Mapping Obesity-related Subtypes And Interconnected Clusters

NCT06960434 | Not Yet Recruiting

• 1 other identifier: Overweight_SHICLIME_2025
• Study Type: observational
• Target: 15
• Locations: 0 countries
• Sites: N/A

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.

Conditions

Obesity; Overweight (BMI > 25)

Keywords

overweight; obesity; prevention; lifestyle; data-driven; clusters

Outcome Measures

Primary Outcomes (1)

• Expert-perceived relationships between identified factors and BMI and expert assessment on identified data-clusters.

  A self-developed questionnaire will be used to investigate and describe expert opinions on (1) the perceived relationsships between specific factors and BMI and (2) the expert assessment regarding data-clusters within BMI categories that has been identified in a previous study. First, the experts are invited to respond to the following question "Do you recognise a relationship between these characteristics and BMI?" on a 3-point likert scale: (1) No, I see absolutely nog relationship. (2) Yes, there may be a relationship, (3) Ik do not know. There will be space for a note. Second, identified data-clusters will be described. The experts are invited to give a brief interpretation of these clusters from their own area of expertise in an open question.

  Baseline

Secondary Outcomes (1)

• Expert interpretation of within-cluster relationships and inter-cluster differences

  One week after baseline

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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

You may not qualify if:

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

Sponsors & Collaborators

• Zuyd University of Applied Sciences: lead
• Statistics Netherlands (CBS): collaborator
• LIME Limburg Measures: collaborator

MeSH Terms

Conditions

Obesity; Overweight

Condition Hierarchy (Ancestors)

Overnutrition; Nutrition Disorders; Nutritional and Metabolic Diseases; Body Weight; Signs and Symptoms; Pathological Conditions, Signs and Symptoms

Central Study Contacts

Iris M Kanera, PhD

CONTACT

+642476766; iris.kanera@zuyd.nl

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: April 14, 2025
• First Posted: May 7, 2025
• Study Start: May 1, 2025
• Primary Completion: June 1, 2025
• Study Completion: September 1, 2025
• Last Updated: May 7, 2025

Record last verified: 2025-04