Self-Reported vs. Measured Height, Weight, and BMI in Young Adults

Self-reported height and weight, if accurate, provide a simple and economical method to track changes in body weight over time. Literature suggests adults tend to under-report their own weight and that the gap between self-reported weight and actual weight increases with obesity. This study investigates the extent of discrepancy in self-reported height, weight, and subsequent Body Mass Index (BMI) versus actual measurements in young adults. Physically measured and self-reported height and weight were taken from 1562 students. Male students marginally overestimated height, while females were closer to target. Males, on average, closely self-reported weight. Self-reported anthropometrics remained statistically correlated to actual measures in both sexes. Categorical variables of calculated BMI from both self-reported and actual height and weight resulted in significant agreement for both sexes. Researcher measured BMI (via anthropometric height and weight) and sex were both found to have association with self-reported weight while only sex was related to height difference. Regression examining weight difference and BMI was significant, specifically with a negative slope indicating increased BMI led to increased underestimation of weight in both sexes. This study suggests self-reported anthropometric measurements in young adults can be used to calculate BMI for weight classification purposes. Further investigation is needed to better assess self-reported vs measured height and weight discrepancies across populations

Neck Circumference Positively Relates to Cardiovascular Risk Factors in College Students

The objective of this study was to determine the relationship between neck circumference (NC) and other anthropometric measures and examine cut-off points for males and females according to existing waist circumference cut-off levels in this age group. Across 8 universities, 1562 students underwent a physical assessment. Spearman rho correlations (ρ) were calculated to determine associations between NC and other continuous variables of health. Receiving operating characteristic curves were constructed to assess the optimal cut-off levels of NC of males and females with central obesity. Participants were predominantly Caucasian (67%), female (70%), and outside of Appalachia 2 (82%). Forty-one percent of males and 34% of females had a BMI ≥ 25 kg/m . In both sexes, significant positive correlations were seen between NC and body weight, BMI, waist circumference, hip circumference, and systolic blood pressure (all p-values \u3c 0.0001). NC ≥ 38 cm for males and ≥33.5 cm for females were the optimal cut-off values to determine subjects with central obesity. NC has been identified to closely correlate with other anthropometric measurements related to disease and could be used as a convenient, low-cost, and noninvasive measurement in large-scale studies

Prospectus, November 19, 1986

https://spark.parkland.edu/prospectus_1986/1030/thumbnail.jp

Food Choice Priorities Change Over Time and Predict Dietary Intake at the End of the First Year of College Among Students in the U.S.

This study assessed food choice priorities (FCP) and associations with consumption of fruits and vegetables (FV), fiber, added sugars from non-beverage sources, and sugar-sweetened beverages (SSB) among college students. Freshmen from eight U.S. universities (N = 1149) completed the Food Choice Priorities Survey, designed for college students to provide a way to determine the factors of greatest importance regarding food choices, and the NCI Dietary Screener Questionnaire. Changes in FCP and dietary intake from fall 2015 to spring 2016 were assessed. Multiple regression models examined associations between FCP and log-transformed dietary intake, controlling for sex, age, race, and BMI. Participant characteristics and FCP associations were also assessed. FCP importance changed across the freshmen year and significantly predicted dietary intake. The most important FCP were price, busy daily life and preferences, and healthy aesthetic. Students who endorsed healthy aesthetic factors (health, effect on physical appearance, freshness/quality/in season) as important for food choice, consumed more FV and fiber and less added sugar and SSB. Busy daily life and preferences (taste, convenience, routine, ability to feel full) predicted lower FV, higher added sugar, and higher SSB consumption. Price predicted lower FV, higher SSB, and more added sugar while the advertising environment was positively associated with SSB intake. FCP and demographic factors explained between 2%–17% of the variance in dietary intake across models. The strongest relationship was between healthy aesthetic factors and SSB (B = −0.37, p \u3c 0.01). Self-rated importance of factors influencing food choice are related to dietary intake among students. Interventions that shift identified FCP may positively impact students’ diet quality especially considering that some FCP increase in importance across the first year of college

Development and Validation of the Short Healthy Eating Index Survey with a College Population to Assess Dietary Quality and Intake

