An Integrative Analysis of Behavioral,Psychological,and Smartphone Use Factors Associated With Sleep Quality: A Cross-Sectional Study of Saudi Undergraduate Students

Abstract

Sleep quality is a critical component of student well-being and academic performance. This study aimed to examine the association between demographic characteristics, health and mental behaviors, smartphone usage, and sleep quality among undergraduate students at Jazan University in Saudi Arabia. A cross-sectional web-based survey was conducted between April and June 2023. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). The questionnaire included demographic data, health behaviors (exercise, diet, stimulant intake, smoking, qat use), anxiety levels, and smartphone usage patterns. Data were analyzed using descriptive statistics, t-tests, ANOVA, and multivariable linear regression. A total of 1564 students participated. Demographic variables were not significantly associated with sleep quality. However, poor sleep quality was significantly associated with several factors. Students who exercised less (mean PSQI = 8.19 vs 7.67; P = .003) or followed an unhealthy diet (8.10 vs 7.33; P = .001) reported worse sleep. High intake of stimulants, such as coffee (>3 times/day: β = 1.08, P = .021), tea, and soft drinks, was also linked to poorer sleep. Anxiety showed a clear dose-response effect, with mild, moderate, and severe anxiety associated with PSQI increases of 0.89, 2.27, and 3.43 points, respectively (all P < .001). Evening (β = .52, P = .016) and bedtime (β = .42, P = .029) smartphone use further predicted worse sleep quality, independent of total usage time. Sleep quality among undergraduate students is primarily influenced by modifiable lifestyle and psychological factors. Interventions promoting healthier routines, reducing stimulant intake, managing anxiety, and improving digital habits are essential to support student well-being.

Keywords

anxiety health behaviors sleep quality smartphone usage undergraduate students

Introduction

Sleep is a fundamental component of both physical and psychological health; however, poor sleep is increasingly prevalent among university students worldwide. Undergraduate students are among the most sleep-deprived populations, often experiencing sleep disturbances, shortened sleep duration, and irregular sleep patterns.^1
-5 This issue is particularly concerning, as sleep deficiency has been consistently linked to impaired cognitive functioning, decreased academic performance, and elevated risks of mental health disorders, including anxiety and depression.^3,5

The rise of technology in education and daily life has intensified this problem. Smartphones have become ubiquitous among students, serving as essential tools for communication, academic work, and entertainment.^3,4 However, excessive smartphone use, especially during evening hours, has been associated with reduced sleep duration, poor sleep quality, and increased sleep latency.⁶ Blue light exposure from screens can suppress melatonin production, delay sleep onset, whereas stimulating content can heighten cognitive arousal, making it harder to fall and stay asleep.^6,7

In addition to technology use, other behavioral and psychological factors play critical roles in shaping sleep quality. High caffeine intake, lack of physical activity, and poor dietary habits have all been linked to disrupted circadian rhythms and difficulty maintaining restful sleep.^1,3,4 Psychological stress and anxiety are also well-documented contributors to sleep disturbance, as they trigger hyperarousal and interfere with the ability to relax at bedtime.⁵

Demographic characteristics such as age and gender have shown mixed associations with sleep outcomes. Studies indicate that certain groups, such as younger male and female students may be more vulnerable to sleep disruption.^1,8 In Saudi Arabia, poor sleep quality is highly prevalent among university students. Studies have reported rates of 60% to 64% in the southern region,^9,10 75.9% among medical students in the central region,¹¹ and about one-third in the eastern region.¹²

Despite the growing body of literature, there is a critical gap in understanding how these variables, especially smartphone usage patterns, interact to influence sleep quality among university students. Recent studies have examined these factors individually, with limited attention to how digital behaviors associate with lifestyle and psychological variables in real-world settings.^3
-5 Thus, a more integrative approach is needed to inform effective health promotion strategies within university environments.

To address this gap, the present study investigates the relationship between sleep quality and a broad set of predictors, including demographic characteristics, health and mental behaviors, and smartphone usage patterns among undergraduate students in Saudi Arabia. The study is guided by the following research questions and hypotheses:

● RQ1: Are demographic characteristics (age, gender, marital status, and area of residence) associated with sleep quality among undergraduates in Saudia Arabia?

H1: Demographic factors will be significantly associated with sleep quality.

● RQ2: Are health and mental behaviors (exercise, healthy diet, daily stimulant beverage consumption, smoking, qat use, and anxiety levels) associated with sleep quality in this population?

H2: Students exhibiting unhealthy behaviors (lack of exercise, unhealthy diet, frequent stimulant consumption) and higher levels of anxiety will report poorer sleep quality.

● RQ3: Is smartphone usage (daily duration, timing of usage, proximity while sleeping, usage upon waking, and bedtime use) associated with sleep quality in this population?

H3: Students who use their smartphones extensively, especially in the evening or at bedtime, will exhibit poorer sleep quality.

● RQ4: When considering all these factors together (demographics, health and mental behaviors, and smartphone usage), which factors most strongly predict poor sleep quality?

H4: Among the factors found to be significantly associated with sleep quality (as identified under H2 and H3), a subset will emerge as key predictors of poorer sleep quality when considered simultaneously in a multivariate analysis.

Methods

Study Design and Sample

A cross-sectional observational study was conducted between April and June 2023 to examine the associations between demographic characteristics, health and mental behaviors, smartphone usage, and sleep quality among undergraduate students at Jazan University in Saudi Arabia. Participants were recruited using a convenience sampling method through popular social media platforms widely used by students, including WhatsApp, Telegram, X (formerly Twitter), and Snapchat.

Inclusion criteria were: (1) current enrollment as an undergraduate student at Jazan University, and (2) provision of voluntary, informed electronic consent. Exclusion criteria: Those not meeting the inclusion criteria were excluded from the study. The study was approved by the University Research Ethics Committee (Reference: REC-44/09/606). The study was reported in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for cross-sectional observational research.¹³

To ensure adequate statistical power for the multivariable linear regression analysis, we followed standard sample size recommendations of 10 to 15 participants per predictor variable.¹⁴ Given the inclusion of 18 predictors in our final model, the minimum required sample size ranged from 180 to 270 participants. Our final sample comprised 1564 completed responses, which is above the threshold for reliable and stable model estimation.

Data Collection

Data were collected using a web-based questionnaire comprising 4 main sections, as shown in Figure 1, as follows:

Section 1: gathered information about demographic characteristics, including age, gender, marital status, and area of residence.

Section 2: assessed the health and mental behaviors, including regular exercise, healthy diet, daily consumption of coffee, tea, energy drinks, and/or soft drinks, smoking, and the level of anxiety. It also investigated whether the consumption of qat, a green plant with stimulant effects similar to amphetamines and commonly used for enhancement and recreation in the Jazan Region,^13,15 is associated with sleep quality.

Section 3: focused on smartphone usage behaviors, including average daily use of a smartphone (in hours), time of day using a smartphone (morning, afternoon, or evening), keeping smartphone close while sleeping, using smartphone immediately when waking up, and using smartphone at bedtime.

Section 4: assessed sleep quality using the Pittsburgh Sleep Quality Index (PSQI), a validated instrument for measuring sleep disturbances over the past month.^16,17 It consists of 19 self-rated questions grouped into 7 components which are subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medications, and daytime dysfunction. A score ranging from 0 (no difficulty) to 3 (severe difficulty) was calculated for each component. Then, the components’ scores were summed to produce a global PSQI score (range 0-21). Higher scores indicate poorer sleep quality.^16,17

Figure 1.

Conceptual design of the questionnaire.

