Sex Differences in the Associations of mental Health,Suicidality,Screentime,and Sleep: A Mediation Effect Analysis of Sleep Using Youth Risk Behavioral Surveillance Survey 2021

Abstract

Objective: This study was conducted to explore the sex differences in the direct and indirect associations among mental health/suicidality, sleep, and screen time. Methods: Using the 2021 Youth Risk Behavior Survey (YRBS) data, 9408 participants were included in the analyses. The associations of endogenous variables (mental health and suicidality), exogenous variable (screen time), mediator (sleep), and covariates (demographic features and risky behaviors) were analyzed using the Structural Equation Model and “medsem” package, as well as logistic regression and bootstrapping methods. To explore the sex differences in the mediation effect, the Likelihood Ratio Test was used for the multiple-group analysis to compare the unconstrained model with the mediation path-constrained model. Results: Screen time had a significant negative association with sleep duration (female: β = −.09, p < .001; male: β = −.04, p < .001), positive relations to mental health problems (female: β = .10, p < .001; male: β = .12; p < .001), and suicidality (female: OR: 1.07, 95% CI: 1.02–1.13; male: OR: 1.06, 95% CI: 1.01–1.23). Sleep duration is negatively associated with mental health problems (female: β = −.16, p < .001; male: β = −.14; p < .001) and negatively associated with the likelihood of suicidality (female: .89, 95% CI: .85–.94; male: OR: .84, 95% CI: .79–.90). Furthermore, female adolescents had higher frequencies in mental health problems, higher odds ratio in suicidality, shorter sleep duration, and bigger mediation effects of sleep, compared to their male counterparts. Conclusion: Mental health and suicidality were affected differently by screen time and sleep between female and male adolescents. Future research may continue to explore sex differences and their underlying reasons.

Keywords

Adolescents mental health/suicidality sleep screen time sex differences

Introduction

In the U.S., one in five adolescents aged 12 to 17 is struggling with mental health problems (Mojtabai & Olfson, 2020; National Healthcare Quality and Disparities Report, 2022), and the number has been increasing since 2011 (Curtin, 2020; CDC, 2023). In 2021, over 40% of adolescents consistently felt sad or hopeless, and 30% experienced poor mental health (CDC, 2023). Furthermore, mental health problems among adolescents often coexist with associated behavioral risks (CDC, 2023). One of the behavioral risks, suicidality, rose by over 50% from 2000 to 2018, and the trend continues (Curtin, 2020; Curtin & Garnett, 2023). Since 2018, suicidality has become one of the top three leading causes of death among U.S. adolescents (Desai & Affrunti, 2023). Mental health problems and suicidality among adolescents have become one of the most urgent public health crises in the U.S.

Researchers have indicated that screen time is associated with mental health problems (Ashton & Beattie, 2019; Babic et al., 2017; Stiglic & Viner, 2019) and suicidality among adolescents (Twenge et al., 2019). During screen time, seeing instant likes and comments on social media by adolescents stimulates immediate pleasure with the consequences of poor emotion and impulsivity control (Wilmer & Chein, 2016), which could lead to mental health problems and associated risk behaviors, including suicidality (Furnham & Cheng, 2019). However, others disagreed and found the association between screen time and mental health/suicidality among adolescents very small and even neglectable (Orben & Przybylski, 2019; Tang et al., 2021).

One potential mediator between screen time and mental health/suicidality is sleep duration, due to its negative association with screen time (Cain & Gradisar, 2010; Dube et al., 2017; Parent et al., 2016) and positive relationship with mental health problems (Colrain & Baker, 2011; Zelinski et al., 2014; Simola et al., 2012) and suicidality (Chiu et al., 2018; Kearns et al., 2020). Short sleep duration during development could reduce brain mass (Mirmiran et al., 1983) and infringe on mental and cognitive functioning (Colrain & Baker, 2011; Zelinski et al., 2014). The enhancement in stress hormones, such as glucocorticoids, following insufficient sleep duration also hinders mental health (Meerlo et al., 2002). The maldevelopment of mental functioning and increased secretion of glucocorticoids could lead to mental health concerns and their associated risky behaviors, including suicide.

