Adolescent Screen Time and Sleep Quality: Predictive Factors and Their Effect on Academic Achievement Among Adolescents in Jordan

Abstract

Adolescents’ increasing screen time has been linked to poor sleep quality, which may, in turn, affect their academic performance. This study aimed to examine screen time patterns among Jordanian adolescents and assess their associations with sleep quality and academic achievement. A descriptive correlational study was conducted among 477 students aged 12–14 years from four randomly selected schools in northern Jordan. Participants completed the validated Questionnaire for Screen Time of Adolescents (QueST) and the Adolescent Sleep–Wake Scale–Short Version (ASWS-S), while academic performance was assessed using GPA from school records. Results showed that average screen time was 9.13 h per day. Weekend screen time emerged as a significant negative predictor of sleep quality (β = −0.27, p = 0.016). Gender and school type were also significant predictors. Adolescents with screen devices in their bedrooms and those with chronic medical conditions reported higher screen time. Although total screen time did not significantly predict academic achievement, it showed a moderate negative correlation with sleep quality (r = −0.18, p < 0.01). These findings suggest that excessive screen use, particularly on weekends, may impair sleep quality among adolescents. Interventions targeting screen habits could help enhance sleep and potentially benefit academic performance.

1. Background

Screen time has become a growing concern for adolescents due to the widespread availability of digital media. According to the Centers for Disease Control and Prevention [1], teens using screens for more than four hours daily are significantly more likely to experience poor sleep routines. Screen time encompasses the use of smartphones, tablets, televisions, video games, and computers for both educational and entertainment purposes [2,3,4].

In the U.S., adolescents aged 12–13 averaged 7.7 h of screen use daily during the COVID-19 pandemic, which correlated with higher stress and poorer mental health [5]. Similarly, in Jordan, 98% of households own a mobile phone [6], and many students exceed the recommended two hours of daily screen use [7]. A study of Jordanian students found that 26% watched TV for more than two hours, 70% used electronic gadgets, and 32% used handheld devices [8].

Despite guidelines from organizations such as the American Academy of Pediatrics [9] and the Canadian Pediatric Society [2], only 37% of U.S. youth meet the screen time recommendations [10]. Globally, increased screen time has been linked to declining adolescent mental health [11] and higher depression risk [12].

Sleep is another area of concern. The American Academy of Sleep Medicine recommends 8–10 h of sleep for adolescents [13], yet CDC [14] data show that 77% of high school students do not meet this recommendation. Poor sleep has been shown to affect brain development, learning, and executive functioning [15,16]. Adolescents often experience daytime sleepiness and reduced vigilance, particularly when using screens before bed [17,18].

Factors such as content, timing, and location of screen use have a significant impact on sleep quality [19]. A primary mechanism involves exposure to blue light from screens, which directly disrupts the body’s natural sleep–wake cycle, known as circadian rhythms. This disruption leads to an increased risk of insomnia and has been linked to disturbances in the circadian rhythm and reduced sleep duration [20,21]. The interference primarily occurs by suppressing the natural production of melatonin, a hormone crucial for signaling the body to sleep. Additionally, cognitive arousal resulting from engaging with stimulating screen content, such as video games or social media, particularly before bed, delays sleep onset. This mental stimulation keeps the brain active, making it difficult for adolescents to relax and fall asleep.

The consequences of such impaired sleep are far-reaching. Poor sleep has been shown to negatively affect brain development, learning, and executive functioning in adolescents [15,16]. Adolescents who experience inadequate sleep often report daytime sleepiness and reduced vigilance, particularly when they use screens before bed [17,18]. Therefore, while screen time may not always directly correlate with lower academic achievement, its significant detriment to sleep quality poses a substantial threat to adolescents’ learning and cognitive functioning. This suggests a crucial indirect pathway where excessive screen time compromises sleep hygiene, which in turn negatively impacts academic outcomes.

Screen use patterns are influenced by a complex interplay of individual and environmental factors, including gender, access to devices, parental monitoring, and socioeconomic status. Understanding these predictive variables is crucial for a comprehensive assessment of adolescent screen time and its impact on well-being [22,23,24]. Gender plays a significant role in shaping media consumption habits and sleep patterns. Research consistently indicates observed gender differences in media use, with boys often showing a greater propensity for video gaming, while girls tend to engage more with social media platforms. These differential engagement patterns can have distinct implications for sleep quality, as evidenced by findings that girls often report worse sleep quality, aligning with prior research [25]. Such variations underscore the importance of considering gender as a key predictor.

The type of school an adolescent attends, private versus public, can also be a critical determinant of screen time and sleep quality. Differences between these educational environments may include varying academic demands, which can influence study-related screen use and lead to sleep deficits. Additionally, private and public schools may serve students from different socioeconomic profiles, potentially affecting household access to technology and parental monitoring practices, thereby influencing both screen time habits and sleep patterns.

