Cardiometabolic Risk Factors Associated with Physical Fitness and Activity Levels: An Exploratory Study of US College Students

Abstract

College students are a vulnerable population at risk of developing and/or experiencing poor physical fitness and insufficient physical activity (PA) levels, both associated with poor cardiometabolic health. Thus, this study assessed the association of physical fitness and domain-specific PA levels on cardiometabolic risk factors (CMRFs) among college students. A volunteer sample of students enrolled in general health and wellness courses at a large, Northeastern U.S. institution from Spring 2023 – Spring 2024 (n=1418, 69.0% male, 89.9% non-Hispanic White) completed an objective health assessment as part of a required course assignment. CMRFs (e.g., cholesterol, blood pressure, fasting blood glucose, triglycerides, waist circumference), cardiorespiratory fitness (e.g., VO₂ max), and markers of muscular strength and endurance (e.g., pushups, curl ups, hand grip, sit and reach) were assessed. Participants were then invited to complete a subjective health assessment (e.g., demographics, PA). Separate, unadjusted linear regressions examined the association of physical fitness and domain-specific PA levels (e.g., moderate and vigorous PA, active transportation PA, muscle-strengthening, meeting PA guidelines) on CMRFs, by biological sex. Socio-demographics of race/ethnicity, sexual orientation, semester standing, and grade point average served as covariates in separate, adjusted models to assess potential associations. Among males and females, cardiorespiratory fitness and markers of muscular strength and endurance were significantly associated with CMRFs, while PA levels and socio-demographics were not. Tailored screening approaches may provide students with the required support to reduce later-life adverse coronary events. Future work is required to improve our understanding of the potential role socio-demographics play in CMRFs among young adults.

Introduction

Physical activity (PA) is one of the most impactful health behaviors an individual can participate in to facilitate positive health and well-being across the lifespan. Adequate PA participation and reduced sedentary time can reduce the risk of chronic conditions and noncommunicable diseases, improve physical and mental health, and reduce premature death.1 Young adulthood, often coinciding with college years, remains a pivotal point in the development of health behaviors and habit formation and requires further investigation.

College students are favorably positioned to develop habitual health-enhancing behaviors (e.g., PA) that they carry into middle or late adulthood,2 which is essential in promoting healthy aging. However, college students are considered to be a vulnerable population due to the potentially stressful transition from high school to college through newfound impendence, exposures to a new environment, and social and academic stressors.3 Current PA guidelines detail that adults should aim to participate in at least 150 to 300 minutes per week of moderate-intensity PA or 75 to 150 minutes per week of vigorous-intensive PA or an equivalent combination, as well as muscle-strengthening activities 2 or more days per week.1 Albeit, it has been identified that approximately 40% of college students do not meet both aerobic and muscle-strengthening guidelines, noting a need for intervention and improved measurements of PA among this group.4 Further, college students are reporting increased rates of hypertension,5 obesity,6 and metabolic syndrome.7 These outcomes can lead to increased risk of cardiovascular disease (CVD), stroke, type two diabetes, and premature death. Specific to educational achievement during college, reduced PA participation and increased sedentary time can diminish academic performance.8 Importantly, these negative outcomes may be prevented or reduced with sufficient participation in PA,1 but previous interventions among college students have been ineffective.9,10

Given the lack of effective methods to promote health among college students, it remains essential to assess predictive risk factors of chronic conditions, including metabolic syndrome. It is often assumed that metabolic syndrome risk only begins as individuals enter older adulthood (i.e., 60+ years), but recent findings identified that exposure to elevated blood pressure and abnormal low-density lipoprotein cholesterol during early adulthood (20 – 39 years) was associated with later-life adverse coronary events (e.g., CVD).11 In turn, recent estimates have highlighted that approximately 48.6% of adults over the age of 20 have CVD.12 Importantly, measurements of waist circumference and body mass index, blood pressure, blood glucose, and blood cholesterol can serve as markers for CVD risk; and alarmingly, about 50% of males and 43% of females aged 20 years and older have hypertension, and over 71% of adults are overweight or obese.12 Despite these findings, many college students are unaware of these CMRFs and their impact on health.13 Understanding the prevalence of CMRFs among college students is essential to reduce metabolic syndrome risk and its potential progression to CVD in later adulthood.