The mediation effect of sleep has been tested; and the results were mixed (Leung & Torres, 2021; Li et al., 2019). Researchers have called for more studies (Stiglic & Viner, 2019; Suchert et al., 2015). This study is conducted to explore the direct and indirect associations among mental health/suicidality, sleep, and screen time. Furthermore, due to sex differences among adolescents in mental health and suicidality (Miranda-Mendizabal et al., 2019; Van Droogenbroeck et al., 2018), screen time use (Jago et al., 2014), screen time effects on mental health (Twenge & Farley, 2020), and sleep duration (Galland et al., 2017; Hysing et al., 2013), female and male adolescents were tested individually as subgroups. We hypothesized that screen time and sleep are associated with mental health and suicidality; sleep, as a mediator, has an indirect effect on the paths between screen time and mental health/suicidality. In addition, the effects vary between female and male adolescents.

Participants and methods

Survey and participants

This study uses the 2021 Youth Risk Behavior Survey (YRBS) cross-sectional data, established by the Centers for Disease Control and Prevention (CDC). The survey assesses health risk behaviors contributing to major health issues among U.S. adolescents. Comparable data was collected at national, state, territorial, tribal, and local levels (Underwood et al., 2020). The YRBS utilizes around 100 questions to cover diverse health risk behaviors, such as mental health, suicidal thoughts, plans, and behaviors, sleep, and screen time (Mpofu et al., 2023). The reliability of YRBS is demonstrated among middle school (Zullig et al., 2006) and high school (Raghupathy & Hahn-Smith, 2012) adolescents in the community. The study protocol for conducting the YRBS received approval from the CDC’s Institutional Review Board (IRB), and the data are publicly accessible.

The YRBS utilized a cluster sample design to create a nationally representative sample from 9th to 12th grades in both public and private middle and high school students (Underwood et al., 2020). For this study, following the previous study (Mantey et al., 2023), participants with missing data on the endogenous variables were excluded from the study. A total of 9408 participants were included in the analyses.

Measures

Mental health

Two endogenous variables are mental health and suicidality. Mental health was collected by the question: how often was your mental health not good (including stress, anxiety, and depression)? A 5-point Likert scale was adopted in the original survey answer and treated as a continuous variable in this study: “never” [=1], “rarely” [=2], “sometimes” [=3], “most of the time” [=4], and “always” [=5].

Suicidality

Three questions in the YRBS 2021 regarding suicidality are: (1) “During the past 12 months, did you ever consider attempting suicide?” and (2) “During the past 12 months, did you make a plan about how you would attempt suicide?” and (3) “During the past 12 months, how many times did you actually attempt suicide?” For this study, following the method used in prior literature (Choi et al., 2015; Kitsantas et al., 2020), we created a binary variable with a coding of “Yes” [=1] if there is an occurrence in any listed questions; otherwise, “No” [=0].

Screen Time

The exogenous variable, screen time, was collected via the question, “How many hours do you spend in front of a TV, computer, smartphone, or other electronic device watching shows or videos, playing games, accessing the internet, or using social media (also called “screen time”, do not count time for schoolwork)?” The answers were coded as “less than 1 hour per day” [=1], “1 hour per day” [=2], “2 hours per day” [=3], “3 hours per day” [=4], “4 hours per day” [=5], and “5 or more hours per day” [=6]. For descriptive statistics, a binary variable, “excessive screen time,” was created in the study and coded as “3 hours or less” [=0] and “more than 3 hours” [=1], following the 24-Hour Movement Guidelines of daily screen time (Neophytou et al., 2019).

Sleep duration

The mediator, sleep duration, was measured by the question, “On an average school night, how many hours of sleep do you get?”. Following the previous study (Meldrum et al., 2020), this study preserved the original coding of the answers for analysis: “4 or less hours” [=1], “5 hours” [=2], “6 hours” [=3], “7 hours” [=4], “8 hours” [=5], “9 hours” [=6], “10 or more hours” [=7]. For descriptive statistics, we also categorized a binary variable, “recommended sleep,” coding as “7 hours or less” [=0] and “8 hours or more” [=1], following the recommended 8-hour sleep duration by the National Sleep Foundation (Hirshkowitz et al., 2015) and the American Academy of Sleep Medicine (Paruthi et al., 2016).

