RegistrationClinicalTrials.gov NCT03328247; https://clinicaltrials.gov/ct2/show/NCT03328247 International Registered Report Identifier (IRRID)RR2-10.1186/s13063-019-3453-x

Objectives To describe trajectories in melanoma survivors’ adherence to monthly total skin self-examination (TSSE) over 12 months, and to investigate whether adherence trajectories can be predicted from demographic, cognitive or emotional factors at baseline.Design A longitudinal observational study nested within the intervention arm of the ASICA (Achieving Self-Directed Integrated Cancer Aftercare) randomised controlled trial.Setting Follow-up secondary care in Aberdeen and Cambridge UK.Participants n=104 adults (48 men/56 women; mean age 58.83 years, SD 13.47, range 28–85 years; mean Scottish Index of Multiple Deprivation score 8.03, SD 1.73, range 2–10) who had been treated for stage 0–IIC primary cutaneous melanoma in the preceding 60 months and were actively participating in the intervention arm of the ASICA trial.Interventions All participants were using the ASICA intervention—a tablet-based intervention designed to support monthly TSSE.Primary and secondary outcome measures The primary outcome was adherence to guideline recommended (monthly) TSSE over 12 months. This was determined from time-stamped TSSE data recorded by the ASICA intervention app.Results Latent growth mixture models identified three TSSE adherence trajectories (adherent −41%; drop-off −35%; non-adherent −24%). People who were non-adherent were less likely to intend to perform TSSE as recommended, intending to do it more frequently (OR=0.21, 95% CI 0.06 to 0.81, p=0.023) and were more depressed (OR=1.31, 95% CI 1.06 to 1.61, p=0.011) than people who were adherent. People whose adherence dropped off over time had less well-developed action plans (OR=0.78, 95% CI 0.63 to 0.96, p=0.016) and lower self-efficacy about TSSE (OR=0.92, 95% CI 0.86 to 0.99, p=0.028) than people who were adherent.Conclusions Adherence to monthly TSSE in people treated for melanoma can be differentiated into adherent, drop-off and non-adherent trajectories. Collecting information about intentions to engage in TSSE, depression, self-efficacy and/or action planning at outset may help to identify those who would benefit from additional intervention.Trial registration number ClinicalTrials.gov Registry (NCT03328247).

Background ehaviour change is a key to addressing many health and healthcare problems and interventions have been designed to improve health outcomes. These behaviour change interventions have been evaluated in many ways, including randomised controlled trials, and over recent decades there has been considerable progress in the conduct and reporting these studies. This paper is a personal retrospection on the changes occurring that have resulted in our current improved methods and their potential for future advancement. Advances There has been steady development of methods for conducting trials, including advances in statistical methods enabled by increase computing power and programmes, greater attention to the recruitment of participants and in the specification of outcomes. Trial reporting has improved, largely due to publication of guidelines for reporting interventions and trials, but until recently the reporting of behaviour change interventions has been quite limited. Developments in the specification of active ingredients of these interventions, the behaviour change techniques, has transformed our ability to report interventions in a manner that facilitates evidence synthesis and enables replication and implementation. However, further work using ontological approaches is needed to adequately represent the evidence contained in the mass of accumulated studies. Meanwhile, attention is gradually being paid to the comparator groups in trials leading to better reporting but with continuing challenges about how control groups are selected. Conclusions These developments are important for the advancements of behavioural science – but also in consolidating the expertise needed to address global social, environmental and health challenges.