Certain socio-demographics can influence cardiometabolic health, including biological sex, race/ethnicity, and sexual orientation. Sex differences in cardiometabolic risk factors an outcomes have been documented, with females being more at risk compared to males.14 When compared to their peers, racial/ethnic minorities15,16 and sexual minorities (i.e., non-heterosexual) 17 experience an increased risk of CVD; which, may be explained by high rates of poor physical and mental health among these groups.18,19 PA levels and physical fitness can also vary by demographics, with women,20 racial/ethnic minorities,21 and sexual minorities22 reporting reduced rates of PA compared to their peers. Recent research has also noted gender and racial/ethnic inequalities in various domain-specific aerobic PA levels (e.g., leisure-time, occupation, active travel) among college students, emphasizing the importance of measuring and examining multiple domains of PA.23 PA is commonly associated with physical fitness1; however, the transition through college is associated with diminished physical fitness and PA levels.24

Due to these understandings, research is necessary to improve health outcomes and health equity among college students. Further, there is limited research simultaneously assessing the role of physical fitness outcomes and various forms of PA on CMRFs among a sample of US college students. Thus, the purpose of this study was to examine the associations between physical fitness outcomes (e.g., VO₂ max, pushups, curl ups, sit and reach, hand grip), and meeting domain-specific PA guidelines (e.g., leisure-time, active travel, total, strength training) and overall PA guidelines (e.g., total aerobic and muscle-strengthening), and on sex-specific CMRFs. This research also sought to examine the role of certain socio-demographic characteristics (e.g., sexual orientation, race/ethnicity, semester standing, and grade point average) on these associations to support health equity.

Methods

Participants

Data for this study was collected from a volunteer sample of college students at a large, Northeastern U.S. university from Spring 2023 – Spring 2024 via an objective health assessment as part of a required general health and wellness course assignment. Participants were then invited to complete a subjective, online survey (Qualtrics, Provo, UT).

Protocol

All participants provided informed consent for both objective and subjective health assessments and trained technicians administered objective health assessment tests. Prior to completing the health assessment, participants completed a pre-participation health screening and an electronic pretest questionnaire that was linked to their health assessment data via identification number. Any participant requiring medical clearance was prevented from participating. Prior to completing the health assessment, participants were instructed to abstain from caffeine for 8 hours, alcohol for 24 hours, smoking/food intake for 2 hours, and exercise for at least 3 hours. The health assessment began with blood pressure measures, followed by a waist circumference measure, and then cardiorespiratory fitness. Blood lipids and glucose were assessed during a follow-up visit on a separate day after an 8 – 12 hour overnight fast. Participants who completed the subjective portion of this study were eligible to enter a drawing to receive a gift card. This study was approved by the university’s Institutional Review Board. This research was carried out fully in accordance to the ethical standards of the International Journal of Exercise Science.25

Participants self-reported their age and biological sex during the objective health assessment pre-consultation questionnaire, and then self-reported their race/ethnicity, sexual orientation, semester standing, and grade point average (GPA; scale: 1.0 – 4.0) on the follow-up subjective survey. Due to small sample size, race/ethnicity, sexual orientation, and semester standing data were modified. Race/ethnicity was collapsed into six categories: non-Hispanic White [White or Caucasian], Black/African American [Black or African American], Hispanic/Latinx [Hispanic or Latino], Asian [Asian American], Multiracial [multiple races selected], and Other [Pacific Islander, Other]. Participants who selected multiple races but also selected Hispanic or Latino were coded as Hispanic/Latin(x). Sexual orientation was collapsed into two categories: Heterosexual [straight/heterosexual] and sexual minority [all other identities]. Semester standing was collapsed into five categories: 1^st year [1 – 2 semesters], 2^nd year [3 – 4 semesters], 3^rd year [5 – 6 semesters], 4^th year [7 – 8 semesters], and 5^th year or more [9+ semesters or graduate student]. These demographics served as covariates in regression models.

The Global Physical Activity Questionnaire (GPAQ),26 assessed volume of leisure-time moderate (MPA) and vigorous (VPA) PA, active transportation (AT), and muscle-strengthening (MS). Weekly minutes of the activity levels were calculated through activity frequency and duration. Weekly minutes were then multiplied by their corresponding MET value to calculate MET-mins/week (VPA = 8.0 METs; MPA = 4.0 METs; AT (e.g., walking, biking) = 3.3 METs). To account for outliers, maximum values were set to two standard deviations above the mean. Participants were categorized as “meeting” or “not meeting” PA guidelines with only leisure-time PA (LTPA) or only transportation PA (TPA) based on ≥600 MET-mins/week threshold in each domain. Total aerobic PA combined both LTPA and TPA totals and categorized participants using the same threshold. Participants were also categorized as “meeting” or “not meeting” MS guidelines based on ≥2 days/week threshold. Using total aerobic and MS guideline categorizations, participants who met both aerobic and MS guidelines were categorized as meeting overall PA guidelines.

The ACSM Static Handgrip Strength Test assessed hand grip strength.27 The ACSM Push-Up Test assessed upper body strength and endurance.27 The Golding Y Curl-up Test assessed abdominal strength and endurance for core stability and back support.28 The Sit and Reach Test assessed hamstring extensibility.28 The Ebbeling Treadmill Test (Single Speed Walk Test) assessed predicted VO₂ max through heart rate predictions of maximal work capacity.

Waist circumference was measured once at the narrowest girth between the umbilicus and the xiphoid process of the sternum using a tension regulated tape measure. Prior to blood pressure measurement, participants sat for 5 minutes with their feet flat on the floor and the measured arm was stationed at heart level. A stethoscope (Littman II, 3 M, Saint Paul, MN) and sphygmomanometer sized for the bladder to encircle 80% of the upper right arm was used to measure blood pressure. A finger prick to collect blood was used to assess cholesterol (total, high-density (HDL) & low-density lipoprotein (LDL)), triglycerides, and fasting blood glucose using the Cholestech LDX (Abbot Labs, Chicago, IL).

The American College of Cardiology/American Heart Association diagnostic criteria was used to categorize at risk participants based on blood pressure (≥121 systolic and ≥81 mm Hg diastolic) measures.29 The American Heart Association/National Heart Lung and Blood Institute’s CMRF diagnostic criteria was used to categorize at risk participants based on fasting blood glucose (≥100 mg/dL; insulin resistance), triglycerides (≥150 mg/dL), as well as sex-specific waist circumference (males: ≥40 inches, females: ≥35 inches) and HDL cholesterol (males: <40 mg/dL, females: <50 mg/dL) measures.30 The 2018 Guidelines for Management of Blood Cholesterol was used to categorize at risk participants on LDL (≥130 mg/dL) and total cholesterol criteria (≥200mg/dL) meaures.31 All five diagnostic criteria were dichotomized into “at risk” or “not at risk” and summed into a discrete variable to capture the total number of CMRFs per participant. Due to small cell sizes (<5%) in the number of risk factors among males and females separately (range: 0 – 5), the range of risk factors was condensed among males to zero to three; females to zero to two. These discrete variables served as independent variables in analyses.

Statistical Analysis

Statistical analyses were conducted using SPSS 29.0 (IBM, Armonk, NY). Descriptive statistics summarized the sample. Linear regressions assessed associations between CMRFs and physical fitness outcomes and domain-specific PA levels in separate models, stratified by biological sex. Sex-specific metabolic syndrome risk factors only included participants who identified as “male” or “female,” respectively. To assess potential associations of specific socio-demographic characteristics on the association between physical fitness and PA on sex-specific CMRFs, race/ethnicity, sexual orientation, semester standing, and GPA served as covariates in linear regression analyses. A priori analyses indicated 85 subjects in models without covariates and 109 subjects in models with covariates would achieve 80% power to detect an effect size of f² = 0.15 for differences at p≤0.05.

Results

The average age of participants (n=1418) was 21 ± 2 years with an average GPA of 3.41 ± 0.41. The majority of participants were male (69.0%), heterosexual (89.9%), non-Hispanic White (89.9%), and in their fourth year of undergraduate studies (34.8%).

Linear regressions examined the potential associations of meeting various PA guidelines on CMRFs by biological sex. In a model without adjustments of covariates (model 1), there were no significant association between PA rates and CMRF among males ((F(4,371)=0.809,p=0.520) (Table 2) or females ((F(4,208)=0.947, p=0.438) (Table 3), respectively. In a model with adjustments of covariates, there were no significant association between PA rates and CMRFs among males ((F(8,340) =1.181, p=0.309) (Table 2) or females ((F(8,183)=0.580, p=0.793) (Table 3).

Table 2.

Linear regression model estimating the associations of physical activity on metabolic syndrome risk factors among US male college students.

Model 1 (n=371)					Model 2 (n=340)
	B (SE)	β	95% CI		B (SE)	β	95% CI
			Lower	Upper			Lower	Upper
Constant	1.290 (0.150)		0.995	1.584	1.988 (0.445)		1.112	2.864
Aerobic LTPA Guidelines	−0.174 (0.142)	−0.068	−0.453	0.105	−0.156 (0.125)	−0.072	−0.402	0.090
Aerobic AT Guidelines	−0.068 (0.108)	−0.033	−0.280	0.145	−0.068 (0.095)	−0.039	−0.255	0.119
MS Guidelines	−0.623 (0.437)	−0.298	−1.483	0.237	−0.480 (0.442)	−0.272	−1.350	0.390
PA Guide	0.586 (0.441)	0.283	−0.281	1.453	0.469 (0.444)	0.268	−0.405	1.342
Sexual Orientation					0.301 (0.187)	0.088	−0.067	0.668
Race/ethnicity					−0.033 (0.027)	−0.066	−0.086	0.021
Semester Standing					−0.036 (0.039)	−0.050	−0.113	0.041
GPA					−0.179 (0.106)	−0.093	−0.388	0.029
R2 = 0.009	R2 = 0.028 ΔR2 = 0.019

Note. p-value:

^*≤0.05,

^**≤0.01,

^***≤0.001;

SE = standard error; CI = confidence interval.

Table 3.

Linear regression model estimating the associations of physical activity on metabolic syndrome risk factors among US female college students.

	Model 1 (n=208)				Model 2 (n=183)
	B (SE)	β	95% CI		B (SE)	β	95% CI
			Lower	Upper			Lower	Upper
Constant	0.847 (0.142)		0.567	1.128	0.977 (0.576)		−0.16	2.115
Aerobic LTPA Guidelines	−0.098 (0.140)	−0.053	−0.373	0.178	−0.043 (0.151)	−0.023	−0.342	0.255
Aerobic AT Guidelines	−0.144 (0.109)	−0.093	−0.360	0.072	−0.142 (0.118)	−0.092	−0.375	0.092
MS Guidelines	0.153 (0.452)	0.103	−0.738	1.043	−0.311 (0.556)	−0.212	−1.409	0.787
PA Guide	−0.226 (0.459)	−0.153	−1.131	0.678	0.202 (0.564)	0.138	−0.912	1.316
Sexual Orientation					0.151 (0.163)	0.072	−0.170	0.473
Race/ethnicity					0.011 (0.037)	0.023	−0.061	0.084
Semester Standing					−0.043 (0.045)	−0.073	−0.132	0.046
GPA					−0.051 (0.150)	−0.027	−0.346	0.244
R2 = 0.018	R2 = 0.026 ΔR2 = 0.008

Note. p-value:

^*≤0.05,

^**≤0.01,

^***≤0.001;

SE = standard error; CI = confidence interval.

Linear regressions examined the potential associations of various physical fitness outcomes on CMRFs by biological sex. In a model without adjustments of covariates (model 1), males (F(5,358)=7.725, p<0.001) demonstrated a significant association between VO₂ max (β= −0.230, p<0.001), pushups (β= −0.138, p=0.039), and hand grip (β= +0.161, p=0.003) on CMRFs; while, females ((F(5,202)=6.029, p<0.001) had a significant association of VO₂ max (β= −0.156, p<0.001), curl ups (β= −0.146, p=0.040), and hand grip (β= +0.196, p=0.005) on CMRFs (Table 4).

Table 4.

Linear regression model estimating the associations of physical fitness outcomes on metabolic syndrome risk factors among US male college students.

	Model 1 (n=358)				Model 2 (n=325)
	B (SE)	β	95% CI		B (SE)	β	95% CI
			Lower	Upper			Lower	Upper
Constant	2.437 (0.495)		1.463	3.411	2.391 (0.678)		1.056	3.726
VO2 max	−0.038 (0.009)	−0.230***	−0.055	−0.020	−0.031 (0.009)	−0.188***	−0.049	−0.013
Push ups	−0.009 (0.005)	−0.138*	−0.018	0.000	−0.011 (0.005)	−0.166*	−0.021	−0.002
Curl Ups	−0.002 (0.005)	−0.021	−0.011	0.008	−0.005 (0.005)	−0.061	−0.014	0.005
Sit and Reach	0.007 (0.005)	0.070	−0.003	0.017	0.008 (0.005)	0.083	−0.002	0.019
Hand Grip	0.007 (0.003)	0.161**	0.003	0.012	0.008 (0.005)	0.164**	0.002	0.013
Sexual Orientation					0.409 (0.210)	0.104	−0.004	0.821
Race/ethnicity					−0.035 (0.032)	−0.059	−0.099	0.029
Semester Standing					−0.014 (0.045)	−0.017	−0.103	0.074
GPA					−0.132 (0.120)	−0.060	−0.369	0.105
R2 = 0.099	R2 = 0.122 ΔR2 = 0.023

Note. p-value:

^*≤0.05,

^**≤0.01,

^***≤0.001;

SE = standard error; CI = confidence interval.

In a model with adjustments of covariates, males ((F(9, 325)=4.878, p<0.001) demonstrated a significant association between VO₂ max (β= −0.188, p<0.001), pushups (β= −0.166, p=0.017), and hand grips (β= +0.164, p=0.005) on CMRFs, with no covariates significantly predicting CMRFs. Among females ((F(9, 177)=3.124, p=0.002), there was a significant association of pushups (β= −0.187, p=0.022) and hand grip (β= +0.178, p=0.017), with no covariates significantly predicting CMRFs (Table 5).

Table 5.

linear regression model estimating the associations of physical fitness outcomes on metabolic syndrome risk factors among US female college students.

	Model 1 (n=203)				Model 2 (n=177)
	B (SE)	β	95% CI		B (SE)	β	95% CI
			Lower	Upper			Lower	Upper
Constant	1.580		0.713	2.447	1.663 (0.679)		0.322	3.004
VO2 max	−0.019 (0.008)	−0.156*	−0.036	−0.002	−0.015 (0.009)	−0.132	−0.033	0.002
Push ups	−0.009 (0.005)	−0.145	−0.018	0.000	−0.011 (0.005)	−0.187*	−0.021	−0.002
Curl Ups	−0.009 (0.004)	−0.146*	−0.018	0.000	−0.006 (0.005)	−0.095	−0.016	0.004
Sit and Reach	−0.009 (0.006)	−0.103	−0.021	0.003	−0.011 (0.007)	−0.120	−0.024	0.003
Hand Grip	0.012 (0.004)	0.196**	0.004	0.021	0.011 (0.005)	0.178*	0.002	0.02
Sexual Orientation					0.183 (0.157)	0.085	−0.126	0.493
Race/ethnicity					−0.011 (0.035)	−0.023	−0.080	0.058
Semester Standing					−0.02 (0.043)	−0.035	−0.105	0.065
GPA					−0.069 (0.132)	−0.039	−0.329	0.192
R2 = 0.133	R2 = 0.144 ΔR2 = 0.011

Note. p-value:

^*≤0.05,

^**≤0.01,

^***≤0.001;

SE = standard error; CI = confidence interval.

