The Effects of a Pre-Existing University Course About Sleep on Students’ Subjective Sleep Quality, Sleep Beliefs, and Pre-Sleep Arousal

1. Introduction

Sleeping well is a crucial requirement for good health. Studies have shown the importance of good sleep for different health domains (Irwin, 2015). Conversely, sleep problems seem related to the development of several psychiatric and medical disorders, especially at young ages (Scott et al., 2021; Lewandowski et al., 2011). Nevertheless, in Western societies, sleep health is frequently overlooked for several individual and/or psychosocial reasons: leisure activities, busy schedules, work, school, etc. All these factors make it difficult to establish a daily life routine, resulting in increased sleep variability and risk of developing a fully-fledged sleep disorder (Becker et al., 2017). This scenario is particularly plausible in university students, whose efforts to match personal commitments with university schedules and studying time often take a toll on sleep, causing a possible social jetlag situation (Vrabec et al., 2022; de Medeiros Lopes et al., 2022). Indeed, it was shown that this cohort reports numerous sleep complaints, such as insomnia (Schlarb et al., 2012; Chowdhury et al., 2021; Sun et al., 2025), poor sleep quality (Becker et al., 2017; Milojevich et al., 2016), reduced sleep duration (Becker et al., 2017, Schlarb et al., 2012, Milojevich et al., 2016), increased sleep onset latency (Becker et al., 2017, Schlarb et al., 2012), and other sleep disruptions (Milojevich et al., 2016). Notably, typical young adult behaviors, such as consuming psychotropic substances (e.g., alcohol, caffeine, nicotine, etc.) (Brito et al., 2024; De Rosa et al., 2025) or playing video games for long sessions (De Rosa et al., 2024), may exacerbate these disturbances.

As a consequence, sleep issues in this cohort have been related to several negative outcomes, such as increased depression (Dinis et al., 2018), anxiety (Zhang et al., 2024), stress (Wallace et al., 2017; Gardani et al., 2022), eating disorders (Sun et al., 2025), suicidal ideation (Russell et al., 2019), and impaired academic performance (Turner et al., 2021; Kwok et al., 2021).

Therefore, monitoring sleep and developing intervention programs for university students appears urgent and necessary. In this regard, it is important to understand how and with which methods it would be more feasible to carry out this kind of intervention. The golden standard approach is represented by the cognitive-behavioral therapy for insomnia (CBT-I). Indeed, by working on different areas, i.e., sleep hygiene education, relaxation techniques, stimulus control, sleep restriction, and cognitive restructuring, CBT-I has been proven to yield beneficial effects on university students’ sleep, mental health and academic performance (see Chandler et al., 2022, for a review). However, CBT-I is usually expensive, time-consuming, and not always applicable, especially for huge populations. Considering this, a possible approach in university contexts could be to use only those effective tools of CBT-I that can be easily disseminated among students. To this aim, a core part of CBT-I, the one based on sleep hygiene education, could be an appropriate intervention in this context. In fact, according to the literature, dysfunctional beliefs about sleep may lead to the development of maladaptive behaviors and negative habits, resulting in increased pre-sleep arousal and, consequently, in worse sleep quality (Wang et al., 2025; Jin et al., 2018; Humphries et al., 2022). Therefore, providing correct notions about mechanisms and functions of sleep may contribute to reshaping dysfunctional beliefs and thus to reducing pre-sleep arousal and improving overall sleep quality.

A few past studies have actually shown promising positive effects of sleep hygiene education on sleep quality and dysfunctional beliefs about sleep (see Blunden et al., 2012, for a review). Through different strategies, such as sleep education programs (Brown et al., 2006; Kira et al., 2014), in-person lectures (Levenson et al., 2016), and web-based modules on sleep hygiene (Quan et al., 2013), these studies showed an association between knowledge about sleep and improved sleep quality. In a pioneering study, Baroni and colleagues developed a specific sleep course for university students to assess its impact on sleep-related behaviors, mood, and anxiety (Baroni et al., 2018). The course consisted of semester-long bi-weekly didactic lectures and weekly seminars and covered historical and cultural elements of sleep, sleep physiology and pathology, and dream interpretation. Students were administered a battery of questionnaires to assess sleep, sleepiness, depression, and anxiety at baseline, at the end of the course, and at a two-month follow-up. A control group followed a parallel course on psychopathology. Results showed positive outcomes with increased sleep hygiene, lower sleep onset latency, and reduced depression and anxiety, which were also maintained at follow-up.

Baroni et al.,’s (2018) results prompt to explore the possibility that also already existing university sleep courses can serve as intervention strategies for students. Compared to ad hoc programs, they would represent a more ecological approach and produce more generalizable results by reducing possible biases linked to participants’ expectations.

