Objectives (1) To develop reference values for health-related fitness in European children and adolescents aged 6–18 years that are the foundation for the web-based, open-access and multilanguage fitness platform (FitBack); (2) to provide comparisons across European countries. Methods This study builds on a previous large fitness reference study in European youth by (1) widening the age demographic, (2) identifying the most recent and representative country-level data and (3) including national data from existing fitness surveillance and monitoring systems. We used the Assessing Levels of PHysical Activity and fitness at population level (ALPHA) test battery as it comprises tests with the highest test–retest reliability, criterion/construct validity and health-related predictive validity: the 20 m shuttle run (cardiorespiratory fitness); handgrip strength and standing long jump (muscular strength); and body height, body mass, body mass index and waist circumference (anthropometry). Percentile values were obtained using the generalised additive models for location, scale and shape method. Results A total of 7 966 693 test results from 34 countries (106 datasets) were used to develop sex-specific and age-specific percentile values. In addition, country-level rankings based on mean percentiles are provided for each fitness test, as well as an overall fitness ranking. Finally, an interactive fitness platform, including individual and group reporting and European fitness maps, is provided and freely available online (www.fitbackeurope.eu). Conclusion This study discusses the major implications of fitness assessment in youth from health, educational and sport perspectives, and how the FitBack reference values and interactive web-based platform contribute to it. Fitness testing can be conducted in school and/or sport settings, and the interpreted results be integrated in the healthcare systems across Europe.

Background: The main aims of this study were to explore the associations between time spent in bed (as a proxy of sleep duration) and sleep quality with overweight/obesity status in a large sample of young adults. Methods: In this cross-sectional study, participants were 2100 university students (49.6% of women). We used Pittsburgh Sleep Quality Index (PSQI) questionnaire to assess time spent in bed and sleep quality. Body-mass index (BMI) was self-reported and dichotomized as normal (<25 kg/m²) vs. overweight/obesity (≥25 kg/m²) status. Results: In model 1, both short (10 h/day, OR = 3.38; 95% CI 2.12 to 5.40) time spent in bed were associated with a greater likelihood of being overweight/obese. In model 2, poor sleep quality (>5 points, OR = 1.45; 95% CI 1.14 to 1.83) was associated with a greater likelihood of being overweight/obese. After entering time spent in bed and sleep quality simultaneously into the model 3, both short (OR = 2.64; 95% CI 1.23 to 5.66) and long (OR = 3.27; 95% CI 2.04 to 5.23) time spent in bed and poor sleep quality (OR = 1.40; 95% CI 1.10 to 1.78) were associated with overweight/obesity status. Conclusions: Our results show that both short and long time spent in bed and poor sleep quality are associated with overweight/obesity status in young adults. Special interventions and policies that use both sleep duration and sleep quality as protective factors against overweight/obesity are warranted.

The main purpose of the study was to explore the associations of sleep duration and sleep quality with physical activity (PA). In this cross-sectional study, participants were 894 elderly individuals (mean age 80 ± 3 years; 56.0% women) living in nursing homes. PA, sleep duration, and sleep quality (based on the Pittsburgh Sleep Quality Index (PSQI)) were self-reported. The associations of sleep duration and sleep quality with PA at the nursing home level were analyzed using generalized estimating equations with clustering. Participants reporting short sleep duration (9 h; OR = 2.61; 95% CI 1.35–5.02) and good sleep quality (<5 points; OR = 1.59; 95% CI 1.19–2.12) were more likely to report sufficient PA. When sleep duration and sleep quality were entered into the same model, the same associations remained. This study shows that elderly individuals who report short sleep duration are less likely to meet PA guidelines, while those who report long sleep duration and good sleep quality are more likely to meet PA guidelines. Strategies aiming to improve sleep duration and sleep quality are warranted.

