Journal of Psychosocial Nursing and Mental Health Services

Original Article 

Prevalence of Mental Health Disorders Among Undergraduate University Students in the United States: A Review

Harmeet Kaur Kang, PhD, MSN, BSN, RN; Christopher Rhodes, MSN, RN; Emerald Rivers, MSN, RN; Clifton P. Thornton, MSN, RN, CPNP; Tamar Rodney, PhD, RN, PMHNP-BC, CNE

Abstract

The aim of the current review is to describe the prevalence and demographic correlates of mental health disorders among undergraduate university students in the United States. A search strategy was built and conducted using PubMed, PsycINFO, and CINAHL to identify studies published between 2009 and 2019 on the prevalence of mental health disorders, as defined in the fourth and fifth editions of the Diagnostic and Statistical Manual of Mental Disorders, in undergraduate students in the United States. A total of 12 studies were included in the final data extraction. The highest prevalence rates were identified in eating disorders, which ranged from 19% to 48%, followed by compulsive disorders (2% to 12.27%), depression (22%), posttraumatic stress disorder (8%), and sleep disorders (9.4% to 36%). The identified prevalence of mental health disorders is high, and the subsequent impact on this population is worrying. There is an urgent need to develop strategies for early screening and management of mental health services in university settings. [Journal of Psychosocial Nursing and Mental Health Services, xx(x), xx–xx.]

Abstract

The aim of the current review is to describe the prevalence and demographic correlates of mental health disorders among undergraduate university students in the United States. A search strategy was built and conducted using PubMed, PsycINFO, and CINAHL to identify studies published between 2009 and 2019 on the prevalence of mental health disorders, as defined in the fourth and fifth editions of the Diagnostic and Statistical Manual of Mental Disorders, in undergraduate students in the United States. A total of 12 studies were included in the final data extraction. The highest prevalence rates were identified in eating disorders, which ranged from 19% to 48%, followed by compulsive disorders (2% to 12.27%), depression (22%), posttraumatic stress disorder (8%), and sleep disorders (9.4% to 36%). The identified prevalence of mental health disorders is high, and the subsequent impact on this population is worrying. There is an urgent need to develop strategies for early screening and management of mental health services in university settings. [Journal of Psychosocial Nursing and Mental Health Services, xx(x), xx–xx.]

The mental health of university students is an important global concern. This issue has garnered attention from organizations such as the World Health Organization, which developed the World Mental Health International College Student Initiative in collaboration with the American Psychological Association to examine the prevalence of mental health disorders among college students (October 2014 to February 2017). This report found that more than one third of students screened positive for at least one of six mental health disorders, with 27% showing prevalence of any mental health disorder during a 12-month period, and a lifetime prevalence of 28.7% (Auerbach et al., 2018).

The growth and development of a country is dependent on young adult populations (Committee on Improving the Health, Safety, and Well-Being of Young Adults et al., 2015). The transition from high school to college can be a distressing period (Kadison, 2004) that requires students to develop and exercise greater autonomy with a shift in their roles and expectations. They gain more freedom to explore aspects of life and are tasked to make important decisions regarding their education, future career, leisure activities, romantic relationships, and other decisions that may have lifelong implications (Arnett, 2000; Sussman & Arnett, 2014). Concurrently, university students face tremendous stress attributed to changes in relationship status, sexual orientation, peer group pressure, and course selection (Arnett, 2000). These are all factors that may contribute to mental health disorders among college students (Slavich & Auerbach, 2018).

Anxiety, depression, and substance use disorders have frequently been reported in university students (Auerbach et al., 2018; de Girolamo et al., 2012; Kessler et al., 2007), which have been associated with poor academic performance (Auerbach et al., 2016; Auerbach et al., 2018; Bruffaerts et al., 2018) and suicidal ideation (Mortier et al., 2018). It is well known that early identification of mental health issues and the provision of mental health services on university campuses improves resilience among students, leading to improvements in interpersonal skills, creative work, and innovations (Douce & Keeling, 2014). Student participation in mental health counseling services allows for improved mental health, higher attendance rates, and increased satisfaction with quality of life (Arria et al., 2013).

