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The Human Behaviour-Change Project (HBCP) is creating an online Knowledge System that uses Artificial Intelligence, in particular Natural Language Processingand Machine Learning, to extract information from behaviour change intervention evaluation reports to answer key questions about the evidence. It is a collaboration between behavioural scientists, computer scientists and system architects.The Knowledge System is being curated with annotations of published reports according to the Behaviour Change Intervention Ontology also being developed as part of the project. The ontology is used to organise knowledge contained in behaviour change intervention evaluation reports.The Knowledge System will continually search publication databases to find behaviour change intervention evaluation reports, extract and synthesise the findings, provide up-to-date answers to questions, and draw inferences about behaviour change. Practitioners, policy makers and researchers will be able to query the system to obtain answers to variants of the key question: ‘What intervention(s) work, compared with what, how well, with what exposure, with what behaviours, for how long, for whom, in what settings and why?

Background Investigations about mental health report prevalence rates with fewer studies investigating psychological and social factors influencing mental health during the Covid-19 pandemic. Study aims: (1) identify sociodemographic groups of the adult population at risk of anxiety and depression and (2) determine if the following social and psychological risk factors for poor mental health moderated these direct sociodemographic effects: loneliness, social support, threat perception, illness representations. Methods Cross-sectional nationally representative telephone survey in Scotland in June 2020. If available, validated instruments were used, for example, Patient Health Questionnaire (PHQ-4) to measure anxiety and depression. Simple linear regressions followed by examination of moderation effect. Results A total of 1006 participants; median age 53 years, 61.4% female, from all levels of area deprivation (i.e., 3.8% in the most deprived decile and 15.6% in the most affluent decile). Analyses show associations of anxiety and depression with sociodemographic (age, gender, deprivation), social (social support, loneliness) and psychological factors (perceived threat and illness representations). Mental health was poorer in younger adults, women and people living in the most deprived areas. Age effects were exacerbated by loneliness and illness representations, gender effects by loneliness and illness representations and deprivation effects by loneliness, social support, illness representations and perceived threat. In each case, the moderating variables amplified the detrimental effects of the sociodemographic factors. Conclusions These findings confirm the results of pre-Covid-19 pandemic studies about associations between sociodemographics and mental health. Loneliness, lack of social support and thoughts about Covid-19 exacerbated these effects and offer pointers for pre-emptive action.

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Additional file 1: The theory-based intervention. The content and format of the intervention developed in this study. (DOC 240 KB)

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This folder contains materials generated by the Human Behaviour-Change Project: a multidisciplinary collaboration between behavioural scientists, computer scientists and system architects.

BACKGROUND: Understanding the mechanisms through which behavior change techniques (BCTs) can modify behavior is important for the development and evaluation of effective behavioral interventions. To advance the field, we require a shared knowledge of the mechanisms of action (MoAs) through which BCTs may operate when influencing behavior. PURPOSE: To elicit expert consensus on links between BCTs and MoAs. METHODS: In a modified Nominal Group Technique study, 105 international behavior change experts rated, discussed, and rerated links between 61 frequently used BCTs and 26 MoAs. The criterion for consensus was that at least 80 per cent of experts reached agreement about a link. Heat maps were used to present the data relating to all possible links. RESULTS: Of 1,586 possible links (61 BCTs × 26 MoAs), 51 of 61 (83.6 per cent) BCTs had a definite link to one or more MoAs (mean [SD] = 1.44 [0.96], range = 1-4), and 20 of 26 (76.9 per cent) MoAs had a definite link to one or more BCTs (mean [SD] = 3.27 [2.91], range = 9). Ninety (5.7 per cent) were identified as "definite" links, 464 (29.2 per cent) as "definitely not" links, and 1,032 (65.1 per cent) as "possible" or "unsure" links. No "definite" links were identified for 10 BCTs (e.g., "Action Planning" and "Behavioural Substitution") and for six MoAs (e.g., "Needs" and "Optimism"). CONCLUSIONS: The matrix of links between BCTs and MoAs provides a basis for those developing and synthesizing behavioral interventions. These links also provide a framework for specifying empirical tests in future studies.

Three studies were conducted to examine the effect of group identification and normative content of social identities on healthy eating intentions and behaviour. In Study 1 (N = 87) Australian participants were shown images that portrayed a norm of healthy vs. unhealthy behaviour among Australians. Participants' choices from an online restaurant menu were used to calculate energy content as the dependent variable. In Study 2 (N = 117), female participants were assigned to a healthy or unhealthy norm condition. The dependent variable was the amount of food eaten in a taste test. Social group identification was measured in both studies. In Study 3 (N = 117), both American identification and healthiness norm were experimentally manipulated, and participants' choices from an online restaurant menu constituted the dependent variable. In all three studies, the healthiness norm presented interacted with participants' group identification to predict eating behaviour. Contrary to what would be predicted under the traditional normative social influence account, higher identifiers chose higher energy food from an online menu and ate more food in a taste test when presented with information about their in-group members behaving healthily. The exact psychological mechanism responsible for these results remains unclear, but the pattern of means can be interpreted as evidence of vicarious licensing, whereby participants feel less motivated to make healthy food choices after being presented with content suggesting that other in-group members are engaging in healthy behaviour. These results suggest a more complex interplay between group membership and norms than has previously been proposed.

