PLoS ONE plos plosone PLOS ONE 1932-6203 Public Library of Science San Francisco, CA USA 10.1371/journal.pone.0245327 PONE-D-20-24157 Research Article Medicine and health sciencesMedical conditionsInfectious diseasesViral diseasesCOVID 19 Medicine and health sciencesEpidemiologyMedical risk factors Medicine and health sciencesEpidemiologyPandemics Medicine and health sciencesMental health and psychiatry Biology and life sciencesPsychologySocial psychology Social sciencesPsychologySocial psychology Biology and life sciencesPsychologyEmotionsAnxiety Social sciencesPsychologyEmotionsAnxiety Biology and life sciencesNeuroscienceCognitive scienceCognitive psychologyClinical psychology Biology and life sciencesPsychologyCognitive psychologyClinical psychology Social sciencesPsychologyCognitive psychologyClinical psychology Medicine and health sciencesHealth careSocioeconomic aspects of health Medicine and health sciencesPublic and occupational healthSocioeconomic aspects of health Psychological impacts from COVID-19 among university students: Risk factors across seven states in the United States Student COVID psychological risk https://orcid.org/0000-0003-2296-7602 Browning Matthew H. E. M. Conceptualization Data curation Formal analysis Investigation Methodology Project administration Resources Software Supervision Validation Visualization Writing – original draft Writing – review & editing 1 * https://orcid.org/0000-0001-9591-1269 Larson Lincoln R. Conceptualization Data curation Investigation Methodology Visualization Writing – review & editing 2 Sharaievska Iryna Conceptualization Data curation Formal analysis Investigation Methodology Project administration Software Writing – review & editing 3 Rigolon Alessandro Conceptualization Data curation Methodology Writing – review & editing 4 McAnirlin Olivia Data curation Writing – review & editing 1 https://orcid.org/0000-0002-1258-7983 Mullenbach Lauren Data curation Writing – review & editing 2 Cloutier Scott Data curation Writing – review & editing 5 Vu Tue M. Formal analysis Methodology Visualization Writing – review & editing 6 Thomsen Jennifer Data curation Writing – review & editing 7 Reigner Nathan Data curation Methodology Visualization Writing – review & editing 8 Metcalf Elizabeth Covelli Data curation Writing – review & editing 7 D'Antonio Ashley Data curation Writing – review & editing 9 https://orcid.org/0000-0003-0392-8915 Helbich Marco Methodology Software Writing – review & editing 10 Bratman Gregory N. Methodology Writing – review & editing 11 Alvarez Hector Olvera Methodology Writing – review & editing 12 Virtual Reality & Nature Lab, Department of Parks, Recreation and Tourism Management, Clemson University, Clemson, SC, United States of America Department of Parks, Recreation and Tourism Management, North Carolina State University, Raleigh, NC, United States of America Department of Parks, Recreation and Tourism Management, Clemson University, Clemson, SC, United States of America Department of City and Metropolitan Planning, The University of Utah, Salt Lake City, UT, United States of America Sustainability and Happiness Research Lab, School of Sustainability, Arizona State University, Tempe, AZ, United States of America Advanced Computing & Data Science, Clemson Computing & Information Technology, Clemson University, Clemson, SC, United States of America Department of Society and Conservation, W.A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT, United States of America Recreation, Park, and Tourism Management Department, College of Health and Human Development, Pennsylvania State University, PA, United States of America Forest Ecosystems and Society, College of Forestry, Oregon State University, Corvallis, OR, United States of America Department of Human Geography and Spatial Planning, Faculty of Geosciences, Utrecht University, Utrecht, The Netherlands Environment & Well-Being Lab, School of Environmental and Forest Sciences, University of Washington, Seattle, WA, United States of America School of Nursing, Oregon Health & Science University, Portland, OR, United States of America Lin Chung-Ying Editor Hong Kong Polytechnic University, HONG KONG

The authors have declared that no competing interests exist.

 * E-mail: mhb2@clemson.edu 7 1 2021 2021 16 1 e0245327 4 8 2020 28 12 2020 2021 Browning et al This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Background

University students are increasingly recognized as a vulnerable population, suffering from higher levels of anxiety, depression, substance abuse, and disordered eating compared to the general population. Therefore, when the nature of their educational experience radically changes—such as sheltering in place during the COVID-19 pandemic—the burden on the mental health of this vulnerable population is amplified. The objectives of this study are to 1) identify the array of psychological impacts COVID-19 has on students, 2) develop profiles to characterize students' anticipated levels of psychological impact during the pandemic, and 3) evaluate potential sociodemographic, lifestyle-related, and awareness of people infected with COVID-19 risk factors that could make students more likely to experience these impacts.

 Methods

Cross-sectional data were collected through web-based questionnaires from seven U.S. universities. Representative and convenience sampling was used to invite students to complete the questionnaires in mid-March to early-May 2020, when most coronavirus-related sheltering in place orders were in effect. We received 2,534 completed responses, of which 61% were from women, 79% from non-Hispanic Whites, and 20% from graduate students.

 Results

Exploratory factor analysis on close-ended responses resulted in two latent constructs, which we used to identify profiles of students with latent profile analysis, including high (45% of sample), moderate (40%), and low (14%) levels of psychological impact. Bivariate associations showed students who were women, were non-Hispanic Asian, in fair/poor health, of below-average relative family income, or who knew someone infected with COVID-19 experienced higher levels of psychological impact. Students who were non-Hispanic White, above-average social class, spent at least two hours outside, or less than eight hours on electronic screens were likely to experience lower levels of psychological impact. Multivariate modeling (mixed-effects logistic regression) showed that being a woman, having fair/poor general health status, being 18 to 24 years old, spending 8 or more hours on screens daily, and knowing someone infected predicted higher levels of psychological impact when risk factors were considered simultaneously.

 Conclusion

Inadequate efforts to recognize and address college students’ mental health challenges, especially during a pandemic, could have long-term consequences on their health and education.

 The author(s) received no specific funding for this work. Data Availability All relevant data are within the manuscript and its Supporting Information files. Outbreaks COVID-19 1 Introduction

A large number of studies support that the conclusion that the novel coronavirus (SARS-CoV-2) and its corresponding disease (COVID-19) have dramatically impacted people's mental health and behavior [1–5], with very few studies suggesting otherwise [6]. Mental health hotlines in the United States experienced 1,000% increases during the month of April, when most people were under lockdown because of the pandemic [7]. Some medical facilities have seen more deaths from suicide, presumably because of exceedingly poor mental health, than from COVID-19 infections [8]. Substance disorders in many people who were previously abstinent are expected to relapse during COVID-19, which will cause long-term economic and health impacts [9].

Although impacts are felt across populations—and especially in socially-disadvantaged communities and individuals employed as essential workers—college students are among the most strongly affected by COVID-19 because of uncertainty regarding academic success, future careers, and social life during college, amongst other concerns [10]. Even before the pandemic, students across the globe experienced increasing levels of anxiety, depressive moods, lack of self-esteem, psychosomatic problems, substance abuse, and suicidality [11]. Therefore, students may need additional resources and services to deal with the physical and mental health repercussions of the disease.

University administrators could best serve students if they better understood the impacts of COVID-19 and the risk factors of its psychological impacts. These impacts are of critical importance to warrant immediate mental health interventions focused on prevention and treatment [12]. Psychiatric and counseling services have historically been underutilized by college students [13, 14]. Understanding what subpopulations may suffer from unique combinations of psychological impacts may facilitate targeted interventions and successful treatment and coping strategies for individuals at greatest risk.

A recent review highlights some of the documented psychological impacts of COVID-19 on college students [15]. Many feel increased stress levels and anxiety and depressive symptoms as a result of changed delivery and uncertainty of university education, technological concerns of online courses, being far from home, social isolation, decreased family income, and future employment. These impacts have been observed in universities across the world [10].

Studies of the psychological impacts of COVID-19 on college studies in the United States, however, have been limited in their generalizability [10] due to examination of single institutions only [10, 16, 17]. We are aware of no studies that have been conducted with college students at multiple institutions across the United States during the pandemic. These schools collectively represent a somewhat unique context within higher education. The United States educates large numbers of students from around the world [18, 19]. Diverse student bodies may show different risk factors from more culturally-homogenous student bodies because of the diversity of value orientations [20] and sources of media consumption [16, 21–23]. Further, colleges in the United States cost more than higher education institutions nearly anywhere else in the world [24]; therefore, financial concerns may be particularly apparent in the United States. The United States also experienced the lowest global recovery rate from infection–in other words, the highest mortality rate post-infection–in the weeks leading up to the timing of the current study (April and May, 2020) [25]. This country continues to witness the highest incidence and mortality rates among Global North countries [26]. Such high rates aggravate the psychological impacts of the disease on infected and non-infected individuals [1].

In the current study, we investigate the psychological impacts of COVID-19 and associated risk factors on college students at seven universities across the United States. Our objectives are three-fold: 1) identify the array of psychological impacts COVID-19 has on students, 2) develop profiles to characterize students' anticipated levels of psychological impact during the pandemic, and 3) evaluate potential sociodemographic, lifestyle-related, and awareness of people infected with COVID-19 risk factors that could make students more likely to experience these impacts.

 2 Methods 2.1 Study population

In spring 2020, 14,174 participants were recruited cross-sectionally from representative and targeted samples at seven large, state universities, which in sum enroll more than 238,000 students. Universities included Arizona State University in Tempe, AZ (approximately 52,000 undergraduate/graduate students enrolled in 2019); Clemson University in Clemson, SC (approx. 25,000); North Carolina State University in Raleigh, NC (approx. 34,000); Oregon State University in Corvallis, OR (approx. 29,000); Pennsylvania State University in State College, PA (approx. 54,000); University of Montana in Missoula, MT (approx. 11,000); and The University of Utah in Salt Lake City, UT (approx. 33,000). One institution (North Carolina State University) was able to utilize a university-wide representative sample. Other institutions used targeted samples in the home college(s) or department(s) of the corresponding author. Selection of sampling scheme (i.e., representative or targeted) was determined by human subject review board allowances and listserv availability. (Recruitment occurred over email listservs and course website announcements.)

This research was deemed exempt from the Clemson University Institutional Review Board. Also, all subjects provided written consent when they completed the online survey.

Recruitment started as soon as human subject approval was awarded and occurred over a two-to-three-week window at each institution. Because approval took longer at some institutions, nationwide recruitment was staggered. No compensation for participation was provided. Sampling frames and recruitment windows are detailed in Table 1.