Background: Investigating and improving the effects of behaviour change interventions requires detailed and consistent specification of all aspects of interventions. An important feature of interventions is the way in which these are delivered, i.e. their mode of delivery. This paper describes an ontology for specifying the mode of delivery of interventions, which forms part of the Behaviour Change Intervention Ontology, currently being developed in the Wellcome Trust funded Human Behaviour-Change Project. Methods: The Mode of Delivery Ontology was developed in an iterative process of annotating behaviour change interventions evaluation reports, and consulting with expert stakeholders. It consisted of seven steps: 1) annotation of 110 intervention reports to develop a preliminary classification of modes of delivery; 2) open review from international experts (n=25); 3) second round of annotations with 55 reports to test inter-rater reliability and identify limitations; 4) second round of expert review feedback (n=16); 5) final round of testing of the refined ontology by two annotators familiar and two annotators unfamiliar with the ontology; 6) specification of ontological relationships between entities; and 7) transformation into a machine-readable format using the Web Ontology Language (OWL) and publishing online. Results: The resulting ontology is a four-level hierarchical structure comprising 65 unique modes of delivery, organised by 15 upper-level classes: Informational, Environmental change, Somatic, Somatic alteration, Individual-based/ Pair-based /Group-based, Uni-directional/Interactional, Synchronous/ Asynchronous, Push/ Pull, Gamification, Arts feature. Relationships between entities consist of is_a. Inter-rater reliability of the Mode of Delivery Ontology for annotating intervention evaluation reports was a=0.80 (very good) for those familiar with the ontology and a= 0.58 (acceptable) for those unfamiliar with it. Conclusion: The ontology can be used for both annotating and writing behaviour change intervention evaluation reports in a consistent and coherent manner, thereby improving evidence comparison, synthesis, replication, and implementation of effective interventions.

Background: Behaviour change interventions (BCI), their contexts and evaluation methods are heterogeneous, making it difficult to synthesise evidence and make recommendations for real-world policy and practice. Ontologies provide a means for addressing this. They represent knowledge formally as entities and relationships using a common language able to cross disciplinary boundaries and topic domains. This paper reports the development of the upper level of the Behaviour Change Intervention Ontology (BCIO), which provides a systematic way to characterise BCIs, their contexts and their evaluations. Methods: Development took place in four steps. (1) Entities and relationships were identified by behavioural and social science experts, based on their knowledge of evidence and theory, and their practical experience of behaviour change interventions and evaluations. (2) The outputs of the first step were critically examined by a wider group of experts, including the study ontology expert and those experienced in annotating relevant literature using the initial ontology entities. The outputs of the second step were tested by (3) feedback from three external international experts in ontologies and (4) application of the prototype upper-level BCIO to annotating published reports; this informed the final development of the upper-level BCIO. Results: The final upper-level BCIO specifies 42 entities, including the BCI scenario, elaborated across 21 entities and 7 relationship types, and the BCI evaluation study comprising 10 entities and 9 relationship types. BCI scenario entities include the behaviour change intervention (content and delivery), outcome behaviour, mechanism of action, and its context, which includes population and setting. These entities have corresponding entities relating to the planning and reporting of interventions and their evaluations. Conclusions: The upper level of the BCIO provides a comprehensive and systematic framework for representing BCIs, their contexts and their evaluations.

