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. Author manuscript; available in PMC: 2025 Oct 1.
Published in final edited form as: Stress Health. 2024 May 15;40(5):e3416. doi: 10.1002/smi.3416

The Longitudinal Mediating Role of Sleep in Associations between COVID-19 Stressors Predicting Mental and Physical Health Outcomes among Emerging Adult College Students

Chelsea D Williams 1, Sneha Gade 1, Kaprea Johnson 2, Roseann E Peterson 1, Oswaldo Moreno 1, Kristina B Hood 1, Arlenis Santana 1, Jasmin Vassileva 1,*, Danielle M Dick 3,*, Ananda B Amstadter 1,*, Karen G Chartier 1, Diamond Y Bravo 4
PMCID: PMC11469949  NIHMSID: NIHMS1992491  PMID: 38748463

Abstract

The current study tested a longitudinal mediation model throughout the COVID-19 pandemic focused on whether students’ housing instability stress and food/financial instability stress at the beginning of the pandemic in spring 2020 (T1) informed sleep dissatisfaction and duration in fall 2020 (T2) and, in turn, physical and mental health in spring 2021 (T3). Further, we tested whether relations varied based on students’ ethnic-racial backgrounds. Participants included 879 Asian, Black, Latine, Multiracial, and White emerging adult college students (Mage = 19.95, SD = .33) from a large public university in the mid-Atlantic region of the United States who attended college during the COVID-19 pandemic and completed surveys about their experiences. Findings indicated a significant mediation process, such that T1 housing instability stress predicted greater T2 sleep dissatisfaction and, in turn, less physical health, greater depressive symptoms, and greater anxiety symptoms at T3. Additionally, T1 food/financial instability stress was significantly associated with less T2 sleep duration but was not, in turn, associated with any T3 outcomes. Findings did not vary by students’ ethnicity/race. Results highlight that sleep dissatisfaction is an important factor that accounts for relations between COVID-19 stressors predicting mental and physical health outcomes throughout the pandemic.

Keywords: Housing Instability Stress, Food/Financial Instability Stress, Sleep, Depression/Depressive Symptoms, Anxiety/Anxiety Symptoms, Physical Health

The COVID-19 pandemic increased stressors for college students, resulting in short- and long-term consequences for their physical and mental health. Specifically, emerging adult college students (i.e., individuals between the ages of 18–25 who were attending college) were impacted by financial, academic, and other social determinant stressors, which contributed to adverse physical and mental health symptoms (Johnson et al., 2022). Importantly, several studies have identified the pandemic as a distinctive event that exacerbated sleep concerns among college students (Benham, 2021; Deng et al., 2021; Ulrich et al., 2021). However, gaps remain in understanding sleep duration and sleep dissatisfaction as potential mediators between COVID-19-induced stressors and physical and mental health.

To address this, the present study used a longitudinal approach to examine the impact of COVID-19 stressors (i.e., housing instability stress and food/financial instability stress) at the beginning of the pandemic in spring 2020 on physical and mental health in spring 2021 (T3) via sleep dissatisfaction and sleep duration in fall 2020 (T2). Further, we tested whether relations varied based on students’ ethnic-racial backgrounds. Given that no prior work has tested this full mediation process, we present previous empirical support for each part of the process, including (a) the direct effects of COVID-19 stressors as predictors of physical and mental health, (b) associations between COVID-19 stressors and sleep, (c) sleep predicting mental and physical health during COVID-19, and (d) ethnic-racial differences.

The Direct Effects of COVID-19 Stressors as Predictors of Physical and Mental Health

College students are often at high risk for stress due to the increased societal pressure to succeed, coupled with the challenges of adjusting to new environments and responsibilities at college (Pascoe et al., 2020). These challenges include managing financial, housing insecurity, and food responsibilities, coping with academic demands, homesickness, navigating healthcare, and other issues (Broton, 2020; El Zein, et al., 2019; Heckman et al., 2014). During the pandemic, emerging adult college students were at risk for experiencing elevated stress and declining mental health (Halliburton et al., 2021) as well as reductions in physical health (LaCaille et al., 2021).

A significant majority (71%) of college students reported that their stress and symptoms of anxiety worsened during the pandemic (Son et al., 2020). These mental health concerns have been linked to long-term diseases like diabetes, heart disease, and stroke (CDC, 2021). The pandemic has further affected the physical health of college students, making them more vulnerable to weight gain and subsequently increasing their risk of chronic diseases, mortality, and other physical ailments (Monroe et al., 2017). Moreover, students from racial and ethnic minority groups report experiencing higher rates of poor health and disease, likely worsened by the pandemic (Centers for Disease Control and Prevention, 2021). Understanding the impact of the COVID-19 pandemic on college students’ mental and physical health, and potential differences in these associations by ethnic-racial background, is essential to support the overall well-being of this population during this critical time in development.

Associations between COVID-19 Stressors and Sleep

Sleep is crucial to an individual’s overall health as it improves brain performance, mood, and physical fitness (NIH, 2021). Additionally, individuals who do not receive enough sleep are at risk for many diseases and disorders (e.g., heart disease, stroke, obesity, and dementia; NIH, 2021). The COVID-19 pandemic resulted in significant changes in sleep patterns, particularly during lockdown. Studies examining differences in sleep during the pandemic found that individuals reported worsened sleep quality, increased sleep latency and insomnia symptoms, and delayed bedtime and wake-time in students (Benham, 2021; Marelli et al., 2020). Several factors related to the COVID-19 pandemic could influence sleep quality and duration, such as housing instability stress and food/financial instability stress.

Housing instability stress.

Housing instability stress involves an individual worrying about paying rent or mortgage, living in unsafe conditions, not having a secure place to live, or having to move due to an inability to pay (Liu et al., 2014). There are various studies focused on housing instability and its influence on sleep. For example, a study with adults found that the prevalence of insufficient sleep was significant among those who reported housing insecurity, compared to those who had secure housing (Liu et al., 2014). However, most of these articles focused on adults overall, rather than college students specifically (Bozick et al., 2021; Liu et al., 2014).

Housing instability was a pre-pandemic stressor among college students, and the issue was exacerbated after the start of COVID-19. Results of a review on the prevalence of housing instability among college students estimates that 45% of students experienced some form of housing instability before the start of the pandemic (Broton, 2020). During the pandemic, students dealt with challenges such as on-campus housing closings, loss of jobs, and the inability to quarantine safely (Soria et al., 2020). Safe and quality housing is essential for the success and well-being of college students, especially in terms of sleep. Several studies found that both food and housing insecurity increased for students during the COVID-19 pandemic (Glantsman et al., 2022; Jones et al., 2021). In a study with university students, homelessness was associated with lower well-being and fewer hours of sleep (Haskett et al., 2020). The majority of this work has been cross-sectional, and limited studies have focused on emerging adult college students’ housing instability and sleep in the context of the COVID-19 pandemic.

Food/financial instability stress.

Financial instability captures challenges in having enough income or resources to meet financial obligations and life expenses, such as food. In a study with college students, researchers found that the COVID-19 pandemic had a negative impact on individuals’ personal financial situation, including job loss, no external financial support, and worry about personal finances (Aristovnik et al., 2020). In another study, researchers found that 25% of the college student participants reported more stress in their financial situation, compared to before the COVID-19 pandemic (Guadiana & Okashima, 2021). Financial demands often contribute to stress, which may cause a reduction in sleep quality, increased daytime sleepiness, increased sleep disturbances, and increased insomnia (Guadiana & Okashima, 2021; Peltz et al., 2020). Existing research suggests that food/financial instability informs sleep issues. The current study builds on work in this area by examining these relations longitudinally with two different forms of sleep (i.e., duration and dissatisfaction) during the pandemic.

Sleep Predicting Mental and Physical Health during COVID-19

Sleep is significant for emerging adult college students’ mental and physical health outcomes, especially during the COVID-19 pandemic. Sleep quality and duration have been identified as crucial factors in determining mental well-being, with insufficient sleep being associated with higher levels of depression, anxiety, and stress (e.g., Rasch & Born, 2013). Additionally, emerging adult college students face unique challenges and transitions, making sleep even more essential for their overall well-being. Similarly, sleep is critical in predicting physical health and well-being among this population (Lund et al., 2010), as it impacts factors such as exercise frequency, class attendance, and overall physical illness. However, research is scarce on the impact of sleep issues on college students’ mental and physical health across the pandemic.

Sleep and mental health.

