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. Author manuscript; available in PMC: 2022 Jan 1.
Published in final edited form as: Child Health Care. 2021 Feb 23;50(3):293–310. doi: 10.1080/02739615.2021.1890078

Prospective associations between pre-sleep electronics use and same-night sleep in healthy school-aged children

Christine J So 1, Matthew W Gallagher 1, Cara A Palmer 2, Candice A Alfano 1
PMCID: PMC8340849  NIHMSID: NIHMS1675551  PMID: 34366538

Abstract

Electronic devices are routinely associated with adverse effects on sleep; however, prospective studies among healthy children are unavailable. This study examined relationships among specific and total electronic device use within the hour before bed and same-night sleep patterns among 55 pre-pubertal children (7–11 years) without medical, psychiatric or sleep disorders. Sleep was assessed via subjective reports and actigraphy for 5 weeknights and pre-bed device use was assessed via daily diary. Neither total devices use nor any single type predicted sleep parameters the same night. The extent to which pre-bed electronics use impacts sleep in healthy children requires further investigation.

Keywords: Sleep, pediatrics, electronic media use, actigraphy, children

Introduction

According to the National Sleep Foundation (NSF), school-aged children ages 6–13 years should receive between 9–11 hours of sleep per night for optimal health and performance (Hirshkowitz et al., 2015), reflecting inter-individual variability in sleep need at all ages (Iglowstein et al., 2003). A significant portion of children nonetheless receives lesser amounts of sleep than recommended. For example, the Centers for Disease Control and Prevention (CDC) reported that, in 2015, 57.8% of middle schoolers across nine states received less than (defined as < 9 hours; Wheaton et al., 2018). Interestingly, this report also stated that younger children (i.e., in grade 6) were more likely to receive short sleep duration compared to older children (i.e., in grade 8) (Wheaton et al., 2018). The most extreme sleep deficits are routinely observed on school nights compared to weekends (Nixon et al., 2008; Williams et al., 2013) indicating that the prevalence of inadequate sleep among youth may be driven by behavioral and environmental factors as much as biological ones (Nixon et al., 2008).

The upsurge of electronic media devices in modern-day society is routinely cited as one factor adversely affecting children’s sleep. A large, multi-wave study found that daily electronics use among children increased by 69 minutes between 1999–2009 (Rideout et al., 2010). A more recent report observed that smartphone ownership incrementally increased among pre-pubertal children from 2015 to 2019, such that the percentage of 8-year olds with smartphones increased from 11% to 19% while the percentage of 11-year olds with smartphones increased from 32% to 53% (Rideout & Robb, 2019). The types of screen-based activities also differ based on developmental stage. For example, “teens” (ages 13–18) spend a greater proportion of their screen time on social media compared to “tweens” (ages 8–12), who spend more time watching television or videos compared “teens” (Rideout & Robb, 2019).

In 2016, the American Academy of Pediatrics (AAP) issued a policy statement focused on children and adolescents 5–18 years old (Council on Communications and Media, 2016) recommending total screen time be limited, electronic devices be removed from children’s bedrooms, and screen time be avoided for at least one hour before bedtime. These recommendations stem from a mounting body of research suggesting deleterious relationships between nighttime electronics use and sleep. One comprehensive review in children ages 5–17 years concluded that screen time is adversely associated with “at least one sleep outcome” in 90% of studies (Hale & Guan, 2015); notably, the vast majority of studies included (81%) were based on cross-sectional data. More recent reviews have similarly concluded screen-based digital media use is associated with poor sleep quality in children and adolescents (Hale et al., 2019; LeBourgeois et al., 2017).

