Obesity, Overweight (BMI > 25)
Conditions
Keywords
overweight, obesity, prevention, lifestyle, data-driven, clusters
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.
Interventions
None listed
Sponsors
Statistics Netherlands (CBS)
LIME Limburg Measures
Zuyd University of Applied Sciences
Study design
Observational model
OTHER
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Healthy volunteers
Yes
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Expert-perceived relationships between identified factors and BMI and expert assessment on identified data-clusters. | Baseline | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Expert interpretation of within-cluster relationships and inter-cluster differences | One week after baseline | An expert panel focus group discussion will be organized using different co-creation methods to further explore on the data-clusters within the BMI categories. By having experts from different fields share knowledge and interpretations, we expect to arrive at descriptions of specific subgroups within different BMI categories and about implications for further research and clinical implications (qualitative data-analysis). |
Contacts
Primary ContactIris M Kanera, PhD
Outcome results
None listed