10.1371/journal.pone.0245327.t001 Sampling frames and recruitment windows across participating universities.
University Recruitment Window Sampling Frame
Description N
Arizona State University, Tempe, AZ April 2 –April 10, 2020 Undergraduate students in large-enrollment course in the School of Sustainability 190
Clemson University, Clemson, SC March 17 –April 12, 2020 All undergraduate and graduate students in the Department of Parks, Recreation and Tourism as well as all undergraduate students enrolled in large-enrollment introductory course in the Department of Communication 1,168
North Carolina State University, Raleigh, NC March 26 –April 10, 2020 Randomly sampled undergraduate and graduate students from across the university 10,000
Oregon State University, Corvallis, OR April 30 –May 11, 2020 All undergraduate and graduate students in the College of Forestry 1,207
Pennsylvania State University, State College, PA April 27 –May 2, 2020 All students in an undergraduate general education course offered by the Recreation, Park, and Tourism Management Department. 141
University of Montana, Missoula, MT April 14 –April 27, 2020 All undergraduate and graduate students in the College of Forestry and Conservation 847
The University of Utah, Salt Lake City, UT March 26 –April 12, 2020 All undergraduate and graduate students in the College of Architecture and Planning 621
Total 14,174
Overall Response Rate 17.9%

Of the 14,174 students invited to participate in the survey, we received 2,534 responses with data on most of the relevant variables; thus, this sample size was available for most of the descriptive statistics and bivariate associations. Missing/not reported data on race/ethnicity and gender occurred in approximately 11% of respondents. Therefore, complete data for multivariate analyses with all risk factors entered simultaneously—including race/ethnicity and gender—were available for 2,140 students. Table 2 provides the sample characteristics.

10.1371/journal.pone.0245327.t002 Characteristics of student respondents (<italic>N</italic> = 2,534)<xref ref-type="table-fn" rid="t002fn001"><sup>a</sup></xref>.
Range
Item Mean (SD) / % (n) Possible Actual
Sociodemographic Factors
    Female 60.8% (n = 1502)
Age
    18 to 24 76.8% (n = 1940)
    25 to 32 18.0% (n = 455)
    33 to 44 3.5% (n = 89)
    45 to 54 1.3% (n = 33)
    55 to 64 0.3% (n = 7)
    65 to 74 0.1% (n = 2)
Race/Ethnicity
    Non-Hispanic White 78.6% (n = 1772)
    Non-Hispanic Black 4.2% (n = 95)
    Non-Hispanic Asian 12.8% (n = 289)
    Hispanic 4.3% (n = 98)
Socioeconomic Status
    Class (paternal) 3.06 (1.03) 1 (working class)– 5 (upper class) 1–5
    Class (maternal) 3.01 (1.00) 1 (working class)– 5 (upper class) 1–5
    Class (self) 2.83 (1.02) 1 (working class)– 5 (upper class) 1–5
    Educational Achievement (paternal) 4.55 (1.64) 1 (less than high school) –7 (doctorate) 1–5
    Educational Achievement (maternal) 4.51 (1.50) 1 (less than high school) –7 (doctorate) 1–5
    Relative Income 3.31 (1.11) 1 (well below average)– 5 (well above average) 1–5
Academic Status
    Graduate Student 19.8% (n = 499)
    Undergraduate Student 81.0% (n = 2053)
Lifestyle-Related Factors
    General Health 3.31 (1.04) 1 (Poor)– 5 (Excellent) 1–5
    Body Mass Index (BMI) 24.11 (4.67) – 5.35–60.08
Time Use (Last 24 Hours)
    Screen time 7.74 (2.81) 0 hours– 12 hours 0–12
    Outdoor time 1.62 (1.70) 0 hours– 12 hours 0–12
    Exercise 0.97 (1.15) 0 hours– 12 hours 0–12
COVID-19 Victim Awareness
    Knowing Someone Infected 24.6% (n = 591)
Institutional Affiliation
    Arizona State University 6.1% (153)
    Clemson University 10.7% (271)
    North Carolina State University 58.3% (1473)
    Oregon State University 8.7% (219)
    Pennsylvania State University 4.7% (119)
    University of Montana 7.6% (193)
    The University of Utah 3.9% (98)

acomplete data for gender and race/ethnicity are available for 2,140 students.

 2.2 Measures 2.2.1 Psychological impacts

2.2.1.1 Qualitative assessment. We expected that it would be difficult to parsimoniously and comprehensively capture the broad array of impacts from COVID-19 on students with quantitative measures. Therefore, we utilized an open-ended questionnaire item that asked respondents, "We are interested in the ways that the coronavirus (COVID-19) pandemic has changed how you feel and behave. What are the first three ways that come to mind?" Three responses were required, and a fourth response was optional. This question was placed at the beginning of the questionnaire to avoid priming and order effects [27, 28].

2.2.1.2 Quantitative assessment. Regarding our selection of quantitative impacts to measure, we chose nine survey items based on information gathered from a review of previous research and new interview data. These nine survey items measured the following concepts: negative emotion states, preoccupation with COVID-19, feeling stressed, worry, and time demands.

Regarding the review of previous research, we examined studies of other large-scale disasters (i.e., the World Trade Center terrorist attacks on September 11, 2001; previous epidemics requiring quarantine), which are almost always associated with psychological impacts on the general population [29]. These studies provided some guidance on what impacts to measure for the impacts of COVID-19 on college students.

Regarding new interview data, the corresponding author of the current study conducted unstructured interviews with adults on their experiences in the early stages of the COVID-19 pandemic. These interviews consisted of recruiting ten participants aged 18 years or older in February 2020. Recruitment occurred in both low-risk and high-risk regions of the United States, including urban areas in Washington and rural areas in Tennessee, Iowa, and South Carolina. The interviews captured the feelings that interviewees experienced during the pandemic.

Negative emotion states comprised four of the survey items. Each item explained one of the basic negative emotions (i.e., being afraid, irritable, guilty, and sad) identified during the development of the positive and negative affect schedule (PANAS) [30]. Items were measured using the visual analogue scale (VAS) to provide data across a wide range of responses (1–100) with minimal participant burden [31]. Prompts asked respondents to indicate the extent to which they felt these things when they thought about the pandemic.

Preoccupation and feeling stressed comprised two more survey items. These were also measured with the VAS. Prompts once again asked respondents to indicate the extent to which they felt these things when they thought about the pandemic.

One more survey item measured worry—specifically anxious arousal. It was measured with a single item ("I worry a lot") from the Penn State Worry Questionnaire (PSWQ) that is strongly associated with the entire 16-item PSWQ, r = 0.80 [32]. Therefore, this single item succinctly captures the concept of worry/anxious arousal. A 5-point Likert-type agree-disagree response scale was used.

Two more survey items measured time demands. These were developed from survey prompts in the eating disorder literature [33]. Specifically, we asked to what extent respondents believed they spent a lot of time/thought on the pandemic, and to what extent they believed they spent too much time/thought on the pandemic. Once again, a 5-point Likert-type agree-disagree response scale was used.

The prompts for all nine of these survey items were delivered as reactions to the coronavirus rather than measures of general psychological states. Example include: "how stressed do you feel when you think about coronavirus," and "to what extent do you agree/disagree with the following: I worry about coronavirus all of the time."

 2.2.2 Risk factors

Sociodemographic factors were self-reported and allowed identification of potential differences in impact levels by gender, age, race/ethnicity, socioeconomic status (SES), and academic status (undergraduate vs. graduate-seeking). SES was measured with perceived social class, which has been shown to accurately represent SES in student populations, using a battery of seven questions on class, parental education, and relative family income [34, 35]. To measure academic status, we asked respondents whether they were in pursuit of an undergraduate or graduate degree.

To account for possible lifestyle-related risk factors, we first considered general health factors such as general health status and body mass index (BMI). Health status was measured with a single item on respondents' "health in general" and a 5-point response scale (poor to excellent) [36]. BMI was calculated from self-reported height and weight. BMI has been implicated as a risk factor or confounder of the psychological impacts of COVID-19 [37, 38].

Another set of plausible lifestyle-related risk factors was time use. We utilized a recent recall question structure from the American Time Use Survey that strongly predicts objective time use and activity measures [39]. Three items were used to ask respondents to indicate how many hours they spent outdoors (at a park, on a greenway/trail, in a neighborhood/yard, etc.), in front of a screen (on a smartphone/computer, watching television, online gaming, etc.), and engaged in moderate or vigorous physical activity that caused an increase in breathing or heart rate (fast walking, running, etc.) in the past 24 hours [40, 41].

Regarding awareness of COVID-19 victims as a potential risk factor, we included two measures of knowing people who were diagnosed with the virus: someone in their family and someone in their community [42].

 2.3 Analyses

To accomplish Objective 1, qualitative data from the open-ended responses were analyzed using content analysis with an inductive approach [43, 44]. Two independent researchers examined the data systematically to identify patterns and codes [43]. Each response was coded separately and reviewed for agreement [45, 46]. Interrater/intercoder agreement (kappa) score was 94.94% [47].

Objective 2 was accomplished in three steps. These included data imputations, data reduction, and profile identification.

We imputed missing values by bag imputation, which fits a machine learning regression tree model for each predictor as a function of all others [48]. In our dataset, 5.2% of the quantitative data were missing and imputed.

Next, we reduced the survey items related to levels of psychological impact into latent constructs using exploratory factor analysis (EFA) with oblimin rotation [49]. Scree plots and Very Simple Structure (VSS) criterion were used to identify the number of factors [50]. The VSS criterion evaluates the magnitude of the changes in goodness of fit with each increase in the number of extracted factors.

Last, using the composite scores from the EFA, we used the identified latent constructs from the psychological impact survey items as input variables in a latent profile analysis (LPA) [51]. Criteria for determining the number of profiles in the LPA included statistical adequacy of the solution and interpretability of each profile [52]. Indices used to determine statistical adequacy included the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and sample-size adjusted Bayesian Information Criterion [53]. For each of these indices, lower values represented better model fit. Also, the entropy criterion was calculated as a measure of classification precision [54]. We favored a parsimonious solution with fewer profiles over a more complex solution if this improved the interpretability of the LPA [53]. Z-scores of the input variables were used to interpret the profiles. The criteria to assign low and high values is not established and so we adopted previous studies' thresholds [53]. These included standardized scores between +0.5 and -0.5 being labelled as moderate, scores above 0.5 being labelled as high, and scores below -0.5 being labelled as low levels of psychological impact from COVID-19.

