Clinical trial NCT06960434: Mapping Obesity-related Subtypes And Interconnected Clusters
Official title: Systemic Interpretation of Personal and Environmental Characteristics in Overweight and Obesity: From Data Patterns to Practical Interventions
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Obesity
Overweight (BMI &gt; 25)
Keywords: overweight
obesity
prevention
lifestyle
data-driven
clusters
Lead sponsor: Zuyd University of Applied Sciences
Sponsor class: OTHER
Collaborators: name: Statistics Netherlands (CBS)
name: LIME Limburg Measures
First posted: 2025-05-07
Enrollment: 15
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: OTHER
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: The study population consists of expert researchers and professionals from diverse domains including obesity research, medical and psychosocial health, lifestyle behavior, and environmental health sciences. Participants are selected using purposive sampling within the professional networks of the research team, followed by snowball sampling to include additional relevant experts. All participants have demonstrable expertise in interpreting data related to BMI or related influencing factors, and are capable of contributing to the interpretation of complex variable clusters from an interdisciplinary perspective.

Brief 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.

Eligibility criteria: Inclusion Criteria:

* researchers or professionals with expertise in obesity, lifestyle, environment, psychosocial or medical factors;
* experience in data interpretation and/or public health;
* able to communicate in Dutch;
* willing to participate in the online survey and/or expert panel meeting

Exclusion Criteria:

* no relevant domain expertise;
* inability to give informed consent

Primary outcomes:
measure: Expert-perceived relationships between identified factors and BMI and expert assessment on identified data-clusters.
timeFrame: Baseline
Secondary outcomes:
measure: Expert interpretation of within-cluster relationships and inter-cluster differences
timeFrame: One week after baseline
Other outcomes:
Locations:
Study officials:
References: