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. Author manuscript; available in PMC: 2022 Aug 1.
Published in final edited form as: J Sleep Res. 2020 Nov 30;30(4):e13243. doi: 10.1111/jsr.13243

Gender Moderates the Relationship Between Media Use and Sleep Quality

Benjamin McManus 1, Andrea Underhill 1, Sylvie Mrug 1, Thomas Anthony 1, Despina Stavrinos 1
PMCID: PMC8164643  NIHMSID: NIHMS1646544  PMID: 33258217

Abstract

With high screen time and poor sleep commonly reported in adolescents, it is important to more fully understand how screen time impacts sleep. Despite similar overall screen times, male and female media preferences and usages differ, making it critical to determine if different domains of screen time differentially affect sleep quality. This study examined whether differing amounts and domains of screen-based media vary in impact on sleep quality of 16-year-old male and female adolescents over a 3-month period. Ninety eight adolescents (Mage = 16.27 years, SD = 0.29; 51% female) completed 2 online surveys spaced 3 months apart and comprised of well-validated self-reported measures of sleep quality, media usage, and depressive symptoms. The various domains of media were categorized into screen-based media with little-to-no peer-to-peer interaction involved (video-only) and screen-based media with interaction a predominant component to the usage (peer-to-peer interaction-involved). Self-reported sleep quality decreased across the 3 month study period. Gender moderated the effect of interactive screen time on sleep quality 3 months later, with interactive screen time associated with better sleep quality in males, but remaining poorer in females. Screen time competes with sleep time and may do so differentially depending on the media domain. Compared to females, interactive components of screen time may lessen worsening sleep quality over time in males. Understanding the relationships among screen time, its content, age, and gender may inform guidelines for educators, parents, and adolescents to help improve sleep quality of adolescents.

Keywords: Screen time, adolescents, sleep quality, electronic use

Introduction

Screen time, which includes the use of electronic media such as smartphones, tablets, gaming devices, desktop computers, or televisions, is recognized as a principal cause of sleep disturbance, especially when engagement occurs near bedtime (Perrault et al., 2019). Over 90% of teens report going online daily (Lenhart, 2015), over 70% of teens reporting insufficient sleep (Wheaton, E., Cooper, & Croft, 2018), and slower accumulation of sleep pressure may increase opportunities for screen time exposure during the evening and bedtime (Crowley, Wolfson, Tarokh, & Carskadon, 2018; Skeldon, Derks, & Dijk, 2016). With evidence that bedtime screen time doubles the odds of inadequate sleep (Carter, Rees, Hale, Bhattacharjee, & Paradkar, 2016) and reducing pre-bedtime screen time by as little as an hour increases sleep quantity and improves daytime performance (Perrault et al., 2019), it is important to more fully understand how screen time impacts sleep. For example, increasing emotional arousal is one mechanism that is more likely to occur when adolescents engage in communication based screen time (Cain & Gradisar, 2010). Although male and female adolescents have similar overall screen time usage, their media preferences differ (Rideout & Robb, 2019), making it critical to determine if the various domains of media impact sleep quality differently. Although little is known about gender differences in the impact of screen time on sleep, research has shown that females are more likely to experience insufficient sleep compared to their male counterparts even when meeting recommended screen time requirements (≤ 2 hours per day) (Xu, Adams, Cohen, Earp, & Greaney, 2019).

The natural changes to the body’s sleep processes transform during adolescence; these changes also vary by gender, but research results are contradictory. Studies have found that adolescent females have poorer sleep quality, more difficulty falling asleep, and shorter sleep duration than their male counterparts (Galland et al., 2017; Marczyk Organek et al., 2015; Maslowsky & Ozer, 2014; Ming, Radhakrishnan, Kang, & Pecor, 2016; Ohida et al., 2004; Wang, Raffeld, Slopen, Hale, & Dunn, 2016). Other research has revealed that older adolescent males, but not their female counterparts, experience more disrupted sleep and more wakefulness after initially falling asleep than younger males (Baker et al., 2016). Still other research has found no differences by gender in adolescent sleep patterns (Williams, Zimmerman, & Bell, 2013). Although the pubertal onset and associated circadian phase delay begins earlier in females (Roenneberg et al., 2004), peak delay occurs between ages 15 to 21 years for both males and females (Hagenauer, Perryman, Lee, & Carskadon, 2009). These issues require further delineation and definition to parse out gender differences in sleep during mid-adolescence. In addition to age and gender, depression also is associated with sleep-related problems (American Psychiatric Association [APA], 2014). Research shows that girls are more likely to experience depressive symptomatology earlier in adolescence than their male counterparts (Bluth, Campo, Futch, & Gaylord, 2017; Lu, 2019; Salk, Petersen, Abramson, & Hyde, 2016). Numerous studies suggest an association between increased screen time and depressive symptoms in adolescents (Carskadon, 2011; Gunnell et al., 2016; Houghton et al., 2018; Hrafnkelsdottir et al., 2018; Li et al., 2018; Maras et al., 2015). Given the gender differences in adolescent sleep processes, depression, and media preferences and patterns, the paucity of literature specifically examining how gender intermingles with screen time’s relationship with adolescent sleep problems is concerning. Gender differences in sleep and depressive symptomatology could make one group more susceptible to the negative impact of screen time on sleep.

