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. Author manuscript; available in PMC: 2023 May 1.
Published in final edited form as: Infant Behav Dev. 2022 Mar 7;67:101707. doi: 10.1016/j.infbeh.2022.101707

Predictors of Television at Bedtime and Associations with Toddler Sleep and Behavior in a Medicaid-Eligible, Racial/Ethnic Minority Sample

Elizabeth B Miller 1, Caitlin F Canfield 2, Helena Wippick 3, Daniel S Shaw 4, Pamela A Morris 5, Alan L Mendelsohn 6
PMCID: PMC9177719  NIHMSID: NIHMS1787031  PMID: 35272177

Abstract

This study examined predictors of TV use at bedtime and associations with toddlers’ sleep and behavior using data from the Smart Beginnings study with 403 Medicaid-eligible, racial/ethnic minority participants from two cities in the United States. We first estimated predictors of TV use at bedtime at 18 months. We then examined whether TV at bedtime was associated with concurrent parent-report of nighttime sleep duration and quality, and later problem behavior at 24 months. Results showed that around half of the sample reported using TV at bedtime with their toddlers, and particularly first-time mothers and those receiving public assistance. We also found that use of TV at bedtime was related to concurrent sleep issues and increases in later problem behavior. Mediational path analyses revealed that TV at bedtime affected behavior via sleep quality. Despite the heterogeneity within this Medicaid-eligible sample, the results underscore the universally harmful effects of TV use at bedtime and lend support for structuring nighttime routines for toddlers to promote better sleep and behavioral outcomes.

Keywords: bedtime, television, toddlers, poverty, racial/ethnic minority, behavior

1.0. Introduction

Sleep is an essential biological function, and as such, the American Academy of Sleep Medicine (AASM) and the American Academy of Pediatrics (AAP) have emphasized healthy sleep behaviors as vital for children’s early brain development and long-term outcomes (Paruthi et al., 2016). Of critical importance to healthy sleep behaviors is the establishment of a bedtime routine – a daily family routine in which parents engage their child in the same activities in the same order on a nightly basis prior to turning out the lights (Mindell et al., 2009) – that limits screen time to maximize the probability of promoting high sleep quality. The AAP Council on Communications and Media (2016) further reiterated the importance of establishing a bedtime routine free of screens to ensure children get adequate sleep each night. Despite these recommendations, recent evidence from birth cohort studies indicates that many children experience screen time (e.g., Dore & Dyria, 2021; Lee et al., 2018), and especially at bedtime (e.g., Beyens & Nathanson, 2019; Garrison, et al., 2011; Staples et al., 2021). These findings are particularly pronounced for low-income and racial/ethnic minority families compared with higher-income white families (Corkin et al., 2019; Duch et al., 2013).

The current study seeks to extend previous literature by examining sociodemographic, psychosocial, and household predictors of using television (TV) at bedtime and the associations between such TV use at bedtime and concurrent toddler nighttime sleep duration, concurrent parent-report of sleep problems, and later problem behavior in the Smart Beginnings study. It also examines mediators of these pathways. The Smart Beginnings study sample is exclusively Medicaid eligible and largely racial/ethnic minority. Therefore, the data allow us to examine additional predictors of TV at bedtime that may be masked by racial/ethnic and income-related differences in sleep and TV use and should therefore be generalizable across low-income populations. The present study is also one of the first to our knowledge to report on links not only between TV at bedtime and sleep duration and quality, but also links with later toddler behavior outcomes as mechanisms for action.

1.1. TV at Bedtime in the Context of Poverty – Theoretical Links

Socioeconomic differences in use of TV at bedtime may be due to both increased parental stress and limited parenting resources associated with poverty (Figure 1). Family stress theory posits that economic stress and emotional/relational distress associated with poverty disrupt parenting practices (McLoyd, 2011), whereas investment models of poverty suggest that low-income parents’ limited resources, particularly time and pay, make engaging in one-on-one interactions with children more difficult and limit responsive parent-child interactions (Mistry et al., 2010). Indeed, many of the factors associated with use of media at bedtime are more prevalent for low-income families. Increased family stress and limited resources may therefore lead to increased reliance on media use, both to entertain children and as part of a bedtime routine (Bagley et al., 2015; Certain & Kahn, 2002). Further, lower maternal education and increased household chaos have been associated with increased screen time overall and, for chaos, specifically at bedtime (Emond et al., 2018; Trinh et al., 2020), whereas maternal stress is associated with less parental monitoring of screen use (Tang et al., 2020). Lower parenting self-efficacy is also associated with longer amounts of daily TV viewing for children ages 3 to 5 years (Jago et al., 2013). Consistent with family stress and parent investment models, these findings are in line with our conceptual framework, which posits that poverty-related factors will influence toddler outcomes by increasing parent stress and reducing resources, specifically by providing less structure at bedtime and increasing TV use. Moreover, the pathways through which poverty impact bedtime reliance on media are in turn hypothesized to influence toddler sleep and behavior, resulting in behavioral disparities.