Considering the long-term benefits of early identification and prompt management of mental health disorders, there is a need for understanding the current prevalence of mental health disorders among university students. Most universities in the United States offer access to resources and mental health services ranging from providing resources online as well as offline (Downs & Eisenberg, 2012). These services include materials to increase awareness, recognize symptoms of mental illness, and the availability of screening and referral services. Despite the availability of these resources, studies have reported continued underuse by university students (Downs & Eisenberg, 2012). Prevalence statistics lay the groundwork for highlighting this need to increase use of mental health resources by university students, with an opportunity to improve the availability of mental health services in university settings in the United States. The current review examines recent studies that describe the prevalence of mental health disorders in undergraduate students in the United States. The authors also reviewed the strengths and limitations of existing studies and suggest future recommendations.

Method

Search Strategy

A search strategy was built with the assistance of a medical research librarian to identify publications exploring the prevalence of mental health disorders in university students in the United States. The following search terms were used: undergraduate, students, university, mental health disorders, and prevalence. MeSH terminology, truncations, and Boolean Operators were used as applicable for PubMed, CINAHL, and PsycINFO databases.

Selection Criteria

Peer-reviewed articles were selected for inclusion in this review if they examined the prevalence of mental health disorders (per criteria listed in the fourth and fifth editions of the Diagnostic and Statistical Manual of Mental Disorders [DSM]) in undergraduate university students in the United States and were published between 2009 and 2019. Exclusion criteria were: studies focused on community colleges, vocational schools, specialty schools (e.g., medicine, pharmacy); focus on specific sub-populations (e.g., Asian American students, athletes); and comparison or intervention studies.

Data Extraction

Data extracted included sample size, population characteristics, classification of mental health disorder, prevalence of the disorder, tools used for assessment, and strengths and limitations of each study. The systematic search strategy identified 1,917 studies. Screening of titles and abstracts excluded 1,687 studies. The full text of 230 studies were reviewed by two authors and 218 studies were excluded after mutual agreement on the basis of the inclusion and exclusion criteria. Therefore, 12 studies met the full eligibility criteria after thorough full-text review. The overall search and screening process is shown in Figure 1, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework (Moher et al., 2009).

Search strategy using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (Moher et al., 2009).Note. DSM = Diagnostic and Statistical Manual of Mental Disorders.

Figure 1.

Search strategy using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (Moher et al., 2009).

Note. DSM = Diagnostic and Statistical Manual of Mental Disorders.

Results

Detailed information regarding the population, mental health diagnosis, and measurement tools of all 12 studies included in this review can be found in Table A (available in the online version of this article). These studies were sorted into five levels by related diagnoses: (1) depression, (2) eating disorders, (3) sleep disorders, (4) compulsive-related disorders/posttraumatic stress disorder (PTSD), and (5) body-focused disorders.

Characteristics of Included Studies.Characteristics of Included Studies.Characteristics of Included Studies.Characteristics of Included Studies.Characteristics of Included Studies.

Table A:

Characteristics of Included Studies.

Depression

One study was included on the prevalence of depression among undergraduate university students. A random sample of 2,500 undergraduate students were selected for survey distribution and 428 students participated. Beck's Depression Inventory II (BDI-II) was used for the assessment of depression. Results showed that 22% of students reported moderate depression with a cut off score of >20 on the BDI-II, and 10% of students reported suicidal thoughts in the past 12 months, 8% had a suicide plan, and 4% had attempted suicide. Two students reported that they sought treatment following a suicide attempt. The prevalence of depression reported was significantly higher among females (25%) than males (17%) (Roberts et al., 2010). The study findings are not generalizable, however, due to a low response rate and the recruitment of participants from only one college.

Eating Disorders

Two articles discussed the prevalence of eating disorders among college students. Symptoms of eating disorders were measured by the Sick, Control, One stone, Fat, Food (SCOFF) screening instrument (U.S. version), a five-question self-report survey with the following questions: (1) Do you make yourself sick (i.e., induce vomiting) because you feel uncomfortably full?; (2) Do you worry that you have lost control over how much you eat?; (3) Have you recently lost more than one stone (14 lb [6.4 kg]) in a 3-month period?; (4) Do you think you are too fat, even though others say you are too thin?; and (5) Would you say that food dominates your life? The survey was conducted via anonymous email to students enrolled at a public institution in the midwestern United States (Eisenberg et al., 2011). A total of 5,021 students were randomly selected, and the survey was completed by 2,822 students in 2005, with a follow-up survey completed by 753 students 2 years later. Initial prevalence (+3 on the SCOFF scale) of eating disorders was 9.4%, and diagnosis of eating disorders was reported by 19% of students in the follow-up survey (Eisenberg et al., 2011). Those scoring positive for eating disorder symptoms included 13.5% of female respondents and 3.6% of male respondents. Those respondents who reported a history of anorexia nervosa and bulimia nervosa were 1.2% and 0.9%, respectively, at the time of the baseline survey (Eisenberg et al., 2011).

