Preschool Sleep Problems and Differential Associations With Specific Aspects of Executive Control in Early Elementary School.

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Preschool Sleep Problems and Differential Associations With Specific Aspects of Executive Control in Early Elementary School a

ab

a

Timothy D. Nelson , Jennifer Mize Nelson , Katherine M. Kidwell , ab

Tiffany D. James

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& Kimberly Andrews Espy

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Department of Psychology, University of Nebraska–Lincoln, Lincoln, Nebraska b

Office of Research, University of Nebraska–Lincoln, Lincoln, Nebraska c

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Department of Psychology, University of Arizona, Tucson, Arizona Published online: 07 Jul 2015.

To cite this article: Timothy D. Nelson, Jennifer Mize Nelson, Katherine M. Kidwell, Tiffany D. James & Kimberly Andrews Espy (2015) Preschool Sleep Problems and Differential Associations With Specific Aspects of Executive Control in Early Elementary School, Developmental Neuropsychology, 40:3, 167-180, DOI: 10.1080/87565641.2015.1020946 To link to this article: http://dx.doi.org/10.1080/87565641.2015.1020946

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DEVELOPMENTAL NEUROPSYCHOLOGY, 40(3), 167–180 Copyright © 2015 Taylor & Francis Group, LLC ISSN: 8756-5641 print / 1532-6942 online DOI: 10.1080/87565641.2015.1020946

Preschool Sleep Problems and Differential Associations With Specific Aspects of Executive Control in Early Elementary School


Timothy D. Nelson Department of Psychology, University of Nebraska–Lincoln, Lincoln, Nebraska

Jennifer Mize Nelson Department of Psychology, University of Nebraska–Lincoln, Lincoln, Nebraska, and Office of Research, University of Nebraska–Lincoln, Lincoln, Nebraska

Katherine M. Kidwell Department of Psychology, University of Nebraska–Lincoln, Lincoln, Nebraska

Tiffany D. James Department of Psychology, University of Nebraska–Lincoln, Lincoln, Nebraska, and Office of Research, University of Nebraska–Lincoln, Lincoln, Nebraska

Kimberly Andrews Espy Department of Psychology, University of Arizona, Tucson, Arizona, and Department of Psychology, University of Nebraska–Lincoln, Lincoln, Nebraska

This study examined the differential associations between parent-reported child sleep problems in preschool and specific aspects of executive control in early elementary school in a large sample of typically developing children (N = 215). Consistent with expectations, sleep problems were negatively associated with performance on tasks assessing working memory and interference suppression inhibition, even after controlling for general cognitive abilities, but not with flexible shifting or response inhibition. The findings add to the literature on cognitive impairments associated with pediatric sleep loss and highlight the need for early intervention for children with sleep problems to promote healthy cognitive development.

Sleep has a profound effect on various aspects of human functioning, although systematic research attention to this critical health behavior in young children is a relatively recent development. Research shows that sleep problems are common among preschool children in Correspondence should be addressed to Timothy D. Nelson, Ph.D., University of Nebraska–Lincoln, 319 Burnett Hall, Lincoln, NE 68588-0308. E-mail: [email protected]