Table 1.

Participant descriptives.

	Total (n=1418)		Male (n=979, 69.0%)		Female (n=439, 31.0%)
	n	%	n	%	n	%
Sexual Orientation
Heterosexual	1217	89.8	861	87.9	356	81.1
Prefer not to disclose	89	6.3	56	5.7	33	7.6
Bisexual	56	4.1	28	2.9	28	6.4
Gay	17	1.2	16	1.6	1	0.2
Lesbian	8	0.6	0	0	4	0.9
Queer	8	0.6	3	0.3	5	1.1
Questioning/unsure	7	0.5	4	0.4	3	0.7
Pansexual	6	0.4	4	0.4	2	0.5
Asexual	6	0.4	1	0.1	5	1.1
Multiple	3	0.2	2	0.1	1	0.2
Other	1	0.1	0	0	1	0.2
Race/ethnicity
Non-Hispanic White	908	65.9	615	62.8	293	66.7
Asian American	140	10.2	106	10.8	34	7.7
Other	128	9.3	94	9.6	34	7.7
Hispanic/Latino/a/x	96	7.0	67	6.8	29	6.6
Multiracial	62	4.5	40	4.1	22	5.0
Non-Hispanic Black	43	3.1	29	3.0	14	3.2
Prefer not to disclose	43	3.1	28	2.9	13	3.0
Class Standing
4th year	480	34.8	339	34.6	141	32.1
3rd year	329	23.8	233	23.8	96	21.9
2nd year	247	17.9	173	17.7	74	16.9
1st year	195	14.1	120	12.3	75	17.1
5th year and graduate studies	129	9.3	88	9.0	41	9.3
Prefer not to disclose	40	2.8	26	2.7	12	2.7

Discussion

This study simultaneously assessed the potential association of physical fitness outcomes and domain-specific PA levels on CMRFs among a sample of US college students, a population that is often unaware of CMRFs. Additionally, this study sought to assess the role of race/ethnicity, sexual orientation, semester standing, and GPA on this association to determine potential influences by socio-demographic characteristics. The findings of the current study identified that meeting various domain-specific aerobic PA guidelines (e.g., LTPA, AT, Total), MS guidelines, and PA guidelines (aerobic and MS) were not significant predictors of the number of CMRFs among males or females, respectively. These findings are in contrast with previous research examining PA levels and CMRFs among young adults, with one study finding that accelerometry-measured LTPA was a significantly negative predictor of cardiometabolic dysregulation among obese young adults32, and another highlighting the dose-response relationship between meeting aerobic PA guidelines (self-report) and lower CMRFs.33 Research also found that young adults who remained physically active during their transition into college were less likely to be affected by CMRFs and poor fitness,34 further solidifying the importance of promoting healthy lifestyle behaviors among this population.

In comparison, various physical fitness outcomes were significantly associated with CMRFs among males and females, respectively. Among males, in models with and without covariates (e.g., sexual orientation, race/ethnicity, semester standing, GPA), VO₂ max and pushups demonstrated a significant negative association with the number of CMRFs in both models, while hand grip demonstrated a significant positive association with the number of CMRFs. Among females, the model without covariates demonstrated a significant negative association of VO₂ max and curl ups on the number of CMRFs, while hand grip demonstrated a significant positive association with the number of CMRFs. In the model with covariates, pushups demonstrated a significant negative association on number of CMRFs, while hand grip demonstrated a significant positive association with the number of CMRFs. Our findings are in line with previous work that found young, college-age adults with below average VO₂ max outputs (via Rockport walk test), compared to those with average or above-average outputs, are at an increased risk of metabolic syndrome.35 Similarly, one study found that higher hand grip strength values were predictive of lower CMRF profiles in males only,36 while the current study confirmed this in both males and females.