Figure 1.

Figure 1.

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Conceptual model linking poverty to bedtime routines, child sleep outcomes and child behavior.

1.2. Relations between TV at Bedtime and Children’s Sleep

Consistently using bedtime routines is one way for parents to promote good sleep practices for young children. Mindell and colleagues (2015) found that infants and toddlers whose parents consistently reported using a bedtime routine slept for longer durations at night than children with parents who reported inconsistent or no bedtime routines, with these children also falling asleep faster, having less frequent and shorter nighttime wakings, and fewer sleep problems. Bedtime routines may not be as beneficial for sleep, however, if they include the use of screens. Several studies have indicated that screen time, particularly in the evening, is associated with several sleep problems, including delayed bedtime and shorter sleep duration, possibly due to arousing content and suppression of endogenous melatonin from emitted blue light (Beyens & Nathanson, 2019; Garrison, et al., 2011). In older children aged 6 to 7 years, increased television viewing at bedtime was associated with not only sleep delays and shorter duration, but also with bedtime resistance and irregularity (Khazaie et al., 2019). The same negative impacts have been seen in toddlers and children aged 3 to 5 years, for whom increased evening television use was associated with sleep problems, including sleep delays and poor sleep consolidation (Beyens & Nathanson, 2019; Garrison et al., 2011), and more night-to-night variability in duration and timing of sleep (Staples et al., 2021).

1.3. Prevalence and Predictors of TV Use at Bedtime

Despite these documented negative associations between TV and children’s sleep, there is wide variation in the literature in terms of the prevalence and predictors of such TV use. Reports of TV use during the infant and toddler periods are generally high, ranging from 65% (Dore & Dynia, 2021) to 80% (Staples et al., 2021). As noted above, many of the predictors of TV use during the infant and toddler years, and particularly at bedtime, include factors associated with poverty-related stress and limited resources, including low levels of maternal education, increased household chaos, racial/ethnic minority status, and primiparity (Dore & Dynia, 2021; Emond et al., 2018; Levine et al., 2019; Trinh et al., 2020). These findings were further supported by two systematic reviews on predictors of screen time (Corkin et al., 2021; Duch et al., 2013). In addition, maternal depression and lower parenting self-efficacy were associated both with increased use of TV as well as such TV use interfering with the parent-child relationship (Coyne et al., 2020; Dore & Dynia, 2021; Jago et al., 2013). Finally, general household norms around TV watching and wider household ecological factors such as adult TV watching, child exposure to adult-directed TV, beliefs about maximum amounts of screen time for children, and use of TV as a reward contributed to higher use of TV in young children (Corkin et al., 2019; Elias & Sulkin, 2019). Although these studies provide a helpful general overview, they are largely comparative between racial/ethnic or SES groups, and therefore have not looked at predictors of TV use in an exclusively low-income, racial/ethnic minority sample like Smart Beginnings, leading to gaps in our understanding of additional predictors of TV at bedtime that may be masked by racial/ethnic and income-related differences in sleep and TV use.

1.4. Consequences for Child Behavior

TV, particularly at bedtime, has been shown to be directly associated with children’s behavior. For example, TV watching for children aged 3 to 4 years was associated with poorer social skills at age 5 (Jackson, 2018; Mistry et al., 2007) as well as increases in aggressive, anxious, and hyperactive behavior (Séguin & Klimek, 2016). Additionally, Christakis and colleagues (2004) found TV exposure at ages 1 and 3 to be associated with attentional problems at age 7, a finding shown to persist into adolescence (Landhuis, et al., 2007). Among younger children, infant exposure to TV at 6 months was associated with problems relating to emotion reactivity, aggression, and externalizing behavior at 18 months (Chonchaiya et al., 2015) and 3 years of age (Manganello & Taylor, 2009).