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the United States, with an estimated 10–21% experiencing issues in this domain by parent report (Byars, Yolton, Rausch, Lanphear, & Beebe, 2012; Mindell, Meltzer, Carskadon, & Chervin, 2009). These statistics are alarming given rapidly emerging evidence that sub-optimal sleep has measurable effects on both brain and behavior (Beebe, 2011; Berger, Miller, Seifer, Cares, & Lebourgeois, 2012; Molfese et al., 2013), and that younger children may be especially sensitive to these effects (Sadeh, Gruber, & Raviv, 2002). A large body of research has demonstrated the effects of poor sleep on cognitive functioning in both children (Beebe, 2011) and adults (Lim & Dinges, 2010); however, research explicating the specific cognitive consequences of sleep problems in preschool children is much more limited and critically needed. Considerable evidence links sleep problems with general cognitive effects at various points in development. A large literature using experimental sleep restriction paradigms demonstrates that sleep loss detrimentally impacts cognitive functioning in adults (see Lim & Dinges, 2010, for review). A smaller but significant body of research has found that sleep problems are also associated with poorer general cognitive functioning in children (e.g., Buckhalt, El-Sheikh, & Keller, 2007; O’Brien et al., 2004). Bub, Buckhalt, and El_Sheikh (2011) reported that increases in sleepiness were associated with poorer growth in verbal abilities over time, suggesting that ongoing sleep problems may affect the trajectory of cognitive development in childhood. Beyond the negative effects of sleep problems on children’s general cognitive functioning, executive control (or executive function) has been identified as a cognitive domain that is particularly sensitive to poor sleep. Conceptualized as a set of “top-down” cognitive processes by which an individual regulates thoughts and behavior, executive control (EC) is typically measured across tasks assessing working memory, flexible shifting, and inhibitory control (Garon, Bryson, & Smith, 2008; Miyake, Friedman, Emerson, Howerter, & Wager, 2000). Studies have linked sub-optimal pediatric sleep with poorer EC in both experimental (e.g., Gruber, Wiebe, Montecalvo, Brunetti, Amsel, & Carrier, 2011) and correlational designs (Sadeh et al., 2002; Steenari et al., 2003) with school-aged children. Theory suggests that the negative effects of poor sleep may be especially potent for “higher-level” cognitive skills such as EC, compared to general cognitive functioning, because of the effect of sleep loss on the prefrontal cortex (e.g., Dahl, 1996; Sadeh, 2007). Along these lines, some research examining infant sleep has found significant links to later executive functioning but not general cognitive ability (Bernier, Beauchamp, Bouvette-Turcot, Carlson, & Carrier, 2013; Bernier, Carlson, Bordleau, & Carrier, 2010), suggesting the sleep–EC association may be particularly important for study. Despite strong evidence that sleep problems have negative effects on children’s EC, very few studies have explored this relationship in preschool children, particularly among typically developing children (Bernier et al., 2013). However, theory and research suggest that preschool may be a particularly relevant developmental period for examining the effects of sleep problems on EC. EC is known to develop rapidly during preschool, corresponding to major changes in the prefrontal cortex (Durston & Casey, 2006; Giedd & Rapoport, 2010; Huttenlocher, 1990). Given the detrimental effects of poor sleep on the prefrontal cortex, specifically (Dahl, 1996; Yoo, Gujar, Hu, Jolesz, & Walker, 2007), sleep problems in preschool may undermine EC development at a critical point, with important long-term implications. Research with infants and very young children has supported this idea, finding that early sleep predicts later EC. Bernier and colleagues found that sleep at age 1 was associated with EC performance at ages 18 months, 2 years, and 4 years (Bernier et al., 2010, 2013). Sadeh and colleagues (2015) report that sleep quality at 1 year significantly predicts attention and behavioral regulation at 3–4 years of age.