The ceaseless use of digital media by adolescents is becoming ubiquitous. In 2018, 45% of teens (ages 14–17) reported being online “almost constantly,” 44% were online “several times a day,” and only 11% indicated going online “less often” (Anderson & Jiang, 2018). These numbers indicate a marked difference from 2014–15 when 24% of teens were online “almost constantly,” 56% “several times a day,” and 20% “less often” (Anderson & Jiang, 2018). Although adolescents are spending increasing amounts of time with digital media, online activities differ by gender. Females are more likely to use their smartphones (Twenge & Martin, 2020), use social media (Rideout & Robb, 2019; Twenge & Martin, 2020; Twenge, Martin, & Spitzberg, 2019; Van Den Eijnden, Koning, Doornwaard, Van Gurp, & Ter Bogt, 2018), and text (Twenge & Martin, 2020). Males, however, spend the vast majority of their digital media time gaming (Anderson & Jiang, 2018; Rideout & Robb, 2019; Twenge & Martin, 2020; Twenge et al., 2019; Van Den Eijnden et al., 2018). These striking differences in how females and males use their online time necessitate research examining the effects of how digital media differentially impact males and females given that how adolescents use media may differentially impact their well-being. For instance, passive usage of social media (viewing news feeds and social media posts) as opposed to active usage (posting updates, commenting on others’ posts), is associated with lower well-being (Krasnova, Wenninger, Widjaja, & Buxmann, 2013) due to enhancing envy (Verduyn et al., 2015), and this effect is stronger in females (Ding, Zhang, Wei, Huang, & Zhou, 2017).

The current study examined how the amount and type of screen time relate to subsequent sleep quality in middle adolescence, and whether these relationships vary by gender. Guided by two of Cain and Gradisar’s proposed mechanisms by which screen time impacts sleep (screen time competes with sleep time; screen time increases emotional arousal delaying sleep onset) (Cain & Gradisar, 2010), we examined two types of screen-based media use: 1) “Video” which included screen-based media with little/no communication (e.g., TV, computer-based video, and web browsing) and 2) “Interactive” which included screen-based media with an interactive or communication component (e.g., video games, IM, emailing, and text messaging). We hypothesized that youth with greater use of screen-based media (especially in the Interactive domain) would display poorer sleep quality after 3 months. Greater use of screen-based media addresses Cain and Gradisar’s mechanism of screen time competing with sleep time, while greater use of interactive screen media addresses their mechanism of emotional arousal delaying sleep onset. Additionally, interactive media has been associated with lower levels of well-being in adolescents depending on usage, such as a passive vs. active usage of social media (Krasnova et al., 2013; Verduyn et al., 2015). Highly interactive media, particularly video games, and can impact sleep quality (Ivarsson, Anderson, Åkerstedt, & Lindblad, 2013; Weaver, Gradisar, Dohnt, Lovato, & Douglas, 2010). Further, given gender differences in screen-time use, we hypothesized that gender would moderate this relationship between screen-based media use and sleep quality. Because females have shown a stronger negative effect of passive social media usage on subjective well-being (Ding et al., 2017), we hypothesized that compared to males, females would display poorer sleep quality with greater Interactive screen time.

Method

Participants

Participants were actively enrolled, licensed adolescent drivers in “REACT,” an 18 month longitudinal study of adolescent driving attention. Drivers were required to be age 16 or 18 at the time of REACT enrollment, to have been issued a driver’s license within two weeks of enrollment, and to be fluent in written and spoken English. Participants were recruited from the following sources via fliers and letters describing the study: 1) local high schools; 2) community outreach events; and 3) targeted advertisements via media such as Facebook and radio. Interested participants were consented and screened for eligibility by a member of the study team.

For the current investigation, one hundred twelve 16-year olds (Mage = 16.27 years, SD = 0.29; 52% female; 65% Black/African American, 31% White/Caucasian, and 4% more than one race or other) participated. Participating adolescents completed REACT’s baseline online survey and a second online survey 3 months later which contained the same measures as the baseline survey. Participants were enrolled between June 1, 2018 and August 19, 2019. Of the 112 who completed the baseline survey, 102 (91%) completed the survey 3-months post baseline. To allow for a clear interpretation of the effects of race, those identifying as more than one race (n = 3) and other (n = 1) were not included in analyses, resulting in a final sample size of n = 98. The 14 excluded participants (n = 10 who did not complete the second survey; n = 4 identifying as more than one race or other) did not significantly differ from the final sample on baseline age, gender, race, depressive symptomology scores, self-reported sleep quality, and screen time. The study protocol was reviewed and approved by a university’s Institutional Review Board for Human Use.