Background: Many global health challenges may be targeted by changing people’s behaviour. Behaviours including cigarette smoking, physical inactivity and alcohol misuse, as well as certain dietary behaviours, contribute to deaths and disability by increasing the risk of cancers, cardiovascular diseases and diabetes. Interventions have been designed to change these health behaviours with a view to reducing these health risks. However, the effectiveness of these interventions has been quite variable and further information is needed to enhance their success. More information is needed about the specific processes that underlie the effectiveness of intervention strategies. Aim: Researchers have developed a taxonomy of 93 behaviour change techniques (i.e. the active components of an intervention that bring about behavioural change), but little is known regarding their potential mechanisms of action (i.e. the processes through which a behaviour change technique affects behaviour). We therefore aimed to examine links between behaviour change techniques and mechanisms of action. Method: First, we conducted a literature synthesis study of 277 behaviour change intervention studies, from which we extracted information on links, described by authors, between behaviour change techniques and mechanisms of action, and identified an average of 10 links per intervention report. Second, behaviour change experts (n = 105) were engaged in a three-round consensus study in which they discussed and rated their confidence in the presence/absence of ‘links’ and ‘non-links’ between commonly used behaviour change techniques (n = 61) and a set of mechanisms of action (n = 26). Ninety links and 460 ‘non-links’ reached the pre-set threshold of 80% agreement. To enhance the validity of these results, a third study was conducted that triangulated the findings of the first two studies. Discrepancies and uncertainties between the studies were included in a reconciliation consensus study with a new group of experts (n = 25). The final results identified 92 definite behaviour change technique–mechanism of action links and 465 definite non-links. In a fourth study, we examined whether or not groups of behaviour change techniques used together frequently across interventions revealed shared theoretical underpinnings. We found that experts agreed on the underlying theory for three groups of behaviour change techniques. Results: Our results are potentially useful to policy-makers and practitioners in selecting behaviour change techniques to include in behaviour change interventions. However, our data do not demonstrate that the behaviour change techniques are effective in targeting the mechanism of action; rather, the links identified may be the ‘best bets’ for interventions that are effective in changing mechanisms of action, and the non-links are unlikely to be effective. Researchers examining effectiveness of interventions in either primary studies or evidence syntheses may consider these links for further investigation. Conclusion: To make our results usable by researchers, practitioners and policy-makers, they are available in an online interactive tool, which enables discussion and collaboration (https://theoryandtechniquetool.humanbehaviourchange.org/); accessed 1 March 2020. This work, building on previous work to develop the behaviour change technique taxonomy, is part of an ongoing programme of work: the Human Behaviour Change Project (www.humanbehaviourchange.org/; accessed 1 March 2020). Funding: This project was funded by the Medical Research Council via its Methodology Panel: ‘Developing methodology for designing and evaluating theory-based complex interventions: an ontology for linking behaviour change techniques to theory’ (reference MR/L011115/1).

Background: Behaviour and behaviour change are integral to many aspects of wellbeing and sustainability. However, reporting behaviour change interventions accurately and synthesising evidence about effective interventions is hindered by lacking a shared, scientific terminology to describe intervention characteristics. Ontologies are standardised frameworks that provide controlled vocabularies to help unify and connect scientific fields. To date, there is no published guidance on the specific methods required to develop ontologies relevant to behaviour change. We report the creation and refinement of a method for developing ontologies that make up the Behaviour Change Intervention Ontology (BCIO). Aims: (1) To describe the development method of the BCIO and explain its rationale; (2) To provide guidance on implementing the activities within the development method. Method and results: The method for developing ontologies relevant to behaviour change interventions was constructed by considering principles of good practice in ontology development and identifying key activities required to follow those principles. The method’s details were refined through application to developing two ontologies. The resulting ontology development method involved: (1) defining the ontology’s scope; (2) identifying key entities; (3) refining the ontology through an iterative process of literature annotation, discussion and revision; (4) expert stakeholder review; (5) testing inter-rater reliability; (6) specifying relationships between entities, and; (7) disseminating and maintaining the ontology. Guidance is provided for conducting relevant activities for each step. Conclusions: We have developed a detailed method for creating ontologies relevant to behaviour change interventions, together with practical guidance for each step, reflecting principles of good practice in ontology development. The most novel aspects of the method are the use of formal mechanisms for literature annotation and expert stakeholder review to develop and improve the ontology content. We suggest the mnemonic SELAR3, representing the method’s first six steps as Scope, Entities, Literature Annotation, Review, Reliability, Relationships.