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Although these studies implicating infant sleep with later EC suggest the importance of early sleep, studies examining sleep problems in preschool and then subsequent EC in the transition to school are more limited. The present study reports on the association between sleep problems and subsequent EC across this transition. Although research has demonstrated the association between child sleep problems and EC in general, few studies have examined the pattern of associations across different aspects of child EC. It is possible that sleep has a differential effect on specific EC abilities such as inhibitory control, flexible shifting, and working memory (Friedman, Corley, Hewitt, & Wright, 2009). In fact, a review of the pediatric sleep literature suggests that there may be a distinct pattern of associations across different aspects of EC, although studies explicitly examining different aspects in the same study are rare. Numerous studies have documented the effects of sleep problems on working memory, with poorer sleep consistently associated with poorer memory in pediatric samples (e.g., Steenari et al., 2003; Vriend et al., 2013). The literature examining the specific effect of sleep on flexible shifting in pediatric samples is limited. Anderson, StorferIsser, Taylor, Rosen, and Redline (2009) found that sleep duration (as measured by actigraphy) was not associated with shifting performance in a sample of adolescents, although self-reported sleepiness was associated with poorer performance in this area, perhaps because sleepiness is a more proximal measure of daytime functioning and captures sleep-related impairment in a way that sleep duration does not. In a longitudinal study, Friedman and colleagues (2009) reported that child sleep problems generally did not correlate with later shifting performance at age 17. The literature on sleep and inhibitory control, however, is more mixed, with some pediatric studies reporting significant effects (e.g., Gruber, Cassoff, Frenette, Wiebe, & Carrier, 2012) but others reporting null findings or even improvement in inhibition with poorer sleep (e.g., Fallone, Acebo, Arnedt, Seifer, & Carskadon, 2001; Sadeh, Gruber, & Raviv, 2003). In a meta-analysis of experimental sleep restriction studies with children, Lundahl, Kidwell, Van Dyk, and Nelson (2015) found that hyperactivity/impulsivity was not significantly affected by sleep loss and, instead, suggested that poor sleep may lead to a “hypoactivity” that delays responses by slowing cognitive tempo (Fallone, Acebo, Seifer, & Carskadon, 2005). Upon closer examination, it is possible that the effect of poor sleep on inhibition depends on the type of inhibition being assessed. Some researchers distinguish between response inhibition, which involves inhibiting overt behavioral responses (e.g., pressing a button), and interference suppression, which involves inhibiting attention to distracting stimuli (Bunge, Dudukovic, Thomason, Vaidya, & Gabrieli, 2002; Nigg, 2000). Although response inhibition may be unaffected by sleep loss (because poor sleep actually “slows down” responses), interference suppression may be affected because the complex task of directing one’s attention is negatively affected by sleep loss. In fact, the Lundahl meta-analysis found significant effects on inattention for children who had been sleep restricted compared to children with optimal sleep. Research explicitly examining the differential effects of sleep loss on such varied aspects of EC, however, has been limited. Overall, there is limited research on the association between sleep and EC using a longitudinal design spanning from preschool to early elementary school in a large typically developing sample. Further, studies examining the pattern of effects on different aspects of EC using performance-based tasks in this critical age period are even more limited. Our study seeks to address this gap by reporting on a longitudinal examination of preschool sleep problems predicting subsequent EC in early elementary school. Based on research with older children, we hypothesized that working

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memory would be negatively affected by preschool sleep problems, but flexible shifting would not be significantly affected. For inhibitory control, we hypothesized a mixed pattern of findings, with negative effects for interference suppression but not response inhibition. This study will add to our understanding of the specific cognitive effects of preschool sleep problems, informing intervention efforts to remediate emerging deficits.


METHOD Participants Participants were 215 children (112 girls and 103 boys) recruited at preschool age for a longitudinal study spanning the preschool period through flyer distribution in a small Midwestern city. Children with diagnosed developmental, behavioral, or language disorders were excluded from initial enrollment, as were those who did not primarily speak English at home. Children who were later diagnosed with developmental or language disorders in the course of longitudinal follow-up were excluded from analyses. As a part of the design for the larger study from which the data are drawn, the sample was oversampled based on socioeconomic risk (59.5% eligibility for public medical assistance defined using the federal poverty guideline for family income/size). The sample was racially and ethnically diverse (139 Caucasian, 7 African-American, 18 Hispanic, and 51 multiracial children included).

