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. Author manuscript; available in PMC: 2017 Nov 1.
Published in final edited form as: Health Psychol. 2016 Aug 11;35(11):1235–1245. doi: 10.1037/hea0000401

Longitudinal Associations between Self-regulation and Health across Childhood and Adolescence

Kristen L Bub 1, Leah E Robinson 2, David Curtis 3
PMCID: PMC5067975  NIHMSID: NIHMS808429  PMID: 27513478

Abstract

Objective

There is some evidence to suggest that one’s ability to delay gratification is associated with a lower body mass index and slower overall weight gain. Less is known about the role that a broader set of self-regulatory skills, including attention focusing, inhibitory control, and impulsivity, might play in fostering not only a healthy weight, but also better overall health and health-related behaviors such as sleep.

Methods

Participants in the NICHD Study of Early Child Care and Youth Development were followed from birth through age 15 beginning in 1991. Self-regulation was assessed when children were 4.5 years old while health-related outcomes were assessed regularly between toddlerhood and adolescence. Structural equation modeling was used to test direct associations between self-regulation and either physical health or sleep in childhood and adolescence.

Results

Findings suggest that there are long-term benefits of self-regulation, indexed by multiple dimensions, for children’s health-related outcomes. Children with better self-regulatory skills demonstrated smaller increases in standardized BMI scores and maintained greater mother-reported health across childhood and adolescence. Further, better self-regulation predicted fewer sleep problems and longer sleep duration when children were 8 and 11 but not when they were 15.

Conclusions

Early self-regulation, marked by numerous skills, appears to have long-term benefits for children’s health-related outcomes. These findings provide some evidence that targeting childhood self-regulatory skills for improvement may help reduce poor health-related outcomes later in life and offer important insight into potential avenues for intervention.

Keywords: Self-regulation, body mass index, physical health, sleep problems, health disparities

Improving the health and well being of young children is of paramount importance to their own future success and productivity as well as the nation’s. Children in the US are increasingly more likely to exhibit poor health outcomes than they were even a decade ago. For example, childhood obesity rates range from 8.4% among 2–5 year olds to 20.5% among 12–19 year olds (Ogden, Carroll, Kit, & Flegal, 2014). Further, more than 16% of adolescents meet the criteria for pre-diabetes (Li, Ford, Zhao, & Mokdad, 2009). Coupled with a rise in poor health is a decline in health behaviors such as sleep. Most typically developing children fail to attain optimal sleep amounts, with significant sleep disruptions occurring in 15% to 30% of 2–5 year olds (Turnbull, Reid, & Morton, 2013) and in 20% to 40% of school-aged children (Archbold, Pituch, Panahi, & Chervin, 2002). Given the widespread consequences of poor childhood health, a substantial rise in chronic health conditions, and a considerable decline in positive sleep habits, identifying factors early in life that predict better health-related outcomes is critical.

One factor that is receiving greater attention in the health literature is self-regulation (SR). Although definitions vary greatly, SR has been broadly defined as the ability to manage emotions, focus attention, and inhibit some behaviors while activating others in accordance with social expectations (Raver, 2004; Rothbart & Bates, 1998). That is, SR involves weighing a more appropriate response that aligns with long-term goals against a more gratifying response that provides immediate satisfaction (Duckworth & Kern, 2011). Some researchers have suggested that SR is best described as two distinct but related processes, including cognitive processes that facilitate self-regulatory behaviors (e.g., executive functions such as working memory, response inhibition, etc.) and behavioral processes that predispose individuals to more impulsive and immediately rewarding behaviors (e.g., reactive under-control, sensation seeking, etc.; Duckworth & Steinberg, 2015). Others suggest that while preschoolers possess all of these cognitive and behavioral skills, they act in a unified manner during early childhood and do not differentiate into distinct processes until later in childhood (Zelazo & Carlson, 2012). When applied to health, SR may reflect the ability to identify and attend to a specific goal (e.g., maintaining a healthy weight), to “tune-out” negative external cues (e.g., advertisements about food), to inhibit engagement in unhealthy behaviors (e.g., frequent eating), and to engage in more positive behaviors (e.g., exercise) as a means to achieve a goal (Schachter, 1971; Singh, 1973). In very young children, health-related SR may be reflected in a child’s ability to respond appropriately to his/her hunger or satiety (Frankel et al., 2012). Although the benefits of SR skills for social, behavioral, and academic outcomes are well established (Blair & Razza, 2007; McClelland, Acock, & Morrison, 2006), studies examining the benefits for a broad range of health-related outcomes are considerably more sparse.