A team at a private university in the northeastern United States recruited 211 college-aged students to complete a survey on binge drinking and disordered eating to determine the relationship between the two conditions (Kelly-Weeder, 2011). Students were asked to report their gender, frequency of consumption of alcoholic beverages, drinking behaviors over the past 2 weeks, and number of drinks consumed on days they were drinking. Participants were also asked questions to evaluate disordered eating via self-reported episodes of binge-eating, frequency of exercise, purposefully eating smaller meals, skipping meals, use of laxatives, fasting, and self-induced vomiting. Binge eating was reported by 48% of respondents without significant differences between genders; 34.5% of females and 39% of male respondents had co-occurrence of binge eating and drinking (Kelly-Weeder, 2011). Another important outcome of this research is the reported high rates of binge drinking among students (63% female, 83% male) (Kelly-Weeder, 2011). Although these findings are important, binge drinking is currently not recognized as a DSM diagnosis; however, it reflects a pattern of drinking that would be a diagnostic criterion for alcohol use disorder.

Sleep Disorders

Five research studies addressed sleep disorders among undergraduate university students (Gaultney, 2010; Lund et al., 2010; Petrov et al., 2014; Taylor et al., 2011; Taylor et al., 2013). The prevalence of symptoms related to sleep disorders ranged from 9.4% to 60%. However, four of five studies had a bias toward female responders (Gaultney, 2010; Petrov et al., 2014; Taylor et al., 2011; Taylor et al., 2013). All studies reported a higher prevalence of sleep disorders in association with other major mental health disorders, such as depression, anxiety, and affective disorders.

A study conducted at a state university in the southeastern United States used the SLEEP-50 instrument to assess sleep disorders among 1,845 students (Gaultney, 2010). The study reported that 27% of students were at risk of at least one sleep disorder, with a majority at risk for developing narcolepsy (16%) and insomnia (12%), followed by restless legs syndrome (RLS)/periodic limb movement disorder (8%), circadian rhythm sleep disorder (7%), affective disorder (7%), obstructive sleep apnea (4%), and hypersomnia (0.4%). This study reported high rates for narcolepsy when compared to the general population, which points to questions about the accuracy of the SLEEP-50 tool for identification of narcolepsy. Further, another limitation of the study is the recruitment of participants from only one university, which limits the generalizability of data (Gaultney, 2010).

One research study indicated a 36% prevalence rate of sleep disorders (Petrov et al., 2014). The sample of this study comprised 1,684 undergraduate students attending the University of Alabama, recruited through an online survey. The Insomnia Severity Index and the Global Sleep Assessment Questionnaire were used as instruments to assess sleep disorders. Results showed that 6.3% of respondents were at risk for two or more sleep disorders (Petrov et al., 2014). Prevalent disorders included insomnia, RLS, and periodic limb movement disorder. Further, students who had previously diagnosed mental health disorders were at greater risk for sleep disorders (12% prevalence) versus students who had no prior mental health disorder diagnosis (5.5% prevalence). Insomnia was reported significantly more by females than males (15.8% and 9.5%, respectively). In regard to ethnicity, there were no significant differences for prevalence of sleep disorders (Petrov et al., 2014).

One study identified the prevalence of insomnia as 9.5% (Taylor et al., 2013). Similarly, another study conducted to assess insomnia and related mental health disorders reported the prevalence of insomnia as 9.4% among undergraduate students, and also reported significantly higher prevalence of mental health disorders among students with insomnia (Taylor et al., 2011).

In a study conducted on the prevalence of poor sleep quality in college students, with a sample of 1,125 students aged 17 to 24 years from an urban university in the midwestern United States, participants responded to a cross-sectional online survey on sleep habits using the Pittsburgh Sleep Quality Index (PSQI), Epworth Sleepiness Scale, Horne-Ostberg Morningness-Eveningness Scale, Profile of Mood States, and Subjective Units of Distress Scale. Students also provided additional information on academic performance, physical health, and psychoactive drug use. The prevalence of poor sleep quality was reported as 60% among this sample of college students (Lund et al., 2010). Further, students reported taking over-the-counter, recreational psychoactive and prescription drugs for sleep or wakefulness. Poor sleep quality was significantly associated with negative moods, confusion, anger, depression, fatigue, and tension (Lund et al., 2010).