Prior to data collection, a pilot test was conducted with 11 students to assess content clarity and relevance, and necessary adjustments were made accordingly. Sections 1-3 of the questionnaire were developed in both English and Arabic by a bilingual research team and focused on individual predictors of sleep quality (eg, demographic and behavioral factors), not as psychometric scales. During the pilot phase, minor revisions were made based on student feedback to enhance clarity and cultural appropriateness. For Section 4, sleep quality (ie, the primary dependent variable) was measured using the validated Arabic version of the PSQI¹⁷ in order to ensure linguistic and contextual relevance for the Saudi student population. The full questionnaire is provided as a Supplemental File.

Data Analysis

Data analysis was performed using Stata/BE 17.0 (Stata Corp, College Station, TX). Participants who completed the full questionnaire were included. Descriptive statistics (eg, frequency distribution, percentages, means and standard deviation [SD]) were first employed to summarize participants’ characteristics and main study variables. T-tests and ANOVA were conducted to compare PSQI scores across groups. To ensure the appropriateness of parametric tests, residuals and normality checks, including Kernel Density Plots and Normal Quantile Graphs, were conducted on PSQI scores. The results indicated an approximately normal distribution with no severe outliers, and no significant deviations from normality were observed.

Multivariable linear regression analysis was then used to identify the factors most significantly associated with sleep quality. Before fitting the final model, multicollinearity among independent variables was assessed by calculating Variance Inflation Factors (VIFs). Any variable with a VIF greater than 10 was considered for removal or modification to address potential collinearity. The final model included only those variables with acceptable VIF values, thereby ensuring the stability and interpretability of our regression results. For all statistical tests, the significance level was set at P < .05.

In total, the model included 18 independent variables and 1 dependent variable (global PSQI score). With a sample size of 1564, the study was sufficiently powered to detect meaningful associations and draw valid inferences.

Results

Associations With Participants’ Demographics Characteristics (H1)

In our study, H1 was not supported because the demographic factors did not directly impact sleep quality, as shown in Table 1. Among the 1564 participants, the vast majority were between 18 and 24 years of age (90.03%), female (83.70%) and unmarried (85.93%). About two-thirds of the participants resided in villages (64.25%) compared to cities (35.74%).

Table 1.

Comparison of Sleep Quality by Participants’ Characteristics.

