Youth Screen Time and Behavioral Health Problems: The Role of Sleep Duration and Disturbances

Abstract

OBJECTIVE

The purpose of the current study was to examine the indirect effect of youth screen time (e.g., television, computers, smartphones, video games, and tablets) on behavioral health problems (i.e., internalizing, externalizing, and peer problems) through sleep duration and disturbances.

METHODS

We assessed a community sample of parents with a child in one of three developmental stages: young childhood (3 – 7 yrs.; N = 209), middle childhood (8 – 12 yrs.; N = 202), and adolescence (13 – 17 yrs.; N = 210). Path analysis was employed to test the hypothesized indirect effect model.

RESULTS

Findings indicated that, regardless of the developmental stage of the youth, higher levels of youth screen time were associated with more sleep disturbances, which, in turn, were linked to higher levels of youth behavioral health problems.

CONCLUSION

Children who have increased screen time are more likely to have poor sleep quality and problem behaviors.

Introduction

The explosion of digital technology ownership in the last five years^1,2 has created a dramatic shift in how youth and families use technology^3,4. Further, the increased access to new digital media (e.g., smartphones and tablets) devices has contributed to a rapid rise in average screen time exposure for children^4,5. Total daily screen time across devices in children 8- to 18-years old has risen from five to approximately eight hours since 1999⁶, far exceeding the American Academy of Pediatric’s (AAP) recommendation of two hours or less⁷.

Excessive screen time in childhood is associated with behavioral health problems^8–10. However, the process by which screen time increases these problems has not been elucidated. One potential mechanism of this association is youth sleep quality: There are established individual associations between youth screen time and compromised sleep duration and quality^11–13 as well as between sleep and a variety of childhood behavioral health outcomes (e.g., internalizing, externalizing, & peer problems)^14–16. The mechanisms by which higher levels of screen time cause sleep disturbances have been attributed to environmental, psychosocial, and biological causes.^11–13 One of these environmental sources is the use of screen-based activities, which often delays bedtime or truncates total sleep time¹¹. One psychosocial source may be arousal due to the content of the media, interfering with the ability to fall and stay asleep¹⁷. And finally, one potential biological mechanism is the effect of screen light on both circadian rhythm and alertness.

Though initial support is promising, only two studies have examined sleep as a link between screen time and youth behavioral health with both finding some support for sleep duration serving in this role^18,19. Each study had limitations that dampen the generalizability of results and implications for modern families. These include: Failure to examine sleep quality, which may be a more important marker of sleep than is duration^14,20–23; limited assessment of screen time (e.g., during the school day); absence of modern media devices (e.g., tablets, smartphones); limited assessment of behavioral health problems; and narrow age ranges (e.g., middle school children), precluding the examination of differences over the course of child development¹⁴.

The purpose of the current study was to address the limitations noted above and provide updated information and recommendations to families. We (1) examine both sleep duration and sleep disturbances, a proxy for sleep quality^24,25; (2) assess screen time after school for all the primary types of devices children use today (e.g., smartphones, tablets, video games, laptops); (3) assess internalizing, externalizing, and peer relationship problems; and (4) use a sample of families with a child in one of three developmental stages: young childhood (3 – 7 yrs.), middle childhood (8 – 12 yrs.), and adolescence (13 – 17 yrs.). These age groups were chosen a priori based typical age divisions of prevention and intervention programs that involve parenting as a primary component (e.g., young children²⁶; middle childhood²⁷; adolescence²⁸) in order to more directly inform the development of programs to help parents manage their children’s screen time at different developmental stages. Of importance, research has long indicated that children have different cognitive skill development and play different roles in the family during these stages of development²⁹. We hypothesized that higher levels of screen time would be indirectly related to higher levels of youth behavioral health problems (i.e., internalizing, externalizing, and peer problems) through lower sleep duration and higher levels of sleep disturbance. We hypothesize that these indirect effects would be significant across the three developmental stages.

Methods

Participants and Recruitment

Parents of 3 to 7 (N = 209), 8 – 12 (N = 202), and 13 – 17 (N = 210) year old children were recruited online through Amazon’s Mechanical Turk (MTurk), the dominant crowdsourcing application in the social sciences. On MTurk, workers browse Human Intelligence Tasks (HITs) by title, keyword, reward, availability, and so on, and complete HITs of interest. Participants are compensated by requesters upon successful completion of tasks (for an introduction to using MTurk³⁰). Participation requirements were being a U.S. resident and having at least a 95% task approval rate on MTurk.