Three of five studies reported higher prevalence of sleep disorders among females compared to males (Gaultney, 2010; Petrov et al., 2014; Taylor et al., 2013). Further, there were no significant associations between ethnic groups and the prevalence of sleep disorders (Gaultney, 2010; Petrov et al., 2014; Taylor et al., 2013).

Posttraumatic Stress Disorder, Compulsive Disorders, and Body-Focused Repetitive Behavior

There were four research studies that addressed the prevalence of PTSD and compulsive spectrum disorders, including compulsive sexual behavior, body-focused repetitive behaviors (BFRBs), body dysmorphic disorder, health anxiety, and obsessive-compulsive disorder (OCD) (Elhai et al., 2012; Houghton et al., 2018; Odlaug et al., 2015; Sulkowski et al., 2011). Most participants were female (58.5% to 71.5%) and Caucasian (56% to 78.3%), with a mean age that ranged from 18.77 to 22.6 years.

Updated DSM-5 PTSD screening instruments were not available in 2012, when two of the included studies were published. Revisions for the current DSM-5 were published in 2013. The authors modified the PTSD Symptom Scale–Self-Report (PSS-SR) to assess the prevalence of PTSD according to the new DSM-5 criteria, and assessed the prevalence of PTSD based on the DSM-IV criteria. The study recruited 625 students, and initially, a stressful life events questionnaire was administered, in which 216 students reported previous trauma exposure (Elhai et al., 2012). The PTSD symptoms scales were administered to the 216 students who reported previous trauma. The prevalence of at least one traumatic exposure was found to be 66.5% per DSM-IV criteria, whereas it was reported to be 59% per DSM-5 criteria. Overall, the prevalence of PTSD per DSM-5 criteria was reported to be 8%. Furthermore, no significant differences in prevalence estimates of PTSD per DSM-IV and DSM-5 were reported in the study (Elhai et al., 2012).

The study on BFRBs included 4,435 undergraduate students from a public university. The sample comprised 69.3% female participants and the mean age of participants was 18.7 years. Students were surveyed for engagement in BFRBs, including daily engagement in hair pulling, skin picking, nail biting, cheek biting, teeth grinding while awake, and skin biting. Study findings reported a high percentage (72%) of students engaged in BFRBs. Cheek biting was the most common subclinical BFRB reported by students. Results showed that 12% of students reported at least one pathological BFRB. Further, of the students who reported pathological BFRBs, 83% reported only one pathological BFRB, whereas two concurrent pathological BFRBs were reported by 13% of students. Prevalence of pathological BFRBs was higher among females (77%) compared to males (23%) (Houghton et al., 2018).

One study was conducted on 358 undergraduate students of a southern United States university to assess the prevalence of OCD and related symptoms. The instruments used for assessing the prevalence of OCD were the Skin Picking Scale, Body Dysmorphic Disorder Questionnaire, Obsessive Compulsive Inventory-Revised, Health Anxiety Inventory-Short Form, Beck Anxiety Inventory, and the Barratt Impulsiveness Scale-15. OCD symptoms were assessed using the DSM-IVTR criteria. Skin picking urge and occasional skin picking was reported by 42% and 38% of students, respectively, with 6% of students reporting that they engaged in pathological skin picking. Thirty-five percent of students reported distress related to skin picking. Trichotillomania urge was reported by 22% of students and 13% reported occasional hair pulling. Three percent of students reported distress associated with hair pulling. Body dysmorphic disorder was reported by 30% of students. Five percent reported distress and functional impairment due to obsession over flaws in physical appearance. Clinically significant OCD symptoms and health anxiety were present in 5% and 7% of students, respectively. There were no significant differences in the incidence of all disorders according to gender (Sulkowski et al., 2011).

Another study was conducted on a sample of 2,108 students from a midwestern university, which comprised 41.8% male and 78% Caucasian participants. The measures used included the Internet Addiction Test (IAT), Patient Health Questionnaire (PHQ), Perceived Stress Scale (PSS), and the Minnesota Impulsive Disorder Interview (MIDI). Study findings revealed that depressive symptoms were reported significantly more by obese male students (28.6%) than overweight (10.6%) and normal weight (9.5%) male students. The prevalence of major depressive disorders was also reported to be significantly greater in obese male students (28.6%) when compared to overweight (9.5%) and normal weight (10.6%) students. Further, the prevalence of trichotillomania was higher among obese male students (5.4%) than overweight and normal weight students. Obese males were at 156% greater risk of being diagnosed with lifetime psychiatric disorders than normal weight male students (Odlaug et al., 2015).