Self-Regulation and Health-Related Outcomes

Research examining the etiology of physical health in both children and adults suggests that deficits in SR skills can have serious negative consequences (Tsukayama, Toomey, Faith, & Duckworth, 2010). For example, Seeyave and colleagues (2009) found that a compromised ability to delay gratification at age 4 predicted a greater risk of being obese at age 11. Schlam et al. (2013) found that the negative effects of failure to delay gratification during childhood on body mass index (BMI) were still present 30 years later. The simultaneous investigation of multiple SR indices (e.g., emotion regulation, inhibitory control, delay of gratification) is rare but those studies that have done so indicate that more than one domain of regulation may play a role in predicting health outcomes. Children with below average self-control and below average delay of gratification skills exhibited more rapid increases in BMI between ages three and twelve than did children with poor skills in only one of these domains or children with no deficits in SR (Francis & Susman, 2009). Graziano, Calkins, and Keane (2010) found that emotion regulation and inhibitory control predicted changes in BMI several years later. Despite compelling evidence linking SR and obesity, the literature on SR and a broader range of health outcomes is relatively scant. Yet, the neurocognitive and behavioral processes relevant for eating behaviors and obesity are also likely salient for other health behaviors. Indeed, Moffitt et al. (2011) found that children with poor self-control during childhood had at least three of six metabolic risk factors measured at age 32. Similarly, Appleton and colleagues (2011; 2013) reported that emotional functioning, including self-regulation and behavioral inhibition, at age 7 predicted cardiovascular disease risk and levels of C-reactive protein (an indicator of low-grade systemic inflammation) in adulthood.

Although data demonstrates that the links between sleep and development are particularly strong during periods of considerable brain maturation such as early childhood (Casey, Galvan, & Hare, 2005), only a handful of studies have examined whether SR in preschool predicts later sleep quality, consistency, or duration. Findings indicate that aspects of SR are associated with sleep (Sadeh, Gruber, & Raviv, 2003). For example, El-Sheikh and Buckhalt (2005) found that greater parent-reported emotional reactivity predicted poorer sleep among 6 to 12 year olds. Shanahan and colleagues (2014) reported that oppositional defiant disorder was associated with increases in sleep problems over time among 9 to 16 year olds. Thus, while there is reason to believe that SR predicts sleep, empirical evidence supporting this association is quite limited. More common are studies examining the effects of sleep quality, consistency, or duration on SR or related skills (Dahl & Harvey, 2007). Tininenko and colleagues (2010) found that truncated sleep predicted increased inattention and hyperactivity, particularly for children in foster care. Similarly, increases in sleep problems have been found to predict poorer cognitive processing and decision-making across childhood (Removed for blind review). It is worth noting that the links between SR and sleep are likely bi-directional, with sleep influencing the development and expression of SR and SR affecting sleep behaviors, although empirical evidence supporting this is sparse. Given the critical role that sleep plays in healthy development (Beebe, 2011) and the fact that young children are particularly vulnerable to the negative effects of sleep disturbances (Dahl, 1996), research identifying early antecedents of sleep problems is greatly needed.