Research has demonstrated significant and positive associations between sleep disturbances and worsened mental health among college student populations (e.g., Kahn et al., 2013; Rasch & Born, 2013). Since the onset of the COVID-19 pandemic, the breadth of sleep research exponentially grew, as sleep concerns increased as a result of the many COVID-19-related stressors (e.g., Becerra et al., 2022; Cunningham et al., 2021, Ingram et al., 2020, Liu et al., 2020; Son et al., 2020). For example, in a cross-sectional study with college students from federally designated Hispanic- and minority-serving four-year public institutions, pandemic-related poor sleep health (e.g., sleeping less than seven hours) was associated with daytime tiredness/fatigue/sleepiness among racial/ethnic minoritized students, which was, in turn, associated with psychological distress (Becerra and colleagues, 2022). Liu and colleagues (2020) also found evidence of poorer sleep among young adults with a history of mental health diagnosis or suspected mental health concerns during the COVID-19 pandemic. Longitudinal studies can shed light on the impact of sleep on mental health among diverse emerging adult college students over time.

Sleep and physical health.

The existing literature on sleep and physical health spans different developmental periods. In a cross-sectional study with adolescents, Tambalis et al. (2018) found that poor sleep quality and short sleep duration were associated with poor diet quality and obesity. Chaput (2016) reported that insufficient sleep among children led to weight gain. Studies involving older adults, such as Dao-Tran and Seib (2017) and Chiu et al. (1999), found connections between sleep disturbances and negative physical health outcomes. These findings demonstrate the importance of sleep quality for physical health across the lifespan.

Focusing on the emerging adult college student population, Lund et al. (2010) established a relation between sleep quality and physical health. This connection is further supported by Bowman et al. (2018), who found that better sleep health was associated with lower odds of obesity. Lund et al. (2010) also conducted a cross-sectional online survey with college students, finding that sleep duration and quality had direct and indirect (through mood and mental state) impacts on physical health, as evidenced by exercise frequency and class attendance affected by physical illness. Despite these studies, there is a scarcity of research on the impact of sleep on physical health during the COVID-19 pandemic, particularly among emerging adult college students.

Ethnic-Racial Differences

Further, there may be ethnic-racial differences in these relations over time that have been largely unexplored. COVID-19 occurred alongside exacerbated racism for people of color, particularly among Black and Asian communities, often referred to as the double pandemic (Starks, 2021). In the early months of COVID-19 spreading, racist remarks that referred to COVID-19 as “the Chinese virus” drastically increased anti-Asian hate, while the murder of George Floyd further highlighted police brutality and anti-Black hate (Starks, 2021). Race-related disparities among people of color, especially Black Americans, existed long before the pandemic due to systemic issues and racism entrenched in the policies and structures in the U.S., but many disparities became worse, such as those pertaining to stress, sleep, and health outcomes.

Prior work that found racial differences in COVID-19-related stressors indicated that Black individuals had the highest levels of stress, followed by Asian and Latine individuals, while White individuals had the lowest levels of stress (Taylor et al., 2020). Regarding sleep during the pandemic, Black emerging adults had the lowest levels of sleep duration and sleep quality compared to American Indian/Alaskan Native, Asian, Black, Latine, and White emerging adults (Yip et al., 2021). People of color also experienced worse mental health declines (Thomeer et al., 2023) and worse physical health (e.g., higher rates of infection and death) than White individuals during the pandemic (Lopez et al., 2021). Collectively, these findings suggest there may be important race-related differences in how COVID-19 stress informs sleep and, in turn, health outcomes among emerging adult college students.

The Current Study

The current study tested a longitudinal mediation model focused on whether students’ housing instability stress and food/financial instability stress at the beginning of the pandemic in spring 2020 (T1) informed sleep dissatisfaction and sleep duration in fall 2020 (T2) and, in turn, physical and mental health in spring 2021 (T3). We also tested whether relations varied based on students’ ethnic-racial backgrounds (i.e., differences among Asian, Black, Latine, Multiracial, and White individuals). We hypothesized that COVID-19 stressors would increase sleep dissatisfaction and lower sleep duration, which would, in turn, be associated with greater anxiety and depressive symptoms and worsened physical health. Given that no prior work has examined ethnic-racial differences in a longitudinal mediation model of stress informing health via sleep during the pandemic, we did not make specific hypotheses regarding expected ethnic-racial group differences, but rather this part of our study was exploratory.