Covariates

Demographic features, such as sex, race/ethnicity, and age, were controlled in the study. Sex is coded as a dichotomous indicator (“female=0” and “male=1”). Race/ethnicity is measured in a series of dummy variables in the study: Native/Indian American, Asian, Black, Hispanic, multiple races, and White [reference]. Age was coded as “12 years old or younger” [=1], “13 years old” [=2], 14 years old [=3], “15 years old” [=4], “16 years old” [=5], “17 years old” [=6], and “18 years old or older.” Being bullied included both being physically bullied and being electronically bullied and coded as “no” [=0] and “yes” [=1]. Substance use (including cigarette smoking, vaping, smokeless tobacco, alcohol, marijuana, prescription medicine, cocaine, inhalants, heroin, methamphetamine, and ecstasy) was coded as “no” [=0] and “yes” [=1].

Statistical analysis

We conducted chi-square tests for descriptive statistics to present the frequencies of each level among variables. The Structural Equation Model (SEM) is utilized to analyze the paths toward the continuous exogenous variable “mental health” (See supplemental document 1 for the path diagram). The model fitness is tested by the Comparative Fit Index (CFI), Root Mean Squared Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR). The indirect (mediation) effects are tested by using the “medsem” package in Stata; the Monte Carlo method was used to test the significance of the indirect effects. The ratio of the mediation effect to the total effect (RIT) and the ratio of the mediation effect to the direct effect (RID) were calculated and analyzed (Zhao et al., 2010). As for the binary endogenous variable “suicidality”, we used logistic regression followed by the bootstrapping method to calculate the odd ratios and mediation effect. Furthermore, to explore the differences in the mediation effects between female and male adolescents, multiple group analyses were conducted using the Likelihood Ratio Test to compare the unconstrained model with the mediation path-constrained model. All the analyses were conducted based on the p-value method (Bursac et al., 2008); for statistical data inference, a statistically significant level was indicated by a p-value <.05. All statistical data analyses were carried out by Stata/SE 18.0 version (College Station, Texas).

Results

Participants demographics

Shown in Table 1, a total of 9408 participants were included in the analysis, with a proportion of males (51.88%) and females (48.12%). Around 75% of participants (n = 7021) were aged from 15 to 17 years old. Almost half of the participants (n=4499) were White followed by Hispanics, Blacks, Asians, people of multi-races, and Native Americans.

Table 1.

Demographic Information of Participants (n=9408).

	Total		Female		Male
	Freq.	Percent. (%)	Freq.	Percent. (%)	Freq.	Percent. (%)
Sex
Male	4881	51.88
Female	4527	48.12
Age
12 years or younger	11	.12	6	.13	5	.10
13 years old	29	.31	18	.40	11	.23
14 years old	1700	18.07	855	18.89	845	17.31
15 years old	2347	24.95	1184	26.15	1163	23.83
16 years old	2356	25.04	1101	24.32	1255	25.71
17 years old	2318	24.64	1128	24.92	1190	24.38
18 years and older	647	6.88	235	5.19	412	8.44
Race
Native	126	1.34	55	1.21	71	1.45
Asian	540	5.74	284	6.27	256	5.24
Black	1543	16.40	765	16.90	778	15.94
White	4499	47.82	2070	45.73	2429	49.76
Hispanic	2118	22.51	1044	23.06	1074	22.00
Multi-races	582	6.19	309	6.83	273	5.59
Total	9408	100	4527	100	4881	100

Descriptive statistics

Table 2 shows the frequencies among the variables. The prevalence of suicidality among female adolescents was 35.3%, compared to their male counterparts of 17.87%. The difference was statistically significant (χ² (1)=358.71, p < .001). Furthermore, the odds of female adolescent’s suicidality are 31% higher than their male counterparts (OR: 1.17, 95% CI: 1.17, 1.47) (Table 5). The prevalences of having mental health problems for the “most time and always” were 40.24% in females and 17.92% in males. The prevalence of excessive screen time was 61.67% in females and 52.80% in males. Around 20% of adolescents had recommended sleep durations of 8 or more hours per night.