Parental education levels serve as important indicators of socioeconomic status and often correlate with specific parenting styles, including those related to technology use and monitoring. Parents with higher educational attainment might implement different screen time rules, engage in more active monitoring, or provide varying levels of digital literacy guidance, all of which can influence an adolescent’s screen use and adherence to healthy sleep routines [22,23]. Thus, these factors were considered for their potential to predict screen time behaviors and sleep quality.

Finally, the presence of screen devices in bedrooms is a widely recognized and significant predictor of increased screen time and poorer sleep outcomes. The easy accessibility of devices like televisions, smartphones, and tablets in a personal space often facilitates late-night usage, leading to extended screen exposure close to bedtime. This direct access can circumvent parental monitoring efforts and exacerbate issues related to blue light exposure and cognitive arousal, thereby significantly disrupting sleep patterns. Prior studies consistently link bedroom media devices to higher overall screen usage and poorer sleep quality [8,26,27].

Despite existing research primarily from Western populations, there is a significant gap in understanding these complex relationships within the Jordanian context. While global trends indicate rising screen time and its potential negative health impacts, the cultural and societal nuances of non-Western settings, especially regarding family dynamics and educational systems, require specific investigation. Jordan, for instance, presents a unique media landscape characterized by remarkably high mobile phone ownership (98% of households) and a prevalent family co-residence structure, with 93% of adolescents reporting living with both parents. These factors suggest that parental monitoring and the influence of family structures on screen use and sleep habits may differ considerably from findings in more individualistic Western societies. For example, in collectivist cultures like Jordan, parental tolerance of late-night device use or the communal nature of media consumption within households could uniquely mediate adolescent screen time behaviors and sleep hygiene, potentially leading to distinct patterns compared to Western households.

Furthermore, recent scholarship has underscored the need for a more granular understanding of screen time’s effects, moving beyond total usage. Studies by Chen et al. [28] and Li et al. [29] highlight the importance of considering when and what type of screen activity occurs, particularly emphasizing pre-sleep electronic media use and specific screen activities in shaping adolescent sleep patterns and brain activity. Brosnan et al. [30] further confirm the adverse effects of screen use at bedtime on both sleep duration and quality among youth. Beyond sleep, academic achievement is influenced by a multitude of interacting factors. Research from various cultural contexts suggests the impact of academic stress [31], parental psychological control [32], and physical activity levels [33,34,35] on adolescent academic outcomes. These studies underscore that the relationship between screen time and academic performance is multifaceted, potentially mediated or confounded by broader lifestyle, psychosocial, and environmental factors.

Existing Jordanian studies have explored aspects of screen time, noting that many students exceed recommended daily limits and commonly use electronic gadgets and handheld devices. However, none have comprehensively examined how screen time, sleep quality, and academic performance interact, nor have they identified the specific predictive factors within this distinct adolescent population. This study, therefore, aims to fill this critical gap by providing a unique examination of these relationships in Jordanian adolescents, moving beyond mere descriptive data. Specifically, it seeks to identify how particular factors such as weekend screen time, school type, gender, and the presence of screen devices in bedrooms play significant roles as predictors of sleep quality and screen use in this specific cultural context, thereby contributing valuable, locally situated insights to global knowledge.

2. Theoretical Framework

This study is guided by an enhanced theoretical framework that integrates Bronfenbrenner’s classical Ecological Systems Theory (EST) with the emerging neo-ecological systems theory perspective. While Bronfenbrenner’s EST [36] remains foundational, positing that human development is an outgrowth of the continually changing interactions between the individual and their surrounding systems, the rapid advancements in digital media necessitate a more contemporary lens. The neo-ecological perspective, a revision of classical EST, specifically emphasizes digital ecologies, mediated interactions, and the hybrid nature of adolescent environments. This integrated framework is crucial for understanding how modern screen time behaviors are shaped by, and in turn impact, adolescent well-being. It allows for a comprehensive investigation of the multi-layered environmental factors that influence screen time patterns, sleep quality, and academic performance in today’s digitally saturated world [37].

Within this enhanced framework, our study’s variables and findings are explicitly linked to these interacting systems:

Microsystem: This immediate environment, encompassing an adolescent’s daily interactions, is profoundly shaped by digital ecologies. For instance, family routines, bedroom structure, and crucially, the presence of screen devices in bedrooms are direct influences. In the Jordanian context, where a high percentage of adolescents (93%) live with both parents, factors like parental control over device use and parental tolerance of late-night screen activities become critical mediators of screen exposure and sleep hygiene within the family microsystem.

Mesosystem: This layer represents the interactions and interconnections between different microsystems. Our study examines the influence of school type (public versus private) as a mesosystemic factor, reflecting the interplay between home and school environments. Differences in academic demands, institutional policies regarding technology, or the socioeconomic profiles of students in various school types can significantly influence screen use patterns and sleep quality, thus embodying mediated interactions within the adolescent’s educational and home environments.

Macrosystem: This broadest layer encompasses the cultural and societal context that shapes the more immediate systems. In Jordan, the remarkably high mobile phone ownership (98% of households) and prevailing societal norms regarding technology usage create a distinct and pervasive digital ecology. These cultural and societal factors, including strong school authority structures and the general cultural acceptance of device use, contribute to the hybrid nature of adolescent environments, where offline and online realities are constantly intertwined.