TV use at bedtime may also be associated with later behavior indirectly though poor sleep duration and quality. Children who get insufficient or poor-quality sleep face a number of behavioral consequences (Paruthi et al., 2016; Sadeh et al., 2015) in addition to many other negative health outcomes, including obesity and depression. Sleep problems, including frequent nighttime wakings and shortened duration of sleep which can result from TV at bedtime, interfere with children’s ability to regulate their emotions and sustain attention, and can increase children’s aggressive behavior. For example, one large study of over 30,000 families found that for toddlers, less sleep at night was associated with higher rates of externalizing problems (Sivertsen et al., 2015). Toddlers not getting enough sleep were more at risk for these problems both concurrently and later at age 5, and the relationship was dose-dependent such that children sleeping the least were the most at risk (Sivertsen et al., 2015). In two additional studies, Komada and colleagues (2011) found an association between poor sleep habits and behavior problems in children between 2 to 5 years of age. Shorter sleep duration was associated with more aggressive behavior, and irregular bedtimes were associated with more aggressive behavior and attention problems. Further, in a large birth cohort study, links between sleep problems and attention problems in infancy and early childhood were shown to persist into adolescence (O’Callaghan et al., 2010). Despite the potential of these findings, limited longitudinal research has been conducted to determine whether sleep duration or quality are specific mechanisms through which TV at bedtime impacts child behavior.

1.5. Present Study

Based on previous findings that many households extensively rely on screens (Dore & Dyria, 2021; Lee et al., 2018) and especially at bedtime (Beyens & Nathanson, 2019; Garrison, et al., 2011; Staples et al., 2021), particularly low-income and racial/ethnic minority families (Corkin et al., 2019; Duch et al., 2013), the present study examined early predictors of TV at bedtime, as well as links from TV habits to concurrent sleep outcomes and later toddler attention problems and aggressive behavior. We addressed two primary research questions: 1) What are the sociodemographic, psychosocial, and household predictors of TV use at bedtime at 18 months?; and 2) Is use of TV at bedtime at 18 months associated with toddlers’ concurrent nighttime sleep duration and sleep problems, and later behavior problems at 24 months?

Consistent with prior literature and in line with our conceptual model, we hypothesized that certain sociodemographic factors such as higher maternal education would predict reduced use of TV at bedtime, whereas others, like public assistance receipt, a proxy for reduced material resources, would predict greater use of TV. In addition, we predicted that higher levels of maternal depression and lower parental self-efficacy would be associated with increased use of TV at bedtime. For our second research question, we hypothesized that TV at bedtime would be associated with reduced hours of sleep, increases in sleep problems, and later increases in attention problems and aggressive behavior. Lastly, we hypothesized the association between bedtime routines involving TV and toddler behavior would be mediated by sleep duration and quality given their importance for child outcomes.

This study is unique in that it was not comparative among racial/ethnic and socioeconomic groups as traditionally studied in the literature, but rather examined other sociodemographic, psychosocial, and household predictors of these behaviors that have been traditionally confounded with race/ethnicity and poverty in comparative studies. Further, by considering links between TV at bedtime and sleep duration and quality and then in turn links with toddler behavior, as well as mechanisms of action, this study enumerates important pathways for how health behaviors in the first two years, a critical period for healthy development, can hinder optimal early outcomes.

2.0. Method

This study was a secondary data analysis of the larger Smart Beginnings randomized controlled trial (RCT) with 403 families taking place in New York, NY and in Pittsburgh, PA. The study was conducted in accordance with the ethical standards of the American Psychological Association. Informed consent was obtained from all study participants. The original study was registered in clinicaltrials.gov, and IRB approval was obtained from all partnering institutions. Smart Beginnings integrates the use of two evidence-based interventions in pediatric primary care with the aim of enhancing early psychosocial development and school readiness of children in poverty through positive parenting practices and reduction of psychosocial stressors. Smart Beginnings includes: 1) a universal primary prevention strategy (Video Interaction Project (VIP); Mendelsohn et al., 2005), which was provided for all families randomly assigned to the intervention group; and 2) a targeted home-based secondary prevention strategy (The Family Check Up (FCU); Dishion & Stormshak, 2007), for intervention families with additional psychosocial or child behavior risks. As this paper was a secondary analysis using the Smart Beginnings data, we have kept our description of the intervention purposely brief as it was not the primary focus of this study; please see Roby et al., (2021) for more detailed information on the Smart Beginnings study.

2.1. Participants

Mothers and infants were enrolled in the Smart Beginnings RCT in postpartum units in NYC (N = 200, treatment arm n = 101) and in Pittsburgh (N = 203, treatment arm n = 100). Descriptive statistics of the sample are listed in Table 1. The sample was entirely composed of Medicaid-eligible, low-income mothers at both sites, with about a third primiparous (first-time birth); however, there were many notable site-specific differences. The majority of mothers in NYC were Latinx (84%), whereas in Pittsburgh they were predominantly Black/African-American (81%). Furthermore, NYC mothers also had much higher rates of marriage (32% vs. 4%, p < .001) and cohabitation (49% vs. 36%, p < .05) but were less likely to be high school graduates (56% vs. 84%, p < .001) compared with mothers in Pittsburgh. Study participant retention across the sites was quite high; retention rates at the 18- and 24- month assessments were 81% and 82%, respectively. Families with and without complete survey data were compared on a wide array of baseline characteristic and none were significant, F(21, 268) = 0.81, p = 0.71.