Measures and Procedures

The online surveys comprised a battery of measures that included well-validated self-reported measures of sleep quality, media usage, and depressive symptoms.

Sleep Quality

The Pittsburgh Sleep Quality Index (PSQI) (Buysse, Reynolds, Monk, Berman, & Kupfer, 1989) provided a measure of subjective sleep quality at baseline and at 3 month follow up. The 19 items of the PSQI were used to calculate seven scales: 1) subjective sleep quality; 2) sleep onset; 3) number of hours of actual sleep; 4) sleep efficiency; 5) sleep disturbances; 6) use of medication as sleep aids; and 7) daytime functioning difficulties. The seven scales ranged from 0 to 3 and were summed to produce a global score with a range of 0–21. A global score greater than 5 indicated clinical levels of poor sleep quality (Buysse et al., 1989). The single-factor global score has been validated in adolescent samples (Raniti, Waloszek, Schwartz, Allen, & Trinder, 2018). The PSQI has been shown to have good internal consistency with an overall Cronbach’s α = 0.83, test-retest reliability (Pearson’s r = 0.85), and validity as indicated with a sensitivity of 89.6% and specificity of 86.5% (Buysse et al., 1989). The Cronbach’s α for the seven PSQI components summed to the global score for baseline was 0.73 and 0.74 at the 3-month follow-up.

Media Usage

At baseline participants completed an adapted version of the Media Multitasking Index (MMI) (Ophir, Nass, & Wagner, 2009). The MMI measured hours spent per week for each of a variety of media including print media, television (TV), computer-based video, music, non-music audio, video/computer games, voice calls, instant messaging (IM), emailing, web surfing, and other computer-based applications. An item on text messaging was added for this study. Shortened versions of the modified MMI indicate high internal consistency in adolescent samples (α = 0.93) and high correlation with the original form (r = 0.84) (Baumgartner, Lemmens, Weeda, & Huizinga, 2017; Baumgartner, Weeda, van der Heijden, & Huizinga, 2014). The Cronbach’s α for the 12 media domains for the current study was 0.77.

Given the focus of this study on different types of electronic screen-based media use, two variables were created to represent two domains of using screen-based media: 1) “Video” – TV, computer-based video, and web-browsing were summed to represent screen-based media with little-to-no communication involved; and 2) “Interactive” – Video games, IM, emailing, and text messaging were summed to represent screen-based media where there is an active usage or communication component to the usage. The inter-item correlations for the media subscales were 0.22 and 0.45 for Video and Interactive, respectively.

Depressive Symptomatology

At baseline, participants completed the 10 item Center for Epidemiological Studies Depression Scale (CES-D-10) (Bjorgvinsson, Kertz, Bigda-Peyton, McCoy, & Aderka, 2013), a short form of the original 20 item CES-D (Radloff, 1977) measuring depressive symptoms. The 10-item scale has good internal consistency (α = 0.86) (Bjorgvinsson et al., 2013). Sum scores ranged from 0 to 30 where higher scores indicated increased depressive symptomatology. Cronbach’s α for the CES-D-10 in this sample was 0.71.

Data Analysis

Preliminary Analyses

Means and frequencies were obtained for continuous and categorical variables, respectively. Race was grouped into two categories: 1) White/Caucasian; and 2) Black/African American. Chi-square (χ2) analyses examined associations between race and gender and independent samples t-tests examined differences between males and females on PSQI global scores at 3 month follow up and baseline, baseline CES-D total scores, total screen time, Video screen time, and Interactive screen time. Independent samples t-tests also examined differences between the two category race variable on PSQI global scores. A paired samples t-test examined differences between baseline and 3-month follow up PSQI global scores. Bivariate correlations examined associations among PSQI global scores at baseline and 3-month follow up, CES-D total score, Video screen time, race, and gender. All statistical analyses were conducted with SAS 9.4 (SAS Institute Inc., 2013) with p < .05 indicating statistical significance.

Main Analyses

A cross-sectional hierarchical multiple linear regression model examined the associations of the two screen-based media dimensions with baseline sleep quality. In Step 1, the baseline PSQI global score was on the two screen-based media scales (Video and Interactive), gender (female, male), race (White/Caucasian, Black/African American), daylight saving time (not during, during), and academic school year (not during, during). Depressive symptomatology as measured by the CES-D-10 was also included as a covariate, because of depression’s relationship with both gender (Bluth et al., 2017; Lu, 2019; Salk et al., 2016) and media use (Carskadon, 2011; Gunnell et al., 2016; Houghton et al., 2018; Hrafnkelsdottir et al., 2018; Li et al., 2018; Maras et al., 2015). At Step 2, interactions of gender with each of the two media variables were entered. Predictors were centered prior to computing the interactions terms. Interactions of race with each of the two media variables were also examined.

