Objectives To define food selectivity and compare indices of food selectivity among children with autism spectrum disorders (ASDs) and typically developing children, and to assess the impact of food selectivity on nutrient adequacy. Study design Food selectivity was operationalized to include food refusal, limited food repertoire, and high frequency single food intake using a modified food frequency questionnaire and 3-day food record. Food selectivity was compared between 53 children with ASDs and 58 typically developing children ages 3–11 years. Nutrient adequacy was assessed relative to the Dietary Reference Intakes. Results Children with ASDs exhibited more food refusal than typically developing children (41.7% vs. 18.9% of foods offered, p < 0.0001). A more limited food repertoire was reported for children with ASDs than typically developing children (19.0 vs. 22.5 foods, p < 0.001). Only four children with ASDs and one typically developing child were reported to demonstrate high frequency single food intake. Children with a more limited food repertoire had inadequate intakes of a greater number of nutrients. Conclusions Our findings suggest that food selectivity is more common in children with ASDs than in typically developing children, and that limited food repertoire may be associated with nutrient inadequacies.

These analyses from 2 nationally representative samples of US girls suggest a drop of about 2(1/2) months in the average age of menarche during the time period between 1963-1970 and 1988-1994. This was paralleled by a concurrent shift in the population distribution of body mass index z-score toward higher relative weights.

After several years of experience with body mass index (BMI)-for-age reference standards in the US, the UK, and elsewhere, reflection on 'how things are going' seems timely. In this issue, Reilly 1 offers a summary of the evidence base for the diagnostic accuracy of BMI in youth and his perspective on what is achieved by a definition of overweight and obesity based on high BMI. To complement this, in our short review, we describe the BMI measure itself, the utility of a BMI z-score (s.d. score), their utility in cross-sectional and longitudinal applications in public health/surveillance, clinical and population-based research settings. Body mass index definedBody mass index is a measure of weight adjusted for height. It is calculated as weight in kilograms divided by the square of height in meters. Although BMI is an imperfect tool -it does not distinguish overweight due to excess fat mass from overweight due to excess lean mass -it is the most commonly used measure for assessing obesity in adults. Other methods of determining adiposity are more accurate, 2 but have limited applicability to screening or studying large populations. The BMI is well correlated with these more direct fatness measures, 3,4 and weight and height are simple, inexpensive, non-invasive measurements that are recorded routinely in clinical and research settings.Others have discussed the limitations of clinical screening for high adiposity by comparing weight centiles to height centiles, 5 and the inaccuracy of assessing overweight by observation or 'eye-balling' has been established. 6,7 Therefore, for screening or for epidemiologic research, using a weight/height index to define obesity has advantages that outweigh its limitations. Despite the likelihood of misclassification of the small percentage of individuals whose high BMI is due to lean muscle mass (e.g. some professional athletes), the great majority of individuals with high BMI have excess body fat. Use of body mass index in children and adolescentsThe BMI is used to assess weight status in children and adolescents as well as adults, but whereas in adults the BMI cut points that define obesity and overweight are not linked to age and do not differ for males and females, in growing children BMI varies with age and sex. Thus, a 5-year-old boy with a BMI of 20 kg/m 2 is likely to be overfat, but a 15-yearold boy with a BMI of 20 kg/m 2 is likely to be lean. As a result, for BMI to be meaningful in children it must be compared to a reference-standard that accounts for child age and sex.

WHAT'S KNOWN ON THIS SUBJECT:Many studies have shown an association between the amount of television-viewing and obesity; evidence is accumulating to link insufficient sleep to obesity, and some studies have shown an inverse association between frequency of family meals and obesity. WHAT THIS STUDY ADDS:Preschool-aged children exposed to 3 household routines of regularly eating dinner as a family, obtaining adequate nighttime sleep, and having limited screenviewing time had an ϳ40% lower prevalence of obesity than children exposed to none of these routines. abstract OBJECTIVE: To determine the association between the prevalence of obesity in preschool-aged children and exposure to 3 household routines: regularly eating the evening meal as a family, obtaining adequate sleep, and limiting screen-viewing time. METHODS: We conducted a cross-sectional analysis of a nationally representative sample of ϳ8550 four-year-old US children who were assessed in 2005 in the Early Childhood Longitudinal Study, Birth Cohort. Height and weight were measured. We assessed the association of childhood obesity (BMI Ն 95th percentile) with 3 household routines: regularly eating the evening meal as a family (Ͼ5 nights per week); obtaining adequate nighttime sleep on weekdays (Ն10.5 hours per night); and having limited screen-viewing (television, video, digital video disk) time on weekdays (Յ2 hours/day). Analyses were adjusted for the child's race/ethnicity, maternal obesity, maternal education, household income, and living in a single-parent household. RESULTS: Eighteen percent of children were obese, 14.5% were exposed to all 3 routines, and 12.4% were exposed to none of the routines. The prevalence of obesity was 14.3% (95% confidence interval [CI]: 11.3%-17.2%) among children exposed to all 3 routines and 24.5% (95% CI: 20.1%-28.9%) among those exposed to none of the routines. After adjusting for covariates, the odds of obesity associated with exposure to all 3, any 2, or only 1 routine (compared with none) were 0.63 (95% CI: 0.46 -0.87), 0.64 (95% CI: 0.47-0.85), and 0.84 (95% CI: 0.63-1.12), respectively. CONCLUSIONS: US preschool-aged children exposed to the 3 household routines of regularly eating the evening meal as a family, obtaining adequate nighttime sleep, and having limited screen-viewing time had an ϳ40% lower prevalence of obesity than those exposed to none of these routines. These household routines may be promising targets for obesity-prevention efforts in early childhood. Pediatrics 2010;125: 420-428