Table 1.

Descriptive Statistics of Smart Beginnings by Site

NYC Sample (N = 200) Pitt Sample (N = 203) Combined Site Sample (N = 403)
Mean / Percent of Sample SD Mean / Percent of Sample SD Mean / Percent of Sample SD
Target Child Characteristics - Baseline
 Gender - Female 49% 50% 50%
 Race
  Asian 2% 0% 1%
  African-American 8% 90% *** 50%
  White 1% 5% * 3%
  Latino 84% 2% *** 42%
  Other 6% 3% 4%
 Child Age in Months - 18 Month Survey 18.76 1.37 19.17 **1.46 18.97 1.43
Primary Caregiver Characteristics - Baseline
 Race
  Asian 3% 0% * 2%
  African-American 8% 81% *** 45%
  White 2% 12% ** 7%
  Latino 84% 3% *** 43%
  Other 3% 3% 3%
 Marital status
  Married 32% 4% *** 18%
  Cohabiting partner 49% 36% * 42%
  Non-cohabiting partner 11% 35% *** 23%
  Biological father is current partner 98% 94% 96%
 Level of education 7.64 3.17 8.67 1.74 8.17 2.59
  HS grad 56% 84% *** 70%
  Some college 32% 37% 34%
 First birth 36% 33% 34%
 Teenaged mom at TC birth 4% 9% * 6%
 Maternal depression 3.15 3.91 3.81 4.16 3.49 4.05
 Maternal parenting self-efficacy 27.84 3.49 29.23 ***1.54 28.55 2.76
Family Household Characteristics - Baseline
 Crowding ratio 1.40 0.57 0.86 ***0.31 1.12 0.53
 Household chaos 3.17 3.27 3.71 3.91 3.44 3.62
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Note. Percents are listed for dichotomous variables; means are listed for continuous variables. Level of education is an ordinal scale from 1 (No formal schooling) to 15 (Completed post-college graduate or professional school), with 7 = High school diploma/technical, 8 = High school diploma/ academic, and 9 = GED.

Site Differences:

*

p < 0.05.

**

p < 0.01.

***

p <0.001.

2.2. Measures

2.2.1. Bedtime TV Behaviors

To understand daily household routines, questions regarding bedtime TV behaviors were administered as part of the parent survey when children were 18 months old. The primary caregiver was asked how many nights each week they used TV as part of getting their child ready for bed. Preliminary analyses revealed this variable to have a bimodal distribution peaking at 0 and 7 nights, respectively, so in addition to utilizing this continuous measure, we created a dichotomous variable indicating whether TV was used any night as part of getting their child ready for bed (1=Yes, 0=No). Parents were also asked additional questions on other forms of media consumption at bedtime, such as tablets or smartphones, but close to 80% responded their children did not use such devices any night (i.e., a unimodal distribution peaking at 0 nights). This distribution is consistent with national data that most screen time for toddlers involves a TV over other screens (72%; Staples et al., 2021). For these reasons, we focused on TV as the primary medium for media at night.

2.2.2. Primary Predictors

As predictors of sleep behaviors and TV use, we included several sociodemographic, psychosocial, and household characteristics collected as part of the baseline survey following the target child’s birth. Predictors were selected based on the theoretical premises of family stress and resource investment models as well as prior work demonstrating associations with use of TV at bedtime for toddlers (Corkin at al., 2021; Duch et al., 2013; Emond et al., 2018; Staples et al., 2021). These variables included sociodemographic characteristics such as first birth, public assistance receipt, including Temporary Assistance for Needy Families (TANF) or Supplemental Security Income (SSI), which have stricter eligibility requirements compared with other programs, as well as level of maternal education and whether the mother was teenaged at target child’s birth. Psychosocial and household predictors included maternal depression as measured by the Edinburgh Postnatal Depression Scale (EPDS; Cox et al., 1987; α = .87), parenting self-efficacy using the Karitane Parenting Confidence Scale (KPCS; Crncec et al., 2008; α = .81) with modified anchor points to aid in understanding in low-literacy populations (D’Alonzo, 2011), an indicator of household chaos using the Chaos, Hubbub, and Order scale (CHAOS; Matheny et al., 2005; α = .79), and an indicator of household crowding as defined by more than one person per room in a dwelling (U.S. Department of Housing and Urban Development, 2014). Analyses also controlled for Smart Beginnings intervention status.

