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The establishment of the Determinants of Diet and Physical Activity (DEDIPAC) Knowledge Hub, 2013-2016, was the first action taken by the 'Healthy Diet for a Healthy Life' European Joint Programming Initiative. DEDIPAC aimed to provide better insight into the determinants of diet, physical activity and sedentary behaviour across the life course, i.e. insight into the causes of the causes of important, non-communicable diseases across Europe and beyond. DEDIPAC was launched in late 2013, and delivered its final report in late 2016. In this paper we give an overview of what was achieved in terms of furthering measurement and monitoring, providing overviews of the state-of-the-art in the field, and building toolboxes for further research and practice. Additionally, we propose some of the next steps that are now required to move forward in this field, arguing in favour of 1) sustaining the Knowledge Hub and developing it into a European virtual research institute and knowledge centre for determinants of behavioural nutrition and physical activity with close links to other parts of the world; 2) establishing a cohort study of families across all regions of Europe focusing specifically on the individual and contextual determinants of major, non-communicable disease; and 3) furthering DEDIPAC's work on nutrition, physical activity, and sedentary behaviour policy evaluation and benchmarking across Europe by aligning with other international initiatives and by supporting harmonisation of pan-European surveillance. [ABSTRACT FROM AUTHOR]

Background: Emerging research suggests that physical activity among children and adolescents decreased during the COVID-19 pandemic. However, a differentiated overview of European youth is lacking. In particular, no systematic analysis has been conducted to date on the impact of heterogeneous pandemic restrictions and school closures within European countries, and with regard to potentially vulnerable groups. Methods: We searched seven databases and included studies for children and adolescents (≤ 19 years) of the WHO European Region that compared physical activity during the COVID-19 pandemic with a pre-pandemic baseline using validated measurement instruments. We used the Oxford Stringency Index and School Closure Index as indicators of restriction stringency. Screening for eligibility, data extraction, assessment of the study risk of bias (using the 'Risk of Bias in Non-randomized Studies - of Exposure' [ROBINS-E]) and certainty grading of evidence (using the GRADE approach), were all done in duplicate. Unpublished data was requested from study authors. Data were pooled in random effects models. An a priori protocol was published, reporting is carried out in accordance with the 'Preferred Reporting Items for Systematic Review and Meta-Analyses' (PRISMA) statement. Results: Of 14,897 non-duplicate records, 26 publications (n = 15,038 pre-pandemic, n = 13,041 during pandemic) met full inclusion criteria. Comparison before and during the COVID-19 pandemic revealed a significant reduction in total physical activity (standardized mean difference [SMD], -0.57 [95%CI, -0.95; -0.20]) and moderate-to-vigorous physical activity (SMD, -0.43 [95% CI, -0.75; -0.10]), corresponding to a decrease of 12 min per day (a 20% reduction of the WHO recommendation). A decrease in sporting activity was also recorded. Subgroup analyses suggested that middle childhood (aged 8–12) and adolescents were particularly affected by the decline. School closures were associated with a reduction in physical activity. The certainty of evidence for all outcomes was low. Conclusions: A sharp decline in all forms of physical activity was recorded among European children and adolescents during the COVID-19 pandemic. This decline was higher during periods of school closure and mainly affected younger schoolchildren and adolescents. Immediate action by policy-makers and practitioners, as well as evidence-based public health strategies, are imperative in reversing this trend. Trial registration: PROSPERO: CRD42023395871 [ABSTRACT FROM AUTHOR]