A hierarchical multiple linear regression examined the association of the two screen-based media dimensions at baseline with subsequent sleep quality at 3 month follow up, as well as gender differences in these relationships. In Step 1, the PSQI global score at 3 month follow up was regressed on the two screen-based media scales at baseline, depressive symptomatology, gender, race, and PSQI global score at baseline. To account for changes in daylight savings time over the 3 months, a 3-level variable was included (no change, less light [end of Daylight Saving Time], more light [beginning of Daylight Saving Time]) with “no change” as the referent. Similarly, a 3-level variable was included to account for changes in the academic year over the 3 months (school begins, school ends, no change). No baseline and 3 month follow up occurred both during summer vacation, so “no change” represents both time periods occurring during the school year and was used as the referent. At Step 2, interactions of gender with each of the two media variables were entered. Significant interactions were followed up with tests of simple slopes for the media variable in each gender group. Exploratory analyses were also conducted using the same models described above with each of the PSQI subscales. A sensitivity analysis of the main analysis examining gender moderation was conducted. To better elucidate the potential role of active communication, video game screen time was separated from the other Interactive items such that the main analyses models described above were conducted with communication-based media (IM, emailing, text messaging), Video media (TV, computer-based video, web surfing), and video games.

Results

At baseline, adolescents reported a median of 33.50 total weekly hours of screen time (4.79 hours of total daily), with a median 13.0 hours of Video screen time and 16.0 hours of Interactive screen time. Average CESD-10 score at baseline was 10.72 (SD = 5.43; range = 2.00 – 29.00) and average baseline PSQI global score was 5.14 (SD = 3.26; range = 1.00 – 18.00). A paired samples t-test indicated a statistically significant difference in sleep quality as measured by PSQI global score between time points, with poorer sleep quality at 3 month follow up compared to baseline (t(97) = 2.28, p = .03). Average self-reported sleep quantity at baseline was 7.15 hours (SD = 1.40; range = 3.00 – 11.00). Baseline variables were reported during the academic school year for the majority of participants (n = 78; 80%) and during Daylight Saving Time (n = 65; 66%). Baseline PSQI global scores were significantly lower in those identifying as Black/African American (M = 4.55, SD = 2.47) compared to those identifying as White/Caucasian (M = 6.38, SD = 4.27; t(96) = 2.68, p = .01), but there was no difference in 3 month follow up PSQI global scores. There were no gender differences in 3 month sleep quality or baseline sleep quality, screen time, depressive symptomatology, being in Daylight Saving Time, or school being in session. See Table 1 for descriptive statistics of variables used in analyses and tests of gender differences.

Table 1.

Descriptive statistics and Gender Differences (n = 98)

Female
 Male
Variable Mean (SD) or n (%) Range Mean (SD) or n (%) Range t or χ2 p
Age 16.28 (0.30) 16.01 – 16.99 16.25 (0.28) 15.81 – 16.99 0.47 0.64
Gender 50 (51%) 48 (49%)
Race 1.33 0.25
Black/African American 31 (62%) 35 (73%)
White/Caucasian 19 (38%) 13 (27%)
Baseline Daylight Conditions 0.13 0.72
Daylight savings time 34 (68%) 31 (65%)
Standard time 16 (32%) 17 (35%)
Baseline School Session 1.97 0.16
In session 37 (74%) 41 (85%)
Out of session 13 (26%) 7 (15%)
Baseline CES-D-10 11.48 (6.14) 2.00 – 27.00 9.94 (4.51) 3.00 – 29.00 1.42 0.16
Baseline PSQI Global 5.54 (3.76) 1.00 – 18.00 4.73 (2.62) 1.00 – 14.00 1.24 0.22
3-Month PSQI Global 6.52 (4.23) 0.00 – 19.00 5.35 (3.17) 1.00 – 15.00 1.55 0.12
Weekly Combined ST (hours) 62.38 (71.65) 5.00 – 334.00 58.29 (93.37) 4.00 – 537.0 0.24 0.81
Weekly Video ST (hours) 23.92 (25.55) 2.00 – 117.00 20.91 (31.42) 0.00 – 175.00 0.52 0.60
Television (hours) 8.10 (7.44) 0.00 – 40.00 7.30 (13.14) 0.00 – 86.00 0.37 0.71
Computer-based Video (hours) 5.14 (7.57) 0.00 – 42.00 7.36 (14.36) 0.00 – 100.00 0.95 0.34
Web Surfing (hours) 10.68 (19.77) 0.00 – 100.00 6.24 (16.06) 0.00 – 86.00 1.22 0.23
Weekly Interactive ST (hours) 38.46 (53.87) 2.00 – 278.00 37.39 (77.16) 2.00 – 514.00 0.08 0.94
Video Games (hours) 5.50 (20.18) 0.00 – 130.00 10.88 (24.26) 0.00 – 168.00 0.97 0.33
Instant Messaging (hours) 11.20 (17.69) 0.00 – 100.00 9.21 (27.45) 0.00 – 168.00 0.40 0.69
Email (hours) 4.82 (13.61) 0.00 – 90.00 1.45 (2.15) 0.00 – 10.00 1.73 0.10
Text Messaging (hours) 15.94 (21.19) 0.00 – 100.00 15.75 (29.22) 1.00 – 168.00 0.04 0.97
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Note: SD = Standard deviation, AA = African American, CES-D-10 = Centers for Epidemiological Studies Depression Scale, PSQI = Pittsburgh Sleep Quality Index, ST = Screen time, hours = hours per week.