Online data collection about screen time has been used successfully, previously³¹. Furthermore, there are several advantages for the use of crowdsourcing methods in clinical and developmental research. First, relatively large samples sizes can be collected quickly³² for a minimal cost allowing researchers to address unanswered questions, particularly about mechanisms that statistically require large sample sizes. Second, a diverse range of participants (e.g., race, SES, household composition) can be recruited from across the United States^32–34. Third, prior research has convincingly demonstrated that data obtained via crowdsourcing methods are as reliable as those obtained via more traditional data collection methods^32,33. Fourth, previous work has also shown that participation and data quality are unaffected by compensation rate or task length³⁵. Fifth, as demonstrated by the current study, crowdsourcing methods afford an opportunity to recruit mothers and fathers, the latter being long underrepresented in clinical research^36,37. Sixth, crowdsourcing methods use identification numbers, which protects respondent anonymity and prevent any individual worker from participating in a single HIT more than once.

Procedure

All study procedures were approved by the BLIND Institutional Review Board. Parents were initially consented online and, after completing surveys, compensated $4.00. For families with multiple children in the target age range, one child was randomly selected through a computer algorithm. Ten attention check items, placed throughout the survey, asked participants to enter a specific response that changed throughout the survey and appeared randomly. To ensure that responses were not random or automated, participants (N = 2) were excluded from the study if they had more than one incorrect response.

Measures

Youth weekly screen time

Parents were asked two questions regarding their child’s screen time: “Now thinking about [target child]’s typical activities, on a typical weekday (“weekend” in second question) how much time does [target child] spend doing each of the following at home?” Parents responded with the number of hours and/or minutes their child engaged in each of the following activities: (1) Watching TV or DVDs; (2) using the computer; (3) playing video games on a console game player (such as: Xbox, Playstation, Wii); (4) playing on a handheld game console like a Gameboy, PSP, or DS; (5) using a tablet computer (such as iPad); and (6) using a smart phone for playing games, watching videos, or surfing the Internet (not including time spent talking on the phone). A daily use (averaged across the weekend and weekday) was calculated by device and then summed across all devices. Due to outliers two standard deviations above the mean that were beyond possible daily totals, such values were winsorized and assigned the highest value at two standard deviations. The method employed in the current study to measure child screen time was similar to those used by major industry reports and peer-reviewed research^38,39.

Sleep disturbances and duration

An abbreviated version of the Children’s Sleep Habit Questionnaire (CSHQ) was used to measure youth sleep disturbances and duration. The CSHQ is a widely used parent-report questionnaire to screen for childhood sleep problems and has been shown to be highly correlated with objective measures sleep functioning such as actigraphy. Parents rated the frequency of sleep behavior for the most recent “typical” week on a four-point Likert scale, with the response options usually (five to seven times per week), sometimes (two to four times per week), rarely (zero to one time per week), and never (less than once a week). A higher score indicates more sleep disturbances. Seven items were chosen to measure sleep disturbances across several domains: daytime sleepiness (“falls asleep while involved in activities”), daytime fatigue (“seems tired during the day”), sleep efficiency (“wakes up during the night”), continuity of sleep (“is restless and moves a lot during sleep”), consistency of sleep (“sleeps about the same amount each day” and “goes to bed at the same time at night”), and sleep latency (“falls asleep within 20 minutes after going to bed”). Items were scored such that higher scores represented more sleep disturbances (current α = .72). To measure sleep duration, parents reported what time their child typically goes to sleep on weeknights and weekend nights separately, and what time they typically wake up on weekdays and weekend days. From these, the amount of sleep was calculated by multiplying the weekday totals by 5 (days), adding it to the weekend totals multiplied by 2, and dividing the total by 7 to indicate average daily sleep duration.

Youth internalizing and externalizing problems

The parent form of the 19-item Brief Problem Monitor (BPM)⁴⁰ measured two indices of youth behavioral health problems: internalizing and externalizing problems. BPM items were selected from the Child Behavior Checklist (CBCL) and Youth Self Report (YSR)⁴¹ using item response theory and factor analysis⁴². The internal consistency, test–retest reliability, and validity of the BPM are excellent^40,42. Each item is rated on a 0 to 2 scale (0 = not true, 1 = somewhat true, or 2 = very true). Higher scores indicate more internalizing (current α = .72) or externalizing (current α = .72) problems.