Discussion

The current review reports recent data estimating the prevalence of mental health disorders among undergraduate students in the United States. This review highlights several current gaps needed to provide a comprehensive, accurate picture of the true prevalence of mental health disorders and the needs of this population. In addition, these findings highlight the need for ongoing assessment that considers social changes, diagnostic criteria changes, and the evolving needs of this population.

Mental health disorders among university students have been given special attention over the past decade. Despite this attention, most prevalence studies have focused on eating disorders (Eisenberg et al., 2011; Hudson et al., 2007), and depression and suicidal ideation (Downs & Eisenberg, 2012; Kisch et al., 2005). There are multiple studies on non-medical use of stimulants, which is a significant concern in this population (McCabe et al., 2005; Wilens et al., 2008). Presently, non-medical use of stimulants is not classified as a disorder according to DSM criteria. In addition, we did not find any studies conducted on substance use disorders among undergraduate university students. There are several other mental health disorders that are not explored in the prevalence studies, such as schizophrenia and attention-deficit/hyperactivity disorder (ADHD), which could also have a significant impact on this population. Further, most studies are centered on mental health disorders among specialty university groups, including medical, health sciences, and graduate students (Laurence et al., 2009; Macauley et al., 2018; Rotenstein et al., 2016). Mental health issues among undergraduate students have been ignored, despite the undergraduate period being considered one of the most distressing (Kadison, 2004), for which students need to adjust to entirely new environments.

Furthermore, to our knowledge, there are no systematic reviews conducted on the prevalence of mental health disorders among undergraduate university students in the United States. A systematic review on global prevalence of depression among undergraduate students was published by Ibrahim et al. (2013), where weighted prevalence of depression was reported as 30.6% globally, whereas in the current review, prevalence of depression was found to be 22% in the United States. One review protocol focusing on mental disorders among university students in low-middle income countries has also been published (January et al., 2018).

After conducting a systematic search strategy, identification, and screening based on eligibility criteria, only 12 studies were found to be relevant, and the majority of these studies had limitations in the methods used, such as use of convenience sampling, online surveys with low response rates (Eisenberg et al., 2011; Odlaug et al., 2015; Petrov et al., 2014; Sulkowski et al., 2011), use of cross-sectional survey design (Odlaug et al., 2015; Sulkowski et al., 2011; Taylor et al., 2013), and use of self-report measures (Elhai et al., 2012; Houghton et al., 2018; Petrov et al., 2014). Authors could not form concrete conclusions, as many of the study samples were not true representations of the population.

The current review included studies published within the past 10 years to gather recent data on the prevalence of mental health disorders among university students. It was presumed that studies published within this time could have captured the long-term effects of violent terrorist attacks, such as 9/11, and deadly school shooting incidents. Such incidents might have a significant effect on children during their childhood development, which may progress to the college/university years. It is well documented that stressful and violent events in childhood can result in PTSD, which can have disastrous effects on students (Nurius et al., 2015), resulting in poor concentration and severe distress (Singer et al., 1995).

We could not find prevalence studies for general mental disorders, such as depression and ADHD, conducted on undergraduate students exclusively. However, there were several studies on these disorders conducted among medical, health sciences, and mixtures of graduate and undergraduate students (Downs & Eisenberg, 2012; Eisenberg et al., 2011; Hudson et al., 2007; Kisch et al., 2005; McCabe et al., 2005; Wilens et al., 2008).

There is a paucity of literature on the prevalence of schizophrenia among university students; however, studies have suggested that the onset of psychotic symptoms occurs in adolescence and persists into early adulthood. One Swedish study reported the “flaring up of symptoms” in late adolescence and the early 20s and a lower risk in the late 20s (Sham et al., 1994). Similarly, Häfner et al. (1993) reported the onset of schizophrenia before the age of 25 years. Within this context, there is a likelihood of a notable prevalence of schizophrenia among undergraduate university students and there is a need for conducting studies on the prevalence of schizophrenia-related disorders among this population.

Limitations

The current study has some limitations. First, we limited our search population to undergraduate students, which excludes graduate students who may also have prevalent mental health disorders. Second, we included only peer-reviewed publications to strengthen the validity, increase generalization, and get an accurate picture of the prevalence of mental disorders among undergraduate students. This decision may have limited otherwise valuable information presented in grey literature. Third, we identified a small number of studies, which demonstrates lack of studies in this field. In addition, included studies were limited to reflect diagnoses identified in the DSM. This limitation reduced our ability to provide comprehensive symptoms that are important but have not reached the diagnostic threshold. These findings signify that additional studies are needed to provide an accurate description of issues impacting the prevalence of mental health disorders among undergraduate university students in the United States.