Variables	N (%)	Sleep quality
Variables	N (%)	Mean	SD	t-value	P-value
Demographic characteristics (H1)
Age groups
18-24 years	1408 (90.03)	7.99	3.50	0.38	.705
>24 years	156 (9.97)	7.88	3.68
Gender
Male	255 (16.30)	8.07	3.55	0.43	.666
Female	1309 (83.70)	7.96	3.52
Marital status
Unmarried	1344 (85.93)	8.03	3.55	1.29	.197
Married	220 (14.07)	7.70	3.29
Area of residence
Village	1005 (64.26)	7.99	3.46	0.14	.886
City	559 (35.74)	7.96	3.62
Health & mental behaviors (H2)
Exercise
No	931 (59.53)	8.19	3.26	2.89	.003*
Yes	633 (40.47)	7.67	3.85
Healthy diet
No	1310 (83.76)	8.10	3.48	3.20	.001*
Yes	254 (16.24)	7.33	3.67
Daily consumption of coffee
None	680 (43.48)	7.70	3.36	8.83	<.001*
Once	570 (36.45)	7.82	3.52
Twice	182 (11.64)	8.36	3.64
Three times	70 (4.48)	9.30	3.72
>Three times	62 (3.96)	9.84	3.87
Daily consumption of tea
None	711 (45.46)	7.53	3.41	8.31	<.001*
Once	554 (35.42)	8.10	3.51
Twice	186 (11.89)	8.51	3.58
Three times	64 (4.09)	9.34	3.71
>Three times	49 (3.13)	9.29	3.72
Daily consumption of energy drinks
None	1153 (73.72)	7.67	3.45	11.47	<.001*
Once	294 (18.80)	8.59	3.49
Twice	70 (4.48)	9.07	3.74
Three times	27 (1.73)	10.22	3.63
>Three times	20 (1.28)	10.35	3.47
Daily consumption of soft drinks
None	674 (43.09)	7.30	3.50	16.72	<.001*
Once	550 (35.17)	8.09	3.47
Twice	212 (13.55)	8.92	3.25
Three times	77 (4.92)	9.49	3.42
>Three times	51 (3.26)	9.49	3.44
Smoking
No	1452 (92.84)	7.93	3.51	−1.93	.053
Yes	112 (7.16)	8.60	3.57
Consumption of Qat
No	1515 (96.87)	7.98	3.53	0.29	.773
Yes	49 (3.13)	7.84	3.31
Feeling anxious
None	316 (20.20)	6.25	3.25	80.23	<.001*
Mild	520 (33.25)	7.28	3.09
Moderate	458 (29.28)	8.75	3.34
Severe	270 (17.26)	10.02	3.56
Smartphone usage behaviors (H3)
Average daily use of smartphone (in hours)
≤1 hour	29 (1.85)	6.97	3.58	10.83	<.001*
>1 < 4 hours	167 (10.68)	6.81	3.41
>4 < 6 hours	296 (18.93)	7.49	3.38
>6 < 8 hours	392 (25.06)	7.99	3.49
>8 hours	680 (43.48)	8.52	3.53
Time to use smartphone
Morning	388 (24.81)	7.89	3.39	4.05	.018*
Afternoon	573 (36.64)	7.71	3.62
Evening	603 (38.55)	8.29	3.49
Keeping smartphone close while sleeping
No	177 (11.32)	7.01	3.23	−3.90	<.001*
Yes	1387 (88.68)	8.10	3.54
Using smartphone immediately when wake up
No	296 (18.93)	7.43	3.55	−2.98	.003*
Yes	1268 (81.07)	8.11	3.50
Using smartphone at bedtime
No	411 (26.28)	7.40	3.31	−3.93	<.001*
Yes	1153 (73.72)	8.19	3.57

Note. Comparison made using t test and ANOVA.

P < .05, the significant level.

Associations With Health and Mental Behaviors (H2)

In line with H2, several health and mental behaviors were significantly associated with sleep quality (Table 1). Participants who did not engage in regular exercise (59.43%) had significantly higher PSQI scores (mean = 8.19) compared to those who exercised (mean = 7.67; P < .05). Similarly, participants reporting an unhealthy diet (83.76%) had higher PSQI scores (mean = 8.10) than those with a healthy diet (mean = 7.33; P < .05).

Regarding the consumption of stimulant beverages (coffee, tea, energy drinks, and soft drinks), many participants indicated that they did not take any coffee (43.48%) or tea daily (45.46%), while 36.35% reported consuming 1 cup of coffee and 35.42% reported consuming 1 cup of tea daily. Less than 4% of participants reported consuming more than 3 cups of coffee daily (3.96%) or more than 3 cups of tea daily (3.1%). Nearly three-quarters (73.72%) of participants reported not consuming an energy drink daily, and just less than half reported (43.09%) consuming a soft drink during the day. Few participants reported smoking (7.16%) or consuming qat (3.13%). Participants who consumed these beverages frequently throughout the day had significantly higher PSQI scores than those who consumed none (P < .05 for all comparisons). Although smoking and qat consumption were examined, neither showed a significant relationship with sleep quality.

Anxiety emerged as a strong predictor. One-fifth of participants reported having no anxiety whereas 33.25% reported mild anxiety, 29.28% reported moderate anxiety, and 17.26% reported severe anxiety. Participants with severe anxiety had significantly higher PSQI scores (mean = 10.02) than those with no anxiety (mean = 6.25; P < .05).