Research assessing demographic differences in CMRFs among young adults remains limited, with these characteristics often serving as confounders that are not discussed. Our findings did not demonstrate any significant findings related to race/ethnicity, sexual orientation, semester standing, and GPA; however, recent findings from a study assessing clustered CMRFs among obese young adults found a higher prevalence of cardiometabolic dysregulation among non-White vs White students.32 These findings are similar to national findings among US adults.15 The current findings are in contrast to previous research that examined cardiovascular biomarkers among young adults by sexual orientation, which demonstrated that sexual minority men (e.g., gay and bisexual) were at an increased risk compared to their heterosexual peers.37 However, the mean age for this cohort was 28.9, which is older than the mean age of 21 in the current study and assessed the role of PA and physical fitness outcomes on CMRFs, not only CMRF prevalence. NHANES data has also identified that cardiovascular health disparities exist by sexual orientation alone,38 and at the intersection of race/ethnicity, sex, and sexual orientation,39 highlighting the complex interactions of these characteristics on health. However, NHANES does not assess physical fitness levels, limiting the ability to explore the association. Albeit research examining cardiovascular health at these demographic intersections among young adults remains vastly understudied, limiting our ability to compare the current study. Our findings, although insignificant, support the literature by providing foundational knowledge in this domain of research to promote health equity.

Regarding practical implications based on these findings, it remains essential to screen for CMRFs among college students to reduce later-life chronic condition risk. Convenient measures of blood pressure and waist circumference provide low-cost approaches to screening for CMRFs, which could be implemented across college campuses. Screening may reduce the likelihood of college students progressing into more severe chronic conditions in later-life, with those most at risk benefiting. Although our findings demonstrate PA was not associated with CMRFs, it still remains paramount to promote PA to improve physical fitness outcomes, which have demonstrated a significant association. Universities should require participation in general health and wellness courses, as required of this study population, to promote knowledge, skills, and abilities in PA participation to promote engagement across the lifespan. As intervention efforts targeting PA, diet, and weight loss among college students remain lackluster,9,10 this requirement may prove beneficial. Universities should also consider reducing barriers to financial and/or environmental access by including campus recreation memberships in student fees, or improving AT (i.e., walking, biking) on campus and/or surrounding areas.40 By improving the built-environment to make it conducive for AT, overall PA rates will increase out by ease of AT accessibility, which can lead to improved physical fitness levels and reduced CMRFs. College campuses are positioned to support healthy habit forming among attendees, but policies and practices are needed to support health and well-being among this group.

Although this study provides valuable insight into the role of physical fitness and activity levels on CMRFs among US college students with added benefits of included socio-demographic characteristics, there are limitations to this work. Firstly, participants were recruited from general health and wellness courses which may skew physical fitness and activity levels. Further, our sample was mostly non-Hispanic White and male which may reduce generalizability, but the inclusion of race/ethnicity and sexual orientation provide novel findings into the role of various socio-demographic characteristics. Secondly, our PA levels were self-reported, which is often an overestimate. Future research should incorporate diverse, robust measures from young adult subpopulations to improve our understanding of how various socio-demographic characteristics influence physical activity and fitness associated with CMRFs.

In summary, cardiorespiratory fitness and markers of muscular strength and endurance were significantly associated with CMRFs; while PA levels were not. The included socio-demographic characteristics of race/ethnicity, sexual orientation, semester standing, and GPA were not significant with CMRFs in any models, but did significantly influence certain physical fitness outcomes among females. Targeted screenings, physical fitness interventions, and policy modifications are needed to reduce CMRF prevalence among college students, ultimately reducing their likelihood of developing metabolic syndrome and CVD later in life. Future research should incorporate measures of socio-demographic characteristics as covariates rather than confounders to improve our understandings of their potential role in physical fitness, PA levels, and CMRFs.