Objectives The aim of this study was to identify which behaviour change techniques (BCTs) were present in intervention and control groups of randomised controlled trials (RCTs) included in a Cochrane systematic review.Setting The RCTs included were conducted in community, primary and/or ambulatory-care settings.Participants The data set was derived from 86 RCTs from an interim update of the Cochrane review of the effectiveness of pharmacist services on non-hospitalised patient outcomes.Primary and secondary outcome measures The primary outcome was the identification of BCTs scheduled for delivery in intervention and control groups of the RCTs. The secondary outcome measure was to identify which BCTs are not being utilised in intervention and control groups of the RCTs.Results The intervention and control groups included 31 and 12 BCTs, respectively. The number of identifiable BCTs/study ranged from 0 to 12 in the intervention groups (mean 3.01 (SD 2.4)) and 0 to 6 in the control groups (mean 0.38 (SD 0.84)). The most commonly identified BCTs in the intervention groups were: instruction on how to perform the behaviour (55%, n=47) (also the most common BCT in control groups); problem solving (29%, n=25); information about health consequences (24%, n=21); social support (practical) (24%, n=21); and social support (unspecified) (23%, n=20) (the second most common BCT in control groups). Thirteen trials had no identifiable BCTs in either group.Conclusion The pharmacist interventions presented in this study did not use the full range of available BCTs. Furthermore, the reporting of BCTs was incomplete for both intervention and control groups, thereby limiting the utility and reproducibility of the interventions. Future interventions should be designed and reported using relevant taxonomies and checklists for example, BCT taxonomy and TIDieR (the template for intervention description and replication).

Background: Contextual factors such as an intervention’s setting are key to understanding how interventions to change behaviour have their effects and patterns of generalisation across contexts. The intervention’s setting is not consistently reported in published reports of evaluations. Using ontologies to specify and classify intervention setting characteristics enables clear and reproducible reporting, thus aiding replication, implementation and evidence synthesis. This paper reports the development of a Setting Ontology for behaviour change interventions as part of a Behaviour Change Intervention Ontology, currently being developed in the Wellcome Trust funded Human Behaviour-Change Project. Methods: The Intervention Setting Ontology was developed following methods for ontology development used in the Human Behaviour-Change Project: 1) Defining the ontology’s scope, 2) Identifying key entities by reviewing existing classification systems (top-down) and 100 published behaviour change intervention reports (bottom-up), 3) Refining the preliminary ontology by literature annotation of 100 reports, 4) Stakeholder reviewing by 23 behavioural science and public health experts to refine the ontology, 5) Assessing inter-rater reliability of using the ontology by two annotators familiar with the ontology and two annotators unfamiliar with it, 6) Specifying ontological relationships between setting entities and 7) Making the Intervention Setting Ontology machine-readable using Web Ontology Language (OWL) and publishing online. Results: The Intervention Setting Ontology consists of 72 entities structured hierarchically with two upper-level classes: Physical setting including Geographic location, Attribute of location (including Area social and economic condition, Population and resource density sub-levels) and Intervention site (including Facility, Transportation and Outdoor environment sub-levels), as well as Social setting. Inter-rater reliability was found to be 0.73 (good) for those familiar with the ontology and 0.61 (acceptable) for those unfamiliar with it. Conclusion: The Intervention Setting Ontology can be used to code information from diverse sources, annotate the setting characteristics of existing intervention evaluation reports and guide future reporting.

Introduction. Acceptance and Commitment Therapy (ACT) explicitly postulates experiential avoidance (EA) and values-based living (VBL) as essential treatment processes. As outcomes from between-subject studies cannot readily be generalized to within-subject processes in individuals, we explored the unfolding of, and relationship between, EA and VBL and levels of pain interference in daily life and emotional well-being within individuals experiencing chronic pain. Methods. Using n-of-1 designs, three participants following a multidisciplinary treatment program filled out a 12-item daily questionnaire (87–110 days). After multiple imputation of missing data, McKnight Time-series analysis procedures were performed for each participant separately. The interrelationships of EA, VBL and pain intensity, and the relationship of EA and VBL beyond pain intensity with both chronic pain outcomes were assessed both concurrently (same day) and prospectively (consecutive days). Results. Both EA and VBL were associated with at least one of five outcome variables (four domains of pain interference and emotional well-being) beyond pain intensity in two participants, but not in the third participant. These associations primarily existed for concurrent, but not consecutive, days. In contrast to VBL, EA was not associated with emotional well-being for any of the three participants. Conclusions. Although the finding that ACT-processes were associated with pain outcomes on concurrent days is consistent with ACT theory, the absence of such associations on consecutive days means that alternative explanations cannot be rule out. One possibility is that pain interference fluctuates within days at a higher variability rate than was currently assessed. Future research should consider using a higher measurement frequency to be able to grasp time-lagged effects.