2.2.3. Outcome Measures

2.2.3.1. Child Sleep.

Child sleep was assessed when target children were 18 months old in two ways. First, the primary caregiver was asked to report the total number of hours their child typically slept at night (duration), which was treated as a continuous variable. Second, sleep problems (a measure of quality) were assessed using the Child Behavior Checklist – Preschool (CBCL; Achenbach & Rescorla, 2000) sleep problems subscale (7 items; sample α=0.72). For this subscale, parents were presented with a list of sleep issues and were asked to report how true the item was of their particular child from 0 (Not True) to 2 (Very True or Often True).

2.2.3.2. Child Behavior Problems.

Child behavior was assessed at 24 months using the CBCL attention problems (5 items; sample α=0.71) and aggressive behavior (19 items; sample α=0.87) subscales. As with the sleep problems subscale, for the attention problems and aggressive behavior subscales, parents were presented with list of behavior issues and were asked to report how true the item was of their child from 0 (Not True) to 2 (Very True or Often True).

2.3. Analysis Plan

Following presentation of descriptive analyses, logistic and ordinary least squares (OLS) regression analyses were then performed to examine sociodemographic, psychosocial, and household characteristics predictors of TV at bedtime (both the continuous and dichotomous measures), while controlling for treatment group. We also controlled for site in our analyses as randomization occurred within each site and because of the notable site-specific differences in our sample. For our second research question, we first tested associations between TV at bedtime and concurrent sleep duration at night and parent-report of sleep problems at 18 months, and then tested associations of TV at bedtime with children’s behavior at 24 months, using OLS regressions, while controlling for site and treatment group. All regression models pooled the sites to increase statistical power and precision of the estimates. Lastly, we conducted mediation path analyses to determine whether TV at bedtime was related to later toddler behavior problems via sleep duration and quality at night. All analyses, including tests of mediation, were conducted using Stata 14 (StataCorp, 2015).

3.0. Results

3.1. Descriptive Results

Descriptive analyses (Table 2) revealed that slightly more than half of the Smart Beginnings sample (52%) reported using TV on any night as part of getting their child ready for bed. Further, the average total hours of nighttime sleep in the sample was close to 10 hours. The CBCL sleep and behavior problems were positively skewed; the average number of reported sleep problems was 2.81 (out of a possible 14), the average number of attention problems was 2.66 (out of a possible 10), and the average number of aggressive behavior problems was 9.52 (out of a possible 38).

Table 2.

Descriptive Statistics Sleep, TV, and Behavior Variables

Mean / Percent of Sample SD
Sleep and Bedtime TV Variables
 TV at bedtime 52%
 Total hours of nighttime sleep 9.70 1.59
 CBCL sleep problems 2.81 2.29
 CBCL aggressive behavior problems 9.52 6.32
 CBCL attention problems 2.66 2.09
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Note. Percents are listed for dichotomous variables; means are listed for continuous variables.

CBCL = Child Behavior Checklist. N = 332.

Bivariate correlations between the TV, sleep, and behavior variables are presented in Table 3 and indicate that in general, the measures were modestly significantly related in the expected direction. TV at bedtime was associated with fewer concurrent total hours of nighttime sleep (r=−0.22, p<.001) and increased sleep problems (r=0.21, p<.001), as well as later aggressive behavior (r=0.17, p<.01) and attention problems (r=0.12, p<.05).

Table 3.

Correlation Table between Sleep, TV, and Behavior Variables

TV at bedtime Total hours of nighttime sleep CBCL sleep problems CBCL aggressive behavior problems CBCL attention problems
TV at bedtime 1.00
Total hours of nighttime sleep −0.22*** 1.00
CBCL sleep problems 0.21*** −0.15** 1.00
CBCL aggressive behavior problems 0.17** −0.05 0.36*** 1.00
CBCL attention problems 0.12* 0.31 0.34*** 0.68*** 1.00
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Note.

*

p < 0.05.

**

p < 0.01.

***

p < 0.001.

3.2. RQ 1 – Predictors of TV at Bedtime

The results for research question 1 are reported in Table 4.

Table 4.

