Conference Agenda

Employees spend a large part of their day at work, making the workplace an important part for physical activity promotion. For global companies, offline health programs are often difficult to implement. As a result, many organizations use digital well-being solutions to support employee health and encourage physical activity.

This case study examines the implementation of a one-month corporate digital physical activity program developed by Stayf Ltd. and implemented at Playrix in September 2025. Playrix employs approximately 3,000 employees worldwide, and 506 employees participated in the program. Participants joined from different countries, including Serbia (173), Ukraine (54), Kazakhstan (46), Georgia (32), Poland (32), Cyprus (20), and other countries (149). The program was realized in a mobile application and included team-based challenges, personal goals, educational webinars, and a collective step goal linked to a charity initiative. Aggregated program data were used to evaluate participation and physical activity indicators.

Participants achieved an average of 9,500 steps per day, with an average of three training sessions per week lasting 23 minutes per session. These values indicate that participants were close to the WHO physical activity recommendations of at least 150 minutes of moderate-intensity physical activity per week. App engagement data showed that participants opened the application on average four times per day, hence the suggestion that the platform supported both activity tracking and social interaction.

This case study suggests that digital well-being programs may support physical activity promotion in geographically distributed organizations and provide digital tools to improve engagement and movement among employees.

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.

Support/Funding Source: This research was funded by the National Institute for Public Health and the Environment.

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, 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: www.sportenbewegenincijfers.nl/en/key-indicators/factsheet

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.Wanda Wendel-Vos

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.

Background: Wearable sensors have the potential to complement lifestyle interventions and support long-term adherence to behavioural change. However, usability remains crucial for sustained engagement with these devices. This study explores usability through user experiences with smartwatches among participants in two lifestyle interventions: a walking group and a lifestyle coaching program.

Methods: In this exploratory mixed-methods study, participants (aged 43-74) wore a Garmin Vivosmart 5 smartwatch for 10–15 weeks without additional instruction. Usability (System Usability Scale; SUS) and user satisfaction (User Satisfaction Evaluation Questionnaire; USEQ) were assessed. Focus groups addressed user experience, perceived added value, and barriers to use and were analyzed using thematic analysis

Results: From the focus groups (N=11) we identified three main themes. Participants reported increased awareness of lifestyle factors (e.g., activity, sleep) by wearing the smartwatch. However, this did not consistently translate into behaviour change. Participants valued smartwatch functions that aligned with personal health goals, such as increasing physical activity. Reported barriers included poor alignment between functions and personal goals, limited knowledge or support to interpret and act on the data, and negative experiences with certain functions (e.g., stress metrics perceived as stress-inducing).

Conclusion: Factors to consider when optimizing usability of wearable sensors include aligning device functions with users’ personal goals and providing sufficient knowledge or support to translate data into actionable behaviour change. Practical guidance on how to use wearable device data may enhance usability and implementation of wearable sensors in the context of lifestyle interventions.

Funding source: Project number 13948, NWO program KIC.

Background: Low physical activity (PA) and high sedentary behavior (SB) decrease energy expenditure and may lead to weight gain. As PA, SB, and sleep together comprise the 24-hour day, these behaviors are interdependent. This study examines the association between 24-h movement behaviors and BMI in Finnish adults.

Methods: A random sample of Finnish adults aged 20 years and older was invited to the population‑based Healthy Finland 2023 health‑examination survey (n= 10,000; participation rate 58%). A sub-sample (n=859) completed continuous 3–7-day wrist-worn accelerometer measurements for sleep, SB, light-intensity physical activity (LIPA) and moderate-to-vigorous physical activity (MVPA). Compositional binary logistic regression analysis, adjusted for age and sex, was used in R to examine associations between 24-h movement behaviors and BMI.

Results: A higher relative proportion of SB to the remaining 24-h movement behaviors (MVPA, LIPA, sleep) was associated with a higher likelihood of both overweight (p=0.007) and obesity (p<0.001). Conversely, a higher relative proportion of MVPA compared to the remaining behaviors was associated with lower odds of overweight (p=0.003) and obesity (p<0.001). Differences in the relative amount of LIPA showed no association with either overweight or obesity.

Conclusions: The results underscore the protective role of MVPA on maintaining a healthy weight, as well as the central role of SB in contributing to adverse weight outcomes within the 24‑hour movement composition. These findings highlight that the relative balance of daily movement behaviors, rather than their absolute durations, is key for weight‑related outcomes.

Purpose: Fabry disease is an inherited metabolic disorder affecting breakdown of glycolipids in cell lysosomes leading to multi-organ disease, particularly the brain, heart and kidneys. This study evaluated associations of physical activity and sedentary behaviour with mental health and quality of life (QoL) in adults with Fabry disease.

Methods: Cross-sectional design study with N=94 adults with Fabry disease. Sitting, standing and stepping were measured using the activPAL4™. Outcomes were the Hospital Anxiety and Depression Scale and the EuroQol five-dimension questionnaire. Analysis was correlations and multiple linear regression.

Results: Time in stepping bouts >10 minutes was correlated with anxiety (r = −0.185). Daily stepping time (r = −0.216), daily steps (r = −0.224) and time in stepping bouts of >2, 5 and 10 minutes (r = −0.265, −0.293 and −0.220, respectively) were correlated with depression. Daily sitting time and prolonged sitting time were adversely correlated with QoL (r = −0.184 and r = −0.150, respectively); increased stepping time, number of steps, and time stepping in bouts of >2 and 5 minutes were correlated favourably (r = 0.337, 0.328, 0.219 and 0.211, respectively). Time in stepping bouts >5 minutes predicted lower depression (β = −0.269). Higher daily steps predicted improved QoL (β = 0.267).

Conclusions: Increased stepping is beneficially associated with depression and QoL in adults with Fabry disease, whereas higher sitting time was associated with lower QoL. Stepping bouts lasting at least 2 to 10 minutes could be particularly important for optimal management.

Funding source: Society for Mucopolysaccharide Diseases (reference CSAC-BAI.BRU.202110).