Maternal correlates of preschool child eating behaviours and body mass index : a cross-sectional study

To the authors’ knowledge, this is the first study to evaluate maternal child feeding practices, maternal parenting characteristics and mother-child interactions as cross-sectional predictors of child eating and/or weight within the one sample. Maternal pressure for her child to eat was a significant positive correlate of fussiness and a negative correlate of enjoyment. Maternal parenting warmth was associated negatively with child BMIz, while mother-child dysfunctional interaction was associated positively with child BMIz. Our findings suggest that childhood obesity research may be better informed by evaluating not just what mothers do (feeding practices) but also how they parent (parenting behaviours and interactions with their child). Longitudinal studies are needed to identify causal influences of parenting on preschool child eating and weight.

The health-related quality of life of overweight and obese adolescents - a study measuring body mass index and adolescent-reported perceptions

Objective. To determine whether the health-related quality of life (HRQOL) of overweight and obese adolescents is significantly lower than that of their healthy weight counterparts, and if so, whether any demographic trends exist and the relative contribution of each HRQOL dimension.

Methods. Cross-sectional analysis of 2,890 students participating in the Pacific Obesity Prevention in Communities Project, Australia. HRQOL was measured using the Pediatric Quality of Life Inventory (PedsQL) adolescent module. Adolescent height and weight were measured by trained field workers and weight categories assigned according to the International Obesity Task Force BMI cut-off points for adolescents. Multivariate linear regression analyses were undertaken to estimate the mean differences in HRQOL scores between (i) overweight and healthy weight, and (ii) obese and healthy weight adolescents, whilst adjusting for gender, age and socioeconomic status quartile.

Results. The sample had a mean age of 14.6 years (range 11–18), 56.2% boys, 20.2% overweight and 6.3% obese. Higher weight status categories were associated with lower HRQOL scores (mean PedsQL scores: healthy weight: 79.1, overweight: 77.7 and obese: 73.7). Relative to the healthy weight group, and after adjustments, overweight and obese adolescents reported 1.44 (p = 0.005) and 5.55 (p < 0.001) lower HRQOL summary scores, respectively. Overweight adolescents reported significantly lower scores in physical and social functioning, whilst obese adolescents reported significantly lower scores in the same dimensions plus emotional functioning. Girls and younger (< 15 years) adolescents reported greater mean negative HRQOL differences associated with excess weight.

Conclusions. Overweight and obesity in adolescents are associated with significantly lower HRQOL scores.

The influence of climate change on Thorthwaite moisture index on the design of light structures

Significant long term changes in the earth’s climate have occurred in the past but recently there has been more severe climate fluctuation than have occurred in the past few centuries. The effect of this climate change on the foundation conditions of roads and low-rise buildings is costing several hundred billion dollars world-wide. A method which tracks this climate change will be of great value for companies and governments. C.W. Thornthwaite (1948) defined the Thornthwaite Moisture Index (TMI) as the first base for his climate classification system and mapping in the United States. There are 3 important factors to predict ground movement: (a) the degree of moisture index change (b) the depth at which this change occurs and (c) the foundation soil type. The water budget model was used by Thornthwaite (1948) to calculate the moisture index. This paper also discusses two typical examples of the use of this model. Originally TMI’s were mainly used to map soil moisture conditions for agriculture but soon became a method to predict environmental and pavement foundation changes.

Involvement of young Australian adults in meal preparation : cross-sectional associations with abdominal obesity and body mass index

Previous research has shown that involvement in meal preparation is positively associated with better diet quality. However, it is unclear whether there is an association between involvement in meal preparation and being overweight or obese. This study investigated whether the level of involvement in meal preparation was associated with objectively measured weight status in young adults. During 2004-2006, a national sample of 1,996 Australian adults aged 26 to 36 years completed a self-administered questionnaire including questions on sociodemographic characteristics, diet, and physical activity. Participants were asked to report who usually prepared the main meal on working days and responses were categorized as “myself,” “shared,” or “someone else.” Waist circumference, weight, and height were measured by trained staff. Moderate abdominal obesity was defined as waist circumference >94 cm for men and >80 cm for women. Overweight was defined as body mass index (calculated as kg/m2) >25. Prevalence ratios were calculated using log binomial regression. After adjusting for age, education, and leisure time physical activity, men who shared the meal preparation had a slightly lower prevalence of moderate abdominal obesity (prevalence ratio=0.92; 95% confidence interval [CI]: 0.86 to 0.99) than those whose meals were usually prepared by someone else. There was no association with having sole responsibility (prevalence ratio=0.99; 95% CI: 0.92 to 1.06). There were no associations between level of involvement in meal preparation and being overweight (shared responsibility prevalence ratio=0.99; 95% CI: 0.92 to 1.07; sole responsibility prevalence ratio=0.98; 95% CI: 0.91 to 1.05). For women, level of involvement was not associated with moderate abdominal obesity (shared responsibility prevalence ratio=0.93; 95% CI: 0.84 to 1.03; sole responsibility prevalence ratio=0.94; 95% CI: 0.86 to 1.03) or being overweight (shared responsibility prevalence ratio=0.93; 95% CI: 0.84 to 1.02; sole responsibility prevalence ratio=0.93; 95% CI: 0.85 to 1.02). In this sample of young adults, level of involvement in meal preparation was not strongly related to weight status.