Procedures In the course of the longitudinal preschool study, parents (typically mothers) completed ratings of child behavior, including the Child Behavior Checklist for Ages 1½–5. This measurement point occurred when children were approximately 5 years, 3 months of age (Mage = 5.23 years). Upon completion of the preschool study, families were subsequently invited to participate in a school age follow-up study beginning in grade 1. The school age follow-up included an initial laboratory visit in the Fall of grade 1, at which time parents provided consent and children assent for participation. At this visit, children were administered the Wechsler Abbreviated Scale of Intelligence (WASI). Families then attended two additional laboratory visits in the Spring of grade 1 (Mage = 7.07 years), during which children were administered a battery of EC tasks. On average, children were 22 months older when they completed the EC tasks in grade 1 compared to when their parents provided ratings of sleep problems in preschool. A total of 325 children participated at age 5 years, 3 months when parent ratings of sleep problems were collected. Of these 325 children, 28 children were lost to follow-up in grade 1 (when EC tasks were administered) due to parents declining to participate in school age followup (n = 6) or parents who were unable to be contacted in time for the grade 1 follow-up. An additional 21 children moved away from the study area, making them unable to participate in laboratory visits. Sixty children were already past grade 1 when the follow-up study began, meaning they were too old for the grade 1 measurement point.


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Measures Sleep problems. Child sleep problems were assessed using the 7-item Sleep Problems scale from the Child Behavior Checklist for Ages 1½–5 inclusive (CBCL/1½–5), a parent-report measure of children’s emotional and behavioral symptoms (Achenbach & Rescorla, 2000). The Sleep Problems Scale of the CBCL is a widely used measure that captures a variety of child sleep problems and has good reliability (a =.75, in the current study). Parents responded to the seven items using a 3-point response scale (Not True (as far as you know), Somewhat or Sometimes True, and Very True or Often True), and all of the items were used to create a latent construct of sleep problems in the model predicting performance on EC tasks. Although some studies have found only modest (but significant) correlations between CBCL sleep items and objective measures of sleep (Gregory et al., 2011), more recent studies have found that the CBCL Sleep Problems Scale as a whole correlates well with evidence-based sleep problems questionnaires and clinician diagnoses of sleep disorders. Becker, Ramsey, and Byers (2014) found that the CBCL Sleep Problems total score correlated significantly (r = .55) with the Children’s Sleep Habits Questionnaire (CSHQ) and sleep psychologist-generated diagnoses of sleep disorders in a clinical pediatric sample. Even more relevant to the current study, Sneddon, Peacock, and Crowley (2013) found that the CBCL 1½–5 version Sleep Problems total score was very strongly correlated with the CSHQ total score (r = .82), which is a well-established evidence-based measure of child sleep problems (Lewandowski, Toliver-Sokol, & Palermo, 2011), in a combined community and clinical sample of preschoolers. Sleep problems in our study did not significantly differ by sex [t(213) = –.38, p = .70] or sociodemographic risk status [t(213) = –1.16, p = .25]. Working memory. Jumping Frog is a computerized visual–spatial span task. Children were oriented to a computer screen template with nine lily pads arranged randomly against a water background, similar to how the Corsi Blocks are presented (Milner, 1971). Children were then asked to watch an animated frog hop from one lily pad to another, before using a touch screen to touch the respective lily pads in the same order as the frog. The forward span portion of the task, in which children responded in forwards, or the same, order as the frog, was used in the present analyses. Children first completed two practice trials, each with a sequence of two lily pads. The scored portion of the task has up to eight blocks of forwards trials (with sequences of two to nine lily pads). The sequence length increased progressively until the child met the discontinuation criteria. Within each sequence block, the child could view up to three trials. If the child correctly responded to the first two trials, the third trial was skipped. If the child incorrectly responded to all three trials in a block, the task was discontinued. The dependent variable was the correct forward trial total. Flexible shifting. In the Shape School (Espy, 1997; Espy, Bull, Martin, & Stroup, 2006), children name stimuli by either color or shape as quickly and accurately as possible, with the demands for each different task condition conveyed in a story about school activities (e.g., calling children’s names for lunch). In the present computerized version, stimuli were cartoon characters whose main body part was a square or a circle colored in blue or red. Some of the stimuli were wearing a hat (the dimension cue) whereas others were hatless. Stimuli were presented one at a time at the center of the screen on a white background. On each trial, the stimulus was visually displayed until the participant responded on a button box with four buttons