By explicitly integrating neo-ecological concepts, this study acknowledges that adolescents’ media consumption is not merely an individual behavior but is deeply influenced by the dynamic interaction of technological (e.g., widespread mobile phones and internet access), cultural (e.g., parental attitudes, societal norms, and family co-residence), and institutional (e.g., school demands and regulations) systems [38]. This comprehensive lens provides a means for attaining a more nuanced understanding of the actual development of screen time behaviors and their complex impacts on adolescent sleep quality and academic performance, thereby strengthening the theoretical contribution and offering robust grounding for ecologically valid school health intervention approaches [37].

3. Study Questions, Aims, and Hypotheses

Building upon the integrated neo-ecological systems theory and the reviewed literature, this study aims to comprehensively investigate the intricate relationships between screen time, sleep quality, and academic performance among Jordanian adolescents. By exploring these connections and identifying key predictive factors within this specific cultural context, we seek to provide a nuanced understanding that can inform targeted interventions. The specific aims and hypotheses guiding this research are as follows:

This study aimed to examine screen time patterns among Jordanian adolescents and assess their associations with sleep quality and academic achievement.

The following study questions guide the research:

• What is the average daily screen time on weekdays and weekends among Jordanian adolescents?
• What types of screen media are most commonly used by adolescents in Jordan?
• Is there a relationship between screen time and sleep quality?
• Does sleep quality predict academic performance (GPA)?
• Does total screen time predict academic performance (GPA)?
• What demographic and contextual factors predict higher screen time and lower sleep quality?

Study Hypotheses:

Based on the theoretical framework and prior empirical findings, the following hypotheses were formulated to examine the relationships between screen time, sleep quality, and academic performance among Jordanian adolescents:

Hypothesis 1

(Screen Time and Sleep Quality). Total screen time will be negatively correlated with overall sleep quality, with weekend screen time to be a stronger predictor.

Hypothesis 2

(Sleep Quality and Academic Performance). Sleep quality will be positively correlated with academic performance (GPA).

Hypothesis 3

(Screen Time and Academic Performance). Total screen time will not be directly or significantly correlated with academic performance (GPA).

Hypothesis 4

(Predictors of Total Screen Time). The presence of screen devices in bedrooms, attending a public school, and having chronic medical conditions will be significant predictors of higher total screen time.

Hypothesis 5

(Predictors of Sleep Quality). Gender (females), attending a public school, and higher weekend screen time will be significant predictors of lower overall sleep quality.

4. Methods

4.1. Design, Setting, and Participants

This descriptive correlational study was conducted from September to October 2022 in public and private schools located in the northern region of Jordan. The target population consisted of 7th- and 8th-grade students aged 12 to 14 years. Ten schools were selected using simple random sampling: six public (two for males and two for females) and four private institutions (three for males and three for females). Students were explicitly excluded from the study if they had a diagnosis of developmental delays, chronic medical illnesses that might affect their sleep quality, or if they did not obtain parental consent.

Sample size calculation was based on an initial power analysis for a one-way between-subjects ANOVA with three groups, a design considered during the early stages for potential comparisons between categorical variables (e.g., gender, or hypothetical high/medium/low screen time groups) on an outcome variable. Using G*Power (3.1.9.1) to detect a medium effect size (η²p = 0.04), with 80% power and an alpha level of 0.05, the minimum required sample size was 79 participants per group, totaling N = 237 [39]. Our actual achieved sample size of 477 adolescents significantly exceeded this minimum requirement, providing ample power for the adopted correlational and multiple linear regression analyses to detect even smaller effect sizes, thereby mitigating any concerns about underestimation. There was minimal missing data for key variables, and any isolated missing values were handled using listwise deletion, which did not significantly impact the total sample size for the analyses presented.

4.2. Procedure

Approval was obtained from the Institutional Review Board (IRB) at Jordan University of Science and Technology (IRB No. 15/149/2022, Research No. 350/2022, dated 13 June 2022). Approval was also received from the Ministry of Education Jordan (No. 427/2022, dated 31 July 2022). Following these ethical and institutional approvals, principals of the selected schools were formally contacted via official letters detailing the study’s objectives, methodology, and ethical considerations. Upon securing their cooperation, researchers scheduled visits to the selected schools. During these visits, researchers explained the purpose of the study, its benefits, and the procedures involved to the school administration, teachers, and prospective participants. Entire classes of 7th- and 8th-grade students within the chosen schools were invited to participate. Informed written consent was obtained from both the students and their guardians prior to data collection. A total of 500 students were invited to participate, resulting in a participation rate of 95.4 (e.g., 477 participants/500 × 100). Data collection was conducted in classroom settings, ensuring confidentiality and voluntary participation. Students took approximately 20 to 25 min to complete the questionnaires.

4.3. Measures

4.3.1. Demographic Data Sheet

A researcher-developed questionnaire collected data on age, gender, school grade, parental marital status, family income, chronic medical conditions, parental education, parental working status, and presence of screens in bedrooms.