Relevance for Clinical Practice

Health care practitioners can play an important role in the early screening of mental health disorders using basic screening tools. Students with positive results on screening tools must be referred for further diagnostic evaluations. Furthermore, psychiatric–mental health (PMH) providers should engage in community outreach and educational activities for students, faculty, and parents. Nurses are uniquely qualified to provide this care, specifically psychiatric nurses and PMH nurse practitioners. Their roles and scope of practice are grounded in the provision of person-centered care, establishing therapeutic communication, and fostering interpersonal relationships, which are foundational to successful engagement in this population (Kane, 2015). This experience would enable nurses to better identify the symptoms and warning signs of mental health disorders and encourage patients to seek early help either inside or outside the university campus. Providers can come up with innovative solutions for timely help and early screening of mental health disorders among students through development of mental health–based technology tools. The use of technology is increasing in this population and innovative methods should be used to engage students. These measures can increase engagement in an anonymous, yet accessible mode and increase availability so that students can have 24-hour access to mental health services in addition to a physical space.

Conclusion

Results of the current review reveal the high prevalence of mental health disorders among undergraduate university students, which points toward the need for strengthening early screening and mental health services in university settings. Further, there is a need to conduct primary studies to determine the prevalence of schizophrenia-related disorders and ADHD among undergraduate students.

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Characteristics of Included Studies.

