Evaluation of the Finnish Diabetes Risk Score as a screening tool for undiagnosed type 2 diabetes and dysglycaemia among early middle-aged adults in a large-scale European cohort. The Feel4Diabetes-study

Aim: To assess the diagnostic accuracy of the FINDRISC for undiagnosed type 2 diabetes mellitus (T2DM) and dysglycaemia (i.e. the presence of prediabetes or T2DM) among early middle-aged adults from vulnerable groups in a large-scale European cohort. Methods: Participants were recruited from low-socioeconomic areas in high-income countries (HICs) (Belgium-Finland) and in HICs under austerity measures (Greece-Spain) and from the overall population in low/middle-income countries (LMICs) (Bulgaria-Hungary). Study population comprised of 2116 parents of primary-school children from families identified at increased risk of T2DM, based on parental self-reported FINDRISC. Sensitivity (Se), specificity (Sp), area under the receiver operating characteristic curves (AUC-ROC) and the optimal cut-offs of FINDRISC that indicate an increased probability for undiagnosed T2DM or dysglycaemia were calculated. Results: The AUC-ROC for undiagnosed T2DM was 0.824 with optimal cut-off =14 (Se = 68%, Sp = 81.7%) for the total sample, 0.839 with optimal cut-off =15 (Se = 83.3%, Sp = 86.9%) for HICs, 0.794 with optimal cut-off =12 (Se = 83.3%, Sp = 61.1%) for HICs under austerity measures and 0.882 with optimal cut-off =14 (Se = 71.4%, Sp = 87.8%) for LMICs. The AUC-ROC for dysglycaemia was 0.663 with optimal cut-off =12 (Se = 58.3%, Sp = 65.7%) for the total sample, 0.656 with optimal cut-off =12 (Se = 54.5%, Sp = 64.8%) for HICs, 0.631 with optimal cut-off =12 (Se = 59.7%, Sp = 62.0%) for HICs under austerity measures and 0.735 with optimal cut-off =11 (Se = 72.7%, Sp = 70.2%) for LMICs. Conclusion: FINDRISC can be applied for screening primarily undiagnosed T2DM but also dysglycaemia among vulnerable groups across Europe, considering the use of different cut-offs for each subpopulation

Do physical activity and screen time mediate the association between European fathers' and their children's weight status? Cross-sectional data from the Feel4Diabetes-study

BACKGROUND: Most research on parenting and childhood obesity and obesity-related behaviours has focused on mothers while fathers have been underrepresented. Yet, recent literature has suggested that fathers uniquely influence their children''s lifestyle behaviours, and hence could also affect their weight status, but this has not yet been scientifically proven. Therefore, the present study aimed to determine whether the association between fathers'' weight status and their children''s weight status is mediated by fathers'' and children''s movement behaviours (i.e. physical activity (PA) and screen time (ST)). METHODS: Cross-sectional data of 899 European fathers and their children were analyzed. Fathers/male caregivers (mean age =¿43.79¿±¿5.92¿years, mean BMI =¿27.08¿±¿3.95) completed a questionnaire assessing their own and their children''s (mean age =¿8.19¿±¿0.99¿years, 50.90% boys, mean BMIzscore =¿0.44¿±¿1.07) movement behaviours. Body Mass Index (BMI, in kg/m2) was calculated based on self-reported (fathers) and objectively measured (children) height and weight. For children, BMI z-scores (SD scores) were calculated to obtain an optimal measure for their weight status. Serial mediation analyses were performed using IBM SPSS 25.0 Statistics for Windows to test whether the association between fathers'' BMI and children''s BMI is mediated by fathers'' PA and children''s PA (model 1) and fathers'' ST and children''s ST (model 2), respectively. RESULTS: The present study showed a (partial) mediation effect of fathers'' PA and children''s PA (but not father''s ST and children''s ST) on the association between fathers'' BMI and children''s BMI (model for PA; coefficient: 0.001, 95% CI: [0.0001, 0.002]; model for ST; coefficient: 0.001, 95% CI: [0.000, 0.002]). Furthermore, fathers'' movement behaviours (PA and ST) were positively associated with their children''s movement behaviours (PA and ST) (model for PA, coefficient: 0.281, SE: 0.023, p <¿0.001; model for ST, coefficient: 0.345, SE: 0.025, p¿<¿0.001). CONCLUSIONS: These findings indicate that the influence of fathers on their children''s weight status partially occurs through the association between fathers'' PA and children''s PA (but not their ST). As such, intervening by focusing on PA of fathers but preferably of both members of the father-child dyad (e.g. engaging fathers and their children in co-PA) might be a novel and potentially effective strategy for interventions aiming to prevent childhood overweight and obesity. Longitudinal studies or intervention studies confirming these findings are however warranted to make meaningful recommendations for health intervention and policy. TRIAL REGISTRATION: The Feel4Diabetes-study is registered with the clinical trials registry http://clinicaltrials.gov , ID: 643708

