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. Author manuscript; available in PMC: 2023 Jul 1.
Published in final edited form as: Behav Sleep Med. 2021 May 18;20(4):380–392. doi: 10.1080/15402002.2021.1924720

Pre-existing Depression and Daytime Sleepiness in Women and Men

Irene Gonsalvez a,b, Jason J Li c, Courtney Stevens d, Justin A Chen e, Cindy H Liu b,c,f,*
PMCID: PMC8599528  NIHMSID: NIHMS1710253  PMID: 34003712

Abstract

Background:

Sleep problems can persist following the treatment of depression and remission of symptoms. The extent to which having a previous history of depression may be associated with current daytime sleepiness is largely unknown.

Methods:

Data were obtained from the spring 2017 American College Health Association-National College Health Assessment (ACHA-NCHA) survey (92 institutions) which assessed self-reported health in U.S. college students (n=41,670). Among the sample, 93.5% were 18–24 year of age, and 69.6% women. Logistic regression estimated the association between reported prior lifetime diagnosis of depression and daytime sleepiness from the past 7 days, while adjusting for depressive symptoms and antidepressant use from the past year. Unadjusted and adjusted logistic regression models stratified by gender were performed.

Results:

Among those who reported problems with sleepiness, 31.6% women and 19.4% men had a pre-existing depression diagnosis. Individuals with pre-existing depression were more likely than those without this diagnosis to report sleepiness problems (women: OR = 1.4, CI = 1.3 – 1.6, p < .001; men: OR = 1.2, CI = 1.0 – 1.4, p < .01). However, this association differed significantly by gender, with women with a pre-existing depression diagnosis having a 13.0% greater likelihood of sleepiness compared to men.

Conclusions:

Those with a pre-existing depression diagnosis, and specifically women, may be at risk for daytime sleepiness even in the absence of current depressive mood-related symptoms. Given that many individuals are at risk for daytime sleepiness, mental health initiatives, including those on college campuses, should incorporate sleep hygiene within their programming.

Keywords: gender, sleep, depression, mental health, students

Introduction

Depression and sleep problems are major public health problems (Bramoweth & Taylor, 2012; James et al., 2018) with one in five people reporting at least one episode of depression in their lifetime (Bromet et al., 2011) and up to 40% of the general population endorsing sleep difficulties (Mai & Buysse, 2008). Depression and sleep problems are connected as up to 90% of the individuals with depression report sleep problems (Tsuno et al., 2005), in contrast to one-third of the general population (Buysse et al., 2008; Victor et al., 2019). Common biological factors (i.e., increased inflammatory dysregulation in response to sleep disturbances) as well as genetic, familial, social and/or environmental elements have all been suggested as potential mechanisms that could contribute independently, but mutually influencing one another in the development of depression and sleep disturbances (Alvaro et al., 2013). While sleep problems are a key diagnostic symptom of depression (American Psychiatric Association, 2013), sleep problems can precede depressive episodes (Asarnow, 2020; Nutt et al., 2008; Sivertsen et al., 2014). Even more, sleep problems can persist following the treatment of depression and remission of symptoms (Giles et al., 1990, 1998; Jindal et al., 2002; Reynolds et al., 2020), and therefore serve as a risk for subsequent depressive episodes (Dombrovski et al., 2008; Ford & Kamerow, 1989; Rush et al., 1986).

A question is the extent to which those who have had a diagnosis of depression struggle with daytime sleepiness, even in the absence of depressive symptoms such as hopelessness or depressed mood. Daytime sleepiness is an understudied construct that is conceptualized separately from the loss of energy or fatigue (Hein et al., 2019) with excessive daytime sleepiness a common behavioral feature of depression that may be related to other sleep problems (e.g., poor sleep quality, insomnia; Hasler et al., 2005). Indeed, up to half of those with a diagnosis of major depression show excessive daytime sleepiness (Chellappa & Araújo, 2006; Hein et al., 2019; Stroe et al., 2010), and emerging evidence suggests daytime sleepiness to be a risk factor for depressive episodes (Jaussent et al., 2011). Although many studies have examined the relations between depression and sleep problems, to our knowledge, no prior studies have focused on the effect of having a pre-existing depression diagnosis on daytime sleepiness, when accounting for current depressive symptoms.

Furthermore, there is also very little that is known about gender differences in daytime sleepiness in men and women who have had a diagnosis of depression, although existing studies show that women experience more daytime sleepiness in general (Hara et al., 2004; Hayley et al., 2013). It is reasonable to expect that daytime sleepiness might persist in women who have had a diagnosis of depression compared to men; sex-specific differences in associations between sleep and mental health symptoms indicate that women show stronger associations overall compared to men (Goldstein-Piekarski et al., 2018; Montagni et al., 2020). Moreover, there is clear evidence that women are more vulnerable in developing depression (Fatima et al., 2016) and separate to this, are twice as likely than men to experience sleep disruptions throughout their lifespan (Mong et al., 2011). Cognitive vulnerabilities such as being prone to rumination along with negative thinking underlie both depression and sleep problems (Y. Liu et al., 2019). Structural brain morphology may be a mechanism that explains problematic sleep and mental health specifically in women (Goldstein-Piekarski et al., 2018). Other sex-specific differences with respect to the regulation of sleep and circadian rhythms may also manifest in the clinical presentation of sleep or depression (Carrier et al., 2017; Theorell-Haglöw et al., 2018).

The college student population is an ideal group to examine daytime sleepiness among those with a pre-existing diagnosis of depression. Depression is present in nearly one-third of the college population (Ibrahim et al., 2013), with 50% reporting daytime sleepiness (Hershner & Chervin, 2014). The unstructured schedules that characterize college life may contribute to daytime sleepiness. For instance, college students who preferred engaging in activities in the evening showed greater daytime sleepiness, other sleep problems, and depressive symptoms (Lin et al., 2021) compared to those who had a preference for mornings (Biss & Hasher, 2012; Gau et al., 2007).

To address these gaps in the literature, we analyzed data from a large U.S. nationwide college health survey that provided information on pre-existing depression diagnosis, depression symptoms, and the experience of daytime sleepiness. We hypothesized that college students who had a pre-existing depression diagnosis would be more likely to experience daytime sleepiness, regardless of depressive mood-related symptoms over the past year. As well, we tested the potential moderating effect of gender. This allowed us to determine whether the relationship between past depression diagnosis and current daytime sleepiness was stronger for women than for men, given prior literature showing women being more vulnerable to depression and problems with daytime sleepiness.

Methods

Data Source and Sample

Data were obtained from the Spring 2017 American Health Association-National College Health Assessment (ACHA-NCHA IIB) Reference Group which included participation of 92 postsecondary educational institutions. Among them, 89 institutions administered web-based surveys, and the remainder paper-based surveys. The non-response rate was 19%. For our analyses, we analyzed responses from undergraduate students, all of whom completed web-based surveys (N=41,670). Approval for secondary data analysis of the de-identified dataset and exemption from human subjects review according to the Institutional Review Board was granted by Mass General Brigham.

This cross-sectional analysis focused on participants with data on all our described measures below. For consistency with published studies (Ball et al., 2009; Chen et al., 2019; C. H. Liu et al., 2019), participants who reported implausible height and weight data were excluded (BMI above 65 or below 16; height above 210 cm or below 120 cm; weight above 180 kg or below 35 kg). Upon restriction, 41,670 degree-seeking undergraduate students were included in analysis. Sociodemographic information is displayed in Table 1.

Table 1.

Demographic Characteristics and Variable Descriptives from the ACHA-NCHA IIC U.S. Students, Spring 2017 (n=41,670)

Variables Rate (%)
 Age (years)
  18–24 93.5
  25+ 6.5
 Gender
  Men 30.4
  Women 69.6
 Race
  White 68.6
  Hispanic 6.8
  Black 4.6
  Asian 10.2
  Multiethnic 9.7
 Sexual orientation
  Heterosexual 81.3
  Gay/lesbian 3.0
  Bisexual 6.3
  Asexual 4.9
  Other 4.5
 Year in school
  1st 28.0
  2nd 23.7
  3rd 24.0
  4th 19.5
  5th or more 4.8
 Transfer student
  Yes 12.7
  No 87.3
 International student
  Yes 5.2
  No 94.8
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Measures

Predictors

Pre-existing Diagnosis of Depression.

Participants indicated “no” or “yes” to the question: “Have you ever been diagnosed with depression?”

Gender.

Self-reported cisgender men and women were categorized based on participants who indicated that their sex at birth aligned with their gender identity (e.g., participant indicated “Female” for sex at birth and “Woman” for gender identity). Those who indicated other gender were omitted from analyses given our intention to compare cis gender men and women.

Outcomes

Daytime sleepiness.

Participants were asked the question: “In the past 7 days, how much of a problem have you had with sleepiness (feeling sleepy, struggling to stay awake) during your daytime activities?” Response options included “no problem at all,” “a little problem,” “more than a big problem,” “a big problem,” and “a very big problem.” The first two response options were collapsed as “no problem” whereas the remainder were collapsed into “problem.”

Data Analysis

A general estimating equation approach was utilized to control for within group differences based on school. Logistic regression was used to estimate the association between pre-existing diagnosis of depression and a problem with sleepiness, with models adjusted in succession for sociodemographic characteristics, depressive symptoms from the past year, and antidepressant use. An interaction term of pre-existing diagnosis of depression and gender was also included in the model. Unadjusted and adjusted logistic regression models stratified by gender were also performed. We set a conservative level of significance at p<.01 and report 99% confidence intervals given the sample size and number of comparisons involved in our approach.

Covariates

Sociodemographic Characteristics.

In the adjusted model we used the following sociodemographic characteristics as covariates (based on their previously described associations with mental health outcomes and sleep): the respondent’s age (Krueger & Friedman, 2009; Trenz et al., 2015), year in school (Bewick et al., 2010), transfer and international student status (Beiter et al., 2015), race/ethnicity (Barnes et al., 2013; Krueger & Friedman, 2009), gender (Eisenberg et al., 2013; Lucena et al., 2020; Müller et al., 2015), and sexual orientation (Dyar et al., 2020; Kerr et al., 2013).

Age group was coded in two different categories: “18–24 years” and “25+ years,” representing traditional-aged versus older undergraduate students (Araas & Adams, 2008; Brittain & Dinger, 2015; Lindley et al., 2008). Year in school was classified using five levels, including 1, 2, 3, 4, and 5+ years. A binary coding was used for transfer and international student status (yes/no).

Race/ethnicity were coded following the responses to the item “How do you usually describe yourself?”, which included “White,” “Black,” “Hispanic or Latino/a,” “Asian or Pacific Islander,” “American Indian, Alaska Native, or Native Hawaiian (AI/AN/NH),” “Biracial or Multiracial,” and “Other.” As multiple selection was possible, answers were recoded into mutually exclusive categories. The selected option was coded if only one option, including “Biracial or Multiracial” category, was chosen. If more than one option was selected, those were recoded as “Multiracial” (which included those who selected “Biracial or Multiracial as a single option). Respondents that selected only “Other” or only “AI/AN/NH” groups were excluded from analysis due to the low sample sizes.

Participants’ sexual orientation was recoded into 5 categories: “Heterosexual,” “Gay/Lesbian,” “Bisexual,” “Asexual,” and “Other” (which included “Pansexual,” “Queer,” “Questioning,” “Same Gender Loving,” and “Another identity.”)

Depressive Symptoms.

Six questions about depressed mood were assessed through self-report, “Felt overwhelmed by all you had to do”; “Felt very lonely”; “Felt things were hopeless” (hopelessness); “Felt exhausted (not from physical activity)” (loss of energy); “Felt very sad” (depressed/sad mood); and “Felt so depressed that it was difficult to function” (symptoms cause significant distress/impairment in functioning). The last four matching DSM-V criteria for depressive mood-related symptoms. In the adjusted model, each of these six items were entered separately.

Answer options included frequency for each feeling or behavior: “No, never”; “No, not in the last 12 months”; “Yes, in the last two weeks”; “Yes, in the last 30 days”; or “Yes, in the last 12 months.” Responses were recoded into 2 different categories creating a 12-month prevalence indicator for each item: No (combining “No, not in the past 12 months” and “No, never.”) and Yes (combining all three ‘yes’ responses, which all fell within the past 12 months).

Antidepressant Use.

Participants were asked to indicate “yes” or “no” if they had taken any antidepressants (e.g., Celexa, Lexapro, Prozac, Wellbutrin, Zoloft) in the last 12 months.

Results

Table 1 displays sociodemographic characteristics and descriptive data of the variables our interest. The majority of participants were between the ages of 18–24 years (93.5%), women (69.6%), White (68.6%), and heterosexual (81.3%). Of our sample, 12.7% were transfer students and 5.2% were international students.