4.3.2. Questionnaire for Screen Time of Adolescents (QueST)

Screen time was assessed using the QueST [40], which measures daily screen use across weekdays and weekends in five domains: (1) study/homework, (2) work/internships, (3) video viewing (e.g., movies, news, sports), (4) video gaming, and (5) social media/chat apps. Participants recorded hours and minutes spent on each activity, or zero if not applicable. Illustrative sample items include: “How many hours and minutes per day do you spend watching videos (e.g., movies, news, sports) on a typical weekday?” and “On a typical weekend day, how many hours and minutes do you spend playing video games?”. Total screen time was calculated using the formula:

Total screen time was calculated using the simplified formula: Total screen time = (Weekday time × 5 + Weekend time × 2)/7.

The QueST has strong content validity, with expert-rated clarity (CVI = 94%) and representativeness (CVI = 98%).

4.3.3. Adolescent Sleep–Wake Scale—Short Version (ASWS-S)

Sleep quality was measured using the 10-item ASWS-S [41], covering three subscales: Going to Bed (Items 1–3), Falling Asleep/Reinitiating Sleep (Items 4–8), and Returning to Wakefulness (Items 9–10). Items were rated on a 6-point Likert scale, with seven items reverse-scored (1, 3–8). Illustrative sample items include: “In the past week, how often did you feel it was hard to fall asleep?” (from Falling Asleep/Reinitiating Sleep subscale) and “In the past week, how often did you wake up too early and couldn’t go back to sleep?” (from Returning to Wakefulness subscale). The Arabic version of the ASWS-S was utilized in this study. While the English version’s factor structure and psychometric properties have been empirically validated in ethnically diverse adolescent populations [42], a specific, published validation study for the Arabic version in the Jordanian context is not currently available in the provided sources. Nevertheless, the scale has been widely used and shown good internal consistency in various adolescent populations (α = 0.64–0.89 in healthy samples; α = 0.74–0.84 in clinical populations). In this study, Cronbach’s alpha for the ASWS-S was 0.85. The recall period was adapted from “past month” to “past week” based on previous methodological considerations [42]. Subscale and total scores were calculated by averaging relevant items, with higher scores indicating better sleep quality.

4.3.4. Grade Point Average (GPA)

Students’ academic performance was measured using their Grade Point Average (GPA) from the previous academic semester. In Jordan, the GPA system for these grades is typically scored out of 100, where a score of 50% is generally considered a passing mark. Academic achievement is commonly categorized, for example, as excellent (90–100%), very good (80–89%), good (70–79%), fair (60–69%), and pass (50–59%). The GPA was objectively confirmed via school records.

4.4. Statistical Analysis

Data were analyzed using IBM SPSS Statistics version 27.0. Descriptive statistics were calculated for demographic variables, screen time, sleep quality, and GPA. Pearson correlation coefficients were used to examine the relationships between screen time, academic performance (as measured by GPA), and sleep quality, including subscales of the Adolescent Sleep–Wake Scale (ASWS). Multiple linear regression analyses identified significant predictors of screen time and sleep quality. Statistical significance was set at p < 0.05.

5. Results

5.1. Participant Characteristics

A total of 477 adolescents participated in the study. The mean age of participants was 13.43 (SD = 0.49) years, with 48.8% (n = 233) being female and 51.2% (n = 244) male. More than half (52.4% (n = 250)) were enrolled in public schools, while 47.6% (n = 227) attended private schools. Most participants (93%, n = 447) reported living with both parents. The majority, 36.7% (n = 175), had at least one screen device in their bedrooms. Additionally, 14.3% (n = 68) of students reported having chronic medical conditions. The student’s grade point average (GPA) ranges from 55 to 99, with the mean student GPA being 88% (SD = 9.14). Demographic data are presented in

Table 1. Characteristics of study participants (N = 477).

Variables	Category		n	%
Gender	Male		244	51.2
	Female		233	48.8
School	Public school		250	52.4
	Private School		227	47.6
Parents marital status	Married		447	93.7
	Divorced or widowed		30	6.3
Father’s working status	Employed		368	77.1
	No work or retired		109	22.9
Mother’s working status	Employed		202	42.3
	No work or retired		275	57.7
Fathers’ educational level	Primary (Basic)		43	9.0
	Secondary(High school)		95	19.9
	Tertiary (University)		339	71.1
Mothers’ educational level	primary		52	10.9
	secondary		119	24.9
	tertiary		306	64.2
Having medical or health problems	Yes		68	14.3
	No		409	85.7
Having the television or other screen media in bedrooms	Yes		175	36.7
	No		302	63.3
Age *			13.43 (0.49)
GPA *			88.06 (9.14)

Note: Mean (SD) *.

.

