Project

The Human Behaviour Change Project

Goal: The aims of the project are to ) develop an ontology of behaviour change interventions b) use artificial intelligence to annotate and synthesise the published literature using the ontology and c) develop a user interface to give academic, practitioner and policy users access to the synthesised literature.

The project is led by Professor Susan Michie (UCL), funded by the Wellcome Trust and is a collaboration of behavioural, computer and information scientists.

For more information, please see:

www.humanbehaviourchange.org.

Updates
0 new
16
Recommendations
0 new
21
Followers
0 new
525
Reads
1 new
4818

Project log

Susan Michie
added an update
We are pleased to have published five new, open-access papers relating to the project, launching the Human Behaviour-Change Project Collection in Wellcome Open Research.
The papers provide an overview of the project, its methods, and present three elements of the Behaviour Change Intervention Ontology.
 
Susan Michie
added 5 research items
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.
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) language 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.
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.
Private Profile
added a research item
Due to the fast pace at which research reports in behaviour change are published, researchers, consultants and policymakers would benefit from more automatic ways to process these reports. Automatic extraction of the reports' intervention content, population, settings and their results etc. are essential in synthesising and summarising the literature. However, to the best of our knowledge, no unique resource exists at the moment to facilitate this synthesis. In this paper, we describe the construction of a corpus of published behaviour change intervention evaluation reports aimed at smoking cessation. We also describe and release the annotation of 57 entities, that can be used as an off-the-shelf data resource for tasks such as entity recognition, etc. Both the corpus and the annotation dataset are being made available to the community.
Lea Deleris
added a research item
We describe an information extraction (IE) approach for knowledge base population of behavior change scientific intervention findings. In this paper, we focus on building a system able to characterize the specific intervention techniques that are undertaken within behavior change intervention studies. We have investigated three different configurations of a general information retrieval based framework for information extraction: a) an unsupervised approach that hinges on specification of a query for each attribute to be extracted and a few parameters for rule-based post-processing; b) a semi-supervised approach, which uses a part of the ground-truth annotations as a training set to automatically learn optimal representation of the queries; and c) a supervised approach that replaces the rule-based post processing by a text classifier. To train and evaluate our system, we make use of a ground-truth data set annotated by behavior science experts. This dataset consists of a total of 226 research papers on smoking cessation.
Susan Michie
added an update
Interested in developing ontologies? We’ve uploaded a detailed summary of our ontology development process to OSF.
 
Private Profile
added a research item
Population age information is an essential characteristic of clinical trials. In this paper, we focus on extracting minimum and maximum (min/max) age values for the study samples from clinical research articles. Specifically, we investigate the use of a neural network model for question answering to address this information extraction task. The min/max age QA model is trained on the massive structured clinical study records from ClinicalTrials.gov. For each article, based on multiple min and max age values extracted from the QA model, we predict both actual min/max age values for the study samples and filter out non-factual age expressions. Our system improves the results over (i) a passage retrieval based IE system and (ii) a CRF-based system by a large margin when evaluated on an annotated dataset consisting of 50 research papers on smoking cessation.
Private Profile
added a research item
Ontologies are classification systems specifying entities, definitions and inter-relationships for a given domain, with the potential to advance knowledge about human behaviour change. A scoping review was conducted to: (1) identify what ontologies exist related to human behaviour change, (2) describe the methods used to develop these ontologies and (3) assess the quality of identified ontologies. Using a systematic search, 2,303 papers were identified. Fifteen ontologies met the eligibility criteria for inclusion, developed in areas such as cognition, mental disease and emotions. Methods used for developing the ontologies were expert consultation, data-driven techniques and reuse of terms from existing taxonomies, terminologies and ontologies. Best practices used in ontology development and maintenance were documented. The review did not identify any ontologies representing the breadth and detail of human behaviour change. This suggests that advancing behavioural science would benefit from the development of a behaviour change intervention ontology. © 2019, The Author(s), under exclusive licence to Springer Nature Limited.
Susan Michie
added an update
We are seeking a behaviour scientist with an outstanding track record and an interest in pushing forward the boundaries of our understanding of behaviour change interventions.
The post involves working on the ‘Human Behaviour-Change Project’ (please see www.humanbehaviourchange.org for more information about the project) The role will involve assisting in the development of the Behaviour Change Intervention Ontology, containing taxonomies of intervention techniques, usage by the target population, mechanisms of action, target behaviours, population and settings.
The role will require close liaison with experts in the field of behaviour change as well as with computer scientists and system architects. The post-holder will also contribute to the annotation of the published research literature, which will be used to train the artificial intelligence system. They will also help co-ordinate and manage the work of this highly complex project, and with organising the dissemination of project information to key groups of stakeholders.
This post is funded until November 2020 in the first instance.
Deadline: 7th February 2019
 