Objective 3 was achieved by modeling unadjusted (bivariate) and adjusted (multivariate) relationships between risk factors and profiles from the LPA. Unadjusted results are presented because multivariate models used a dichotomous outcome variable to distinguish students in the highest profile of psychological impact from those in the moderate or low profiles of impact (see Results for profile development and sample sizes within each profile). Determining risk factors for being in the high impact profile was deemed more important and actionable by university administrators than determining risk factors for each of the lower impact profiles, as would have been accomplished with a multinomial model. Thus, this modeling approach served a practical function; results could inform university administrators with tight budgets on how to prioritize funding for mental health interventions amongst students at greatest risk of high levels of psychological impact. Unadjusted results remained relevant, however, since they served the function of comparing risk factors between each level of impact profile in a simpler format than the output of a multinomial regression model.

For the unadjusted results, risk factors were evaluated with chi-squared contingency tables. Residuals from observed versus expected count comparisons determined the direction of the effect of the risk factors (i.e., more or less likely that a group was classified to a higher impact profile than another profile). Statistical significance of risk factors was calculated with Bonferroni adjustments to reduce Type I Error [55]. Continuous measures were reduced to dichotomous or categorical factors based on clinically meaningful levels, past research, and data distributions. BMI was classified into four categories (less than 18 = underweight; 18 to 24.99 = normal; 25 to 29.99 = overweight; 30.0 and over = obese) [56]. General health was separated into two groups: poor/fair health and good/very good/excellent health [53]. Screen time was separated into less than eight hours on a device and eight or more hours on a device [57]. Time outdoors was split into three groups: Less than 1.00 hour, 1.00 to 1.99 hours, and 2.00 hours or more [58, 59]. Time spent exercising was also split into three groups: 30.00 minutes or less, 30.01 to 59.99 minutes, and 1.00 hour or more [60]. In addition, social class and relative income were split into three levels: below average, average, or above average. Levels of education were split into two levels: less than a 4-year college degree and a 4-year college degree or more [61].

For the adjusted results, we conducted generalized mixed-effects logistic regression to examine risk factors simultaneously and control for random (grouping) effects by institutional affiliation. To avoid collinearity in SES measures, whichever item correlated most strongly with psychological impacts was entered in the model. We used Variance Inflation Factor (VIF) values to test for multicollinearity. The proportion of variance explained was measured with conditional and marginal R2 coefficients of determination [62–64]. Marginal R2 represents the contribution of the predictors, which are modelled as fixed effects, whereas conditional R2 accounts for the additional contribution of institutional affiliation (random effect) in addition to the fixed effects.

As a sensitivity analysis, we ran a logistic regression model with a subsample of respondents from the university that obtained a representative sample (North Carolina State University). This allowed us to evaluate the robustness of our nationwide sample, which otherwise utilized a convenience sampling approach.

Analyses were conducted in Excel for Mac Version 16.38 and R Version 3.6.2.

 3 Results 3.1 Array of impacts

Qualitative data from the open-ended responses demonstrated a broad array of impacts from COVID-19 on college students’ feelings (Table 3) and behaviors (Table 4). The most common changes in how students felt compared to before the pandemic were increased lack of motivation, anxiety, stress, and isolation. For example, one of the students reflected, “I'm normally extremely motivated, and I've never struggled with depression, but have recently felt very sluggish and melancholy.” Another student described their feelings related to isolation as “I feel trapped. I don't have anywhere I need to go since I can't socialize, and I have schoolwork. But yet I still feel trapped due to actual restrictions and suggestions.” The most frequent changes in student behavior compared to before the pandemic included more social distancing, more education changes, and less going out. Other concerning changes ranged from entrapment, boredom, fatigue, hopelessness, guilt, and inconvenience to hygiene, sleep, housing, employment, personal finances, and caretaking. For example, some students expressed their frustration with the financial situation, including one statement indicating: “I am BROKE. I lost my job because of this pandemic and now I can’t pay for groceries.” Other students were concerned about online learning. For example, one student commented: “I am constantly on edge about coursework: Did the computer register I submitted my exam? Did I see everything my teacher posted in Moodle? What happens if my internet goes out and I miss an assignment?”

10.1371/journal.pone.0245327.t003 Open-ended responses on how COVID-19 changed how students feel.
Code Description % Sample Mentioned
Lack of motivation Unmotivated/Hard to concentrate/Unproductive/Procrastinate more/Lazy 21.5%
Anxiety Feeling anxious 17.4%
Stress Overwhelmed 14.6%
Isolation Lonely 13.3%
Worry Worry about health of self or others, health, germs, other people's decisions, my own travel 8.3%
Fear Scared/paranoid/panicked 8.1%
Entrapment Limited/restricted/out of control/trapped/robbed 6.7%
Boredom Feeling bored 6.2%
Uncertainty Uneasy/directionless/confused/inquisitive/surreal 6.0%
Sadness Negative emotions 5.8%
Depression Expressing depressive feelings 5.7%
Annoyance Annoyed/Irritable/frustrated 5.1%
Missing out Disappointment 5.1%
Mental health Mental health is affected 3.9%
Fatigue Exhausted, lack of energy 3.4%
Appreciation Appreciate/grateful for life, friends, health, technology 3.3%
Anger Feeling angry 1.8%
Relaxation calm/relaxed 1.6%
Optimism Optimism/hopefulness/patient 1.5%
Productivity More productive/organized/creative 1.4%
Hopelessness/ Helpless Feeling hopeless/helpless 1.3%
Adaptation Flexible/adjustable to new situations 1.0%
Empathy Empathy towards others 1.0%
Trust Feeling trust/distrust of other people 0.9%
Freedom Independence, being in control 0.8%
Lack of safety Unsecure, precarious 0.8%
Guilt Guilt, shame, privilege 0.5%
Overreaction / Underreaction Primarily feeling overreaction/underreaction from others 0.3%
Inconvenience  Feeling inconvenienced 0.3%

Note: Codes reported by 10% or more of the sample shown in bold.

 10.1371/journal.pone.0245327.t004 Open-ended responses on how COVID-19 changed how students behave.
Code Description % Sample Mentioned
Socializing (less/more) Social distancing 21.1%
Education (change) Change to online, no graduation, canceled school 15.7%
Going out (less) More at home, less out, quarantining, less going to restaurants 12.9%
Exercise (change) More/less exercise, being active 9.5%
Eating pattern (change) Hungry/Eating worse/Cook more/Not eating 7.6%
Carefulness Socially responsible, cautious 7.5%
Hygiene (better) Improved hand washing. Overall cleanliness 7.4%
Sleep/ rest (change) Sleep, lay on the couch 6.9%
Schedule (change) No schedule/Different schedule/Change in daily routines/change in planning/change in work-life balance 5.9%
Housing (change)  Moved in with family members, moved to another location 5.3%
Employment (loss) Temporary or permanently lost job, income 4.3%
Financial worry Concerns about potential or real financial challenges 4.2%
Employment (change) Working from home, working less hours 3.5%
Technology use (change) Using less or more, avoidance 3.4%
Shopping (change) Less shopping, save money 2.9%
Physical contact (less) More aware of people 2.7%
Time (more/less)  Perception of more or less time for activities 2.6%
Travel (change)  Change in traveling plans/vacations 2.0%
Outdoor activities (change) Spending more or less time in the outdoors, outdoor activities 2.0%
Self-reflection Faith, self-reflection 1.7%
News/ media Reading more or less news 1.6%
Leisure activity Hobby/entertainment/projects 1.4%
Physical health Back pain, headache, sore throat, weight gain 1.3%
Caretaking Take care of children, older adults, while working/studying 1.0%
Slow life Slowing down 0.5%
Driving (less) Driving car less 0.2%

Note: Codes reported by 10% or more of the sample shown in bold.

Smaller numbers of students reported positive changes from the COVID-19 pandemic as well. These included optimism, productivity, adaptation, and empathy, as highlighted in the following quotes: “I've affirmed that people are capable of adapting in any circumstances” and “[I felt a] higher degree of empathy toward my community”.

 3.2 Psychological impact profiles

Mean values of the psychological impact survey items are shown in Table 5. Eight of these were included in the EFA. (Feeling guilty demonstrated low communality (h2 = .21) and was removed from further analyses.) All eight items displayed relatively normal distributions (S1 Fig). Criteria of the resulting model were acceptable: Tucker Lewis Index = 0.95; Kaiser-Meyer-Olkin (KMO) factor adequacy measure of sampling adequacy (MSA) = .89 [65]; significant Bartlett's test of sphericity, χ2(28) = 10503, p < .001. The VSS Criterion [50] achieved a maximum of .93 with a two-factor solution, compared to .89 for a one-factor solution or .94 for a three-factor solution (S1 Table, S2 and S3 Figs). We labelled the first factor as "Emotional Distress" since it was composed largely of negative affect items (afraid, irritable, sad, preoccupied and stressed). The second factor was composed of three items dealing with how time was spent presumably in worry during the pandemic (worry, too much time and a lot of time), and so we labelled it "Worry Time." This is a term from clinical psychology that describes time spent reflecting on all the possible impacts of a health concern, including those worries that an individual cannot do anything about [66]. The internal reliability of the factors was high, Cronbach's ⍺ = .87 for Worry Time and .83 for Emotional Distress.

10.1371/journal.pone.0245327.t005 Description of individual COVID-19 psychological impact survey items.
Range
Impact Survey Item Mean (SD) Possible Actual
Worry "I worry about the coronavirus all of the time." 4.01 (1.69) 1 (Strongly disagree)– 7 (Strongly agree) 1–7
Too Much time "I give too much time/thought to coronavirus." 3.97 (1.75) Same ranges as above
Lot of Time "I spend a lot of time thinking about coronavirus." 4.34 (1.78)
Afraid "How afraid do you feel when you think about coronavirus?" 50.40 (27.78) 0 (Not at all)– 100 (Extremely) 0–100
Irritable "How irritable do you feel when you think about coronavirus?" 59.44 (28.91) Same ranges as above
Sad "How sad do you feel when you think about coronavirus?" 60.98 (27.64)
Preoccupied "How preoccupied do you feel when you think about coronavirus?" 53.44 (27.40)
Guilty "How guilty do you feel when you think about coronavirus?" 24.15 (26.38)
Stressed "How stressed do you feel when you think about coronavirus?" 63.97 (26.96)

A three-profile solution fit the data best for the LPA. Information criteria decreased with additional profiles up to a five-profile solution, indicating a better model fit (S2 Table, S4 Fig). The elbow plot suggested minor improvements in model fit after a three-profile solution. Adding a fourth or fifth profile provided less interpretable results. Based on the combined information from the statistical criteria and interpretability, we retained a three-profile solution as our final model.

