Methods: Data were obtained from two cross-sectional panels (2009-10 and 2014) of the New Jersey Child Health Study conducted in four low-income New Jersey cities. Questions from previously validated surveys assessed consumption frequency of fruits, vegetables, SSBs, and sweet and salty snacks. Analyses were confined to 570 children between 5-18 yrs; of which 365 (5-11 yrs: 237, 12-18 yrs: 128) resided in WIC participating households and 205 (5-11 yrs: 138, 12-18 yrs: 67) in income-qualifying non-WIC households. Over half of the sample was African American and 43% were Hispanic. Multivariable analyses were conducted to compute incidence rate ratios (IRRs) using negative binomial regression to compare the differences in eating behaviors of children in WIC vs. Non-WIC households
Results: Household WIC participation was associated with a slightly higher frequency of vegetable consumption among 12-18-year-old children (IRR= 1.25, p=.05); differences were significant among older males (12-18-years-old) (p=.006), and not in females.
Frequency of 100% juice consumption was significantly higher among younger females (5-11-years-old) in WIC households who consumed juice about 44% more frequently (p=.02) compared to similar age girls in non-WIC households. Hispanic children in WIC households reported a lower frequency of SSBs consumption (p=.01); this association was only true among males (p=.02).
Conclusions: Household WIC participation is associated with healthier dietary behaviors among age-ineligible children living in the households, suggesting a positive spillover effect of the program. Proposed changes to WIC packages are likely to have dietary implications not only for WIC participants but also for non-participating children residing in WIC households,
Methods: Data was compiled from the New Jersey Childhood Obesity Study (NJCOB). A random digit dial household phone survey was used to select 1,708 households with at least one child aged 3-18 years from five cities in New Jersey. There were 231 OWOB parent-child dyads in this sample. Bivariate and multivariate analyses were performed to determine the demographic variables significantly associated with the type of weight loss strategy chosen.
Results: Males had higher odds of using PA and both eating and PA when compared to females. Higher income adults had higher odds of using all types of weight loss strategies compared to lower income adults. Adults with college education had higher odds of using eating and both eating and PA when compared to those with high school education. Older children (6-11 and 12-19 years) had higher odds of using PA when compared to younger children (2-5 years). Children of foreign-born parents (> 10 years in the US) had higher odds of using eating to lose weight compared to the children of US born parents. Children overall had higher odds of adopting a weight loss strategy if it was also adopted by the parent. In subgroup analysis, parent-child dyads had higher odds of adopting similar strategies among older children (12-19) and among girls, but this association did not hold true for younger children (2-11 years) and among boys for PA.
Conclusion: Older OWOB children (12-19) and female children had higher odds of adopting their parents’ weight loss strategies. Younger children did not follow the same pattern as their parents and among boys concordance was observed only for eating strategies. Results from the study may inform future family-based weight management interventions.
This work aims to understand how the community layer, represented by the food environment, moderates the association of two other layers and dietary behaviors: the interpersonal layer, represented by receiving health care provider’s (HCP) advice to lose weight, and the policy layer, represented by participation in the Supplemental Nutrition Assistance Program (SNAP), and a policy change within the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC).
Participant data were obtained from a household telephone survey of 2,211 adults in four cities in New Jersey from two cross-sectional panels in 2009-10 and 2014. Community food data were purchased and classified according to previously established protocol. Interaction and stratified analyses determined the differences in the association between HCP advice, SNAP participation, and time (for WIC participants) and eating behaviors by the food environment.
Interaction and stratified analyses revealed that HCP advice was associated with a decrease in SSB consumption when participants lived near a small grocery store, or far from a supermarket, limited service restaurant (LSR), or convenience store. SNAP participation was associated with a higher SSB consumption when respondents lived close to a small grocery store, supermarket, and LSR. There were no differences in fruit and vegetable consumption between two time points among WIC participants, or by food outlet.
The food environment, part of the community layer of SEM, moderated the relationship between the interpersonal layer and dietary behaviors and the policy layer and dietary behaviors. The association between HCP advice and dietary behaviors and SNAP participation and dietary behaviors were both influenced by the food environment in which participants lived.
Obesity is an important modifiable risk factor for chronic diseases. While there is increasing focus on the role of dietary sugars, there remains a paucity of data establishing the association between sugar intake and obesity in the general public. The objective of this study was to investigate associations of estimated sugar intake with odds for obesity in a representative sample of English adults. We used data from 434 participants of the 2005 Health Survey of England. Biomarkers for total sugar intake were measured in 24 h urine samples and used to estimate intake. Linear and logistic regression analyses were used to investigate associations between biomarker-based estimated intake and measures of obesity (body mass intake (BMI), waist circumference and waist-to-hip ratio) and obesity risk, respectively. Estimated sugar intake was significantly associated with BMI, waist circumference and waist-to-hip ratio; these associations remained significant after adjustment for estimated protein intake as a marker of non-sugar energy intake. Estimated sugar intake was also associated with increased odds for obesity based on BMI (OR 1.02; 95%CI 1.00–1.04 per 10g), waist-circumference (1.03; 1.01–1.05) and waist-to-hip ratio (1.04; 1.02–1.06); all OR estimates remained significant after adjusting for estimated protein intake. Our results strongly support positive associations between total sugar intake, measures of obesity and likelihood of being obese. It is the first time that such an association has been shown in a nationally-representative sample of the general population using a validated biomarker. This biomarker could be used to monitor the efficacy of public health interventions to reduce sugar intake.
Measurement error in self-reported sugars intake may explain the lack of consistency in the epidemiologic evidence on the association between sugars and disease risk. This review describes the development and applications of a biomarker of sugars intake, informs its future use and recommends directions for future research. Recently, 24 h urinary sucrose and fructose were suggested as a predictive biomarker for total sugars intake, based on findings from three highly controlled feeding studies conducted in the United Kingdom. From this work, a calibration equation for the biomarker that provides an unbiased measure of sugars intake was generated that has since been used in two US-based studies with free-living individuals to assess measurement error in dietary self-reports and to develop regression calibration equations that could be used in future diet-disease analyses. Further applications of the biomarker include its use as a surrogate measure of intake in diet-disease association studies. Although this biomarker has great potential and exhibits favorable characteristics, available data come from a few controlled studies with limited sample sizes conducted in the UK. Larger feeding studies conducted in different populations are needed to further explore biomarker characteristics and stability of its biases, compare its performance, and generate a unique, or population-specific biomarker calibration equations to be applied in future studies. A validated sugars biomarker is critical for informed interpretation of sugars-disease association studies.