Peer problems

For the third indicator of behavioral health problems, the peer problem subscale of the Strengths and Difficulties Questionnaire⁴³ was used. Responses to each of the five peer problem items (e.g., playing alone; being bullied and generally not liked by other children) were rated on a 3-point Likert scale (0 = not true, 1 = somewhat true, or 2 = certainly true). Psychometric properties are well established^44–46. Higher scores indicate more peer problems (current α = .72).

Data Analytic Plan

Evaluation of the structural model

Path analysis to test the hypothesized structural model was conducted with Mplus 6.0 software⁴⁷. Because previous research recommends examining sleep duration and problems separately^20,23, models were run individually with sleep disturbances and then sleep duration as the link between screen time and behavioral health problems. The following fit statistics were employed to evaluate model fit: Chi-square (χ²: p > .05 excellent), Comparative Fit Index (CFI; > .90 acceptable, > .95 excellent), Root Mean Square Error of Approximation (RMSEA; < .08 acceptable, < .05 excellent) and the Standardized Root Mean Square Residual (SRMR; < .08 acceptable, < .05 excellent)^48,49. As missing data were less than 1% overall for all core variables, full information maximum likelihood estimation techniques were used for inclusion of all available data. The Model Indirect command in Mplus was utilized to calculate a standardized indirect effect parameter and biased-corrected bootstrap confidence intervals. Additionally, the ratio of the indirect effect to the total effect (ab/c)⁵⁰ for each significant indirect effect test was calculated.

Covariates

Although not included in the proposed conceptual model, the effects youth gender, parent marital status (one parent family vs. two-parent), and family income on the model were examined by running a multiple-indicator/multiple-cause MIMIC⁵¹ model in which all major constructs of the final model were regressed on the covariates separately. If paths in the structural model remained significant with the inclusion of these covariates, it was concluded that the control variables did not influence the relations among variables in the model.

Secondary analyses

In order to facilitate recommendations for families on how many hours of youth screen time is disruptive for sleep (i.e., disturbances and duration), one-way analysis of variance (ANOVA) with six levels of screen time (0– 2 hours as recommended by AAP; 2.1 to 4 hours; 4.1 to 6 hours; 6.1 to 8 hours; 8.1 to 10 hours; and 10.1 hours or more) was conducted with sleep disturbances and duration as the dependent variables.

Results

Primary Analyses

Sample demographics by developmental stage (young childhood, middle childhood, and adolescent samples) are presented in Table 1.

Table 1.

Sample demographic characteristics by developmental stage.

	M (S.D.) or Percentage
	Young Childhood n = 209	Middle Childhood n = 202	Adolescence n = 210
Parent Age	31.61 (6.57)	35.39 (6.45)	41.78 (7.66)
Parent (% Mothers)	59.8%	58.4%	62.4%
Parent Race
White	76.0%	77.2%	78.8%
Black	10.6%	10.4%	11.1%
Latino/a	5.8%	6.9%	5.8%
Asian	6.7%	4.0%	3.4%
Other	.9%	1.5%	.9%
Parent Marital Status
Single	18.4%	15.4%	18.8%
Married	58.7%	68.2%	66.3%
Cohabitating	22.8%	16.4%	14.9%
Parent Education
Did not complete H.S.	1.0%	0.0%	0.0%
H.S. or GED	12.5%	12.9%	11.9%
Some College	30.6%	25.2%	35.2%
College Degree	42.5%	41.1%	39.5%
> College Degree	13.4%	20.8%	13.3%
Family Income
Under $30,000	20.5%	19.8%	24.8%
$30,000 – $49,999	32.6%	29.2%	26.2%
$50,000 – $69,999	20.1%	16.8%	20.0%
$70,000 – $99,999	14.8%	18.8%	16.6
$100,000 or more	12.0%	15.4%	12.4
Family Neighborhood
Urban	27.8%	26.2%	25.7%
Suburban	51.7%	52.0%	51.0%
Rural	20.6%	21.8%	23.3%
Number of Children	1.67 (.85)	2.05 (1.36)	1.72 (.93)
Youth Age	4.47 (1.34)	9.46 (1.38)	14.70 (1.40)
Youth Gender (% Girls)	44.0%	46.0%	44.8%
Youth Screen Time (hours)	4.95 (4.1)	5.50 (4.3)	8.80 (5.1)
0 – 2 Hours	16.8%	8.0%	0%
2.1 – 4 Hours	37.0%	34.0%	9.3%
4.1 – 6 Hours	21.6%	25.5%	25.0%
6.1 – 8 Hours	12.0%	15.0%	24.5%
8.1 – 10 Hours	5.9%	8.0%	14.2%
> 10 Hours	6.7%	9.5%	27.0%

