Algorithms to identify correlations between weight and well-being in children's data

The project aims to investigate causal relationships between weight and well-being – how weight and well-being influence each other. This will be done using data-driven methods to quantify the factors linking weight and well-being from birth to early adulthood, based on two Danish child cohorts. Additionally, the study will examine whether behaviors such as sleep, physical activity, and screen time have an impact on these relationships.

Background of the study

The research project is based on the assumption that weight and well-being are interconnected. For example, it is well known that the stigmatization of individuals with high weight can lead to poor mental health and distress, which in turn leads to further weight gain. However, the broader connections between weight and well-being are poorly understood.

In this research project, computer algorithms will attempt to identify patterns and relationships within large datasets from two Danish child cohorts – studies that follow the same group of children over time. These studies track children from 0 to 18 years of age.

The project primarily relies on data from The Danish National Birth Cohort, which includes 96,000 children, supplemented by data from the smaller but more in-depth Copenhagen Prospective Studies on Asthma in Childhood cohort, which includes 700 children.

The aim is to identify new relationships between weight and well-being, to examine how these mechanisms evolve over time, and finally, to see if factors such as sleep, physical activity, and screen time affect these mechanisms.

Title of the study: Investigating the causal interplay and quantifying discovered mechanisms linking weight and well-being from birth to early adulthood