Associations With Smartphone Usage (H3)

The analysis supports H3. Participants who reported using their smartphones for more than 8 hours per day (43.48%) had higher PSQI scores (mean = 8.52) compared to those who used them for ≤1 h (mean = 6.67; P < .05; 25.06%), as shown in Table 1. Smartphone use was nearly evenly distributed across morning (24.81%), afternoon (36.64%), and evening (38.55%). Evening use was associated with the poorest sleep quality (mean = 8.29), compared to morning (7.89) or afternoon use (7.71; P < .05).

In addition, smartphone behaviors such as keeping the phone close while sleeping (88.68%), using it immediately upon waking (81.07%), and using it at bedtime (73.72%) were all significantly associated with higher PSQI scores (P < .05 for all comparisons).

Key Factors Predicting Poor Sleep Quality (H4)

To address H4, a multivariable linear regression was performed to identify the strongest predictors of poor sleep quality, as shown in Table 2. The multivariable analysis identified 3 primary factors significantly associated with poorer sleep quality.

Table 2.

Factors Predicting Poor Sleep Quality Score.

Variables	Coefficient (β)	SE	t-value	P-value
Demographic characteristics
Age groups
18-24 years	Ref
>24 years	.05	0.279	0.18	.856
Gender
Male	Ref
Female	.09	0.236	0.42	.675
Marital status
Unmarried	Ref
Married	.13	0.244	0.54	.589
Area of residence
Village	Ref
City	−.03	0.172	− 0.18	.857
Health & mental behaviors
Exercise
No	Ref
Yes	−.19	0.183	− 1.06	.291
Healthy diet
No	Ref
Yes	.21	0.250	0.84	.399
Daily consumption of coffee
None	Ref
Once	−.17	0.189	− 0.90	.368
Twice	−.15	0.279	− 0.55	.582
Three times	.35	0.439	0.78	.433
>Three times	.08	0.467	2.31	.021*
Daily consumption of tea
None	Ref
Once	.45	0.189	2.35	.019*
Twice	.58	0.284	2.05	.041*
Three times	.94	0.459	2.06	.040*
>Three times	.69	0.505	1.36	.173
Daily consumption of energy drinks
None	Ref
Once	.34	0.227	1.49	.137
Twice	.36	0.427	0.84	.399
Three times	.59	0.724	0.81	.419
>Three times	.66	0.829	0.79	.427
Daily consumption of soft drinks
None	Ref
Once	.37	0.199	1.85	.065*
Twice	.89	0.278	3.22	.001*
Three times	.03	0.425	2.42	.015*
>Three times	.75	0.527	1.41	.158
Smoking
No	Ref
Yes	.44	0.378	1.17	.240
Consumption of Qat
No	Ref
Yes	.35	0.556	0.63	.526
Feeling anxious
None	Ref
Mild	.89	0.235	3.81	<.001*
Moderate	.27	0.244	9.31	<.001*
Severe	.43	0.278	12.33	<.001*
Smartphone usage behaviors
Average daily use of smartphone (in hours)
≤1 hour	Ref
>1 < 4 hours	−.60	0.653	−0.92	.356
>4 < 6 hours	−.27	0.633	−0.42	.674
> 6 < 8 hours	.05	0.629	0.08	.940
> 8 hours	.18	0.624	0.28	.776
Time to use smartphone
Morning	Ref
Afternoon	−.07	0.214	−0.34	.733
Evening	.52	0.213	2.42	.016*
Keeping smartphone close while sleeping
No	Ref
Yes	.29	0.269	1.10	.270
Using smartphone immediately when wake up
No	Ref
Yes	.004	0.220	0.02	.984
Using smartphone at bedtime
No	Ref
Yes	.42	0.193	2.19	.029*

Note. Multivariable linear regression was used.

SE = standard error; REF = reference group.

P < .05, the significant level.

First, high stimulant beverage consumption was a strong predictor. For example, students who consumed coffee more than 3 times daily had PSQI scores higher by 1.08 points (β = 1.08, P = .021) compared to non-consumers. Similar trends were observed for tea and soft drink intake, particularly at higher frequencies.

Second, anxiety levels showed a clear dose-response relationship with sleep quality. Compared to students reporting no anxiety, those with mild, moderate, and severe anxiety had PSQI scores higher by 0.89 (P < .001), 2.27 (P < .001), and 3.43 points (P < .001), respectively.

Third, the timing of smartphone usage was more influential than its duration. Specifically, using a smartphone in the evening was associated with a 0.52-point increase in PSQI score compared to morning use (P = .016), and using a smartphone at bedtime added a further 0.42-point increase relative to students who did not engage in bedtime use (P = .029).

Discussion

Principal Findings, Interpretation, and Implications

This study conducted a multifactorial analysis to examine how demographic, behavioral, and technological factors influence sleep quality among undergraduate students in Jazan in Saudi Arabia. Although existing literature has individually addressed some of these domains, few studies have comprehensively explored their combined effects within a single model. Our findings highlight those behavioral and psychological variables, rather than demographic ones, are the most significant contributors to poor sleep quality in this population, as follows.

H1: Demographic Characteristics and Sleep Quality

Contrary to our first hypothesis (H1), demographic characteristics, including age, gender, marital status, and area of residence, were not significantly associated with sleep quality. This finding is consistent with prior studies that reported no direct relationship between demographic variables and sleep outcomes among university students.⁸ However, other research, such as that by Moussa-Chamari et al,⁸ suggests that demographic differences, particularly gender and age, can influence sleep patterns.

These mixed findings imply that demographic variables may exert indirect effects, potentially mediated by behavioral or psychosocial factors such as anxiety, lifestyle, or digital habits.^1,5 University policies; therefore, could emphasize inclusive wellness programs that address shared behavioral determinants such as stress management and sleep-hygiene promotion rather than tailoring strategies solely by demographic group.

H2: Health and Mental Behaviors as Predictors of Sleep Quality

As hypothesized (H2), health and mental behaviors showed a strong and consistent association with sleep quality. Students who engaged in regular physical activity and maintained a healthy diet reported significantly better sleep, echoing previous research demonstrating the benefits of lifestyle interventions for improving sleep duration and quality.^18,19 On the other hand, excessive consumption of caffeinated and sugary beverages (ie, coffee, tea, and soft drinks) was associated with elevated PSQI scores. This supports prior evidence linking stimulant intake to delayed sleep onset and reduced sleep efficiency.^19
-22

Anxiety was also one of the most prominent predictors of poor sleep in this study. A clear dose-response relationship emerged, with higher anxiety levels associated with progressively poorer sleep quality. This is consistent with previous studies identifying anxiety as a key disruptor of sleep in university students.^8,23 Heightened arousal, intrusive thoughts, and physiological tension may interfere with both sleep onset and continuity.

These findings highlight the need for university-level health-promotion policies that integrate lifestyle and mental-health components. Institutions should implement structured programs encouraging physical activity, balanced nutrition, and moderation of stimulants on campus.³ Routine screening for anxiety and provision of accessible counseling, mindfulness sessions, and cognitive-behavioral therapy (CBT) services can further mitigate psychological contributors to poor sleep.^2,5

H3: Smartphone Usage Patterns and Their Impact on Sleep

Our third hypothesis (H3) was partially supported. Total daily smartphone use was not significantly associated with sleep quality; however, specific patterns of use (ie, bedtime smartphone use, and evening usage) were significant predictors of poor sleep. These results align with existing evidence that evening screen exposure can suppress melatonin secretion and disrupt circadian rhythms, leading to sleep delays and fragmentation.^18,24,25 It is plausible that late-night phone use, particularly for social or emotionally stimulating content, fosters cognitive hyperarousal and contributes to digital dependency.²⁶

Educational campaigns promoting digital hygiene should be incorporated into student-wellness and academic-advisory programs.^3,25,26 Practical measures include awareness workshops on screen-time management, university-wide campaigns to discourage bedtime phone use, and integration of digital health literacy modules into first-year orientation curricula. Broader national health strategies could similarly emphasize the role of digital behaviors in sleep and mental-health outcomes among youth.^25,26

H4: Most Significant Predictors in Multivariable Analysis

Our final regression model addressed RQ4 by examining all predictors simultaneously. Anxiety levels, stimulant consumption, and bedtime smartphone use emerged as the most significant predictors of poor sleep quality. This confirms the hypothesis that a subset of health and digital behavior factors would remain predictive when analyzed simultaneously.

Given these findings, policymakers and university administrators should prioritize integrated interventions that address multiple determinants concurrently. For instance, multidisciplinary wellness frameworks could link sleep-hygiene education, mental-health screening, and digital-wellness initiatives under one student-support system.^4,27

Strengths and Limitations

This study has its own strengths and limitations. Our study’s large sample size, the use of a validated Arabic version of the PSQI, and the examination of multiple factors influencing sleep quality strengthen the validity and breadth of our conclusions. However, reliance on self-reported data may introduce bias,²⁸ and cross-sectional design limits causal insights.²⁹ The short data collection period may overlook seasonal or academic variations. In addition, the convenience sampling and open recruitment approach via social media platforms prevented determining an exact participation rate and may limit representativeness.³⁰ The sample was predominantly female (>80%) which may also limit generalizability to male students. Furthermore, while the questionnaire items in Sections 1-3 were pilot tested, they were not formally psychometrically validated, which may limit the reliability of some measures. Moreover, the lack of direct stress measures constrains our ability to fully contextualize anxiety. To enhance generalizability, future longitudinal research spanning entire semesters, and incorporating validated stress assessments as well as capturing participants’ academic major and year of study, could help in refining our understanding of sleep quality determinants.

Conclusion

Our findings revealed that poor sleep quality in university students is significantly associated with modifiable behavioral and psychological factors, rather than with demographic characteristics. Specifically, insufficient physical activity, unhealthy dietary habits, high consumption of caffeinated and sugary drinks, intense anxiety, and extensive evening or bedtime smartphone usage were linked to poorer sleep quality.

These findings highlight the urgent need for targeted, university-based programs promoting sleep hygiene, digital well-being, healthy routines, and mental health support. By integrating these elements into student health strategies, institutions can address multiple risk factors simultaneously and improve sleep health. Future research should continue to explore these associations in diverse student populations. This will help develop more tailored behavioral interventions for improving sleep health in various academic settings.

Supplemental Material

sj-pdf-1-inq-10.1177_00469580251413526 – Supplemental material for An Integrative Analysis of Behavioral, Psychological, and Smartphone Use Factors Associated With Sleep Quality: A Cross-Sectional Study of Saudi Undergraduate Students

Supplemental material, sj-pdf-1-inq-10.1177_00469580251413526 for An Integrative Analysis of Behavioral, Psychological, and Smartphone Use Factors Associated With Sleep Quality: A Cross-Sectional Study of Saudi Undergraduate Students by Manal Ali Almalki, Manal A. Ahmed, Ali M. Alzahrani, Asim Mehmood, Holly C. Felix, Raghad Almalki, Bashair Alhashemy and Hatun Musawi in INQUIRY: The Journal of Health Care Organization, Provision, and Financing

Footnotes

ORCID iD

Asim Mehmood

Ethical Considerations

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Research Ethics Committee of Jazan University (REC-44/09/606 and 06 April 2023).

Consent to Participate

Informed consent was electronically obtained from all subjects involved in the study.

Author Contributions

Conceptualization: MA, MAA; Methodology & Data Collection: MA, MAA, RA, BA, HM; Formal Analysis: MA, AMA; Writing and Original Draft Preparation: MA; Review & Editing: MA, AMA, AM, HCF.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The datasets used during the current study are available from the corresponding author on reasonable request.

Supplemental Material

Supplemental material for this article is available online.

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