Current Study

With mounting evidence showing that SR skills in both childhood (e.g., Raver et al., 2011; Riggs, Sakuma, & Pentz, 2007) and adulthood (Stadler, Oettingen, & Gollwitzer, 2010) can be improved, the need to establish links between these skills and a range of outcomes has increased. Such evidence may provide much needed insight into novel avenues for reducing poor health across the lifespan. Despite indications linking SR and health-related outcomes, critical gaps remain in our understanding of these associations. For example, most research has relied on cross-sectional rather than longitudinal data and has used a single index of SR that does not adequately capture this multi-dimensional construct. Thus, we know little about the long-term benefits of multiple SR skills for health-related outcomes. Importantly, only a handful of studies have examined health-related outcomes other than obesity. Because children are now commonly being diagnosed with a range of health issues that were once thought of as adult problems (Removed for blind review), understanding the effects of SR on a broader set of health-related outcomes is important. Finally, with several notable exceptions, study participants have tended to be adolescents or adults rather than very young children. Yet, we know that healthy habits are established early and thus understanding how to improve health-related outcomes may have considerable long-term benefits. We begin to address these gaps by using a comprehensive battery of SR measures, by examining several health-related outcomes, and by investigating SR collected when children were 4.5 years old and its relation to health 3.5 (i.e., age 8), 6.5 (i.e., age 11), and 10.5 (i.e., age 15) years later. We expected that children with better SR skills would demonstrate smaller changes in z-BMI scores, maintain better mother-reported general health, and experience fewer sleep problems as well as longer sleep in childhood and adolescence.

Methods

Sample

Data from the National Institute of Child Health and Human Development (NICHD) Study of Early Child Care and Youth Development (SECCYD), a comprehensive, longitudinal study of children’s development from birth through adolescence was used for this study. Participants were recruited from ten locations around the US, resulting in a diverse sample reflecting the demographics of the catchment areas from which they came. Maternal reports, direct assessments, and child/adolescent reports of demographic factors, health indicators, and self-regulatory skills were gathered regularly from 1-month to age 15. Detailed information on sample selection and study procedures can be found elsewhere (see NICHD ECCRN, 2001). Of the 1,364 original study participants, 1,023 had data on at least one key outcome variable; these individuals comprise our analytic sample. Mean comparisons between participants (n = 1,023) and non-participants (n = 341) on a range of factors revealed that study participants were more likely to be male (M = .58 for participants and M = .50 for non-participants; t = 2.73, p <.01) and less likely to experience chronic working poverty across early childhood (M = .13 for participants and M = .26 for non-participants; t = 2.91, p <.01). Each data collection site obtained human subjects approval by their university for all assessments.

Measures

Outcome Variables

Physical health

Children’s height and weight were measured during laboratory visits when children were approximately 4.5, 8, 11, and 15 years old. Body mass index (BMI) was calculated as the child’s weight (kilograms) divided by the square of their height (centimeters) using standard software available from the Centers for Disease Control (CDC). Scores were then standardized to provide a z-score at each age (herein referred to as z-BMI scores). Additionally, maternal reports of the child’s health and injury history were collected via phone when children were 4.5, 8, 11, and 15 years old. Using a 4-point Likert scale (1 = poor to 4 = excellent), mothers were asked to consider their child’s health over the last 6 months and respond to the following question: “In general, how would you rate your child’s health” (herein referred to as mother-rated general health). Single-item indices of general health have been shown to be reliable and valid measures of individual health, correlating with multi-item assessments of physical functioning and emotional well-being (e.g., DeSalvo et al., 2006).