Because diet quality (DQ) is associated with risk of chronic disease and is a common construct assessed in health-related research, validated tools to assess DQ are needed that have low respondent and researcher burden. Thus, content experts develop the Short Healthy Eating Index (sHEI) tool and an associated scoring system. The sHEI scoring system was then refined using a classification and regression tree (CRT) algorithm methodology with an iterative feedback process with expert review and input. The sHEI scoring system was then validated using a concurrent criterion validation process that included the sHEI DQ scores (calculated from responses from 50 participants) being compared to the participants’ Healthy Eating Index scores derived from 24 h recalls. The total HEI score from the CRT algorithm highly correlated with the 24 h recall HEI score (0.79). For individual food group items, the correlation between the CRT algorithm scoring and the 24 h recall data scoring ranged from 0.44 for refined grains to 0.64 for whole fruits. The sHEI appears to be a valid tool for estimating overall dietary quality and individual items (with correlations \u3e 0.49) for fruits, vegetables, dairy, added sugar, sugar from sugar-sweetened beverages, and calcium

Assessment of the dining environment on and near the campuses of fifteen post-secondary institutions

Objective: The present study evaluated the restaurant and dining venues on and near post-secondary campuses varying in institution size. Design: The Nutrition Environment Measures Survey for Restaurants (NEMS-R) was modified to evaluate restaurants as fast food, sit down and fast casual; and campus dining venues as dining halls, student unions and snack bar/cafe ́s. ANOVA with post hoc Tukey’s B and T tests were used to distinguish differences between dining venues and associated institutions by size. Setting: The study was conducted at fifteen US post-secondary institutions, 2009–2011. Subjects: Data presented are from a sample of 175 restaurants and sixty-eight on-campus dining venues. Results: There were minimal differences in dining halls by institution size, although medium-sized institutions as compared with small-sized institutions offered significantly more healthful side dish/salad bar items. Dining halls scored significantly higher than student unions or snack bar/cafe ́s on healthful entre ́es, side dish/salad bar and beverages offerings, but they also had the most barriers to healthful dietary habits (i.e. all-you-can-eat). No differences were found by restaurant type for NEMS-R scores for total restaurant dining environment or healthful entre ́es and barriers. Snack bars had more healthful side dishes (P 5 0?002) and fast-food restaurants had the highest level of facilitators (i.e. nutrition information; P 5 0?002). Conclusions: Based on this evaluation in fifteen institutions, the full campus dining environment provides limited support for healthy eating and obesity prevention. The quality of campus dining environments can be improved via healthful offerings, providing nutrition information and other supports to facilitate healthy eating and prevent unwanted weight gain

Multilevel Structural Equation Modeling of Students’ Dietary Intentions/Behaviors, BMI, and the Healthfulness of Convenience Stores

When dietary behaviors are habitual, intentions are low, and environmental cues, such as the consumer food environment, might guide behavior. How might intentions to eat healthily and ultimately actual dietary behaviors, be influenced by the consumer food environment (including the availability and affordability of healthy foods) in convenience stores? This study will determine pathways between the healthfulness of convenience stores and college students' dietary intentions/behaviors, and body mass index (BMI)

Redesign, Field-Testing, and Validation of the Physical Activity Campus Environmental Supports (PACES) Audit.

This paper describes the redesign, field-testing, and convergent validity of a practical tool-Physical Activity Campus Environmental Supports (PACES) audit. Methods. The audit includes two parts: (1) PACES-Programs, which is comprised of questions regarding populations served, fees, programs (recreation/fitness classes and intramurals), proximity, adequacy of facilities, and marketing, and (2) PACES-Facilities, which is comprised of questions regarding built environment (aesthetics, bike racks, stairs, and universal design), recreation equipment, staff, amenities, and access. Each item criterion is specifically scored using a five-point, semantic-differential scale ranging from limited to extensive environmental support. A few questions utilize select all that apply for a summed score. PACES training, interrater reliability, and data collection are all accessible via an online portal. PACES was tested on 76 college campuses. Convergent validity was examined by comparing the PACES-Programs questions to Healthy Campus Initiatives-Programs questions (HCI-Programs) and comparing the PACES-Facilities questions to questions contained in the Physical Activity Resource Assessment (PARA) Instrument. Statistical analyses included Cronbach\u27s alpha, ANOVA, latent profile analysis, and Spearman correlations. Results.The PACES-Programs audit includes 10 items for a potential total of 73 points (α = 0.72) and PACES-Facilities audit includes 15 items for a potential total of 77 points (α = 0.837). Most (77.8%) of the 153 facilities assessed scored in the most healthful range (20-42), which was mainly due to the extensiveness of the aerobic equipment/amenities and the competence/accessibility of staff. Significant differences in PACES-Total and PACES-Programs scores were associated with campus size and PACES-Facilities across regions. For the paired validation assessments, correlations were significant between PACES-Programs and HCI-Programs ((n=41) r=0.498, p \u3c 0.001) and PACES-Facilities and PARA (n=29) for both features (r=0.417, p=0.024) and amenities (r=0.612, p \u3c 0.001), indicating moderate convergent validity. Conclusion. The PACES audit is a valid, reliable tool for assessing the quality of recreation facilities and programs in a variety of college campus environments