Methods

Participants and Procedure

Data were collected as a part of a multi-cohort, longitudinal study called Spit for Science (Dick et al., 2014). Participants for the current study included 879 Asian (23.2%), Black (18.7%), Latine (9.8%), Multiracial (7.7%), and White (40.6%) emerging adult college students 18 to 25 years of age (M age = 19.95, SD = 0.33, range = 19.27– 21.87) from a large public university in the mid-Atlantic region of the U.S. who attended college before and during the COVID-19 pandemic, and completed surveys about their experiences. The 5 ethnic-racial groups that from the larger study included enough participants to test for ethnic-racial differences. The cohort utilized for this study began participating in 2017, and although the larger study included more cohorts, the current study is a subset of students who completed surveys about their pandemic experiences.

Each year, students were invited to complete a survey. In the current study, we describe Time 1 (T1) as the survey completed during the beginning of the pandemic in spring of 2020, T2 as the survey completed in the fall of 2020, and T3 as the survey completed in the spring of 2021. Students who chose to participate completed an online survey at each time point and were compensated $10 for their time completing each survey. Data were collected and managed through REDCap (Research Electronic Data Capture; Harris et al., 2009). The Institutional Review Board approved all study measures and procedures.

Measures

Due to the large-scale nature of the title blinded for review study, measures were shortened to reduce participant burden. Item response theory model fitting was used after the first survey was administered to select items to include in subsequent surveys, including the time points of data used in the current study (Overstreet et al., 2017).

T1 housing instability stress.

A single item from the Coronavirus Health Impact Survey (CRISIS; Nikolaidis et al., 2021) measured participants’ stability of living situation (i.e., “To what degree are you concerned about the stability of your living situation?”). Response options were on a 5-point rating scale from 1 = Not at all to 5 = Extremely. Higher scores indicated higher stress in relation to the instability of their living situation.

T1 food/financial instability stress.

A single item from the CRISIS (Nikolaidis et al., 2021) measured participants’ food/financial instability stress (i.e., “Do you worry whether your food will run out because of a lack of money?”). Response options were on a 2-point that was coded 0 = No, 1= Yes. Higher scores indicated higher food/financial instability.

T2 sleep duration.

A single item from the Coronavirus Health Impact Survey (CRISIS; Nikolaidis et al., 2021) measured participants’ sleep hours (i.e., “How many hours per night do you sleep on average?”). Response options were on a 6-point rating scale from 1 = < 5 hours to 5 = 10+ hours. A higher score indicated a higher number of hours of sleep.

T2 sleep dissatisfaction.

A single item from the Coronavirus Health Impact Survey (CRISIS; Nikolaidis et al., 2021) measured participants’ sleep dissatisfaction (i.e., “How satisfied or dissatisfied have you been with your sleep patterns?”). Response options were on a 5-point rating scale from 1 = very satisfied to 5 = very dissatisfied. A higher score indicated greater sleep dissatisfaction.

T3 anxiety symptoms.

Four subset items from the anxiety subscale from the Symptoms Checklist-90 (SCL-90; Derogatis et al., 1973) were used to measure participants’ anxiety symptoms. Participants were asked to indicate how often they had anxiety symptoms (e.g., “spells of terror or panic”) within the last 30 days. Response options were on a 5-point rating scale from 0 = not at all to 4 = extremely. The mean of items was computed, and higher scores indicated greater anxiety symptoms. Internal reliability for anxiety symptoms was .90.

T3 depressive symptoms.

Four subset items from the depression subscale from the Symptoms Checklist-90 (SCL-90; Derogatis et al., 1973) were used to measure participants’ depressive symptoms. Participants were asked to indicate how often they had depressive symptoms (e.g., “feeling blue”) within the last 30 days. Response options were on a 5-point rating scale from 0 = not at all to 4 = extremely. The mean of items was computed, and higher scores indicated greater depressive symptoms. Internal reliability for depressive symptoms was .91.

T3 physical health.

A single item from the Coronavirus Health Impact Survey (CRISIS; Nikolaidis et al., 2021) measured participants’ physical health (i.e., “How would you rate your overall physical health?”). Response options were on a 5-point rating scale from 1 = poor to 5 = excellent. A higher score indicated better physical health.

