ArticleImplementation science : IS2017
The Human Behaviour-Change Project: harnessing the power of artificial intelligence and machine learning for evidence synthesis and interpretation.
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.
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.
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.
Who cites it
112 citing papers in PubMed, 8 syntheses or guidelines pooled it.
- Behavioural components and delivery features of early childhood obesity prevention interventions: intervention coding of studies in the TOPCHILD Collaboration systematic review.The international journal of behavioral nutrition and physical activity · 2025Pooled it
- Content and quality of physical activity ontologies: a systematic review.The international journal of behavioral nutrition and physical activity · 2023Pooled it
- The Cooperation Databank: Machine-Readable Science Accelerates Research Synthesis.Perspectives on psychological science : a journal of the Association for Psychological Science · 2022Pooled it
- Mindfulness for smoking cessation.The Cochrane database of systematic reviews · 2022Pooled it
- The Implementation of Behavior Change Techniques in mHealth Apps for Sleep: Systematic Review.JMIR mHealth and uHealth · 2022Pooled it
- Determinants of Physical Health Self-Management Behaviours in Adults With Serious Mental Illness: A Systematic Review.Frontiers in psychiatry · 2021Pooled it
- Common Elements of Practice, Process and Implementation in Out-of-School-Time Academic Interventions for At-risk Children: a Systematic Review.Prevention science : the official journal of the Society for Prevention Research · 2020Pooled it
- Reporting randomised trials of social and psychological interventions: the CONSORT-SPI 2018 Extension.Trials · 2018Guideline
- Mechanisms of change of a multifaceted implementation strategy on fidelity to a guideline for the prevention of mental health problems at the workplace: a mechanism analysis within a cluster-randomized controlled trial.Implementation science : IS · 2025Trial
- Which behaviour change techniques are effective to promote physical activity and reduce sedentary behaviour in adults: a factorial randomized trial of an e- and m-health intervention.The international journal of behavioral nutrition and physical activity · 2020Trial
- Behavioral Interventions to Increase Physical Activity Among Dementia Caregivers: A Narrative Review of Behavior Theory, Behavior Change Techniques, and Mechanisms of Behavior Change.Geriatrics (Basel, Switzerland) · 2026Review
- Artificial Intelligence and Healthcare Policy: A Bibliometric Analysis of Global Research Trends.Healthcare (Basel, Switzerland) · 2026Article
- Unpacking weight management interventions measuring eating disorder risk in adults: coding of components of interventions in a systematic review.Journal of eating disorders · 2026Review
- Digital Episodic Future Thinking Intervention (Luminaut): Co-Design and Iterative Development Study.JMIR human factors · 2026Article
- Overcoming Psychological Barriers to Treatment Adherence: The Case of Antihypertensive Medication.Cardiology clinics · 2026Review
- AI Methods for Implementation Science (AIM-IS): developing a framework, toolkit, and reporting standard for the responsible use of AI in implementation practice and research.Implementation science : IS · 2026Article
- From Dismissal to Partnership: Patient Experiences of Recurrent Urinary Tract Infection Healthcare Informed by the Theoretical Domains Framework and Behaviour Change Theory.Health expectations : an international journal of public participation in health care and health policy · 2026Article
- Article
- Motivational effectiveness of prosocial public health messaging to reduce respiratory infection risk: a systematic review and meta-analysis.Communications medicine · 2025Article
- Conceptualizing Young People's Experiences of Climate Change Awareness: A Narrative Review.Annals of the New York Academy of Sciences · 2025Review
52 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
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.
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What OpenQuestion holds
Registered trials
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.