Abstract Background National diabetes audits in the UK show room for improvement in the quality of care delivered to people with type 2 diabetes in primary care. Systematic reviews of quality improvement interventions show that such approaches can be effective but there is wide variability between trials and little understanding concerning what explains this variability. A national cohort study of primary care across 99 UK practices identified modifiable predictors of healthcare professionals’ prescribing, advising and foot examination. Our objective was to evaluate the effectiveness of an implementation intervention to improve six guideline-recommended health professional behaviours in managing type 2 diabetes in primary care: prescribing for blood pressure and glycaemic control, providing physical activity and nutrition advice and providing updated diabetes education and foot examination. Methods Two-armed cluster randomised trial involving 44 general practices. Primary outcomes (at 12 months follow-up): from electronic medical records, the proportion of patients receiving additional prescriptions for blood pressure and insulin initiation for glycaemic control and having a foot examination; and from a patient survey of a random sample of 100 patients per practice, reported receipt of updated diabetes education and physical activity and nutrition advice. Results The implementation intervention did not lead to statistically significant improvement on any of the six clinical behaviours. 1,138,105 prescriptions were assessed. Intervention (29% to 37% patients) and control arms (31% to 35%) increased insulin initiation relative to baseline but were not statistically significantly different at follow-up (IRR 1.18, 95%CI 0.95–1.48). Intervention (45% to 53%) and control practices (45% to 50%) increased blood pressure prescription from baseline to follow-up but were not statistically significantly different at follow-up (IRR 1.05, 95%CI 0.96 to 1.16). Intervention (75 to 78%) and control practices (74 to 79%) increased foot examination relative to baseline; control practices increased statistically significantly more (OR 0.84, 95%CI 0.75–0.94). Fewer patients in intervention (33%) than control practices (40%) reported receiving updated diabetes education (OR = 0.74, 95%CI 0.57–0.97). No statistically significant differences were observed in patient reports of having had a discussion about nutrition (intervention = 73%; control = 72%; OR = 0.98, 95%CI 0.59–1.64) or physical activity (intervention = 57%; control = 62%; OR = 0.79, 95%CI 0.56–1.11). Development and delivery of the intervention cost £1191 per practice. Conclusions There was no measurable benefit to practices’ participation in this intervention. Despite widespread use of outreach interventions worldwide, there is a need to better understand which techniques at which intensity are optimally suited to address the multiple clinical behaviours involved in improving care for type 2 diabetes. Trial registration ISRCTN, ISRCTN66498413. Registered April 4, 2013

Abstract Implementing research findings into healthcare practice and policy is a complex process occurring in diverse contexts; it invariably depends on changing human behaviour in many parts of an intricate implementation system. Questions asked with the aim of improving implementation are multifarious variants of ‘What works, compared with what, how well, with what exposure, with what behaviours (for how long), for whom, in what setting and why?’. Relevant evidence is being published at a high rate, but its quantity, complexity and lack of shared terminologies present challenges. The achievement of efficient, effective and timely synthesis of evidence is facilitated by using ‘ontologies’ to systematically structure and organise the evidence about constructs and their relationships, using a controlled, well-defined vocabulary.

