BackgroundFitness and physical activity are important for cardiovascular and mental health but activity and fitness levels are declining especially in adolescents and among girls. This study examines clustering of factors associated with low fitness in adolescents in order to best target public health interventions for young people.Methods1147 children were assessed for fitness, had blood samples, anthropometric measures and all data were linked with routine electronic data to examine educational achievement, deprivation and health service usage. Factors associated with fitness were examined using logistic regression, conditional trees and data mining cluster analysis. Focus groups were conducted with children in a deprived school to examine barriers and facilitators to activity for children in a deprived community.ResultsUnfit adolescents are more likely to be deprived, female, have obesity in the family and not achieve in education. There were 3 main clusters for risk of future heart disease/diabetes (high cholesterol/insulin); children at low risk (not obese, fit, achieving in education), children ‘visibly at risk’ (overweight, unfit, many hospital/GP visits) and ‘invisibly at risk’ (unfit but not overweight, failing in academic achievement). Qualitative findings show barriers to physical activity include cost, poor access to activity, lack of core physical literacy skills and limited family support.ConclusionsLow fitness in the non-obese child can reveal a hidden group who have high risk factors for heart disease and diabetes but may not be identified as they are normal weight. In deprived communities low fitness is associated with non-achievement in education but in non-deprived communities low fitness is associated with female gender. Interventions need to target deprived families and schools in deprived areas with community wide campaigns.

Objectives1. to investigate whether 20 m multi-stage shuttle run performance (20mSRT), an indirect measure of aerobic fitness, could discriminate between healthy and overweight status in 9–10.9 yr old schoolchildren using Receiver Operating Characteristic (ROC) analysis; 2. Investigate if cardiometabolic risk differed by aerobic fitness group by applying the ROC cut point to a second, cross-sectional cohort.DesignAnalysis of cross-sectional data.Participants16,619 9–10.9 year old participants from SportsLinx project and 300 11–13.9 year old participants from the Welsh Schools Health and Fitness Study.Outcome MeasuresSportsLinx; 20mSRT, body mass index (BMI), waist circumference, subscapular and superilliac skinfold thicknesses. Welsh Schools Health and Fitness Study; 20mSRT performance, waist circumference, and clustered cardiometabolic risk.AnalysesThree ROC curve analyses were completed, each using 20mSRT performance with ROC curve 1 related to BMI, curve 2 was related to waist circumference and 3 was related to skinfolds (estimated % body fat). These were repeated for both girls and boys. The mean of the three aerobic fitness thresholds was retained for analysis. The thresholds were subsequently applied to clustered cardiometabolic risk data from the Welsh Schools study to assess whether risk differed by aerobic fitness group.ResultsThe diagnostic accuracy of the ROC generated thresholds was higher than would be expected by chance (all models AUC >0.7). The mean thresholds were 33 and 25 shuttles for boys and girls respectively. Participants classified as ‘fit’ had significantly lower cardiometabolic risk scores in comparison to those classed as unfit (p<0.001).ConclusionThe use of the ROC generated cut points by health professionals, teachers and coaches may provide the opportunity to apply population level ‘risk identification and stratification’ processes and plan for “at-risk” children to be referred onto intervention services.

BackgroundThis study examines obesity and factors associated with obesity in children aged 11–13 years in the UK.Methods1147 children from ten secondary schools participated in a health survey that included blood samples, fitness test and anthropometric measures. Factors associated with obesity were examined using multilevel logistic regression.FindingsOf the children examined (490 male; 657 female) a third were overweight, 1 in 6 had elevated blood pressure, more than 1 in 10 had high cholesterol, 58% consumed more fat than recommended, whilst 37% were classified as unfit. Children in deprived areas had a higher proportion of risk factors; for example, they had higher blood pressure (20% (deprived) compared to 11% (non-deprived), difference: 9.0% (95%CI: 4.7%–13.4%)). Obesity is associated with risk factors for heart disease and diabetesMaintaining fitness is associated with a reduction in the risk factors for heart disease (high blood pressure and cholesterol) but not on risk factors for diabetes (insulin levels). In order of importance, the main risk factors for childhood obesity are being unfit, having an obese father, and being large at birth.ConclusionThe high proportion of children with risk factors suggests future interventions need to focus on community and policy change to shift the population norm rather than targeting the behaviour of high risk individuals. Interventions need to focus on mothers’ lifestyle in pregnancy, fathers’ health, as well as promoting fitness among children. Obesity was not associated with deprivation. Therefore, strategies should be adopted in both deprived and non deprived areas.