Bivariate correlations indicated a moderate correlation between poorer baseline sleep quality and poorer 3 month follow up sleep quality (r = .51, p < .01) as well as greater baseline depressive symptomatology and poorer 3 month follow up sleep quality (r = .41, p < .01). Poorer baseline depressive symptomatology was moderately correlated with both poorer baseline sleep quality (r = .66, p < .01) and greater Interactive screen time (r = .40, p < .01). See Table 2 for correlations among all variables used in analyses.

Table 2.

Correlations among Variables Included in Regression Models

Variable 1 2 3 4 5 6
1.3 Month PSQI -
2.Baseline PSQI .51** -
3.Video ST .06 .12 -
4.Interactive ST .01 .25* .44** -
5.Baseline CES-D-10 .41** .66** .16 .40** -
6.Male Gender −.13 −.08 −.09 .02 −.10 -
7.Black/African American −.08 −.19 −.01 .02 −.05 .12
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Note:

*

p < .05

**

p < .01

PSQI = Pittsburgh Sleep Quality Index, ST = Screen time, CES-D-10 = Centers for Epidemiology Studies Depression Scale, Spearman’s Rho presented for gender and race.

Primary Analyses

Step 1 of the cross-sectional baseline regression model estimating self-reported baseline sleep quality was significantly predicted by the variables included in the model (F = 12.94, p < .01, R2 = .50). Being in Daylight Savings Time was uniquely associated with poorer baseline sleep quality (β = .43, p = .02). Race was associated with baseline sleep quality, such that being Black/African American was associated with better baseline sleep quality (β = −.42, p = .01). Higher baseline depressive symptomatology measured by CES-D-10 scores was also significantly associated with poorer self-reported baseline sleep quality (β = .63, p < .01). Inclusion of the interactions of gender with the two media scales in Step 2 did not significantly improve the model (ΔR2 = .02, F = 1.27, p = .29). The interaction of race with the two media scales was also examined but neither interaction term was significant.

Step 1 of the hierarchical linear regression estimating self-reported sleep quality at 3 months post-baseline was significant (F = 4.87, p < .01, R2 = .36). Self-reported sleep quality at baseline was uniquely associated with sleep quality 3 months later (β = .43, p < .01). School beginning session during the 3 month period was associated with lower sleep quality at 3 month follow up (β = .64, p = .02), and there was marginal evidence suggesting an association of Interactive screen time with lower sleep quality at 3 month follow up (β = −.25, p = .06). Step 2 of the regression was statistically significant and accounted for additional 7% of variance in sleep quality 3 months post-baseline (ΔR2 = .07, F = 4.93, p < .01). The gender by Interactive interaction was significant, indicating the effect of Communication screen time on sleep quality 3 months post-baseline differed between males and females. See Table 3 for full results for sleep quality regression analyses. Simple slope analyses revealed that greater Interactive screen time was associated with better sleep quality 3 months later for males (β = −.48, p < .01), but not females (β = .24, p = .27) (Figure 1). Similar to the cross-sectional baseline model, the interaction of race with the two media scales was also examined but neither interaction term was significant. The PSQI subscales were also examined with similar hierarchical models. Interactive screen time was uniquely associated with only daytime functioning difficulties, such that greater Interactive screen time was associated with lower daytime functioning difficulties 3 months post-baseline (β = −.32, p = .02). Gender did not moderate the effect of either screen time scale on any of the PSQI subscales. Analyzing video games separately from the communication-based Interactive screen time (IM, text messaging, and emailing) and Video screen time indicated only marginal evidence that gender moderated the effect of communication-based screen time on 3-month sleep quality (β = −.63, p = .09) and no moderation of the effect of video games (β = −.11, p = .78) or Video screen time (β = .05, p = .83) on 3-month sleep quality.

Table 3.

Standardized Coefficients for Predictors in Regression Models

Predictor Variable Baseline PSQI 3 mo PSQI
Step 1
 Baseline PSQI - .43**
 Daylight Savings Time .43* .51
 Standard Time Occurs during 3 mo. - .33
 School In Session .03 .64*
 School Out of Session during 3 mo. - .12
 Black/African American −.42* .21
 Male −.00 −.10
 CES-D-10 .63** .22
 Video Screen time .06 .06
 Interactive Screen time −.04 −.24
 R2 .50** .36**
Step 2
 Male x Video Screen time −.19 .05
 Male x Interactive Screen time .36 −.71**
 R2 Change .02 .07**
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Note:

*

p < .05

**

p < .01

PSQI = Pittsburgh Sleep Quality Index, CES-D-10 = Centers for Epidemiological Studies Depression Scale

Figure 1.