Table 2.

Descriptive Statistics of the Frequencies.

	Total Freq. (%)	Female Freq. (%)	Male Freq. (%)
Suicidality
Yes	2458 (26.13)	1586 (35.3)	872 (17.87)
No	6950 (73.87)	2941 (64.97)	4009 (82.13)
			*p < .001
Mental health concerns
Never	2175 (23.12)	454 (10.03)	1721 (35.26)
Rarely	1995 (21.21)	765 (16.90)	1230 (25.20)
Sometimes	2541 (27.01)	1486 (32.83)	1055 (21.61)
Most of the time	1837 (19.53)	1236 (27.30)	601 (12.31)
Always	860 (9.14)	586 (12.94)	274 (5.61)
Screentime (per day)
Less than 1 hour	576 (6.12)	183 (4.04)	393 (8.05)
1 hour	399 (4.24)	179 (3.95)	220 (4.51)
2 hours	1238 (13.16)	556 (12.28)	682 (13.97)
3 hours	1826 (19.41)	817 (18.05)	1009 (20.67)
4 hours	1578 (16.77)	766 (16.92)	812 (16.64)
5 or more hours	3791 (40.30)	2026 (44.75)	1765 (36.16)
Sleep duration
4 or less hours	1036 (11.01)	506 (11.18)	530 (10.86)
5 hours	1441 (15.21)	789 (17.43)	652 (13.36)
6 hours	2353 (25.01)	1138 (25.14)	1215 (24.89)
7 hours	2529 (26.88)	1184 (26.15)	1345 (27.56)
8 hours	1543 (16.40)	677 (14.95)	866 (17.74)
9 hours	361 (3.84)	161 (3.56)	200 (4.10)
10 hrs or more	145 (1.54)	72 (1.59)	73 (1.50)

Note. *Pearson Chi-square p-value.

Table 3 shows that adolescents with excessive screen time have a higher likelihood of suicidality and mental health concerns. Among adolescents with recommended screen time, female adolescents have higher prevalences of suicidality (28.47% vs. 13.89%) and most time or always mental health problems (33.09% vs. 12.41%). Among adolescents with excessive screen time, females have high prevalences of suicidality (39.11% vs. 21.42%) and most time or always mental health problems (44.70% vs. 22.85%). In addition, among adolescents with recommended sleep duration, females reported high prevalences of suicidality (25.82 vs. 12.29%) and most time or always mental health problems (30.55% vs. 12.73%). Among short sleep duration, female adolescents also have higher prevalences of suicidality (37.35% vs. 19.56%) and most time or always mental health problems (42.69% vs. 19.51%).

Table 3.

Chi-Square Between Screentime and Mental Health, and Suicidality.

	Total		Female		Male
	Recom ST^a; n (%)	Excess ST^b; n (%)	Recom ST^a; n (%)	Excess ST^b; n (%)	Recom ST^a; n (%)	Excess ST^b; n (%)
Suicidality
Yes	814 (20.15)	1644 (30.62)	494 (28.47)	1092 (39.11)	320 (13.89)	552 (21.42)
No	3225 (79.85)	3725 (69.38)	1241 (71.35)	1700 (60.89)	1984 (86.11)	2025 (78.58)
		p < .001		p < .001		p < .001
Mental health
Never	1213 (30.03)	962 (17.92)	246 (14.18)	208 (7.45)	967 (41.97)	754 (29.26)
Rarely	954 (23.62)	1041 (19.39)	359 (20.69)	406 (14.54)	595 (25.82)	635 (24.64)
Sometime	1012 (25.06)	1529 (28.48)	556 (32.05)	930 (33.31)	456 (19.79)	599 (23.34)
Most time	616 (15.25)	1221 (22.74)	407 (23.46)	829 (29.69)	209 (9.07)	392 (15.21)
Always	244 (6.04)	616 (11.47)	167 (9.63)	419 (15.01)	77 (3.34)	197 (7.64)
Total	4039 (42.93)	5369 (57.07)	1735 (38.32)	2792 (61.68)	2.304 (47.20)	2577 (52.80)
		p < .001		p < .001		p < .001