Analytic Approach

To test research questions in the current study, we used path analyses in Mplus version 8.4 (Muthén & Muthén, 2019). The estimator that was used for analyses was maximum likelihood (ML). Full information maximum likelihood was used to handle missing data (Arbuckle, 1996). There was 1–3% missing data at T1, 31% at T2, and 60–61% missing at W3 on variables included in the current study. Three fit indices were used to examine model fit: the comparative fit index (CFI), the root-mean-square-error of approximation (RMSEA), and the standardized root-mean-square residual (SRMR). Model fit was considered good if the CFI was greater than or equal to .95, the RMSEA was less than or equal to .05, and the SRMR was less than or equal to .05 (Hu & Bentler, 1999).

We tested for differences by ethnicity/race by using nested models, in which a less constrained model (e.g., no paths were constrained based on ethnicity/race) was compared to a more constrained model (e.g., a fully constrained model in which all paths were constrained to be equal based on ethnicity/race). The grouping variable was ethnicity/race, which compared 5 groups: Asian, Black, Latine, Multiracial, and White individuals. A chi-square difference test was used. A significant chi-square test indicates significant ethnic-racial differences in relations in the model, and the process is repeated to determine which paths are significantly different based on ethnicity/race. If the chi-square test is not significant, it suggests no significant ethnic-racial differences are detected, and the fully constrained model is accepted as the final model.

We used the RMediation web application (Tofighi & MacKinnon, 2011) to formally test mediation, in which confidence intervals are computed for mediated effects. Mediation is statistically significant if the confidence interval does not contain zero.

Results

First, we examined the correlations, means, and standard deviations for study variables (Table 1). Then, to test research questions, we specified a model of the associations between T1 housing instability stress and T1 food/financial instability stress predicting T3 depressive symptoms, anxiety symptoms, and physical health mediated by T2 sleep duration and T2 sleep dissatisfaction. Further, we included students’ age as a covariate in all analyses predicting outcomes. Next, we compared the unconstrained model [χ2 (df = 10) = 12.39] to the fully constrained model [χ2 (df = 86) = 101.72] to test for ethnic-racial group differences. The chi-square difference test was not statistically significant [Δχ2 (Δdf = 76) = 89.33, p = .14], indicating that there were no significant ethnic-racial differences in relations. Thus, the fully constrained model was accepted as the final model, which had a good fit: χ2 (df = 86) = 101.72, p = .12; RMSEA = 0.03 [90% C.I. = 0.00 – 0.06]; CFI = .97, SRMR = .13. Unstandardized coefficients are presented below, and standardized coefficients are presented in Figure 1.

Table 1.

Correlations, means, and standard deviations for variables among Asian American, Black American, Latine, Multiracial, and White Emerging Adult College Students (N = 879)

1 2 3 4 5 6 7 8
1. Age --
2. T1 Housing Instability Stress .02 --
3. T1 Food/Fin Instability Stress −.01 .44*** --
4. T2 Sleep Duration −.05 −.05 −.12*** --
5. T2 Sleep Dissatisfaction .07* .14*** .02 −.32*** --
6. T3 Physical Health .01 −.13*** −.07* .03 −.19*** --
7. T3 Depressive Symptoms .04 .16*** .07* −.09** .25*** −.31*** --
8. T3 Anxiety Symptoms .02 .12*** .09** −.11** .21*** −.26*** .76*** --
Mean 19.95 1.99 .17 3.33 3.32 3.53 1.60 .91
SD .33 1.18 .38 1.08 1.06 .97 1.20 1.05
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Note. Fin = Financial. T = Time. T1 was spring semester 2020 (first semester of the pandemic), T2 was fall semester 2020, and T3 was spring semester 2021.

*

p < .05.

**

p < .01.

***

p < .001.

Figure 1. Final constrained mediation model of T1 (Time 1) housing and food instability predicting T3 physical and mental health via T2 sleep duration and sleep dissatisfaction.

Figure 1

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Note. Coefficients are standardized. Coefficients are constrained to be equal across Black, White, Asian, Multiracial, and Latine emerging adult college students. Significant paths are bolded. Fin = Financial. Age was included as a covariate predicting the outcomes but is not displayed for ease of illustration. * p < .05. ** p < .01. *** p < .001.

