Associations between lifestyle behaviors and sleep problems in university students: a cross-sectional analysisABSTRACT
Introduction: Lifestyle behaviors such as nutrition, substance use, physical activity and stress management are independently associated with less risk of sleep problems, but there few evidence evaluating whether the combination of lifestyle behaviors associates with sleep problems. The aim of this study was to evaluate the associations between lifestyle behaviors and sleep problems in university students.
Methods: The study had cross-sectional design. Data was gathered using an online survey (RedCap) enrolling university student from 11 universities in Brazil. The Short Multidimensional Inventory Lifestyle Evaluation (U-SMILE) was used to assess six lifestyle behaviors (diet, substance use, physical activity, stress management, social relationships, and environment exposure), while the DSM-5-TR self-rated level 1 cross-cutting symptom measure and the Pittsburgh Sleep Quality Index (PSQI) were used to assess sleep problems. Poisson regression was used to assess the associations between lifestyle and sleep problems.
Results: A total of 747 participants (56.1% women, 42.6% ≤20 years old) participated the study. Participants with an overall healthier lifestyle, accounting six behaviors, had a lower prevalence of sleep problems compared to those with an unhealthier lifestyle (RP=0.59; 95%CI=0.48–0.72; p<0.001). Diet (RP=0.74; 95%CI=0.60–0.92; p=0.006), social support (RP=0.71; 95%CI=0.57–0.88; p=0.002), and environmental exposure (RP=0.80; 95%CI=0.64–0.99; p=0.044) were independently associated with a higher prevalence of sleep problems.
Conclusion: A healthy lifestyle was associated with a lower prevalence of sleep problems, with diet, social support, and environmental exposure being the key contributing factors.
KEYWORDS
Sleep problems; Lifestyle; Students; Sleep; Behaviors
RESUMO
Introdução: Comportamentos relacionados ao estilo de vida, como alimentação, uso de substâncias, atividade física e manejo do estresse, estão independentemente associados a menor risco de problemas de sono. No entanto, há poucas evidências que avaliem se a combinação desses comportamentos está associada a problemas de sono. O objetivo deste estudo foi avaliar as associações entre comportamentos de estilo de vida e problemas de sono em estudantes universitários.
Métodos: O estudo possui delineamento transversal. Os dados foram coletados por meio de um questionário online (RedCap), com a participação de estudantes de 11 universidades do Brasil. O Short Multidimensional Inventory Lifestyle Evaluation (U-SMILE) foi utilizado para avaliar seis comportamentos de estilo de vida (alimentação, uso de substâncias, atividade física, manejo do estresse, relacionamentos sociais e exposição ambiental). Para avaliar os problemas de sono, foram utilizados o DSM-5-TR Self-Rated Level 1 Cross-Cutting Symptom Measure e o Pittsburgh Sleep Quality Index (PSQI). As associações entre estilo de vida e problemas de sono foram analisadas por meio de regressão de Poisson.
Resultados: Participaram do estudo 747 estudantes (56,1% mulheres, 42,6% com ≤20 anos). Os participantes com um estilo de vida geral mais saudável, considerando os seis comportamentos, apresentaram menor prevalência de problemas de sono em comparação àqueles com estilo de vida menos saudável (RP=0,59; IC95%=0,48–0,72; p<0,001). Alimentação (RP=0,74; IC95%=0,60–0,92; p=0,006), suporte social (RP=0,71; IC95%=0,57–0,88; p=0,002) e exposição ambiental (RP=0,80; IC95%=0,64–0,99; p=0,044) foram independentemente associados a maior prevalência de problemas de sono.
Conclusão: Um estilo de vida saudável esteve associado a menor prevalência de problemas de sono, sendo a alimentação, o suporte social e a exposição ambiental os principais fatores contribuintes.