Sleep problems

Maternal perceptions of children’s sleep problems were assessed using the 28-item Children’s Sleep Habits Questionnaire (CSHQ; Owens, Spirito, & McGuinn, 2000) when children were approximately 8 and 11. The CSHQ measures the frequency of child sleep behaviors, habits, and difficulties using a three-point scale (1=usually, 2=sometimes, 3=rarely). Two subscales were used here: night wakings was the average of three items (e.g., “my child awakes more once during night”) and daytime sleepiness was the average of four items (e.g., “my child seems tired during the day”). Possible scores ranged from 1 to 3 with higher scores indicating more problems. Reliability for the two composite variables was relatively low (Night wakings: α=.53 at age 8 and α=.56 at age 11; Daytime sleepiness: α=.57 at age 8 and α=.65 at age 11), likely due to the small number of items in each scale. Mothers also reported on the number of hours per day the study child slept. Finally, children’s self-reported sleep problems were assessed at ages 11 and 15 using an adaptation of the CHSQ (Owens et al., 2000). This 8-item questionnaire captures the frequency (1= never, 3=sometimes, 5= always) of problems like night wakings, difficulty falling asleep, difficulty waking in the morning, and feeling tired in school. Items were summed to create a single variable reflecting general sleep problems. Possible scores ranged from 8 to 40 with higher scores indicating more sleep problems. The composite demonstrated moderate internal consistency (α=0.77 at age 11 and α=0.78 at age 15). The CHSQ is a widely used measure of sleep problems in clinical and non-clinical samples (Owens et al., 2000). Because these same sleep problems variables were not administered at age 3, we selected two similar indices of sleep as control variables: frequency of night wakings in the last week and mother’s perception of sleep as a problem in the last week (1=not much, 2=somewhat, and 3=quite a bit). Together, these two variables provide a coarse index of sleep problems during early childhood and help account for the possibility that sleep problems are chronic. Note that we do not have an index of sleep duration at this age.

Predictor Variables

Self-regulation

A total of five observed indicators were used to represent self-regulation at age 4.5. Children’s ability to delay gratification was assessed using Mischel's (1974) self-imposed waiting task. Children were offered a choice between receiving a small immediate reward of their favorite food or waiting (for a total of 7 minutes) for a larger reward. The amount of time (in minutes) the child waited before touching either plate of food was recorded. Children’s impulsivity was assessed using the Children’s Stroop Task (Gerstadt, Hong, & Diamond, 1994). Children were given a deck of 18 cards and were instructed to say, “day” when he/she saw a black card and “night” when he/she saw a white card. A total score reflecting the percentage of incorrect responses was calculated. The Stroop Task has adequate reliability in this sample (α=0.79) and has been shown to correlate with the frontal lobe development that inhibits impulsive behavior (Gerstadt et al., 1994). Children’s sustained attention was assessed using the Continuous Performance Task (CPT; Rosvold, Mirsky, Sarason, Bransome, & Beck, 1956). Children were shown a series of familiar pictures (e.g., butterfly, fish, flower) along with a critical stimulus picture (a chair) and were instructed to press a button when the chair appeared. A single variable was created representing the number of times the child did not press the button when the stimulus appeared. The CPT is one of the most widely used measures of sustained attention and has adequate test-retest reliability as well as high predictive and content validity (Halperin, Sharma, Greenblatt, & Schwartz, 1991). Finally, maternal perceptions of her child’s self-regulatory skills were assessed using Child Behavior Questionnaire (CBQ; Rothbart, Ahandi, Hershey, & Fisher, 2001). Two subscales were used: attention focusing, which measures the ability to focus, sustain attention, and complete assigned tasks (e.g., has a hard time keeping their mind on an activity, becomes absorbed in a picture book) and inhibitory control, which measures how well a child can delay impulses (e.g., can lower voice when asked to, has trouble sitting still when told). Mothers responded on a seven-point scale (1= extremely untrue to 7=extremely true). Responses were averaged and higher scores indicate greater SR skills. Both subscales demonstrate adequate reliability (α=0.74 for attention and α = 0.75 for inhibition) and have been used in previous studies to measure self-regulation (Clark, Prior, & Kinsella, 2002).