	Recom sleep^c; n (%)	Short sleep^d: n (%)	Recom sleep^c; n (%)	Short sleep^d; n (%)	Recom sleep^c; n (%)	Short sleep^d; n (%)
Suicidality
Yes	375 (18.30)	2083 (28.31)	235 (25.82)	1351 (37.35)	140 (12.29)	732 (19.56)
No	1674 (81.70)	5276 (71.69)	675 (74.18)	2266 (62.65)	999 (87.71)	3010 (80.44)
		p < .001		p < .001		p < .001
Mental health
Never	720 (35.14)	1455 (19.77)	172 (18.90)	282 (7.80)	548 (48.11)	1173 (31.35)
Rarely	480 (23.43)	1515 (20.59)	187 (20.55)	578 (15.98)	293 (25.72)	937 (25.04)
Sometime	426 (20.79)	2115 (28.74)	273 (30.00)	1213 (33.54)	153 (13.43)	902 (24.10)
Most time	290 (14.15)	1547 (21.02)	204 (22.42)	1032 (28.53)	86 (7.55)	515 (13.76)
Always	133 (6.49)	727 (9.88)	74 (8.13)	512 (14.16)	59 (5.18)	215 (5.75)
Total	2049 (21.78)	7359 (78.22)	910 (20.10)	3.617 (79.90)	1139 (23.33)	3742 (76.67)
		p < .001		p < .001		p < .001

^aRecom ST indicates recommended screentime of less than 4 hours.

^bExcess ST indicates excessive screentime of 4 hours and more.

^cRecom sleep indicates recommended sleep duration of 8 hours or less.

^dShort sleep indicates excessive screentime of 7 hours or less.

Direct paths

SEM analyses were individually conducted for the total population, female adolescents, and male adolescents; and the models were good fits (CFI:.94. RMSEA:.07. SRMR:.02; CFI:.94. RMSEA:.06. SRMR:.02; and CFI:.90. RMSEA:.08. SRMR:.03, respectively for the total adolescents, female, and male) (Xia & Yang, 2018). The results indicated that screen time has a significant negative association with sleep duration. For every level increase in screen time, there is a 0.06 level decrease in sleep duration in adolescents (β=−.06, p < .001), a 0.09 level decrease among females (β=−.09, p < .001), and a 0.04 level decrease among males (β=−.04, p < .01). Furthermore, screen time was positively associated with suicidality (Table 4). For every level increase in screen time, there is a 7% increase in the likelihood of suicidality among total adolescents (OR: 1.07, 95% CI: 1.03–1,11), a 7% increase among female adolescents (OR: 1.07, 95% CI: 1.02–1.13), and a 6% increase for male adolescents (OR: 1.06, 95% CI: 1.01–1.23) (Table 5).

Table 4.

SEM Analysis of Mental Health and Sleep Duration.

	Total		Female		Male
	β coefficient	p Value	β coefficient	p Value	β coefficient	p Value
Mental health
Female (male as ref.)	.77	<.001
Screentime	.11	<.001	.10	<.001	.12	<.001
Sleep	−.15	<.001	−.16	<.001	−.14	<.001
Being bullied	.51	<.001	.44	<.001	.61	<.001
Substance	.38	<.001	.37	<.001	.38	<.001
Age	.03	<.01	.02	.13	.04	<.01
Race (White as ref.)
Asian	−.01	.87	−.08	.22	.06	.43
Black	−.23	<.001	−.27	<.001	−.19	<.001
Hispanic	−.03	.29	−.04	.36	−.03	.53
Native	.07	.48	.01	.97	.12	.38
Multiple	.01	.78	.01	.83	−.001	.99
Sleep duration
Female (male as ref.)	−.07	<.05
Screen time	−.06	<.001	−.09	<.001	−.04	<.01
Being bullied	−.25	<.001	−.29	<.001	−.29	<.001
Substance	−.34	<.001	−.33	<.001	−.35	<.001
Age	−.10	<.001	−.06	<.01	−.13	<.001
Race (White as ref.)
Asian	−.15	<.05	−.20	<.05	−.08	.38
Black	−.40	<.001	−.29	<.001	−.48	<.001
Hispanic	−.09	<.01	−.06	.24	−.11	<.05
Native	−.25	<.05	−.38	<.05	−.16	.32
Multiple	−.16	<.01	−.22	<.01	−.06	.50

Table 5.