T1 Housing Instability Stress Predicting T3 Outcomes via T2 Sleep

Findings indicated that T1 housing instability stress predicted greater T2 sleep dissatisfaction (b = .15, p = .00) and, in turn, less T3 physical health (b = −.15, p = .00), greater T3 depressive symptoms (b =.25, p = .00) and greater T3 anxiety symptoms (b = .17, p = .00). Mediation analyses indicated that T2 sleep dissatisfaction was a significant mediator of the relation between T1 housing instability stress and T3 physical health because the confidence interval did not contain zero (95% CI = −.04, −.01). Further, T2 sleep dissatisfaction was a significant mediator of the relation between T1 housing instability stress and T3 depressive symptoms because the confidence interval did not contain zero (95% CI = .01, .07). T2 sleep dissatisfaction was also a significant mediator of the relation between T1 housing instability stress and T3 anxiety symptoms because the confidence interval did not contain zero (95% CI = .01, .05). Regarding direct effect paths, T1 housing instability stress was only significantly associated with greater T3 depressive symptoms (b = .16, p = .01) but did not predict T3 physical health (b = −.08, p = .11) or T3 anxiety symptoms (b = .08, p = .15).

T1 Food/Financial Instability Stress Predicting T3 Outcomes via T2 Sleep

T1 food/financial instability stress was significantly associated with less T2 sleep duration (b = −.28, p = .04); however, T2 sleep duration was not significantly associated with T3 physical health (b = −.01, p = .79), T3 depressive symptoms (b = −.05, p = .40), or T3 anxiety symptoms (b = −.06, p = .35). Regarding direct effects, T1 food/financial instability stress did not predict T3 physical health (b = −.09, p = .57), T3 depressive symptoms (b = .14, p = .49), or T3 anxiety symptoms (b = .24, p = .19).

In terms of controls, individuals’ age was not significantly associated with T3 physical health (b = .10, p = .58), T3 depressive symptoms (b = .04, p = .87), or T3 anxiety symptoms (b = −.04, p = .84).

Discussion

The present study focused on the impact of stressors on sleep, and their effects on college students’ mental and physical health, while also testing for ethnic-racial differences. Housing and financial insecurity impact sleep (Bozick et al., 2021; Liu et al., 2014) and college students had higher rates of food and housing instability, which has been further exacerbated during the pandemic (Broton, 2020; Glantsman et al., 2022). Additionally, sleep has been associated with physical and mental health (Reid et al., 2006; João et al., 2018). However, most previous work consisted of cross-sectional designs and did not account for the longitudinal impact of various stressors on overall health (João et al., 2018; Dinis & Bragança, 2018; Lund et al., 2010). The current study expands the literature by examining the longitudinal associations between the impact of stressors on sleep and, in turn, physical and mental health over time among a diverse sample of emerging adult college students.

We hypothesized that housing instability stress would impact sleep dissatisfaction which would, in turn, impact depressive symptoms, anxiety symptoms, and physical health. Results supported this hypothesis. These findings are consistent with past literature, which found associations between students having difficulty finding stable housing and their ability to have satisfying sleep, as well as between housing instability and poorer mental and physical health (Bozick et al., 2021). Although previous studies found evidence to support these associations in separate studies cross-sectionally (Bozick et al., 2021; Liu et al., 2014), few studies have found these associations longitudinally in a sample of emerging adult college students. Our study was also conducted during the COVID-19 pandemic, which is an area that needs more attention to enable a better understanding of how it has impacted individuals’ sleep and health.

Despite studies (e.g., Guadiana & Okashima, 2021; Peltz et al., 2020) demonstrating the relation between food insecurity and sleep outcomes, as well as food insecurity and physical and mental health outcomes, the current study did not find associations between food/financial insecurity, sleep, and health outcomes. There was a significant association between food/financial insecurity and sleep duration but sleep duration was not related to physical or mental health outcomes. These results could be due to the question wording (i.e., “Do you worry whether your food will run out because of a lack of money?”), which may have left more room for interpretation. Additionally, during the pandemic some students may have been living at home with their families where they could access food, even if they did not feel financially stable. Future research with more detailed assessments of financial instability that include questions about food security, as well as other questions that indicate financial hardship unrelated to food (e.g., worry about not having enough money to cover expenses) would provide a comprehensive view of the impact of food/financial instability on sleep.

