Differences in Body Mass Index Z-Scores Among Children and Adolescent Participants of NHANES 2003–2016 with Different Temporal Eating Patterns

Objectives To determine if body mass index z-scores (zBMI) differ among children and adolescents who reported an early temporal eating pattern when compared to those who reported other temporal eating patterns. Methods Data from children (6–12 years) and adolescents (13–19 years) participating in the National Health and Nutrition Examination Survey 2003–2016 were used. Children and adolescents were analyzed separately because of the phase delay in circadian rhythms experienced during adolescence. Energy intake and timing of intake was assessed using two 24-hour dietary recalls. Children (n = 2,565) and adolescents (n = 2,321) were included if they reported plausible intakes (≥500 kcals/day) with the same temporal eating patterns on both days. Temporal eating patterns were defined by the following morning and evening parameters: consumed 20% of total caloric intake before 10 AM (EAM); didn't consume 20% before 10 AM (LAM); consumed at least 80% of total caloric intake before 7 PM (EPM); didn't consume 80% before 7 PM (LPM). These parameters provided four temporal eating patterns: EAM-EPM, EAM-LPM, LAM-EPM, LAM-LPM. A covariate adjusted general linear models, adjusted for the complex sampling design, was used to compare zBMI of those participating in the different patterns. Data are reported as means ± SEM and P < 0.0125 was considered significant. Results zBMI for EAM-EPM (n = 887; 0.460 ± 0.037) children was lower when compared to LAM-LPM (n = 455; 0.641 ± 0.056; P = 0.007) children. For adolescents, zBMI for EAM-EPM (n = 445; 0.516 ± 0.048; P = 0.005), EAM-LPM (n = 172; 0.370 ± 0.089; P = 0.001), and LAM-LPM (n = 839; 0.549 ± 0.039; P = 0.011) adolescents were lower when compared to LAM-EPM (n = 1,097; 0.683 ± 0.035) adolescents. No other differences were observed. Conclusions Children between the ages of 6 and 12 who reported consuming an early eating pattern have a lower zBMI when compared to those who reported consuming a later eating pattern. Adolescents who didn't consume 20% of their total energy intake before 10 am and did consume more than 80% of their total intake before 7 pm have a greater zBMI than their peers who consistently reported consuming alternative eating patterns. Funding Sources None

Consumption of ultra-processed foods and socioeconomic position: a cross-sectional analysis of the Brazilian Longitudinal Study of Adult Health

The objective of the study was to estimate the contribution of ultra-processed foods to total caloric intake and investigate whether it differs according to socioeconomic position. We analyzed baseline data from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil 2008-2010; N = 14.378) and data on dietary intake using a food frequency questionnaire, assigning it into three categories: unprocessed or minimally processed foods and processed culinary ingredients, processed foods, and ultra-processed foods. We measured the associations between socioeconomic position (education, per capita household income, and occupational social class) and the percentage of caloric contribution of ultra-processed foods, using generalized linear regression models adjusted for age and sex. Unprocessed or minimally processed foods and processed culinary ingredients contributed to 65.7% of the total caloric intake, followed by ultra-processed foods (22.7%). After adjustments, the percentage of caloric contribution of ultra-processed foods was 20% lower among participants with incomplete elementary school when compared to postgraduates. Compared to individuals from upper income classes, the caloric contribution of ultra-processed foods was 10%, 15% and 20% lower among the ones from the three lowest income, respectively. The caloric contribution of ultra-processed foods was also 7%, 12%, 12%, and 17% lower among participants in the lowest occupational social class compared to those from high social classes. Results suggest that the caloric contribution of ultra-processed foods is higher among individuals from high socioeconomic positions with a dose-response relationship for the associations.

Session 3: Goal Setting; Weighing and Measuring

This chapter (Session 3) focuses on teaching clients how to set effective weight loss goals by choosing goals that are reasonable, active, short term, and time limited. Methods of evaluating goals are presented, such as tracking progress over time and sharing goals with others. This chapter also discusses the importance of weighing and measuring food and beverages by using measuring utensils or prepackaged meal options to promote accurate recording of total caloric intake.

Session 3: Goal Setting; Weighing and Measuring

This chapter (Session 3) focuses on teaching clients how to set effective weight loss goals by choosing goals that are reasonable, active, short term, and time limited. Methods of evaluating goals are presented, such as tracking progress over time and sharing goals with others. This chapter also discusses the importance of weighing and measuring food and beverages by using measuring utensils or prepackaged meal options to promote accurate recording of total caloric intake.

The Effect of Calorie Labels on Caloric Intake and Restaurant Revenue: Evidence from Two Full-Service Restaurants

Field experiment data were used to study the effect of numeric calorie labels in two full-service restaurants. Ultimately, both field experiments, despite using different experimental designs, reached the same conclusion: the numeric calorie label had no significant effect on total caloric intake. However, results revealed the addition of a traffic light symbol to the numeric label led to a 67.8-kcal reduction in average calories ordered. Furthermore, results showed restaurant revenue is unlikely to be affected by the addition of calorie labels on menus. The results have implications for restaurant labeling laws that are being considered around the world.