Logistic Regression of Suicidality.

Suicidality	Total		Female		Male
Suicidality	Odds ratio	95% CIa	Odds ratio	95% CIa	Odds ratio	95% CIa
Female (male as ref.)	1.31	[1.17, 1.47]
Screentime	1.07	[1.03,1.11]	1.07	[1.02,1.13]	1.06	[1.01,1.23]
Sleep	.87	[.84,.91]	.89	[.85,.94]	.84	[.79, .90]
Being bullied	2.53	[2.24,2.86]	2.32	[1.98,2.72]	2.88	[2.37,3.50]
Substance	3.01	[2.67,3.59]	2.99	[2.56,3.49]	3.06	[2.54,3.69]
Age	.91	[.87,.95]	.85	[.80,.90]	.98	[.92,1.06]
Race (White as ref.)
Asian	1.25	[.98,1.61]	1.28	[.93,1.75]	1.19	[.80,1.78]
Black	1.27	[1.08,1.49]	1.25	[1.02,1.54]	1.27	[.98,1.64]
Hispanic	1.07	[.93,1.24]	1.06	[.88,1.27]	1.10	[.88,1.37]
Native	1.56	[.99,2.46]	1.18	[.63,2.22]	2.12	[1.11,4.10]
Multiple	1.19	[.95,1.49]	1.11	[.83,1.48]	1.32	[.92,1.88]

^a95% CI indicates 95% confidence interval.

Sleep duration is negatively associated with mental health problems. For every one-level increase in sleep duration, there is a 0.15 level decrease in the frequency of having mental health problems for total adolescents (β = −.15, p < .001), a 0.16 level decrease among females (β = −.16, p < .01), and a 0.14 level decrease in males (β = −.14, p < .001). Sleep duration was inversely associated with suicidality (Table 4). For every level increase in sleep duration, there is a 13% decrease in the likelihood of suicidality in adolescents (OR: .87, 95% CI: .84–.91), an 11% decrease among female adolescents (OR:.89, 95% CI:.85–.94), and a 16% decrease in male adolescents (OR: .84, 95% CI: .79–.90) (Table 5).

Furthermore, as shown in Table 4 and Table 5, compared to their male counterparts, on average, female adolescents have a higher frequency of mental health problems (β = .77, p < .001), a higher likelihood of suicidality (OR: 1.31, 95% CI: 1.17–1.47), and a shorter sleep duration (β = −.07, p < .05) (See supplemental document 2 for the path diagram with statistic indices).

Mediation effect and group comparison

The Monte Carlo results indicated that sleep duration has an 8% mediation effect on the total relationship between screen time and mental health problems among U.S. adolescents. The mediation effects vary between male and female adolescents. Among female adolescents, around 13% (p < .001) of the total effect of screen time on mental health is mediated by sleep duration, whereas for male adolescents, it is 4% (p < .001). Furthermore, the Likelihood Ratio Test, by comparing the unconstrained model with the mediation-constrained model, indicated that the mediation effects between female and male adolescents were significantly different (χ² (3) = 8.30, p < .05). The bootstrapping method revealed that sleep duration is also a significant mediator of the path between screen time and suicidality. The result of the Likelihood Ratio Test indicated that the mediation effects of sleep on suicidality vary between female and male adolescents (χ² (3) = 14.52, p < .01), where the mediation effect is bigger among female adolescents than male adolescents as shown in the bootstrapping method results (Table 6).

Table 6.

Mediation Effects of Sleep Duration and Group Comparison.

	Total		Female		Male
	RIT^a/RID^b	p value	RIT^a/RID^b	p value	RIT^a/RID^b	p value
Screentime -sleep-mental health	.08/.09	p < .001	.13/.14	p < .001	.04/.04	p < .001
			Likelihood ratio test: LR chi2 (3) = 8.3 p < .05

	Coefficient	95% CI (BC)^c	Coefficient	95% CI (BC)^c	Coefficient	95% CI (BC)^c
Screentime -sleep-suicidality	.012	[.007, .017]	.016	[.01, .025]	.009	[.002, .017]
			Likelihood ratio test: LR chi2 (3) = 14.52 p < .01

^aThe total effect of screentime on mental health is mediated by sleep duration.

