RIVM disseminates its findings through various channels, including peer-reviewed scientific articles, official reports and scientific abstracts for conferences.
The complete list of publications can be found on our Dutch website.
Scientific abstracts
Accepted abstracts for HEPA conference 2026. Our research team will present the results of our latest research at the conference.
Purpose
To estimate the impact of policy interventions regarding physical activity (PA) on future public health in the Netherlands, we developed a life course disease microsimulation model (LCDM). The impact estimation from this model of PA on health, costs, and quality of life will facilitate decision making concerning PA interventions by national policy makers.
Methods
The LCDM, an existing microsimulation model, was extended to include PA and PA-related health outcomes. Self-reported PA from a Dutch national health survey was used to model PA as MET minutes/week. For model input regarding health outcomes, we studied recent literature on the association of PA-level with the odds/risk of developing major non-communicable diseases type 2 diabetes, stroke, ischemic heart disease, congestive heart failure, breast cancer, colon cancer & dementia. Costs, Quality Adjusted Life Years (QALY’s), and workforce productivity serve as secondary outcomes. In an additional literature study, PA policy interventions with a significant effect on PA levels were identified. To illustrate the potential impact of PA policy interventions on public health in 2050, the results of these interventions were modelled and compared with a ‘usual’ policy projection (i.e. without PA policy interventions).
Results
For our primary health outcomes, we are currently running projections with the LCDM, and are able to present the results at the HEPA conference.
Conclusions
These results will be used to model or estimate the evidence based public health impact of national policies concerning PA.
Purpose
Sensor-based measurements are increasingly used to analyse 24/7 physical behaviour (PB) in cohort and monitoring studies, providing detailed information on physical activity (PA), sedentary time, and sleep. However, it remains unclear which sensor-derived characteristics best describe 24/7 PB and how these relate to health. This knowledge is valuable for designing new, sensor-based activity guidelines. This study is a first step towards a ‘Dutch Physical Activity Index’ (DPAI), a set of components designed to: I) capture relevant 24/7 PB characteristics from sensor data, II) be derived from common sensor protocols, and III) be simple and attractive to communicate, for instance in guidelines.
Methods
Potential components (e.g., distribution of moderate-to-vigorous PA or maximal sedentary time) were formulated based on literature and expert discussions. When necessary, additional exploratory analyses were performed on cross-sectional accelerometer data from the Doetinchem Cohort Study (2013-2017, n=1,991). We then evaluated the practical feasibility of extracting these from sensor data. The proposed components are currently being refined iteratively based on these analyses and expert feedback. Next, we will determine prevalence within various sensor protocols and explore associations with health-related factors.
Results
At the HEPA conference, we will present the selected components, prevalence estimates, and preliminary exploratory analyses of associations with health outcomes.
Conclusions
We expect to provide detailed insights into various 24/7 PB patterns, serving as input for a DPAI. This index may be an interesting step towards new, sensor-based PA guidelines.
Purpose
Electric cyclists are becoming increasingly visible in Dutch streets. This study examines the prevalence of electric bicycle users, analyzes their demographic characteristics, and explores motivations, destinations and travel distances. For the first time, these figures, coming from the national health survey infrastructure, will be presented at an international conference.
Methods
Cross-sectional data were obtained from a national questionnaire (Additional module Physical activity and accidents/Lifestyle Monitor, RIVM, VeiligheidNL, Statistics Netherlands), including a representative sample of Dutch cyclists aged 12+ (2021: n= 6,481; 2023: n=8,107). Descriptive statistics were used to examine demographic characteristics of electric bicycle users (e.g. age, disabilities), as well as to identify reasons for electric bicycle use (e.g. cycling faster), trip purposes (e.g. commuting), and distances traveled. Significant differences in electric bicycle use between groups and years was determined at p<0.05.
Results
Electric bicycle use increased from 29% in 2021 to 37% In 2023. In 2023, electric bicycle users were more often women, people aged 50+ and individuals with a lower level of education. The main reasons for choosing an electric over a conventional bicycle were ease of cycling (59%) and the desire to cycle longer distances (49%). The most common purpose for cycling was recreational trips (59%). The newest data of 2025 will be available summer 2026.