Although some previous work has found that sleep duration is related to mental and physical health in student populations (Guadiana & Okashima, 2021; Peltz et al., 2020) other work has found that other sleep indices were better predictors of mental and physical health concerns during the pandemic (Benham, 2020). Researchers purport that during lockdown, the length of time that people were sleeping did not differ significantly from pre-pandemic but sleep disturbances, quality, latency, daytime dysfunction, and use of sleep medication increased. The lack of changes in sleep duration could have influenced our findings. During the lockdown period, sleep duration may not be as sensitive as sleep satisfaction when focusing on the links between stressors, sleep, and health. Future work should include more sleep indices to investigate how stressors like housing, food, and financial insecurity influence sleep and health.

An exploratory aspect of our study was to test whether findings varied by emerging adult college students’ ethnic-racial backgrounds. Results indicated that the ways in which stressors impacted sleep and subsequent mental and physical health over time in the current study were not significantly different for Asian, Black, Latine, Multiracial, and White emerging adult college students. Although race-related disparities have been found for stress (Taylor et al., 2020), sleep (Yip et al., 2021), mental health (Thomeer et al., 2023), and physical health (Lopez et al., 2021), none of these prior studies nor any to our knowledge have examined ethnic-racial differences in how students’ stress informed sleep and, in turn, physical and mental health over time throughout the pandemic. Therefore, the design and research questions across this previous related work and our study were different, which may have contributed to the divergent findings.

Ethnic-racial differences may emerge with larger samples, and/or samples that include emerging adults who are not attending college. It is possible that pandemic experiences were more similar among students in our study given that they were all attending college when the pandemic began, masking potential race-related differences. Overall, given that disparities have been found for individuals of color compared to white individuals across stress, sleep, and health, it is important for future work to continue testing ethnic-racial differences in these associations throughout the pandemic to understand current and enduring future implications.

Limitations and Future Directions

The current study has several limitations that should be considered to enhance future research. First, the data collection method in this study involved several single-item measures (e.g., physical health). The reliance on single-item measures may compromise the reliability of the findings. Second, it was unclear whether students’ reports of food/financial instability and housing instability referred to only students or also to their families. Many students moved home with their families during the beginning of the pandemic when universities shut down, which could have affected their responses. To address both of these limitations, future research should consider utilizing multi-item scales, which would allow for a more precise examination of the variables under investigation.

Third, the measures were all reported by students, which could have introduced self-reporter bias. A consideration of a variety of measures (e.g., fitbit technology to measure sleep), will be an important future direction. Relatedly, beyond assessing sleep duration and dissatisfaction, the current study could have benefited from considering additional sleep quality indices, such as sleep onset latency, sleep efficiency, sleep fragmentation, and restorative sleep, to name a few. By evaluating multiple indices of sleep quality, researchers can obtain a more comprehensive understanding of sleep patterns and their relations with health outcomes. Expanding the scope of sleep quality assessment in future studies would provide a more nuanced view of the complex interplay between stress, sleep, and health factors.

Additionally, the study’s population was limited to emerging adults residing in the mid-Atlantic region of the United States who were attending college. The experiences of this group during the COVID-19 pandemic may not be generalizable to individuals in other regions of the country or emerging adults who were not attending college during the pandemic. Further, data were collected at one 4-year mid-Atlantic university, and findings may not be applicable to students attending different types of universities with varying access to resources and in different regions. To enhance the generalizability of the findings, future research should aim to engage a nationally representative sample that includes various age groups of students attending various types of colleges. This would help determine if the location, age group, and college were confounding variables, and ensure that the results can be applied more broadly to a diverse population.

Last, the current study specifically tested sleep duration and sleep dissatisfaction as potential mediators between COVID-19-induced stressors and physical and mental health during the beginning of the pandemic (i.e., spring 2020, fall 2020, and spring 2021). There are other models that also may be plausible in explaining emerging adults’ experiences, as well as differences that may emerge later in the pandemic. For example, mental and physical health may instead be mediators, rather than outcomes, and these relations may vary over time. Understanding other potential longitudinal processes that resulted from stressful COVID-19 experiences and whether experiences varied as the pandemic continued warrants future investigation.

Implications and Conclusions

In light of the study findings, several implications and conclusions can be drawn that may contribute to current policies and inform future research directions. Health is a dynamic attribute influenced by myriad factors and can be challenging to restore once deteriorated. Therefore, prevention of health issues is of paramount importance. Our research demonstrates a clear link between housing instability stress, sleep dissatisfaction, and mental and physical health outcomes. It is crucial to share these findings with stakeholders who can address these interconnected issues, such as researchers, clinicians, university administrators, and physicians.