Analyses Predicting TV at Bedtime from Sociodemographic, Psychosocial, and Household Characteristics

TV Use at Bedtime
AOR (SE)
Sociodemographic Characteristics
 Teenaged mom at TC birth 0.33 (0.20)
 First birth 2.61 (0.78)***
 TANF or SSI receipt 2.86 (1.00)**
 HS grad 0.97 (0.06)
Psychosocial Characteristics
 Maternal depression 1.04 (0.03)
 Maternal parenting self-efficacy 1.03 (0.05)
Household Characteristics
 Crowding ratio 1.01 (0.30)
 Household chaos 1.04 (0.04)
Covariates
 SB intervention group 1.27 (0.32)
 Site 0.76 (0.51)
  Pseudo R2 = 0.10
χ2(14, 326) = 40.99, p<.001
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Note. N = 326. AOR = adjusted odds ratio. TANF = Temporary Assistance to Needy Families. SSI = Supplemental Security Income.

*

p < 0.05.

**

p < 0.01.

In regression analyses for this outcome, we used a hierarchical logistic model in which we examined both unstandardized and standardized regression coefficients for the continuous measure of TV at bedtime and adjusted odds ratios (AOR) for the dichotomous version. Sociodemographic variables were included in step one and caregiver psychosocial and household characteristics were added in step two. Because there were no differences in the predictive significance of sociodemographic variables between steps one and two, only the full model is shown in Table 4. Further, because both the continuous and dichotomous versions of the TV use at bedtime measure provided consistent findings, for parsimonious presentation we only display the results with the dichotomous measure in Table 4; however, the continuous results are available upon request. Results indicated that primiparous mothers (AOR= 2.61, p < 0.001) were more likely to use TV as part of putting their children to bed at night, as were mothers who received TANF or SSI support (AOR= 2.86, p < 0.01). The Smart Beginnings intervention did not predict TV use at bedtime.

3.3. RQ 2 – Associations between TV at Bedtime and Toddler Sleep and Behavior

The results for research question 2 are reported in Table 5.

Table 5.

Analyses Relating TV at Bedtime, Concurrent Toddler Sleep at 18 Months, and Toddler Behavior at 24 Months
Total Hours of Nighttime Sleep CBCL Sleep Problems CBCL Aggressive Behavior Problems CBCL Attention Problems
b (SE) β b (SE) β b (SE) β b (SE) β
TV use at bedtime −0.50 (0.17)** −0.15 1.05 (0.26)*** 0.23 2.02 (0.76)** 0.16 0.53 (0.25)* 0.13
SB intervention group 0.18 (0.17) 0.06 −0.09 (0.25) −0.02 −0.39 (0.74) −0.03 0.02 (0.25) 0.01
Site −0.97 (0.17)*** −0.30 −0.39 (0.26) −0.08 0.72 (0.76) 0.06 0–.13 (0.25) −0.03
R2= 0.14
F(3, 309) = 16.50, p<.001		 R2= 0.05
F(3, 313) = 5.57, p=.001		 R2= 0.03
F(3, 268) = 3.23, p=0.02		 R2= 0.02
F(3, 268) = 1.49, p=0.22
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Note. b = unstandardized regression coefficient. β = standardized regression coefficient. Standard errors in parentheses. OLS estimates reported in table. N = 332.

*

p < 0.05.

**

p < 0.01.

***

p < 0.001.

We first estimated whether TV at bedtime was associated with concurrent toddler sleep using OLS regressions controlling for site and treatment group. Results indicated that TV at bedtime was associated with fewer concurrent total hours of nighttime sleep (b= −0.50, β= −0.15, p < 0.01) and increased sleep problems (b=1.05, β= 0.23, p < 0.001).The Smart Beginnings intervention was not associated with either outcome.

Next, for associations with later toddler behavior problems, we ran a series of models predicting two CBCL subscale outcomes – attention problems and aggressive behavior problems at 24 months – using OLS regressions and controlling for site and treatment group. Results indicated that TV at bedtime was associated both with later aggressive behavior (b= 2.02, β= 0.16, p < 0.01) and attention problems (b=0.53, β= 0.13, p < 0.05). The Smart Beginnings intervention was not associated with either later outcome.

Finally, we conducted path analyses to determine if TV at bedtime was associated with toddlers’ later aggressive behavior and attention problems at 24 months via total nighttime sleep duration and sleep problems at 18 months. We included sleep problems at 24 months as a covariate in order to test for the direction of effects between toddlers’ sleep and behavior outcomes. Results are displayed in Figure 2 and indicate that the relationship between TV at bedtime and toddlers’ later aggressive behavior and attention problems was mediated by parent-reported sleep problems (quality), though not by reductions in total hours of nighttime sleep (duration). The results further lend credence to the established direction of effects in this paper.

Figure 2.

Figure 2.

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Mediation model predicting a) attention problems and b) aggressive behavior from TV use at bedtime through parent-reported sleep problems.