Table 2 presents rates of pre-existing depression diagnosis, current symptoms of depression, and problems with daytime sleepiness, both for the overall sample and stratified by gender. Among our total sample, 21.2% indicated receiving a pre-existing diagnosis of depression, with more women (24.1%) than men (14.6%) indicating having had this diagnosis. Overall, the vast majority among all students reported having felt overwhelmed (89.1%) or exhausted (85.5%) in the prior 12 months. Of note, women endorsed a higher rate of all depressive symptoms compared to men. Use of antidepressants in the past 12 months were reported by 2.7% of the overall sample, again a greater proportion of women (3.1%) compared to men (1.9%) reporting using antidepressants. Among the entire sample, 44.5% reported problems with sleepiness in the past seven days, with more women (46.8%) reporting this as a problem compared to men (39.1%).

Table 2.

Distribution of Rates of Pre-existing Depression (Dx), Current Depressive Mood-Related Symptoms and Current Daytime Sleepiness of ACHA-NCHA IIC U.S. Students by Gender, Spring 2017 (n=41,670)

Variables Total Men Women
Pre-existing depression dx (lifetime)
 Yes 21.2 14.6 24.1
 No 78.8 85.4 75.9
Depressive mood-related symptoms within last year
 Felt hopeless 53.0 45.8 56.1
 Felt overwhelmed 89.1 80.9 92.7
 Felt exhausted 85.5 77.5 88.9
 Felt lonely 64.6 57.2 67.8
 Felt very sad 69.2 59.8 73.3
 Felt so depressed it was difficult to function 39.6 33.7 42.2
Took antidepressants in the past 12 months
 Yes 2.7 1.9 3.1
 No 97.3 98.1 96.9
Problems with Daytime Sleepiness within last 7 days
 Yes 44.5 39.1 46.8
 No 55.5 60.9 53.2
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Table 3 shows the rates of problems with sleepiness by gender and pre-existing depression diagnosis. Among those who indicated a problem with sleepiness with a pre-existing depression diagnosis, 31.6% were women, and 19.4% were men. To examine the effect of gender and pre-existing depression diagnoses on problems with sleepiness, we conducted a series of analyses which began with an unadjusted model, followed by three additional models that first adjusted for the sociodemographic characteristics, then additional adjustment of depressive symptoms in the past 12 months, and finally, the additional adjustment of antidepressant use in the past 12 months. The unadjusted model showed significant main effects for pre-existing depression diagnosis (OR=1.85, CI=1.62–2.11, p<.001) and gender (OR=1.25, CI=1.17–1.33, p<.001), as well as a significant interaction between pre-existing depression and gender (OR=1.19, CI=1.02–1.38, p=.003). These effects remained after every analysis which adjusted for the following: sociodemographic characteristics, past-year depressive symptoms, and antidepressant use. Thus, the final model which adjusted for all these covariates showed a significant main effect for pre-existing depression diagnosis (OR=1.16, CI=1.00–1.33, p=.008) with greater problems of daytime sleepiness among those with a pre-existing depression diagnosis versus those without such a diagnosis; and gender (OR=1.06, CI=0.99–1.14, p=.02), with higher rates of daytime sleepiness among women compared to men. A significant interaction between pre-existing depression and gender (OR=1.26, CI=1.08–1.47, p<.001) was observed.

Table 3.

Rate of Daytime Sleepiness by Gender and Pre-existing Depression Diagnosis (Dx) from the ACHA-NCHA IIC, Spring 2017 (n=41,670)

Men Women
No pre-existing depression dx Pre-existing depression dx No pre-existing depression dx Pre-existing depression dx
No problems with sleepiness 88.5 11.5 82.6 17.4
Problems with sleepiness 80.6 19.4 68.4 31.6
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Given the significant interaction between pre-existing depression and gender observed in all models above, we conducted additional analyses stratified by gender. These analyses examined the association between pre-existing depression and problems with sleepiness separately in men and women. As before, these analyses began with an unadjusted model, followed by three additional models that first adjusted for the sociodemographic characteristics, then additional adjustment of depressive symptoms in the past 12 months, and finally, the additional adjustment of antidepressants use in the past 12 months. As displayed in Table 4, those with a pre-existing depression diagnosis were significantly more likely than those without a diagnosis to report problems with daytime sleepiness. However, this association was consistently stronger for women than for men for each of the models. After accounting for all the covariates, women with a pre-existing depression diagnosis were 43% more likely to have a problem with daytime sleepiness compared to women without a diagnosis, whereas men with a pre-existing depression diagnosis were 20% more likely to report sleepiness as a problem relative to men without a diagnosis.

Table 4.

Likelihood of Reported Problems with Daytime Sleepiness based on Pre-existing Depression Diagnosis (Dx) by Gender from the ACHA-NCHA IIC, Spring 2017 (n=41,670)

Unadjusted Adjusted1 Adjusted2 Adjusted3
Men OR 95% CI OR 95% CI OR 95% CI OR 95% CI
 No pre-existing depression dx 1.0 -- 1.0 -- 1.0 -- 1.0 --
 Pre-existing depression dx 1.85*** 1.62–2.10 1.82*** 1.59–2.09 1.21** 1.05–1.40 1.20** 1.04–1.39
Women
 No pre-existing depression dx 1.0 -- 1.0 -- 1.0 -- 1.0 --
 Pre-existing depression dx 2.20*** 2.04–2.36 2.19*** 2.03–2.36 1.44*** 1.33–1.57 1.43*** 1.32–1.56
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*

p<.05

**

p<.01

***

p<.001

1

Adjusted model controlled for: race, age, year in school, sexual orientation, transfer student status, and international student status

2

Adjusted model controlled for: race, age, year in school, sexual orientation, transfer student status, and international student status, and depressive mood-related symptoms reported over the past 12 months which included feeling hopeless, overwhelmed, exhausted, very lonely, very sad, and so depressed it was difficult to function.

3

Adjusted model controlled for: race, age, year in school, sexual orientation, transfer student status, and international student status, and depressive mood-related symptoms reported over the past 12 months which included feeling hopeless, overwhelmed, exhausted, very lonely, very sad, and so depressed it was difficult to function; took antidepressants over the past 12 months

Discussion

In this study we examined the relationship between pre-existing depression diagnoses and current problems with daytime sleepiness using a national, large-scale dataset of college students, a population where depression and sleep problems are highly prevalent. We present three main findings. First, depression and daytime sleepiness were found to be prevalent among the college students. In our sample, 1 in 5 students reported a past diagnosis of depression and over 40% of students reported a current problem with daytime sleepiness during daytime activities. Second, we also found that having had a depression diagnosis was associated with higher likelihood of current daytime sleepiness problems, and this relationship held even after controlling for depressive symptoms and antidepressant use over the past year. Third, we observed a significant interaction effect between gender and pre-existing depression diagnoses in relations with sleepiness problems, with this association between having had a pre-existing depression diagnosis and sleepiness problems consistently stronger for women than for men.