Author(s)TitleMental Health Disorder (DSM)Population/Sample DescriptionPrevalenceTool(s)Other
(Elhai et al., 2012)Posttraumatic stress disorder in DSM-5: Estimates of prevalence and symptom structure in a nonclinical sample of college studentsPosttraumatic stress disorder (DSM-IV & DSM-5)Setting:Ohio Public University Sample size:625 recruited, 216 administered PTSD symptoms scale Age: Mean: 19.36 years Sex/Gender:Female: 71.2% Ethnicity:Hispanic/Latino: 5%; Caucasian: 75%; African American: 18.3%; Asian: 5.5%Overall, 66% of participants endorsed at least one traumatic event that met DSM IV criteria and 59.3% with DSM-5 criteria.Stressful life events screening questionnaire (SLLSQ) PTSD symptoms scale self-report (PSS-SR) Center for Epidemiological Studies-Depression Scale (CES-D).Strengths: The study investigates the prevalence of PTSD per DSM-IV and DSM-5 criteria Limitations: Self-reported measure for PTSD rather than structured diagnostic interviews
(Gaultney, 2010)The prevalence of sleep disorders in college students: Impact on academic performanceObstructive Sleep Apnea (OSA); Periodic Limb Movement Disorder/Restless Leg Syndrome (PLMD/RLS); Insomnia; Narcolepsy; Circadian Rhythm Disorders (CRDs); Affective Disorder; Hypersomnia; Sleep State Misperception (SSM)Setting:Large state university in the southeastern United States. Sample size:1,845 Age: Mean: 20.38 years Sex/Gender:Female: 71% Ethnicity:70% White, 17% African/American, 5% Other, 4% Asian, 4% Latino.27% at risk for at least one sleep disorderSLEEP-50Strengths: Assessed all major sleep disorders Limitation:Survey results instead of DSM diagnosis from provider. There was an inflated Narcolepsy scale. Further validation of this scale is needed.
(Houghton, Alexander, Bauer, & Woods, 2018)Body-focused repetitive behaviors: More prevalent than once thought?Body-focused repetitive behaviors (BFRBs)Setting:A large public university Sample size:4,435 Age: Mean: 18.77 years Sex/Gender:Female: 69.3% Ethnicity:Not reported12.27% met criteria for a BFRB disorderQuestions related to BFRBs within the last month. BFRBs de fined as daily engagement in hair pulling, skin picking, nail biting, cheek biting, teeth grinding while awake, and/or skin bitingStrength:Assessment of multiple forms of BFRBs Limitation: Assessment tool not psychometrically validated. Participants' diagnostic status not verified by a trained evaluator
(Eisenberg, Nicklett, Roeder, & Kirz, 2011)Eating disorder symptoms among college students: Prevalence, persistence, correlates, and treatment-seekingEating disordersSetting: A large midwestern public university Sample size: 5,021 recruited, survey completed by 2,822 Sex/Gender:Female: 57.3% Ethnicity: White/Caucasian: 68.4%, African American/Black: 7%, Hispanic/Latino: 3.4%, Asian/Pacific Islander: 15.5%, Arab/Middle Eastern: 1.1%, more than above: 4.5%, other: 0.2%13.5% in females, 3.6% in males 19% diagnosis of eating disorder in follow-up surveyUS version of SCOFF screening instrument: PHQ-9Strengths: Use of baseline and follow-up survey used to study the persistence of eating disorders Limitations: Measure used to identify eating disorder symptoms (SCOFF) is a screening tool, not a clinical diagnostic tool. Follow-up survey has non-response bias.
(Kelly-Weeder, 2011)Binge drinking and disordered eating in college studentsDisordered eating behaviors; binge drinking behaviorsSetting:Private university in the northeastern United States Sample size:211 Age: 18 to 26 years; Mean: 20.7 Sex/Gender:Females: 65.9% Ethnicity:80.6% Caucasian, 7.1% Asian/Pacific Islander, 6.2% Hispanic, 2.8% Multiracial, 2.4% African/American, 0.95% other.48% of students reported binge eating disorders 83% of males and 63% of females met criteria for binge drinking 34.5% of females and 39% of male respondents had co-occurrence of binge eating and drinkingSelf-reported behaviors regarding number of alcoholic drinks, eating behaviors, and weight loss behaviorsStrength: Assessment of variety of eating disorders Limitations:No use of validated tool, small sample size, convenience sampling
(Odlaug et al., 2015)Compulsive sexual behavior in young adultsCompulsive sexual behaviorSetting:Large public midwestern university Sample size:2,108 completed survey, 1,765 after exclusion Age: Mean: 22.6 years Sex/Gender:58.5% female Ethnicity:78.3% White, 10.9% Asian American, 2.0% Hispanic, 1.2% African American, 1.2% African, 4.0% other.2% of students met criteria for current compulsive sexual behavior (23 males and 13 females)Minnesota Impulsive Disorders Interview (MIDI)Strengths: Random selection of subjects Limitations: Compulsive sexual behavior rates were based on a self-report scale without an in-person evaluation; Limited generalizability with 78% white students
(Petrov, Lichstein, & Baldwin, 2014)Prevalence of sleep disorders by sex and ethnicity among older adolescents and emerging adults: Relations to daytime functioning, working memory and mental healthSleep disordersSetting:University of Alabama Sample size:1,684 Age: 17 to 25 years Sex/Gender:Male: 23.2% Ethnicity:76.8% Non-Hispanic White; 15.3% BlackOverall, 36% at risk for at least one sleep disorder, and 6.3% at risk for two or more sleep disorders Insomnia = 14.3%; Restless legs syndrome = 8.4%; Periodic limb movement disorder = 7.8%Insomnia Severity Index (ISI) The Global Sleep Assessment Questionnaire (GSAQ)Strength:Assessed the work memory capacity (WMC) and its relationship with sleep disorders Limitations: Self-reported data, not randomly selected–limits generalizability, women were oversampled, GSAQ validated from a sample of mostly non-Hispanic White middle-aged adults