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(each coded for one of the response options—blue, red, square, and circle). After children’s responses, the computer program then automatically advanced to the next trial. Participants first completed a color baseline condition, consisting of 4 practice and 20 test trials naming stimulus color. Thereafter, during a shape baseline condition (always administered after the color baseline condition), participants completed another 4 practice and 20 test trials during which they were required to name the stimuli by shape. Finally, participants moved on to the switch condition, where the hatted and hatless stimuli were interleaved, and the child had to switch between naming the hatless stimuli by color and the hatted stimuli by shape for 4 practice trials and 64 test trials. Within the switch condition, after the first starting trial, 1/3 of trials were shift trials, where the relevant dimension on the current stimulus differed from that in the previous trial. The dependent variable used to represent flexible shifting in the present study was the proportion correct on the shift trials in the switch condition. Inhibition. Two inhibition tasks were administered to evaluate response inhibition and interference suppression, respectively. A computerized Go/No-Go task (adapted from Simpson & Riggs, 2006) was used to assess response inhibition. Children were presented with pictures of colored fish and asked to “catch” these fish by responding on a button box. On less frequent “nogo” trials (25% of trials), a stimulus image of a shark appeared and children were instructed to “let it go” by withholding the button press response, hence requiring response inhibition. Feedback was provided in the form of a fishing net, which broke when children made an error of commission by pressing the button in response to the shark stimulus. The “go” and “nogo” stimuli were presented for up to 750 msec, with an inter-stimulus interval of 500 msec. The dependent variable was d prime (d’) (standardized difference between the z-score of false alarm right-tail p and z-score of hit rate right-tail p, where false alarm rate is percent correct on shark trials and hit rate is percent correct on fish trials). Funny Animals, a computerized Stroop-like task in which children were presented animals whose heads and bodies often mismatch (adapted from Wright, Waterman, Prescott, & MurdochEaton, 2003), was used to assess interference suppression. Children were required to name the relatively less salient bodies of farm animal stimuli while resisting the interference created by more salient head information. Animal heads were made more salient both by the placement of the stimulus on the computer screen such that the animal head was at the center of the screen, and by the presentation of a fixation cross at the center of the screen where the head would appear for 500 msec at the start of each trial. In an initial knowledge checking stage, children were asked to name each of five complete animals (same head and body), as well as then name the heads and bodies separately. This was to ensure that the Stroop-like portion of the task was not confounded with a lack of knowledge of how to identify or name the animals. Children were then presented with a series of test trials, in which 40% of trials were conflicting (different head and body), 40% were non-conflicting (same head and body), and 20% were irrelevant (head was a square with facial features, not an animal head). Children verbally named each animal as quickly as possible as it came on the screen (within a time window of 2000 ms). The dependent variable was the proportion of correct responses on the conflict trials. General intellectual abilities. The WASI (Wechsler, 1999) was used to assess the general intellectual functioning of each child. The WASI is a well-validated brief intelligence test for individuals ages 6 to 90 years. It contains four subtests (Vocabulary, Similarities, Block Design, and Matrix Reasoning), which yield a Full Scale IQ score (M = 100.86, SD = 11.67, in the

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current sample). The Full Scale IQ score was controlled for as a covariate in the structural equation model to isolate the effects of sleep problems on EC performance above and beyond general intelligence.