^bThe direct effect of screentime on mental health is mediated by sleep duration.

^c95% CI (BC) indicates bias corrected 95% confidence interval.

Covariates

As shown in Table 4 and Table 5, both being bullied and substance use were positively associated with mental health problems (β = .51, p < .001; β = .38, p < .001, respectively) and the likelihood of suicidality (OR: 2.53, 95% CI: 2.24–2.86; OR: 3.01, 95% CI: 2.67–3.59, respectively), and negatively associated with sleep duration (β = −.25, p < .001; β = −.34, p < .001, respectively). The same patterns of associations were shown in both male and female adolescents (Figures 1 and 2).

Figure 1.

Path Diagram with Covariates.

Figure 2.

Path Diagram among Exogenous Variable (Screen Time), Endogenous Variables (Mental Health and Suicidality), and Mediator (Sleep) with Statistic Indices. Note. β refers to β coefficient. OR refers to Odds Ratio (Lower Bound of the 95% Confidence Interval-Upper Bound of the 95% Confidence Interval). *** refers to p < .01, ** refers to p < .01.

Moreover, compared to White adolescents, Black adolescents had a lower frequency of having mental health problems (β = −.23, p < .001). Female Black adolescents and Male Native American adolescents had a higher likelihood of suicidality (OR: 1.25, 95% CI: 1.02–1.54; OR: 2.12, 95% CI: 1.11–4.10). Among female adolescents, compared to their White counterparts, Asian, Black, Native American, and people with multiple races had shorter sleep durations. Among male adolescents, compared to their White counterparts, Black and Hispanic adolescents were associated with shorter sleep duration. Age in male adolescents was positively associated with mental health frequency (β = .04, p < .01); age in females was negatively associated with the likelihood of suicidality (OR: .85, 95% CI: .80–.90). Age is negatively associated with sleep duration among total adolescents ((β = −.10, p < .001).

Discussion

This study explores the direct effects of screen time and sleep on mental health problems, as well as the mediation effect of sleep duration among U.S. adolescents. Furthermore, female adolescents and male adolescents were tested and separated as subgroups. Overall, our results indicated that longer screen time and shorter sleep duration were associated with greater mental health problems and higher suicidality. The direct effects of screen time and sleep duration, as well as the indirect effect of sleep duration, were stronger among female adolescents compared to their male counterparts.

Consistent with previous findings, this study revealed that, among U.S. adolescents, screen time is positively associated with mental health problems (Ashton & Beattie, 2019; Babic et al., 2017; Roberts & Duong, 2017; Stiglic & Viner, 2019) and suicidality (Twenge et al., 2019; Twenge et al., 2017); and negatively related to sleep duration (Cain & Gradisar, 2010; Dube et al., 2017; Parent et al., 2016). This study further revealed the differences in the effects of screen time on sleep duration, mental health, and suicidality among female and male adolescents. Consistent with previous studies, female adolescents had higher likelihoods of having longer screen time (Jago et al., 2014), shorter sleep duration (Baker et al., 2020; Galland et al., 2017; Hysing et al., 2013), and having mental health problems and suicidality (Miranda-Mendizabal et al., 2019; Van Droogenbroeck et al., 2018). However, unlike the previous UK study (Twenge & Farley, 2020), this study revealed that U.S. male adolescents had stronger associations between screen time and mental health and suicidality compared to their female counterparts. A possible explanation for the discrepancy could be sleep duration, which is included as a factor in the analysis and has been proven to be affected more by screen time among female adolescents compared to male adolescents.

Like previous studies, this study observed that sleep duration was positively associated with mental health problems (Colrain & Baker, 2011; Zelinski et al., 2014; Simola et al., 2012) and suicidality (Chiu et al., 2018; Kearns et al., 2020); as a mediator, sleep duration significantly mediated the paths between screen time and mental health and suicidality (Nuutinen et al., 2014; Tao et al., 2017). This study also revealed that the negative associations of sleep duration on mental health (Agathão et al., 2020) and suicidality were higher among female adolescents than male counterparts.

