of high maternal body mass index, elevated fasting plasma leptin concentrations, and low fasting plasma insulin concentrations at baseline explained an addi- tional 4% of the total variance in adiposity at follow-up. Conclusions. Although parental obesity and meta- bolic variables such as insulinemia and leptinemia at baseline account for a small percentage of the variance in adiposity at follow-up, early childhood obesity is the dominant predictor of obesity 5 years later. These results suggest that strategies to prevent childhood obesity must be initiated at a very early age. Pediatrics 2002;110:299 – 306; childhood obesity, growth and development, parent– child relationship, overweight tracking, Pima Indians.
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It is emerging convincingly that the genesis of Type 2 Diabetes and Coronary Heart Disease begins in childhood, with childhood obesity serving as an important factor. There has been a phenomenal rise in proportions of children having obesity in the last four decades especially in the developed world. Studies emerging from different parts of India within last decade are also indicative of similar trend. Examination of the factors involved in weight gain and obesity in developing countries where socioeconomic changes are going on, is crucial for predicting the future impact, because the problem of obesity is emerging at a time when under nutrition remains a significant public health problem.
Physical activity and fitness of normal-weight pre- pubertal girls predisposed to obesity did not differ from those of girls without a history of familial obe- sity; however, there was a wide range of PALs in all the groups. This study enrolled only children who were of normal-weight-for-height and between 12% and 30% body fat. It is highly possible that those children who are already obese at this age, and who were, therefore, omitted from this study, may have been very inactive. Given that children of obese par- ents are more likely to be obese themselves, and these obese children were omitted from the study, the comparison of our groups may be biased. Such that with respect to activity, the children of obese parents used in this study may not be representative of children of obese parents in general. The girls in this study are part of a longitudinal study examining the predictors of weight and fat gain; therefore, we could not enroll obese children. These girls will be followed for 2 years to determine whether low levels of physical activity and fitness, in combination with a predisposition to familial obesity, pose heightened risk for the development of later obesity. The results of the longitudinal aspect of the study must be awaited before any judgment can be made about the potential role of activity as a pathway through which parents transmit predisposition to obesity in their children.
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We did not ﬁ nd any associations for the PDD-NOS subtype of ASD. PDD-NOS does not have any speci ﬁ c diagnostic crite- ria, but it is used as a subthreshold category for children who have some autistic features but not suf ﬁ cient to meet the speci ﬁ c diagnostic criteria for autistic disorder or Asperger disorder. The PDD-NOS subtype encompasses a heterogeneous group of children in our study sample, ranging from children with congenital syndromes and profound mental retardation to high-functioning children with milder symptoms of au- tism. The fact that the associations with paternal obesity were observed only for
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rather than maternal obesity to be more strongly associated with risk of ASD. Our results cannot be directly compared with previous studies because we evaluated different domains of development by using the ASQ, a validated screening rather than diagnostic tool. Nevertheless, our findings provide suggestive evidence for a differential role of paternal obesity on the personal- social domain (attributes close to those evidenced in ASD). Research in embryo development suggests that there are potential mechanisms through epigenetic alterations to sperm that could have downstream impact. 42 The presence of pleiotropic
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Children and adolescents with CAH have a higher risk of obesity. Glucocorticoid dosage, chronologic age, ad- vanced bone age maturation, and parental obesity con- tributed to elevated BMI SDS, whereas birth weight and length, serum leptin levels, glucocorticoids used, and fludrocortisone dosage were not associated with obesity. Because our cross-sectional study covers only 1 time point per patient, there is a need for longitudinal studies analyzing the different factors contributing to the devel- TABLE 3 Relative Risks and Odds Ratios of Obesity (ie, BMI SDS of >2.0)
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ent obesity is positively associated with parental re- port of teen obesity. Whether parental obesity is related to increased accuracy with respect to report- ing adolescent obesity is unclear. The impact of pa- rental distress on the subjective identification of teen obesity is also unclear. Because assessment and treat- ment of obesity often involve parents, how parental weight status, objectivity about weight status, and/or distress regarding weight status interact may be important areas of further inquiry. Parental iden- tification of obesity may be an indicator that the teen’s family is concerned about weight status and may be more amenable to intervention than families who do not acknowledge weight problems. Alterna- tively, parental distress over parental obesity may impact treatment negatively. In addition, discrepan- cies in adolescent and parental reports of weight problems are certainly a reflection of discrepant per- ceptions, and may be an indicator of other underly- ing issues in the parent–teen relationship that can exacerbate the obesity problem.
Childhood obesity has reached epidemic proportions worldwide  and its health consequences are considerable . Obesity is a complex condition in which a myriad of risk factors interact within and between several levels of influence . Social ecological frameworks posit that childhood obesity is influenced by energy intake and expenditure patterns, which are embedded within the familial and wider community contexts [4-6]. An un- derstanding of the multiple influences on obesity, in- cluding within individual, familial, and neighborhood levels, will improve population efforts to address childhood obesity. For example, at the individual level, regular intake of sugar-sweetened beverages  and physical inactivity  have been associated with childhood obesity. Similarly, through shared genetics and lifestyles, parental obesity has been identified as a risk factor for childhood obesity [4,9,10]. Within wider community contexts, neighborhood parks, sports and recreational facilities, and the presence of nearby convenience stores and fast food restaurants have been associated with childhood obesity, albeit inconsist- ently [6,11-14]. Neighborhood disadvantage has been more consistently associated with childhood obesity [15,16]. However, it remains unclear how factors within these dif- ferent levels of influence interact to determine obesity.