RESULTS


Analysis Plan We used structural equation modeling to examine the associations between preschool sleep problems and later EC performance across tasks measuring working memory, flexible shifting, and inhibition (both response inhibition and suppression interference). Structural equation modeling offers advantages in being able to model both latent and manifest variables, and to have multiple dependent variables in the model simultaneously (Kline, 2002). Sleep problems was modeled as a latent construct using all seven items as indicators, and performance on various EC tasks was represented using manifest variables. All EC tasks were modeled simultaneously in a single model in which the tasks were allowed to correlate. Child sex and full-scale IQ were controlled for as covariates. The model was estimated using maximum likelihood estimation (ML) in Mplus version 7.2 (Muthén & Muthén, 1998-2014). See Table 1 for descriptive statistics and correlations among observed variables. All variables demonstrated acceptable distributional properties. Structural Equation Model The results of the structural equation model are presented in Figure 1. The overall model demonstrated adequate fit, χ2 (52) = 76.14, p = .02, RMSEA = .05, CFI = .94, and all item loadings contributing to the latent sleep problems construct were significant. Sleep problems significantly predicted poorer performance (i.e., fewer correct trials) on the working memory TABLE 1 Descriptive Statistics and Correlations of Observed Variables

M

SD

Range

Sleep Problems

215

2.24

2.36

0–11

—

214 214 215

8.33 .81 2.39

2.05 .14 .65

3–14 .29–1 0–3.12

214

.92

.09

.57–1

n Sleep Problems (total score) Working Memory Flexible Shifting Response Inhibition Interference Suppression IQ

215 100.86 11.67

***p < .001. **p < .01. *p < .05.

76–146

Working Memory

Flexible Shifting

Response Inhibition

Interference Suppression

–.16* –.12 .05

— .17* .23***

— — .19**

— — —

— — —

–.23***

.27***

.31***

.18**

—

–.11

.31***

.21**

.05

.21**


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FIGURE 1 Structural equation model results. *p < .05. **p < .01. ***p < .001.

task, such that children who had more sleep problems had worse working memory, β = –.15, SE = .07, t = –2.15, p = .03. Sleep problems did not significantly predict flexible shifting, β = –.12, SE = .07, t = –1.56, p = .12. Sleep problems also did not significantly predict response inhibition, β = .09, SE = .08, t = 1.12, p = .26; however, sleep problems did significantly predict interference suppression, β = –.26, SE = .07, t = –3.61, p < .001, such that children with more sleep problems had a lower percentage of correct answers on conflict trials in the task. Of note, the latent sleep problems construct did not significantly correlate with full scale IQ (r = –.09, p = .23).

DISCUSSION This study examined the longitudinal association between sleep problems in preschool and subsequent EC performance in elementary school, with an emphasis on explicating the pattern of associations across different aspects of EC in a longitudinal sample that was oversampled for socioeconomic risk. Consistent with our expectations, we found evidence of different pattern associations between sleep problems and various aspects of EC. Specifically, results indicated that greater sleep problems was associated with poorer performance on a working memory task, and we found a complex pattern of results for inhibition, with sleep problems predicting performance on an interference suppression task but not on a response inhibition task. Importantly, the associations were found after controlling for IQ, suggesting a unique association