The three levels of psychological impact from COVID-19 resulting from the LPA are depicted in Fig 1. Positive z-scores indicate higher levels of impact and negative z-scores indicate lower levels of impact, compared to the average. Profile 1 ("high") represented students with higher than average levels of the two factors measuring psychological impacts (Emotional Distress, Worry Time) stemming from COVID-19. Profile 2 ("moderate") represented students with moderate levels of the two factors, and profile 3 ("low") represented students with low levels of the two factors. Regarding profile membership, 45.2% of students (n = 1,146) were within the high impact profile, whereas 40.4% (n = 1,025) were in the moderate profile and 14.3% (n = 363) were in the low profile.

10.1371/journal.pone.0245327.g001 COVID-19 psychological impact profiles derived from z-scores of eight items reduced to two factors using data from college students across the United States (n = 2,534).

Means and standard errors shown.

 3.3 Risk factors

A summary of the risk factors with significant differences between impact profiles based on bivariate Chi-square tests is depicted in Fig 2. With respect to sociodemographic factors, women were more likely to be at risk than men (χ2(2) = 88, p < .001). Specifically, women were more likely to be in the high profile (residuals (RES) = 8.02, p < .001) and less likely to be in the moderate (RES = -2.75, p = .036) or low (RES = -7.54, p < .001) profile. Men demonstrated the opposite pattern. We did not observe differences by academic status (χ2(2) = .3, p = .9), although we did observe differences by age (χ2(4) = 15, p = .005). Students who were 18 to 24 years old were more likely to be in the moderate profile (RES = 3.81, p = .0013), and students who were 25 to 32 years old were less likely to be in the moderate profile (RES = -3.03, p = .022) than other profiles. No other significant differences between age groups by profile were found, p > .05.

10.1371/journal.pone.0245327.g002 <p>Sociodemographic (a), lifestyle (b), and COVID-19 victim awareness (c) risk factors associated with high, moderate, and low psychological impact profiles for students across the United States. Residuals from Pearson's chi-squared tests depict likelihood of profile membership based on risk factor. Only significant factors (<italic>p</italic> < .05) are reported. Reference groups include men; over 32 age; other race/ethnicity; average/above average SES (social class and relative family income); good/very good/excellent general health; less than 2 hours of time outdoors; less than 8 hours of screen time; and not knowing someone infected (COVID-19).</p> </caption> <graphic mimetype="image" position="float" xlink:href="info:doi/10.1371/journal.pone.0245327.g002" xlink:type="simple"/> </fig> <p>We also observed racial/ethnic and SES differences in psychological impact levels. Specifically, we found differences by race/ethnicity (<italic>χ</italic><sup>2</sup>(6) = 18, <italic>p</italic> = .007) with non-Hispanic Whites being more likely to be in the low profile (RES = 2.98, <italic>p</italic> = .035) and Non-Hispanic Asians being less likely to be in the low impact profile (RES = -3.42, <italic>p</italic> = .0076). No differences in impact profiles were observed for non-Hispanic Black students or Hispanic students, although sample sizes were small (<italic>n</italic> = 95 and 98, respectively). Parental educational achievement measures further showed no differences in profiles, <italic>p</italic> = .5 for maternal and .9 for paternal. No differences were observed for parental social class either, <italic>p</italic> = .1 for maternal and .2 for paternal. In contrast, student social class (<italic>χ</italic><sup>2</sup>(4) = 14, <italic>p</italic> = .008), and relative family income (<italic>χ</italic><sup>2</sup>(4) = 14, <italic>p</italic> = .008) differed by impact profile. Students who reported above-average social class were more likely to be in the low profile (RES = 3.07, <italic>p</italic> = .019), and students who reported below-average relative family income were more likely to be in the high profile (RES = 3.38, <italic>p</italic> = .0065). No other significant differences between ethnoracial groups or SES measures by profile were found, <italic>p</italic> > .05.</p> <p>Lifestyle-related factors predicted differences in impact profiles. For instance, general health predicted assignment to different impact profiles (<italic>χ</italic><sup>2</sup>(2) = 41, <italic>p</italic> < .001). Students with fair/poor health were more likely to be in the high profile (RES = 5.90, <italic>p</italic> < .001) and less likely to be in the moderate (RES = -2.67, <italic>p</italic> = .045) or low profile (RES = -4.58, <italic>p</italic> < .001). Students with good/very good/excellent health displayed the opposite pattern. No difference in impact profiles was observed for BMI (<italic>χ</italic><sup>2</sup>(6) = 9, <italic>p</italic> = .2). We observed differences in impact profiles by time outdoors (<italic>χ</italic><sup>2</sup>(4) = 13, <italic>p</italic> = .01) and screen time (<italic>χ</italic><sup>2</sup>(2) = 14, <italic>p</italic> = .001) but not by exercise time (<italic>χ</italic><sup>2</sup>(4) = 6, <italic>p</italic> = .2). Students who reported spending two or more hours outdoors were less likely to be in the high profile (RES = -3.17, <italic>p</italic> = .014), and students who reported spending more than eight hours on a device were more likely to be in the high profile (RES = 3.06, <italic>p</italic> = .013) and less likely to be in the moderate profile (RES = -3.67, <italic>p</italic> = .0014). Students spending less than eight hours on a device displayed the exact opposite trend. No other pair-wise comparisons in lifestyle-related factors were significant, <italic>p</italic> > .05.</p> <p>Lastly, knowing someone who was infected with COVID-19 increased the likelihood of being at risk of psychological impacts (<italic>χ</italic><sup>2</sup>(2) = 14, <italic>p</italic> < .001). Students who knew someone in their family or community who was infected were more likely to be in the high profile (RES = 3.06, <italic>p</italic> = .013) and less likely to be in the moderate profile (RES = -3.67, <italic>p</italic> = .0014). Students who did not know an infected person displayed the opposite pattern.</p> <p>Five variables remained significant predictors of impact profiles in models adjusting for all risk factors simultaneously while controlling for institutional affiliation (<xref ref-type="table" rid="pone.0245327.t006">Table 6</xref>). The SES measure entered in these models was social class of student, because it correlated more highly with psychological impact levels than other measures (<xref ref-type="supplementary-material" rid="pone.0245327.s005">S5 Fig</xref>). Students who were women, fair/poor general health, 18 to 24 years old, reporting 8 or more hours of screen time, and who knew someone infected with COVID-19 were more likely to be in the high profile. Non-Hispanic Asian students were marginally more likely to be in the high impact profile, <italic>p =</italic> .091. Effect sizes varied; women were approximately twice as likely to be assigned to the high impact profile as the moderate/low profile. Other predictors increased (or decreased) the likelihood of being in the high impact profile by approximately 20% to 40%. No institutions emerged as significant random effects (<xref ref-type="supplementary-material" rid="pone.0245327.s006">S6 Fig</xref>). VIF values < 2.0 indicated no multicollinearity. Approximately 7% of the variance was explained by the predictors and institutional affiliations.</p> <table-wrap id="pone.0245327.t006" position="float"> <object-id pub-id-type="doi">10.1371/journal.pone.0245327.t006</object-id> <label>Table 6</label> <caption><title>Results of mixed-effects binary logistic regression modelling likelihood of risk factors predicting assignment to high COVID-19 psychological impact profile for students in seven United States universities (<italic>N</italic> = 2,140) <sup>a</sup>.
Log odds (95% CI)
Female 2.018 (1.675, 2.431) ***
Age (18 to 24) 1.375 (1.099, 1.719) **
Race/Ethnicity
    Non-Hispanic White 0.868 (0.623, 1.208)
    Non-Hispanic Asian 1.274 (0.962, 1.687) ^
Class (Self) 0.908 (0.802, 1.028)
General Reported Health 0.619 (0.497, 0.772) ***
BMI 1.036 (0.91, 1.178)
Time Use (Last 24 Hours)
    Screen time 1.216 (1.008, 1.466) *
    Outdoor time 0.947 (0.834, 1.076)
    Exercise 1.039 (0.93, 1.161)
Academic Status (Graduate) 0.946 (0.755, 1.186)
Knowing Someone Infected 1.453 (1.173, 1.799) ***
Marginal R2 / Conditional R2 (%) 6.8 / 7.3
ICC 0.005

Note

^p < .10

*p < .05

**p < .01

***p < .001. Predictors with p < .10 shown in bold. Due to identification with multiple races and missing values or other categories (i.e., mixed, prefer not to answer) for race/ethnicity and gender, 2,140 responses were available for multivariate analysis. These models were adjusted for random effects of institutional affiliation (S6 Fig), ICC = Intra-Class Coefficient.

Sensitivity analyses with a subsample of respondents from the representative sample at North Carolina State University identified a similar set of predictors of psychological impact levels (S3 Table). Gender, age, general health, and knowing someone infected remained significant predictors. In contrast, screen time was no longer significant. Being Non-Hispanic Asian as marginally significant, p = .070, and social class was significant, p = .0038. Students of above average social class were 23.0% less likely to be assigned to the high impact profile.

 4 Discussion 4.1 Key findings and interpretation of results

To evaluate the psychological impacts of COVID-19 on students in the United States, we collected over 2,500 survey responses from students at seven universities in late-February to mid-May 2020. Qualitative data from open-ended responses showed students experienced largely negative impacts of COVID-19 on psychological health and lifestyle behaviors. Among the most commonly reported changes were lack of motivation, anxiety, stress, and isolation, as well as social distancing, education changes, and going out less. Similar findings were reported by another study exploring the impact of COVID-19 on students at a single college in the United States, revealing increases in sedentary lifestyle, anxiety, and depressive symptoms [16]. A global study examining experiences of students in 62 countries, including one university in the United States, found that students’ expressed concerns about their academic and professional careers, as well as feelings of boredom, anxiety and frustration [10]. Increased anger, sadness, anxiety and fear were also reported by students in China [67]. Students in Switzerland reported a decrease in social interaction and higher levels of stress, anxiety, and loneliness [68]. More generally, adults have reported decreases in physical activity and food consumption increases during the COVID-19 pandemic quarantine compared to beforehand, as well as increases in binge drinking on average [69], which was identified in a small portion of our student respondents as well. Slight differences between our studies' results and results from studies conducted elsewhere may be due to the differences in student experience by geographical location. The United States is providing relatively little financial relief to college students during the pandemic compared to other Global North countries [70].

Quantitative survey measures captured the majority of the content that students entered in the open-ended responses (i.e., worry, stress, and fear) and informed the development of impact profiles. Students were assigned to one of three profiles—low (14% of the sample), moderate (40%) and high (45%)—based on the psychological impacts they reported experiencing in response to COVID-19.

In unadjusted models, students who were women, non-Hispanic Asian, in fair/poor health, of below-average relative family income, or someone who knew a family/community member infected with COVID-19 were at risk of higher levels of psychological impact. Students who were non-Hispanic White, above-average social class, spent at least two hours outside in the past day, or spent less than eight hours on screens in the past day were at less risk.

