Using the Nutrition Facts Label to Make Food Choices Is Associated with Healthier Eating among 8th and 11th-Grade Students: An Analysis of Statewide Representative Data from the 2019-2020 Texas School Physical Activity and Nutrition Survey.

BACKGROUND: Nutrition labels are a tool to inform and encourage the public to make healthier food choices, but little information is available about use in multi-ethnic adolescent populations in the U.S. The purpose of this study was to examine associations between the level of nutrition label usage and healthy/unhealthy eating behaviors among a statewide representative sample of 8th and 11th-grade students in Texas. METHODS: We analyzed cross-sectional associations between the Nutrition Facts label use and eating behaviors from a statewide sample of 8th and 11th-grade students in Texas, (n = 4730, weighted n = 710,731, mean age = 14.7 ± 1.6 years; 49% female, 51% Hispanic), who completed the 2019-2020 Texas School Physical Activity and Nutrition (TX SPAN) survey. Students self-reported their level of nutrition label usage to make food choices (5-point Likert scale from "Never" to "Always") and previous day consumption of 26 food items (13 healthy, 13 unhealthy). The 26 food items were used to calculate a Healthy Eating Index (HEI) score (0-100), a Healthy Foods Index (HFI) score (0-100), and an Unhealthy Foods Index (UFI) score (0-100). Weighted linear regression models were employed to examine the associations between self-reported use of nutrition labels to make food choices and HEI, HFI, and UFI scores. Marginal predicted means of HEI, HFI, and UFI scores were calculated post hoc from linear regression models. The odds of consuming specific individual food items for nutrition label usage were also calculated from weighted logistic regression models. All linear and logistic regression models were adjusted for grade, sex, Body Mass Index (BMI), race/ethnicity, economic disadvantage, and percentage of English language learners by school. RESULTS: A total of 11.0% of students reported always/almost always using nutrition labels to make food choices, 27.9% reported sometimes using them, while 61.0% indicated they never/almost never used nutrition labels to make food choices. The average HEI score among students in the sample was 47.7 ± 5.9. Nutrition Facts label usage was significantly and positively associated with HEI (b = 5.79, 95%CI: 4.45, 7.12) and HFI (b = 7.28, 95%CI:4.48, 10.07), and significantly and negatively associated with UFI (b = -4.30, 95%CI: -6.25, -2.34). A dose-response relationship was observed between nutrition label usage and HEI, HFI, and UFI scores, such that the strength of these associations increased with each one-point increase in nutrition label usage. Students who reported using nutrition labels always/almost always to make food choices had significantly higher odds of consuming healthy foods including baked meat, nuts, brown bread, vegetables, whole fruit, and yogurt (ORrange = 1.31-3.07), and significantly lower odds of consuming unhealthy foods including chips, cake, candy, and soda (ORrange = 0.48-0.68) compared to students who reported never/almost never using the Nutrition Facts label. CONCLUSIONS: Using the Nutrition Facts labels to make food choices is beneficially associated with healthy and unhealthy eating among 8th and 11th-grade students, although the proportion of students using nutrition labels to make their food choices was low. Public health efforts should be made to improve nutrition literacy and encourage nutrition label use among secondary students in the United States.

Ten-Year Changes in Accelerometer-Based Physical Activity and Sedentary Time During Midlife: The CARDIA Study.

We describe 10-year changes in accelerometer-determined physical activity (PA) and sedentary time in a midlife cohort of the Coronary Artery Risk Development in Young Adults Study, within and by race and sex groups. Participants (n = 962) wore the accelerometer with valid wear (≥4 of 7 days, ≥10 hours per day) at baseline (2005-2006; ages 38-50 years) and 10-year follow-up (2015-2016; ages 48-60 years). Data were calibrated to account for accelerometer model differences. Participants (mean age = 45.0 (standard deviation, 3.5) years at baseline) had reduced accelerometer counts overall (mean = -65.5 (standard error (SE), 10.2) counts per minute/day), and within race and sex groups (all P < 0.001). Sedentary time increased overall (mean = 37.9 (SE, 3.7) minutes/day) and within race and sex groups, whereas light-intensity PA (mean = -30.6 (SE, 2.7) minutes/day) and moderate- to vigorous-intensity PA (mean = -7.5 (SE, 0.8) minutes/day) declined overall and within race and sex groups (all P < 0.001). Significant differences in 10-year change estimates were noted by race and sex groups for accelerometer counts, sedentary time, and moderate- to vigorous-intensity PA bouts; black men had the greatest reductions in PA compared with other groups. PA declines during midlife were characterized by reductions in light-intensity PA with increases in sedentary time, which may have important health consequences. Targeted efforts are needed to preserve PA, regardless of intensity level, across midlife.

Texas Adolescent Tobacco and Marketing Surveillance System's Design.

OBJECTIVES: To provide a full methodological description of the design of the wave I and II (6-month follow-up) surveys of the Texas Adolescent Tobacco and Marketing Surveillance System (TATAMS), a longitudinal surveillance study of 6th, 8th, and 10th grade students who attended schools in Bexar, Dallas, Tarrant, Harris, or Travis counties, where the 4 largest cities in Texas (San Antonio, Dallas, Fort Worth, Houston, and Austin, respectively) are located. METHODS: TATAMS used a complex probability design, yielding representative estimates of these students in these counties during the 2014-2015 academic year. Weighted prevalence of the use of tobacco products, drugs and alcohol in wave I, and the percent of: (i) bias, (ii) relative bias, and (iii) relative bias ratio, between waves I and II are estimated. RESULTS: The wave I sample included 79 schools and 3,907 students. The prevalence of current cigarette, e-cigarette and hookah use at wave I was 3.5%, 7.4%, and 2.5%, respectively. Small biases, mostly less than 3.5%, were observed for nonrespondents in wave II. CONCLUSIONS: Even with adaptions to the sampling methodology, the resulting sample adequately represents the target population. Results from TATAMS will have important implications for future tobacco policy in Texas and federal regulation.