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between poor sleep and specific EC abilities above and beyond general intellectual functioning. Also consistent with our expectations, no significant association between sleep problems and performance on a flexible shifting task was found. The finding that sleep problems were related to aspects of EC, but not to general intelligence, is consistent with both developmental sleep theory and previous research. Theory suggests that the negative effects of poor sleep should be most apparent on tasks with a substantial cognitive load, particularly for those assessing abilities centered in the prefrontal cortex (Dahl, 1996; Sadeh, 2007). Ample research has demonstrated the specific effects of sleep loss on prefrontal functioning (Beebe & Gozal, 2002; Jones & Harrison, 2001; Yoo et al., 2007), and studies like ours that link sleep problems to executive abilities specifically are consistent with this literature. Further, our findings are consistent with infant sleep literature showing that sleep affects later executive control but not IQ (e.g., Bernier et al., 2013). This study is among the first to document a specific pattern of associations in which sleep problems have varying associations with different aspects of EC in young children. Of particular interest is the finding of different associations on different types of inhibition. Consistent with expectations, we found a significant association between sleep problems and performance on an interference suppression task but not on a response inhibition task. The interference suppression task required the child to ignore/suppress extraneous information and, instead, to focus on the most relevant aspects of the stimuli to respond correctly. This is a demanding cognitive task for children, and it is likely that those with sub-optimal prefrontal functioning resulting from suboptimal sleep patterns would have more difficulty on this complex task. In contrast, the response inhibition task required the child merely to refrain from enacting a behavioral response (i.e., pressing a button) when a certain stimulus was presented. Given the “slowed cognitive tempo” that is characteristic of sleep problems, this may actually help children who might otherwise act impulsively to inhibit behavioral responses, resulting in a lack of noticeable impairment in this domain for children with sleep problems. The finding is consistent with the meta-analysis by Lundahl and colleagues (2015) finding no significant effect of sleep restriction on children’s hyperactivity/impulsivity symptoms, but our study provides a rare comparison of interference suppression versus response inhibition associations in the same sample. It is worth noting, however, that reaction time was not significantly correlated with sleep problems (r = –.07, p = .31) in our sample, so we cannot necessarily conclude that sleep problems result in a generalized slowing of responses. Clearly, further examination of why sleep problems are associated with performance on some EC tasks, but not others, is needed. Consistent with our expectations, we did not find a significant association for preschool sleep problems on flexible shifting performance in early elementary school. One possibility for this null finding is that flexible shifting is thought to be a more complex, later developing executive ability among the EC abilities measured in this study (Best, Miller, & Jones, 2009; Garon et al., 2008). In this case, problems with sleep in preschool may not affect the development of this ability as profoundly as others that are at a more critical point in development while the child is having sleep problems. Alternatively, the effect of preschool sleep problems on flexible shifting may be delayed and more apparent in later assessments when this aspect of EC is more fully formed. The current study, with a relatively short amount of time between measurement of sleep problems and EC, may have been limited in its ability to capture the sleep-shifting effect, whereas longitudinal studies with a longer lag may be better suited for this purpose. It is also


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possible that if sleep problems persist beyond preschool, the negative effects on flexible shifting could be more apparent. In addition to describing a pattern of associations between pediatric sleep problems and aspects of EC, the current study does so at a particularly important point in development. The transition from preschool to elementary school is a critical time during which academic and behavioral demands—many of which require strong EC abilities—increase substantially. Further, difficulties with this transition can set the stage for later academic and behavioral problems (Hair, Halle, Terry-Humen, Lavelle, & Calkins, 2006). Sleep problems in preschool can undermine the healthy development of critical cognitive abilities such as EC, which, in turn, can negatively affect early learning. Early learning deficits could then change long-term academic trajectories, especially without appropriate intervention to remediate the emerging cognitive problems and address the underlying causes of early deficits. Given the crucial role of EC in the transition to elementary school, the associations with earlier sleep problems in this domain merit considerable attention. The findings of the current study have notable implications for assessment and intervention. In light of the documented associations between sleep problems and various aspects of EC, one obvious implication is the need to identify sleep problems early and deliver targeted interventions when problems are present. Sleep problems in preschool are common (Byars et al., 2012; Mindell et al., 2009), so it is easy to explain away these issues as normal and something that a child will outgrow. While some children may eventually “outgrow” their sleep problems, the current study suggests that the development of key cognitive abilities may be already affected by problematic sleep in preschool. Therefore, early identification and intervention to address sleep problems in preschool may help limit the negative impact during a critical period in EC development. Preschool is also a good time for intervention because of the general malleability of health behaviors at this age (Haemer, Ranade, Barón, & Krebs, 2013) and the demonstrated effectiveness of behavioral sleep interventions with young children (Mindell, Kuhn, Lewin, Meltzer, & Sadeh, 2006). Effective intervention may help improve a child’s sleep across development, decreasing the risk of ongoing problems. Interestingly, large-scale longitudinal research by Friedman and colleagues (2009) found that while sleep problems at age 4 did not predict EC at age 17, changes in sleep across development did. Therefore, it is important to promote a healthier sleep trajectory, especially for children who have early sleep problems, again highlighting the importance of early identification and appropriate intervention. Resolving sleep issues early, when they are most responsive to treatment, may support more optimal cognitive development down the road. Another potential clinical implication of this study is the ability to target cognitive remediation efforts to address the specific deficits resulting from sleep problems. The current study suggests that not all cognitive functions have the same association with sleep problems. Therefore, the areas most affected—working memory and interference suppression inhibition— may be critical domains for intervention to ameliorate the negative cognitive effects of chronic poor sleep. Such interventions could be delivered in combination with behavioral treatments aimed at addressing the underlying sleep problems. It is also possible that the pattern of EC deficits described in this study could help to identify cases in which sleep is a possible concern. If the pattern proves to be unique to sleep problems, a profile of deficits in working memory and interference suppression could serve as a red flag indicating that the clinician should consider assessing for an underlying sleep problem. Further research