In multivariate models controlling, being a woman, being younger (18 to 24 years old), having poor/fair general health, reporting more screen time, and knowing someone infected were statistically significant risk factors. SES and identifying as non-Hispanic Asian were additional significant risk factors in the subsample of respondents obtained from representative sampling, whereas screen time was not significant in this sensitivity analysis.

These risk factor findings generally match those found in other studies that employed a case study approach within single United States universities/colleges. One longitudinal study of students at a public university in Nevada (n = 205) found that anxiety and depressive symptoms were greater in April 2020 than in prior months [17]. Women reported greater disruption to daily activities, mental and physical health, and personal finances than men. Contrary to our unadjusted findings, Asian or Asian-American students in the Nevada study reported lower levels of anxiety and depression than other races. A second longitudinal study with undergraduate students (n = 217) at a small liberal arts college in New Hampshire also found increases in anxiety, depression, and sedentary time during April 2020 relative to prior months [16]. COVID-19 risk factors for college students at other countries have been strikingly similar, as explained below.

Over ten studies, including several with college student populations, identify women as being at greater risk of psychological distress during the COVID-19 pandemic [1, 10, 21, 71–77]. Women are generally prone to depression and anxiety disorders [14], and although initial evidence indicated men were more susceptible to infection [77], our study supports the assertion that women appear to be more strongly impacted by the long-term psychological impacts of the pandemic. This observation may be attributable to higher levels of pre-existing psychopathology in women as well as gender differences in fear processing, which could translate to exacerbations of symptoms [78]. Also, male students tend to have higher confidence in the computer skills necessary for the transition to online course delivery [10]. Meanwhile, women are more concerned about impacts on their professional career and ability to study than men, on average [10]. One study attributed these gender differences to greater emotional expression, less tolerance for uncertainty, and less-effective coping strategies amongst students who are women [75]. Women have also reported being more susceptible to "emotional hunger" and subsequent increased food intake than men during COVID-19 quarantine; these behaviors can lead to weight gain and poor mental health [73].

Our findings that fair/poor general health is a risk factor has been documented in numerous other populations during COVID-19 [79, 80]. In addition to comorbidity between mental and physical health status, people with pre-existing health problems and those with poor mental health show lower preparedness for disasters and suffer disproportionately more from disaster-related outcomes [81].

Several reasons explain our findings that younger students may be at greater risk than older students. Younger students (i.e., 18 to 24 years old, regardless of academic status) tend to be more worried about their future education and ability to pay for college education than older students [10]. Younger people also engage in social media more than older people during the pandemic [12, 82]. Given the dominance of the COVID-19 pandemic in the news, younger "always-on" students may be exposed to greater amounts of risk-elevating messages, which can lead to anxiety and poor mental health [16, 75].

Regarding our findings that non-Hispanic Asian students may be at greater risk than other races/ethnicities, several studies show higher psychological distress from COVID-19 in this population [10]. Asians and Asian Americans have reported being discriminated against by other students on social media during the pandemic [83]. Further, this population has experienced long-standing barriers to receiving and participating in mental health services [84].

The current study provides some support toward the mounting evidence that excessive screen time, including during the pandemic, may negatively impact mental health [85]. People who manage COVID-19 anxiety with excessive use of smartphones and other screen-based technology inadvertently learn more about the virus from the news, which fuels anxiety and ongoing coping through screens, thus causing a downward spiral [82]. Excessive use of digital media also detracts from time that could be spent on other health-promoting activities such as outdoor recreation [86]. Our study supports these relationships, suggesting negative impacts of screen time and positive impacts of "green time" on students' psychological health. The unadjusted analyses suggested that outdoor time predicted psychological impacts of COVID-19, although this variable was not significant in multivariate models. Other studies justify its consideration as a risk factor by university administrators. Both outdoor recreation [87] and nature exposure [88, 89] can improve psychosocial and eudaimonic well-being [90, 91]. Recent studies of people across the world show protective psychological effects of park and green space access during the pandemic [92] as well as lower rates of infection and mortality [93].

The finding that knowing someone infected is a risk factor for psychological impacts of COVID-19 is intuitive. Familiarity can increase the salience and perceived risk of becoming infected and dealing with subsequent health concerns, like COVID-19-related death [79]. Also, the threat of death from COVID has been associated with students' mental health and explainable by unhealthy levels of smartphone use [82].

As suggested in our unadjusted analyses and the multivariate model with the representative sample, SES may influence students' mental health during the pandemic. This might be a result of financial concerns affecting college students and their families [10]. SES has been documented as a predictor of COVID-19 fear and mental health concerns in other populations [10, 74, 79, 94–98]. Students coming from low-SES families may be more concerned about basic needs, like food and shelter, caused by loss of their or their parent's income [99]. Furthermore, since low-SES families are more susceptible to COVID-19 infection [98], students may be more concerned for their own and their families' safety.

 4.2 Recommendations for universities

Given the large percentage of students assigned to the high psychological impact profile, universities would be well-served to address the mental health needs of their entire student body. Select programs that have promoted mental health—such as those at the University of Connecticut, University of Kentucky, and Northeastern—include virtual group exercise and meditation/mindfulness sessions, accountability buddies and exercise challenges and tele-medicine/counseling visits [99]. These group meetings may be helpful not only in lowering anxiety but also in decreasing the sense of isolation reported by the students in this study. Digital interventions for students with clinical levels of anxiety or depression as well as potential for self-harm or suicide can involve automated and blended therapeutic interventions (such as apps and online programs), calls/text messages to reach those with less digital resources, suicide risk assessments, chatlines and forums, and other technologies to monitor risk either passively or actively [80]. Recently, Chen et al. [100] recommended a six-step intervention for the reduction in psychological impact risk amongst Chinese college students. These steps included the delivery of positive pandemic-related information, reduction in negative behavior, learning about stress management techniques, improvements in family relationships, increases in positive behavior, and adjustments in academic expectations.

Given the likelihood of ongoing psychological distress from COVID-19, universities may also consider helping students maintain healthy mindsets rather than avoiding stress [101]. In support of this proposition are recent findings that cognitive and behavioral avoidance (i.e., avoiding situations where exposure is possible and difficult thoughts about the pandemic) was the most consistent predictor of increased anxiety and depressive symptoms during the pandemic [17]. Cognitive reappraisal of stressful situations can alter their negative impacts [102, 103]. Training students to shift their educational experience mind-set to one that focuses on the "silver linings" and emerging opportunities may lead to "stress-related growth" and "toughening" [104, 105]. Adaptive mindsets can also help reorganize priorities to develop deeper relationships and greater appreciation of life [106], as well as help students to adjust to new ways of learning. Since a portion of the students in this study reported feeling less motivated, productive, and able to focus, switching to an adaptive mindset may help students persevere in their education and later in life. Finally, mindset reappraisals can increase well-being, decrease negative health symptoms, and boost physiological functioning under acute stress when a family member becomes infected or the pandemic creates rapid shifts in policies and procedures that affect students [107, 108].

Universities can further develop platforms that facilitate safe student social interaction. Many students seek out social interaction during their university experience [109–111]. However, as the findings of this study revealed, students’ opportunities for socializing significantly decreased in the early stages of COVID-19. Missing "going out" and important milestone events (e.g., graduation, last sporting event) was a frequent response from our student participants. Other studies found that in order to maintain students' mental health during the first wave of the COVID-19 pandemic, they communicated online with close family members or roommates at least daily [10]. With college students, physical distancing does not and should not require "social distancing" [101]. Both synchronous (i.e., Zoom) and asynchronous (i.e., Facebook group) online interactions can foster bonding and bridging social connection [112–115], which can extend beyond social media posts and email listservs. Normal venues where people congregate such as places of worship, gyms, cafeterias, yoga studios and classes can be replicated online or even held outdoors in temperate weather on a schedule similar to what was in place prior to the pandemic [116]. Other recently-successful interventions include the facilitated online sharing of recipes, books, and podcasts as well as virtual movie, game, trivia, or happy hour nights [99]. Providing support to student organizations to coordinate these virtual social activities could accelerate the availability of these resources.

Colleges and universities also have a moral obligation to boost their outreach to particularly vulnerable groups–that is, populations at risk of high levels of psychological impact from COVID-19 [14]. As documented in the impact profiles of our study, people at increased risk include women, younger students, students with pre-existing health concerns, students spending at least one-third of their day (including time spent sleeping) on screens, and students with family or community members who are infected with COVID-19. Monitoring and reporting rates of anxiety, depression, self-harm, suicide and other mental health issues within these groups is necessary to allocate counseling services and intervene pre-emptively and at times of acute symptomology [80]. Further, universities can provide accommodations for assignments and exams using a more personalized approach to learning and create enhanced opportunities for virtual social interactions with peers. These efforts may help at-risk groups succeed academically, build stronger relationships, and enhance their sense of belonging during distant learning [117].

Students in this study also expressed stress and anxiety associated with changes in education mode during the pandemics. As previous research has found, academic success may be supported with virtual town halls, regular email check-ins, virtual office hours, and peer mentoring [104]. Globally, students' satisfaction with university response to COVID-19 is predicted by students' satisfaction with pre-recorded videos during online course delivery, sufficient information on exams, satisfaction with teaching staff, satisfaction with websites and social media information with regular updates from the university, hopefulness, (lack of) boredom, (lack of) study issues, being on scholarship, being able to pay for school, and study discipline (social sciences tend to be less impacted than hard sciences or engineering) [10]. Universities may be encouraged by findings from another study on the switch to online courses; this study found many students were not challenged by the transition because of their aptitude toward digital learning and new technologies [118]. However, another study found new software platforms can be a challenge for some students [10].

 4.3 Strengths and limitations

The primary strength of this study is the development of psychological impact profiles using data from universities across the United States. This sampling approach is also a limitation, however. Whereas all the included universities were teaching exclusively online during the study, their respective states and localities may have experienced differing levels of social distancing policy and enforcement. Another limitation related to the sample is the high percentage of non-Hispanic Whites. This occurrence was likely the result of the demographic composition of the colleges and departments targeted for recruitment [119]. Selection bias related to which students participated in the study questionnaire based on interest and access/availability is also possible [3].

Another limitation is the quantitative assessment of the psychological impacts of COVID-19, which could have limited the utility of our impact profiles. We did not measure substance abuse, which is expected to be a ramification of the virus [116] and which anxious individuals are prone to under-report [120]. Such counterproductive coping behaviors could be particularly problematic for college students [121]. Further, because our predictors explained a small amount of variance of the profiles, other unmeasured (or better measured) factors might predict students’ psychological risk. For example, our single-item measures of leisure time activities could be improved with a more comprehensive assessment of time budgets such as those employed in episodic time use surveys [122].

