PRECIS: PRecision Evidence for Childhood obesity prevention InterventionS

Global Centre for Preventive Health and Nutrition

PRECIS will bring together the best international evidence around complex community-based interventions to prevent child and adolescent obesity. The project has been funded by the NHMRC Ideas scheme for 4 years (2021–2024).


Childhood obesity remains a major threat to population health. In the last two decades, there have been many community-wide initiatives aiming to prevent or reduce childhood obesity, and to promote healthier nutrition and activity behaviours, with mixed results. In order to generate greater insights into how and why interventions vary in effectiveness, we need new methods to combine and analyse the results of these interventions.


PRECIS will uniquely combine data from community-based obesity prevention interventions from around the world, and apply novel methods, including artificial intelligence, to identify what combinations of factors drive the largest reductions in standardised body mass index, and understand the full breadth of intervention impacts.


The findings from this project will allow us to develop guidance to support effective, equitable and efficient community-based obesity prevention, optimised to the unique characteristics of diverse communities.


University of Newcastle

University of Hawaii

Tufts University

University of Auckland

Over the past 20 years, our trials have shown that childhood obesity is preventable through community-based interventions (CBIs). However, there is significant variation in effectiveness, and the most important drivers of this variation are unclear.

The overall objective of this project is to generate the best possible evidence to demonstrate how CBIs can have the greatest impact on childhood obesity.

The data available from our completed and ongoing large-scale studies include a wide range of exposure, outcome and process data at the individual, community, and intervention levels. We will collate and harmonise all variables across the datasets to create a data repository with standardised exposure and outcome metrics and data structures.

In order to understand the predictors of success in these interventions, we will employ both traditional epidemiological analysis, and novel artificial intelligence (specifically machine learning) analysis.

The major aims of PRECIS are:

1) Identify and validate combinations of individual, community and intervention characteristics in community-based obesity prevention interventions that most strongly predict intervention effectiveness.

2) Identify and quantify the broader impacts (co-benefits) of community-based obesity prevention and the impact of these additional benefits on cost-effectiveness estimates.

3) Establish an international knowledge translation and exchange network for community-based obesity prevention.

This approach will facilitate a nuanced understanding of the context and mechanisms of community-based obesity prevention, and optimise community responses in the future.

PRECIS is an international collaboration, including leaders in community-based obesity prevention from three countries. If you are working in this area and are interested in being involved in PRECIS or contributing data to the repository, please be in touch with lead investigator, Dr Melanie Nichols


Dr Melanie Nichols, Deakin University

Associate Professor Kathryn Backholer, Deakin University

Professor Boyd Swinburn, University of Auckland

Dr Victoria Brown, Deakin University

Dr Thin Nguyen, Deakin University

Professor Christina Economos, Tufts University

Professor Steve Allender, Deakin University

Alfred Deakin Professor Anna Peeters, Deakin University

Emeritus Professor Marj Moodie, Deakin University

Professor Rachel Novotny, University of Hawaii

Professor Liliana Orellana, Deakin University

Professor Luke Wolfenden, University of Newcastle