Moreover, a clearer comprehension of the mechanisms through which these courses can achieve their effects on sleep quality is also needed. Besides the changes of dysfunctional beliefs, for instance, pre-sleep arousal is still a poorly investigated outcome variable. High levels of agitation are not unusual among university students, especially during the examination periods (Rajendran et al., 2022), and this may not only be deleterious to sleep but, as a result, to academic performance. Indeed, according to neurobiological theories, primary insomnia may be caused by the effect of hyperarousal on the sleep-inducing brain system (Riemann et al., 2010). We hypothesize that learning the basic principles of how sleep really exerts its restorative role could be reassuring and drastically reduce pre-sleep arousal.

Therefore, here we investigate whether, in a sample of students attending a bachelor’s degree program in Psychology, a pre-existing university course on Sleep Psychology can yield positive outcomes on subjective sleep quality, mainly driven by a reduction in dysfunctional beliefs about sleep and in pre-sleep arousal.

2. Materials and Methods

2.1. Subsection

A sample of 75 volunteer students (F = 40, M = 35, Mean^age+SD = 21.5 ± 3.78) from the University of Campania “L. Vanvitelli” (N = 56, F = 24, M = 32, Mean^age+SD = 21.7 ± 4.42), Caserta, Italy, and from the University of Florence (N = 19, F = 16, M = 3, Mean^age+SD = 20.9 ± 1.84) Florence, Italy, took part in the study.

At both universities, students were enrolled in a bachelor’s degree program in Psychology. Specifically, of the total sample, 50 students (F = 22, M = 28, Mean^age+SD = 21.5 ± 2.40), corresponding to the experimental group (S), were attending a three-month mandatory course in Sleep Psychology, whereas 25 students (F = 18, M = 7, Mean^age+SD = 21.5 ± 1.83) were attending a course of the same duration (and amount of hours) in History of Psychology, and were used as a control group (H).

Inclusion criteria were to be at least 18 years old and enrolled for the first time in the Sleep Psychology course (for S) or in History of Psychology (for H).

For both groups, during the first lesson, participants were invited to take part in a survey on sleep in the student population. Volunteers were asked to fill in a consent form and complete a battery of questionnaires, including the Pittsburgh Sleep Quality Index (PSQI; Buysse et al., 1989), the Pre-Sleep Arousal Scale (PSAS; Nicassio et al., 1985), and the Dysfunctional Beliefs and Attitudes About Sleep (DBAS; Morin et al., 2007), as well as a series of ad hoc questions for demographic information. They were informed that they would be asked to complete other similar questionnaires later on during the course, but were blind to the specific research objectives. At the end of the course, participants were administered the same set of questionnaires (PSQI, DBAS, PSAS).

The structure and content of the Sleep Psychology course were analogous in both Universities. The course was structured in 3-hour in-person lectures (two per week) and covered the following topics: methods and techniques in sleep research, sleep phenomenology, sleep functions, sleep regulation and circadian rhythms, sleep deprivation effects on daytime functioning, and the role of sleep for memory consolidation processes. The History of Psychology course followed the same structure and organization, but covered the general history of psychology, with no information on sleep education.

2.1.1. Instruments

Pittsburgh Sleep Quality Index (PSQI; Buysse et al., 1989; Curcio et al., 2013): the PSQI is a self-report questionnaire used to assess sleep timing and quality over the past month. It consists of seven subscales, with scores ranging between 0 and 3, which investigate: subjective sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disturbances, use of sleep medications, and daytime functioning. The PSQI also provides a global score ranging between 0 and 21: higher scores denote poorer sleep quality; individuals scoring > 5 can be classified as poor sleepers.

Pre-Sleep Arousal Scale (PSAS; Nicassio et al., 1985; Palagini and Manni, 2016): the PSAS is a 16-item questionnaire that investigates subjective pre-sleep arousal. Items range from 1 (not at all) to 5 (very), with higher scores indexing higher arousal. They yield two subscores, one referring to somatic and one to cognitive arousal. A global score, ranging between 16 and 80, is obtained by summing all the items.