Theoretically, there are several mechanisms by which screen time may individually or interactively affect sleep quality (Cain & Gradisar, 2010), including sleep displacement (i.e., screen time use that extends into the nighttime sleep period), heightened physiological and/or psychological arousal related to media content that disrupts sleep, and/or the arousing effects of blue light (i.e., blue light emitted from devices suppresses the release of melatonin, promoting alertness). At present however, the impact of each of these individual factors on children’s sleep quantity and/or quality is unclear. Such questions may be particularly salient for certain populations of youth, such as those with more of an eveningness chronotype (e.g., Arrona-Palacios, 2017) and children entering adolescence (Carskadon, 2011), both of which are associated with a delay in sleep timing.

In addition to the preponderance of cross-sectional studies, longitudinal and experimental studies in youth are beginning to emerge, but results are somewhat equivocal. For example, in a sample of adolescents assessed every 3–4 months over a one-year period, van der Schuur et al. (2018) did not find associations between typical media multitasking behaviors and subjective sleep problems. These results are similar to a recent prospective study among young healthy adults that found increased use of social media at bedtime was not associated with worse sleep the same night (Das-Friebel et al., 2020). Conversely, among 10–11 year-old Finnish children, greater report of usual television and computer use was found to predict shorter self-reported sleep duration 18 months later (Nuutinen et al., 2013). Developmental differences, methodological variation in sleep measurement, types of media use examined, and timing of assessments may account for inconsistent findings, but relationships nonetheless remain unclear. Fewer studies have utilized night-to-night prospective designs, which would allow for more rigorous examination of these relationships. No such studies are available among healthy pre-pubertal children.

An additional limitation of previous research is the common reliance on subjective report of both sleep and electronic media use (Beyens & Nathanson, 2019; Calamaro et al., 2012; Helm & Spencer, 2019; Nathanson & Beyens, 2018). In addition to potential shared method variance, subjective reports are prone to various types of biases, including potential influence by public messages regarding negative relations between media use and sleep. Accordingly, literature reviews have specified measurement error due to self-report to be a considerable limitation of most prior studies (Hale & Guan, 2015; LeBourgeois et al., 2017). The use of actigraphy for assessing children’s sleep, by comparison, allows for unbiased, reliable recording of sleep parameters across multiple consecutive days. Although a few studies have utilized actigraphy (Fobian et al., 2016; Helm & Spencer, 2019; Perrault et al., 2019), most have examined sleep based on average values across the week rather night-to-night media use and sleep. The latter approach would provide more meaningful information about the potential impacts of pre-sleep electronics use and on sleep quality. Further, discrepancies between objective measurement and subjective report of sleep are common in both clinical and healthy samples of children (e.g., Alfano et al., 2015), highlighting a need to assess relationships with pre-sleep media from the perspective of both objective sleep parameters and perceived sleep quality.

Lastly, though many findings are based on school- and population-based surveys, the use of such samples may obscure important inter-individual differences in ‘screen time’-sleep relationships among children (e.g., Calamaro et al., 2012; Chaput et al., 2014; Nathanson & Beyens, 2018). Specifically, since both physical and psychological health have been shown to influence associations between electronics use and sleep patterns (Becker & Lienesch, 2018; Chen et al., 2006; Nixon et al., 2008), studies using well-characterized, healthy child samples represent a neglected but necessary step in discerning specific relationships, with direct implications for interpreting outcomes among specific (e.g., clinical, at-risk) populations of children.

To address some of these limitations, and in response to the AAP’s (Council on Communications and Media, 2016) call for prospective studies investigating media use in school-aged children, the current study examined total and specific types of electronic media use during the hour before bedtime as a predictor of objective and subjective sleep patterns across five weekdays in a sample of healthy children. Sleep was assessed using both daily diaries completed by children and parents as well as wrist actigraphy. In the absence of prior studies using similar methodologies, we examined media’s impact on total sleep time, sleep onset latency, and sleep efficiency based on both objective and subjective sleep measures. In addition to total electronic devices, we examined the impact of TVs, mobile devices, computers, and videogames individually on sleep. Consistent with finding from cross-sectional studies, we hypothesized that greater total and each specific type of electronics device used in the hour before bed would predict worse objective and subjectively-reported sleep that night.

