Conference Agenda

The Government’s Get Finland Moving initiative aims to increase physical activity across all age groups. The programme promotes physical activity through a phenomenon‑based approach that spans for example school days, studies, working life, conscription, and services for older people. It is cross‑sectorally funded, with measures supported by seven ministries.

Systematic monitoring has been established to assess the programme’s results and impacts. A multidisciplinary research and expert group has developed a comprehensive monitoring framework, under which each of the programme’s 35 measures is followed using defined action, process, output, and impact indicators.

Progress toward the core objective—increasing physical activity in every age group—is tracked through population‑based studies that examine changes in overall activity levels and physical functioning. Additional research focuses on the societal impact of physical activity counselling, outdoor recreation, and school-aged children’s movement. For schoolchildren, data are collected from the same schools and pupils before and after the introduction of funded measures to increase physical activity.

The purpose of the results reporting is to generate evidence on the programme’s effectiveness and impacts. An external evaluation will also be carried out. This assessment will consider the counterforces that have influenced physical activity promotion during the government term as well as the process‑related lessons learned from cross‑sectoral cooperation.

Background

Physical activity (PA) is important for secondary prevention of cardiovascular diseases (CVDs), yet factors associated with device-measured PA in this population are underexplored. This study examines the link between device-measured PA and demographic, socioeconomic, lifestyle factors, and comorbidity in individuals with CVDs.

Methods

This cross-sectional study used data from a large Swedish cohort and included adults aged 50-64 years with major CVDs. Moderate-to-vigorous physical activity was measured using accelerometers and grouped into tertiles. Demographic, socioeconomic, lifestyle factors, and comorbidities were obtained through questionnaires and registry data. Associations with PA levels were analysed using multiple ordinal logistic regression.

Results

In total, 1,484 participants (32% women; median age 60.2 years) were included. Lower odds of having a higher PA level were significantly associated with being in the oldest age group (OR = 0.58; 95% CI: 0.44, 0.77), female sex (OR = 0.74, 95% CI: 0.59, 0.92), completion of secondary school (OR = 0.73, 95% CI: 0.54, 0.98), regular/occasional smoking (OR = 0.37; 95% CI: 0.26, 0.51), and having one (OR = 0.61, 95% CI: 0.48, 0.77) or two or more comorbidities (OR = 0.45, 95% CI: 0.30, 0.68). A healthy diet was significantly associated with higher PA levels (OR = 1.84; 95% CI: 1.47, 2.32).

Conclusions

In this population, engagement in PA is largely associated with lifestyle factors, demographic characteristics, and comorbidity, whereas some commonly used socioeconomic indicators may be of lesser relevance. These findings identify characteristics that may inform future research supporting PA promotion in high-risk groups.

Background: Excessive screen use among children has been linked to poorer mental health. This study examined the association between screen time and symptoms of mental health and beahavioural problems in children, and whether associations varied by sleep duration or physical activity.

Methods: This cross-sectional study included 7768 nine‑year‑old Swedish children (46% girls). Parents reported their children’s mental health symptoms using the Strengths and Difficulties Questionnaire, which includes five subscales: emotional problems, conduct problems, hyperactivity/inattention, peer problems and prosocial behaviour. Screen time and sleep duration were parent‑reported, and vigorous physical activity (VPA) was measured using accelerometers. Data were analysed with generalized estimating equations.

Results: On weekdays, most children (76%) accumulated the recommended 0–2 hours of daily screen time, whereas only one third met this level on weekends. Higher screen time was negatively associated with all subscales, showing a dose–response pattern. The findings were consistent across weekdays and weekends. Children with short sleep duration had higher hyperactivity and emotional symptom scores regardless of weekend screen time. Among children with medium or long sleep duration, both outcomes tended to increase with higher levels of weekend screen time. No interaction effects were seen for VPA.

Conclusions: Higher screen time was associated with symptoms of mental health, with stronger associations at higher exposure levels. These findings underscore the importance of monitoring screen use in school‑aged children and considering screen‑related behaviours in strategies to support their mental well‑being.

Funding source: This work was supported by the Knowledge Foundation and the Swedish Research Council.

Background: Electric cyclists are becoming increasingly visible in Dutch streets. This study examines the prevalence of electric bicycle users and analyzes their demographic characteristics, as well as 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 between groups and years were 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 was traveling to shops, friends, etc. (72%). The newest 2025 data will be available in 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.

Support/Funding Source: Ministry of Health, Welfare and Sports, the Netherlands.

Purpose: The 24-hour human movement behaviour is inherently complex and heterogeneous, making it challenging to represent with a single metric. Accelerometers provide more information than simply time spent at different intensity levels. From raw acceleration signals, a wide array of indicators can be extracted, reflecting multiple dimensions such as intensity, duration, volume, frequency, accumulation pattern, timing, regularity, and complexity. This study aimed to identify distinct movement behaviour phenotypes and examine how variations in these phenotypes relate to cardiometabolic health.
Methods: In this cross-sectional study, 1009 adults from Luxembourg (aged 25–79) with at least four valid days of accelerometer data were included. A reversed graph embedding technique reduced the complexity of movement behaviours into a two-dimensional tree structure, allowing the identification of distinct phenotypes. Linear regression models were applied to explore associations between the tree dimensions, movement behaviour indicators, and cardiometabolic health biomarkers.
Results: Four movement behaviour phenotypes emerged: inactive, irregularly active, regularly active, and regularly low active. Both tree dimensions showed favourable associations with cardiometabolic outcomes, indicating that greater overall activity volume—regardless of its regularity—may benefit health. Among the indicators, average 24-hour acceleration and total step count were the strongest contributors to phenotypic variation.
Conclusion: This study demonstrates that a novel dimensionality reduction approach can effectively capture the multifaceted nature of movement behaviours, highlight key movement behaviour indicators, and distinguish between meaningful phenotypes. The findings enhance understanding of behavioural heterogeneity and emphasize movement dimensions most relevant to cardiometabolic health.