Additional Control Variables

Maternal reports of children’s sex (1=male) and race (1=African American), as well as her years of education were collected when children were one month of age. Family income was collected via maternal report when children were 1, 6, and 15 months of age, as well as at 2, 3, and 4.5 years of age. An income-to-needs ratio at each age was computed (US Bureau of the Census, 2004) and then dichotomized such that families with an income-to-needs ratio less than 1.85 were given a score of 1 while all others were given a score of 0. To create an index reflecting the chronicity of poverty, families who were working poor (i.e., had a score of 1 on the dichotomous indicators) for at least five of the six epochs were given a score of 1; all others were given a score of 0. Children’s temperament was assessed via maternal report using the 55-item Early Infant Temperament Questionnaire when children were 1 and 6 months (EITQ; Medoff-Cooper, Carey, & McDevitt, 1993). The EITQ measures the frequency (1= almost never to 6 = almost always) with which their child exhibited a set of behaviors (e.g., “My baby’s initial reaction to a new babysitter is rejection”). At each age, items were averaged to provide an overall measure of difficulty, with higher scores indicating a more difficult temperament. An average of the 1 and 6 month scores was created for the current analyses. Data collection site was also included to account for possible variation across data collection locations.

Analysis Plan

Structural equation modeling (SEM) in MPlus version 7.0 was used to investigate direct effects of SR skills on children’s health-related outcomes. We began by fitting a measurement model to confirm that the five indicators we selected adequately reflected SR. Next, direct pathways from SR to either z-BMI scores and mother-reported general health or night wakings, daytime sleepiness and/or general sleep problems were added to the measurement model. Outcome variables were allowed to correlate. For the models predicting z-BMI and mother-reported general health at 8, 11, and 15, we controlled for z-BMI scores at 4.5 on the z-BMI variable and mother-reported general health at 4.5 on the mother-reported general health variable, thereby creating residualized change scores. Although identical measures were not available at earlier ages, we wanted to account for the possibility that some children experienced chronic sleep problems. Thus, for all sleep models, we controlled for measures of night wakings and overall sleep problems assessed at age 3. Finally, because sleep duration is one of the most commonly used measures in sleep research we also wanted to investigate whether SR predicted sleep duration. Thus, we re-estimated our sleep models and included an index of sleep duration at ages 8 and 11 (duration was not assessed at age 15). Note that because parents did not report on duration at age 3, this model is less conservative than our original sleep models and considered a supplemental analysis. Each model controlled for the effects of child sex, race/ethnicity, temperament, chronic working poverty status, maternal education, and data collection site on SR. Model fit was assessed using typical fit statistics including chi-square, CFI, and RMSEA. A small and non-significant chi-square value, a CFI close to 1, and an RMSEA value less than .08 all indicate good fit (Hu & Bentler, 1999). It is worth noting that chi-square estimates are sensitive to large sample sizes and are considered quite conservative (Browne & Cudeck, 1993). Missing data were handled using Full Information Maximum Likelihood.

Results

Preliminary Analyses

Sample means and standard deviations for all variables can be found in Table 1. Standardized BMI scores varied somewhat with age and mothers rated their children’s general health quite highly at each assessment. Mothers in this sample did not perceive sleep to be a problem for their children, while children and adolescents reported modest levels of sleep problems at ages 11 and 15. On average, children obtained 9.5 hours of sleep at age 8 and 8.9 hours at age 11, somewhat below the recommended 10–11 hours for children this age. Correlations between all key outcome variables and the indicators of SR can be found in Table 2. Many key study variables are correlated in the expected direction but associations are small and thus may be explained by the large sample size. Finally, results from the confirmatory factor analysis supported our hypothesis that delay of gratification (B = 1.00), attention focusing (B = .176, p <.05), inhibitory control (B = .189, p <.05), impulsivity (B = −2.41, p <.05), and sustained attention (B = .035, p <.001) adequately reflect self-regulation at 4.5 years of age. Model fit was excellent (χ2 = 6.08, p < .05; CFI = .991; RMSEA = .043, n.s.).

Table 1.