Figure 1.

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Simple slopes of interactive screen-based media usage on PSQI score by gender.

Discussion

The current study examined whether different domains of screen-based media vary in their impact on sleep quality in middle adolescents. Sleep is critical for proper brain functioning, particularly for the developing brains of adolescents. With poor sleep quality, detriments could be seen in the performance of daily activities, in the ability to learn and apply new information (e.g., in school, sports, driving, or work).

Adolescents in this study reported a median screen-time of 4.79 hour per day, which is over twice the recommended limit for this age group by the American Academy of Pediatrics (< 2 hours/day), but is similar to the screen-time characteristics of a national sample of adolescents (Rideout & Robb, 2019). Average sleep quality reported by the youth was above the PSQI global score of 5 indicating clinical levels of poor sleep quality and became worse over the 3-month follow up period regardless of gender. The majority of adolescents in this study reported baseline sleep quality during Daylight Saving Time when the evening daylight may contribute to less sleep (Figueiro & Rea, 2010). Although the 3-month period occurred during the school year for the majority of the sample (62%), many of these adolescents had school begin or end during this period (38%) or saw a change in time/daylight (51%) during the 3-month period. These light and schedule changes may have contributed to greater variability in time in bed, sleep onset latency and subsequently more negative mood (Bei, Manber, Allen, Trinder, & Wiley, 2016). Similarly, the average levels of depressive symptomatology indicated the presence of some clinical symptoms of depression. These descriptive results are consistent with prior research demonstrating that both poor sleep quality and symptoms of depression are common among adolescents (Carskadon, 2011; Crowley et al., 2018; Lu, 2019). Past research has demonstrated racial differences in sleep quality with African American reporting shorter sleep durations throughout adolescence (Marczyk Organek et al., 2015; Maslowsky & Ozer, 2014). In contrast, our findings indicated compared to adolescents identifying as White/Caucasian race, adolescents identifying as Black/African American had significantly better sleep quality at baseline as indicated by both bivariate models and models with covariates. Additional factors affecting sleep that were not measured in this sample may explain this unexpected finding, such as parental attachment (Tu, Marks, & El-Sheikh, 2017) and family and household behaviors (Spilsbury, Patel, Morris, Ehayaei, & Intille, 2017).

Our first hypothesis that those with greater use of screen-based media, particularly with Interactive screen time, would display poorer sleep quality at 3 months post-baseline was not supported by the findings. Only self-reported sleep quality at baseline was uniquely associated with sleep quality at 3-month follow up. However, the correlation between Interactive screen time and baseline PSQI suggests screen time may be associated with poor sleep quality concurrently but not over time when controlling for the stability of sleep quality. However, this study investigated a 3 month period and shorter intervals of measurement may be needed to identify the effects of greater use of screen-based media on subsequent sleep quality. Similarly, concurrent measurements of screen-based media use over time to may also demonstrate these effects since media use is not stable over time. Additionally, because the Interactive screen time variable did not explicitly measure social media or differentiate between active vs. passive usage of the media, it may not have been sensitive towards the differential effects of usage on related outcomes that have been demonstrated in passive usage of social media in adolescents (Krasnova et al., 2013; Verduyn et al., 2015). Similarly, depressive symptomatology was predictive of concurrent sleep quality but not over 3 months. Past work requiring adolescents to stop engaging in screen time at specific times during school nights, showed that screen-based media alone was associated with lower sleep quality over the course of a single month (Perrault et al., 2019). The same direct effect of screen time on sleep quality was not replicated here. However, there were differences between the studies. First, sleep quality was measured over a longer time frame in this sample with no restrictions on screen time. Second, pre-bedtime screen time was not measured or controlled (experimentally or statistically). These differences may provide a more naturalistic measurement of total screen time and its effect on sleep quality over 3 months. There are other factors known to be associated with decreased sleep quality in adolescents (Baker et al., 2016; Bruce, Lunt, & McDonagh, 2017; Carskadon, 2011; Crowley et al., 2018; Fischer, Lombardi, Marucci-Wellman, & Roenneberg, 2017; Skeldon et al., 2016), and these results may not have shown screen-time as a predictor of poorer sleep quality due to a stronger influence of unmeasured factors among this sample of adolescents. Rather than a longer circadian period or increased sensitivity to evening light in older adolescents, a slower accumulation of sleep pressure as adolescence progresses may additionally increase opportunities for light exposure during the evening (Crowley et al., 2018; Skeldon et al., 2016). If adolescents have a pre-determined time and purpose for waking (such as going to school), it is reasonable to posit that their sleep quality would be decreased due to the slower homeostatic build-up (Bruce et al., 2017; Skeldon et al., 2016). Another consideration is that the activities comprising the communication-based media may require the user to physically be closer to the medium, especially with regards to portable devices like cell phones. As such, the proximity of the electronic devices to the face is more likely to disrupt sleep, especially if used at night and in bed. Although the physical logistics of using these media were not measured, they may contribute to the effects shown in this study. Additionally, adolescent sleep quality could be impacted by external pressures from school or sports as well as by not having set bedtime rules (Crowley et al., 2018). These factors were not measured in the current study and may be partly responsible for the lack of relationships between screen time and subsequent poor sleep quality, but it may also be explained by the stability of poor sleep quality over time.