Susan Michie
added an update
Want to know more about our work on automating information extraction from behaviour change intervention reports? Read our new blog post here - https://www.ibm.com/blogs/research/2018/12/ai-help-change-behaviour/
 
Susan Michie
added an update
Interested in getting involved in our project and informing our work? Please tell us your interests and expertise by taking our stakeholder survey - https://bit.ly/2NaqIDa
 
Pol Mac Aonghusa
added a research item
This paper describes our approach to construct a scalable system for unsupervised information extraction from the behaviour change intervention literature. Due to the many different types of attribute to be extracted, we adopt a passage retrieval based framework that provides the most likely value for an attribute. Our proposed method is capable of addressing variable length passage sizes and different validation criteria for the extracted values corresponding to each attribute to be found. We evaluate our approach by constructing a manually annotated ground-truth from a set of 50 research papers with reported studies on smoking cessation.
Lea Deleris
added a research item
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.
Susan Michie
added an update
We've recently pre-registered an upcoming systematic review of ontologies - 'Advancing methods to develop behaviour change interventions: a review of relevant ontologies'. Read more about our planned methods here - http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42017079990
 
Susan Michie
added an update
We recently published a commentary which argues for the use of ontologies in implementation science. Ontologies can be used to structure and organise evidence, and to define consistent shared terminologies, to address the challenge of effectively synthesising evidence despite the ever-growing, and highly complex evidence base.
You can read our commentary here -
 
Michael P Kelly
added 2 research items
Objective: To demonstrate that six common errors made in attempts to change behaviour have prevented the implementation of the scientific evidence base derived from psychology and sociology; to suggest a new approach which incorporates recent developments in the behavioural sciences. Study design: The role of health behaviours in the origin of the current epidemic of non-communicable disease is observed to have driven attempts to change behaviour. It is noted that most efforts to change health behaviours have had limited success. This paper suggests that in policy-making, discussions about behaviour change are subject to six common errors and that these errors have made the business of health-related behaviour change much more difficult than it needs to be. Methods: Overview of policy and practice attempts to change health-related behaviour. Results: The reasons why knowledge and learning about behaviour have made so little progress in alcohol, dietary and physical inactivity-related disease prevention are considered, and an alternative way of thinking about the behaviours involved is suggested. This model harnesses recent developments in the behavioural sciences. Conclusion: It is important to understand the conditions preceding behaviour psychologically and sociologically and to combine psychological ideas about the automatic and reflective systems with sociological ideas about social practice.
Lea Deleris
added a research item
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.
Susan Michie
added an update
Our protocol paper for the Human Behaviour Change Project has recently been published in Implementation Science. It outlines the aims, methods and intended applications of the project. Read the full paper here - https://t.co/y47WQplQgn
 
Susan Michie
added an update
HBCP team members Susan Michie, Robert West, Marie Johnston and Marta Marques, and HBCP affiliate Rachel Carey, all presented at this year’s European Health Psychology Society Conference.
Susan Michie and Robert West jointly gave an extended ‘State of the art’ presentation.
Read more about the conference, and download the presentations here - https://www.ucl.ac.uk/human-behaviour-change/news/EHPS-2017
 
Marta M. Marques
added an update
Marta Marques, Research Associate of the HBCP was a guest speaker at the e-&m-health Special Interest Group meeting at the annual International Society of Behavioural Nutrition and Physical Activity (ISBNPA) conference.
More information about this presentation can be found here: http://www.ucl.ac.uk/human-behaviour-change/news/ISBNPA-conference.
 
Marta M. Marques
added an update
Principal Investigator of the HBCP presented a keynote address at Informatics for Health 2017 in Manchester (24th-26th of April)
More information on the HBCP can be found here: http://www.ucl.ac.uk/human-behaviour-change
 
Susan Michie
added an update
We are recruiting one Research Associate and one Senior Research Associate to join the HBCP. More information at: http://www.ucl.ac.uk/human-behaviour-change/recruitment
 
Susan Michie
added an update
Presentation of the HBCP at the 3rd CBC Digital Health Conference
 
Susan Michie
added an update
Presentation of the HBCP at the Symposium of the Society for the Study of Addiction_York 2016
 
Susan Michie
added an update
The video recording from the HBCP Launch event is now available on the website:
Please also see the brochure attached.
 
Susan Michie
added a project goal
The aims of the project are to ) develop an ontology of behaviour change interventions b) use artificial intelligence to annotate and synthesise the published literature using the ontology and c) develop a user interface to give academic, practitioner and policy users access to the synthesised literature.
The project is led by Professor Susan Michie (UCL), funded by the Wellcome Trust and is a collaboration of behavioural, computer and information scientists.
For more information, please see: