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31 October 2026

22 Pages

Napping Practices and Their Associations with Eating Habits, Health Indicators, and Lifestyle Among University Students

Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University, Riyadh 11433, Saudi Arabia
Youth2026, 6(4), 157;https://doi.org/10.3390/youth6040157
This article belongs to the Section Youth Health and Wellbeing
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Abstract

Background: Daytime napping is common among university students, yet its relationship with lifestyle, eating behaviors, and academic-related outcomes remains unclear. Objective: To examine the associations between nap duration and eating habits, lifestyle factors, napping motivations, and academic-related outcomes among university students. Methods: A cross-sectional study was conducted among 245 university students recruited from a single university in Riyadh, Saudi Arabia. Participants were classified according to nap duration as no-nap, short (≤35 min), mid (36–90 min), or long (>90 min). Self-reported data were collected on napping behavior, eating habits, lifestyle characteristics, academic-related outcomes, and motivations for napping. Group differences were then examined across nap-duration categories. Continuous variables were compared using one-way analysis of variance (ANOVA) or Welch’s ANOVA, while categorical variables were analyzed using chi-square or Fisher’s exact tests, as appropriate. Results: Of the 245 participants, 16 (6.5%) were classified as no-nap, 30 (12.2%) short-nap, 119 (48.6%) mid-nap, and 80 (32.7%) long-nap groups. No significant differences were observed across nap-duration groups in academic-related outcomes, including academic level, GPA, study hours, concentration measures, or perceived nap benefits. Similarly, BMI and physical activity did not differ significantly across groups. In contrast, several lifestyle and eating behaviors differed significantly across nap-duration groups. Sedentary behavior differed significantly (p = 0.004), with 37.5% of participants in the long-nap group reporting more than 7 h of daily sitting. Emotional eating also differed significantly across groups (p = 0.003), with the no-nap group reporting the highest mean rating, followed by the long-nap group. Late-night eating differed significantly across groups (p < 0.001), with the long-nap group reporting the highest mean rating. Napping frequency (p = 0.002), napping location (p < 0.001), and replacement-napping motivation (p = 0.027) also differed significantly across nap-duration groups. Conclusions: In this exploratory cross-sectional study, nap duration was associated with selected lifestyle and eating behaviors and motivations for napping, while no statistically significant between-group differences were detected in the academic-related outcomes assessed, including GPA, study hours, concentration, or perceived academic benefits. Further research using objective measures of nighttime sleep and daytime sleepiness is needed to clarify the relationships between napping, lifestyle behaviors, and academic functioning.

1. Introduction

Short daytime naps have been associated with improvements in alertness and certain aspects of cognitive functioning; however, the magnitude and consistency of these reported benefits may depend on nap timing and duration, as well as the characteristics of the population and study setting (Chen et al., 2018; Mantua & Spencer, 2017). Engaging in early napping for durations ranging from 10 to 35 min has been substantiated to enhance afternoon performance across various dimensions, including learning capacity, memory retention, executive functioning, and vigilance (BaHammam et al., 2021; Lau et al., 2015; Ru et al., 2019). Aside from the numerous advantages associated with napping, it remains underappreciated as a legitimate form of sleep, with considerable cultural disparities evident (Alger et al., 2019). The tendency to sleep in the afternoon is recognized in North American and Northern European societies, where roughly 41–74% of healthy American adults take at least one nap per week (Knutson, 2015; McDevitt et al., 2012). In Saudi Arabia, daytime napping is a commonly reported sleep-related behavior, particularly during the afternoon (Alharbi & Bajaifar, 2024; BaHammam et al., 2021). Cultural practices, daily schedules, nighttime sleep patterns, and environmental conditions such as high daytime temperatures may potentially influence napping behavior (Putilov & Verevkin, 2026); however, the relative contribution of these factors has not been well established.
Longer daytime naps may have different immediate and longer-term correlates. Immediately following a longer nap, individuals may experience sleep inertia, characterized by temporary confusion, lethargy, and reduced cognitive alertness (Du et al., 2022; Hilditch et al., 2016). Longer or habitual napping has also been associated in observational studies with sedentary behavior and several health-related factors, including obesity, elevated triglycerides, hypertension, and depressive symptoms (BaHammam et al., 2021; Yang et al., 2024). However, these associations should not be interpreted as causal. In the present study, long-naps were defined as those lasting >90 min. Nap duration, frequency, and motivation for napping may therefore help explain differences in napping patterns and their associated outcomes (Du et al., 2022; Duggan et al., 2018).
Because daytime napping occurs within the broader context of students’ daily routines, it may coexist with differences in lifestyle and eating behaviors. Behaviors such as prolonged sedentary time, late-night eating, and emotional eating were therefore considered as relevant indicators of students’ everyday behavioral patterns that may accompany different napping practices. A later chronotype (an evening-oriented sleep–wake preference) has been associated with longer daytime nap duration and later-timed eating behaviors among university students (Barley et al., 2023), supporting the relevance of examining napping alongside eating and lifestyle behaviors. Given the exploratory nature of the study, a broad range of behavioral and napping-related variables was considered to characterize the context in which daytime napping occurs. Napping motivation was also assessed to help characterize possible reasons underlying different nap-duration patterns. We hypothesized that nap duration would be associated with selected lifestyle behaviors, eating patterns, napping motivations, and perceived academic outcomes among university students. Despite the widespread practice of daytime napping among university students in Saudi Arabia, limited evidence is available on its relationship with lifestyle, eating behaviors, and academic-related outcomes. Therefore, this study aimed to examine the associations between nap duration and health indicators, lifestyle factors, eating behaviors, and academic-related outcomes among university students in Saudi Arabia.

2. Materials and Methods

2.1. Study Design

An analytical cross-sectional survey was conducted to investigate the associations between nap duration and health indicators, eating behaviors, lifestyle factors, and academic-related outcomes among university students.

2.2. Participants and Setting

The study was conducted among university students in Riyadh, Saudi Arabia. Participants were undergraduate and graduate students aged 18–35 years. Data were collected from December 2025 to February 2026 using a structured, self-administered questionnaire. Participants were recruited from King Saud University in Riyadh, Saudi Arabia.

2.3. Sample Size Calculation

Sample size was determined a priori using G*Power 3.1.9.7 software (Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany) (Faul et al., 2007), based on a one-way analysis of variance (ANOVA) comparing the four nap-duration groups (no-nap, short, mid, and long). The calculation assumed a medium effect size of f = 0.25, a significance level of α = 0.05, and statistical power of 80%. The resulting minimum required sample size was N = 179 participants. A total of 245 participants were ultimately included. The sample size calculation assumed approximately equal allocation across the four groups and was used as an a priori overall recruitment target rather than as evidence that each individual group comparison was adequately powered.

2.4. Sampling, Recruitment, and Eligibility Criteria

The questionnaire was distributed electronically through the university’s official student communication system and was made available to all King Saud University students during the data-collection period from December 2025 to February 2026. Because the university-wide distribution system did not provide information on the number of individuals who opened or started the questionnaire, these data were not recorded by the research team. Consequently, the numbers of survey starts, screening failures, discontinuations, and exclusions before completion were unavailable, and a conventional response rate could not be calculated. A total of 245 eligible students provided complete responses and were included in the final analytical sample. Eligibility was assessed through self-reported screening questions included in the questionnaire. Participants were required to confirm that they met the eligibility criteria and provide informed consent before accessing the main survey. The eligibility criteria concerned reported sleep disorders (including sleep apnea, narcolepsy, and insomnia), use of medications known to affect sleep patterns, pregnancy, and breastfeeding; none of these were verified against medical records.

2.5. Data Collection

The questionnaire assessed napping behaviors, dietary patterns, health indicators, lifestyle factors, academic characteristics, concentration, and perceived benefits of napping. The survey was distributed through the university’s official student email system during the study period. The questionnaire was administered electronically using Google Forms (Google LLC, Mountain View, CA, USA). To minimize duplicate submissions, access to the electronic questionnaire required authentication through the participant’s institutional university email account. This authentication was used solely for access control and to permit only one response per institutional account and was not recorded, exported, or included in the research dataset. Because the questionnaire required completion of the key study items before submission, incomplete questionnaires were not included in the analytical dataset. A small number of item-level missing responses were identified in the final dataset for specific academic variables and are reported in the relevant table. Ethical approval for this study was obtained from the Institutional Review Board/Permanent Committee for Research Ethics at King Saud University (KSU) (Ref. No: KSU-HE-25-1240; date of approval: 12 November 2025). Participation was voluntary, and electronic consent was obtained from all participants before accessing the main survey. No identifying information was collected, and all responses were managed confidentially and used solely for research purposes. The study procedures were conducted in accordance with relevant ethical guidelines and regulations (including the principles of the Declaration of Helsinki, where applicable).

2.6. Survey Instrument

The questionnaire consisted of the Napping Motivation items adapted from Duggan et al. (2018), together with author-developed items addressing specific daytime napping practices, lifestyle behaviors, eating patterns, and academic-related characteristics relevant to the study objectives and the local cultural and social context, including daytime napping and late-night eating. The questionnaire was intentionally kept brief to minimize participant burden and encourage completion. Faculty members with relevant expertise reviewed the items for relevance, clarity, and alignment with the study objectives, followed by a small-group clarity review with three students from the target population. The questionnaire was revised accordingly prior to administration. The questionnaire was exploratory, and not intended to provide a comprehensive assessment of nighttime sleep quality, daytime sleepiness, or overall dietary quality; therefore, standardized instruments such as the Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS) were not included in the original study protocol.
The questionnaire was administered in Arabic, the language used for data collection. Except for the napping motivation items in Section 2C, all questionnaire sections were developed directly in Arabic, and the English wording presented in the manuscript represents a translation of the administered Arabic items. The napping motivation items were adapted from the English-language DREAM questionnaire (Duggan et al., 2018) and translated into Arabic for administration. The translation and adaptation were performed by the author. No formal forward-translation and back-translation procedure was undertaken.
The survey consisted of six main sections:
Section 1: Demographic and Academic Information: age, gender, marital status, and monthly household income, reported in Saudi riyals (SAR). Academic information included academic level, field of study, cumulative GPA, and average daily study hours. Cumulative GPA was self-reported using the 5-point grading scale used by the institution and was categorized into predefined GPA ranges. Daily study hours were assessed as an indicator of study behavior.
Section 2: Napping Behavior:
A-
The no-nap group represents participants who answered “No” to the question, “Do you typically take naps during the daytime?” Participants responding “Yes” were asked, “What is the typical duration of your nap during the daytime?”.
B-
Napping Frequency and Duration: Napping frequency was assessed among participants who reported typically taking daytime naps using the question, “On average, how many days per week do you take a nap during daytime?”. Response options were “Rarely,” “One day a week,” “Two days a week,” “3–4 days,” “5–6 days,” and “Every day.” Average nap duration was assessed using the following categories: <10, 10–20, 21–35, 36–60, 61–90, and >90 min. These categories were subsequently grouped as short (≤35 min), mid (36–90 min), and long (>90 min). Preferred napping times (morning, early afternoon, late afternoon, evening), Napping location (bedroom, living room, library, prayer room, other).
C-
Napping Motivation, adapted from (Duggan et al., 2018): Planned napping (scheduled rest periods), Replacement napping (compensating for lost nighttime sleep), Appetitive napping (enjoyment-based napping), Emergency napping (overwhelming sleepiness). Each item was rated independently on a 5-point Likert scale (1 = Never, 2 = Rarely, 3 = Sometimes, 4 = Often, 5 = Always). Higher mean ratings indicate a greater frequency of napping for the stated reason. Napping motivation was assessed using items derived from the DREAM questionnaire (Duggan et al., 2018), and the four items were analyzed separately and were not combined to form a composite score. As each motivation was represented by a single item, internal-consistency reliability was not applicable.
D-
Nap Benefit score: This exploratory composite score was derived from four experience-based items administered to participants who reported typically taking daytime naps (n = 229). These items evaluate the impact of napping on academic performance, ability to study and concentrate, post-nap alertness, and nighttime sleep. Coding for the four items was as follows: academic performance difference 0–2 (0 = worse, 1 = no difference, 2 = better); difficulty studying without a nap 0–3 (0 = never to 3 = always); post-nap drowsiness reverse-coded 0–3 (0 = always to 3 = never); and nighttime sleep disruption 0–2 (0 = yes, a lot to 2 = no effect). These items were combined to provide a descriptive composite score called “Nap benefit score” (0–10), representing the overall reported benefit and potential adverse effects of napping. Items reflecting adverse effects (post-nap drowsiness and nighttime sleep disruption) were reverse-coded so that higher scores consistently indicated more favorable effects of napping. The resulting score was used as an exploratory summary measure rather than as a validated psychometric scale. The measure demonstrated acceptable internal consistency (Cronbach’s α = 0.73).
Section 3: Eating Habits and Dietary Patterns
A-
Meal Pattern score: Meal patterns were assessed using seven author-developed items covering meal frequency (main meals and snacks per day), meal-skipping frequency, and regularity of the eating schedule. Each item was numerically coded according to the predefined response categories, with higher scores assigned to more favorable eating patterns (e.g., regular meal timing, appropriate meal/snack frequency, and less frequent meal skipping). The item scores were summed to generate a composite meal pattern score; higher scores indicated more regular and favorable meal patterns (0–20). Because these items were developed specifically for the present study and were not derived from a previously validated dietary instrument, the composite score should be considered an author-developed exploratory measure rather than a validated scale (Cronbach’s α = 0.58).
B-
Eating Behavior: Three individual eating-behavior items assessed emotional eating, social eating, and late-night eating. Each item was rated separately on a 5–point Likert scale, with higher ratings indicating greater frequency of the respective behavior. These items were analyzed separately and were not combined into a composite score.
C-
Snacks and Beverage Consumption: Assesses frequency of consuming sweets (candies, chocolate), savory snacks (chips/pastries), soft drinks/sweetened beverages, and caffeinated drinks (coffee, tea, energy drinks) using a 5–point scale (1 = Never to 5 = Daily). A composite score (4–20) was calculated by summing the four items; higher total scores indicate more frequent overall consumption of snacks and beverages (Cronbach’s α = 0.714).
Section 4: Health Status Indicators
A-
Anthropometric Measures: Participants self-reported their height (cm) and weight (kg) through the questionnaire. These measurements were not independently verified or obtained from medical records. BMI was calculated from the reported height and weight as weight (kg)/height2 (m2).
B-
Health History and Current Status: Participants were asked to report whether they had a history of any of the following chronic health conditions: obesity, diabetes, elevated blood lipids (dyslipidemia), high blood pressure (hypertension), heart disease, and thyroid disease. Participants were also asked to report current medication use (yes/no). Medication use was self-reported and was not verified against prescriptions, medical records, or pharmacy records. Other items in this section included energy levels throughout the day (1–10 scale) and self-reported general health status (excellent, very good, good, average, weak).
Section 5: Lifestyle Factors
Lifestyle behaviors included smoking status, physical activity, sedentary behavior, screen time, and usual mode of transportation. Smoking status was categorized as non-smoker, occasional smoker, former smoker, or regular smoker. Participants first indicated whether they engaged in physical activity (yes/no). Those who answered “Yes” reported exercise frequency (days/week), duration (minutes/day), and intensity, categorized as light, moderate, or vigorous based on examples provided in the questionnaire. Participants who reported no exercise did not complete the subsequent exercise-related questions. Sedentary behavior and screen exposure were assessed separately using self-reported average daily sitting time and screen time, respectively, both expressed in hours/day. Usual transportation was categorized as walking, cycling, public transportation, or driving. Each lifestyle variable was analyzed separately; no composite physical-activity or lifestyle score was calculated.
Section 6: Academic Concentration and Perceived Napping Benefits
A-
Concentration and Academic Performance: Assess students’ concentration and attention during learning activities. Responses were numerically coded and presented as mean ratings, where higher ratings indicate more favorable outcomes (greater concentration and less distraction). Concentration during lectures is coded on a 1–5 scale: 1 = less than 15 min, 2 = 15–30 min, 3 = 31–60 min, 4 = 61–110 min, and 5 = more than 2 h. The concentration measure was an author-developed ordinal item, and its response categories, as administered, did not explicitly represent the 111–120-min interval between the 61–110-min and >2-h categories. Study distraction frequency is also coded on a 1–5 scale: 1 = always, 2 = often, 3 = sometimes, 4 = rarely, and 5 = not at all.
B-
Perceived Academic Achievement Benefit (Napping): Three separate items assessed participants’ general perceptions regarding the potential academic benefits of napping, where higher ratings indicate greater perceived benefits. These items were administered to all participants, including those who did not typically take daytime naps. Perceived academic performance benefit was coded 0–3 (0 = does not help, 1 = no difference, 2 = to some extent, 3 = yes, to a great extent). Perceived effect on lecture comprehension was coded 0–2 (0 = reduces, 1 = no effect, 2 = improves). Perceived academic achievement benefit was coded 0–2 (0 = no, 1 = not sure, 2 = yes). These items were analyzed separately and were not combined into a composite score.

2.7. Statistical Analysis

Data were analyzed using IBM SPSS Statistics version 24.0 (IBM Corp., Armonk, NY, USA). Continuous variables were summarized as mean ± standard deviation (SD), and categorical variables as frequencies (n) and percentages (%). The electronic questionnaire required completion of key study variables before submission; however, a small number of observations were missing for specific academic variables. Specifically, GPA had one missing observation (valid n = 244), and average daily study hours had two missing observations (valid n = 243). Analyses involving these variables were therefore based on their respective valid sample sizes.
Participants were classified into four groups according to reported nap duration: no-nap, short (≤35 min), mid (36–90 min), and long (>90 min). Continuous variables were compared across groups using ANOVA. Categorical variables were compared using the chi-square test or Fisher’s exact test, as appropriate.
To examine differences between these groups, the following statistical tests were applied:
  • Differences in continuous variables across the nap-duration groups were examined using one-way analysis of variance (ANOVA). The assumptions of approximate normality and homogeneity of variances were assessed, with Levene’s test used to evaluate equality of variances. Standard one-way ANOVA with Tukey’s HSD post-hoc test was used when the homogeneity assumption was met, whereas Welch’s ANOVA followed by Games–Howell post-hoc comparisons was used when equal variances could not be assumed. This approach was applied to demographic and health indicators (age and BMI), lifestyle factors (exercise frequency and duration), dietary variables (meal pattern score, emotional eating, social eating, late-night eating, and snacks/beverage consumption score), napping motivation, nap benefit score, academic- related measures (lecture concentration, study distraction, and perceived academic benefits of napping items), and energy levels.
  • Associations between the nap-duration groups and categorical variables were evaluated using Pearson’s chi-square test, Fisher’s exact test, or Fisher–Freeman–Halton Exact test, as appropriate. Monte Carlo-estimated p-values were reported where applicable. These tests were applied to demographic characteristics (gender, marital status, and income), lifestyle factors (smoking status, exercise intensity, daily screen time, sedentary/sitting time, and mode of transportation), health indicators (chronic disease history and current medication use), and napping-specific behaviors (weekly napping frequency, preferred napping time, and napping location)
  • Spearman’s Rank-Order Correlation: Bivariate associations between nap duration/napping frequency and continuous or ordinal variables of interest (age, GPA, average daily study hours, meal pattern score, individual eating-behavior items, snack/beverage consumption, screen time, sitting time, and nap benefit score) were examined among the 229 participants who reported napping (Supplementary Table S1). This analysis was not part of the prespecified analytical framework and is reported as a supplementary, exploratory analysis.
  • For outcomes showing statistically significant overall differences or associations across nap-duration groups, effect-size estimates were calculated to characterize the magnitude of the observed effects (Supplementary Table S2). Follow-up pairwise comparisons were conducted for significant findings to identify the specific nap-duration groups contributing to the overall association (Supplementary Table S3). For categorical variables showing a significant omnibus association with nap-duration groups, adjusted standardized residuals were examined as cell-level follow-up analyses to identify cells contributing to the observed association (Supplementary Table S4).
Statistical significance was set at p < 0.05 (two-sided) for all analyses. F- and χ2 statistics were additionally reported where applicable to complement statistical significance. The study was designed as an exploratory, hypothesis-generating investigation examining differences across multiple predefined domains relevant to daytime napping, including lifestyle behaviors, eating behaviors, napping characteristics and motivations, health indicators, and academic-related outcomes. No multiplicity adjustment was applied across the omnibus analyses. Multiplicity was addressed within the post-hoc analyses using Tukey’s HSD for continuous outcomes and Bonferroni-adjusted p-values for categorical pairwise comparisons. Both statistically significant and non-significant results were considered, with attention to the magnitude and direction of observed differences rather than statistical significance alone.

3. Results

The study included 245 students with a mean age of 24.51 ± 5.8 years. Most participants were female (168; 68.6%) and single (191; 78.0%). Monthly income was distributed across categories, with 27.8% (n = 68) reporting >20,000, 21.6% (n = 53) reporting 10,001–20,000, 12.7% (n = 31) reporting 5000–10,000, and 16.3% (n = 40) reporting <5000; 21.6% (n = 53) preferred not to disclose their income.
Table 1 indicates that participant characteristics differed across nap-duration groups primarily for age, marital status, and sedentary time. Age varied significantly across nap-duration groups (F (3, 241) = 7.292, p < 0.001). Post-hoc comparisons revealed that participants in the short-nap group were significantly older than those in the mid-nap group (28.53 ± 6.1 vs. 24.34 ± 5.6 years; mean difference = 4.19 years, 95% CI [1.2, 7.2], p = 0.002) and the long-nap group (28.53 ± 6.1 vs. 23.03 ± 5.4 years; mean difference = 5.51 years, 95% CI [2.4, 8.6], p < 0.001). No other pairwise differences were statistically significant. Marital status also differed significantly across groups (Fisher–Freeman–Halton Exact = 14.88, p = 0.010), with a higher proportion of single participants in the mid- (79.8%) and long-nap (86.3%) groups than in the short-nap group (53.3%). Bonferroni-adjusted pairwise comparisons indicated that only the short- and long-nap groups differed significantly (p < 0.006). Regarding lifestyle factors, a significant between-group difference was also observed for average daily sedentary (sitting) time (Fisher–Freeman–Halton Exact = 26.364, p = 0.004). The short-nap group most commonly reported sitting 6–7 h/day (40.0%), the mid-nap group most commonly reported 4–5 h/day (41.2%), and the long-nap group had the highest proportion sitting more than 7 h/day (37.5%). Bonferroni-adjusted pairwise comparisons indicated a statistically significant difference only between the mid- and long-nap groups (p = 0.042) (Supplementary Table S3).
Table 1. Comparison of participants’ characteristics by nap-duration group.
No statistically significant differences in health status or physical activity indicators were detected across nap-duration groups. Although the short-nap group had a slightly lower BMI (23.75 ± 3.00), BMI did not differ significantly between nap-duration groups (Welch’s F (3, 58.44) = 0.387, p = 0.763), and neither did chronic disease history (p = 0.557). Similarly, among participants who reported engaging in physical activity (n = 91), neither exercise frequency (days/week; F (3, 87) = 0.884, p = 0.453), exercise duration (min/day; F (3, 87) = 0.435, p = 0.729), nor exercise intensity (Fisher–Freeman–Halton Exact = 3.278, p = 0.795) differed significantly across nap groups (Table 1).
Comparison of participants’ academic outcomes across nap-duration groups revealed no statistically significant differences in any of the academic measures assessed. Most participants were bachelor’s students (55.9%), followed by postgraduates (27.8%) and Introductory year (16.3%). Fields of study were distributed, with the largest group in Education/Humanities/Other (30.2%), then STEM (26.9%), Health Sciences (21.6%), and Business/Law/Media (21.2%). The most reported GPA was 4.51–5.0 (39.8%), though 18.9% preferred not to disclose. Most students reported studying 2–4 h per day (59.3%). No statistically significant differences were observed across the nap-duration groups regarding academic level (χ2(6) = 9.177, p = 0.164), field of study (Fisher’s exact, p = 0.464), GPA (Fisher’s exact, p = 0.363), and daily study hours (Fisher’s exact, p = 0.203). No statistically significant differences were observed across nap-duration groups in concentration, study distraction, or perceived academic benefits of napping (all p > 0.05; Table 2).
Table 2. Comparison of participants’ academic outcomes and perceived napping benefits by nap-duration group.
Participants’ napping behavior showed that most students, 64 (27.9%), napped 3–4 days per week, preferred napping at early afternoon (12:00–3:00 p.m.) (117 [51.1%]), and usually napped in their bedroom (194 [84.7%]). Comparison of participants’ napping behavior across nap-duration groups showed significant differences in the distribution of napping frequency and location. Napping frequency per week differed significantly across groups (χ2 (10) = 27.841, p = 0.002). Daily napping was reported by 20.0% of participants in the long-nap group, compared with 10.9% and 10.0% in the mid- and short-nap groups, respectively. Bonferroni-adjusted pairwise comparisons indicated a statistically significant difference only between the mid- and long-nap groups (p = 0.006). Napping location also showed a highly significant association with duration (Fisher–Freeman–Halton Exact = 35.924, p < 0.001). Bonferroni-adjusted pairwise comparisons showed that the short-nap group differed significantly from both the mid- and long-nap groups (both p < 0.003), whereas the mid- and long-nap groups did not differ significantly. The short-nap group reported napping in the living room (40.0%) or prayer room (13.3%) more frequently, whereas the mid- and long-nap groups predominantly reported napping in the bedroom (89.1% and 92.5%, respectively). Regarding self-reported motivations, replacement napping had the highest mean ratings (3.66 ± 1.22), followed by emergency napping (3.50 ± 1.15). Replacement napping differed significantly across nap-duration groups (F (2, 226) = 3.672, p = 0.027), with the long-nap group reporting the highest mean ratings (3.86 ± 1.2). Tukey’s HSD showed that the long-nap group had a significantly higher rating than the short-nap group (p = 0.020); no other pairwise differences were statistically significant. Emergency napping also showed the highest mean ratings in the long-nap group (3.73 ± 1.2), although the between-group difference was not statistically significant (p = 0.08). These findings indicate differences in reported reasons for napping across nap-duration groups; however, they do not establish that longer naps were compensatory for inadequate or poor-quality nighttime sleep, as nighttime sleep duration and quality were not assessed in the present study.
Lastly, the composite nap benefit score (possible range: 0–10), with higher scores indicating greater self-reported benefit based on participants’ experiences, did not differ significantly across nap-duration groups, F (2, 226) = 2.169, p = 0.117. Group means ranged from 4.86 to 5.67 (Table 3; Supplementary Tables S2–S4).
Table 3. Comparison of participants’ napping behavior and motivation by nap-duration group.
Comparison of participants’ eating habits and dietary patterns by nap duration group indicated significant differences across nap-duration groups for specific eating behaviors, particularly emotional eating (F (3, 241) = 4.699, p = 0.003) and late-night eating (F (3, 241) = 8.730, p < 0.001). The short-nap group had the lowest mean ratings for both behaviors (2.07 ± 1.0 and 2.30 ± 1.1, respectively), indicating lower frequencies of emotional and late-night eating. Post-hoc Tukey comparisons revealed that the no-nap group had the highest mean emotional eating rating (3.06 ± 1.0) and differed significantly from the short-nap group (p = 0.036). The mid- and long-nap groups had intermediate mean ratings (2.27 ± 1.2 and 2.71 ± 1.3, respectively) and differed significantly from each other at the threshold of statistical significance (p = 0.050); no other pairwise comparisons were significant. For late-night eating, the long-nap group reported the highest mean rating (3.41 ± 1.0) and differed significantly from the no-nap, short-nap, and mid-nap groups (p = 0.050, p < 0.001, and p = 0.004, respectively). No other pairwise comparisons were statistically significant (no-nap: 2.63 ± 1.2; short-nap: 2.30 ± 1.1; mid-nap: 2.87 ± 1.2). Although the long-nap group had a lower total meal pattern score (12.75 ± 3.2) and a higher total snacks and beverage consumption score (14.80 ± 3.0), these differences were not statistically significant. No significant between-group differences were observed for meal patterns (p = 0.098), social eating (p = 0.963), or overall snacks and beverage consumption (p = 0.07). Thus, these findings do not provide sufficient evidence of differences in these broader dietary pattern domains across nap-duration groups (Table 4).
Table 4. Comparison of participants’ eating habits and dietary patterns by nap-duration group.
Spearman’s rank-order correlation analyses were conducted as supplementary, exploratory analyses to examine the direction and magnitude of associations of nap duration and napping frequency with the continuous and ordinal variables of interest (Supplementary Table S1). Overall, some of the correlations were statistically significant but were predominantly weak. Nap duration showed weak negative association with age (ρ = −0.262) and meal-pattern score (ρ = −0.133), and weak positive associations with late-night eating (ρ = 0.279), sitting time (ρ = 0.199), screen time (ρ = 0.162), snack and beverage consumption (ρ = 0.160), and emotional eating (ρ = 0.146). Napping frequency showed weak positive associations with late-night eating (ρ = 0.167), sitting time (ρ = 0.134), and age (ρ = 0.139), whereas the association with reported napping benefits was moderate in magnitude (ρ = 0.370) and was the strongest observed association. Neither nap duration nor napping frequency showed an appreciable association with GPA or study hours. These correlation findings broadly complement the nap-group comparisons presented in Table 1, Table 2, Table 3 and Table 4 but should be interpreted cautiously. These findings are exploratory and hypothesis-generating and require confirmation in studies with prespecified hypotheses and appropriate adjustment for multiple comparisons.