Abstract Background Behaviour change is key to addressing both the challenges facing human health and wellbeing and to promoting the uptake of research findings in health policy and practice. We need to make better use of the vast amount of accumulating evidence from behaviour change intervention (BCI) evaluations and promote the uptake of that evidence into a wide range of contexts. The scale and complexity of the task of synthesising and interpreting this evidence, and increasing evidence timeliness and accessibility, will require increased computer support. The Human Behaviour-Change Project (HBCP) will use Artificial Intelligence and Machine Learning to (i) develop and evaluate a ‘Knowledge System’ that automatically extracts, synthesises and interprets findings from BCI evaluation reports to generate new insights about behaviour change and improve prediction of intervention effectiveness and (ii) allow users, such as practitioners, policy makers and researchers, to easily and efficiently query the system to get answers to variants of the question ‘What works, compared with what, how well, with what exposure, with what behaviours (for how long), for whom, in what settings and why?’. Methods The HBCP will: a) develop an ontology of BCI evaluations and their reports linking effect sizes for given target behaviours with intervention content and delivery and mechanisms of action, as moderated by exposure, populations and settings; b) develop and train an automated feature extraction system to annotate BCI evaluation reports using this ontology; c) develop and train machine learning and reasoning algorithms to use the annotated BCI evaluation reports to predict effect sizes for particular combinations of behaviours, interventions, populations and settings; d) build user and machine interfaces for interrogating and updating the knowledge base; and e) evaluate all the above in terms of performance and utility. Discussion The HBCP aims to revolutionise our ability to synthesise, interpret and deliver evidence on behaviour change interventions that is up-to-date and tailored to user need and context. This will enhance the usefulness, and support the implementation of, that evidence.

Abstract Background Health partnerships often use health professional training to change practice with the aim of improving quality of care. Interventions to change practice can learn from behavioural science and focus not only on improving the competence and capability of health professionals but also their opportunity and motivation to make changes in practice. We describe a project that used behavioural scientist volunteers to enable health partnerships to understand and use the theories, techniques and assessments of behavioural science. Case studies This paper outlines how The Change Exchange, a collective of volunteer behavioural scientists, worked with health partnerships to strengthen their projects by translating behavioural science in situ. We describe three case studies in which behavioural scientists, embedded in health partnerships in Uganda, Sierra Leone and Mozambique, explored the behaviour change techniques used by educators, supported knowledge and skill development in behaviour change, monitored the impact of projects on psychological determinants of behaviour and made recommendations for future project developments. Discussion Challenges in the work included having time and space for behavioural science in already very busy health partnership schedules and the difficulties in using certain methods in other cultures. Future work could explore other modes of translation and further develop methods to make them more culturally applicable. Conclusion Behavioural scientists could translate behavioural science which was understood and used by the health partnerships to strengthen their project work.

Delayed cancer diagnosis leads to poorer patient outcomes. During short consultations, General Practitioners (GPs) make quick decisions about likelihood of cancer. Patients' facial cues are processed rapidly and may influence diagnosis.To investigate whether patients' facial characteristics influence immediate perception of cancer risk by GPs.Web-based binary forced choice experiment with GPs from Northeast Scotland.GPs were presented with a series of pairs of face prototypes and asked to quickly select the patient more likely to have cancer. Faces were modified with respect to age, gender, and ethnicity. Choices were analysed using Chi-squared goodness-of-fit statistics with Bonferroni corrections.Eighty-two GPs participated. GPs were significantly more likely to suspect cancer in older patients. Gender influenced GP cancer suspicion, but this was modified by age: the male face was chosen as more likely to have cancer than the female face for young (72% of GPs;95% CI 61.0-87.0) and middle-aged faces (65.9%; 95% CI 54.7-75.5); but 63.4% (95% CI 52.2-73.3) decided the older female was more likely to have cancer than the older male (p = 0.015). GPs were significantly more likely to suspect cancer in the young Caucasian male (65.9% (95% CI 54.7, 75.5)) compared to the young Asian male (p = 0.004).GPs' first impressions about cancer risk are influenced by patient age, gender, and ethnicity. Tackling GP cognitive biases could be a promising way of reducing cancer diagnostic delays, particularly for younger patients.

