Evidence map›Paper›PMID 29047393›Full record

ArticleImplementation science : IS2017

The Human Behaviour-Change Project: harnessing the power of artificial intelligence and machine learning for evidence synthesis and interpretation.

Susan Michie, James Thomas, Marie Johnston, Pol Mac Aonghusa, John Shawe-Taylor, Michael P Kelly, Léa A Deleris, Ailbhe N Finnerty, Marta M Marques, Emma Norris and 2 more

Abstract read
In one paragraph

Article in Implementation science : IS, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 112 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
112citing papers in PubMed, 8 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

112 citing papers in PubMed, 8 syntheses or guidelines pooled it.

  1. Pooled it
  2. Content and quality of physical activity ontologies: a systematic review.The international journal of behavioral nutrition and physical activity · 2023
    Pooled it
  3. The Cooperation Databank: Machine-Readable Science Accelerates Research Synthesis.Perspectives on psychological science : a journal of the Association for Psychological Science · 2022
    Pooled it
  4. Mindfulness for smoking cessation.The Cochrane database of systematic reviews · 2022
    Pooled it
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52 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Susan MichieUCL Centre for Behaviour Change, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK. s.michie@ucl.ac.uk.
James ThomasEPPI-Centre, Department of Social Science, University College London, London, UK.
Marie JohnstonHealth Psychology, University of Aberdeen, Scotland, UK.
Pol Mac AonghusaIBM Research - Ireland, Dublin, Ireland.
John Shawe-TaylorDepartment of Computer Science, UCL, London, UK.
Michael P KellyPrimary Care Unit, Institute of Public Health, University of Cambridge, Cambridge, UK.
Léa A DelerisIBM Research - Ireland, Dublin, Ireland.
Ailbhe N FinnertyUCL Centre for Behaviour Change, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK.
Marta M MarquesUCL Centre for Behaviour Change, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK.
Emma NorrisUCL Centre for Behaviour Change, University College London, 1-19 Torrington Place, London, WC1E 7HB, UK.
Alison O'Mara-EvesEPPI-Centre, Department of Social Science, University College London, London, UK.
Robert WestDepartment of Epidemiology and Public Health, University College London, London, UK.

Funding

Cancer Research UKMedical Research Council MR/J005037/1Wellcome Trust 201,524/Z/16/ZWellcome Trust 201524/Z/16/Z
6 · The paper itself

Abstract

backgroundBehaviour 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?'.

methodsThe 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.

Indexed as

Artificial IntelligenceHealth BehaviorHealth PolicyAlgorithmsHumansMachine LearningArtificial intelligenceBehaviour change interventionsEvidence synthesisImplementationMachine learningNatural language processingOntology

Identifiers

PMID29047393
PMCPMC5648456

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.