Scores on the dietary guideline index for children and adolescents are associated with nutrient intake and socio-economic position but not adiposity 1-3

Diet quality indices reflect overall dietary patterns better than single nutrients or food groups. The study aims were to develop a measure of adherence with dietary guidelines applicable to child and adolescent populations in Australia and determine the association between index scores and food and nutrient intake, socio-demographic characteristics, and measures of adiposity. Data were analyzed from 4- to 16-y-old participants of the 2007 Australian Children’s Nutrition and Physical Activity Survey (n = 3416). The Dietary Guideline Index for Children and Adolescents (DGI-CA) comprises 11 components: 5 core food groups, wholegrain bread, reduced-fat dairy foods, extra foods (nutrient poor and high in fat, salt, and added sugar), healthy fats/oils, water, and diet variety (possible score of 100). The index criteria were age specific. The mean DGI-CA score was low (53.6 ± 0.4), similar between boys and girls, and differed by age; the youngest children scored higher than the oldest children (P < 0.0001). Higher DGI-CA scores were associated with lower energy intake, energy density, total and saturated fat, and sugar intake; higher protein, carbohydrate, fiber, calcium, iron, vitamin C, vitamin A, folate, phosphorous, magnesium, zinc, and iodine intakes; and a higher polyunsaturated:saturated fat ratio (P < 0.0001). DGI-CA scores were associated with socio-economic characteristics and measures of family circumstance. Weak positive associations were observed between DGI-CA score and BMI or waist circumference Z-scores in the 4- to 10-y and 12- to 16-y age groups only. This index is the first validated index in Australia and one of the few international indices to describe the diet quality of children and adolescents.

Does an increase in body mass index over 10 years affect knee structure in a population-based cohort study of adult women?

Introduction : Although obesity is a modifiable risk factor for knee osteoarthritis (OA), the effect of weight gain on knee structure in young and healthy adults has not been examined. The aim of this study was to examine the relationship between body mass index (BMI), and change in BMI over the preceding 10-year period, and knee structure (cartilage defects, cartilage volume and bone marrow lesions (BMLs)) in a population-based sample of young to middle-aged females.

Methods : One hundred and forty-two healthy, asymptomatic females (range 30 to 49 years) in the Barwon region of Australia, underwent magnetic resonance imaging (MRI) during 2006 to 2008. BMI measured 10 years prior (1994 to 1997), current BMI and change in BMI (accounting for baseline BMI) over this period, was assessed for an association with cartilage defects and volume, and BMLs.

Results : After adjusting for age and tibial plateau area, the risk of BMLs was associated with every increase in one-unit of baseline BMI (OR 1.14 (95% CI 1.03 to 1.26) P = 0.009), current BMI (OR 1.13 (95% CI 1.04 to 1.23) P = 0.005), and per one unit increase in BMI (OR 1.14 (95% CI 1.03 to 1.26) P = 0.01). There was a trend for a one-unit increase in current BMI to be associated with increased risk of cartilage defects (OR 1.06 (95% CI 1.00 to 1.13) P = 0.05), and a suggestion that a one-unit increase in BMI over 10 years may be associated with reduced cartilage volume (-17.8 ml (95% CI -39.4 to 3.9] P = 0.10). Results remained similar after excluding those with osteophytes.

Conclusions : This study provides longitudinal evidence for the importance of avoiding weight gain in women during early to middle adulthood as this is associated with increased risk of BMLs, and trend toward increased tibiofemoral cartilage defects. These changes have been shown to precede increased cartilage loss. Longitudinal studies will show whether avoiding weight gain in early adulthood may play an important role in diminishing the risk of knee OA.

Prevalence of obesity and the relationship between the body mass index and body fat : cross-sectional, population-based data

Background: Anthropometric measures such as the body mass index (BMI) and waist circumference are widely used as convenient indices of adiposity, yet there are limitations in their estimates of body fat. We aimed to determine the prevalence of obesity using criteria based on the BMI and waist circumference, and to examine the relationship between the BMI and body fat.

Methodology/Principal Findings: This population-based, cross-sectional study was conducted as part of the Geelong Osteoporosis Study. A random sample of 1,467 men and 1,076 women aged 20–96 years was assessed 2001–2008. Overweight and obesity were identified according to BMI (overweight 25.0–29.9 kg/m2; obesity $30.0 kg/m2) and waist circumference (overweight men 94.0–101.9 cm; women 80.0–87.9 cm; obesity men $102.0 cm, women $88.0 cm); body fat mass was assessed using dual energy X-ray absorptiometry; height and weight were measured and lifestyle factors documented by self-report. According to the BMI, 45.1% (95%CI 42.4–47.9) of men and 30.2% (95%CI 27.4–33.0) of women were overweight and a further 20.2% (95%CI 18.0–22.4) of men and 28.6% (95%CI 25.8–31.3) of women were obese. Using waist circumference, 27.5% (95%CI 25.1–30.0) of men and 23.3% (95%CI 20.8–25.9) of women were overweight, and 29.3% (95%CI 26.9–31.7) of men and 44.1% (95%CI 41.2–47.1) of women, obese. Both criteria indicate that approximately 60% of the population exceeded recommended thresholds for healthy body habitus. There was no consistent pattern apparent between BMI and energy intake. Compared with women, BMI overestimated adiposity in men, whose excess weight was largely attributable to muscular body builds and greater bone mass. BMI also underestimated adiposity in the elderly. Regression models including gender, age and BMI explained 0.825 of the variance in percent body fat.

Conclusions/Significance: As the BMI does not account for differences in body composition, we suggest that gender- and age-specific thresholds should be considered when the BMI is used to indicate adiposity.