Conclusions
These findings give policymakers insight in who uses an electric bicycle and why, as a first step to better tailor cycling and physical activity policies to different population groups.
Purpose
Current policy programs on physical activity (PA) in the Netherlands are expiring. To determine focus areas for new national policy, the Ministry of Health, Welfare and Sports wants to gain a broad perspective on future societal developments and a prediction of the future PA trends.
Methods
This study comprises two parts: an exploration of future societal developments (qualitative research) and an analysis of the future development of PA key-indicators (quantitative research). For the first part, workshops with experts and citizens were organized. In these workshops the PA system was mapped out using causal loop diagrams and societal developments were linked to specific elements in this system map. Societal developments came from a DESTEP-analysis looking at demographic, economic, socio-cultural, technological, ecological and political-legal factors and developments. In the second part, a system dynamic model was used to model the future development of a selection of key-indicators up to 2040. In this analyses a broad range of scenarios were taken into account.
Results
Results provide a description of expected future societal developments that influence PA of the Dutch population. The analysis also gives insight into long-term future developments of several PA key-indicators, for example “weekly sports participation”, “number of sport accommodations” and “adherence to physical activity guidelines”. These results will be presented at the HEPA 2026 conference.
Conclusions
This study will form a basis for future policy on PA by providing insight into recent and anticipated developments. This creates a foundation for broad thinking with a long-term perspective in policy.
Purpose
The Netherlands has a unique system in place for systematic monitoring of sports and physical activity, with the use of Key-indicators. For the first time, the factsheet containing the essential information about the Key-indicators has been translated into English, thereby extending the reach to the global community. To support the goal of an active and sportive society, key-indicators have been established to address important facilitators and outcome measures, providing a clear signaling function for the national government. An additional aim is to disseminate local-level data for every Key-indicator when feasible. Each indicator is represented by a single numerical value, enabling the presentation of complex information in a communicable and visually appealing overview.
Methods
Key-indicators were developed collaboratively with stakeholders in 2014 and formalized by the Minister of Health, Welfare and Sport. A network of organizations was appointed to ensure implementation and maintenance, including Statistics Netherlands, VeiligheidNL, NOC*NSF, Mulier Institute, the Knowledge Centre for Sport & Physical Activity, and RIVM, which coordinates the network. The network convenes biannually, to ensure quality, comparability, and continuity. In 2024, the key-indicators were evaluated with the network, policymakers, and researchers. The overview of 25 Key-indicators is updated regularly as new data become available at Factsheet Key Indicators Sports and Physical Activity | sportenbewegenincijfers.nl.
Results
Key-indicator numbers are updated at least every four years, most annually or biennially. Policymakers have ongoing access to the latest online overview, enabling timely policy adjustments based on emerging trends.
Conclusions
The English translation of the Key-Indicators increases international visibility and supports broader collaboration in public health and sports policy.
Purpose
The primary aim was to advise our Ministry of Health, Welfare and Sports in the Netherlands how to best implement sensor-based physical activity (PA) measurement in the existing national health surveillance infrastructure.
Methods
Based on multiple previous studies and two recent pilot studies, the advice was formulated by the RIVM, Statistics Netherlands and Amsterdam UMC. It consisted of the following aspects: which infrastructure the sensors needed to be implemented to, frequency of measurement, sample size, sensor-placement (i.e. wrist or thigh-worn), costs, and implementation.
Results
We recommend expanding the existing Health Survey with sensors immediately. In that timeline, they will be fully implemented in 2030, the year where the new PA guidelines will be published by the World Health Organization (WHO). To make optimal use of the Health Survey infrastructure and obtain reliable data, continuous year-round measurement is recommended, allowing for annual activity rates and more accurate insights into subpopulations. Based on the current Health Survey sample design and pilot data, an estimated 1,626 adults per year are expected to wear a sensor. Since we are currently unable to obtain reliable results from a wrist-worn sensor, we now recommend a thigh-worn sensor, as it can accurately measure various activities. Costs for establishing the new infrastructure will be around €250.000, costs for the first year of sensor-measurement around €485.000.
Conclusions
We advise addition of the sensors to the Health Survey immediately for complete implementation in 2030. Accurate measurement of PA along the spectrum is important because detailed understanding tailors policies.