Background: Meeting global health challenges requires effective behaviour change interventions (BCIs). This depends on advancing the science of behaviour change which, in turn, depends on accurate intervention reporting. Current reporting often lacks detail, preventing accurate replication and implementation. Recent developments have specified intervention content into behaviour change techniques (BCTs) – the ‘active ingredients’, for example goal-setting, self-monitoring of behaviour. BCTs are ‘the smallest components compatible with retaining the postulated active ingredients, i.e. the proposed mechanisms of change. They can be used alone or in combination with other BCTs’ (Michie S, Johnston M. Theories and techniques of behaviour change: developing a cumulative science of behaviour change. Health Psychol Rev 2012;6:1–6). Domain-specific taxonomies of BCTs have been developed, for example healthy eating and physical activity, smoking cessation and alcohol consumption. We need to build on these to develop an internationally shared language for specifying and developing interventions. This technology can be used for synthesising evidence, implementing effective interventions and testing theory. It has enormous potential added value for science and global health. Objective: (1) To develop a method of specifying content of BCIs in terms of component BCTs; (2) to lay a foundation for a comprehensive methodology applicable to different types of complex interventions; (3) to develop resources to support application of the taxonomy; and (4) to achieve multidisciplinary and international acceptance for future development. Design and participants: Four hundred participants (systematic reviewers, researchers, practitioners, policy-makers) from 12 countries engaged in investigating, designing and/or delivering BCIs. Development of the taxonomy involved a Delphi procedure, an iterative process of revisions and consultation with 41 international experts; hierarchical structure of the list was developed using inductive ‘bottom-up’ and theory-driven ‘top-down’ open-sort procedures (n = 36); training in use of the taxonomy (1-day workshops and distance group tutorials) (n = 161) was evaluated by changes in intercoder reliability and validity (agreement with expert consensus); evaluating the taxonomy for coding interventions was assessed by reliability (intercoder; test–retest) and validity (n = 40 trained coders); and evaluating the taxonomy for writing descriptions was assessed by reliability (intercoder; test–retest) and by experimentally testing its value (n = 190). Results: Ninety-three distinct, non-overlapping BCTs with clear labels and definitions formed Behaviour Change Technique Taxonomy version 1 (BCTTv1). BCTs clustered into 16 groupings using a ‘bottom-up’ open-sort procedure; there was overlap between these and groupings produced by a theory-driven, ‘top-down’ procedure. Both training methods improved validity (both p

Background: Following surgery for melanoma, there is a high risk of recurrence and it is recommended that patients perform regular total skin self-examination (TSSE) to complement physician follow-up. However practice is sub-optimal and reported barriers to TSSE include lack of training, waning motivation and the time required. The Achieving Self-directed Integrated Cancer Aftercare (ASICA) intervention was developed based on behaviour change theory [Information-Motivation-Behavioural skills model (IMB) and Control Theory]. Aims: This study assessed acceptability and feasibility of ASICA. Methods: The intervention involves text message prompts from the nurse-lead (N), information and TSSE demonstration by tablet, monitoring of TSSE, photographing any skin changes and sending to N via tablet, feedback and follow-up by N. Interviews were conducted with 21 people previously treated for cutaneous melanoma. The intervention was piloted with 20 people treated for melanoma. Results: Interviewees were positive about the use of technology to remind and instruct on TSSE, conducting it in their homes and getting feedback quickly. Pilot participants were strongly positive; 15 adhered well to the intervention, 4 intermittently and one withdrew. Symptoms were reported by seven and two had further surgery, one with a recurrent melanoma. Several practical issues were identified. Participants’ intention and self-efficacy increased. Conclusions: The ASICA intervention was acceptable to most patients and feasible following small technical adjustments. It is now proposed to evaluate the intervention in a randomised controlled trial.