Methods

Participants

A total of 55 pre-pubertal children (Tanner stages 1–2 assessed via the Self-Administered Rating Scale for Pubertal Development; Carskadon & Acebo, 1993) between ages 7–11 years [n=31 female; Mage=9.11 (1.36)] and a parent participated in a larger study examining sleep and emotional health in children. Exclusion criteria of the larger study included (a) past/present suicidal ideation; (b) evidence of International Classification of Sleep Disorders (American Academy of Sleep Medicine, 2005) sleep disorder(s) assessed via diagnostic clinical interview; (c) full-scale IQ below 85; (d) body mass index >95th percentile (based on common association with sleep apnea); (e) chronic medical condition potentially affecting sleep; (f) medication use potentially impacting mood/sleep; and (g) abnormal sleep schedules evidenced by averaged total sleep time < 8 hours or > 11 hours, consistent with population based estimates (Holley, Hill & Stevenson, 2010; Williams, Zimmerman & Bell, 2013, or variable nightly sleep/wake times greater than 90 minutes based on parent report or one week of actigraphy.

The majority of children identified as Caucasian (n=33), followed by African American (n=17), biracial/other (n=3), and Asian American (n=2). Children primarily identified as Non-Hispanic/Latino (n=38). Approximately 75% (n=40) of the sample were in school during the time they completed the assessment. The majority of participating caregivers were parents married to their child’s father/mother (n=38). Yearly household income varied, with a median range of $60,000–80,000. See Table 1 for sample characteristics.

Table 1.

Demographic Characteristics of Study Sample

N 55
Age: M (SD) 9.11 (1.36)
Female: n (%) 31 (56%)
Race: n (%)
 Caucasian 33 (60%)
 African-American 17 (31%)
 Asian-American 2 (4%)
 Biracial/Other 3 (5%)
Ethnicity: n (%)
 Non-Hispanic/Latino 38 (69%)
 Hispanic/Latino 17 (31%)
Parent Marital Status: n (%)
 Married to child’s father/mother 38 (69%)
 Married to another partner 3 (6%)
 Single 9 (16%)
 Divorced 4 (7%)
 Widowed 1 (2%)
Yearly Household Income: n (%)
 <$20,000 3 (6%)
 $20,000–$40,000 9 (16%)
 $40,000–$60,000 10 (18%)
 $60,000–$80,000 9 (16%)
 $80,000–$100,000 9 (16%)
 >$100,000 15 (27%)
Holiday Status during participation: n (%)
 No (Participated during school year) 40 (73%)
 Yes (Participated during school holiday) 13 (24%)
 Partial 2 (3%)
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Note. N=55; M=Mean; SD=Standard deviation.

Procedures

Participants were recruited from the community in Houston, TX via mailings and print advertisements for a study on sleep and emotion. The protocol was approved by the Institutional Review Board at the University of Houston, and written informed consent/assent was obtained by families prior to participation. Children and a parent arrived at the Sleep and Anxiety Center of Houston at the University of Houston for the initial baseline assessment, during which eligibility was confirmed via semi-structured clinical interviews. Other information collected included demographics, psychosocial functioning, and sleep habits. Within one week following the initial assessment, children completed five weekdays of objective and subjective sleep monitoring at home via actigraphy and sleep diaries.

Measures

Background/General information

Demographic information was obtained from a parent, including the child’s age, sex, and race/ethnicity, parental marital status and level of education, and total household income (Table 1).