Multilevel Structural Equation Modeling of Students\u27 Dietary Intentions/Behaviors, BMI, and the Healthfulness of Convenience Stores.

BACKGROUND: When dietary behaviors are habitual, intentions are low, and environmental cues, such as the consumer food environment, might guide behavior. How might intentions to eat healthily and ultimately actual dietary behaviors, be influenced by the consumer food environment (including the availability and affordability of healthy foods) in convenience stores? This study will determine pathways between the healthfulness of convenience stores and college students\u27 dietary intentions/behaviors, and body mass index (BMI).METHODS: Through multilevel structural equation modeling, a comparison was made of students\u27 healthful meal intentions (HMI); intake (fruits/vegetables, %kcal/fat, sugar-sweetened beverages (SSBs) and whole-grains); and measured BMI; as well as the healthfulness of convenience stores (fruits/vegetables availability/quality, healthy food availability/affordability). Data was collected on 1401 students and 41 convenience stores across 13 US college campuses.RESULTS: Controlling for gender, HMI was negatively associated with SSBs (β = -0.859) and %kcal/fat (β = -1.057) and positively with whole-grains (β = 0.186) and fruits/vegetables intake (β = 0.267); %Kcal/fat was positively (β = 0.098) and fruits/vegetables intake (β = -0.055) negatively associated with BMI. Campus level, fruits/vegetables availability were positively associated to HMI (β = 0.214, β = 0.129) and directly/negatively to BMI (β = -2.657, β = -1.124).CONCLUSIONS: HMI modifies dietary behaviors, with energy from fat and fruit/vegetable intake the most predictive of weight. Availability of fruit/vegetables in convenience stores make it easier for young adults to eat well

Development and Validation of the Vending Evaluation for Nutrient-Density (VEND)ing Audit

Background: This paper describes the development and validation of the Vending Evaluation for Nutrient-Density (VEND)ing audit to comprehensively evaluate vended products based upon healthfulness, price and promotion, and machine accessibility. Methods: A novel vending nutrient-density score was created to determine the healthfulness of vended snack/beverage products. Field tested in United States colleges, VENDing audit (Σnutrient-density + 10 x % healthy products) and Support sub-scores (price + promotion + accessibility) were calculated for snack/beverage machines. Higher scores indicate more healthful vending options and supports for choosing healthfully. Nutrition Environment Measures Survey-Vending (NEMS-V) was used to validate the nutrient-density score for a sub-sample of machines. Sensitivity and specificity were computed by comparing the number of healthy snacks/beverages determined by NEMS-V and the VENDing nutrient-density scores. Results: Researchers conducted the VENDing audit on 228 snack/beverage vending machines at 9 universities within the United States and used both VENDing and NEMS-V on 33 snack and 52 beverage vending machines. Mean VENDing audit scores were 4.5 ± 2.0 (2.6, 3.4) and 2.6 ± 2.0 (0, 12) for snack/beverage machines, respectively. The number of products considered healthy assessed with both the VENDing nutrient-density scores and the NEMS-V were positively correlated for beverages (r = 0.687, p \u3c 0.001) and snacks (r = 0.366, p \u3c 0.05). The sensitivity was excellent for beverages (0.83) and moderate for snacks (0.69); while the specificity was moderate for both beverages (0.66) and snacks (0.50). Conclusions: The VENDing audit uses unique, valid, an