Abstract
Childhood obesity remains a major public health issue and priority area for action. Promisingly, obesity prevention interventions in the first 2000 days of life have shown modest effectiveness in improving health behaviours and healthy weight status in children. Yet, researchers in this field face several challenges. This can lead to research waste and impede progress towards delivering effective, scalable solutions. In this perspective article, we describe some of the key challenges in early childhood obesity prevention and outline innovative and collaborative solutions to overcome these. Combining these solutions will accelerate the generation of high-quality evidence that can be implemented into policy and practice.
Highlights
The prevalence of overweight and obesity in early childhood has continued to increase from an estimated 30.3 million (4.9%) children aged under 5 years in 2000 up to 38.3 million (5.6%) in 2019 [1]
We describe some of the key challenges in early childhood obesity prevention and outline innovative and collaborative solutions to overcome these
We present several proposed solutions to collectively address these challenges in early childhood obesity prevention (Figure 2)
Summary
The prevalence of overweight and obesity in early childhood has continued to increase from an estimated 30.3 million (4.9%) children aged under 5 years in 2000 up to 38.3 million (5.6%) in 2019 [1]. The question of which components drive behaviour change can be addressed by combining individual participant data with intervention components in a predictive modelling approach These models can quantitatively explore which intervention components are associated with effectiveness, compared with usual care, for priority population groups. The Communities for Healthy Living trial is testing the roll out of an integrated service with three times the sites over a three-year period, compared to the parallel group pilot [61] Use of these different study designs can accelerate intervention testing and evaluate interventions in ‘real world’ contexts to reduce challenges of implementation at scale.
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