The multiple-group function in Mplus was used to determine model fit across all three developmental stages, but paths in the model were freely estimated by youth developmental stage. When sleep disturbances was included in the model, direct paths from youth screen time to behavioral health problems were non-significant across all developmental stages and thus, these paths were dropped in order to determine model fit. This final model demonstrated excellent fit, χ² (9, N = 613) = 10.73, p > .15, RMSEA = .03, 95% CI .00 – .088, CFI = 1.0, SRMR = .03, and is displayed by each child developmental stage in Figure 1. The standardized estimates of direct and indirect effects are presented in Table 2 along with bias-corrected bootstrap confidence intervals for all effects for each of the three developmental stages.

Figure 1.

The indirect effect of youth screen time on behavioral health problems through sleep disturbances.

Note: Y = Young Childhood; M = Middle Childhood; A = Adolescence; * = p < .05; ** = p <.01; non-significant paths indicated by dashed line.

Table 2.

Standardized estimates for the final structural model by youth developmental stage.

Paths in the model	Standardized Estimate [95% CI]
	Young	Middle	Adolescence
Sleep Disturbances
Screen Time – Sleep Disturbances	.17 [.04 – .30]	.15 [.02 – .29]	.23 [.10 – .36]
Sleep Disturbances – Internalizing Problems	.29 [.16 – .41]	.39 [.28 – .51]	.37 [.25 – .49]
Sleep Disturbances – Externalizing Problems	.39 [.27 – .50]	.47 [.37 – .58]	.38 [.26 – .50]
Sleep Disturbances – Peer Problems	.30 [.17 – .42]	.31 [.19 – .44]	.29 [.17 – .42]
Screen Time IND Internalizing Problems	.05 [.004 – .09]	.06 [.004 – .12]	.08 [.03 – .14]
Screen Time IND Externalizing Problems	.07 [.01 – .12]	.07 [.01 – .14]	.09 [.03 – .15]
Screen Time IND Peer Problems	.05 [.01 – .09]	.05 [.001 – .10]	.07 [.02 – .12]
Internalizing WITH Externalizing	.25 [.12 – .38]	.45 [.34 – .56]	.21 [.08 – .35]
Internalizing WITH Peer Problems	.23 [.10 – .35]	.34 [.22 – .47]	.45 [.34 – .56]
Externalizing WITH Peer Problems	.03 [−.11 – .16]	.29 [.16 – .41]	.24 [.12 – .37]
Sleep Duration
Screen Time – Sleep Duration	−.29 [−.48 – −.10]	−.53 [−.71 – −.36]	.14 [−.01 – .29]
Sleep Duration – Internalizing Problems	−.05 [−.20 – .11]	−.06 [−.21 – .08]	−.01 [−.16 – .14]
Sleep Duration – Externalizing Problems	−.10 [−.24 – .04]	−.22 [−.38 – −.06]	−.09 [−.23 – .05]
Sleep Duration – Peer Problems	−.06 [−.18 – .06]	−.05 [−.21 – .11]	.15 [−.01 – .31]
Internalizing WITH Externalizing	.33 [.17 – .48]	.55 [.43 – .68]	.32 [.18 – .47]
Internalizing WITH Peer Problems	.29 [.15 – .43]	.42 [.29 – .55]	.51 [.39 – .64]
Externalizing WITH Peer Problems	.13 [−.04 – .30]	.39 [.24 – .53]	.35 [.22 – .47]

Note: IND = Indirect Effect; CI = Confidence Intervals; CI that do not contain zero can be considered statistically significant

The statistically significant standardized estimates of pathways in the sleep disturbances model (Figure 1) were consistent across all three developmental stages: As predicted, higher levels of youth screen time were associated with higher levels of sleep disturbances which, in turn, were related to higher levels of internalizing, externalizing, and peer problems. Further, the indirect effect of youth screen time on youth internalizing, externalizing, and peer problems through sleep disturbances was significant across all developmental stages (see Table 2). The ratio of the indirect effect to the total effect for youth screen time on problem behaviors for the young, middle, and adolescent children ranged from 33% to 50% for internalizing problems, from 33% to 89% for externalizing problems, and from 44% to 98% for peer problems.