The ToyBox pre-school obesity prevention intervention for use in Scotland : results of a feasibility cluster randomised controlled trial (cRCT)

Objective: The ToyBox intervention was successful at increasing physical activity and reducing sedentary behaviour in pre-school children across Europe. The intervention involves teacher-led activities over 18 weeks which aim to increase physical activity, reduce sedentary behaviour, and promote healthy snacking and water consumption. We adapted the Toybox Europe intervention for preschools in Scotland using a co-production approach. This study aimed to test the feasibility of our adapted intervention in children attending preschools in relatively deprived areas of Glasgow, Scotland, who are considered hard to reach. Methods: The feasibility cRCT involved six preschools (three intervention, three control); control was usual curriculum. Participants were 3-5 year old children attending preschools in Glasgow, UK, and their parents. Outcomes of interest were recruitment rates, willingness to be randomised, attrition rates, questionnaire completion rate and acceptability of measurement methods. Measurements were taken at baseline and 18 weeks; anthropometry, physical activity, sleep and sedentary time using the activPal accelerometer (wear time = 7 days; 3 days considered valid), body composition via bioelectrical impedance analysis (BIA), and measures of diet and home screen time via parental questionnaire. Results: Cluster level recruitment rate was 9% (11/112 preschools) and the individual level recruitment rate was 18% (42/231 children). 36 children (16 girls) provided at least one valid measurement at baseline and follow-up (attrition rate = 16.6%). All clusters were willing to be randomised. Anthropometric measures were acceptable and feasible. Parental questionnaire response rates were low (20%). 61% of the sample provided valid accelerometer data at baseline, 27% for baseline and followup. BIA was not feasible due to poor participant compliance with protocol. Conclusions: Recruitment rates of both preschools and children was lower than anticipated compared with Toybox Europe. However, for those children who took part, the adapted intervention and the measurement methods appeared acceptable and feasible. An ongoing process evaluation will help identify ways in which recruitment of preschools, and recruitment and retention of participants, can be maximised in areas of deprivatio

GATEKEEPER’s Strategy for the Multinational Large-Scale Piloting of an eHealth Platform: Tutorial on How to Identify Relevant Settings and Use Cases

Background: The World Health Organization’s strategy toward healthy aging fosters person-centered integrated care sustained by eHealth systems. However, there is a need for standardized frameworks or platforms accommodating and interconnecting multiple of these systems while ensuring secure, relevant, fair, trust-based data sharing and use. The H2020 project GATEKEEPER aims to implement and test an open-source, European, standard-based, interoperable, and secure framework serving broad populations of aging citizens with heterogeneous health needs. Objective: We aim to describe the rationale for the selection of an optimal group of settings for the multinational large-scale piloting of the GATEKEEPER platform. Methods: The selection of implementation sites and reference use cases (RUCs) was based on the adoption of a double stratification pyramid reflecting the overall health of target populations and the intensity of proposed interventions; the identification of a principles guiding implementation site selection; and the elaboration of guidelines for RUC selection, ensuring clinical relevance and scientific excellence while covering the whole spectrum of citizen complexities and intervention intensities. Results: Seven European countries were selected, covering Europe’s geographical and socioeconomic heterogeneity: Cyprus, Germany, Greece, Italy, Poland, Spain, and the United Kingdom. These were complemented by the following 3 Asian pilots: Hong Kong, Singapore, and Taiwan. Implementation sites consisted of local ecosystems, including health care organizations and partners from industry, civil society, academia, and government, prioritizing the highly rated European Innovation Partnership on Active and Healthy Aging reference sites. RUCs covered the whole spectrum of chronic diseases, citizen complexities, and intervention intensities while privileging clinical relevance and scientific rigor. These included lifestyle-related early detection and interventions, using artificial intelligence–based digital coaches to promote healthy lifestyle and delay the onset or worsening of chronic diseases in healthy citizens; chronic obstructive pulmonary disease and heart failure decompensations management, proposing integrated care management based on advanced wearable monitoring and machine learning (ML) to predict decompensations; management of glycemic status in diabetes mellitus, based on beat to beat monitoring and short-term ML-based prediction of glycemic dynamics; treatment decision support systems for Parkinson disease, continuously monitoring motor and nonmotor complications to trigger enhanced treatment strategies; primary and secondary stroke prevention, using a coaching app and educational simulations with virtual and augmented reality; management of multimorbid older patients or patients with cancer, exploring novel chronic care models based on digital coaching, and advanced monitoring and ML; high blood pressure management, with ML-based predictions based on different intensities of monitoring through self-managed apps; and COVID-19 management, with integrated management tools limiting physical contact among actors. Conclusions: This paper provides a methodology for selecting adequate settings for the large-scale piloting of eHealth frameworks and exemplifies with the decisions taken in GATEKEEPER the current views of the WHO and European Commission while moving forward toward a European Data Space