We also hypothesized that gender moderated the relationship between the two domains of screen-based media use and sleep quality. This hypothesis was partially supported. Increased reported screen-based media usage involving interaction or communication was associated with lower PSQI global scores at the 3-month follow-up for males, but not for females. Although the assessment of Interactive screen-based media did not distinguish between active and passive usage of the media similarly to literature on social media usage (Verduyn et al., 2015), this finding may be due to the gender differences in the form of interaction in these screen-based media. Males are more likely to be playing video games and females are more likely to be on social media (Rideout & Robb, 2019; Twenge & Martin, 2020; Twenge et al., 2019; Van Den Eijnden et al., 2018). Although this study did not distinguish between video games involving communications compared to no communication, the males may have been engaged in more active communication, particularly if video games were multiplayer games involving direct exchanges. Conversely, the females may have been engaged in more passive interactions similar to the passive usage of social media shown to be associated with poorer well-being (Krasnova et al., 2013; Verduyn et al., 2015). However, there was no significant interaction of gender with screen-based media usage not involving interaction or potential communication. It is important to note that these results do not indicate screen time involving interaction improves sleep quality for males. Regardless of gender, sleep quality was significantly poorer at 3 month follow up and there were no gender differences in sleep quality scores at baseline or 3 month follow up. Rather, these findings suggest the degree to which sleep quality worsens over time as a function of interactive screen time is experienced differentially between males and females such that the effect is lessened in males.

This relationship between Interactive screen time and subsequent sleep quality differed by gender despite no gender differences between the two media-use domains (with and without an interactive component). The improvement in sleep quality over the 3 months with greater Interactive screen time shown only in males may indicate that males may benefit from active interaction similarly to findings shown in social media usage research. In examination of passive vs. active usage of social media, active usage has been associated with increased perceptions of social support and well-being (Verduyn, Ybarra, Résibois, Jonides, & Kross, 2017). Although the content and more nuanced detail regarding the communication components of Interactive screen time were not assessed (e.g., multiplayer vs. single player video games, browsing social media feeds vs. direct messaging or posting), these findings suggest the males’ engagement with media included in the Interactive screen time scale (Video games, IM, emailing, and text messaging) may have involved communication and active usage. However, the analysis of video games separately indicated the gender did not moderate the effect of either communication-only items or video games alone on 3 month sleep quality. Although playing video games presleep have been shown to be associated with delayed sleep onset (Weaver et al., 2010) and the content (e.g., violent vs. nonviolent) can impact sleep quality (Ivarsson et al., 2013), the amount and type of communication within video games and subsequent effects on sleep is largely unknown. Greater detail on the types of video games played and the amount of potential communication involved (e.g., multiplayer games vs. single player games) is needed to understand the effect that communication involved screen time may have on sleep quality. Similarly, since social media usage was not directly measured, additional considerations of the content and exchanges of the conversations within IM, emailing, and text messaging may provide additional explanation for these findings. Examination of the individual PSQI subscales indicated Interactive screen time was associated with better daytime functioning specifically, suggesting better daytime functioning may drive improvements in sleep quality over 3 months.

Research has shown that although males and females talk with their friends for a comparable amount of time, males spend less of that time discussing problems than do females (Rose, Smith, Glick, & Schwartz-Mette, 2016). Males are less likely than females to disclose problems to their friends and to partake in co-rumination (Glick & Rose, 2011; Jose, Wilkins, & Spendelow, 2012; Rose & Asher, 2004; Rose & Rudolph, 2006; Rose et al., 2016; Smith & Rose, 2011). Additionally, males may tie their psychological well-being to their social connections less strongly than their female counterparts (Miething et al., 2016). Although males and females both establish strong social friendship networks, males are less likely to experience disruption of these networks and report feelings of tenseness and sadness resulting from interactions within these networks (Miething et al., 2016). Given these gender differences in social interactions, it is likely that females may continue to think about their online interpersonal interactions longer than males, and although their screen time may be over, sleep time may be delayed for time to reflect. Additionally, females may have more impassioned online interactions than males and need a recovery time from emotional arousal prior to sleep onset. Finally, communication-based media provide optimal conditions for emotional arousal (Zillmann, 2006). Dependent upon whether active or passive usage of these media, these findings suggest males may experience emotional arousal to a lesser degree. These interpretations are consistent with one of Cain and Gradisar’s proposed mechanisms by which screen time impacts sleep: screen time increases emotional arousal delaying sleep onset (Cain & Gradisar, 2010). Future work on male and female media use that would be more nuanced could assess these proposed mechanisms directly.