MIMIC models tested the demographic effects of youth gender, parent marital status, and family income on the associations in the model for each age group. All the major constructs of the model were regressed on the control variables separately. All paths in the structural model across all three samples were largely unaffected (i.e., remained significant without large reductions in effect size) by the inclusion of these control variables; thus, it was concluded that the control variables did not influence the original relations among variables in the model.

Next, the model was run with sleep duration in the model instead of disturbances. This model demonstrated excellent fit, χ² (9, N = 613) = 13.89, p > .10, RMSEA = .05, 95% CI .00 – .10, CFI = .98, SRMR = .04, see Table 2 for standardized estimates and 95% confidence intervals. Youth screen time was related to sleep duration for young and middle childhood, but not for adolescence. Sleep duration was only related to youth externalizing problems in middle childhood; all other paths from sleep duration to behavioral health outcomes were not significant. This model was not considered further (e.g., MIMIC models were not tested).

Secondary analyses

See the bottom of Table 1 for overall mean screen time and percentage of sample in each category. To examine how many hours of screen time is disruptive for sleep, sleep disturbances initially and then duration served as a dependent variable and six levels of youth screen time served as the independent variable in an ANOVA. A significant effect emerged for young childhood (F (5, 203) = 2.43, p < .05) and adolescence (F (4, 199) = 3.74, p < .01), but not for middle childhood (F (5, 194) = .60, p > .10); however, the pattern of means for middle childhood are consistent with the adolescent sample. Contrasts were performed in order examine the significant differences between screen time categories (see Figure 2 for estimated sleep disturbance means by screen time category). For middle childhood and adolescence, though only significant in adolescence (p < .01), 10 plus hours of screen time is associated with higher levels of sleep disturbances than all other levels of screen time. In contrast, for young childhood age, a steep increase in sleep disturbances begins after 6 hours of screen time (p < .05) and increases again after 8 hours (p < .01).

Figure 2.

Estimated means for sleep disturbances and duration by youth screen time.

When sleep duration served as the dependent variable, a significant effect emerged for young childhood (F (5, 202) = 2.8, p < .05) and middle childhood (F (5, 194) = 12.05, p < .001), but not for adolescence (F (4, 199) = .19, p > .10) (see Figure 2 for estimated sleep duration means by screen time category). For the middle childhood sample, sleep duration was not different for 0 – 2 and 2.1 – 4 hours of screen time (p > .15), whereas after 4 hours of screen time, sleep duration decreased significantly with each 2-hour increase in screen time (all p < .05). A similar pattern of effects emerged for the young childhood sample; sleep duration was not different for 0 −2, 2.1 – 4, and 4.1 – 6 (all p > .05) but decreased for each 2-hour increase in screen time afterward (all p < .05).

Discussion

This study examined the indirect effect of youth screen time on behavioral health problems through sleep duration and disturbances. Findings indicated that, regardless of the developmental stage of the youth, higher levels of youth screen time were associated with more sleep disturbances, which, in turn, were linked to higher levels of youth internalizing, externalizing, and peer problems. Contrary to expectation, sleep duration only served to link screen time to behavioral health problems for externalizing problems in middle childhood.

The consistency of findings for sleep disturbances across developmental stages ranging in age from 3 to 17 provides substantial support for the roles of youth screen time and sleep disturbances in youth behavioral health problems. Beyond the significant links in the model, the effect size of the indirect effect from youth screen time to all three types of behavioral health problems through sleep disturbances was substantial at each developmental stage. As most research has focused on young children¹⁵, the findings for older children and adolescents are particularly important.