Actigraphy

Objective estimates of participants’ sleep were assessed using the AMI Micro Motionlogger Sleep Watches (Ambulatory Monitoring, Inc., Ardsley, NY). Actigraphs are watch-shaped devices that continuously record movement via accelerometers, and use validated algorithms to determine sleep-wake patterns (Cole et al., 1992; Sadeh et al., 1994). Present data were stored in one-minute epochs, and sleep was scored via the Sadeh algorithm (Sadeh et al., 1994), a validated method for scoring pediatric sleep (Meltzer et al., 2012). Although polysomnography is viewed as the “gold-standard” of measuring objective sleep, actigraphy is commonly used due to its portability, affordability, ease of use in a naturalistic environment, and high reliability/validity compared to polysomnography (Littner et al., 2003). Participants were instructed to wear the watch continuously throughout the day and night for five days, and to push an event marker button on the watch to indicate time-in- and out-of-bed. Based on recommendations from previous studies (Acebo et al., 1999), participants were included in the present study if they had at least five nights of actigraphy data collected. In the event that a particular actigraphy variable was missing on a night (e.g., event marker was not pushed), we did not supplement with subjective data from the sleep diaries due to the separate focus of objective and subjective sleep parameters as predictor variables. The vast majority of children (89%) had no nights of missing data for primary actigraphy variables, while 7% were missing at least one variable from one night and another 4% were missing at least one variable from two nights.

Total sleep time (TST) was calculated as the number of minutes asleep during time-in-bed. Sleep-onset latency (SOL) was calculated as the number of minutes between the event marker (i.e., indicator of bedtime) and first epoch of sleep. Sleep efficiency (SE) was calculated as the proportion of time spent asleep to the proportion of time spent in bed (i.e., time-in-bed), indicated as a percentage. Time-in-bed was calculated as the number of minutes between the event marker button pushes.

Sleep diary/Electronic media use

Subjective measurement of sleep across the actigraphy week and nighttime electronics use were assessed using sleep diaries. Participants and their parent were instructed to complete the “End of Day” portion of the daily diary at bedtime, including # and time of day caffeinated drinks were consumed, medications taken during the day, and electronic devices used within one hour of bedtime. Specifically, each night, youth checked a box if they used a) TV, b) videogames, c) computer, and/or d) mobile phone before bed (for a range 0–4 devices per night, with higher numbers indicating greater number of devices used). Participants were instructed to complete the “Morning” portion upon awakening, including self-reported bedtime, wake time, SOL, and TST.

Data analysis

All preliminary analyses were conducted using SPSS (version 25). Weekly averages (i.e., across the five days of data collection) for electronics use, as well as objective and subjective sleep parameters, were first used to calculate descriptive statistics for primary study variables. Correlations were then conducted to examine bivariate relationships.

All primary analyses were conducted using Mplus (version 8). To examine whether electronics use predicted sleep parameters, daily-level analyses were conducted using hierarchical linear modeling (HLM; Bryk & Raudenbush, 1992) which is ideally suited to examine nested data structures such as daily diary studies, and allowed us to examine between- and within-child variations in sleep outcomes and electronics usage. First, we specified null models to quantify the intraclass correlation (ICC) for each media type and overall media usage. We then specified a series of random effects HLM models in which each of the four media types along with total media usage was specified as a predictor of various sleep outcomes. Maximum likelihood estimation was used to account for any missing data and to allow for inclusion of all participants. A Bonferroni correction was used to adjust for the number of comparisons, with a critical p-value of .005.

Results

Preliminary analyses

No sex-based differences in total nighttime media use were found, though boys reported a significantly greater number of nights where video games were used (p=.006). There were no other sex-based differences on frequency of electronic media use or objective sleep variables. Self-reported sleep duration differed as a function of race such that Caucasian children reported longer TST than did African-American children (p=.01), but objectively-measured TST revealed no significant differences. There were no other differences in sleep or electronics use based on race/ethnicity. Children’s age was positively correlated with nighttime weekly television (r=.37, p<.01) and smartphone (r=.35, p<.05) usage, such that older age was associated with greater nighttime television and smartphone usage. No statistically significant differences in study variables were observed based on the timing of participation (i.e., whether children were in school or versus summer/holiday), such that children who participated during the school year and who participated during the summer/break did not exhibit significantly different sleep behaviors.