We were primarily interested in reactions to the pandemic rather than how people were feeling/behaving during the pandemic. Therefore, we did not employ standardized measures of stress, anxiety, depression, or well-being. This limits our findings from being directly compared to other studies and pooled in meta-analyses.

Lastly, our measures were retrospective rather than longitudinal, which decreases our ability to say with confidence that the reported impacts were caused by COVID-19. However, we are fairly confident that the findings are attributable to the pandemic given our survey prompts. They specified students' responses to COVID-19 rather than asked generalized psychological states, and the findings strongly aligned with those of longitudinal studies of college students during the pandemic [16, 17, 37, 123–125].

 5 Conclusion

Our cross-sectional study found that being a woman, being of younger age, experiencing poor/fair general health, spending extensive time on screens, and knowing someone infected with COVID-19 were risk factors for higher levels of psychological impact during the pandemic among college students in the United States. Unadjusted analyses also suggested that students who were non-Hispanic White, were not non-Hispanic Asian, were of higher-SES, or spent at least two hours outside experienced lower levels of psychological impact. That said, all students surveyed reported being negatively affected by the pandemic in some way, and 59% of respondents experienced high levels of psychological impact.

At the time that these data were collected, the education of over 1.5 billion students across the world were affected by COVID-19 [126]. Rates of student psychological distress were as high as 90% [17, 127]. Students must "Maslow before they can Bloom; " in other words, their basic physiological, psychological, and safety needs must be met prior to them focusing on–much less excelling–in academic life [99]. We recommend that university administrators take aggressive, proactive steps to support the mental health and educational success of their students at all times, but particularly during times of uncertainty and crisis–notably, the COVID-19 pandemic.

 Supporting information Distributions and relationships between COVID-19 psychological impact survey items, including histograms, Pearson correlation coefficients, and scatter plots.

(DOCX)

 Diagram of EFA on COVID-19 psychological impact survey items.

(DOCX)

 Scree plot of EFA on COVID-19 psychological impact survey items.

(DOCX)

 Elbow plot of the information criteria for the latent profile analysis.

(DOCX)

 Correlations between socio-economic measures and the two psychological impact profiles.

(DOCX)

 Conditional mean values ("condval") and standard deviations of institutional affiliation (university) random effects from mixed-effects logistic regression predicting high versus medium/low psychological impact profile from COVID-19.

(DOCX)

 Item loadings and fit statistics of EFA on COVID-19 psychological impact survey items.

(DOCX)

 Fit indices, entropy and model comparisons for estimated latent profile analyses models.

(DOCX)

 Results of binomial logistic regression modelling likelihood of risk factors predicting high versus low/moderate levels of COVID-19 psychological impact for students at North Carolina State University, where a representative sample was collected (<italic>N</italic> = 1,312).

(DOCX)

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Krakauer Hubner von C, Bruscatto ML, Lima RD. Distress among Brazilian university students due to the Covid-19 pandemic: survey results and reflections. medRxiv.org. 2020. doi: 10.1101/2020.06.19.20135251 10.1371/journal.pone.0245327.r001 Decision Letter 0 Wickens Christine M Academic Editor 2021 Christine M Wickens This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Submission Version 0

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Psychological distress from COVID-19 among university students: Development of a risk classification scheme using data from seven states in the United States

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Dear Dr. Browning,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

When reading the Reviewers’ comments, you will see that both Reviewers had concerns about how the analyses were conducted and/or reported. In my own review of the manuscript, I questioned the interpretation of both scree plots as indicating three-factor solutions. In both cases, only one eigenvalue exceeded the value of 1 and the drop-off in the plot could be interpreted to suggest a single-factor solution. Both Reviewers also recommended avoiding the term ‘Risk’ in naming groups identified by the latent profile analysis. Beyond the examination of risk factors or vulnerable groups to an adverse impact of COVID-19 (chi-square analyses), the authors might also consider including a multivariate analysis of these factors, rather than focusing exclusively on unadjusted analyses.

I would echo the concerns of Reviewer 1 regarding the short review of existing literature; there has been much research published on this topic in the last 6 months that would be informative to the reader. Both reviewers also had concerns about the recommendations made based on these data. I encourage you to consider the Reviewers' feedback and invite you to revise and resubmit your manuscript.

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Reviewers' comments:

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Reviewer #1: Partly

Reviewer #2: Partly

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Reviewer #1: I Don't Know

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #2: Yes

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Reviewer #1: The effects a pandemic has on the mental health of students are significant.

This study contains a lot of information, but the analysis performed and the interpretation of the data raises several questions. There are serious problems with the manuscript in its current form and I am not sure whether the study can be published on POne or not.

Regarding the introduction, a greater review of the scientific literature could be reflected. In fact, there are several current studies showing the effects of COVID lockdown on the mental health of college students in various countries.

A major problem is that the manuscript does not provide information on how the sample was specifically recruited (eg, type of sampling). Other important information from the sample is the mean age of the participants, the previous mental state and the taking of medication.

Regarding the interview and questionnaires, why not use validated instruments to assess mental health status? The use of different items generated ad hoc or selected from other forms does not guarantee the reliability of the measure extracted from them.

Regarding the exploratory factor analysis, nothing is indicated about the fulfillment of assumptions of normality, linearity or multicollinearity of the scores.

The main finding is that there are three different risk profiles for the students in the sample and several associated risk factors. However, the concept of risk factor does not seem appropriate. How to guarantee that other third variables are not those that account for the relationships found between these risk factors and the three profiles found?

Table 1 has an error. The total is not the sum of the participants presented in the column.

Finally, the recommendations made exceed the data found. I think they should adjust more to the results and limit themselves more to them

Reviewer #2: Summary and overall impression

This is an interesting and timely investigation of the impact of the first wave of the COVID-19 pandemic on university students in seven US states. The authors identified a range of psychological and behavioural COVID-19 related impacts which they used to classify participants into High, Moderate, and Low Impact groups (in the manuscript, the authors used the term ‘risk’ instead of ‘impact’). Latent profile analysis revealed that being a female, non-Latino Asian, and from a low SES were risk factors of inclusion in the High Impact group. Protective factors included being a White man.

I believe the main finding of this research is valuable in that it shows how the COVID-19 pandemic differentially impacts students along sociodemographic lines in a large and geographically dispersed US college student sample.

Specific areas for improvement - Major

I don’t believe the Behavioural Impacts listed in this study are appropriate given the pandemic context. Behaviour limiting may have more to do with state-issued directives (e.g., stay home orders) than personal choices to limit activities. For example, where I am, none of the behaviours listed are allowed because of state directives, not because of personal choice. During the period of data collection in each of the data locations, what were the COVID-19-related restrictions on these six activities? Had all study shifted online? Related to this, it appears participants had no opportunity to indicate whether the activities are something they would ever engage anyway (not everyone goes to the gym). Finally, in addition to weather, the time spent on activities in the past 24 hours would be highly variable based on local restrictions, the degree to which the participant’s study, work and other activities have shifted online, etc. For these reasons, I’d remove the Behavioural Impacts from consideration in this paper.

Single item factors are not interpretable. Two factor solutions should be accepted for the Psychological Impact items (and Behavioural Impact items, if you choose to keep them).

The authors have labelled the profiles High Risk, Moderate Risk, and Low Risk. These profiles would be more accurately labelled High Impact, Moderate Impact, and Low Impact. The sociodemographic, lifestyle, and COVID-19 characteristics are then Risk Factors for finding oneself in the High Impact group.

In the recommendations, the first two recommendations seem obvious but the third - targeted support for especially vulnerable groups – is more compelling and should be the focus of this section. The final recommendation seems impracticable given restrictions, social distancing, stay at home orders, etc.

Specific areas for improvement - Minor

The description of the analyses is spread across the Analyses and the Results section making it hard to follow. It should be presented as a single 'narrative' of the data analyses in the Results section.

I recognise that the authors’ expertise is in outdoor activities, but the inclusion of outdoor activity variables and literature feel ‘shoe-horned’ into the study and manuscript. I would remove references to outdoor activities and their contribution to psychological wellbeing as, in many cases, this is simply not an option.

There is an implication that screen time is a ‘bad thing’. During COVID-19, screens are how students access study, paid work, friends, and families. In this sense, increases in screen time is inevitable and desirable and should not be framed as the anti-outdoor activity in the way they might be in a non-COVID world.

The open-ended response tables probably belong in the supplementary materials.

The authors mention basic negative emotion states but don’t say what they are. What are they?

Why was BMI included?

Sub-headings would provide more structure to the manuscript (e.g., Psychological impacts, Behavioural impacts, Lifestyle risk factors, Sociodemographic risk factors, etc.).

Avoid using terms interchangeable - when talking about risk factors you also refer to them variables, characteristics, and attributes.

Participant numbers and characteristics should be included in the Participants section.

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Reviewer #2: Yes: Francesca E. Collins

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 10.1371/journal.pone.0245327.r002 Author response to Decision Letter 0 Submission Version 1

25 Sep 2020

Reviewer 1

1.1. The effects a pandemic has on the mental health of students are significant. This study contains a lot of information, but the analysis performed and the interpretation of the data raises several questions. There are serious problems with the manuscript in its current form and I am not sure whether the study can be published on POne or not.

We appreciate your suggestions for improvement below and hope our revisions make the manuscript appropriate for publication in PLOS ONE.

1.2. Regarding the introduction, a greater review of the scientific literature could be reflected. In fact, there are several current studies showing the effects of COVID lockdown on the mental health of college students in various countries.

We conducted a forward search on the studies of college students previously listed in our paper, including the first article on the topic (Cao et al., 2020), as indicated by Grubic, Badovinac & Johri (2020). That first article has been cited more than 350 times. The forward search allowed us to expand our review of the extant literature and consider nearly 40 empirical and review articles on the impacts of COVID-19 amongst college students. Further, we considered the articles included in a systematic review on the psychological impacts of COVID on students, amongst other populations (Sankhi & Marasine, 2020). The updated review of the literature is briefly included in the introduction and extensively in the discussion.

Cao, W., Fang, Z., Hou, G., Han, M., Xu, X., Dong, J., & Zheng, J. (2020). The psychological impact of the COVID-19 epidemic on college students in China. Psychiatry research, 112934.

Sankhi, S., and Marasine, N. R. (2020). Impact of COVID-19 Pandemic on Mental Health of the General Population, Students, and Health Care Workers: A Review. preprints.org. doi:10.20944/preprints202007.0616.v1.