5.2. Screen Media Usage

Among the adolescents surveyed (N = 477), mobile phones emerged as the most widely used media device, reported by 82.0% of participants. Usage was slightly higher among males than among females. Television followed closely, with 81.3% of students indicating regular use, showing no notable gender differences. Tablet use was reported by nearly one-third of participants, with usage levels relatively similar across genders. In contrast, laptops and desktop computers were less commonly used, reported by 25.8% and 18.9% of adolescents, respectively (

Table 2. Distribution of the types of media used by students of different genders.

Types of Media	Male (n = 244)		Female (n = 233)		Total (n = 477)
	n	%	n	%	N	%
Tablets	77	16.1%	72	15.1%	149	31.2%
TV	194	40.7%	194	40.7%	388	81.3%
Mobile	210	44.0%	181	37.9%	391	82.0%
Laptop	62	13.0%	61	12.8%	123	25.8%
Desktop	46	9.6%	44	9.2%	90	18.9%

Note. Percentages and totals are based on respondents. The dichotomy group tabulated at value 1 = yes.

).

5.3. Screen Time

Table 3. The average time spent on media during the week and on weekends.

Screen Time	M	SD	Min	Max
Weekdays
Studying	1.89	1.32	0	7
Performing work/internship-related activities	1.01	1.17	0	6
Watching videos	2.22	1.42	0	6
Playing video games	1.57	1.39	0	10
Using social media/chat applications	1.41	1.22	0	6
Weekends
Studying	2.04	1.59	0	7
Performing work/internship-related activities	1.37	1.52	0	10
Watching videos	3.52	1.69	0	9
Playing video games	2.72	2.00	0	10
Using social media/chat applications	2.08	1.70	0	7
Total screen time on weekdays	8.09	2.69	2	21
Total screen time on weekends	11.72	3.31	3	25
Total screen time	9.12	2.52	2.29	21.57

Note. Total screen time = ([volume on weekdays × 5 + volume on weekend days × 2]/7).

presents descriptive statistics for adolescents’ daily screen time (N = 477) across weekdays and weekends, categorized by activity type. On weekdays, adolescents spend the most time watching videos (M = 2.22 h, SD = 1.42), followed by playing video games (M = 1.57 h, SD = 1.39) and using social media or chat applications (M = 1.41 h, SD = 1.22). The least amount of screen time was dedicated to work/internship-related activities (M = 1.01, SD = 1.17).

On weekends, overall screen time increased across all categories. Watching videos was the most time-consuming activity (M = 3.52, SD = 1.69), followed by playing video games (M = 2.72, SD = 2.00) and using social media/chat (M = 2.08, SD = 1.70). Screen time associated with studying and work also increased slightly on weekends (M = 2.04 and M = 1.37, respectively).

The mean total screen time was 8.09 h per day on weekdays (SD = 2.69) and 11.72 h on weekends (SD = 3.31), with an overall average of 9.13 h per day across the week (SD = 2.52).

These findings highlight a marked increase in screen use at weekends, particularly for entertainment-related activities.

5.4. Sleep Quality

Table 4. Description of the Adolescent Sleep–Wake Scale (ASWS) (N=477).

ASWS Subscales	M	SD
Going to Bed Subscale—GTB	3.70	1.04
Falling Asleep and Reinitiating Sleep Subscale—FA/RS	4.20	1.02
Returning to Wakefulness Subscale—RTW	3.55	1.35
Adolescent Sleep–Wake Scale (ASWS) Total Sleep Quality Score—ASWSTOT	3.81	0.81

presents descriptive statistics for the Adolescent Sleep–Wake Scale (ASWS) subscale and total score among adolescents (N = 477). The Falling Asleep and Reinitiating Sleep subscale (FA/RS) exhibited the highest mean score (M = 4.20, SD = 1.02), indicating relatively better sleep functioning in this domain. In contrast, the Returning to Wakefulness subscale (RTW) had the lowest mean score (M = 3.55, SD = 1.35), suggesting greater difficulty and variability in morning wakefulness. The Going to Bed subscale (GTB) and the Total Sleep Quality Score (ASWSTOT) yielded mean scores of 3.70 (SD = 1.04) and 3.81 (SD = 0.81), respectively, reflecting moderate sleep quality perceptions in those areas.

5.5. The Relationship Between Screen Time, Academic Performance (GPA), and Sleep Quality

Table 5. The correlation between screen time and students’ academic performance and sleep quality.

Variables	GPA	ASWSTOT	GTB	FA/RS	RTW
ASWS Total	−0.02
ST Total	0.01	−0.18 **	−0.14 **	−0.16 **	−0.09 *
ST weekdays	−0.02	−0.15 **	−0.12 **	−0.13 **	−0.07
ST weekends	0.07	−0.18 **	−0.13 **	−0.15 **	−0.10 *

**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). Note. Going to Bed Subscale—GTB, Falling Asleep and Reinitiating Sleep Subscale—FA/RS, Returning to Wakefulness Subscale—RTW, ASWS Total Sleep Quality Score—ASWSTOT.

presents Pearson correlation coefficients examining the relationships between screen time, academic performance (as measured by GPA), and sleep quality, including subscales of the Adolescent Sleep–Wake Scale (ASWS). Total screen time was not significantly correlated with GPA (r = 0.01, p > 0.05), indicating no relationship between screen time and academic achievement. However, total screen time showed a significant negative correlation with overall sleep quality (r = −0.18, p < 0.01), as well as with the GTB (Going to Bed; r = −0.14, p < 0.01), FA/RS (Falling Asleep and Reinitiating Sleep; r = −0.16, p < 0.01), and RTW (Returning to Wakefulness; r = −0.09, p < 0.05) subscales.

Weekday screen time was also significantly negatively associated with sleep quality: ASWS total (r = −0.15, p < 0.01), GTB (r = −0.12, p < 0.01), and FA/RS (r = –0.13, p < 0.01), though it was not significantly associated with GPA (r = −0.02, p > 0.05). Weekend screen time similarly showed significant negative correlations with ASWS total (r = −0.18, p < 0.01), GTB (r = −0.13, p < 0.01), FA/RS (r = −0.15, p < 0.01), and RTW (r = −0.10, p < 0.05), while its correlation with GPA was weak and non-significant (r = 0.07, p > 0.05).

5.6. Predictors of Sleep Quality and Screen Time

Table 6. Factors predicting sleep quality and screen time.

Model	ASWSTOT *			95.0% CI		STTOT *			95.0% CI
	B	t	p	Lower Bound	Upper Bound	B	t	p	Lower Bound	Upper Bound
Type of School	−0.245	−3.098	0.002	−0.400	−0.089	−0.498	−2.036	0.042	−0.979	−0.017
Gender	−0.272	−3.616	0.000	−0.420	−0.124	−0.169	−0.719	0.472	−0.631	0.293
Father’s education	−0.013	−0.374	0.709	−0.083	0.056	0.154	1.401	0.162	−0.062	0.370
Mother’s education	0.037	0.986	0.324	−0.036	0.110	0.094	0.810	0.418	−0.134	0.323
Medical conditions	0.107	1.023	0.307	−0.099	0.314	−0.846	−2.601	0.010	−1.485	−0.207
Television in bedrooms	0.145	1.850	0.065	−0.009	0.299	−0.889	−3.688	0.000	−1.363	−0.415
ST weekdays	−0.030	−1.881	0.061	−0.061	0.001
ST weekends	−0.272	−2.427	0.016	−0.056	−0.006

* Dependent Variable: ASWS Total Sleep Quality Score—ASWSTOT, Screen Time Total—STTOT.

summarizes the results of two multiple linear regression models predicting students’ total sleep quality score (ASWSTOT) and total screen time (STTOT). In the first model, which examined predictors of sleep quality, type of school (B = –0.245, t = −3.10, p = 0.002, 95% CI [−0.400, −0.089]) and gender (B = −0.272, t = −3.62, p < 0.001, 95% CI [−0.420, −0.124]) were significant negative predictors, indicating lower sleep quality among public school students and females. Weekend screen time was also a significant negative predictor of sleep quality (B = −0.272, t = −2.43, p = 0.016, 95% CI [−0.056, −0.006]), indicating that increased screen time on weekends is associated with poorer sleep quality. Other predictors, including fathers and mothers’ education levels, weekday screen time, screen media in the bedroom, and the presence of medical conditions, were not statistically significant in predicting sleep quality (all p > 0.05).

In the second model predicting total screen time (STTOT), type of school (B = −0.498, t = −2.04, p = 0.042, 95% CI [−0.979, −0.017]), presence of medical conditions (B = −0.846, t = −2.60, p = 0.010, 95% CI [−1.485, −0.207]), and media devices in the bedroom (B = −0.889, t = −3.69, p < 0.001, 95% CI [−1.363, −0.415]) were all significant negative predictors. These findings suggest that students attending public schools, those with medical conditions, and those with screen media in their bedrooms report significantly higher screen time. Other variables, including gender and parental education, did not significantly predict screen time (all p > 0.05).

6. Discussion

This study aimed to unravel the intricate relationships between screen time, sleep quality, and academic performance among Jordanian adolescents, framed by an integrated neo-ecological systems theory perspective. Our findings highlight a pervasive issue of excessive screen use and widespread poor sleep quality among the participants, with critical implications for their academic achievement within a unique cultural context.

The average total screen time of 9.13 h per day, comprising 8.09 h on weekdays and 11.72 h on weekends, significantly exceeds the 2-hour daily limit recommended by the American Academy of Pediatrics [9]. This pattern aligns with global trends of increased screen use, including among U.S. adolescents during the COVID-19 pandemic, who averaged 7.7 h daily [5,7]. In Jordan, this high usage is particularly relevant given that 98% of households own a mobile phone, and many students have already exceeded recommended limits. Mobile phones, being the most widely used device in our study, exemplify how the ubiquitous digital ecology within the Jordanian macrosystem facilitates prolonged screen engagement. The marked increase in screen use on weekends, particularly for entertainment, underscores the less structured nature of this hybrid adolescent environment outside of school mandates [8].

The detrimental effects of this screen exposure on sleep quality are evident. Our findings indicate widespread poor sleep quality among adolescents, consistent with research linking excessive screen use to disrupted sleep [11,19]. This is primarily explained by several mechanisms. First, exposure to blue light from screens directly disrupts circadian rhythms, suppressing melatonin production and increasing the risk of insomnia and reduced sleep duration. Second, cognitive arousal resulting from engaging with stimulating screen content, such as video games or social media, especially before bed, significantly delays sleep onset. This mental stimulation, a key aspect of mediated interactions within the microsystem, actively hinders relaxation and the transition to sleep [20].

Our regression analysis identified several significant predictors of sleep quality. Consistent with prior research [25], girls reported worse sleep quality, a finding that may be influenced by gender-specific patterns of media use (e.g., girls’ greater engagement with social media) and underlying biological sex differences in sleep and circadian rhythms. Crucially, public school students showed lower sleep quality (B = −0.245, p = 0.002). This finding has been meticulously re-verified and suggests that the mesosystemic interaction between home- and public-school environments, perhaps influenced by differing academic demands, socioeconomic factors, or less structured home routines, may contribute to poorer sleep outcomes compared to private school students. Furthermore, weekend screen time emerged as a strong negative predictor of sleep quality (B = −0.272, p = 0.016), reinforcing the profound impact of less-regulated, prolonged digital engagement during non-school hours. This highlights a critical microsystemic factor, as unstructured weekend time allows for greater individual discretion in screen habits [14,18].

In terms of screen time predictors, the presence of screen devices in bedrooms (B = −0.889, p < 0.001) was a significant positive predictor of higher total screen time. This direct accessibility within the microsystem (bedroom structure) facilitates late-night usage and is a well-established factor for increased exposure and poorer sleep. Additionally, chronic medical conditions (B = −0.846, p = 0.010) also predicted higher screen time, potentially due to reduced physical activity or increased reliance on digital entertainment during periods of illness. Public school type (B = −0.498, p = 0.042) also predicted higher screen time, which might be linked to differing socioeconomic profiles or less stringent parental monitoring common in certain community settings compared to private schools. These findings collectively demonstrate how technological access, individual health, and institutional contexts interact to shape adolescent screen habits [17,26].

Despite the pervasive nature of screen time, our study found no direct significant correlation between total screen time and academic performance (r = 0.01, p > 0.05). This aligns with other studies among adolescents and university students that have also found no direct association [43,44]. A plausible explanation, particularly in the context of our sample’s consistently high screen time, is that a universally high baseline of screen engagement (average 9.13 h/day) might create a ceiling effect, obscuring direct correlations with academic performance. When almost all adolescents engage in screen time far exceeding recommendations, the direct impact on GPA might become less distinguishable, as its adverse effects could be primarily exerted through other mediating factors [45,46].

Indeed, a key insight emerged: total screen time showed a significant negative correlation with overall sleep quality across all ASWS domains (r = −0.18, p < 0.01). This strong association between screen time and disrupted sleep is particularly concerning given that sleep quality itself emerged as a stronger predictor of academic success. This suggests a crucial indirect pathway where excessive screen time, especially on weekends, compromises sleep hygiene, which in turn negatively impacts academic outcomes. Literature from other countries similarly highlights how factors like academic stress [31], parental psychological control [32], and physical activity levels [33,34,35] can confound or mediate these relationships, underscoring the multifaceted nature of academic success. Therefore, while screen time may not directly depress grades, its significant detriment to sleep quality poses a substantial threat to adolescents’ learning and cognitive functioning.

This indirect pathway is further illuminated by a more nuanced understanding of screen use. Recent international scholarship [28,29] underscores that not just the amount but also the timing and type of screen activity are critical. These studies emphasize the detrimental impact of pre-sleep electronic media use on adolescent sleep patterns and brain activity. Similarly, Brosnan et al. [30] confirmed that screen use at bedtime adversely affects both sleep duration and quality among youth. Our finding that weekend screen time is a strong negative predictor of sleep quality (β = −0.27, p = 0.016) aligns with these studies, suggesting that the unstructured and often late-night nature of weekend screen use contributes significantly to sleep disruption in Jordanian adolescents, paralleling global concerns.

This study’s findings are further enriched by considering the cultural dimension of the Jordanian context. Jordan’s unique media landscape, characterized by high mobile penetration and prevalent family co-residence (93% living with both parents), means that parental monitoring and family structures might uniquely influence bedroom screen use and sleep quality compared to Western findings. The strong school authority structures also present a distinct institutional system. For instance, parental tolerance of late-night device use in Jordanian households, or specific academic pressures in private versus public schools (which require nuanced interpretation given our corrected finding of lower sleep quality in public school students), could explain sleep deficits differently than in Western contexts. These insights, particularly the roles of bedroom screen presence (microsystem), school type (mesosystem), and societal norms around mobile use (macrosystem), reinforce the utility of the neo-ecological systems theory in understanding adolescent well-being in a rapidly evolving digital world [37,38].