4. Discussion

Napping has become an important yet often overlooked aspect of student life, particularly among university students facing academic and personal pressures. To the best of our knowledge, no earlier study in Saudi Arabia has comprehensively examined the relationship between napping practices and health indicators, lifestyle factors, eating habits, and academic-related outcomes among university students. In this exploratory study, we examined these associations using unadjusted analyses; therefore, the observed relationships should be interpreted cautiously and should not be considered causal. The main findings of this study were that nap duration differed significantly according to age, marital status, sedentary time, emotional eating, late-night eating, napping frequency, napping location, and replacement napping. Longer naps were associated with younger age, being single, greater sitting time, more frequent late-night eating, more frequent daytime napping, and greater self-reported replacement napping motivation. In contrast, no significant differences were observed in BMI, physical activity, academic characteristics, concentration measures, or perceived nap benefits across the nap-duration groups.
In this study of 245 students, napping was a common behavior. The largest proportion of participants reported napping 3–4 days per week (64, 27.9%), with early afternoon (12:00–3:00 p.m.) being the most preferred time for naps (117, 51.1%). Most students also reported taking their naps in the bedroom (194, 84.7%). Based on nap duration, 30 students (12.2%) were classified in the short-nap group (≤35 min), 119 (48.6%) in the mid-nap group (36–90 min), and 80 (32.7%) in the long-nap group (>90 min). These patterns differ from those reported by Lovato et al. in their study of Australian university students, where 68% of students reported napping once or twice per week, and 32% reported napping three or more times weekly. Similar to the present findings, most naps occurred during the early afternoon period (Lovato et al., 2014; Demirkol et al., 2025). Longer naps were also common in Bahammam et al. and Lovato et al. studies (100% and 77%, respectively), although the duration categories differed between the two studies (Bahammam et al., 2005; Lovato et al., 2014). Students in the long-nap group also reported more frequent napping during the week. Napping location differed significantly across nap-duration groups. The short-nap group used a wider range of locations, whereas those in the mid- and long-nap groups were more likely to nap in the bedroom (89.1% and 92.5%, respectively). These findings describe differences in napping frequency and location across nap-duration groups; however, the underlying reasons for these patterns were not assessed in this study.
Napping may arise from different motivations, which can influence napping patterns and perceived effects after waking (Duggan et al., 2018). Previous research has classified nappers using characteristics such as nap frequency, daytime sleepiness, and nap duration (Macchi et al., 2002; Milner et al., 2006; Vela-Bueno et al., 2008). From a motivational perspective, napping has also been described as restorative, undertaken to compensate for inadequate prior sleep; prophylactic, undertaken in anticipation of future sleep loss; or appetitive, undertaken for enjoyment, relaxation, or personal preference (Milner & Cote, 2009). In the present study, motivation items were designed to capture participants’ reported reasons for napping, including replacement, emergency, and other motives. These categories broadly reflect the purposes described in previous frameworks but were not intended to represent direct or validated equivalents of the classifications (Duggan et al., 2018).
People who nap by choice, including those who nap for enjoyment or relaxation, may experience different reported effects from daytime sleep. Previous research has reported associations between habitual daytime napping and health-related outcomes, although the evidence is mixed and does not establish that napping itself confers health benefits. For example, habitual napping has been associated with larger total brain volume in a Mendelian-randomization study, whereas evidence regarding mortality remains inconsistent (Paz et al., 2023). Restorative napping is generally described as occurring in response to insufficient or disrupted sleep; however, the motivations for napping may overlap, and these categories should not be interpreted as indicators of underlying health status (Stang, 2015). This may help explain our finding that, among the various motivations for napping, replacement napping was the only motive that differed significantly across groups, with the long-nap group reporting the greatest self-reported replacement-napping motivation. A similar pattern was observed for emergency napping, although the difference did not reach statistical significance. Moreover, our findings showed that despite differences in nap duration and underlying motivations, reported overall benefits of napping did not vary significantly across the nap-duration groups. This may indicate that short naps do not necessarily confer greater self-reported academic or functional benefits than longer naps. Importantly, these classifications describe reported reasons for napping and should not be interpreted as evidence of underlying sleep deficiency or health status in the present study.
In the present exploratory analysis, differences across nap-duration groups were observed for selected lifestyle and eating behaviors, whereas no statistically significant differences were detected for academic and health-related outcomes assessed. Age differed significantly across nap-duration groups, with participants in the short-nap group being older than those in the mid- and long-nap groups. This finding represents an observed difference in mean age across nap-duration groups. Because the primary analyses did not adjust for potential confounders, age may have influenced some of the observed associations between nap duration and lifestyle, dietary, or academic outcomes. Therefore, these findings should be interpreted cautiously, and future studies using multivariable-adjusted analyses are warranted to determine whether the observed associations persist after accounting for age.
Nap duration also varied significantly by sedentary behavior, with students in the long-nap group reporting the highest daily sitting time; 37.5% reported sitting for more than 7 h per day. In contrast, physical activity did not differ significantly across nap-duration groups. Overall, 62.9% of participants reported not engaging in regular exercise. These findings indicate differences in reported sedentary time across nap-duration groups but do not establish a relationship between sedentary behavior and physical inactivity or explain the underlying mechanisms. Previous studies have reported associations between sedentary behavior, sleep duration, and sleep-related outcomes. In a large study by Ju et al. (2025), which included 23,130 adults aged 20 years and older, a dose–response relationship was observed between greater sedentary time, sleep duration, and risk of sleep disorders. Similarly Peltzer and Pengpid (2019), in a study of 3266 university students from five ASEAN countries, reported associations between sedentary behavior, internet and smartphone use, and short sleep. These findings provide contextual evidence for an association between sedentary behavior and sleep-related outcomes; however, the present cross-sectional study cannot determine whether sedentary behavior influences napping or whether other factors account for the observed differences.
A noteworthy finding was that students in the no-nap group reported a higher mean emotional eating rating than those in the short-nap group (3.06 ± 1.0 vs. 2.07 ± 1.0, p = 0.036). This association may reflect differences in psychological or behavioral factors such as stress, mood, or fatigue; however, these factors were not directly assessed and therefore cannot be established as explanations for the observed finding. Late-night eating was significantly more common among long-nap participants (3.41 ± 1.0). One explanation is that differences in sleep timing or daily routines, including a later chronotype (an evening-oriented sleep–wake preference), may contribute to both later eating and longer daytime naps, although chronotype and sleep timing were not directly measured in the present study (Barley et al., 2023). Reverse causality is also possible, whereby longer daytime naps may influence subsequent meal timing or nighttime sleep schedules. Previous studies have reported that unhealthy eating behaviors, such as heavy evening meals, replacing main meals with snacks, and eating shortly before bedtime, are associated with poorer sleep quality. In contrast, broader dietary measures, including meal pattern regularity, social eating, and overall snacks and beverage consumption, did not differ significantly across nap-duration groups.
Previous studies have reported associations between nighttime sleep characteristics and BMI. In a study of 1306 Korean adolescents, longer total and weekend sleep duration were associated with lower BMI z-scores, and shorter sleep was linked to a higher risk of overweight and obesity (Choi et al., 2023). Similarly, poorer sleep quality was associated with higher BMI among Malaysian university students (Kwan et al., 2021). In contrast, no statistically significant differences in BMI or chronic disease history were detected across nap-duration groups in the present study. A Saudi study from the Qassim region also reported no significant association between BMI and poor sleep among undergraduate female students (Alfarhan et al., 2018). However, these findings should not be interpreted as directly comparable because the present study assessed daytime nap duration rather than nighttime sleep duration or overall sleep quality. The absence of a comprehensive assessment of nighttime sleep duration and quality limits our ability to determine how daytime napping relates to overall sleep characteristics or clinical health indicators in this sample.
Another notable finding was that no statistically significant differences were detected across nap-duration groups in GPA, study hours, concentration-related measures, or perceived academic effects of napping. GPA, which was included as a self-reported measure of academic achievement, did not differ significantly across nap-duration groups (p = 0.363). Similarly, daily study hours and measures of concentration and perceived academic effects showed no significant between-group differences. Although the short-nap group reported numerically higher ratings for several perceived academic benefits, these findings should be interpreted as students’ subjective perceptions rather than evidence that napping objectively improved academic performance.
The literature presents mixed findings on the relationship between napping and cognitive or academic outcomes. Observational studies of habitual napping have reported different associations with cognitive outcomes, including poorer episodic memory, although no clear association was found with executive functioning or attention (Mednick, 2024). In contrast, some experimental studies have reported that a daytime nap may support certain aspects of cognitive performance, particularly memory, under controlled conditions (Chen et al., 2018; Mantua & Spencer, 2017; Zare et al., 2023). These findings may not be directly comparable with the present study because of differences in study design, population, and the distinction between acute cognitive effects of a nap and habitual nap duration. In Saudi Arabia, BaHammam et al. reported that daytime sleepiness, but not napping, was negatively associated with academic performance among medical students (BaHammam et al., 2012). In the present study, no statistically significant differences were detected across nap-duration groups for the academic and concentration-related outcomes assessed. However, these findings should not be interpreted as evidence that nap duration has no relationship with academic functioning. Together, these findings suggest that the relationship between napping and academic or cognitive performance may be multifactorial and may not be explained by nap duration alone.
One possibility is that other sleep-related factors, such as nap timing, quality, frequency, and the adequacy of nighttime sleep, may play a more influential role in determining academic outcomes than duration alone. This interpretation is supported by previous evidence showing that different napping motivations are associated with distinct napping patterns, nighttime sleep characteristics, and health status (Du et al., 2022). Because nighttime sleep quality and daytime sleepiness were not assessed in the present study, this interpretation remains speculative. Future studies incorporating validated instruments such as the PSQI and ESS, together with objective measures of sleep where feasible, could provide greater insight into these relationships. Therefore, a broader assessment of sleep behavior using validated and objective tools may be needed to better understand how napping relates to academic performance.
This study has several strengths. It examined napping behavior alongside eating habits, lifestyle factors, and academic-related outcomes within the same student sample, allowing a broader view of how these factors may be interrelated. Categorizing participants by nap duration also offered a more detailed perspective on variation in napping patterns. To the best of our knowledge, this is the first study to investigate these associations among this population in Saudi Arabia.
A number of limitations may also be considered when interpreting the results. Because the study used a cross-sectional design, causal relationships cannot be established. The reliance on self-reported data may also have introduced recall and reporting bias, and self-selection may have influenced participation and the observed associations. The response rate could not be determined because the number of individuals who received or viewed the survey invitation was not available. The study was conducted at a single university in Riyadh, and participants were self-selected; therefore, the findings may not be generalizable to university students across Saudi Arabia. Although a no-nap group was included (n = 16), its small size limited robust comparisons with the napping groups. Therefore, findings involving the no-nap group should be interpreted cautiously and confirmed in larger studies with more balanced group sizes. Another limitation is that nighttime sleep quality, total sleep duration, and daytime sleepiness were not assessed using standardized, validated instruments. Consequently, we could not determine whether longer naps reflected inadequate or poor-quality nighttime sleep or greater daytime sleepiness. Potential confounding by factors such as age, sex, academic demands, lifestyle behaviors, and sleep-related characteristics should also be considered, as the analyses were primarily unadjusted. In addition, the exploratory analyses involved multiple comparisons, increasing the possibility of chance findings. Similarly, eating-behavior items were designed to capture selected behaviors relevant to the study objectives rather than provide a comprehensive assessment of dietary quality. Finally, some study measures, including the meal pattern and nap benefit scores, were developed or adapted for the present survey and should therefore be considered exploratory rather than fully validated psychometric instruments.
Future studies would benefit from using validated, and where needed culturally adapted, instruments across the remaining study domains. Objective sleep assessment using actigraphy or other wearable devices, complemented by sleep diaries and direct measures of nighttime sleep duration and quality, could provide a more comprehensive characterization of sleep behavior. Assessment of chronotype, stress, and daytime sleepiness may help clarify factors associated with napping patterns. Academic outcomes could also be assessed using objective measures, such as academic records or standardized performance measures, rather than relying solely on self-reported outcomes.