Dysfunctional Beliefs and Attitudes About Sleep (DBAS; Morin et al., 2007; Devoto et al., 2016): the DBAS consists of 30 items, with responses given on a 100 mm visual analogue scale, which evaluate sleep-related beliefs, with higher scores denoting more dysfunctional beliefs. Items are divided into five subscales: 1) beliefs about consequences of insomnia (i.e., Consequences), e.g., “when I have trouble sleeping one night, I am concerned about the long-term consequences on my health”, 2) worry/helplessness about sleep (i.e., Control/Predictability), e.g., “I feel that I am no longer able to sleep well”, 3) expectations about sleep (i.e., Sleep Expectations), e.g., “I need 8 hours of sleep to function well during the day”, 4) attributions for poor sleep (i.e., Causal Attribution), e.g., “my insomnia is due to a chemical imbalanced sleep medication use”, and 5) sleep medication use (i.e., Sleep Practices), e.g., “Medication is probably the only solution to sleeplessness”. Furthermore, the questionnaire provides a global score corresponding to the average of all items.

2.2. Statistical Analysis

To test differences between pre- and post-course scores at the PSQI, PSAS, and DBAS in both groups, we used a repeated-measures ANOVA (rmANOVA), with Group (S and H) as between factor and Condition (Pre and Post) as within factor. In case of significance, post-hoc tests were performed with the Tukey correction. Possible between-group differences in age and gender were analyzed, respectively, with the Student’s t-test and the Independent Chi-Square (2) Test.

All analyses were performed with Jamovi 2.3.21 (The jamovi project, 2025); statistical significance was set at p ≤ 0.05. Descriptive statistics are reported as mean ± standard deviation. The partial 2 was reported as a measure of effect size.

3. Results

3.1. Sample Characteristics

The two groups (S and H) did not differ in age (Student’s t = 0.04, p = .970) but differed in gender distribution (X² = 8.25, p = .004).

3.2. Changes in Subjective Sleep Quality (PSQI Scores) from Baseline to Post-Course Evaluation

Table 1 displays statistics of the rmANOVA on PSQI scores.

The sample overall reported a reduction of the PSQI global score and of scores at the Sleep Quality and Sleep Latency subscales after attending courses (main effects of Condition). The analysis also yielded significant interaction effects for the Sleep Quality and Sleep Disturbances subscales. Specifically, in the S group scores at these two subscales were significantly reduced from pre- to post-test (t = 6.27, p = <.001; t = 3.90, p = .001), indicating improved sleep quality and reduced sleep disturbances. Furthermore, Sleep Quality subscores were lower (e.g., improved) at the post-test in the S compared to the H group (t = 2.69, p = .044).

3.3. Changes in Subjective Pre-Sleep Arousal (PSAS Scores) from Baseline to Post-Course Evaluation

Pre-sleep arousal appeared reduced at the post-course assessment independent of group, with regard to the global score as well as to both subscores (Table 2). Moreover, we found a significant interaction for the cognitive component, with the S group reporting lower cognitive arousal following the course compared to the baseline assessment (t = 3.98, p = .001).

3.4. Changes in Dysfunctional Beliefs and Attitudes About Sleep (DBAS Scores) from Baseline to Post-Course Evaluation

Table 3 displays statistics of the rmANOVA on DBAS scores.

As for the DBAS global score, the analysis yielded a significant main effect of Condition, indicating that scores were decreased from pre- to post-test independent of group. However, the significant interaction Group x Condition shows that this decrease was more pronounced in the S group. Specifically, post-hoc analysis showed that post-test scores of the S group were significantly reduced compared to pre-test scores of the same group (t = 7.56, p = <.001) and to both pre- and post-test scores of the H group (t = 4.12, p = <.001 and t = 2.67, p = .045, respectively).

Moving to DBAS subscores, we found a main effect of Condition for all subscales (e.g., a reduction of scores independent of group), as well as a main effect of Group for the Sleep Practices Subscale, indicating a lower presence of dysfunctional beliefs and attitudes about sleep practices in the S group independent of time point of measurement.

The analysis also yielded a significant interaction for the Consequences Subscale: specifically, the S group reported lower scores after attending the course compared with the pre-course evaluation of the same group (t = 6.61, p = <.001) and of the H group (t = 2.93, p = .021).

4. Discussion

In this study, we evaluated whether a pre-existing course in Sleep Psychology, compared to a course in History of Psychology of equivalent duration, determined changes in subjective sleep quality, subjective pre-sleep arousal, and beliefs about sleep in a sample of university students attending a bachelor’s degree program in Psychology.

At baseline, participants of both groups reported rather poor sleep quality (average PSQI > 7). This observation confirms extant data on typical university students’ sleep issues (Becker et al., 2017; Sun et al., 2025) and further underlines the need for effective intervention strategies.

Our analysis on subjective sleep quality (measured through the PSQI) yielded post-test improvements in the global score, as well as in the Sleep Quality and Sleep Latency subscores, in the overall sample. Interaction effects, clearly indicating that the improvements prominently involved the S group, were found for the Sleep Quality and Sleep Disturbances subscales. Indeed, examining data at a descriptive level, changes from pre- to post-test scores were more marked in the S group both for the PSQI global score and for most of the subscores.

