The prevention of metabolic syndrome risk development during young adulthood is critical in mitigating future cardiometabolic disease. Metabolic syndrome, defined as a cluster of cardiometabolic risk factors, is associated with a more than twofold greater risk of cardiometabolic disease. As many as 76.7% of U.S. young adults have at least one metabolic syndrome risk factor (1). This high prevalence of risk factors is concerning because they are often asymptomatic, go undetected, and cluster with other developed risk factors over time (2,3). Importantly, this critical public health issue may be preventable with early lifestyle intervention (4), which would reduce the overall economic burden of medical costs and improve the cardiometabolic health of the nation’s young adults.
For most U.S. youth, entering young adulthood is associated with the start of college, a period of dramatic environmental and social adjustment that results in significant behavioral change (5). The transition from high school to college occurs concurrently with an accelerated increase of metabolic syndrome risk (6). Although the lifestyle behaviors associated with metabolic syndrome in the general population are known, including excess sedentary behavior (SB), low moderate- to vigorous-intensity physical activity (PA), poor diet and sleep habits, and elevated stress (7), longitudinal research is needed to identify the changes in lifestyle behaviors most associated with advances in metabolic syndrome risk during this transitional period. Moreover, the college environment presents an ideal opportunity to identify such behavioral changes as well as the psychosocial or environmental factors that help to explain them. Together, this information can be used with established frameworks such as the social ecological model to develop a population-specific theoretical framework to guide prevention efforts helping young adults adopt positive behaviors that will likely track throughout life.
This article describes the study design and conceptual approach of the Health E Start study, a prospective longitudinal cohort study that will 1) identify key lifestyle behaviors associated with metabolic syndrome risk progression by assessing changes in objective measures of PA, SB, and sleep, as well as validated measures of diet and other risk-related behaviors, and 2) explore changes in psychosocial and environmental antecedents of observed behavior changes, including social determinants of health. The study will use various levels of the social ecological model (e.g., inter-/intraindividual and community), including constructs from social cognitive theory and self-determination theory to explain observed behavioral changes. Data will facilitate the design of a targeted intervention for the prevention of metabolic syndrome risk.
The Health E Start study is funded by a Research Enhancement Award (R15) from the National Heart, Lung, and Blood Institute (NHLBI), which supports small-scale research projects and strengthens the research environment at undergraduate-focused institutions, while giving undergraduate students an opportunity to gain significant biomedical research experience. This mechanism provides many opportunities to improve the capacity for research at institutions that do not typically receive federal funding, with a primary focus on developing the next generation of scholars, many of whom would not typically have the opportunity to work on externally funded research. Support includes direct funds for the project and project personnel, including undergraduate researcher pay, travel support, and publication/professional development. Accordingly, study design decisions, including scope, duration, and measures, were chosen to strike the best balance between rigor of research and opportunities for the training of student researchers.
Graduating high school seniors (n = 150, 16–20 yr old), moving from their childhood homes to independent living at any 4-yr college in the United States, will be included. Participants must plan to return to their childhood homes (near Elon University) after the first year of college. These criteria allow for the assessment of change in behavior and metabolic syndrome risk across the transition to and throughout the first year of college and immediately after 1 yr of college exposure. Exclusion criteria are 1) continued habitation in the childhood home throughout the first year of college, to ensure that behavioral choices made by the participant are not affected by parents/guardians; 2) current pregnancy, or planned pregnancy within 18 months of enrollment, to ensure participants can safely complete assessments (i.e., dual-energy x-ray absorptiometry [DXA]); 3) injury/condition that prevents habitual PA, so that participants are able to perform habitual behaviors; and 4) known cardiovascular, renal, or metabolic disease, being immunocompromised, having a body mass index ≥35 kg·m−2, or current use of any medication associated with primary outcomes, so that change in metabolic syndrome risk factors can be effectively assessed.
Recruitment will target distributional representation of U.S. student enrollment in undergraduate programs in fall 2019, based on sex and race/ethnicity. This includes approximately 57% female, 51% White, 13% Black/African American, and 36% other (i.e., American Indian/Alaskan Native, Asian, Native Hawaiian/other Pacific Islander, or two or more races), with at least 21% who identify their ethnicity as Hispanic/Latino (8). This sampling strategy was chosen to provide the greatest generalizability to the target population (i.e., U.S. college students, so that data are appropriate for the design of behavioral interventions in the early college years). Recruitment strategies will include social media advertisements targeting individuals fitting our study demographic within a 50-mile radius of Elon University, direct mailed postcards, posted advertisements in local establishments, and e-mails sent to students in public/private high schools within the surrounding counties.