5. Conclusions

In this exploratory cross-sectional study, nap duration was associated with selected lifestyle patterns and reasons for napping, whereas no statistically significant differences were detected across nap-duration groups for any of the academic-related outcomes assessed, including GPA, study behavior, concentration, and perceived academic benefits. These findings add to the evidence base describing how napping duration relates to students’ everyday behaviors and napping motives. The observed associations, particularly those involving sedentary behavior and replacement-napping motivation, may provide useful directions for future research investigating the behavioral context of daytime napping among university students. Extended daytime naps may occur within different sleep or lifestyle patterns; however, nighttime sleep duration and quality were not directly assessed in this study, and therefore no conclusions can be drawn regarding whether longer naps reflect insufficient or poor-quality nighttime sleep. Nevertheless, the findings should be interpreted cautiously as hypothesis-generating rather than confirmatory. Future longitudinal studies incorporating objective sleep assessments, validated measures, and appropriate adjustment for potential confounders are needed to further clarify these relationships.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/youth6040157/s1, Table S1: Bivariate correlations (Spearman’s rho) between nap duration/frequency and study variables; Table S2: Effect sizes for significant group comparisons; Table S3: Pairwise comparisons of significant outcomes across nap-duration groups; Table S4: Cell-level follow-up analysis of categorical variables significantly associated with nap-duration groups using adjusted standardized residuals.

Funding

This research was funded by the Ongoing Research Funding program, (ORF-2026-840), King Saud University, Riyadh, Saudi Arabia.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board/Permanent Committee for Research Ethics at King Saud University (KSU) (Ref. No: KSU-HE-25-1240; date of approval: 12 November 2025).

Data Availability Statement

The datasets generated during and/or analyzed during this study are not publicly available due to data protection requirements but are available from the corresponding author upon reasonable request, subject to applicable data protection requirements and institutional/ethical approval, where required.

Conflicts of Interest

The author declares no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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