3.4. Robustness Check

Our primary analyses controlled for site because randomization occurred within each site and because of notable site-specific differences (Table 1). Nonetheless, based on research documenting racial/ethnic differences in use of TV at bedtime (Corkin et al., 2019; Duch et al., 2013), as a robustness check we controlled for maternal race rather than site. The results are nearly identical to the ones presented in this paper. Therefore, for parsimonious presentation, they are not included here; however, they are available upon request.

4.0. Discussion

This study examined sociodemographic, psychosocial, and household predictors of TV use at bedtime, as well as associations with toddler sleep and later toddler behavior in a sample of exclusively racially/ethnically diverse, low-income parents with young children. As previous studies of sleep and TV behaviors were mainly comparative between racial/ethnic and socioeconomic groups, this paper allowed us to examine additional predictors of these TV behaviors that have been traditionally confounded with race/ethnicity and poverty in other comparative studies. Further, by considering links not only between TV at bedtime and sleep quality and duration, but also links with later toddler behavior, this study utilized mediation path analyses to enumerate the importance of health behaviors, such as sleep and media practices, and relations between young children’s nighttime routines with later child problem behavior.

4.1. Overview of Study Findings

With regard to predictors of TV at bedtime, our findings largely affirmed study hypotheses and converged with previous literature. Though lower than national estimates (65–80%; Dore & Dynia, 2021; Staples et al., 2021), around 50% of parents in our sample reported using TV as part of putting their toddlers to bed. Further, sociodemographic variables predicted bedtime routines in the expected directions; parity and public assistance receipt (a proxy for reduced material resources) were associated with increased use of TV at bedtime, consistent with recent work (e.g., Coyne et al., 2020; Trinh et al., 2020). We did not, however, find evidence for associations between established household characteristics, such as chaos, and TV at bedtime, which diverges from other recent studies (Emond et al., 2018). It is possible that the lack of relations between TV at bedtime and these household characteristics was partly because of high positive skewness in the current sample; almost all parents in the Smart Beginnings study reported low levels of household chaos. Interestingly, we also did not find evidence that the Smart Beginnings intervention, designed to promote responsive parenting and reduce psychosocial stressors, was associated with reduced use of TV at bedtime, perhaps because media use was not an explicit focus of the intervention.

In addition, in line with medical evidence cited by the AAP (2016) and prior literature (e.g., Garrison et al., 2011; Mindell et al., 2015), TV at bedtime was associated with fewer concurrent hours of nighttime sleep and more parent-reported sleep problems. We further found evidence of relations between toddler use of TV at bedtime at 18 months and subsequent behavior at 24 months; use of TV at bedtime was related to increases in aggressive behavior as well as attention problems. These findings are in line with previous research documenting relations between TV use generally and increased aggression and attention problems from toddlerhood through adolescence (Christakis et al., 2004; Chonchaiya et al., 2015; Landhuis, et al., 2007; Manganello & Taylor, 2009; Séguin & Klimek, 2016). Moreover, none of the impacts of the Smart Beginnings intervention on sleep and behavior outcomes reached the threshold for statistical significance, though all were generally in the expected direction. Although surprising given the focus of the intervention on responsive parenting, this finding may be due to the young ages of the participants. Whereas sleep and behavior problems are common in toddlerhood, they may not be clinically meaningful. Our data supports this possibility, as most of the variability was in the low end of these scales, resulting in the potential difficulty of achieving significant intervention-induced change on these outcomes.

In addition to relations between TV at bedtime with increases in later toddler behavior problems, we further found evidence that this relationship was mediated by parent reports of toddler sleep problems, though not by total hours of nighttime sleep. That is, although TV at bedtime was related to fewer hours of sleep at night, the association between TV at bedtime and toddlers’ aggressive behavior was partially accounted for by sleep problems (a measure of quality) rather than duration. This study is one of the first to examine sleep problems as an explicit mechanism through which TV use at bedtime is associated with later behavior problems. These findings are in line with recent research examining these questions separately, which have highlighted that toddler sleep problems in particular are related to subsequent behavior (Komada et al., 2011; O’Callaghan et al., 2010; Sivertsen et al., 2015). Recent research has also indicated that relations between media use and sleep duration and efficiency are moderated by children’s effortful control, such that for children with high effortful control, there were few associations with sleep duration, perhaps because these children were better able to disengage from media activities (Clifford et al., 2020). Effortful control may be similarly related to later behavior problems, which may account for the lack of significant mediation via sleep duration in the current study. Future research should further explore this complex association.