Objective and subjective sleep variables were positively correlated (r’s=.51-.60, p<.01), indicating a general congruence between self-reported sleep and actigraphy. Average number of nights where television was used was positively correlated with average self-reported SOL, such that on average, more television use was associated with longer self-reported SOL (r=.28, p<.05). No other forms of electronic media were broadly correlated with objective or subjective sleep variables. See Table 3 for zero-order correlations.

Table 3.

Zero-Order Correlations among Continuous Study Variables

1 2 3 4 5 6 7 8 9 10 11 12 13 14
1. Age ---
2. Act-Bedtime .33* ---
3. Act-Sleep onset time .40*
 .89** ---
4. Act-Sleep offset time .26 .67** .78** ---
5. Act-SOL .001 −.14 .11 .01 ---
6. Act-TST −.23 −.35** −.43** .15 −.23 ---
7. Act-SE .12 .14 .06 −.08 −.17 .28* ---
8. Sleep log-SOL .08 −.05 .12 .06 .51** −.04 .08 ---
9. Sleep log-TST −.17 −.28 −.23 .27 .02 .60** −.26 −.14 ---
10. Sleep log- Wakings −.16 −.39** −.38** −.17 .16 .27 −.13 −.07 .28* ---
11. TV .37** .09 .22 .15 .23 −.24 −.22 .28* −.21 .11 ---
12. Video Games .17 .26 .21 .17 −.13 .01 .22 −.10 −.22 −.21 −.09 ---
13. Computer −.02 .03 −.08 −.18 −.14 −.08 .08 −.14 −.25 −.13 −.15 .03 ---
14. Smartphone .35* −.03 −.02 −.04 −.05 .00 .06 .07 .08 .01 −.03 .12 .08 ---
15. Other −.02 .18 .09 .07 −.12 −.01 .09 −.18 −.06 −.18 −.07 −.11 .20 -
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Note. N=55; Averaged values across 5 nights for sleep and electronic media use were used. Act=Actigraphy; SOL=Sleep onset latency; TST=Total sleep time; Wakings=Number of nightly awakenings; TV=Television.

*

p<.05.

**

p<.01.

Rates of nighttime electronic media use

On average, children endorsed using four electronic devices across the five days of assessment (see Table 2). Within specific device type, children reported watching televisions most frequently (on average twice per week), followed by computer use, then videogames and smartphones.

Table 2.

Descriptive Statistics for Study Variables: M (SD)

Actigraphy:
 Bedtime 9:29 PM (00:55)
 Sleep onset time 9:55 PM (01:07)
 Sleep offset time 6:51 AM (01:01)
 SOL (minutes) 21.43 (14.20)
 TST (hours) 8.49 (.70)
 SE (percentage) 94.74 (3.86)
Sleep Log:
 SOL (minutes) 13.98 (14.07)
 TST (hours) 9.21 (.80)
 Number of Nightly Awakenings .24 (.38)
Electronic Device Frequency (totaled across 5 Weekdays):
 Total electronics used 3.98 (2.69)
 Television 2.19 (1.78)
 Video Games .54 (1.02)
 Computer .71 (1.39)
 Smartphone .44 (1.18)
 Other .10 (.45)
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Note. N=55; Averaged values across 5 nights for sleep and electronic media use were used. M=Mean; SD=Standard deviation; SOL=Sleep onset latency; TST=Total sleep time; SE=Sleep efficiency.

Intra-individual variability in electronic media use

We examined the extent to which usage of different electronic media varied between and within children across each daily assessment. The results of null models partitioning variance and the ICC for each media type and overall media usage are presented in Table 4. For each media type examined and overall media use, there was significant variability both within- and between-children in usage patterns.

Table 4.