ObjectivesStudy objectives were to investigate the prevalence and causes of prescribing errors amongst foundation doctors (i.e. junior doctors in their first (F1) or second (F2) year of post-graduate training), describe their knowledge and experience of prescribing errors, and explore their self-efficacy (i.e. confidence) in prescribing.MethodA three-part mixed-methods design was used, comprising: prospective observational study; semi-structured interviews and cross-sectional survey. All doctors prescribing in eight purposively selected hospitals in Scotland participated. All foundation doctors throughout Scotland participated in the survey. The number of prescribing errors per patient, doctor, ward and hospital, perceived causes of errors and a measure of doctors' self-efficacy were established.Results4710 patient charts and 44,726 prescribed medicines were reviewed. There were 3364 errors, affecting 1700 (36.1%) charts (overall error rate: 7.5%; F1:7.4%; F2:8.6%; consultants:6.3%). Higher error rates were associated with : teaching hospitals (pConclusionsPrescribing errors are frequent and of complex causation. Foundation doctors made more errors than other doctors, but undertook the majority of prescribing, making them a key target for intervention. Contributing causes included work environment, team, task, individual and patient factors. Further work is needed to develop and assess interventions that address these.

BackgroundAlthough most people with Type 2 diabetes receive their diabetes care in primary care, only a limited amount is known about the quality of diabetes care in this setting. We investigated the provision and receipt of diabetes care delivered in UK primary care.MethodsPostal surveys with all healthcare professionals and a random sample of 100 patients with Type 2 diabetes from 99 UK primary care practices.Results326/361 (90.3%) doctors, 163/186 (87.6%) nurses and 3591 patients (41.8%) returned a questionnaire. Clinicians reported giving advice about lifestyle behaviours (e.g. 88% would routinely advise about calorie restriction; 99.6% about increasing exercise) more often than patients reported having received it (43% and 42%) and correlations between clinician and patient report were low. Patients' reported levels of confidence about managing their diabetes were moderately high; a median (range) of 21% (3% to 39%) of patients reporting being not confident about various areas of diabetes self-management.ConclusionsPrimary care practices have organisational structures in place and are, as judged by routine quality indicators, delivering high quality care. There remain evidence-practice gaps in the care provided and in the self confidence that patients have for key aspects of self management and further research is needed to address these issues. Future research should use robust designs and appropriately designed studies to investigate how best to improve this situation.

BackgroundInterventions for dietary and physical activity changes in obese adults may be less effective for participants with additional obesity-related risk factors and co-morbidities than for otherwise healthy individuals. This study aimed to test the feasibility and acceptability of the recruitment, allocation, measurement, retention and intervention procedures of a randomised controlled trial of an intervention to improve physical activity and dietary practices amongst obese adults with additional obesity related risk factors.MethodPilot single centre open-labelled outcome assessor-blinded randomised controlled trial of obese (Body Mass Index (BMI)≥30 kg/m2) adults (age≥18 y) with obesity related co-morbidities such as type 2 diabetes, impaired glucose tolerance or hypertension. Participants were randomly allocated to a manual-based group intervention or a leaflet control condition in accordance to a 2∶1 allocation ratio. Primary outcome was acceptability and feasibility of trial procedures, secondary outcomes included measures of body composition, physical activity, food intake and psychological process measures.ResultsOut of 806 potentially eligible individuals identified through list searches in two primary care general medical practices N = 81 participants (63% female; mean-age = 56.56(11.44); mean-BMI = 36.73(6.06)) with 2.35(1.47) co-morbidities were randomised. Scottish Index of Multiple Deprivation (SIMD) was the only significant predictor of providing consent to take part in the study (higher chances of consent for invitees with lower levels of deprivation). Participant flowcharts, qualitative and quantitative feedback suggested good acceptance and feasibility of intervention procedures but 34.6% of randomised participants were lost to follow-up due to overly high measurement burden and sub-optimal retention procedures. Participants in the intervention group showed positive trends for most psychological, behavioural and body composition outcomes.ConclusionsThe intervention procedures were found to be acceptable and feasible. Attrition rates were unacceptably high and areas for improvements of trial procedures were identified.Trial registrationControlled-Trials.com ISRCTN90101501.