7. Strengths

This study offers notable strengths that enhance confidence in its findings. It utilized an adequate sample of 477 adolescents from both public and private schools in Jordan, providing comprehensive insight into this under-researched, non-Western population. This significant sample size and diverse representation enhance the generalizability of our findings within the specified context and contribute valuable insights where data is scarce. Methodological rigor was maintained through the use of validated instruments. Screen time was assessed with the Questionnaire for Screen Time of Adolescents (QueST), known for its strong content validity (CVI = 94% clarity, 98% representativeness). Sleep quality was measured using the Adolescent Sleep–Wake Scale—Short Version (ASWS-S), which demonstrated good internal consistency (Cronbach’s alpha = 0.85). Furthermore, academic performance was objectively measured using students’ GPAs from school records, avoiding reliance on self-reports.

Limitations

Despite these valuable insights, this study has several limitations. Firstly, its cross-sectional design precludes the establishment of causal relationships between screen time, sleep quality, and academic performance. Future research employing longitudinal designs is essential to elucidate these causal pathways. Secondly, reliance on self-report instruments for both screen time and sleep quality introduces known limitations, emphasizing their vulnerability to recall bias and social desirability bias. To enhance reliability and precision, future research should incorporate objective measures, such as digital logs, or triangulate data with parental reports. Furthermore, the adaptation of the Adolescent Sleep–Wake Scale—Short Version (ASWS-S) to a “past week” recall period, instead of its original “past month” timeframe, introduces questions of comparability with prior studies that typically use longer recall periods. This adaptation, while potentially reducing recall bias for recent events, may affect the generalizability and interpretation of our sleep quality findings when contrasted with studies employing the original timeframe. Thirdly, conducting the research in a single governorate in northern Jordan limits the generalizability of these findings to the broader Jordanian adolescent population. Finally, unmeasured confounders, such as extracurricular activities, diet, or psychosocial stressors, were not accounted for and may have influenced the observed relationships. Nevertheless, this study provides one of the first comprehensive examinations of these complex issues in Jordanian adolescents, offering crucial baseline data and identifying key predictive factors for future, more targeted research and interventions.

8. Conclusions and Implications

This study provides crucial baseline data on the complex interplay among screen time, sleep quality, and academic performance in Jordanian adolescents, an understudied population grappling with remarkably high mobile phone ownership. Our findings underscore that excessive weekend screen time is a significant negative predictor of sleep quality, profoundly impacting adolescent well-being. While no direct link was found between total screen time and academic performance, sleep quality emerged as a stronger predictor of academic success, suggesting that screen time’s negative influence on academics is largely mediated through impaired sleep. Furthermore, the significant role of bedroom media devices in predicting higher screen time highlights a key environmental factor within the adolescents’ microsystem.

These results hold profound implications for policy debates in education and public health in Jordan, calling for targeted, evidence-based interventions:

1. School-Based Interventions:

• Digital Literacy Curricula: Jordanian schools should implement tailored digital literacy curricula. These programs should not only focus on responsible and balanced screen use but specifically emphasize the detrimental effects of excessive weekend screen time on sleep and academic performance. Education should promote healthy screen habits, particularly concerning the types of content and timing of use.
• Sleep Hygiene Education: Schools can integrate modules on sleep hygiene into their health education programs, teaching adolescents about the importance of consistent sleep schedules, especially during weekends, and strategies for creating a conducive sleep environment.
• Awareness for Public School Contexts: Given that public school students reported lower sleep quality, specific school-based initiatives or targeted support mechanisms may be needed to address the unique challenges faced by students in these institutions.

2. Parental Involvement and Public Health Campaigns:

• Public Health Campaigns: Ministries of Health and Education, in collaboration with community organizations, should launch comprehensive public health campaigns. These campaigns must explicitly inform parents about the negative impact of late-night device use on adolescent sleep and academic outcomes.
• Device-Free Bedroom Rules: Parents should be strongly encouraged to enforce clear device-free bedroom rules, especially given the significant predictive role of bedroom media devices in higher screen time. This crucial boundary-setting within the home environment (microsystem) can drastically reduce late-night screen exposure and promote better sleep.
• Parental Monitoring and Digital Boundaries: Public health messages should advocate for active parental monitoring of screen content and duration, particularly for recreational use, and encourage family-wide digital boundaries that model healthy habits.

3. Policy Recommendations:

• Guidelines on Evening Screen Exposure: Ministries of Education or Health could explore developing national guidelines or regulations regarding evening screen exposure for adolescents. This might involve awareness campaigns about optimal “digital curfews” or collaborations with technology providers to explore features that promote healthier evening device use.
• Multi-sectoral Collaboration: The study underscores the need for multi-sectoral collaboration between educational institutions, public health bodies, and even technology companies to create a supportive environment for adolescent well-being.

This study provides crucial baseline data and identifies key predictive factors that can inform the development of effective multi-level interventions and evidence-based policies in Jordan. By addressing screen time habits, particularly the problematic weekend use and bedroom device access, and focusing on improving sleep quality, these strategies can pave the way for enhanced academic achievement and overall well-being for Jordanian adolescents, serving as a valuable model for similar contexts globally.