Within and Between Children Variance in Electronic Media Usage

Variance
Media Type: Within Between ICC
 Television .156** .091** .369
 Video Games .071** .027** .278
 Computer .059** .065** .523
 Smart Phone .034** .048* .586
 Total Electronics .305** .216** .414
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Note. N=55 across 5 nights of data. ICC=Intraclass correlation.

*

p<.05.

**

p<.01.

Nighttime electronic media use predicting sleep

A series of HLM models was specified to examine whether any form of electronic media use was associated with self-reported or objective sleep outcomes. Separate models were specified for each media type with each sleep outcome, and the effect of media was specified as a random effect to allow for potential variation within and between children. Results of the HLM analyses are presented in Table 5. Neither total number of electronic devices nor any of the four specific device types were found to be a significant predictor of any sleep outcome variable, and all effect sizes were small (ESr’s < .15). Thus, while there was intra-individual variability in media usage and sleep, greater overall use of electronic media did not predict worse sleep outcomes.

Table 5.

HLM Models Examining Electronic Media Usage as Predictor of Sleep Outcomes.

Intercept Media effect
Media Type: Sleep Outcome: beta SE p beta SE p
Television Act-SOL 20.86 2.11 < .01 2.93 2.80 .30
Act-TST 503.18 6.64 < .01 10.73 7.45 .167
Act-SE 94.76 .57 < .01 .23 .59 .70
Sleep log-SOL 12.96 2.11 < .01 2.93 2.61 .26
Sleep log-TST 9.07 .17 < .01 .07 .17 .70
Sleep log-Wakings .27 .07 < .01 −.05 .07 .48
Video Games Act-SOL 21.45 1.43 < .01 −.80 4.31 .85
Act-TST 506.79 5.96 < .01 10.13 12.59 .42
Act-SE 94.78 .53 < .01 .68 .720 .34
Sleep log-SOL 14.58 2.08 < .01 -2.48 3.09 .42
Sleep log-TST 9.07 .16 < .01 .01 .21 .97
Sleep log-Wakings .25 .06 < .01 −.04 .09 .65
Computer Act-SOL 22.85 2.04 < .01 -6.41 3.53 .07
Act-TST 509.55 6.13 < .01 -10.75 9.67 .27
Act-SE 94.85 .54 < .01 .08 .76 .92
Sleep log-SOL 14.55 2.11 < .01 -1.77 3.13 .57
Sleep log-TST 9.10 .16 < .01 −.18 .22 .41
Sleep log-Wakings .24 .06 < .01 .03 .11 .81
Smart Phone Act-SOL 21.06 2.03 < .01 8.06 5.95 .18
Act-TST 506.81 6.09 < .01 12.99 12.85 .31
Act-SE 94.80 .54 < .01 .65 .96 .50
Sleep log-SOL 13.72 2.04 < .01 6.33 5.58 .26
Sleep log-TST 9.05 .16 < .01 .29 .31 .35
Sleep log-Wakings .24 .05 < .01 .03 .15 .87
Total Act_SOL 20.64 2.42 < .01 1.56 2.15 .47
Electronics Act-TST 504.47 7.34 < .01 4.15 6.01 .49
Act-SE 94.46 .64 < .01 .48 .49 .33
Sleep log-SOL 12.90 2.44 < .01 1.75 1.94 .37
Sleep log-TST 9.07 .17 < .01 .04 .13 .72
Sleep log-Wakings .25 .07 < .01 −.01 .06 .89
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Note. N=55 across 5 nights of data. SE=Standard error; Act=Actigraphy; SOL=Sleep onset latency; TST=Total sleep time; SE=Sleep efficiency; Wakings=Number of nightly awakenings.