The high rates of depression and daytime sleepiness among college students in our study are consistent with prior studies (Amaral et al., 2018; Auerbach et al., 2018; Chen et al., 2019; Ibrahim et al., 2013; Kaur & Singh, 2017; C. H. Liu et al., 2019; Lund et al., 2010; Sarokhani et al., 2013; Yang et al., 2003). Systematic reviews and meta analyses have estimated the prevalence of depression among college students to be around 33% (Ibrahim et al., 2013; Sarokhani et al., 2013). In fact, our rate of lifetime prevalence of 21% is equal to the rate obtained in a recent study showing a 21.2% lifetime prevalence of depression in college students (Auerbach et al., 2018). Sleep-related problems in the college population have been found to be as high as 60% in some studies (Lund et al., 2010), with approximately half of these samples experiencing sleep difficulties (52.3%; Amaral et al., 2018; 44%; Yang et al., 2003), including daytime sleepiness (45%; Kaur & Singh, 2017).

In our study, the experience of daytime sleepiness appears to persist among individuals with a pre-existing diagnosis of depression, regardless of those with current symptoms of depression such as hopelessness or depressed mood. Students who have had a depression diagnosis may continue to report daytime sleepiness after their mood-related depressive symptoms resolve, increasing their vulnerability for depression relapse and recurrence (Buysse et al., 1997; Perlis et al., 1997). This work builds upon the work of earlier studies of clinically remitted depressed patients (Ford & Kamerow, 1989; Rush et al., 1986) and more recent work involving the evaluation of post treatment patients in remission . For instance, our work is consistent with findings by Nierenberg et al. (2010) who show that sleep disturbances, including sleep onset/mid-nocturnal and early morning insomnia, and hypersomnia, persist beyond the active phase of the disease in up to a 70% of those who in remission. Our results provide a novel contribution to the field by underscoring daytime sleepiness as an important behavioral feature to detect among those with a diagnosis of depression.

There may be several reasons that explain the persistence of daytime sleepiness in those who have had depression. The genetic association between daytime sleepiness and depressive symptoms have been suggested (Lessov-Schlaggar et al., 2008). Another interpretation is that those who have had depression in the past may have an underlying higher perception sensitivity or a negative perspective. Individuals with depression have been shown to attribute a more negative meaning to both neutral and negative stimuli and exhibit patterns of negative thinking (rumination, self-blame, catastrophizing [Beck’s schema theory; Beck & Haigh, 2014]); further, perceptions of time have been explicitly shown to be affected by these negative attributions (Ratcliffe, 2012). This may result in a propensity for our respondents to perceive and report daytime sleepiness problems, thus representing an underlying common cognitive/behavioral phenotype on those that have suffered depression.

These interpretations may also be interconnected. Sleep studies found that patients with depression with architectural sleep changes had more negative cognitions than those with no objective sleep changes (Giles et al., 1998), linking changes in sleep structure to disturbances in affective information processing (Perlis et al., 1995); suggesting that those with sleep changes have more of a negative thinking pattern that could further contribute to depression. That being said, previous studies have shown that even those depressed patients with no alterations in their sleep by polysomnographic recordings reported sleep complaints (Feinberg et al., 1982), highlighting the possibility that the subjective nature of reported and experienced sleep problems may be attributable to either the depression.

Our results also indicate that past depression diagnoses affect daytime sleepiness differently in women compared to men, with women two times as likely to report daytime sleepiness after controlling for depressive mood-related symptoms within the last year. To our knowledge, the only study to date to explore daytime sleepiness and lifetime depression demonstrated a significant relationship not only between sleepiness and current depression, but also between lifetime history of depressive illness and reported daytime sleepiness in women (Hayley et al., 2013). Although no comparison was made to men, this observed relationship was shown independent of several possible explanatory lifestyle and health factors (tobacco smoking, obesity, physical inactivity, obstructive sleep apnea, etc.), suggesting that this association may, in part, be mediated by degree of sleepiness pathology. Their findings, in addition to our focus on women with a diagnosis of depression suggest that it may be important to attend to daytime sleepiness particularly for women, even when the depression may be well controlled, given the implications for negative health outcomes.

This persistence in daytime sleepiness among women with a depression diagnosis may be due in part to women being prone to rumination along with negative thinking, which are considered cognitive vulnerability factors and predictors for both depression and sleep problems (Y. Liu et al., 2019). Women have been found to have more negative attributions, perseverative thinking, and perceived stress than men (Amaral et al., 2018), which may contribute to their higher rates of sleep problems (taking longer to sleep, and functioning more poorly during the daytime), even when women may not differ from men on sleep duration or quality (Becker et al., 2018). Interestingly, when comparing depressed women with and without daytime sleepiness, no differences in sleep efficienty or quality were found and however, the group with daytime sleepiness actually reported sleeping more hours than the group without daytime sleepiness. This finding reinforces the role that cognitive vulnerabilities may have on these gender differences (Calati et al., 2010), while calling attention to the possibility of too much sleep as a potential risk factor for depression. As well, women, endorse more frequently the presence of significant sleep disturbance even when they do not differ from men on objective measures of sleep (Vitiello et al., 2004), suggesting sex-related differences in self-perception of sleep. These associations could be even more robust for women who have had a depression diagnosis.

The growing research on gender differences in sleep has focused on hormonal status (Mong et al., 2011), the regulations of sleep and circadian rhythms (Carrier et al., 2017) and body composition which may contribute to further gender differences in diagnosis, clinical presentation and possibly treatment effect of sleep disturbances (Theorell-Haglöw et al., 2018). Given this, future research may consider these particular mechanisms in understanding women’s perceptions of their sleep.

Limitations

Our study has several limitations. We relied on survey data, which reflects self- reported data without a corroborated clinical diagnosis or clinical validation. The definition of pre-existing diagnosis of depression was predicated on one’s report of having ever received a diagnosis; as such, we are unable to discern between current vs past/treated depression (though we are able to partially address this limitation by adjusting for current symptoms of depression). As well, the survey did not include a standardized instrument to assess depression or depressive symptoms (though items included were largely representative of symptoms used as part of a diagnosis of depression). Daytime sleepiness was assessed through a one-item, self-rated (not standardized) measure, and an additional limitation is that this sleepiness could reflect a side effect of sleep aids or use of substances beyond antidepressants; sleepiness could also be confounded by experiences of stress experienced by college students. Furthermore, along with the already mentioned sex differences between objective and subjective sleep perception, women and men may hold a different interpretation of the term “sleepiness,” which could account for the higher rates of sleep problems in women compared with men (Morris et al., 2018). We focused on pre-existing depression diagnosis, however, other conditions comorbid with depression, such as generalized anxiety or post-traumatic stress symptoms, as well as other stress experiences may contribute to daytime sleepiness (Breslau et al., 1997; Theorell-Haglöw et al., 2006). These factors and their interrelations warrant further investigation in future research. Finally, the data obtained is cross-sectional and therefore causality cannot be determined. Future research that would address these limitations is needed.