A very similar profile was found for PSAS scores. Both the global score and the subscores showed improvements at the post-course evaluation, independent of group, although the S group showed greater changes between pre-test and post-test values in all three scores. An interaction effect was shown for the cognitive component of pre-sleep arousal, indicating that the improvement in this component was driven by the S group.

As for dysfunctional beliefs and attitudes about sleep, the analysis showed that they were significantly reduced in both groups from baseline to post-test. However, as indexed by the significant interaction observed for the DBAS global score, this improvement was more pronounced in students of the Sleep Psychology course. DBAS scores at all the subscales (i.e., Consequences, Control and Predictability, Sleep Expectations, Causal Attribution, and Sleep Practices) also showed reductions across time-points independent of group, although, looking at the raw data, it appears plausible that, here again, the change was mainly driven by the Sleep Psychology students. Indeed, although the interaction effect reached significance only for the Consequences subscale, the gap between pre- and post-test values is greater in the S group for all subscales except Causal Attribution. In sum, dysfunctional beliefs and attitudes about sleep appear globally reduced mainly in the S group, with a more pronounced reduction in the Consequences subscale, which stands for a better understanding of the real consequences of poor sleep on daytime functioning.

Overall, our results confirm previous data indicating that knowledge about sleep plays an important role in determining sleep quality. Indeed, numerous studies have shown the importance of understanding sleep mechanisms and the functioning of the sleep-wake rhythm to reduce the risk of developing possible sleep disorders (see Chandler et al., 2022, and Albakri, Drotos, and Meertens, 2021, for reviews). Unsurprisingly, teaching about sleep mechanisms is an essential part of education programs for sleep issues (Gruber, 2013) and represents the first step for the implementation of cognitive-behavioral therapy for sleep disorders (Schneider et al., 2019). In this regard, our data on the changes in dysfunctional beliefs and attitudes about sleep are consistent with the fact that an important component of sleep education consists in restructuring erroneous beliefs about sleep, which often stem from common thinking and can have a relevant impact on sleep quality by generating maladaptive behaviors and habits.

Among previous studies on the topic, our findings specifically parallel results from Baroni and colleagues (2018), who observed increased sleep hygiene, lower sleep onset latency, and reduced depression and anxiety after a university course about sleep. Of note, at variance with Baroni et al., (2018), we adopted a more ecological approach by employing a pre-existing mandatory university course about sleep rather than developing one for the specific purpose of the study. Moreover, our data add to their results through a specific investigation on pre-sleep arousal, which is considered to play an essential role in the development and persistence of insomnia (Riemann et al., 2010; Schneider et al., 2019). In line with our results, in an interesting study on twins and siblings, Schneider and collaborators (2019) found that insomnia symptoms were more associated with the cognitive component than with the somatic component of pre-sleep arousal. Both components, nevertheless, appear to have a genetic trace and, beyond their trigger effect on the onset of insomnia, represent important maintenance factors.

A remarkable observation in our study is that certain subjective sleep dimensions appear more impacted than others by learning about sleep mechanisms through the course. Indeed, subjective sleep quality and sleep disturbances appeared particularly improved, whereas sleep duration and efficiency remained unchanged from pre- to post-test (as well as the use of sleep medications, whose lack of variations is clearly explained by the already very limited use at baseline). It can be hypothesized that sleep education may preferentially benefit the psychological components of the experience of sleep, i.e., its general perception, irrespective of its specific determinants. This is mirrored by our findings about pre-sleep arousal, which was especially diminished in its psychological component.

Furthermore, it must be pointed out that PSQI global scores, though significantly reduced at the post-test, stayed relatively high (reaching 6.78 in the S group, indicating that participants still fall in the range of poor sleepers). According to literature (Dietrich et al., 2016), this confirms that sleep education, despite being a powerful tool to counter sleep disturbance, may not be sufficient to re-establish a “good” sleep pattern. Undoubtedly, CBT-I remains the preferred strategy for the thorough treatment of poor sleep issues and, all the more, of diagnosed sleep disorders. Nonetheless, from an applicative standpoint, our results appear very encouraging in promoting the use of university courses about sleep as an effective prevention strategy for sleep disturbances. Sleep courses can be an ecological, low-cost, and feasible strategy to improve university students’ sleep, and they could be easily implemented in all university courses, generally regarding health. This would have a specific benefit on this population (in terms of mental and physical health and academic performance) as well as more generally promoting the importance of sleep for health, which still appears, globally, under-recognized (see Lim et al., 2023).