To add to the literature, this study also revealed that the mediation effects of sleep duration on the paths between screen time and mental health and suicidality were also bigger among female adolescents than their male counterparts. Males and females have different circadian rhythms (Kervezee et al., 2018; Meers et al., 2019; Mong & Cusmano, 2016), and females tend to have earlier bedtimes and wake-up times than males (Fischer et al., 2017). Screen time, especially at night, disturbs early bedtimes (Cain & Gradisar, 2010; Dube et al., 2017; Parent et al., 2016). Compared to males, females with late bedtimes were more likely to have poorer sleep quality, including shorter sleep duration, waking up at night, and difficulty returning to sleep (Duffy et al., 2011; Kervezee et al., 2018). In turn, compared to males, poorer sleep quality among females caused by screen time could further lead to more severe mental health problems and suicidality.

There are several limitations in the study. The YRBS 2021 data relies on self-report instruments, which are subject to bias. The data only applies to adolescents who attend school on the day when the survey was being distributed; therefore, it could not represent all persons. The data is collected by cross-sectional surveys; therefore, analyses based on the data can only provide an indication of association instead of causation. The definitions of mental health, suicide ideation, suicide plan, and suicide attempt are not clearly stated in the survey; therefore, participants might have different perceptions on the matter and engender bias. More general limitations for the use of the YRBS data are available in the overview report (Underwood et al., 2020; Mpofu et al., 2023). In addition, this study did not separate suicide ideation, suicide plans, and suicide behaviors; instead, it combined variables. There might be differences in patterns among ideation, planning, and attempts.

Conclusion

This study found that among U.S. adolescents, the effects of screen time on mental health and suicidality are greater among male adolescents compared to their female counterparts. The effect of screen time on sleep duration was greater among female adolescents. The direct effects of sleep duration on mental health and suicidality, as well as the mediation effects of sleep duration on the paths between screen time and mental health and suicidality, were greater among female adolescents compared to their male counterparts. Furthermore, female adolescents have higher prevalences of excessive screen time, shorter sleep duration, mental health problems, and suicidality.

Supplemental Material

Supplemental Material - Sex Differences in the Associations of mental Health, Suicidality, Screentime, and Sleep: A Mediation Effect Analysis of Sleep Using Youth Risk Behavioral Surveillance Survey 2021

Supplemental Material for Sex Differences in the Associations of mental Health, Suicidality, Screentime, and Sleep: A Mediation Effect Analysis of Sleep Using Youth Risk Behavioral Surveillance Survey 2021 by Shuo Feng, Renming Liu, Aditi Tomar, and Ping Ma in Psychological Reports.

Supplemental Material

Supplemental Material - Sex Differences in the Associations of mental Health, Suicidality, Screentime, and Sleep: A Mediation Effect Analysis of Sleep Using Youth Risk Behavioral Surveillance Survey 2021

Footnotes

Author Contributions

Shuo Feng: Conceptualization, Methodology, Formal analysis, Writing – original draft, Writing – review & editing. Renming Liu: Conceptualization, Writing – review & editing. Aditi Tomar: Methodology, Writing – review & editing. Ping Ma: Conceptualization, Supervision

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Shuo Feng

Data Availability Statement

The YRBS 2021 data is publicly available at:

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Shuo Feng is a doctoral student in the Department of Health Behavior at Texas A&M University. His research interests include sleep health and digital communication technologies and their association with mental health.

Renming Liu is a doctoral student in the Department of Educational Psychology at Baylor University. Her research interests include behavioral analysis and intervention for children with special needs and mental health.

Aditi Tomar is a postdoctoral research associate at the Gillings School of Global Public Health, University of North Carolina Chapel Hill. Her research focuses on examing the effects of factors such as communication on health.

Ping Ma is an associated professor in the Department of Health Behavior at Texas A&M University. Her research interests include children's health, health promotion for children and adolescents, and social determinants of health.

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