4.2. Implications

These findings are consistent with our theoretical framework grounded in family stress and resource models of poverty that posit the manner in which parents structure children’s nighttime activities like sleep routines and screen time can affect children’s behavior (Figure 1). The pathways through which poverty impacts children’s development in the context of screens and sleep are illustrated through associations with TV at bedtime, which in turn influence toddler sleep, resulting in disparities in problem behaviors. The findings are also consistent with AAP and AASM recommendations for healthy sleep and screen time, and based on the scope of these findings, support continued adherence to these guidelines for parents and in anticipatory guidance from clinicians. In fact, screening for nighttime media use may be useful in assessing parent-reported problems related to children’s sleep and/or behavior, and raises the need for additional guidance in this area as part of a comprehensive plan to address these issues. Importantly, despite the heterogeneity within the Smart Beginnings sample, the findings underscore the universally harmful effects of TV use at bedtime, which behooves front-line workers in medical offices and other social support institutions such as Women, Infants, and Children Nutritional Supplement (WIC) to continue reiterating messages regarding the potential negative impacts of TV use at bedtime to parents. As prior experimental work demonstrates (Mindell et al., 2016), parent education around healthy sleep confers benefits.

4.3. Limitations and Future Directions

Some study limitations should be noted. First, the results of this study are not causal and should not be interpreted as such; however, extensive robustness checks and the use of both continuous and dichotomous measures of our primary predictor lend credence to the findings. Second, there were challenges in measurement with the sole reliance on parent report for all data. In particular, the measures of sleep relied on parent report versus more objective actigraphy data monitoring rest and activity cycles. Nonetheless, in the absence of such actigraphy data, parents can serve as a reliable alternative source for assessing sleep problems and TV use, particularly in very young children (Sadeh, 1996; Staples et al., 2021). Future studies can employ multiple informants and methods about sleep routines and TV use.

Future studies should attempt to parse out how screens at bedtime may be mediated by parent behaviors related to both bedtime difficulty and confidence managing their toddlers’ sleep. That is, it may be that by 18 months of age, behavioral challenges related to bedtime increase the likelihood of TV use as part of the bedtime routine, suggesting that the relation between TV use at bedtime and behavioral problems may be part of a reciprocal process. Future work can further explore this question by examining how TV use is associated with child outcomes and parenting behaviors focused on structuring routines. Future work can also examine how sleep and TV habits change as children age and how such changes may contribute to later outcomes in early adolescence, another period of rapid hormonal change and brain development. Lastly, future research can also continue to examine if limits on TV in and of themselves are related to better child outcomes, or if it is a more indirect relationship, as possibly suggested in this study, in that households with these limits are likely to promote healthy sleep habits overall, which might be more influential than sleep duration per se on later child behavior.

4.4. Conclusion

In sum, this study examined sociodemographic, psychosocial, and household predictors of use of TV at toddlers’ bedtime, as well as associations between toddler sleep and later toddler behavior outcomes in an exclusively low-income, racially/ethnically-diverse sample of very young children. Despite the heterogeneity within this exclusively Medicaid-eligible and racial/ethnic minority sample, the results underscore the universally harmful effects of TV use at bedtime. The findings further lend support for the importance of focusing on TV at bedtime and parent structuring of toddlers’ bedtime activities to potentially promote later child outcomes.

Highlights.

  • Half of the SB sample reported using TV at bedtime with their toddlers

  • Particularly first-time mothers and those on public assistance

  • TV at bedtime was related to sleep duration and quality as well as problem behavior

  • TV at bedtime affected problem behavior via poorer 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 R01HD076390. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

CRediT authorship contribution statement

Elizabeth B. Miller: Conceptualization, Methodology, Software, Supervision, Data curation, Visualization, Writing- Original draft preparation, Writing- Reviewing and Editing. Caitlin F. Canfield: Conceptualization, Methodology, Visualization, Writing- Original draft preparation, Writing- Reviewing and Editing. Helena Wippick: Conceptualization, Methodology, Investigation, Writing- Original draft preparation, Writing- Reviewing and Editing.Daniel S. Shaw: Conceptualization, Methodology, Writing- Original draft preparation, Writing- Reviewing and Editing.Pamela A. Morris: Conceptualization, Methodology, Writing- Original draft preparation, Writing- Reviewing and Editing.Alan L. Mendelsohn: Conceptualization, Methodology, Writing- Original draft preparation, Writing- Reviewing and Editing.

The authors declare no actual or perceived conflict of interest in the conduct and reporting of research.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Contributor Information

Elizabeth B. Miller, NYU Grossman School of Medicine

Caitlin F. Canfield, NYU Grossman School of Medicine

Helena Wippick, New York University.

Daniel S. Shaw, University of Pittsburgh

Pamela A. Morris, New York University

Alan L. Mendelsohn, NYU Grossman School of Medicine

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