Discussion

Electronic media devices are ubiquitous, with up to 98% of children in the U.S. living in homes with mobile devices (Rideout, 2017). Despite clear advantages, the upsurge of electronics use among youth in recent years also raises concerns, including potential to adversely affect sleep. Such concerns are warranted in light of findings suggesting declines in the sleep duration of children over time (Matricciani et al., 2012) and a wealth of cross-sectional research linking sleep disruption with increased use of screen-based devices. Fewer longitudinal and experimental studies are available, but collective findings paint a picture that is more complex than simple dose-response relationships. The present study sought to examine specific relationship between night-to-night pre-bedtime media use and sleep among a sample of healthy children. Consistent with other studies (Helm & Spencer, 2019), we found a positive correlation between average number of nights including pre-sleep television use and average subjective SOL across the week. However, on a night-to-night basis, we did not observe significant relationships between pre-sleep electronics use and sleep. That is, neither the total number of electronic devices nor any specific device type used (i.e., television, mobile phones, computer, videogames) was associated with sleep parameters that same night, including actigraphy-assessed TST, SOL or SE.

Although a majority of previous research has found significant associations between children’s use of electronic media and sleep (for reviews, see Cain & Gradisar, 2010; Hale & Guan, 2015), the null findings reported here are not entirely without precedent. For example, studies examining relationships between sleep duration, weight and other health behaviors (nutrition, exercise, etc.) in youth did not find electronics usage to influence these relationships (Chen et al., 2006; Nixon et al., 2008). Another study including youth from nine European countries found that after controlling for covariates such as weight, screen time was not uniquely predictive of sleep duration (Hense et al., 2011). The inclusion of normal-weight, healthy youth in the present study may therefore be an important distinction, underscoring the potential impact of inter-individual child differences. Importantly, the current sample was without any medical conditions, psychiatric disorders, clinically-significant symptoms, or reported sleep difficulties. In addition to the high overlap between psychiatric disorders and sleep disturbance (American Psychiatric Association, 2013), several studies have found associations between greater electronic media use and increased sleep anxiety in youth (Cain & Gradisar, 2010). Similarly, a recent meta-analysis examining the effectiveness of blue-light blockers found that the intervention was more effective for those with sleep and/or psychiatric disorders versus healthy individuals (Shechter et al., 2020). As such, use of electronic devices at night may differentially impact sleep patterns in youth with physical or mental difficulties compared to children without these problems. These are critical questions for future research to address.

It is important to emphasize that the current study examined electronic media use during the pre-bedtime period only. Other prospective studies have found bidirectional relationships between sleep duration and total electronic media use across the 24-hour period (Magee et al., 2014; Poulain et al., 2019). It is possible therefore that media use in the hour before bed is less consequential for healthy child sleep patterns than total ‘screen time’ consumption across the day. However, other studies examining night-to-night sleep patterns among adolescents found sleep duration to be unaffected by daily computer use (Fuligni & Hardway, 2006) as well as TV and videogames (Hamilton et al., 2020). Together with findings from experimental studies that have not reported meaningful associations between pre-sleep electronics use and same night sleep (Harris et al., 2015), we concur with others researchers (Fuligni & Hardway, 2006; Hale et al., 2019) that the relative contribution of screen-based media in disrupting youth sleep patterns may be dependent on a range of factors. Future work systematically examining putative mediating/moderating variables in well-characterized samples of youth are needed to disentangle these relationships.

The current study focused on the timing of electronic media use in relation to sleep (i.e., the hour before bedtime) but location of use also deserves consideration. One theorized mechanism through which electronic screens are believed to impact upon sleep, namely melatonin suppression via blue light, may be largely dependent upon proximity of screens to children’s eyes. In fact, because data suggest young children’s eyes absorb more blue light from digital screens compared to adults (Behar-Cohen et al., 2011), use of electronic devices while lying in bed may be particularly powerful in disrupting children’s sleep. Relatedly, a recent study found that school-aged children exhibited greater melatonin suppression in response to light exposure compared to adults (Lee et al., 2018). We regret that we did not assess whether children used their devices in their bed in the current study. Other theorized mechanisms of sleep disruption such as sleep displacement and arousing content (e.g., social stressors) may be less dependent on location of us than timing (Cain & Gradisar, 2010; Van den Bulck, 2007). For example, a cross-sectional study among adolescents found social stress related to media use better predicted SOL and daytime sleepiness than social media use per se (van der Schuur et al., 2019).