Grubic, N., Badovinac, S., and Johri, A. M. (2020). Student mental health in the midst of the COVID-19 pandemic: A call for further research and immediate solutions. Int J Soc Psychiatry 66, 517–518. doi:10.1177/0020764020925108.

1.3. A major problem is that the manuscript does not provide information on how the sample was specifically recruited (eg, type of sampling). Other important information from the sample is the mean age of the participants, the previous mental state and the taking of medication.

58% of the sample was from a representative sample of undergraduate students at one university (North Carolina State University, NCSU). Other university samples were convenience samples.

To ensure the convenience samples did not bias our results, we reran all analyses with only the representative subsample (S1, Table A3). The findings remain essentially the same.

We agree that a lack of pre-COVID-19 data is a limitation of our study; however, we developed items to measure reactions to the coronavirus pandemic. These items were not intended for a repeated-measures design. We clarify this in the manuscript:

[Methods] The prompts for all nine of these survey items were delivered as reactions to the coronavirus rather than measures of general psychological states. Example include: "how stressed do you feel when you think about coronavirus," and "to what extent do you agree/disagree with the following: I worry about coronavirus all of the time."

[Discussion] We were primarily interested in reactions to the pandemic rather than how people were feeling/behaving during the pandemic. Therefore, we did not employ standardized measures of stress, anxiety, depression, or well-being. This limits our findings from being directly compared to other studies and pooled in meta-analyses.

Regarding previous health status, our interest was on psychological impacts and symptoms, not on psychopathology as would have been indicated by medication use. We kindly remind the reviewer that we already include a general health status as well as BMI.

Table 1 now presents the age of participants within the categorical groupings that they were measured. Thank you for noticing that these were omitted. Age has also been considered as a risk factor in unadjusted and adjusted models.

1.4. Regarding the interview and questionnaires, why not use validated instruments to assess mental health status? The use of different items generated ad hoc or selected from other forms does not guarantee the reliability of the measure extracted from them.

We agree that previously validated items would have guaranteed reliability. However, as explained in our comment above, we were interested in reactions to the pandemic rather than how people were feeling/behaving during the pandemic. Our measures allowed us to capture responses to the pandemic using a cross-sectional study design. Standardized measures like GAD and PHQ would have allowed reporting the prevalence of depressive and anxiety symptoms. Without baseline measures, we would not have been able to determine how the pandemic changed the severity of these symptoms. Therefore, we believed using these survey items with prompts asking about reactions to the pandemic were appropriate for our study objectives.

That said, we now acknowledge that our measures limit our ability to directly compare results to other studies or pool our effects in future meta-analyses. We include this limitation in the manuscript:

[Discussion] We were primarily interested in reactions to the pandemic rather than how people were feeling/behaving during the pandemic. Therefore, we did not employ standardized measures of stress, anxiety, depression, or well-being. This limits our findings from being directly compared to other studies and pooled in meta-analyses.

1.5. Regarding the exploratory factor analysis, nothing is indicated about the fulfillment of assumptions of normality, linearity or multicollinearity of the scores.

We added histograms to show the relatively normal distributions of the survey items included in the EFA (S1, Figure A1). Also, we report on the lack of multicollinearity in the new multivariate regression model.

1.6. The main finding is that there are three different risk profiles for the students in the sample and several associated risk factors. However, the concept of risk factor does not seem appropriate. How to guarantee that other third variables are not those that account for the relationships found between these risk factors and the three profiles found?

To clarify our terminology, we now refer to the outputs of the latent profile analysis as three levels of psychological Impacts (thus, "Psychological Impact Profiles" or "Impact Profiles" for short).

We believe the reviewer is concerned about unmeasured confounding. To address this concern, we report the results of a mixed-effects logistic regression with adjustments for all risk factors simultaneously entered in the model and random effects for institutional affiliation.

1.7. Table 1 has an error. The total is not the sum of the participants presented in the column.

Thank you for noticing this error. It has been corrected. We reviewed results in the other tables to double-check accuracy as well. No further changes were necessary.

1.8. Finally, the recommendations made exceed the data found. I think they should adjust more to the results and limit themselves more to them

We agree that the recommendation involving outdoor recreation exceeds the data found. It has been removed.

Our recommendations are now more focused on specific findings from this study: changes in behavior and emotional state of students. Specifically, we provide recommendations on how university administration may lower students’ isolation by facilitating opportunities for socially-distanced interactions amongst students and their peers, between students and faculty, and between students and the larger campus community. We also provide recommendations on what techniques could be used to lower students’ level of anxiety and stress, as well as increase their motivation and general wellbeing. Lastly, we expanded our recommendations for working with vulnerable populations.

Reviewer 2

2.1. This is an interesting and timely investigation of the impact of the first wave of the COVID-19 pandemic on university students in seven US states. The authors identified a range of psychological and behavioural COVID-19 related impacts which they used to classify participants into High, Moderate, and Low Impact groups (in the manuscript, the authors used the term ‘risk’ instead of ‘impact’). Latent profile analysis revealed that being a female, non-Latino Asian, and from a low SES were risk factors of inclusion in the High Impact group. Protective factors included being a White man. I believe the main finding of this research is valuable in that it shows how the COVID-19 pandemic differentially impacts students along sociodemographic lines in a large and geographically dispersed US college student sample.

Thank you for the nice summary.

2.2. I don’t believe the Behavioural Impacts listed in this study are appropriate given the pandemic context. Behaviour limiting may have more to do with state-issued directives (e.g., stay home orders) than personal choices to limit activities. For example, where I am, none of the behaviours listed are allowed because of state directives, not because of personal choice. During the period of data collection in each of the data locations, what were the COVID-19-related restrictions on these six activities? Had all study shifted online? Related to this, it appears participants had no opportunity to indicate whether the activities are something they would ever engage anyway (not everyone goes to the gym). Finally, in addition to weather, the time spent on activities in the past 24 hours would be highly variable based on local restrictions, the degree to which the participant’s study, work and other activities have shifted online, etc. For these reasons, I’d remove the Behavioural Impacts from consideration in this paper.

We agree behavioral limitations are suspect to bias and misinterpretation, since pre-COVID levels were not available and lockdown measures (and weather) dictate activities. We removed these variables from the manuscript entirely.

We retain the activities in the last 24 hours because that question format mirrors the time diary approach of the American Time Use Survey. This asks about activity participation in the past 24 hours and correlates highly with objective measures (see https://www.bls.gov/tus/atususersguide.pdf and Tudor-Lock, Johnson & Katzmarzyk, 2010). The use of a time use measure with very recent reporting minimizes the recall bias common when questions reflect “average” behaviors over longer time horizons.

Tudor-Locke, C., Johnson, W. D., & Katzmarzyk, P. T. (2010). Frequently reported activities by intensity for US adults: the American Time Use Survey. American journal of preventive medicine, 39(4), e13-e20.

2.3. Single item factors are not interpretable. Two factor solutions should be accepted for the Psychological Impact items (and Behavioural Impact items, if you choose to keep them).

We now employ Very Simple Structure (VSS) criterion and scree plots to determine the number of factors. We find that a two-factor solution fits the psychological impact data best, and that both factors are represented by multiple survey items. Therefore, the single-item concern is no longer applicable.

Again, we removed behavioral impact items entirely.

2.4. The authors have labelled the profiles High Risk, Moderate Risk, and Low Risk. These profiles would be more accurately labelled High Impact, Moderate Impact, and Low Impact. The sociodemographic, lifestyle, and COVID-19 characteristics are then Risk Factors for finding oneself in the High Impact group.

We thank the reviewer for identifying this necessary change in terminology. We have made the necessary changes. We use "Psychological Impact Profiles" (not "Psychological Risks"). Again, behavioral items are removed based on your good feedback earlier. Sociodemographic, lifestyle-related, and awareness of infected individual characteristics are "Risk Factors".

2.5. In the recommendations, the first two recommendations seem obvious but the third - targeted support for especially vulnerable groups – is more compelling and should be the focus of this section. The final recommendation seems impracticable given restrictions, social distancing, stay at home orders, etc.

We agree that the recommendation involving outdoor recreation exceeds the data found. It has been removed.

Our recommendations are now more focused on specific findings from this study: changes in behavior and emotional state of students. Specifically, we provide recommendations on how university administration may lower students’ isolation by facilitating opportunities for socially-distanced interactions amongst students and their peers, between students and faculty, and between students and the larger campus community. We also provide recommendations on what techniques could be used to lower students’ level of anxiety and stress, as well as increase their motivation and general wellbeing. Lastly, we expanded our recommendations for working with vulnerable populations.

2.6. The description of the analyses is spread across the Analyses and the Results section making it hard to follow. It should be presented as a single 'narrative' of the data analyses in the Results section.

We reorganized the two sections to match another paper utilizing LPA and logistic regression recently published in PLOS ONE (Ekblom-Bak et al., 2020).

Ekblom-Bak, E., Stenling, A., Salier Eriksson, J., Hemmingsson, E., Kallings, L. V., Andersson, G., et al. (2020). Latent profile analysis patterns of exercise, sitting and fitness in adults – Associations with metabolic risk factors, perceived health, and perceived symptoms. PLOS ONE 15, e0232210–13. doi:10.1371/journal.pone.0232210.

2.7. I recognize that the authors’ expertise is in outdoor activities, but the inclusion of outdoor activity variables and literature feel ‘shoe-horned’ into the study and manuscript. I would remove references to outdoor activities and their contribution to psychological wellbeing as, in many cases, this is simply not an option.

This potential coping strategy was a focus given the increasing number of articles on the importance of greenspace/outdoor recreation during the pandemic, as well as debate over whether to keep public parks open (see citations below). However, since outdoor time no longer predicted psychological impact in multivariate models, we have minimized our discussion of it.

Amerio, A., Brambilla, A., Morganti, A., Aguglia, A., Bianchi, D., Santi, F., et al. (2020). COVID-19 Lockdown: Housing Built Environment’s Effects on Mental Health. IJERPH 17, 5973–10. doi:10.3390/ijerph17165973.

Bell, M. L. (2020). Relationships between local vegetation level and human mobility patterns during COVID-19 for Maryland, USA. in, 1–1.

Dümpelmann, S. (2020). Urban Trees in Times of Crisis: Palliatives, Mitigators, and Resources. One Earth 2, 402–404. doi:10.1016/j.oneear.2020.04.017.

Dzhambov, A. M., Lercher, P., Browning, M., Stoyanov, D., Petrova, N., Nedkov, S., et al. (2020). Does indoor greenery provide an escape and support mental health during the COVID- 19 quarantine? Environ. Res., 1–39.

