_edited.jpg)
Tools
Equal-Life Tools
The Tools page on the Equal-Life Toolbox website is organised into key sections that support various stages of research on the exposome and its impact on children's mental health:
-
Define and Test: Focuses on conceptual models and definitions, linking to the Guidebook and Glossary.
-
Collect, Measure, Model: This section includes tools for internal exposome, multimodal data collection, physical exposome, and social exposome, along with links to relevant resources.
-
Algorithms for Data Analysis and Health Impact Assessment: Provides tools for integrative data analysis and health effect analysis.
-
Inform and Involve: Emphasises stakeholder involvement with participatory tools.
1
The Guidebook
Our guidebook offers concepts and definitions to help you understand the exposome and its components, particularly the factors influencing mental health and cognitive development in children of different ages at different places and circumstances.
3
Multimodal data collection and enrichment
We develop novel methods for exposome data collection and enrichment, based on new and complementary data sources. It aims to offer new insights into aspects of the built and natural environment that are not covered by conventional datasets.
4
Internal Exposome Tools
We discover novel internal biomarkers in relation to children’s mental health. What is a biomarker? How biomarkers are used in Equal-Life? Explore our biological protocols
5
Physical
Exposome Tools
We create a detailed description of the external exposome by developing comprehensive data and open-source models for the built and natural environments, air pollution, and noise
Algorithms for Data Analysis and Health Impact Assessment
We analyze how integrated exposures affect children's mental health and development by harmonizing cohort and school study data. Using machine learning and statistical methods, we model these relationships, considering moderation and mediation effects, to provide insights and recommendations.
7
Statistical Models
We develop and apply machine learning and other statistical methods to model the relationship between exposome and mental health and cognitive development in children, including moderation and mediating effects