Implications

Public health campaigns, including those that take place on college campuses should consider addressing sleep problems in tandem with mental health initiatives. Campaigns for preventing sleep problems as well as the implementation of interventions for those identified with sleep problems are warranted. Existent literature supports the implementation of CBT (cognitive behavioral therapy) to improve sleep as well as relaxation techniques and mindfulness to strengthen its effects on comorbid mental health problems (Friedrich & Schlarb, 2018). For those with a pre-existing diagnosis of depression, behavioral activation (e.g., activities that increase positive mood such as exercising or spending time with friends) may be beneficial for those with a pre-existing diagnosis of depression, even among those without depressive symptoms, as it may help to mitigate daytime sleepiness. Based on our data, strategies may be even more salient and impactful for those with a past diagnosis of depression and in this time in history given emerging work demonstrating associations between the young adult sleep problems during the COVID-19 pandemic (Hyun et al., 2021; C. H. Liu et al., 2020). Particular attention is needed to address these concerns among women given their elevated rates of depression and problems with sleep and the strong association between these two concerns when compared to men.

Conclusion

Individuals who have had a depression diagnosis may be prone to daytime sleepiness even when depressive mood-related symptoms are largely absent from the past year. The associations in our study suggest that daytime sleepiness as a behavioral feature to be studied among those with a pre-existing diagnosis of depression. This association appeared to be greater for women than for men, such that women may be more likely experience higher daytime sleepiness than men once other symptoms remit. As such, investigations should take into account sex-specific models in understanding the link between depression and sleep. Our results also underscore the importance of widescale assessment for daytime sleepiness and with sleep disturbances. Raising awareness of sleep problems, especially among those with a pre-existing diagnosis of depression and women, may help to prevent negative consequences from daytime sleepiness, with poorer academic functioning being just one of the negative outcomes among the college student population. When considering college students who may be more vulnerable to sleep problems, universities may wish to consider campus-based interventions (Prichard & Hartmann, 2019) that focus on promoting sleep hygiene within the context of school schedules (McCabe et al., 2018), and with peers (Robbins & Niederdeppe, 2017), and the application of mobile technologies (sleep trackers) with the college student population to improve sleep (Baron et al., 2018; De Zambotti et al., 2019; Pope et al., 2019).

Acknowledgements

We are grateful to the American College Health Association for providing and approving the use of this dataset: American College Health Association-National College Health Assessment, Spring 2017. Hanover, MD: American College Health Association [producer and distributor]; (2017–04-17 of distribution). The opinions, findings, and conclusions presented/reported in this article/presentation are those of the author(s) and are in no way meant to represent the corporate opinions, views, or policies of the American College Health Association (ACHA). The ACHA does not warrant nor assume any liability or responsibility for the accuracy, completeness, or usefulness of any information presented in this article/presentation. Support for preparing this manuscript was provided through the Mary A. Tynan Faculty Fellowship and a NIMH K23 MH 107714–01 A1 award (to C.H.L.) and a Willamette University Atkinson Research Award (to C.S.). No financial disclosures were reported by the authors of this paper.

Data availability statement

Data analyzed in this study were a re-analysis of existing data, which are available at locations cited in the reference section.