Background: To advance understanding of intervention effects, and improve theory development, a methodology for linking behavior change techniques (BCTs) to the processes through which they change behavior (i.e. their mechanisms of action (MoAs)) is required. Purpose: To triangulate evidence for hypothesized BCT-MoA links obtained in previous literature synthesis and expert consensus studies and present the results in an interactive, online tool. Methods: Two previous studies generated evidence on the frequency of links between the same 56 BCTs and 26 MoAs (1456 possible links). Concordance between the findings of the two studies were compared statistically. Uncertainties and differences between the two studies were reconciled by 16 behavior change experts using consensus development methods. Results: Multilevel models revealed agreement across the two studies for 25 of the 26 MoAs; the literature synthesis explained from 35.1% to 45.0% of variance in the expert consensus. The pre-set criteria for a link was achieved in both studies for 37 links and for 460 ‘non-links’. The reconciliation study resolved a further 55 links resulting in a total of 92 (37+55) BCT-MoA links covering 51 of 56 BCTs and 22 of 26 MoAs, and 465 (460+5) ‘non-links’. Conclusions: This triangulation of two distinct sources of evidence provides urgently needed guidance on how BCTs may affect the mechanisms that change behavior. The data are available online in an interactive Theory and Technique Tool (https://theoryandtechniquetool.humanbehaviourchange.org/). They provide a resource for behavior change intervention designers, researchers and theorists, supporting intervention design, research synthesis, and collaborative research.

BACKGROUND: Understanding the mechanisms through which behaviour change techniques (BCTs) can modify behaviour is important for the development and evaluation of effective behavioural interventions. To advance the field, we require a shared knowledge of the mechanisms of action (MoAs) through which BCTs may operate when influencing behaviour. PURPOSE: To elicit expert consensus on links between BCTs and MoAs. METHODS: In a modified Nominal Group Technique study, 105 international behaviour change experts rated, discussed, and re-rated links between 61 frequently used BCTs and 26 MoAs. The criterion for consensus was that at least 80% of experts reached agreement about a link. Heat maps were used to present the data relating to all possible links. RESULTS: Of 1,586 possible links (61 BCTs × 26 MoAs), 51 of 61 (83.6%) BCTs had a definite link to one or more MoAs (mean (SD) = 1.44 (0.96), range= 1-4), and 20 of 26 (76.9%) MoAs had a definite link to one or more BCTs (mean (SD) = 3.27 (2.91), range=9). Ninety (5.7%) were identified as ‘definite’ links, 464 (29.2%) as ‘definitely not’ links, and 1032 (65.1%) as ‘possible’ or ‘unsure’ links. No ‘definite’ links were identified for 10 BCTs (e.g. ‘Action Planning’ and ‘Behavioural Substitution’) and for six MoAs (e.g. ‘Needs’ and ‘Optimism’).CONCLUSIONS: The matrix of links between BCTs and MoAs provides a basis for those developing and synthesizing behavioural interventions. These links also provide a framework for specifying empirical tests in future studies. Note: This article has been accepted for publication in Annals of Behavioral Medicine. Published by Oxford University Press.

TIDieR-based description of training provided to facilitators in the IDEA trial. (DOCX 30Â kb)

Main outcome definitions and operationalizations. (DOCX 32Â kb)

Labels and descriptions of behaviour change techniques delivered during the IDEA intervention. (DOCX 30Â kb)

TIDieR-based intervention description for the IDEA trial. (DOCX 33Â kb)

CONSORT checklist. (DOCX 42Â kb)

Cost of delivering the IDEA intervention in primary care (nâ =â 22). (DOCX 34Â kb)

Methods for engaging stakeholders in the HBCP. (DOCX 23Â kb)

Twelve-month clinician self report behaviour questionnaire. (PDF 75Â kb)

Baseline postal questionnaire incorporating organisational and clinical questionnaires and behaviour simulation measures. (PDF 827Â kb)

Methodological approach to the development of the BCI Ontology, manual and automated annotation and machine learning and reasoning algorithms. (DOCX 30Â kb)

Prisma checklist. (DOCX 41 kb)

Transcripts of Video 1 and Video 2. (DOCX 20 kb)

Contingent valuation methods (CVM) are integral in valuating non-market environmental issues. Numerous mechanisms have been proposed and analysed, as well as numerous studies on willingness to pay (WTP) and willingness to accept (WTA) discrepancies. Despite the concentrated and persistent focus on achieving efficient mechanisms, controversies and limitations remain. This paper applies an open-ended approach to the referendum (majority voting) method for contingent valuation as advised by Green et al. (1998). This is undertaken via economic experiments with induced values, and by comparison of two novel majority voting mechanisms and one widely accredited voluntary contribution mechanism. The preliminary findings show the majority voting mechanism induce more incentive compatible values and reduce WTP WTA discrepancy compared to the voluntary contribution method. Thus, given promising results, this paper recommends further investigation into the novel open-ended referendum method, termed the undisclosed cost voting mechanism (UCVM).