Physical activity trackers (PAT) can be devices that record movement data. Ownership of PAT have the potential for increasing PA behaviour during adolescence.

The aim was to analyse the ownership of PAT among Finnish adolescents aged 11y, 13y, and 15y, between 2016 to 2024, and its association with PA.

Repeated cross-sectional data (2016-2024) from the Finnish School-age Physical Activity (F-SPA) study were conducted on ownership of PATs (mobile apps, smart watches). Binominal logistic regression analyses (reference = no ownership) were performed with study year and PA as independent variable.

There were increases in ownership of all PATs from 2016 (apps = 52%; watches = 27%) to 2024 (apps = 65%; OR = 1.2, CI =1.1-1.3; watches = 43%; OR = 1.9, CI = 1.7-2.1). Simply having PAT was associated positively with increased PA from 0-2 days to 3-4days (Apps: OR=1.6, CI = 1.5-1.8; watches: OR = 1.7, CI = 1.5-1.8).

Although weekly use of apps continues to increase since 2016, weekly use of watches has not changed between 2022 and 2024 among Finnish adolescents. There were modest associations between ownership with increased PA levels.

Implications for Policy and Practice:

Regulations on mobile phone restrictions during school times limits the accuracy of the data from apps, which may increase the interest in wearables linked with apps. Alternatively, less use of apps for PAT may occur as the phone’s sensor are not allowed to measure activity for a large portion of the day (i.e. school time). Investigations on PAT use are warranted.

Background and aim of the study: This study examined whether a polygenic risk score for cognitive function (PRS-COG) modifies the association between cumulative physical activity (PA) from childhood to midlife and cognitive changes in midlife.

Methods: We utilized data (n=1353, 57% female) from the Cardiovascular Risk in Young Finns Study, an ongoing population-based longitudinal study that began in 1980. PA was assessed at all study phases (1980–2018) with a standardized questionnaire every 3–9 years. Cumulative PA was determined from childhood to midlife (ages 9–48). Cognitive functions (learning and memory, working memory, reaction time, and information processing) were evaluated using the Cambridge Neuropsychological Test Automated Battery in 2011 and 2018. Associations between cumulative PA and cognitive changes were analysed using linear regression models. Models were adjusted for age, sex, education, cardiometabolic risk factors, and health behaviors. The moderating effect of PRS-COG to cumulative PA was evaluated by adding an interaction term for the mean centred variables.

Results: PRS-COG and cumulative PA from childhood to midlife did not show statistically significant interaction effects on changes in any cognitive domain. Higher levels of cumulative PA were associated with a smaller decrease in information processing in midlife (β=0.05, p=0.008). Cumulative PA was not associated with changes in other cognitive domains.

Conclusions: These findings suggest that promoting PA across the lifespan may be associated with a smaller decrease in information processing in midlife, regardless of individuals’ genetic architecture.

Support/Funding Source: The study was supported by the Finnish Ministry of Education and Culture (OKM/15/626/2024).

Background:

This study describes the feasibility of LifeLab Dublin, a co-designed, nine week, health literacy intervention for young adolescents (12-15 years) in socioeconomically disadvantaged schools.

Methods:

A pre-post-follow-up, single-arm, mixed methods, feasibility study design was adopted, guided by the Orsmond and Cohn objectives for feasibility studies. Semi-structured interviews (n=15) and focus groups (n=12) assessed the feasibility and acceptability of LifeLab. Surveys assessed adolescents’ health literacy, wellbeing, and health related quality of life were administered at baseline, post-intervention, and at 3-or 6-months follow-up. Teacher surveys post-intervention assessed feasibility. Researcher records monitored recruitment, attrition, and study management procedures. Data were first analysed independently using an inductive reflexive thematic analysis, descriptive statistics, independent and paired samples t-tests, and one-way ANOVAs; with data sources integrated during write up.

Results:

Four schools, 345 students and 16 teachers participated in this study between January 2023 and September 2025. While participants largely indicated positive experiences with LifeLab, there were some suggested improvements across the lessons, visits, photovoice project, and teacher training. Significant increases in adolescents' health literacy were observed from baseline to post-intervention (p<.001) and baseline to follow-up (p<.001). Family and autonomy increased from baseline to follow-up (p<.001) and post-intervention to follow-up (p<.001). Similarly, school environment increased from baseline to follow-up (p=.024) and post-intervention to follow-up (p=.021).

Conclusion:

Findings support LifeLab Dublin as a feasible and acceptable programme for young adolescents and teachers as it is aligned with curricular objectives and addresses issues faced in everyday life.