Secondary analyses provided additional data in order to facilitate recommendations for families on how many hours of youth screen time is disruptive for sleep. At all three developmental stages, average screen time is substantially above that recommended by AAP⁷ but comparable to major industry reports⁶. For young children, sleep disturbances appear to emerge after 6 hours of daily screen time whereas for middle childhood and adolescence, these disturbances increase only at high levels of daily screen time use (10 hours or more). Sleep duration appears to decrease progressively after 4 or 6 hours of daily screen time for preadolescents. As both duration and quality of sleep are important for children and adolescents²⁵, the findings suggest that for preadolescents screen time above 4 to 6 hours daily is disruptive and for adolescents screen time above 10 hours daily is disruptive. Of importance, the findings are limited to sleep; higher levels of screen time may disrupt functioning in other areas (e.g., academic performance).

As sleep hygiene is important in the treatment of behavioral health difficulties⁵², the present findings suggest practitioners should assess for youth screen time and, if appropriate, include recommendations to reduce children’s screen time. As the adoption of mobile media devices continues to grow, it will be particularly important to provide parents with effective strategies for managing their child’s screen time (e.g., technology-specific parenting strategies).

There are several limitations of the current study. First, the data are cross-sectional, raising questions about the direction of effects and temporal precedence that are better addressed by longitudinal designs. Second, due to the crowdsourcing methodology, all variables in the model were from a single reporter. As this is a potential issue of shared method variance and parent’s reports of adolescent sleep and internalizing may be biased⁵³, the use of multiple reporters and methods (e.g., actigraphy) on constructs of interest could strengthen confidence of findings in future work. Third, we did not separate out weekend from weekday screen time and sleep. These are important topics for families; however, research has not yet addressed weekday vs. weekend measurement of these constructs. Research designs utilizing daily-diary or ecological momentary assessment methodologies will be well suited to examine the roles weekday vs. weekend assessment play. Fourth, our assessment of youth screen time did not account for overlapping use of multiple devices. Though our average screen time hours were in line with major industry reports⁶, future research should take into account potential device overlap and examine if simultaneous use of multiple devices further increases risk for behavioral health problems. Furthermore, screen time outside the home (e.g., friend’s home) was not considered in our assessment. Fifth, the current study did not include self-reported medical or behavioral health issues (e.g., ADHD). This is an important potential confounder and future research should include this information as potential covariates. Additionally, our assessment of sleep disturbances and youth problem behavior used measures not yet validated for children under six.

Sixth, the online nature of participant recruiting in the present study precludes the examination of parents who may not use the internet, possibly as a result of their perceptions of technology. Given that approximately 15% of adults in the United States do not use the internet⁵⁴, it will be important in future research to include these families. Seventh, the present study’s focus on negative effects of screen time precluded the examination of potential positive effects of screen time. For example, the importance of technology for a child’s academic success provides a source of unique tension for parents as they attempt to balance the positive and negative effects of screen time. Future research should incorporate positive effects of screen time, such as academic success, as an additional potential outcome associated with child screen time. Eighth, our measure of sleep disturbances was an abbreviated version of the full CSHQ. Though most items and subscales from this measure were not of interest to the current investigation, further research on the validity of the brief version is needed. Ninth, future research will benefit from utilizing objective methods of assessing sleep duration and quality²⁵.

An additional limitation of the current investigation is the simplification of the process by which high levels of screen time influence sleep and problem behaviors. Though necessary for initial stages of inquiry into this growing topic of research, future research should seek to examine the mechanisms involved for each pathway in the current model and do so with the child’s developmental stage as a central context. For example, it is plausible that the mechanisms by which high levels of screen time is related to sleep disturbance, which in turn is related to behavioral health, would differ depending on the child’s developmental stage. For example, adolescents may engage in screen time rather than sleeping whereas young children may be overstimulated by the games and, therefore, have a more difficult time settling in when it is time to sleep. Further, a developmentally-informed approach to identifying mechanisms of influence will greatly enhance both the breadth and confidence in findings as well as the broad scale generalizability of findings to families and children.

Conclusions

The use of mobile media devices continues to increase in childhood. The current findings suggest that excessive screen time is associated with reduced sleep duration in the preadolescent years and sleep disturbances in three age groups ranging from 3 to 17 years. In turn, sleep disturbances, and to a lesser extent duration, are associated with behavioral health problems. Effective interventions to decrease screen time need to be developed and tested for their effects on these negative outcomes. Once mechanisms have been identified within and across children at different developmental stages, interventions targeting these mechanisms can be designed and implemented. Potential targets for intervention may include psychoeducation for both parent and child regarding the consequences of excessive screen time, as well as targeting screen time indirectly through interventions aimed at improving parental efficacy when managing this behavior.