Sleep Patterns & Predictors of Disturbed Sleep
Anaisha Aggarwal • March 4, 2026
Sleep is a vital element of well-being, especially for college students who face academic stress, social pressure, and irregular schedules that can disrupt healthy sleep routines. Lund et al. (2009) conducted an empirical study to identify how common disturbed sleep is among undergraduate students and to examine the specific behaviors and factors that contribute to poor sleep quality. The goal of the study was to examine differences between weekend and weekday sleep patterns, analyze how sleep disturbances impact physical and psychological health, and quantify overall sleep patterns in this population.
This study utilized a cross-sectional survey design with a large sample of undergraduate students at a Midwestern university. The authors, Hannah G. Lund, Brian D. Reider, Annie B. Whiting, and J. Roxanne Prichard, collected self-report data on students' usual bedtime and wake times, weekend versus weekday schedules, daytime sleepiness, and the use of medications or substances to alter alertness or sleep. The main assessment tool used was the Pittsburgh Sleep Quality Index (PSQI), which categorizes individuals as either “poor sleepers” or “good sleepers.” This classification is based on seven components of sleep, including sleep latency, disturbances, duration, efficiency, use of sleeping medication, daytime dysfunction, and overall sleep quality. Using descriptive statistics and regression analyses, the study identified predictors of poor sleep.
The findings revealed concerning trends regarding college student well-being. More than 60% of students were classified as “poor quality sleepers” according to the PSQI assessment. A notable percentage of students reported using prescription medications, over-the-counter sleep aids, stimulants, and even alcohol to modify their sleep schedules or maintain wakefulness. Additionally, students demonstrated irregular sleep patterns, going to bed and waking up significantly earlier on weekdays compared to weekends. This pattern reflects the concept of “social jetlag,” which is associated with disturbances in circadian rhythm. Finally, poor quality sleepers reported higher levels of psychological distress and more physical health complaints compared to good quality sleepers, indicating a strong relationship between sleep disturbance and overall well-being challenges.
This study highlights that poor sleep quality among college students is a major concern due to its impact on overall well-being. The strong association between sleep quality and both psychological and physiological health problems suggests that universities should prioritize sleep education and consider structural changes that promote healthier schedules. Implementing such changes could improve productivity, academic performance, and the overall campus environment. Moreover, because sleep is a modifiable behavior, even small improvements in student habits, such as reducing substance use to regulate sleep and maintaining more consistent sleep schedules, could lead to improvements in academic performance and emotional well-being.
Because the study utilized a large sample and well-established measurement tools, the insights it provides are valuable. However, the cross-sectional design limits the ability to determine causation. Although the findings demonstrate strong associations, they do not prove that poor sleep directly causes psychological or physical distress. Future research could use longitudinal or experimental designs to examine how targeted sleep interventions influence student well-being over time. Doing so would provide clearer evidence regarding the long-term benefits of improving sleep hygiene in college populations.
Author:
Anaisha Aggarwal, Emory University
Editor:
Mary Yeboah, Emory University
Article Reviewed:
Lund, H. G., Reider, B. D., Whiting, A. B., & Prichard, J. R. (2009). Sleep Patterns and Predictors of Disturbed Sleep in a Large Population of College Students. Journal of Adolescent Health, 46(2), 124–132. https://doi.org/10.1016/j.jadohealth.2009.06.016
To measure positive emotions and mental health, the researchers used questionnaires. College students were divided into two groups based on how often they ran long distances. Positive emotions were measured at four time points (T1, T2, T3, T4), which represented the start of the study and then 30, 60, and 90 days later. The running group included 52 students at each stage, while the control group included 37 students. Mental health was measured using different dimensions, including depression, obsession‑compulsion, and anxiety. This design allowed the researchers to track how regular long‑distance running might shape both emotional well‑being and mental health over time. When looking at the results for positive emotions, the researchers found that students who ran regularly had higher scores across all measured aspects, including feeling energetic, confident, and relaxed. The only category that did not show much change was the “quietness” category. For mental health, students who did not run or rarely ran had higher scores in almost all of the negative mental health areas. Their scores indicated that their levels of depression and anxiety were above normal. This suggests that students who do not run regularly may be more likely to experience psychological stress. In contrast, students who ran at least once a week had mental health scores within the normal range, indicating healthier mental well‑being. Together, these results show that regular long‑distance running is associated with better mental health and more positive emotional experiences (Feng, Zhou et al. 2020).