References

  1. Alvaro PK, Roberts RM, & Harris JK (2013). A Systematic Review Assessing Bidirectionality between Sleep Disturbances, Anxiety, and Depression. Sleep, 36(7), 1059–1068. 10.5665/sleep.2810 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Amaral AP, Soares MJ, Pinto AM, Pereira AT, Madeira N, Bos SC, Marques M, Roque C, & Macedo A (2018). Sleep difficulties in college students: The role of stress, affect and cognitive processes. Psychiatry Research, 260, 331–337. 10.1016/j.psychres.2017.11.072 [DOI] [PubMed] [Google Scholar]
  3. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5®). American Psychiatric Pub. [Google Scholar]
  4. Araas TE, & Adams TB (2008). Protective Behavioral Strategies and Negative Alcohol-Related Consequences in College Students. Journal of Drug Education, 38(3), 211–224. 10.2190/DE.38.3.b [DOI] [PubMed] [Google Scholar]
  5. Asarnow LD (2020). Depression and sleep: What has the treatment research revealed and could the HPA axis be a potential mechanism? Current Opinion in Psychology, 34, 112–116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Auerbach RP, Mortier P, Bruffaerts R, Alonso J, Benjet C, Cuijpers P, Demyttenaere K, Ebert DD, Green JG, Hasking P, Murray E, Nock MK, Pinder-Amaker S, Sampson NA, Stein DJ, Vilagut G, Zaslavsky AM, & Kessler RC (2018). The WHO World Mental Health Surveys International College Student Project: Prevalence and Distribution of Mental Disorders. Journal of Abnormal Psychology, 127(7), 623–638. 10.1037/abn0000362 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Ball J, Ford J, Russell A, Williams L, & Hockey R (2009). Data Cleaning for Height and Weight—1973–78 and 1946–51 Cohorts. https://www.alswh.org.au/images/content/pdf/InfoData/Data_Dictionary_Supplement/DDSSection3Data%20Cleaning%20for%20Height%20and%20Weight.pdf
  8. Barnes DM, Keyes KM, & Bates LM (2013). Racial differences in depression in the United States: How do subgroup analyses inform a paradox? Social Psychiatry and Psychiatric Epidemiology, 48(12). 10.1007/s00127-013-0718-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Baron KG, Duffecy J, Berendsen MA, Mason IC, Lattie EG, & Manalo NC (2018). Feeling validated yet? A scoping review of the use of consumer-targeted wearable and mobile technology to measure and improve sleep. Sleep Medicine Reviews, 40, 151–159. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Beck AT, & Haigh EAP (2014). Advances in Cognitive Theory and Therapy: The Generic Cognitive Model. Annual Review of Clinical Psychology, 10(1), 1–24. 10.1146/annurev-clinpsy-032813-153734 [DOI] [PubMed] [Google Scholar]
  11. Becker SP, Jarrett MA, Luebbe AM, Garner AA, Burns GL, & Kofler MJ (2018). Sleep in a Large, Multi-University Sample of College Students: Sleep Problem Prevalence, Sex Differences, and Mental Health Correlates. Sleep Health, 4(2), 174–181. 10.1016/j.sleh.2018.01.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Beiter R, Nash R, McCrady M, Rhoades D, Linscomb M, Clarahan M, & Sammut S (2015). The prevalence and correlates of depression, anxiety, and stress in a sample of college students. Journal of Affective Disorders, 173, 90–96. 10.1016/j.jad.2014.10.054 [DOI] [PubMed] [Google Scholar]
  13. Bewick B, Koutsopoulou G, Miles J, Slaa E, & Barkham M (2010). Changes in undergraduate students’ psychological well-being as they progress through university. Studies in Higher Education, 35(6), 633–645. [Google Scholar]
  14. Biss RK, & Hasher L (2012). Happy as a lark: Morning-type younger and older adults are higher in positive affect. Emotion, 12(3), 437. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Bramoweth AD, & Taylor DJ (2012). Chronic Insomnia and Health Care Utilization in Young Adults. Behavioral Sleep Medicine, 10(2), 106–121. 10.1080/15402002.2011.587067 [DOI] [PubMed] [Google Scholar]
  16. Breslau N, Roth T, Rosenthal L, & Andreski P (1997). Daytime sleepiness: An epidemiological study of young adults. American Journal of Public Health, 87(10), 1649–1653. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Brittain DR, & Dinger MK (2015). An Examination of Health Inequities among College Students by Sexual Orientation Identity and Sex. Journal of Public Health Research, 4(1). 10.4081/jphr.2015.414 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Bromet E, Andrade LH, Hwang I, Sampson NA, Alonso J, De Girolamo G, De Graaf R, Demyttenaere K, Hu C, & Iwata N (2011). Cross-national epidemiology of DSM-IV major depressive episode. BMC Medicine, 9(1), 90. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Buysse DJ, Angst J, Gamma A, Ajdacic V, Eich D, & Rössler W (2008). Prevalence, course, and comorbidity of insomnia and depression in young adults. Sleep, 31(4), 473–480. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Buysse DJ, Frank E, Lowe KK, Cherry CR, & Kupfer DJ (1997). Electroencephalographic sleep correlates of episode and vulnerability to recurrence in depression. Biological Psychiatry, 41(4), 406–418. 10.1016/S0006-3223(96)00041-8 [DOI] [PubMed] [Google Scholar]
  21. Calati R, Gaspar-Barba E, Cruz-Fuentes CS, Nenclares A, Jimenez-Genchi A, De Ronchi D, & Serretti A (2010). Excessive daytime sleepiness in depressed women. Psychiatry Research, 179(2), 171–175. [DOI] [PubMed] [Google Scholar]
  22. Carrier J, Semba K, Deurveilher S, Drogos L, Cyr-Cronier J, Lord C, & Sekerovick Z (2017). Sex differences in age-related changes in the sleep-wake cycle. Frontiers in Neuroendocrinology, 47, 66–85. [DOI] [PubMed] [Google Scholar]
  23. Chellappa SL, & Araújo JF (2006). Excessive daytime sleepiness in patients with depressive disorder. Brazilian Journal of Psychiatry, 28(2), 126–129. [DOI] [PubMed] [Google Scholar]
  24. Chen JA, Stevens C, Wong SH, & Liu CH (2019). Psychiatric symptoms and diagnoses among US college students: A comparison by race and ethnicity. Psychiatric Services, 70(6), 442–449. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. De Zambotti M, Cellini N, Goldstone A, Colrain IM, & Baker FC (2019). Wearable sleep technology in clinical and research settings. Medicine and Science in Sports and Exercise, 51(7), 1538. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Dombrovski AY, Cyranowski JM, Mulsant BH, Houck PR, Buysse DJ, Andreescu C, Thase ME, Mallinger AG, & Frank E (2008). Which symptoms predict recurrence of depression in women treated with maintenance interpersonal psychotherapy? Depression and Anxiety, 25(12), 1060–1066. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Dyar C, Feinstein BA, Stephens J, Zimmerman AR, Newcomb ME, & Whitton SW (2020). Nonmonosexual stress and dimensions of health: Within-group variation by sexual, gender, and racial/ethnic identities. Psychology of Sexual Orientation and Gender Diversity, 7(1), 12–25. 10.1037/sgd0000348 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Eisenberg D, Hunt J, & Speer N (2013). Mental Health in American Colleges and Universities: Variation Across Student Subgroups and Across Campuses. The Journal of Nervous and Mental Disease, 201(1), 60–67. 10.1097/NMD.0b013e31827ab077 [DOI] [PubMed] [Google Scholar]
