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.

Within lifestyle behavior research, the sex of populations causes differences in behaviors and outcomes of studies. This cross-sectional study investigated lifestyle behavior patterns in college students, examining sex differences in four areas: Nutrition, physical activity, sleep, and stress. Data from over 1100 college freshmen across 8 United States universities were used for this cross-sectional analysis. Self-reported data assessed fruit and vegetable intake, fat percent intake, physical activity, perceived stress, and sleep quality. Statistical analysis included Pearson chi-squared and Mann–Whitney’s U tests for scores by sex. Likewise, healthy cut-offs were used to determine frequency of participants within range of the five tools. Males reported higher intake of both fruits and vegetables, and percent energy from fat than females. Males also reported higher physical activity levels, lower stress levels, and poorer sleep quality than females. Of the five self-reported tools, males were found to have a larger frequency of participants with healthy ranges than females. In a large college freshmen sample, sex was found to be related to general lifestyle behaviors which strengthen results reported in the previous literature. These findings shed light on the need for lifestyle behavior interventions among at-risk college students to enhance their behaviors to healthy levels.

This study aimed to identify whether a research participant’s body-mass index (BMI) can be correctly identified from their facial image (photograph) in order to improve data capturing in dissemination and implementation research. Facial BMI (fBMI) was measured using an algorithm formulated to identify points on each enrolled participant’s face from a photograph. Once facial landmarks were detected, distances and ratios between them were computed to characterize facial fatness. A regression function was then used to represent the relationship between facial measures and BMI values to then calculate fBMI from each photo image. Simultaneously, BMI was physically measured (mBMI) by trained researchers, calculated as weight in kilograms divided by height in meters squared (adult BMI). Correlation analysis of fBMI to mBMI (n = 1210) showed significant correlation between fBMI and BMIs in normal and overweight categories (p < 0.0001). Further analysis indicated fBMI to be less accurate in underweight and obese participants. Matched pair data for each individual indicated that fBMI identified participant BMI an average of 0.4212 less than mBMI (p < 0.0007). Contingency table analysis found 109 participants in the ‘obese’ category of mBMI were positioned into a lower category for fBMI. Facial imagery is a viable measure for dissemination of human research; however, further testing to sensitize fBMI measures for underweight and obese individuals are necessary.

Objective: To examine health behavior and environmental perception differences among vegetarian and nonvegetarian students. Participants: First-year university students (n = 1078) from eight United States universities. Methods: Data were obtained from base 2015 and followup 2016 assessments. Vegetarians and nonvegetarians were compared for anthropometrics, lifestyle behaviors, and campus environmental perceptions (CEPS). Results: Vegetarians had smaller waist circumference, lower systolic blood pressure, higher fruit and vegetable consumption, lower percentage of energy obtained from fat, and higher perceived stress. Vegetarians expressed a lower rating of perceptions of health policies on campus. Conclusion: A clear difference in indicators of physical health does not appear, however, vegetarian students show positive dietary patterns which can promote positive health outcomes. Further, vegetarians had lower perceptions of health policies on campus. Results can be used by administrators to ensure policies are in place to support health of students as currently vegetarian students see limitations in the environmental health policies.

College students face unique challenges with leading healthful lifestyles. Using a community-based participatory research approach, college student research partners at two land-grant universities collected data and developed a tailored intervention to improve the well-being of college students. To inform the design of the program, college students were trained to conduct a needs assessment that included a campus-wide survey on the health behaviors of college students, environmental audits of health policies and food pantries on campus, and stakeholder interviews with campus health professionals. Outcomes of the needs assessment data highlighted university students ranked their health as “good” but nutrition health as “fair/poor.” Low or very low food security was self-reported by 36.9% of participants and had an overall diet quality score of 47.6 ± 10.1 out of 100. Health professional interview data indicated campuses provide healthful resources to students, but students are not aware those resources exist. Utilizing the needs assessment data previously mentioned, the nominal group technique was then used for student research partners to collaboratively determine the best intervention approaches and develop a wellness program. Student partners identified (1) education, (2) sharing of campus resources, and (3) incentives as important areas of intervention. Using the data collected, the student research partners developed a program titled, The College Cooking Connection, to address health-related quality of life in college students. Using a community-based participatory research approach to program planning, educators and researchers have a greater likelihood of addressing the current needs of the population they are targeting and developing a successful intervention to meet those health concerns. This study aims to partner with young adult university students to understand the college environment and allow the target community to be involved with the development of intervention programs for their campus.

Purpose: There is little investigation into the causes of food addiction. The aim of this study was to determine the impact of early life influences on the development of food addiction in college-attending young adults aged 18–29. Methods: This study utilized a sequential explanatory mixed-methods research design. College-attending young adults were invited to complete an online survey measuring Adverse Childhood Experiences (ACEs), food addiction, depression, anxiety, stress, and demographic information. Correlations between food addiction and the other variables were analyzed and significant variables were placed into a nominal logistic regression model to predict the development of food addiction. Participants who met the criteria for food addiction were invited to participate in interviews to examine their childhood eating environment and when their symptoms emerged. Interviews were transcribed and thematically analyzed. Quantitative analysis was conducted using JMP Pro Version 16.0 and qualitative analysis was conducted using NVIVO Software Version 12.0. Results: Survey respondents (n = 1645) had an overall 21.9% prevalence of food addiction. Significant correlations were observed between food addiction and ACEs, depression, anxiety, stress, and sex (p < .01 for all). Depression was the only significant predictor of the development of food addiction (OR = 3.33 95% CI 2.19, 5.05). The most common eating environment described by interview participants (n = 36) was an emphasis on diet culture, ideal body image, and restrictive environments. Symptoms frequently emerged after transitioning into college and having the ability to make their own food choices. Conclusion: These results show the impact of early life eating environments and young adulthood mental health on the development of food addiction. These findings contribute to the understanding of underlying causes of food addiction. Level of evidence: Level V, Opinions of authorities, based on descriptive studies, narrative reviews, clinical experience, or reports of expert committees.