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Over six weeks, participants took part in weekly 60-minute vinyasa flow yoga sessions, followed by 30-minute guided meditation. Both activities were led by trained faculty members from the University of Rhode Island College of Pharmacy and conducted in a private classroom within the College of Pharmacy building. These sessions provided participants with a structured and accessible opportunity to step away from academic demands and explore the stress-relieving effects of yoga and meditation. To assess changes in participants’ stress, anxiety, and mindfulness, the researchers used three self-report questionnaires. Students’ scores on each questionnaire were evaluated before and after the intervention using numerical values and categorical scales (low, medium, and high) specific to each instrument (Lemay et al., 2019). The Beck Anxiety Inventory (BAI) measured the severity of anxiety symptoms, the Perceived Stress Scale (PSS) evaluated how stressed students felt in their daily lives, and the Five Facet Mindfulness Questionnaire (FFMQ) assessed mindfulness skills across five dimensions (Lemay et al., 2019).
The study used an online focus group method that included 18 graduate students at a very high research activity university. Participants were verified as both neurodivergent and STEM students based on their responses to a graduate student listserv and information from the university’s disability services office. Degree programs were classified as STEM by considering the majors offered and their alignment with National Science Foundation research areas (Syharat et al., 2023). To gather data, the researchers asked participants open-ended questions about their general experiences in graduate school. The online focus-group format allowed students not only to share their own experiences, but also to reflect on and respond to others’ stories, which enriched the data collected. Responses from earlier focus groups were used to refine and develop questions for later groups. Questions typically addressed topics such as individual strengths and challenges, relationships with peers and mentors, understanding of neurodiversity, and experiences with accommodations in both past and current educational settings.
Results showed that students were more likely to listen to music alone. Specifically, 31.03% of respondents reported rarely listening with others in the past week, while 47.41% reported only sometimes doing so. Music can help validate and process emotions, and this may be more effective when students listen alone, as it provides a more intimate space for engagement. Furthermore, 37.04% of students often listened to music to match their mood, and 36.11% of students did so very often. 91.67% of participants also used music to influence their mood in various ways, such as alleviating anxiety, escaping from reality, or getting through difficult times.
The participants were asked to read and evaluate therapy scenarios generated from human-AI and human-human interactions. The human therapy scenarios are transcripts adapted from Cognitive Behavioral Therapy (CBT) textbooks, and the Pi therapy conversations were generated by Pi using the same client opening statements. The participants were divided into four groups. Group 1 assessed two human-human interactions. Group 2 evaluated one human-human interaction, followed by one human-AI interaction. Group 3 focused on one human-AI interaction, followed by one human-human interaction. Group 4 assessed both human-AI interactions. The participants would read the scenario, assess the quality of described counseling, and identify whether it was human-human or human-AI. The scenarios were ranked on a 5-point Likert scale with questions focused on communication, understanding, credibility, and therapeutic techniques. The mean quality score was 3.70 for human-AI therapy transcripts and 3.43 for human-human transcripts. The percentage of correctly identified human-human transcripts (59.3%) was higher than correctly identified human-AI transcripts (49.2%). Kuhail et al. concluded, using the Mann-Whitney U test, that there was a statistically significant difference in therapists’ rating of human-human and human-AI therapy, with human-AI therapy rated at a higher quality. In addition, therapists could not reliably tell the difference between human-human and human-AI therapy. The use of a chi-square test confirmed that therapists’ identification of human therapists and Pi was not statistically significant.
This study uses an ex post facto research design, in which researchers compare preexisting groups without manipulating variables. The researcher obtained a sample of 197 children aged 8 to 12 from five migrant farmers’ primary schools located in Uzo Uwani, a rur,m al suburb of Enugu State, Nigeria, which serve a population of 3,488 children. To measure the children’s well-being and mental health in the context of Nigerian children, the research adopted and made modifications to the Children’s Mental Health Questionnaire (CMHQ) and Children’s Wellbeing Index (CWI). CCMHQ measures children’s mental health using 15 items that assess levels of depression anxiety, suicidal thoughts, and related behaviors on a four point Likert scale. CWI measures children’s well-being through six dimensions: self-acceptance, life satisfaction, personal growth, positive interpersonal relationships, adaptability, and quality of life, using yes/no response questions. Linear regression was further applied to analyze the questionnaire data and identify relationships between nutrition, mental health, and wellbeing. For the relationship between nutrition and mental health, results show a strong positive correlation between nutrition and children’s mental health in the studied migrant farmers’ primary school in the specified area of Nigeria, with a coefficient of R = 0.915. The coefficient of determination indicates that 83% of the variation in mental health outcomes could be explained by nutritional factors. With a p-value of 0.000 in the linear regression test of nutrition and mental health, it is supported that nutrition is creating a meaningful and positive impact on their mental health. For the relationship between nutrition and well-being, results show that there is a high and positive correlation between nutrition and the well-being of the children in the studied migrant farmers’ primary schools, with a correlation coefficient of R = 0.949. The coefficient of determination of 0.901 showcases that 90% of the variation in the well-being of children who share a similar background with the sample could be explained by nutrition. The p-value of 0.000 from the linear regression test again shows that nutrition has a meaningful effect on their well-being.
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