Background: Media use may influence metabolic syndrome (MetS) in children. Yet, longitudinal studies are scarce. This study aims to evaluate the longitudinal association of childhood digital media (DM) use trajectories with MetS and its components. Methods: Children from Belgium, Cyprus, Estonia, Germany, Hungary, Italy, Spain and Sweden participating in the IDEFICS/I.Family cohort were examined at baseline (W1: 2007/2008) and then followed-up at two examination waves (W2: 2009/2010 and W3: 2013/2014). DM use (hours/day) was calculated as sum of television viewing, computer/game console and internet use. MetS z-score was calculated as sum of age- and sex-specific z-scores of four components: waist circumference, blood pressure, dyslipidemia (mean of triglycerides and HDL-cholesterol−1) and homeostasis model assessment for insulin resistance (HOMA-IR). Unfavorable monitoring levels of MetS and its components were identified (cut-off: ≥ 90th percentile of each score). Children aged 2–16 years with ≥ 2 observations (W1/W2; W1/W3; W2/W3; W1/W2/W3) were eligible for the analysis. A two-step procedure was conducted: first, individual age-dependent DM trajectories were calculated using linear mixed regressions based on random intercept (hours/day) and linear slopes (hours/day/year) and used as exposure measures in association with MetS at a second step. Trajectories were further dichotomized if children increased their DM duration over time above or below the mean. Results: 10,359 children and adolescents (20,075 total observations, 50.3% females, mean age = 7.9, SD = 2.7) were included. DM exposure increased as children grew older (from 2.2 h/day at 2 years to 4.2 h/day at 16 years). Estonian children showed the steepest DM increase; Spanish children the lowest. The prevalence of MetS at last follow-up was 5.5%. Increasing media use trajectories were positively associated with z-scores of MetS (slope: β = 0.54, 95%CI = 0.20–0.88; intercept: β = 0.07, 95%CI = 0.02–0.13), and its components after adjustment for puberty, diet and other confounders. Children with increasing DM trajectories above mean had a 30% higher risk of developing MetS (slope: OR = 1.30, 95%CI = 1.04–1.62). Boys developed steeper DM use trajectories and higher risk for MetS compared to girls. Conclusions: Digital media use appears to be a risk factor for the development of MetS in children and adolescents. These results are of utmost importance for pediatricians and the development of health policies to prevent cardio-metabolic disorders later in life. Trial registration: ISRCTN, ISRCTN62310987. Registered 23 February 2018- retrospectively registered. [ABSTRACT FROM AUTHOR]

Background: A pan-European approach to evaluate policy impact on health behaviour requires the employment of a consensus set of established and relevant indicators. Methods: As part of the Joint Programming Initiative on a Healthy Diet for a Healthy Life, the Policy Evaluation Network PEN identified key indicators of health behaviours and their determinants. These key indicators are already, or have the potential to be, adopted by large European Union surveillance systems for the assessment of policy impact. The iterative selection process included consultations in two rounds via email prior to a 2-days expert workshop. The experts collated a list of dietary behaviour, physical activity and sedentary behaviour indicators for European policy monitoring in young and adult populations based on existing frameworks and literature reviews. The expert panel was composed of researchers, policy makers and representatives of major European surveillance systems and related initiatives, as well as, representatives of organisations providing monitoring data, such as the European Commission and Eurostat. Results: The process provided two lists of key indicators including 37 diet 'policy' indicators and 35 indicators for dietary behaviour and their 'determinants'; as well as 32 physical activity 'policy' indicators and 35 indicators for physical activity, sedentary behaviour and their 'determinants'. Conclusion: A total of 139 key indicators related to the individual, the setting and the population level, and suitable for the assessment of dietary behaviour, physical activity and sedentary behaviour were prioritised by policy makers and researchers with the ultimate aim to embed policy evaluation measures in existing surveillance systems across the European Union. In a next step, data sources and suitable instruments will be identified to assess these key indicators. [ABSTRACT FROM AUTHOR]

Background: Research has suggested the positive impact of physical activity on health and wellbeing in older age, yet few studies have investigated the associations between physical activity and heterogeneous trajectories of healthy ageing. We aimed to identify how physical activity can influence healthy ageing trajectories using a harmonised dataset of eight ageing cohorts across the world. Methods: Based on a harmonised dataset of eight ageing cohorts in Australia, USA, Mexico, Japan, South Korea, and Europe, comprising 130,521 older adults (Mage = 62.81, SDage = 10.06) followed-up up to 10 years (Mfollow-up = 5.47, SDfollow-up = 3.22), we employed growth mixture modelling to identify latent classes of people with different trajectories of healthy ageing scores, which incorporated 41 items of health and functioning. Multinomial logistic regression modelling was used to investigate the associations between physical activity and different types of trajectories adjusting for sociodemographic characteristics and other lifestyle behaviours. Results: Three latent classes of healthy ageing trajectories were identified: two with stable trajectories with high (71.4%) or low (25.2%) starting points and one with a high starting point but a fast decline over time (3.4%). Engagement in any level of physical activity was associated with decreased odds of being in the low stable (OR: 0.18; 95% CI: 0.17, 0.19) and fast decline trajectories groups (OR: 0.44; 95% CI: 0.39, 0.50) compared to the high stable trajectory group. These results were replicated with alternative physical activity operationalisations, as well as in sensitivity analyses using reduced samples. Conclusions: Our findings suggest a positive impact of physical activity on healthy ageing, attenuating declines in health and functioning. Physical activity promotion should be a key focus of healthy ageing policies to prevent disability and fast deterioration in health. [ABSTRACT FROM AUTHOR]