  29. Fatima Y, Doi SAR, Najman JM, & Mamun AA (2016). Exploring Gender Difference in Sleep Quality of Young Adults: Findings from a Large Population Study. Clinical Medicine & Research, 14(3–4), 138–144. 10.3121/cmr.2016.1338 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Feinberg M, Gillin JC, Carroll BJ, Greden JF, & Zis AP (1982). EEG studies of sleep in the diagnosis of depression. Biological Psychiatry, 17(3), 305–316. [PubMed] [Google Scholar]
  31. Ford DE, & Kamerow DB (1989). Epidemiologic study of sleep disturbances and psychiatric disorders: An opportunity for prevention? Jama, 262(11), 1479–1484. [DOI] [PubMed] [Google Scholar]
  32. Friedrich A, & Schlarb AA (2018). Let’s talk about sleep: A systematic review of psychological interventions to improve sleep in college students. Journal of Sleep Research, 27(1), 4–22. 10.1111/jsr.12568 [DOI] [PubMed] [Google Scholar]
  33. Gau SS-F, Shang C-Y, Merikangas KR, Chiu Y-N, Soong W-T, & Cheng AT-A (2007). Association between morningness-eveningness and behavioral/emotional problems among adolescents. Journal of Biological Rhythms, 22(3), 268–274. [DOI] [PubMed] [Google Scholar]
  34. Giles DE, Etzel BA, & Biggs MM (1990). Risk factors in unipolar depression: II. Relation between proband REM latency and cognitions of relatives. Psychiatry Research, 33(1), 39–49. [DOI] [PubMed] [Google Scholar]
  35. Giles DE, Kupfer DJ, Rush AJ, & Roffwarg HP (1998). Controlled comparison of electrophysiological sleep in families of probands with unipolar depression. American Journal of Psychiatry, 155(2), 192–199. [DOI] [PubMed] [Google Scholar]
  36. Goldstein-Piekarski AN, Greer SM, Saletin JM, Harvey AG, Williams LM, & Walker MP (2018). Sex, sleep deprivation, and the anxious brain. Journal of Cognitive Neuroscience, 30(4), 565–578. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Hara C, Rocha FL, & Lima-Costa MFF (2004). Prevalence of excessive daytime sleepiness and associated factors in a Brazilian community: The Bambuí study. Sleep Medicine, 5(1), 31–36. [DOI] [PubMed] [Google Scholar]
  38. Hasler G, Buysse DJ, Gamma A, Ajdacic V, Eich D, Rössler W, & Angst J (2005). Excessive daytime sleepiness in young adults: A 20-year prospective community study. The Journal of Clinical Psychiatry, 66(4), 521–529. [DOI] [PubMed] [Google Scholar]
  39. Hayley AC, Williams LJ, Berk M, Kennedy GA, Jacka FN, & Pasco JA (2013). The relationship between excessive daytime sleepiness and depressive and anxiety disorders in women. Australian & New Zealand Journal of Psychiatry, 47(8), 772–778. [DOI] [PubMed] [Google Scholar]
  40. Hein M, Lanquart J-P, Loas G, Hubain P, & Linkowski P (2019). Prevalence and risk factors of excessive daytime sleepiness in major depression: A study with 703 individuals referred for polysomnography. Journal of Affective Disorders, 243, 23–32. [DOI] [PubMed] [Google Scholar]
  41. Hershner SD, & Chervin RD (2014). Causes and consequences of sleepiness among college students. Nature and Science of Sleep, 6, 73. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Hyun S, Hahm HC, Wong GTF, Zhang E, & Liu CH (2021). Psychological correlates of poor sleep quality among US young adults during the COVID-19 pandemic. Sleep Medicine, 78, 51–56. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Ibrahim AK, Kelly SJ, Adams CE, & Glazebrook C (2013). A systematic review of studies of depression prevalence in university students. Journal of Psychiatric Research, 47(3), 391–400. [DOI] [PubMed] [Google Scholar]
  44. James SL, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N, Abbastabar H, Abd-Allah F, Abdela J, & Abdelalim A (2018). Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017. The Lancet, 392(10159), 1789–1858. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Jaussent I, Bouyer J, Ancelin M-L, Akbaraly T, Peres K, Ritchie K, Besset A, & Dauvilliers Y (2011). Insomnia and daytime sleepiness are risk factors for depressive symptoms in the elderly. Sleep, 34(8), 1103–1110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Jindal RD, Thase ME, Fasiczka AL, Friedman ES, Buysse DJ, Frank E, & Kupfer DJ (2002). Electroencephalographic sleep profiles in single-episode and recurrent unipolar forms of major depression: II. Comparison during remission. Biological Psychiatry, 51(3), 230–236. [DOI] [PubMed] [Google Scholar]
  47. Kaur G, & Singh A (2017). Excessive daytime sleepiness and its pattern among Indian college students. Sleep Medicine, 29, 23–28. [DOI] [PubMed] [Google Scholar]
  48. Kerr DL, Santurri L, & Peters P (2013). A Comparison of Lesbian, Bisexual, and Heterosexual College Undergraduate Women on Selected Mental Health Issues. Journal of American College Health, 61(4), 185–194. 10.1080/07448481.2013.787619 [DOI] [PubMed] [Google Scholar]
  49. Krueger PM, & Friedman EM (2009). Sleep duration in the United States: A cross-sectional population-based study. American Journal of Epidemiology, 169(9), 1052–1063. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Lessov-Schlaggar CN, Bliwise DL, Krasnow RE, Swan GE, & Reed T (2008). Genetic Association of Daytime Sleepiness and Depressive Symptoms in Elderly Men. Sleep, 31(8), 1111–1117. 10.5665/sleep/31.8.1111 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Lin C-Y, Imani V, Griffiths MD, Broström A, Nygårdh A, Demetrovics Z, & Pakpour AH (2021). Temporal associations between morningness/eveningness, problematic social media use, psychological distress and daytime sleepiness: Mediated roles of sleep quality and insomnia among young adults. Journal of Sleep Research, 30(1), e13076. [DOI] [PubMed] [Google Scholar]
  52. Lindley LL, Barnett CL, Brandt HM, Hardin JW, & Burcin M (2008). STDs Among Sexually Active Female College Students: Does Sexual Orientation Make a Difference? Perspectives on Sexual and Reproductive Health, 40(4), 212–217. 10.1363/4021208 [DOI] [PubMed] [Google Scholar]
  53. Liu CH, Stevens C, Conrad R, & Hahm HC (2020). Evidence for elevated psychiatric distress, poor sleep, and quality of life concerns during the COVID-19 pandemic among U.S. young adults with suspected and reported psychiatric diagnoses. Psychiatry Research, 292, 113345. 10.1016/j.psychres.2020.113345 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Liu CH, Stevens C, Wong SH, Yasui M, & Chen JA (2019). The prevalence and predictors of mental health diagnoses and suicide among US college students: Implications for addressing disparities in service use. Depression and Anxiety, 36(1), 8–17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Liu Y, Zhang N, Bao G, Huang Y, Ji B, Wu Y, Liu C, & Li G (2019). Predictors of depressive symptoms in college students: A systematic review and meta-analysis of cohort studies. Journal of Affective Disorders, 244, 196–208. 10.1016/j.jad.2018.10.084 [DOI] [PubMed] [Google Scholar]
  56. Lucena L, Polesel DN, Poyares D, Bittencourt L, Andersen ML, Tufik S, & Hachul H (2020). The association of insomnia and quality of life: Sao Paulo epidemiologic sleep study (EPISONO). Sleep Health. 10.1016/j.sleh.2020.03.002 [DOI] [PubMed]