By knowing the prevalence of obesity in ID subjects, we can see the magnitude of this problem so it can be made for countermeasures planning, such as assessing the cardiorespiratory endurance level, developing exercise model which is applicable, and can provide an obesity treatment especially in ID subjects. General practitioners or health professionals have and important role and responsibility in preventing and diagnosing weight problems. Finally, these efforts may reduce obesity complications and improve quality of life for ID subjects with obesity.
The questionnaire included questions regarding the socioeconomic background (household income, parents’ highest education background), birth weight, history of prematurity and whether the child was breastfed and the duration as well as the family history of obesity and diabetes. Each parent was required to guess the status of the weight of their children either as “normal weight” or “overweight”. To simplify the assessment, we did not include the “obese” category. Questionnaires were provided in English and Malay language.
Parents’ perceived lack of control. Another parental barrier prevalent in the literature was the belief of lack of control over child’s lifestyle choices as a result of time constraints, child preferences, and familial beliefs about behavior change (Mikhailovich & Morrison, 2007; Pettigrew & Roberts, 2007). As suggested by Pocock, Trivedi, Wills, Bunn, and Magnusson (2010), parents often cited child food preferences, low motivation to exercise, and familial beliefs about their children’s inability to change behavior as reasons for unhealthy lifestyles. Mothers felt undermined in their attempt to feed their children a healthy diet. They complained of fathers, grandparents, and schools disrupting their attempts to consistently provide healthy foods (Jackson, Mannix, Faga, & McDonald, 2005; Pettigrew & Roberts, 2007; Pocock et al., 2010). Mothers claimed that grandparents weakened parental efforts at providing a healthy diet by permitting children to eat anything they desired when grandparents cared for the children while the mother was at work. In addition, mothers acknowledged feeling like “spoilsports” if they attempted to limit junk foods (Pocock et al., 2010). Furthermore, Eckstein et al. (2006) found 26% of caregivers of overweight children were concerned about their child’s weight status, but most felt they could not motivate their children to increase their physical activity level.
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While the inclusion of a broad range of covariates in this study allows for a thorough picture of psycho-social influences on FFEs of children during early childhood in Australia, it is recognised that this may increase the risk of type 1 errors. While no adjustments were made for this, it can be seen in Table 4 that the many of p-values are quite low and as such the interpretation of the ma- jority of results would not differ with adjustment. Des- pite being cross-sectional in nature, this study is strengthened by the large sample of participants repre- senting all states and territories in Australia. Single par- ents were represented at a rate comparable to the 15% reported nationally, and distribution of participants in the high and middle income groups were represented similarly, although low income families were underrep- resented . This under-representation of low income families is likely to be a limitation of this study which impacts the generalisability and application of these re- sults, particularly in obesity prevention initiatives. Al- though anthropometric data in this study were self-reported, steps were taken to ensure the biological plausibility of included cases, as is considered a quality feature given that approximately 41% of large epidemio- logical studies do not address biological implausibility . Similar to what has been reported in other studies, anthropometric data deemed biologically implausible values was higher in boys, although, contrary to other studies implausible data were higher in younger children [65, 66]. No differences in demographic characteristics were between children classified as underweight com- pared with other BMI categories.
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Of the 3179 subjects, 11 were excluded from the analysis due to refusal to take part in the study or school absence. Thus, data from a total of 3168 subjects (1645 boys and 1523 girls) were analyzed (participation rate: 99.7%). The characteristics of parents (fathers and moth- ers) by the overweight status of their children are shown in Table 1. Parents with overweight children had signifi- cantly higher BMI than those with non-overweight chil- dren (P < 0.001). Children with one or both parents be- ing overweight were more likely to be in the overweight group (P < 0.001). The employment status of the parents (fathers and mothers) was not related to the child’s overweight. There was a statistically significant differ- ence between parental perceptions on childhood obesity and the child’s overweight status (P < 0.001).
instrument used in the present study was designed considering the cognitive skills and literacy levels of children aged 7-10 years. It simplifies recall by prompting only the most relevant food items and physical activities, so the questionnaire remains brief and easy to complete. The cognitive tasks required for estimating portion size, frequency, and averaging may not be compatible with the perceptual and conceptual capacities of children who have not reached the stage of abstract reasoning which typically occurs between 10 and 11 years of age [45-47]. It is also important to mention the use of multinomial regression–an advanced statistical method capable of differentiating the impact of eating behavior on overweight versus obesity while controlling statistically for other factors. Moreover, sequential block entry of thematic groups of variables (parental BMI, physical activity, family income and educational level) allowed some insight into their capacity to alter the relative contributions of food frequency items.