As with all research, the current work is subject to limitations. The primary limitation of this research was the reliance on adolescent self-report for both screen time and sleep quality. Self-reports are prone to social desirability bias, acquiescence, misunderstanding, over or under estimation of time, and other errors. Objective measures of sleep and screen time would be preferable, particularly the use of actigraphy with luxometer for measuring ambient light and screen time reports from mobile devices. Having both objective and subjective measures of screen time and sleep would have been ideal. A second limitation is the limited assessment of adolescent screen-time. This study only assessed time spent with different types of media, but not the content of the television programs, videos, or websites, which could shed more light on whether the screen-based media viewed were truly passive (e.g., scrolling through news feeds, watching videos) or active, direct exchanges (e.g., multi-player video game) and thus possibly arousing an emotional response. The version of the MMI used in this study does not explicitly include social media as one of the reported media, which potentially limits interpretation of these findings. Similarly, the social component of the media assessed by the MMI was not directly measured. The devices used by the adolescents was not measured but may have a considerable effect on sleep disruption, especially mobile and portable devices that would likely be held close to the eyes. The lack of information concerning when adolescents were utilizing screens is a considerable limitation. If screen time is to compete with sleep time, one would naturally assume that screen time is primarily at night and adolescents are choosing screen time over sleep time. However, that may not be the case. Regardless of when adolescents are using screens, it is reasonable to presume they are choosing screens over another activity. If said activity (homework, chores, personal hygiene) must be accomplished prior to bedtime, then screen time is in indirect competition with sleep time. Furthermore, measuring the content of the communication component involved in the video games, IM, emails, and text messages would help researchers quantify the emotional response elicited. If the content of the communication is emotionally charged, confrontational, or otherwise troublesome to the adolescent, it could be more likely to remain on his/her mind long after the communication is over, and this rumination could delay sleep onset. More mundane communications could be less emotionally arousing, allowing for earlier sleep onset. Additionally, possible underlying mechanisms for poor sleep quality such as adolescents’ sleep pressure, circadian period, chronotype, and sources of external pressure (school, sports) were not considered here. Finally, a larger sample size may provide additional insight into these findings. However, the power for small, medium, and large effects in this sample was calculated to be .66, .72, and .80, respectively.

Future research should incorporate objective measures of sleep quality, screen time, and possibly even emotional responses to screen time. Building upon previous research on active vs. passive usage of social media communication’s impact on well-being (Verduyn et al., 2015), future research should consider a similar delineation in communication within interactive screen time and its relationship to adolescent sleep over time. Adolescent exposure to blue-spectrum light was not included in the current research but is an important component in the understanding of decreased sleep quality among adolescents as are measures of biological sleep processes and other possible external factors impacting sleep quality. The devices being used and proximity to the eyes when used should also be considered in future work. A comprehensive understanding of the relationship among screen time, age, and gender may provide policy makers the evidence-based tools needed to make informed, effective guidelines for parents and adolescents for increasing the sleep quality of our adolescent population.

This work is novel in suggesting the effect of electronic media screen time on sleep quality is not “one size fits all,” and gender differences may be considered as a focal factor in this relationship. This study assessed sleep quality over 3 months in a diverse sample at mid-adolescence when circadian delays peak. This work informs the next steps of research in this area to clearly distinguish the content of screen time and the need for experimental studies to discover dose-response effects of the timing of screen time on adolescent sleep. By investigating different domains of screen time, this study is among the first to go beyond considering how quantity of screen time viewed by adolescents impacts sleep quality. That is, although the screen time may be detrimental to sleep quality, the various media of screen time may not be equally detrimental.

Statement of Significance.

This work is novel in suggesting the effect of electronic media screen time on sleep quality is not “one size fits all,” and gender differences may be considered as a focal factor in this relationship. This study assessed sleep quality over 3 months in a diverse sample at mid-adolescence when circadian delays peak. This work informs the next steps of research in this area to clearly distinguish the content of screen time and the need for experimental studies to discover dose-response effects of the timing of screen time on adolescent sleep. By investigating different domains of screen time, this study is among the first to go beyond considering how quantity of screen time viewed by adolescents impacts sleep quality.

Acknowledgments

Research reported in this publication was supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under Award Number R01HD089998. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Special thanks to the UAB Translational Research for Injury Prevention Laboratory for data collection and entry, and support from the UAB Edward R. Roybal Center for Translational Research in Aging in Mobility (NIH/NIA grant no. 5 P30 AG022838–09) and a grant from the National Institute on Aging (NIH/NIA grant no. 5 R01 AG005739–24).

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