PALAVRAS-CHAVE
Problemas de sono; Estilo de vida; Estudantes; Sono; Comportamentos
INTRODUCTION
Sleep problems refers to difficulties in falling or remaining asleep, experiencing low-quality sleep, and daytime sleepiness.1 They are associated with irritability, agitation, and incapacity to process information2 and to an increased risk of developing other mental health problems and cognitive deficits.3 Further, sleep problems often result in lower performance in daily life tasks4 and are common among university students.5 Previous studies reported prevalence of sleep problems in university students ranging from 27% to 936, including insomnia (36.4%) and obstructive sleep apnea (21.5%).7
Sleep problems are multifactorial8, and some evidence supports the notion that lifestyle behaviors are associated with their emergence.9-11 These factors constitute multidimensional construct, which encompasses life habits and such as physical activity, diet, sleep, smoking, substance abuse, stress management, social support, and environmental exposure.12
During higher education years, students are at a high risk of adopting unhealthy lifestyle behaviors such as lower physical activity levels, poor dietetic habits, sleep irregularly and high consumption of alcohol and other substances.13,14 Accordingly, evidence suggests that students with these unhealthy lifestyle conducts tend to experience worse sleep outcomes.9 For instance, higher leisure time physical activity associates with 29% lesser risk of poor sleep quality.15 Further, long periods of screen time, including the use of computers, cellphones and the internet, for example, is associated with shorter and lower-quality sleep.16 A significant negative correlation between social media use and sleep health (r=−0.131) was found in students.17
On the other hand, effective stress management and a solid social support system are individually related to better sleep.18 Moreover, studies indicate an increase of smoking and alcohol consumption are associated with poor sleep quality among adolescents.19 Lastly, Wang et al. (2023)20, found that bedtime habits and neighborhood environment combined with stress and self-control, increase the risk of having sleep problems.
Besides that, as sleep issues are multifaceted, it is important to understand not just how these factors interact independently but also how they collectively influence sleep. Although there is evidence that lifestyle is associated with sleep within students, most of them explore just the independent association between lifestyle behaviors and sleep. However, it is well known that lifestyle behaviors do not occur in isolation, and have a strong relationship among them.21,22 Therefore, studies investigating the combination of multiple lifestyle behaviors with sleep problems are more likely to better explain how they associate.
Given the aforementioned, our study aimed to evaluate the associations between lifestyle behaviors (combined and independently) and sleep problems in a sample of undergraduate and postgraduate students from Brazil.
METHODS
Study design
This cross-sectional study was developed with data from the MENTAL-UNI study. The MENTAL-UNI was a pilot study for The UNIversity Student's LIFEstyle and Mental Health Study (UNILIFE-M) cohort.23 This study was approved by the Ethics and Research Committee (CAAE 55481422.5.1001.5346). This study was conducted following the STROBE guidelines for cross-sectional studies.24
Sample
The study sample was conveniently selected and included students from 11 Brazilian universities, covering all macroregions of the country. Participants were recruited through researchers’ and the institution's social media platforms (WhatsApp, Facebook, Instagram, and Twitter), general media, university networks, and emails sent to students. Furthermore, recruitment was done via face-to-face visits to lectures, flyers and advertising through the campus.
The universities participating in this study were: Federal University of Santa Maria (UFSM), Pontifical Catholic University of Rio Grande do Sul (PUCRS), Federal University of Santa Catarina (UFSC), Federal University of Sergipe (UFS), State University of Montes Claros (UNIMONTES), Federal University of Pará (UFPA), Federal University of Rio Grande do Norte (UFRN), Federal University of Rio de Janeiro (UFRJ), University of Brasília (UnB), University of São Paulo (USP), and Federal University of Ceará (UFC).
Inclusion criteria
University students aged ≥18 who were enrolled in undergraduate or postgraduate programs at the participating universities were included in this study. Individuals who did not fully complete the sleep assessment instrument were excluded from our analytical sample.
Procedures
The study was conducted through an online survey using the Research Electronic Data Capture (RedCap) platform (The model that demonstrates how the survey is inserted into the platform can be found at the link: https://redcap.huwc.ufc.br/surveys/?s=YF8H3WWCW7D4R8WC). After reading and agreeing to the Informed Consent Form, participants answered a questionnaire, which covered sociodemographic and academic aspects, further, lifestyle behaviors and sleep problems were assessed. Data collection took place from June to November 2022.
Lifestyle Assessment
The Short Multidimensional Inventory Lifestyle Evaluation (SMILE-C) (25) instrument assessed students’ lifestyles. This instrument allows a multidimensional lifestyle assessment, providing scores for each of its seven domains: diet, substance use, physical activity, stress management, social support, sleep and environment exposure, as well as an overall lifestyle score. There were 27 questions to assess the frequency of behaviors considered healthy, with a response scale ranging from 1 to 4, where a higher score is indicative of a healthier lifestyle.25 Since this study's outcome is sleep problems, the sleep domain was excluded from the general score, resulting in a total of six domains used.
Lifestyle scores were calculated as follows: the diet domain consists of five items (score range: 1–20), assessing eating habits and meal patterns; substance use includes four items (1–16), evaluating alcohol, tobacco, and other drug use; physical activity consists of one item (1–4), assessing engagement in regular physical exercise; stress management comprises six items (1–24), covering psychological, behavioral, and meaning-related coping strategies; social support includes six items (1–24), assessing social relations and interpersonal interactions; and environmental exposure consists of one item (1–4), reflecting screen use close to bedtime.25
The SMILE-C generates continuous scores for each lifestyle domain and for overall lifestyle, reflecting the frequency of behaviors considered healthy according to the instrument. Higher scores indicate a greater frequency of healthier behaviors, whereas lower scores reflect unlifestyle patterns. As the instrument does not provide established clinical or guideline-based cutoff points, lifestyle classifications in this study were based on relative distinctions within the sample, ensuring internal consistency while preserving the behavioral meaning of each domain.25
Sleep Problems
The presence of sleep problems was assessed at two levels. First, all participants answered the DSM-5-TR self-rated level 1 cross-cutting symptom measure.26 Second, only those with a positive screening at the DSM-5-TR self-rated level 1 cross-cutting symptom measure answered the Pittsburgh Sleep Quality Index (PSQI). The DSM-5-TR self-rated level 1 cross-cutting symptom measure was used as a tracking instrument for symptoms that can be related to any mental disorder.26 The instrument consists of 13 domains that cover depression, anger, mania, anxiety, somatic symptoms, suicidal ideation, psychosis, sleep problems, memory, repetitive thoughts and behaviors, dissociation, personality functioning, and substance use.26 The scale consists of 23 questions that are answered by the participant using a scale of 0 to 4 points (0= Not at all. 1= A little bit, rarely, less than one or two days. 2= Mild, several days. 3= Moderate, more than half the days. 4= Severe, nearly every day), considering the experiences of the past two weeks.26 In our study, we utilized only the sleep domain to screen for potential cases of sleep problems. Scores above two points in each domain require an additional assessment.26 Therefore, this instrument served as the indicator for symptoms of sleep problems, and if participants reached the mild cutoff (≥ 2), the participant responded to the sleep problems instrument.26 Participants who scored <2 on the sleep domain were retained in the sample and classified as non-cases (i.e., without sleep problems).
To assess sleep, the Pittsburgh Sleep Quality Index (PSQI) instrument was employed27. It consists of seven components of sleep: subjective sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction.27,28 In the present study, only the sleep disturbance component (Component 5) was used to assess sleep problems. The sleep disturbance component is derived from item 5 (Items 5b to 5j), which assess different reasons for sleep disturbance. Each item is scored on a scale ranging from 0 (no difficulty) to 3 (severe difficulty). The sum of the item scores results in a total sleep disturbance score ranging from 0 to 27 points, with higher scores indicating more severe sleep problems.29 According to the PSQI scoring guidelines, each component score ranges from 0 to 3 and is derived from the summed item scores. For Component 5, scores are classified as follows: 0 = 0 (no difficulty); 1–9 = 1; 10–18 = 2; and 19–27 = 3 (severe difficulty). Based on this classification, we assume a cutoff point of ≥10 points on the sleep disturbance score (moderate difficulty) as to indicate the presence of sleep problems28,29
Sociodemographics
Sociodemographic information was used to characterize the sample. This information included sex, age, ethnic group, previous diagnosis of mental disorders, family history of mental disorders, and university affiliation.
Statistical analysis
Data distribution was tested using the Shapiro-Wilk test. Descriptive analyses were performed utilizing relative and absolute frequencies for categorical variables, and medians with interquartile intervals for continuous data with non-parametric distribution (total and each domain).
Lifestyle variables were categorized for analytical purposes using the sample median. This approach was adopted because the lifestyle domain scores showed non-parametric distribution, for which the median represents a robust and appropriate measure of central tendency, minimizing the influence of extreme values. Moreover, this classification reflects the structure of the SMILE-C instrument, which yields continuous scores without predefined clinical cutoffs.
Crude and adjusted associations between lifestyle (combined and independently) and sleep problems were tested by Poisson regression with robust variance models with the reference group being unhealthy lifestyle. The results expressed in Prevalence Ratio (PR) and its respective 95% confidence intervals (95%CI) and significance levels. Following previous literature, it was tested sex, age, ethnic group, family history of mental disorders, income, work, and level of education30,31, as potential covariates. Those with values p<0,20 were incorporated into the adjusted models. All variables were included simultaneously in the models. Analyzes were performed with an alpha of 5%, using STATA version 15.
As this is a pilot study of the survey, post hoc sample size analyzes were conducted to test the statistical power of the analysis, using the GPower software version 3.1.9.7. Using an alpha of 0.05, odds ratio of 0.30 and coefficient of determination (R²) of 0.92, the power found was 0.94.
Results
A total of 918 participants were involved in the study, with 171 excluded due to incomplete data in the lifestyle or sleep assessment instrument. The analytical sample consisted a total of 747 individuals, predominantly women (56.1%), students aged ≤20 years (42.0%), and mostly non-white (55.9%). Additionally, 109 students (14.6%) had a prior diagnosis of a mental disorder. The majority of the sample consisted of students from the Federal University of Pará (UFPA) - 130 (17.7%). The prevalence of sleep problems within our sample was 39.4%. This information is available in table 1.
Table 2 presents the medians of each lifestyle domain and general scores and table 3, the association's analyses. In the crude analysis, participants with a healthier overall lifestyle had a lower prevalence of sleep problems (RP=0.56; 95%CI=0.46–0.68; p<0.001). A healthy diet (RP=0.60; 95%CI=0.49–0.73; p<0.001), higher physical activity frequency (RP=0.73; 95%CI=0.60–0.89; p=0.002), better social support (RP=0.60; 95%CI=0.49–0.72; p<0.001), better stress management (RP=0.70; 95%CI=0.58–0.84; p<0.001), and healthier environmental exposure (RP=0.72; 95%CI=0.58–0.88; p=0.002) were associated with a reduced prevalence of sleep problems. In the adjusted analysis, these associations remained significant for a healthy diet (RP=0.74; 95%CI=0.60–0.92; p=0.006), better social support (RP=0.71; 95%CI=0.57–0.88; p=0.002), healthier environmental exposure (RP=0.80; 95%CI=0.64–0.99; p=0.044), and an overall healthy lifestyle (RP=0.59; 95%CI=0.48–0.72; p<0.001).
Average lifestyle scores (overall and domains) of participants in the MENTAL-UNI study (n=747)
Crude and adjusted associations of lifestyle behavior (subdomains and overall) and sleep problems in participants of the MENTAL-UNI study.
Discussion
The results suggest that a healthier lifestyle, combining six lifestyle behaviors, is associated with a 41% lower prevalence of sleep problems in a sample of university students from different macroregions in Brazil. Furthermore, our results suggest that a healthier diet, greater social support and healthier environmental exposure are associated with a lower prevalence of sleep problems, after adjusting for relevant covariates. To our knowledge, this is the first study to examine the correlations between comprehensive lifestyle factors, encompassing various domains, and sleep issues among Brazilian university students across diverse fields of study.
There is limited evidence investigating the combination of multiple lifestyle behaviors with sleep problems in the Brazilian population, in particular, among students. A study carried out with the Brazilian adult and older adult population observed that individuals with lifestyle-related risk behaviors, including smoking, excessive alcohol consumption, sedentary lifestyle, and sedentary behavior were at greater risk of experiencing sleep problems.10 Among students, a study in Malasya found that those with unhealthier lifestyle behaviors were more likely to experience poor sleep quality compared to those with healthier lifestyles32 and that the lifestyle can play a mediating role between circadian rhythm and sleep quality among students.33 These findings align with our study and suggest that sleep problems and lifestyle behaviors are intrinsically associated in people from different ages and cultures.
It was found that particularly a healthier diet, greater social support and environmental exposure are associated with a lower risk of sleep problems. An unhealthy dietary pattern, involving an excessive consumption of sugar and a low intake of dairy products, is associated with a higher prevalence of sleep problems in university students.34 Therefore, students with an unhealthy diet were more likely to experience poor sleep quality.35 It is possible that diet may interfere with hormonal release, promoting abnormal levels of glucocorticoids and cortisol, which could lead to an increased desire to consume unhealthy foods, rich in calories, fats, and free sugars.36 However, the mechanisms of this association seem to exists bidirectionality, where irregular sleep may also trigger alterations in dietary intake and stress, leading to alterations in decision-making and influencing the need for instant rewards provided by unhealthy foods.37
Our results indicate that higher levels of social support are associated with lower odds of sleep problems among students. There is evidence suggesting that university students with higher levels of social support have a reduced probability of experiencing poor sleep quality.38 Additionally, students with good social support are also more likely to have greater sleep duration, enhanced sleep efficiency, favorable subjective sleep quality, and less sleep latency.18 It is well established that social support is associated with positive mental and emotional health outcomes, including lower levels of rumination and fewer depressive symptoms.39 These factors may represent a plausible explanatory pathway for the observed association between social support and sleep problems, as both rumination and depressive symptoms have been consistently linked to poorer sleep quality and sleep disturbances.40
Besides that, our study also found an inverse association between environmental exposure and sleep problems. In note, it is important to highlight that the questions used in this domain of the SMILE questionnaire are mainly focused on exposure to screen time. The negative impact of screen time on sleep is well documented in the literature41 with high levels of it being associated with poor sleep quality and shorter sleep duration, fatigue, daytime sleepiness, and lack of concentration in adolescents and college students.42 A possible explanation for this factor may be the negative effect of short-wavelength blue light on melatonin production43, which is an important hormone in the regulation of the circadian cycle and sleep onset.44
This study has some limitations. Due to its cross-sectional nature, it is not possible to infer causality from the observed associations or assess the potential bidirectionality between the associations, and reverse causality cannot be ruled out. Moreover, data used is self-reported, therefore, susceptible to memory bias and social desirability bias. Also, as all the mostly self-report on-line surveys, the sample was convenience-selected and may not accurately reflect the Brazilian university student's population, thus, it requires caution in extrapolations. Furthermore, although the study included several universities, it is a pilot study, so the sample was unevenly distributed among the institutions, which may further limit the generalizability of the results. The absence of established cut-off points for classifying lifestyle domains assessed by the SMILE led to the use of the sample median as a classification criterion. However, this approach does not ensure the identification of genuinely healthy habits, as scores above the median may still reflect unfavorable behavioral patterns when the overall sample presents inadequate lifestyles, without alignment with established health guidelines. Lastly, sleep problems were initially identified using the DSM-5 Level 1 Cross-Cutting Symptom Measure as a screening tool, which does not have perfect sensitivity and may have resulted in false negatives, potentially leading to an underestimation of the prevalence of sleep problems. In the second stage of the assessment, sleep problems were assessed exclusively through the domain of sleep disorders, without considering the overall score of the instrument, which may result in a lack of relevant issues related to sleep.
It is important to mention that this study has strengths, such as the use of validated instruments to assess sleep problems and overall lifestyle, including different lifestyle-related domains, diverging from the majority of the current evidence in the literature, focusing on assessing the specific domains. In addition, it is a multicenter study, including participants from different regions of the country and various study fields. As this study should be considered a pilot investigation, it provided an opportunity to test analytical procedures and explore potential association patterns. In this context, the broader UNILIFE-M Cohort23 represents a valuable opportunity to replicate and extend the present findings using longitudinal and cross-national data, allowing future studies to more robustly examine the temporal and potentially bidirectional relationships between lifestyle behaviors and mental health outcomes among university students.
Conclusions
Our findings demonstrate that university students with a healthy lifestyle had a lower prevalence of sleep problems. Diet, social support, and environmental exposure were the only independent lifestyle behaviors associated with the prevalence of sleep problems.
SOURCE OF FUNDING
DATA AVAILABILITY STATEMENT
The data supporting the findings of this study are not publicly available due to ethical and privacy restrictions.
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