Another important aspect of our study is the inclusion of pre-pubertal children. Despite the increasing prevalence of electronics use at younger ages, the majority of research has been conducted in young adult and adolescent samples, yet emerging findings are suggestive of developmental differences in the impact of device use on sleep (Twenge et al., 2019). The transition to adolescence marks a period of dramatic developmental shifts in biological, cognitive, emotional and social functioning that might render the effects of nighttime electronics more potent. In conjunction with homeostatic and circadian alterations that independently delay sleep onset (Crowley et al., 2007), adolescents are highly sensitive to acceptance and rejection in their peer relationships (Brown, 2004). Evening social interaction with peers via portable devices may be perceived as critical for ensuring acceptance, and therefore serve as a major source of stress even at night. Younger children do not prioritize peer relationships to the same extent and may be less likely to use devices for social purposes. In fact, the Common Sense census found that children 8 years and younger spend a substantial amount of their “online” time watching videos (Rideout, 2017). Parents of younger children are also more likely to monitor use of electronics, serving as an additional potential mechanism by which content and reasons for use might differ (i.e., be less arousing) among younger compared to older children.

The present study is the first to prospectively examine the impact of children’s electronic media use in the hour before bedtime on the same night’s sleep, using both objective and subjective sleep measures. Nevertheless, several limitations should be noted in addition to those already discussed. The size of the present sample may have limited our ability to detect statistically significant relationships, given the small effect sizes observed. Additionally, participants were required to complete a survey at the end of the day, which may have inadvertently influenced their typical nightly routines. The specific age range of our sample is also noteworthy, since children endorse overall lower levels of electronic device use than adolescents. We did observe a positive association between age and TV use as well as smartphone use, suggesting that future studies should explicitly examine how development might moderate these relationships. Pre-pubescent children also experience stronger homeostatic sleep pressure compared to adolescents (McLaughlin Crabtree & Williams, 2009), potentially providing a buffer against sleep-impeding effects of electronic media in the evening. Further, because we only assessed pre-sleep electronics use during weekday nights, findings may not extent to weekendsAlthough we assessed use of multiple forms of electronic devices in the hour prior to bedtime, we did not assess for the duration or specific timing of devices or consumption across the 24-hour day. This is an important limitation for future studies to address. Higher cumulative daily doses of electronic media (versus pre-sleep media) may be a more meaningful disruptor of nightly sleep, as suggested by other prospective studies (Magee et al., 2014; Marinelli et al., 2014; Poulain et al., 2019), though precise mechanism remain unclear.

Clinical Implications.

In our study, children endorsed using an average of four electronic devices in the hour before bedtime across the five day assessment period; watching television being most common. Concern that widespread use of electronic screens at night may be detrimental to children’s sleep is also common. However, a majority of research examining these relationships has relied on correlational designs that do not provide rigorous tests of night-to-night relationships. In the current study, neither total number of electronic devices nor any one type of device used in the hour before bedtime was associated with same night sleep patterns according to actigraphy or subjective sleep reports. These findings highlight a need for more rigorous designs and robust methods investigating these relationships in children, including attention to the role of potential moderation factors such as child psychiatric and medical conditions, location of media use, and parental supervision across different stages of development.

Acknowledgements:

The authors would like to thank the National Institute of Mental Health [grant number #R21 MH099351] for funding the present study, as well as the families who participated.

This study was funded by the National Institute of Mental Health [grant number #R21 MH099351] awarded to the last author.

Footnotes

Declaration of Interest statement:

The authors report no other potential conflicts of interest.

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