This prospective, longitudinal study will assess young adult metabolic syndrome risk development and associated lifestyle behaviors over approximately 15 months. Potential psychosocial and environmental antecedents of observational behavioral changes coinciding with the transition to college will also be assessed to facilitate the development of a theoretical model for future intervention design. Participants will be recruited in two cohorts (n = 75 for each), with recruitment and testing for the first cohort beginning during the spring of 2022.
A broad array of measures are included in this study to ensure assessment of traditional risk factors used to diagnose metabolic syndrome, as well as nontraditional risk factors that may provide further information into the advancement of cardiometabolic risk. Moreover, we focus on the objective assessment of lifestyle behaviors with the addition of several subjective measures to provide context to observed behaviors.
Traditional risk factors
Height and weight will be measured using a stadiometer (Health-O-Meter 209HR; Pelstar LLC, McCook, IL) and digital scale (Patient Aid PA-550XL; Patient Aid, Profound Health LLC, Austin, TX), to the nearest 0.1 cm and 0.1 lb, respectively. Waist and hip circumference will be measured using a tension-sensitive tape measure (Gulick II Plus; MABIS Healthcare, Inc., Lake Forest, IL) to the nearest 0.1 cm. Circumference measures will be taken in accordance with guidelines from the World Health Organization, with waist circumference being measured halfway between the iliac crest and the last palpable rib and hip circumference taken at the largest part of the hips. All measures will be taken in duplicate. If a difference between measures of ≥0.5 cm is noted, a third measure will be taken.
Venous blood samples will be collected after an overnight fast to investigate standard metabolic syndrome biomarkers (e.g., high-density lipoprotein cholesterol, glucose, and triglycerides) and other cardiometabolic risk markers. Samples will be analyzed using the Piccolo Xpress Chemistry Analyzer and Lipid Panel Plus Cassette (Abaxis, Inc., Union City, CA) and enzyme-linked immunosorbent assays (R&D Systems, Minneapolis, MN). Insulin resistance will be calculated using the homeostasis model (HOMA-IR), calculated as fasting insulin (μU·mL−1) × fasting glucose (mg·dL−1)/405 (9).
Peripheral and central blood pressures (systolic, diastolic, and mean arterial pressure) will be assessed in triplicate after 20 min of supine rest using an automated oscillometric device (Vicorder, SMT Medical, Wuerzburg, Germany). Participants will be supine with the upper arm supported to ensure alignment with the heart (10).
Nontraditional risk factors
Heart rate variability will provide a measure of autonomic nervous system function. Time- and frequency-domain metrics (e.g., root mean square of successive differences in R-R intervals and high/low frequency power) as well as nonlinear measures (e.g., sample entropy) (11) will be derived from a 5-min electrocardiography (ECG) recording (Biopac ECG 100C; BIOPAC Systems Inc., Goleta, CA) after voiding of the bladder and 10 min of supine rest. Raw ECG signal will be analyzed using Kubios software (v3.4; Kubios Oy, Kuopio, Finland).
Body composition will be measured using DXA (Lunar Prodigy; GE Healthcare, Madison, WI). One whole-body, spine, and dual-femur scan will provide total body fat mass (kg), fat-free soft tissue mass (kg), total body and % body fat, lumbar spine and proximal femur bone area (cm2), bone mineral content (g), and areal bone mineral density (g·cm−2).
Aortic arterial stiffness will be assessed via carotid–femoral pulse wave velocity (PWV; Vicorder). PWV is the speed of a forward traveling pressure wave as it moves from a central to a peripheral site and is influenced by arterial stiffening. Increased PWV results in greater arterial wave reflection to the heart and an augmentation of central systolic blood pressure and myocardial load (12).
Proinflammatory markers, including C-reactive protein and interleukin 6, will be assessed using enzyme-linked immunosorbent assays (R&D Systems), serving as potential links between lifestyle-related factors and atherosclerosis development in early life.
