NCT06960434

Mapping Obesity-related Subtypes And Interconnected Clusters

Systemic Interpretation of Personal and Environmental Characteristics in Overweight and Obesity: From Data Patterns to Practical Interventions

• Status: Not Yet Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 15 (estimated)
• Sponsor: Zuyd University of Applied Sciences · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Accepted

Summary

In the Netherlands, about half of all adults are currently living with overweight. This number is expected to rise to as much as 64% by the year 2050, especially among younger adults aged 18 to 44. Overweight and obesity increase the risk of chronic conditions such as heart disease, diabetes, and joint problems. However, there is no single cause behind these issues. Instead, they result from a complex combination of factors - including nutrition, physical activity, sleep, stress, income, environment, and even air quality. These factors often influence each other and vary from person to person. This study aims to better understand these patterns and connections. By analyzing large sets of data, researchers are identifying different subtypes of people with overweight or obesity. These subtypes reflect groups of individuals who share similar personal, lifestyle, and environmental characteristics. Understanding these differences makes it possible to develop more personalized lifestyle advice and support. That way, care and prevention efforts can be better tailored to what people actually need and what works best for them in practice. Experts from various fields are helping interpret the results, so that scientific insights can be translated into practical solutions for individuals, communities, and healthcare settings.

Detailed description

Overweight and obesity are increasingly prevalent in the Netherlands. Obesity-related health issues are complex and influenced by multiple interacting variables, including personal behaviors, socioeconomic status, environmental characteristics, and health conditions. This study seeks to validate and enrich the results of an ongoing exploratory data analysis by involving experts in the interpretation of identified factor clusters related to BMI categories. This mixed methods study includes a quantitative component (an online survey) and a qualitative component (expert panel group discussions). Experts are recruited through purposive and snowball sampling and participate in interpreting variable clusters, assessing associations, and drawing conclusions on implications for further research and practical application.

Conditions

• Obesity
• Overweight (BMI > 25)

Timeline

• Start date: 2025-05-01
• Primary completion: 2025-06-01
• Completion: 2025-09-01
• First posted: 2025-05-07
• Last updated: 2025-05-07