  57. Lund HG, Reider BD, Whiting AB, & Prichard JR (2010). Sleep Patterns and Predictors of Disturbed Sleep in a Large Population of College Students. Journal of Adolescent Health, 46(2), 124–132. 10.1016/j.jadohealth.2009.06.016 [DOI] [PubMed] [Google Scholar]
  58. Mai E, & Buysse DJ (2008). Insomnia: Prevalence, impact, pathogenesis, differential diagnosis, and evaluation. Sleep Medicine Clinics, 3(2), 167–174. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. McCabe BE, Troy AS, Patel HH, Halstead V, & Arana M (2018). Development of a university campus healthy sleep promotion program. Issues in Mental Health Nursing, 39(3), 264–268. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Mong JA, Baker FC, Mahoney MM, Paul KN, Schwartz MD, Semba K, & Silver R (2011). Sleep, Rhythms, and the Endocrine Brain: Influence of Sex and Gonadal Hormones. Journal of Neuroscience, 31(45), 16107–16116. 10.1523/JNEUROSCI.4175-11.2011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Montagni I, Qchiqach S, Pereira E, Tully PJ, & Tzourio C (2020). Sex-specific associations between sleep and mental health in university students: A large cross-sectional study. Journal of American College Health, 68(3), 278–285. [DOI] [PubMed] [Google Scholar]
  62. Morris JL, Rohay J, & Chasens ER (2018). Sex differences in the psychometric properties of the Pittsburgh sleep quality index. Journal of Women’s Health, 27(3), 278–282. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Müller S, Rohde P, Gau JM, & Stice E (2015). Moderators of the Effects of Indicated Group and Bibliotherapy Cognitive Behavioral Depression Prevention Programs on Adolescents’ Depressive Symptoms and Depressive Disorder Onset. Behaviour Research and Therapy, 75, 1–10. 10.1016/j.brat.2015.10.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Nierenberg AA, Husain MM, Trivedi MH, Fava M, Warden D, Wisniewski SR, Miyahara S, & Rush AJ (2010). Residual symptoms after remission of major depressive disorder with citalopram and risk of relapse: A STAR*D report. Psychological Medicine, 40(1), 41–50. 10.1017/S0033291709006011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Nutt D, Wilson S, & Paterson L (2008). Sleep disorders as core symptoms of depression. Dialogues in Clinical Neuroscience, 10(3), 329. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Perlis ML, Giles DE, Buysse DJ, Tu X, & Kupfer DJ (1997). Self-reported sleep disturbance as a prodromal symptom in recurrent depression. Journal of Affective Disorders, 42(2–3), 209–212. 10.1016/S0165-0327(96)01411-5 [DOI] [PubMed] [Google Scholar]
  67. Perlis ML, Giles DE, Fleming GM, Drummond SPA, & James SP (1995). Sustained facial muscle activity during REM sleep and its correlation with depression. Journal of Affective Disorders, 35(4), 163–171. 10.1016/0165-0327(95)00065-8 [DOI] [PubMed] [Google Scholar]
  68. Pope ZC, Barr-Anderson DJ, Lewis BA, Pereira MA, & Gao Z (2019). Use of wearable technology and social media to improve physical activity and dietary behaviors among college students: A 12-week randomized pilot study. International Journal of Environmental Research and Public Health, 16(19), 3579. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Prichard JR, & Hartmann ME (2019). Follow-up to Hartmann & Prichard: Should universities invest in promoting healthy sleep? A question of academic and economic significance. Sleep Health, 5(4), 320–325. [DOI] [PubMed] [Google Scholar]
  70. Ratcliffe M (2012). Varieties of Temporal Experience in Depression. Journal of Medicine and Philosophy, 37(2), 114–138. 10.1093/jmp/jhs010 [DOI] [PubMed] [Google Scholar]
  71. Reynolds S, Orchard F, Midgley N, Kelvin R, & Goodyer I (2020). Do sleep disturbances in depressed adolescents improve following psychological treatment for depression? Journal of Affective Disorders, 262, 205–210. [DOI] [PubMed] [Google Scholar]
  72. Robbins R, & Niederdeppe J (2017). How online peer-to-peer conversation shapes the effects of a message about healthy sleep. Prevention Science, 18(2), 141–151. [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Rush AJ, Erman MK, Giles DE, Schlesser MA, Carpenter G, Vasavada N, & Roffwarg HP (1986). Polysomnographic findings in recently drug-free and clinically remitted depressed patients. Archives of General Psychiatry, 43(9), 878–884. [DOI] [PubMed] [Google Scholar]
  74. Sarokhani D, Delpisheh A, Veisani Y, Sarokhani MT, Manesh RE, & Sayehmiri K (2013, September 25). Prevalence of Depression among University Students: A Systematic Review and Meta-Analysis Study [Review Article]. Depression Research and Treatment; Hindawi. 10.1155/2013/373857 [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Sivertsen B, Harvey AG, Lundervold AJ, & Hysing M (2014). Sleep problems and depression in adolescence: Results from a large population-based study of Norwegian adolescents aged 16–18 years. European Child & Adolescent Psychiatry, 23(8), 681–689. [DOI] [PubMed] [Google Scholar]
  76. Stroe AF, Roth T, Jefferson C, Hudgel DW, Roehrs T, Moss K, & Drake CL (2010). Comparative levels of excessive daytime sleepiness in common medical disorders. Sleep Medicine, 11(9), 890–896. [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Theorell-Haglöw J, Lindberg E, & Janson C (2006). What are the important risk factors for daytime sleepiness and fatigue in women? Sleep, 29(6), 751–757. [DOI] [PubMed] [Google Scholar]
  78. Theorell-Haglöw J, Miller CB, Bartlett DJ, Yee BJ, Openshaw HD, & Grunstein RR (2018). Gender differences in obstructive sleep apnoea, insomnia and restless legs syndrome in adults–What do we know? A clinical update. Sleep Medicine Reviews, 38, 28–38. [DOI] [PubMed] [Google Scholar]
  79. Trenz RC, Ecklund-Flores L, & Rapoza K (2015). A comparison of mental health and alcohol use between traditional and nontraditional students. Journal of American College Health, 63(8), 584–588. [DOI] [PMC free article] [PubMed] [Google Scholar]
  80. Tsuno N, Besset A, & Ritchie K (2005). Sleep and depression. The Journal of Clinical Psychiatry. [DOI] [PubMed]
  81. Victor R, Garg S, & Gupta R (2019). Insomnia and depression: How much is the overlap? Indian Journal of Psychiatry, 61(6), 623. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Vitiello MV, Larsen LH, & Moe KE (2004). Age-related sleep change: Gender and estrogen effects on the subjective–objective sleep quality relationships of healthy, noncomplaining older men and women. Journal of Psychosomatic Research, 56(5), 503–510. [DOI] [PubMed] [Google Scholar]
  83. Yang C-M, Wu C-H, Hsieh M-H, Liu M-H, & Lu F-H (2003). Coping with Sleep Disturbances Among Young Adults: A Survey of First-Year College Students in Taiwan. Behavioral Medicine, 29(3), 133–138. 10.1080/08964280309596066 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Data analyzed in this study were a re-analysis of existing data, which are available at locations cited in the reference section.

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