PA and SB will be objectively assessed as part of the 24-h activity cycle (13) via triaxial ActiGraph GT9X Link accelerometers (ActiGraph, LLC, Fort Walton Beach, FL) and activPAL4 posture monitors (PAL Technologies Ltd., Glasgow, Scotland). ActiGraph accelerometers will also provide an objective, validated assessment of total sleep time, night-to-night sleep variability, social jetlag, and sleep efficiency (14). Participants will wear the devices on their nondominant wrist and mid-thigh, respectively, 24 h·d−1 for a minimum of 7 d, immediately after visit 1 and visit 3 (high school senior spring and college spring semester, respectively; Fig. 1). To cross reference the wearable monitor data during analysis, a logbook will be provided to record nonwear, bed, and wake times. Wearables will be mailed with a prepaid return label via United States Postal Service. Wearables will be initialized and their data downloaded using respective software suites (i.e., ActiLife v6.13.3 and PAL v8). The computation of 24-h activity cycle metrics will be performed using the R package GGIR (v2.0-0; R Foundation for Statistical Computing, Vienna, Austria) (15) and PAL software suite.
The Patient-Reported Outcomes Measurement Information System pediatric item banks will provide valid and reliable, subjective assessments of sleep disturbances, quality, and satisfaction (sleep disturbance) and daytime impairments related to sleep (sleep-related impairment) (16). The Pittsburgh Sleep Quality Index will provide information on perceived sleep, including sleep quality, sleep latency, habitual sleep efficiency, and estimated sleep duration on weeknights and weekend nights (17).
Dietary factors, with a focus on total energy (kcal), added sugars (g), fats (g), and fruit and vegetable servings, will be assessed using the National Cancer Institute’s automated, self-administered 24-h dietary recall, the ASA24® (version ASA24-2020) on three nonconsecutive days (including one weekend day). ASA24® has been shown to perform comparatively to a self-administered or interviewer-administered questionnaire (18). Dietary recall will occur concurrently with visits 1, 2, and 3 (high school senior spring, college fall semester, and college spring semester, respectively; Fig. 1), with the first assessment being completed in person to encourage compliance while allowing participants to ask questions and gain comfort with using the system. A series of follow-up reminder messages and/or phone calls will encourage completion of remaining recalls.
Risk-related behaviors, including vaping and alcohol consumption, will be assessed using individual items from the National Institute on Minority Health and Health Disparities’ PhenXToolkit and the Alcohol Use Disorders Identification Test (19).
Psychosocial and environmental antecedents
Antecedents of behavioral changes will be assessed at various levels of the social ecological model (e.g., inter-/intraindividual and community), using several social cognitive theory and self-determination theory constructs (20,21). A comprehensive listing of scales can be found in Table 1. Briefly, self-determination theory outcomes include intra-/interindividual-level psychological aspects such as perceived autonomy, competence, and relatedness (34); social cognitive theory outcomes include constructs such as behavioral self-regulation (27) and self-efficacy for PA, healthy eating, and sleep hygiene measures (35). Furthermore, the Neighborhood Environment Walkability Scale for Youth (24) will be used to assess the environment for PA. It is a valid, reliable tool for neighborhood environment correlates of youth PA. Nutrition will be assessed using individual items previously used in longitudinal studies in youth (25), which collect data on home nutrition environments, weight-related perceptions, and eating behaviors. Finally, the Sleep Hygiene Index (26) will be used to assess sleep habits and is a valid, internally consistent tool for assessing sleep behaviors associated with potential sleep problems.
TABLE 1 -
Construct to be Measured, Theory, and Instrument or Method Used with the Corresponding Number of Items.
||Number of Items
|Social determinants of health
||PhenX Toolkit—Social Determinants of Health Collection: Core Questions
||Perceived Stress Scale: produces a continuous score
||Pittsburg Sleep Quality Index: produces a continuous score
||Patient-Reported Outcomes Measurement Information System, Sleep-Related Impairment Child-Report Bank: produces a continuous score
||Patient-Reported Outcomes Measurement Information System, Sleep Disturbance Child-Report Bank: produces a continuous score
||Neighborhood Environment Walkability Scale for Youth: produces a continuous score
||FLASHE: originally modified from Project Eat-II Survey, the 2010 National Youth Physical Activity and Nutrition Survey, and the TREC Idea Study
|Sleep environment and practices
||Sleep Hygiene Index: produces a continuous score
|Behavioral self-regulation: delayed gratification
||Chen K. Definition and Measurement of Delay Gratification. Newark (NY): Unpublished manuscript. University of Medicine and Dentistry of New Jersey; 1998.