Objectives The main purpose of the present study was to explore the associations between sleep quality and insufficient physical activity. Design Cross-sectional. Setting Faculties in Croatia. Participants 2100 university students (1049 men and 1051 women) aged 18–24 years were recruited. Primary outcome To assess the domains of sleep quality (independent variables) and ‘insufficient’ physical activity (dependent variable), we used previously validated Pittsburgh Sleep Quality Index and International Physical Activity questionnaires. Logistic regressions were used to calculate the associations between the sleep quality and ‘insufficient’ physical activity. Results When sleep quality domains were entered separately into the model, very bad subjective sleep quality (OR 3.09; 95% CI 1.50 to 6.56), >60 min of sleep latency (OR 2.17; 95% CI 1.39 to 3.39), <7 hours of sleep (OR 1.56; 95% CI 1.24 to 1.96), 1/week (OR 1.61; 95% CI 1.03 to 2.52), use of sleep medication >1/week (OR 3.35; 95% CI 1.83 to 6.10), very big daytime dysfunction problem (OR 2.78; 95% CI 1.57 to 4.93) and poor sleep quality (1.53; 95% CI 1.23 to 1.91) were associated with ‘insufficient’ physical activity. When all sleep quality domains were entered simultaneously into the model, the same significant associations remained, except for sleep disturbances. Both models were adjusted for gender, body mass index, self-rated health, life satisfaction, socioeconomic status, presence or absence of chronic diseases, smoking status, binge drinking and psychological distress. Conclusions Our results show that ‘poor’ sleep quality is associated with ‘insufficient’ physical activity in young adults. In order to improve, special strategies and policies that leverage ‘good sleep’ quality are warranted.

This study aimed to determine the associations between the self-reported sleep duration and self-rated health in young adults. In this cross-sectional study, participants were 689 young adults (mean age 20 ± 1.35 years, 49.8% female). Sleep duration and self-rated health, as the main outcome of interest, were measured as self-reported. As potential covariates, we included sex, age, smoking status, alcohol consumption, physical activity, sedentary behavior, psychological distress, and body mass index. Approximately 30% of participants slept 7-8 hours, 17.4% were short sleepers (categories < 6 hours and 6-7 hours), and 53.9% were long sleepers (categories 8-10 hours and > 10 hours of sleep). In an unadjusted model, compared with the reference category (7-8 hours of sleep), those who slept < 6 hours (odds ratio 0.20; 95% confidence interval 0.08 to 0.48) and between 6-7 hours (odds ratio 0.43; 95% confidence interval 0.26 to 0.69) were less likely to have good self-rated health. In an adjusted model, short (< 7 hours) and long sleep (> 10 hours) were both associated with poor self-rated health. Our results suggest that both short (< 7 hours) and long (> 10 hours) sleepers have lower odds of having good self-rated health after adjusting for potential covariates. Health professionals should pay more attention to young adults, who have both short and long period of sleep, in order to prevent health problems and potential acute or chronic diseases.

Purpose: To determine normative data for gait speed and height-normalized gait speed in community-dwelling older men and women. Materials and Methods: In this cross-sectional study, we recruited 565 men and women aged ≥ 60 years old. Age was calculated from the date of birth and further classified into four categories: (1) 60– 65 years, (2) 66– 70 years, (3) 71– 75 years and (4) ≥ 76 years. Gait speed was assessed by a pressure platform (ZEBRIS, Munich, Germany) in meters per second (m/s). Height and weight were objectively measured. Height-normalized gait speed was calculated by dividing gait speed by height. We created the 20th, 40th, 60th and 80th percentile curves for both outcome measures using Cole’s Lambda (L), Mu (M) and Sigma (S) method. Results: Mean gait speed and height-normalized gait speed was 1.24 (standard deviation 0.28) and 0.75 (0.17). Significant age-related decline in gait speed for both sexes was observed (p < 0.001). Being a woman (β = - 0.09, p < 0.001), being older (β = - 0.02, p < 0.001) and having higher body mass index values (β = - 0.02, p < 0.001) were significantly associated with slower gait speed. Conclusion: Gait speed significantly declines with age in both older men and women. Providing normative data can be used in screening and monitoring “slow” walkers to prevent from foot pain and higher risk of falls.