(Sulkowski, Mariaskin, & Storch, 2011)Obsessive-compulsive spectrum disorder symptoms in college studentsObsessive-compulsive spectrum disorder (OCSD); Body dysmorphic disorder (BDD); Obsessive compulsive disorders (OCD)Setting:University in the southern United States Sample size:358 Sex/Gender:Females: 71.5%; Males: 28.4% Age: Mean: 19.45 years Ethnicity:Caucasian: 56%, Black/African American: 18%, Hispanic/Latino:12%, Asian: 9%, Mixed: 4%6% pathological skin picking 3.1% hair pulling disorder 4.7% BDD symptoms 5.9% OCD symptoms 6.7% anxiety symptomsThe Skin Picking Scale (SPS) Massachusetts General Hospital Hair Pulling Scale Body Dysmorphic Disorder Questionnaire Obsessive-Compulsive Inventory-Revised Health Anxiety Inventory-Short Form Beck Anxiety Inventory Barratt Impulsiveness Scale-15Strengths: Use of psychometrically validated tools Limitations:Cross-sectional, single university, oversampled for females, self-report measures, OCSDs not widely accepted by researchers and mental health community
(Taylor, Bramoweth, Grieser, Tatum, & Roane, 2013)Epidemiology of insomnia in college students: Relationship with mental health, quality of life, and substance use difficultiesInsomniaSetting:University of North Texas Sample Size: 1,039 Age:Mean: 20.39 years Sex/Gender:Female: 72% Ethnicity:66.4% Caucasian, 12.9% African American, 10.4% Hispanic, 5.6% Asian/Pacific Islander, 4.5% other9.5% insomnia 26.9% met severity, frequency, and duration criteria but did not report an insomnia complaintHealth Questionnaire (HQ) Pittsburg Sleep Quality Index (PSQ) Insomnia Severity Index (ISI) Epworth Sleepiness Scale (ESS) Multidimensional Fatigue Inventor (MFI) Quick Inventory of Depressive Symptomatology (QIDS) State Trait Inventory/Trait Scale – 20 Statement (STAI) Perceived Stress Scale (PSS) Quality of Life Enjoyment and Satisfaction Questionnaire-Short Form (Q-LES-Q-SF) 14 Alcohol Use Disorders Identification Test (AUDIT) Marijuana Problem Scale (MPS)Strengths: Reported sleep disorders per DSM-5 criteria Limitations: The cross-sectional nature of the data collection, the lack of objective measurement of sleep, and use of self-report instruments rather than clinical interviews to diagnose insomnia
(Taylor et al., 2011)Insomnia and mental health in college studentsInsomniaSetting:Large university in the southwestern United States Sample size:373 Age: Mean: 21 years Sex/Gender:60.9% women Ethnicity:66.2% Caucasian, 15.3% African American, 9.1% Hispanic, 6.7% Asian American, and 2.7% did not report ethnicity9.4 % InsomniaSymptom Check List-90 (SCL-90)Strength: Prevalence was calculated after controlling the comorbid medical conditions Limitation:Addressed the relation of insomnia symptoms and mental health symptomatology cross-sectionally
(Lund, Reider, Whiting, & Prichard, 2010)Sleep patterns and predictors of disturbed sleep in a large population of college studentsSleep patterns/poor sleep qualitySetting:A large private university in the Midwest. Sample Size:1125 Age: Range 17–24 yrs. Sex/Gender:Males: 37.3% Females: 62.6% Ethnicity:Caucasian: 86%, Asian or Pacific Islander: 5%, African American: 2%, Biracial: 2%, American Indian or Alaskan Native: 1%, Hispanic: 0.4%, No response: 3%.60% poor-quality sleepers per the PSQIThe Pittsburgh Sleep Quality Index (PSQI) The Epworth Sleepiness Scale (ESS) The Horne-Ostberg Morningness-Eveningness Scale (MES) The Subjective Units of Distress Scale (SUDS) The Profile of Mood States (POMS).Strengths: Use of standardized and validated instruments Limitations: Sample consisted of college students from one geographic area, cross-sectional survey design, female responders bias
(Roberts, Glod, Kim, & Hounchell, 2010)Relationships between aggression, depression, and alcohol, tobacco: Implications for healthcare providers in student healthDepressionSetting: A large private university in the northeast United States Sample Size:428 Sex/Gender:Females: 63% Age:Range 18 to 21 years Ethnicity:White: 67% Asian: 11%, Black: 5%, Hispanic/Latino: 4%22% moderate depression 10% suicidal thoughts 8% suicide plan 4% attempted suicide 36% aggressionSurvey Questionnaire on 30 selected items from NCRBS survey about alcohol, drugs, tobacco, and violence Beck Depression Inventory II Overt Aggression ScaleStrengths: Random selection of students Limitations: Small sample size, female over-representation in sample, low response rate.
Authors

Dr. Kaur Kang is Professor, Chitkara School of Health Sciences, Chitkara University, Punjab, India; and Mr. Rhodes is PhD Student, Ms. Rivers is PhD and DNP Student, Mr. Thornton is PhD Student, and Dr. Rodney is Assistant Professor, School of Nursing, Johns Hopkins University, Baltimore, Maryland.

The authors have disclosed no potential conflicts of interest, financial or otherwise.

Address correspondence to Harmeet Kaur Kang, PhD, MSN, BSN, RN, Professor, Chitkara School of Health Sciences, Chitkara University, Punjab, India, 140401; email: harmeet.kaur@chitkara.edu.in.

Received: April 24, 2020
Accepted: July 28, 2020
Posted Online: November 12, 2020

10.3928/02793695-20201104-03

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