|Behavioral self-regulation: positive self-reinforcement
||Assessment of Frequency of Self-Reinforcement: produces a continuous score
|Self-efficacy for pa, healthy eating, and achieving sleep recommendations
||FLASHE: originally modified from the Perceived Competence Scale
|Motivation surrounding PA and healthy eating
||FLASHE: originally modified from the Self-Regulation Questionnaire
|Social support and perceived norms for PA, healthy eating, and achieving sleep recommendations
||FLASHE: originally modified from the Patient-centered Assessment and Counseling for Exercise (PACE) Project
|Barriers to PA and healthy eating
||FLASHE: originally modified from the Go Girls! 2 Study (PA) and the National Cancer Institute’s Food Attitudes and Behaviors Survey (Healthy Eating)
|Attitudes regarding PA
||FLASHE: originally modified from Motl et al. 2000. Preventive Medicine 31, 584–594
|Autonomy, competence, and relatedness
||Subscales of the Basic Needs Satisfaction in General Scale: produces a continuous score
FLASHE, questions adapted from the Family Life, Activity, Sun, Health, and Eating study; N/A, not applicable; PA, physical activity; SCT, social cognitive theory; SDT, self-determination theory.
Health history, demographics, and social determinants of health, including age, gender, race/ethnicity, socioeconomic status (i.e., parental household income, parental education level, and participant employment status); educational history (i.e., public vs private school); social support and social networks; medication and supplement use; family history of chronic disease; menstrual history; and history of weight loss will also be recorded using questions from the PhenXToolkit (22).
Perceived psychological stress will be assessed using the Perceived Stress Scale (PSS-10). The PSS-10 is a valid tool for the assessment of frequency of feeling a lack of control, anger, mood swings, and other aspects of overall life stress in college-aged samples (23).
Participants will complete in-person and remote testing over approximately 15 months (Fig. 1). First, in-person testing will occur during the spring semester of the high school senior year (visit 1) to capture initial metabolic syndrome risk, behavioral characteristics, and psychosocial/environmental outcomes while in the home environment. Next, changes in lifestyle behaviors and psychosocial/environmental outcomes will be assessed via self-report questionnaires and mailed wearables during the fall (visit 2) and spring (visit 3) semesters of the first year of college, while participants are on their respective college campuses across the United States. Finally, changes in metabolic syndrome risk factors will be assessed at a follow-up in-person testing visit during the summer, immediately after the end of the first year of college (visit 4).
In designing this study, there were several considerations unique to the R15 mechanism. Specifically, the inclusion of undergraduate students within the implementation and management of the project offers diverse, intensive research and training opportunities. A comprehensive training plan was developed to include undergraduate students in all aspects of the research process, including the proposal of nested projects, technical skills training at the primary and collaborator’s institutions, data collection, mentored statistical analysis, and the dissemination of findings via conferences and manuscripts. Moreover, the role of a study coordinator was divided into several smaller roles to include students in the processes of recruitment, communication, and data management. The details of this training plan were clearly defined within the funding application, with emphasis on how such experiences would encourage students to consider careers within the biomedical sciences. A brief overview can be found in Table 2.
TABLE 2 -
Proposed Undergraduate Engagement in Research Training Plan.
|Established undergraduate research process
||Students complete a minimum of two semesters of mentored research that requires them to explore the current literature, complete research integrity training, generate a novel research question nested within the project, undergo comprehensive technical skills training, play an active role in data collection, and disseminate their findings.
|Technical skills training
||Fall 2021; refresher training annually
||Skills training sessions will occur at Elon University (led by PI: behavioral measures, and the Director of Student Health Services: clinical skills, e.g., phlebotomy), at UNC Chapel Hill (led by coinvestigator Stoner; cardiovascular measures), and Wake Forest Medical School (led by coinvestigator Moore; psychosocial measures). These training sessions will take on many forms, including hands-on tutorials, prerecorded videos, and personal pilot testing. Although the bulk of training will occur at the start of the project (fall 2021/early spring 2022), annual refreshers will be scheduled to onboard new student researchers and maintain testing reliability.
||Throughout the life of the project, the role of a typical study coordinator will be split into several smaller roles with undergraduate researchers employed in these roles as part of their work on the project. These roles will include coordinators for recruitment, communications, and data quality. Guidance for each of these roles will be provided in an ongoing manner through weekly in-person (or virtual) meetings with the PI.
|Virtual and in-person laboratory visits
||At least one per semester
||Each semester, one or more visits will be scheduled with coinvestigators and/or research laboratories at local institutions. This will provide students with the opportunity to meet and hear from successful researchers and their graduate students about their research, and to learn about careers in biomedical sciences by witnessing the science first hand.