Future research can build on our findings to further explore these relations. University resources should be devoted to assisting students in securing affordable housing throughout their academic career. Pre-pandemic research showed that 45% of students were experiencing housing insecurity and these numbers have increased since the start of the pandemic. The rising costs of basic needs like housing and food counselors, clinicians, and physicians should consider the established correlations when working with emerging adult college students. For instance, when university students seek mental health counseling, therapists, counselors, and licensed clinical social workers should inquire about, and provide resources for supporting the basic needs, as financial hardships can affect mental health over time. Additionally, university administrators should strive to bridge the gap between student housing availability, affordability, and concerns, ensuring a safe and comfortable environment for students. Nevertheless, our results can inform strategies to mitigate the longitudinal onset of various health problems by leveraging diverse platforms and fostering collaboration among stakeholders.

Acknowledgments

The Spit for Science Working Group: Director: Karen Chartier Co-Director: Ananda Amstadter. Past Founding Director: Danielle M. Dick. Registry management: Emily Lilley, Renolda Gelzinis, Anne Morris. Data cleaning and management: Katie Bountress, Amy E. Adkins, Nathaniel Thomas, Zoe Neale, Kimberly Pedersen, Thomas Bannard & Seung B. Cho. Data collection: Kimberly Pedersen, Amy E. Adkins, Peter Barr, Holly Byers, Erin C. Berenz, Erin Caraway, Seung B. Cho, James S. Clifford, Megan Cooke, Elizabeth Do, Alexis C. Edwards, Neeru Goyal, Laura M. Hack, Lisa J. Halberstadt, Sage Hawn, Sally Kuo, Emily Lasko, Jennifer Lend, Mackenzie Lind, Elizabeth Long, Alexandra Martelli, Jacquelyn L. Meyers, Kerry Mitchell, Ashlee Moore, Arden Moscati, Aashir Nasim, Zoe Neale, Jill Opalesky, Cassie Overstreet, A. Christian Pais, Tarah Raldiris, Jessica Salvatore, Jeanne Savage, Rebecca Smith, David Sosnowski, Jinni Su, Nathaniel Thomas, Chloe Walker, Marcie Walsh, Teresa Willoughby, Madison Woodroof & Jia Yan. Genotypic data processing and cleaning: Cuie Sun, Brandon Wormley, Brien Riley, Fazil Aliev, Roseann E. Peterson & Bradley T. Webb.

Spit for Science has been supported by Virginia Commonwealth University, P20 AA017828, R37AA011408, K02AA018755, P50 AA022537, and K01AA024152 from the National Institute on Alcohol Abuse and Alcoholism, and UL1RR031990 from the National Center for Research Resources and National Institutes of Health Roadmap for Medical Research. This research was also supported by the Center for the Study of Tobacco Products at Virginia Commonwealth University. The content is solely the responsibility of the authors and does not necessarily represent the views of the NIH or the FDA. Data from this study are available to qualified researchers via dbGaP (phs001754.v4.p2) or via spit4science@vcu.edu to qualified researchers who provide the appropriate signed data use agreement. We would like to thank Dr. Danielle Dick for founding and directing the Spit for Science Registry from 2011–2022, and the Spit for Science participants for making this study a success, as well as the many University faculty, students, and staff who contributed to the design and implementation of the project. The authors have no conflict of interest to report.

We would also like to acknowledge The Minoritized Ethnic and Racial Students’ Experiences Working Group: Active Members: Oswaldo Moreno (Chair) Chelsea D. Williams (Former Chair), Diamond Y. Bravo, Karen G. Chartier, Natese Dockery, Maria J. Elias, Lisa Fuentes, Isis Garcia-Rodriguez, Cindy Hernandez, Terrell A. Hicks, Kristina B. Hood, Kaprea F. Johnson, Jasmine Lewis, Tanya Middleton, Benjamin Montemayor, Geovani Muñoz, Rumbidzai Mushunje, Roseann E. Peterson, & Arlenis Santana; Former Members: Ashlynn Bell, Eryn N. DeLaney, Sneha Gade, Chaz Goodman, William Gordon, Sydney Judge, Diamond Reese, & Jennifer Rodriguez, & Chloe J. Walker.

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