Gillis, K. (2020). Nature-based restorative environments are needed now more than ever. Cities & Health 00, 1–4. doi:10.1080/23748834.2020.1796401.

Gordon, L. (2020). Can virtual nature be a good substitute for the great outdoors? The science says yes. The Washington Post, 1–6. Available at: https://www.washingtonpost.com/video-games/2020/04/28/can-virtual-nature-be-good-substitute-great-outdoors-science-says-yes/.

Hanzl, M. (2020). Urban forms and green infrastructure – the implications for public health during the COVID-19 pandemic. Cities & Health 00, 1–5. doi:10.1080/23748834.2020.1791441.

Honey-Rosés, J., Anguelovski, I., Chireh, V. K., Daher, C., van den Bosch, C. C. K., Litt, J. S., et al. (2020). The impact of COVID-19 on public space: an early review of the emerging questions – design, perceptions and inequities. Cities & Health 00, 1–17. doi:10.1080/23748834.2020.1780074.

Kleinschroth, F., and Kowarik, I. (2020). COVID‐19 crisis demonstrates the urgent need for urban greenspaces. Frontiers Eco. Env. 18, 318–319. doi:10.1002/fee.2230.

Klompmaker, J. O., Hart, J. E., Holland, I., Sabath, M. B., Wu, X., Laden, F., et al. (2020). County-level exposures to greenness and associations with COVID-19 incidence and mortality in the United States. medRxiv, 1–14. doi:10.1101/2020.08.26.20181644.

Lu, Y., Zhao, J., Wu, X., and Lo, S. M. (2020). Escaping to nature in pandemic: a natural experiment of COVID-19 in Asian cities. osf.io, 1–35. doi:10.31235/osf.io/rq8sn.

McCunn, L. J. (2020). The importance of nature to city living during the COVID-19 pandemic: Considerations and goals from environmental psychology. Cities & Health 00, 1–4. doi:10.1080/23748834.2020.1795385.

Pouso, S., Borja, A., Fleming, L. E., Gómez-Baggethun, E., White, M. P., and Uyarra, M. C. (2020). Maintaining contact with blue-green spaces during the COVID-19 pandemic associated with positive mental health. osf.io. doi:https://doi.org/10.31235/osf.io/gpt3r.

Ramkissoon, H. (2020). COVID 19 Place Confinement, Pro-Social, Pro-environmental Behaviors, and Residents' Wellbeing: A New Conceptual Framework. fpsyg-11-02248.tex, 1–11. doi:10.3389/fpsyg.2020.02248.

Rice, W. L., Mateer, T. J., Reigner, N., Newman, P., Lawhon, B., and Taff, B. D. (2020). Changes in recreational behaviors of outdoor enthusiasts during the COVID-19 pandemic: analysis across urban and rural communities. Journal of Urban Ecology 6, 37–8. doi:10.1093/jue/juaa020.

Rousseau, S., and Deschacht, N. (2020). Public Awareness of Nature and the Environment During the COVID-19 Crisis. Environ Resource Econ 12, 1–11. doi:10.1007/s10640-020-00445-w.

Saadi, D., Schnell, I., Tirosh, E., Basagaña, X., and Agay-Shay, K. (2020). “There’s no place like home? The psychological, physiological, and cognitive effects of short visits to outdoor urban environments compared to staying in the indoor home environment, a field experiment on women from two ethnic groups.” Environ. Res., 109687. doi:10.1016/j.envres.2020.109687.

Samuelsson, K., Barthel, S., Colding, J., Macassa, G., and Matteo Giusti (2020). Urban nature as a source of resilience during social distancing amidst the coronavirus pandemic. OSF Preprint.

Slater, S. J., Christiana, R. W., and Gustat, J. (2020). Recommendations for Keeping Parks and Green Space Accessible for Mental and Physical Health During COVID-19 and Other Pandemics. Preventing Chronic Disease 17, 200204–8. doi:10.5888/pcd17.200204.

Stieger, S., Lewetz, D., and Swami, V. (2020). Psychological Well-Being Under Conditions of Lockdown: An Experience Sampling Study in Austria During the COVID-19 Pandemic. psyarxiv.com. doi:https://doi.org/10.31234/osf.io/qjhfp.

Understanding Psychosocial Factors Influencing Outdoor Recreation During the COVID-19 Pandemic (2020). Understanding Psychosocial Factors Influencing Outdoor Recreation During the COVID-19 Pandemic. Soc Nat Resour, 1–23.

Venter, Z., Barton, D. N., Gundersen, V., Figari, H., and Nowell, M. (2020). Urban nature in a time of crisis: recreational use of green space increases during the COVID-19 outbreak in Oslo, Norway. osf.io, 1–28. doi:https://doi.org/10.31235/osf.io/kbdum.

You, H., Wu, X., and Guo, X. (2020). Distribution of COVID-19 Morbidity Rate in Association with Social and Economic Factors in Wuhan, China: Implications for Urban Development. IJERPH 17, 3417–14. doi:10.3390/ijerph17103417.

2.8. There is an implication that screen time is a ‘bad thing’. During COVID-19, screens are how students access study, paid work, friends, and families. In this sense, increases in screen time is inevitable and desirable and should not be framed as the anti-outdoor activity in the way they might be in a non-COVID world.

We agree that increased screen time is inevitable. However, a growing body of literature suggests screen time is associated with access to unhealthy amounts of information and reactions to COVID-19 (see references below). Further, we find excessive screen time remains a significant predictor of psychological impacts in multivariate models. We maintain our reporting of it in the discussion.

Dubey, S., Biswas, P., Ghosh, R., Chatterjee, S., Dubey, M. J., Chatterjee, S., et al. (2020). Psychosocial impact of COVID-19. Diabetes & Metabolic Syndrome: Clinical Research & Reviews 14, 779–788. doi:10.1016/j.dsx.2020.05.035.

Duong, V., Pham, P., Yang, T., Wang, Y., and Luo, J. (2020). The Ivory Tower Lost: How College Students Respond Differently than the General Public to the COVID-19 Pandemic. arxiv.org

Hart, P. S., Chinn, S., and Soroka, S. (2020). Politicization and Polarization in COVID-19 News Coverage. Sci Commun, 107554702095073–19. doi:10.1177/1075547020950735.

Holmes, E. A., O'Connor, R. C., Perry, V. H., Tracey, I., Wessely, S., Arseneault, L., et al. (2020). Multidisciplinary research priorities for the COVID-19 pandemic: a call for action for mental health science. The Lancet Psychiatry 7, 547–560. doi:10.1016/S2215-0366(20)30168-1.

Huckins, J. F., daSilva, A. W., Wang, W., Hedlund, E., Rogers, C., Nepal, S. K., et al. (2020). Mental Health and Behavior of College Students During the Early Phases of the COVID-19 Pandemic: Longitudinal Smartphone and Ecological Momentary Assessment Study. J Med Internet Res 22, e20185. doi:10.2196/20185.

Liu, Q., Zheng, Z., Zheng, J., Chen, Q., Liu, G., Chen, S., et al. (2020). Health Communication Through News Media During the Early Stage of the COVID-19 Outbreak in China: Digital Topic Modeling Approach. J Med Internet Res 22, e19118–12. doi:10.2196/19118.

Stieger, S., Lewetz, D., and Swami, V. (2020). Psychological Well-Being Under Conditions of Lockdown: An Experience Sampling Study in Austria During the COVID-19 Pandemic. psyarxiv.com. doi:https://doi.org/10.31234/osf.io/qjhfp.

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2.9. The open-ended response tables probably belong in the supplementary materials.

Ordinarily, we might agree with the reviewer, but the available empirical evidence on the range of psychological and behavioral impacts of COVID-19 on students is relatively limited. Placing these tables in the supplementary materials would reduce their visibility and utility. Further, summarizing these findings in the main text without tables in the main text would increase in the overall length of the manuscript, which is already long.

Ultimately, we are open to the PLOS ONE editor's suggestions on this, but if the journal allows, we trust that reporting these tables in a prominent location would ensure readers would appreciate easily finding them.

2.10. The authors mention basic negative emotion states but don’t say what they are. What are they?

We now identify these states in the manuscript:

Negative emotion states comprised four of the survey items. Each item explained one of the basic negative emotions (i.e., being afraid, irritable, guilty, and sad) identified during the development of the positive and negative affect schedule (PANAS).

2.11. Why was BMI included?

BMI has been implicated as a possible risk factor of psychological impacts from COVID-19 and a covariate in multivariate models predicting these impacts in past work (please see references below). We now justify the inclusion of BMI in the article using the below references.

Huang, Y., Wang, Y., Zeng, L., Yang, J., Song, X., Rao, W., et al. (2020). Prevalence and Correlation of Anxiety, Insomnia and Somatic Symptoms in a Chinese Population During the COVID-19 Epidemic. Front. Psychol., 1–9. doi:10.3389/fpsyt.2020.568329/full.

Xiao, J., Jiang, Y., Zhang, Y., Gu, X., Ma, W., and Zhuang, B. (2020). The Impact of Psychology Interventions on Changing Mental Health Status and Sleep Quality in University Students during the COVID-19 Pandemic. medRxiv. doi:10.1101/2020.09.01.20186411.

2.12. Sub-headings would provide more structure to the manuscript (e.g., Psychological impacts, Behavioural impacts, Lifestyle risk factors, Sociodemographic risk factors, etc.).

We appreciate this recommendation. We have added these sub-headings and hope they make the manuscript structure easier to follow.

2.13. Avoid using terms interchangeable - when talking about risk factors you also refer to them variables, characteristics, and attributes.

We thank the reviewer for identifying this inconsistency. We have made the necessary changes.

2.14. Participant numbers and characteristics should be included in the Participants section.

We thank the reviewer for calling our attention to the fact that PLOS articles often have sample characteristics in the methods section. We have made this change.

Submitted filename: Response to Reviewers.docx

 10.1371/journal.pone.0245327.r003 Decision Letter 1 Lin Chung-Ying Academic Editor 2021 Chung-Ying Lin This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Submission Version 1

29 Dec 2020

Psychological impacts from COVID-19 among university students: Risk factors across seven states in the United States

PONE-D-20-24157R1

Dear Dr. Browning,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Chung-Ying Lin

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PLOS ONE

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Reviewer #2: All comments have been addressed

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Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #2: Yes: Francesca Collins

 10.1371/journal.pone.0245327.r004 Acceptance letter Lin Chung-Ying Academic Editor 2021 Chung-Ying Lin This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

30 Dec 2020

PONE-D-20-24157R1

Psychological impacts from COVID-19 among university students: Risk factors across seven states in the United States

Dear Dr. Browning:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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on behalf of

Dr. Chung-Ying Lin

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PLOS ONE