||Every other week (alternates with journal clubs) the PI’s laboratory will meet to discuss project progress and career-building opportunities. Students will be encouraged to take ownership over small aspects of the project and to present formal progress reports to provide opportunities to develop presentation/research discussion skills. Students will also be paired with other laboratory members in a peer-mentoring relationship to build technical skills, confidence, and a sense of cohesiveness among team members.
||Every other week (alternates with laboratory meetings) the PI’s laboratory will meet with other undergraduate research students within the Exercise Science Department to discuss current literature. Two students will be assigned to help lead the discussion on a rotating basis.
||Years 2 and 3
||Collaborations between undergraduate researchers in exercise science (PI; Dr. Higgins) and mathematics and statistics (coinvestigator; Dr. Weaver) will facilitate mentored statistical analysis and joint-authored manuscripts. This type of team training is reminiscent of the typical “team science” that is standard in biomedical research.
|Local, regional, and national conference presentations
||February–June years 2 and 3
||Students will present the findings of their research at local, regional, and/or national conferences with the aid of funding from the R15 and Elon’s undergraduate research program. Such conferences are great opportunities for students to develop scholarly communication skills and explore careers in the biomedical sciences through career fairs and other organized/informal networking.
||Throughout the research experience, students will be guided through the dissemination of their findings in manuscript format via one-on-one mentorship, writing seminars provided by the grant team, and workshops provided by the university. Students aim to publish at least one paper as a direct result of their research.
The study facilitates the development of two to four unique undergraduate researchers per year, over the 3-yr grant period. Undergraduate researchers from related areas (e.g., exercise science, public health, and psychology) will propose novel questions that nest within the parent project, and students from other departments (i.e., mathematics and statistics) will work with the project’s biostatistician to provide statistical support for their peers’ projects. Interested undergraduate students will be recruited from the campus community through posted advertisements on the undergraduate research program website, word of mouth, and community e-mails. Moreover, to encourage diversity within our study team, several programs will be targeted during team recruitment efforts, such as Elon University’s Odyssey Scholars, a competitive and selective merit-based program that supports academically strong students who are from disadvantaged backgrounds, are first-generation college students, or are from underrepresented groups. Each student will commit to a minimum of 1 yr on the project. It is our hope that this immersive research experience, along with the resources provided by NHLBI funding, including funds for undergraduate researcher pay, conference travel, and other professional development, will deepen the research experience while providing students with a comprehensive understanding of potential biomedical career pathways through the collaborations developed with coinvestigators and their laboratories.
To support and implement the training of these undergraduate researchers, the interdisciplinary management team includes researchers from three higher education institutions in North Carolina. Monthly meetings throughout the life of the project will monitor study progress and identify any concerns regarding conduct, unanticipated problems, or serious adverse events during testing. Such events will be reported to the Institutional Review Board within 48 h of their occurrence using an incident report form. During testing visits, all data will be entered directly into a secure REDCap database by the research team. To ensure participant anonymity, participants will be assigned a study identifier, and their identifiable information will be stored in a separate section of the database, with restricted access. Data will undergo quality checks, including screening of outcome range, consistency, and outlier checks. Any data decisions will be discussed during the research team meetings and recorded in a codebook to provide an audit trail.
Primary aim (metabolic syndrome behavioral risk measurement)
To address the study’s primary aim, a principal components analysis (PCA) will be used to convert traditional metabolic syndrome risk factors to a single metabolic syndrome risk score (36). Using only data from visit 1 (high school senior spring), we will apply PCA with an orthogonal varimax rotation. We will examine a scree plot of the eigenvalues to determine the number of components to retain. Using the scoring coefficients obtained from visit 1 to obtain risk scores for the follow-up data, we will compute within-person changes in the metabolic syndrome risk scores from visit 1 to visit 4 (summer post first year of college) for use as the response variables in linear models. Similarly, within-person change scores will be computed between visits 1, 2, and/or 3 (as appropriate for each outcome) for key behavioral variables. We will initially evaluate the relationships between the change-in-metabolic-syndrome-risk scores and the change-in-behavior scores using separate linear regression models for each behavior. Then, the overall F-test for each model, if significant at the 5% level, will be interpreted by estimating appropriate linear combinations of the model parameters along with 95% confidence intervals. Finally, we will use a forward selection approach with fivefold cross validation to build a model that simultaneously includes multiple behavioral variables. As an exploratory analysis, similar modeling approaches will be individually applied for each of the nontraditional risk factors.