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replicating our findings and determining if this pattern of associations is in fact unique to sleep problems would be important in considering the potential value of this kind of profile analysis in differential diagnosis. More broadly, we reiterate existing recommendations to incorporate sleep assessment into ADHD evaluations, given the possibility that sleep problems could result in EC impairment that resembles ADHD (e.g., American Academy of Pediatrics, 2011). Several limitations of the current study should be noted. First, preschool sleep problems was measured using a parent report scale rather than objective measurement. Parents may not be accurate reporters of sleep problems, and the multifaceted nature of the CBCL Sleep Problems Scale makes it difficult to distinguish which types of sleep problems are associated with EC impairment. On the other hand, the CBCL Sleep Problems Scale has been frequently used in pediatric sleep research, has demonstrated convergent validity with evidence-based measures of sleep problems and clinician diagnoses (Becker et al., 2014; Sneddon et al., 2013), and may capture important perceptions of problems that may not show up on objective assessments such as actigraphy (e.g., bedtime resistance, nightmares, not wanting to sleep alone). To obtain a more complete picture of child sleep, future research should combine objective and subjective measurements of this construct (Gregory & Sadeh, 2012). Second, the current study employed a relatively short longitudinal window between the measurement of preschool school and elementary school EC (average lag of about 22 months). The longitudinal design is a strength, but some effects may take longer to unfold. Therefore, research using longer longitudinal designs to examine the associations between preschool sleep problems and subsequent aspects of EC performance is recommended. Third, our study did not examine various environmental and biological factors that could be important for understanding the sleep–EC relationship. The role of the family environment, including family stress and adverse events, as well as possible neurodevelopmental mechanisms underlying the overlap between sleep problems and EC deficits, merit future study. Finally, our study did not include measurement of change in key variables. Longitudinal studies employing repeated measurement of both sleep and EC would allow for an examination of how these constructs dynamically interact over time. Despite these limitations, the current study has a number of notable strengths, including a multimethod longitudinal design with strong measurement of EC using developmentally appropriate tasks. We were able to examine relationships between sleep problems and different aspects of EC within a critical developmental period, in a typically developing sample, making a valuable contribution to the pediatric sleep and EC literature. Overall, the current study suggests that sleep problems in preschool are associated with EC in early elementary school, with specific associations with working memory and interference suppression inhibition even after controlling for IQ. These findings add to the literature on cognitive impairments associated with pediatric sleep loss and highlight the need for early intervention for children with sleep problems to promote healthy cognitive development.

ACKNOWLEDGMENTS We thank the participating families and acknowledge the invaluable assistance with data collection and coding by research technicians and undergraduate and graduate students of the Developmental Cognitive Neuroscience Laboratory at the University of Nebraska–Lincoln.

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FUNDING This work was supported by NIH grant MH065668.


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