Prevalence of overweight and obesity in children from urban and rural Niagara

Purpose: The influence of environment in the development of overweight and obesity is an ongoing concern. This investigation examined the influence of urbanization on the rates of childhood overweight and obesity. Method: 2167 (1090M, 1077F) grade four children from 75 schools in Ontario's Niagara Region were sampled. A sophisticated algorithm overlaying electoral boundaries, population densities, and the knowledge of community members was used to classify schools into one of three location categories: urban {N= 1588), urban fringe {N= 379), and rural (A^= 234). Each subject was measured for: height, weight, and aerobic performance (Leger). Physical activity was evaluated with the self-report Participation Questionnaire (free-time and organized sport activities), and teacher's evaluations of student activity. Overweight (overweight and obesity combined) was measured both as a continuous (BMI) and categorical variable (BMI category), to evaluate the prevalence by location. A multivariate analysis was used to test for a suppression effect. Results: BMI and BMI category did not differ significantly by location or gender, and no evidence of a gender interaction existed. According to both a linear and logistic regression, physical activity or fitness levels did not suppress the influence of location on BMI and BMI category. Age, gender, free-time activity, organized sports, fitness level, and number of siblings, were all found to significantly influence overweight. Conclusions: It is plausible that the prevalence of overweight does not differ in urban and rural children from the Niagara Region. Further investigation is recommended, examining subjects by individual location of residence, in multiple regions throughout Ontario.

Examining the potential application of childhood stature in assessing adolescent overweight and obesity

Background: Increasing Overweight and Obesity (OwOb) prevalence in pediatric populations is becoming a public health concern in many countries. The purpose of this study was to determine if childhood stature components, particularly the Leg Length Index (LLI = [height - sitting height]! height), were useful in assessing risk of OwOb in adolescence. Methods: Data was from a longitudinal study conducted in south Ontario since 2004. Approximately 2360 students had body composition measurements including sitting height and standing height at baseline. Among them, 1167 children (573 girls, 594 boys) who had weight and height measured at the 5 th year follow-up, were included in this analysis. OwOb was defined using age and sex specific BMI (kg!m 2 ) cut-off points corresponding to adults' BMI ~ 25. Results: Overall, 34% (n=298) of adolescents were considered as OwOb. The results from logistic regression analysis indicated that with 1 unit increase in LLI the odds of OwOb decreased 24% (Odds Ratio, [95% Confidence Interval], 0.76, [0.66-0.87]) after adjusted for age, sex and baseline waist circumference. Further adjusting for birth weight, birth order, breastfeeding, child's physical activity, maternal smoking, education, mother's age at birth and mother's BMI, did not change the relationship. Our results also indicated that mother's smoking status is associated with LLI. Discussion: Although LLI measured at childhood in this study is related to OwOb risk in adolescents, the underlying mechanism is unclear and further study is needed.

The Association of Sleep Disorder, Obesity Status, and Diabetes Mellitus among US Adults—The NHANES 2009-2010 Survey Results

To examine the association between sleep disorders, obesity status, and the risk of diabetes in adults, a total of 3668 individuals aged 40+ years fromtheNHANES 2009-2010 withoutmissing information on sleep-related questions,measurements related to diabetes, and BMI were included in this analysis. Subjects were categorized into three sleep groups based on two sleep questions: (a) no sleep problems; (b) sleep disturbance; and (c) sleep disorder. Diabetes was defined as having one of a diagnosis from a physician; an overnight fasting glucose > 125 mg/dL; Glycohemoglobin > 6.4%; or an oral glucose tolerance test > 199mg/dL. Overall, 19% of subjects were diabetics, 37% were obese, and 32% had either sleep disturbance or sleep disorder. Using multiple logistic regression models adjusting for covariates without including BMI, the odds ratios (OR, (95% CI)) of diabetes were 1.40 (1.06, 1.84) and 2.04 (1.40, 2.95) for those with sleep disturbance and with sleep disorder, respectively. When further adjusting for BMI, the ORs were similar for those with sleep disturbance 1.36 (1.06, 1.73) but greatly attenuated for those with sleep disorders (1.38 [0.95, 2.00]). In conclusion, the impact of sleep disorders on diabetes may be explained through the individuals’ obesity status.

Unpacking Pieces of a Puzzle: Understanding Obesity-Related Health Risk through Lifestyle Behaviours and Well-Being

The primary objective of this non-experimental study was to examine the differences based on obesity-related health risk in terms of physical activity, sedentary behaviour and well-being in adults. Participants (N = 50; Mage = 38.50, SDage = 14.21) were asked to wear a SenseWear Armband (SWA) across a seven day monitoring period followed by a questionnaire package. Using the National Institute of Health’s (1998) criteria, participants were classified as either least, increased, or high risk based on waist circumference and Body Mass Index scores. Differences between these classifications were found in the amount of time spent in active energy expenditure for bouts of ten minutes or more (p = .002); specifically between least and high risk (p < .05). No other differences (p > .05) emerged. Participants’ also perceived the SWA as a practical and worthwhile device. Overall, these findings provide practical applications and future directions for health promotional research.