Secondary aim (metabolic syndrome behavioral cluster analysis)
To address the secondary aim, we will compute change scores for psychosocial and environmental constructs from visit 1 (high school senior spring) to each of the follow-up visits (fall/spring of the first year of college), as well as change scores in the behavioral measures. We will then fit separate linear mixed effect models to evaluate the relationships between the changes in the theoretical constructs and each behavior.
The planned sample size is powered for detecting changes in metabolic syndrome risk scores. Using PCA scores, we can assume that the standard deviation is 1. Further, we assume that the within-person correlation between scores from visit 1 (high school senior spring) and visit 4 (summer post first year of college) is at least 0.5. Under these assumptions, enrolling at least 90 participants would provide at least 80% power to detect a 0.3 standard deviation (SD) between mean scores, considered a small to moderate effect size. Allowing for up to 40% loss, we will inflate this sample size to 150.
Previous studies have reported retention rates of 65.2%–85.5%, likely due to relocation-related communication barriers and loss of interest as students transition from the parental home into the college environment (37,38). Together with our oversampling procedure, we will encourage participation and retention through a ramped incentive structure to ensure adequate statistical power. Participants will receive $25 after visit 1 and $75 after visit 4 (high school senior spring and summer post first year of college, respectively; Fig. 1). Participants will also receive $25 upon returning the activity monitors to incentivize wear and device return. Furthermore, participants will receive entries into a prize drawing for one of two $500 awards. Entries will accrue in increasing numbers for the progressive completion of each aspect of the project. Further, we will acquire multiple means of contact to avoid loss to follow-up due to transitional changes in e-mail addresses or phone numbers (local area high schools to colleges across the United States). Using these strategies, we conservatively estimate a 75% retention rate, based on our own data and those of others who used longitudinal assessment in college students (37–39).
Limitations and strengths
One limitation of this study is the relatively small sample size. Although the study will be powered to detect changes in metabolic syndrome risk scores in the overall cohort, it will be underpowered to evaluate sex and race-specific associations. Furthermore, the study is limited to 15 months of follow-up rather than including consecutive years in college. However, our study design has several strengths. The study will use remote assessment of the transition to college that objectively measures changes in PA, SB, and sleep via preinitialized, mailed wearable devices. This novel approach will allow for valid assessment regardless of a student’s college location. Furthermore, recruitment of students who intend to return to their parental home during the summer after year 1 of college will ensure their ability to attend a follow-up testing visit. Finally, the ramped incentive structure motivates participants to attend follow-up testing through immediate monetary benefit as well as increased odds of winning the overall prize.
Ethical approval was obtained from the Institutional Review Board at Elon University (no. 21-165). Informed consent will be attained from all participants ≥18 yr of age or from the parent/guardian of participants <18 yr old, coupled with assent from the minor participant, before participation. The study will be performed in accordance with the Department of Health and Human Services Common Rule (45 CFR part 46).
The Health E Start study is the first comprehensive, longitudinal assessment of the relationship between behavior change and metabolic syndrome risk, done in an ecologically valid manner that assesses actual behavior change from the home environment to college campuses across the United States. Our goal is to develop a theoretically driven intervention for the prevention of metabolic syndrome risk development in first year of college students using a population-specific theoretical framework to facilitate positive behavioral change and/or maintenance. The design of interventions in the early college years will contribute to reductions in the future burden of cardiometabolic disease by helping young adults to adopt positive behaviors that will likely track throughout life. We plan to disseminate our findings via the production of manuscripts and conference presentations. Additionally, through the collection of prospective behavior change and metabolic syndrome risk development data over the course of students’ transitions from high school through their first year of college, we hope to embolden the research environment at Elon University by positioning the investigative team for future research and grant funding opportunities. Data collected from this study will fill an important knowledge gap and enable the design of focused educational, environmental, and policy initiatives for the prevention of metabolic syndrome risk development in the more than one-third of U.S. high school graduates who pursue higher education on a yearly basis.
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the American College of Sports Medicine.
The authors have no potential, perceived, or real conflict of interest to disclose. Research reported in this publication was supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number R15HL159650. The sponsors had no involvement with study design and will have no involvement with data collection, analysis, or interpretation, manuscript writing, or the decision to submit the manuscript for publication.
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