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practice in China today together with the lack of physical activity since small children are held by parents most of the time. No significant associations between children’s relative weight (BMI SDS) and eating behaviour were observed in the present study. We also considered if adjusting parental weight could affect the correlation between children ’ s BMI SDS and CEBQ scales. Therefore we also checked the simple correlation without control- ling anything. The result remains the same and we there- fore suggest that this could probably be explained by the young age of children in this study, as some eating beha- viours are harder to detect at early age.
current chewers were pooled together as ever chewers in the calculations of prevalence and incidence rates of dia- betes. Body mass index (BMI) was calculated from the body weight in kg divided by the squared body height in meters and obesity I and II were defined as a BMI ≥ 25 kg/m 2 and ≥ 30 kg/m 2 , respectively, as recom- mended for Asian populations . The age at new diagnosis of T2DM was divided into < 40, 40-49, 50-59, 60-69 and ≥ 70 years for appropriate comparison with the age-specific prevalence of betel nut chewing reported by Tung et al. . P < 0.05 was considered as statistically significant.
This was the first study with a representative sample to explore association of family-based factors with over- weight and obesity among Pakistani school-aged chil- dren. Higher parental education was significantly associated with overweight and obesity among both boys and girls. Children with parents having college-level or higher education were independently more likely to be overweight as compared to children with parents having high school-level or lower education. Higher parental education was independent predictor of higher BMI. Positive association of childhood obesity with higher par- ental education had been observed in the developing countries [16,21]; however, studies in the developed countries had shown inverse association of parental edu- cation with obesity [18,19,42]. Children whose both par- ents were working had significantly higher rates of overweight and obesity than those whose mother was a housewife; however, in adjusted regression analyses, the effect did not remain significant. Maternal employment had been associated with childhood obesity previously
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The mean systolic BP of the girls was 110±11 mmHg and mean diastolic BP was 69±8 mmHg. Twelve (4%) girls had systolic BP and 48 (16.1%) girls had diastolic BP between 90th and 95th percentile whereas 38 (12.7%) girls had systolic BP and 23 girls (7.7%) had diastolic BP more than 95th percentile. There was a significant association between systolic BP and obesity (X2 = 5.79, df = 2) and diabetes (X2 =9.76, df=2) in grandmother (p<0.05). There was also a significant association of diastolic BP with prevalence of cardio-vascular disease in both parents (X2 = 11.26, df =2) especially with that in mothers (X2 =11.27, df = 2) (p<0.05). Systolic BP showed an association with mother education (X2 = 18.81, df = 8) (p<0.05. Central obesity and hypertension is prevalent in school going girls in Mumbai city and show a significant association with prevalence of diseases in parents and grandparents.
The ACHI had recognized the value of a comprehensive database for deter- mining the actual statewide and local burden of obesity and for long-term follow-up of efﬁcacy of political changes in the state. Thus, with grant funding, a comprehensive database was created, and data collected pri- marily for parental risk notiﬁcation were transformed into politically sup- portive information. Aggregate results of the ﬁrst year of BMI assessments were reported in September 2004 ac- cording to schools, school districts, 75 counties in the state, legislative and congressional districts, and for the state as a whole according to various student demographic characteristics (eg, gender, grade, race). Wide varia- tions in obesity prevalence were ob- served across the state, with some school districts having more than 50% of their children in 1 of the 2 highest obesity-risk categories (at risk for overweight and overweight). This in- formation was relayed to local paren- tal advisory committees established in Act 1220, local school boards, and leg- islative committees and was widely disseminated through the print and television media.
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one approach to measure parenting stress would have provided a more thorough understanding of how differ- ent types of stress may influence obesity risk for pre- schoolers. Second, although, where possible, we used validated measures to assess our behavioral outcomes, we used parental report rather than objective measures, which may have introduced bias or random error. The message used to recruit families to the intervention study of learning tips on ‘raising happy, healthy pre- schoolers’ could have primed parents to answer ques- tions in a way that made their children seem healthier. Third, based on the self-selection method of recruit- ment, it is possible that there may be systematic differences between those who choose to sign up for a parenting program and those that do not choose to sign up; parents who did sign up may have a heightened interest in or concern about their child’s health. This possible over-reporting of healthful behaviours caused both by the use of parental report measures and the self-selection bias of our recruitment methods may have biased our results towards our null hypothesis that par- enting stress is not associated with child obesity risk or risk behaviours such as physical activity and TV viewing. Fourth, the results may not be generalizable to socioeco- nomically advantaged populations, as there are inherent demographic differences between our participants and the general U.S. population. While the inability to generalize the findings may be a limitation, ethnically di- verse, low-income families were purposefully recruited as they bear a disproportionate burden of health related issues  and would benefit most from the PTT pro- gram. Fifth, the confidence intervals around our esti- mates for TV viewing were quite wide, suggesting that, due to our relatively small sample size, the null findings in this study could be the result of a Type II error. Finally, due to our cross-sectional study design, we are unable to determine changes over time. Parenting stress and parenting behaviours exhibited under stress may affect children’s obesity risk differently as they age.