Background: Advancements in mobile phone technology offer huge potential for enhancing the timely delivery of health behavior change interventions. The development of smartphone-based health interventions (apps) is a rapidly growing field of research, yet there have been few longitudinal examinations of how people experience and use these apps within their day-to-day routines, particularly within the context of a hybrid Web- and app-based intervention. Objective: This study used an in-depth mixed-methods design to examine individual variation in (1) impact on self-reported goal engagement (ie, motivation, self-efficacy, awareness, effort, achievement) of access to a weight management app (POWeR Tracker) when provided alongside a Web-based weight management intervention (POWeR) and (2) usage and views of POWeR Tracker. Methods: Thirteen adults were provided access to POWeR and were monitored over a 4-week period. Access to POWeR Tracker was provided in 2 alternate weeks (ie, weeks 1 and 3 or weeks 2 and 4). Participants’ goal engagement was measured daily via self-report. Mixed effects models were used to examine change in goal engagement between the weeks when POWeR Tracker was and was not available and whether the extent of change in goal engagement varied between individual participants. Usage of POWeR and POWeR Tracker was automatically recorded for each participant. Telephone interviews were conducted and analyzed using inductive thematic analysis to further explore participants’ experiences using POWeR and POWeR Tracker. Results: Access to POWeR Tracker was associated with a significant increase in participants’ awareness of their eating (β 1 =0.31, P =.04) and physical activity goals (β 1 =0.28, P =.03). The level of increase varied between individual participants. Usage data showed that participants used the POWeR website for similar amounts of time during the weeks when POWeR Tracker was (mean 29 minutes, SD 31 minutes) and was not available (mean 27 minutes, SD 33 minutes). POWeR Tracker was mostly accessed in short bursts (mean 3 minutes, SD 2 minutes) during convenient moments or moments when participants deemed the intervention content most relevant. The qualitative data indicated that nearly all participants agreed that it was more convenient to access information on-the-go via their mobiles compared to a computer. However, participants varied in their views and usage of the Web- versus app-based components and the informational versus tracking tools provided by POWeR Tracker. Conclusions: This study provides evidence that smartphones have the potential to improve individuals’ engagement with their health-related goals when used as a supplement to an existing online intervention. The perceived convenience of mobile access to information does not appear to deter use of Web-based interventions or strengthen the impact of app access on goal engagement. A mixed-methods design enabled exploration of individual variation in daily usage of the app-based tools. [J Med Internet Res 2014;16(10):e201]

Under pressure: the latest on managing hypertension. (PDF 291 kb)

Under pressure: the latest on managing hypertension. (PDF 291 kb)

Electronic version of questionnaire. (PDF 89 kb)

Background: Researchers, practitioners and policymakers develop interventions to change behavior based on their understanding of how behavior change techniques (BCTs) impact the determinants of behavior. A transparent, systematic and accessible method of linking BCTs with the processes through which they change behavior (i.e. their mechanisms of action (MoAs)) would advance understanding of intervention effects, and improve theory and intervention development. Purpose: To triangulate evidence for hypothesized BCT-MoA links obtained in two previous studies and present the results in an interactive, online tool.Methods: Two previous studies generated evidence on links between 56 BCTs and 26 MoAs based on their frequency in literature synthesis and on expert consensus. Concordance between the findings of the two studies was examined using multilevel modelling. Uncertainties and differences between the two studies were reconciled by 16 behavior change experts using consensus development methods. The resulting evidence was used to generate an online tool.Results: The two studies showed concordance for 25 of the 26 MoAs and agreement for 37 links and for 460 ‘non-links’. A further 55 links were resolved by consensus (total of 92 (37+55) hypothesized BCT-MoA links). Full data on 1456 possible links was incorporated into the online interactive Theory and Technique Tool (url link removed for anonymity peer-review).Conclusions: This triangulation of two distinct sources of evidence provides guidance on how BCTs may affect the mechanisms that change behavior and is available as a resource for behavior change intervention designers, researchers and theorists, supporting intervention design, research synthesis, and collaborative research

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.

Changing behaviour is necessary to address many of the threats facing human populations. However, identifying behaviour change interventions likely to be effective in particular contexts as a basis for improving them presents a major challenge. The Human Behaviour-Change Project harnesses the power of artificial intelligence and behavioural science to organise global evidence about behaviour change to predict outcomes in common and unknown behaviour change scenarios.

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.