Better4All Personalized Intervention

The BETTER4U (Preventing obesity through Biologically and bEhaviorally Tailored inTERventions for you) project, funded by the European Union, aims to address obesity through biologically and behaviourally tailored interventions. Obesity is a major public health issue influenced by genetic, metabolic, and lifestyle factors. Despite current weight management interventions, many individuals face challenges due to these varied influences. The BETTER4U project seeks to improve weight management by incorporating artificial intelligence (AI) and polygenic risk scores (PRS) to personalize interventions. The goal is to test the effectiveness of these personalized interventions in improving weight loss compared to standard care, using advanced monitoring tools and AI models.

The BETTER4ALL personalized intervention is a multicentre, open-label, parallel-group randomized controlled trial (RCT) involving seven study sites across Cyprus, France, Greece, Poland, Portugal, Spain, and Sweden. A total of 1,022 participants with overweight or obesity (BMI ≥ 25 kg/m²), aged 18-65 years, will be enrolled. Participants will be randomized into two groups: an intervention group receiving personalized lifestyle recommendations based on AI and PRS, and a control group receiving standard care recommendations. The intervention will last six months, followed by a six-month follow-up assessment.

The intervention's key aspects include wearable devices and a mobile application to monitor participants' behaviour, including physical activity, sleep, and eating habits. The intervention also integrates genetic, metabolic, and environmental data to provide tailored recommendations for weight loss. Participants' outcomes will be assessed regarding BMI, weight loss maintenance, changes in clinical biomarkers, body composition, and other lifestyle parameters.

This RCT will provide valuable insights into the effectiveness of personalized weight management strategies. AI-driven personalized recommendations and real-time monitoring represent a significant shift from traditional, one-size-fits-all approaches. The results of this study could offer a more effective and sustainable model for obesity management, particularly by accounting for individual genetic predispositions and lifestyle factors. Furthermore, by evaluating the impact of the intervention on a wide range of health outcomes, including biomarkers and psychosocial factors, the study will provide a comprehensive understanding of how personalized interventions can improve overall health and weight management.

In addition to contributing to the scientific understanding of obesity and its management, this project has the potential to influence public health strategies, offering a more personalized, data-driven approach to obesity prevention and treatment. By integrating genetic, environmental, and lifestyle factors, the BETTER4U intervention could pave the way for future innovations in digital health and obesity management.