Descriptive Statistics for Outcome, Predictor, and Control Variables Across Time

Sample 4.5 Years Old 8 Years Old 11 Years Old 15 Years Old Other Agesb
Rangea Mean/% (SD) Mean/% (SD) Mean/% (SD) Mean/% (SD) Mean/% (SD)
Outcome Variablesc
  Body Mass Index Z-score (kg/m2) −5.6 – 4.21 .377d (.988) .542 (1.05) .540 (1.11) .571 (.995)
  Mother-Reported General Health 1 – 4 3.39d(.65) 3.55 (.60) 3.49 (.63) 3.47 (.64)
  Sleep as Problem 1 – 3 1.34d(.59)
  Frequent Night Wakings 1 – 3 1.14 (.28) 1.15 (.20) 2.77d (1.95)
  Daytime Sleepiness 1 – 3 1.12 (.22) 1.13 (.25)
  Sleep Duration 5 – 14 9.55 (.81) 8.97 (.87)
  General Sleep Problems 8 – 45 19.55 (5.16) 24.01 (5.59)
Predictor Variables
  Delay of Gratification (minutes) 0 – 7.00 4.48 (3.01)
  Sustained Attention (prop. correct) .07 – 1.00 .741 (.19)
  Impulsivity (% incorrect) 0 – 87.5 25.34 (20.53)
  Attention Focusing 1.25 – 6.88 4.71 (.85)
  Inhibitory Control 2.00 – 6.70 4.66 (.78)
Control/Moderator Variables
  Male 0 – 1 51.7%
  African-American 0 – 1 12.9%
  Child Temperament 1 – 4 3.25 (.43)
  Chronic Working Poverty 0 – 1 13.6%
  Maternal Education 4 – 21 14.23 (2.51)
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Note:

a

Ranges reflect sample values across all assessment points;

b

Other ages include 1 month (male, African American, Maternal Education), 36 months (Sleep as a problem and frequent night wakings), an average of 1 and 6 months (temperament), and a sum across 1 month to 4.5 years (Chronic working poverty status);

c

unless otherwise noted, measure units are dichotomous or categorical scales;

d

Indicates that these variables served as controls for the relevant outcomes.

Table 2.

Intercorrelations among Outcome Variables and Indicators of Self-Regulation

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 15 16
1. Z-BMI – 8 --
2. Z-BMI – 11 .87*** --
3. Z-BMI –15 .77*** .83*** --
4. General Health – 8 −.11*** −.11*** −.10** --
5. General Health – 11 −.09** −.14*** −.09** .32*** --
6. General Health –15 −.10** −.10** −.09*** .32*** .33*** --
7. Night Wakings – 8 .004 .05 .01 −.12*** −.04 −.07* --
8. Night Wakings – 11 .07 .07* .06 −.03 −.06* −.08* .33*** --
9. Sleepiness – 8 .03 .10** .04 −.16*** −.10** −.08* .18*** .23*** --
10. Sleepiness – 11 .08* .13*** .09* −.06* −.12** −.06 .19*** .47*** .36*** --
11. Sleep Probs. – 11 .01 .06 .05 −.07* −.07* −.10** .12*** .06* .11*** .12*** --
12. Sleep Probs. – 15 .03 .06* .08* −.09** −.07* −.16*** .04 .05 .07* .07* .37*** --
13. Morningness - 15 .04 .04 .03 −.003 .08* .05 −.02 −.03 −.02 −.05 −.19*** −.29*** --
14. Delayed Gratify −.06 −.11** −.14*** .15*** .04 −.01 −.04 −.10** −.09* −.11** −.05 −.01 −.01 --
15. Sustain Attention .03 −.04 −.03 .09** .02 .03 −.13*** −.07* −.05 −.06 −.06 −.004 .02 .23*** --
16. Impulsivity .02 .01 .08* .001 −.03 −.01 .01 −.03 −.05 .02 −.03 −.03 .03 −.12** −.08* --
17. Attention Focusing −.07* −.07* −.15*** .14*** .09** .10** −.05 −.09** −.08* −.06 −.08* .05 .03 .20*** .09** −.12** --
18. Inhibitory Control −.07* −.07* −.13*** .11*** .08* .12*** −.07* −.06+ −.05 −.07* −.09** .003 .002 .18*** .14*** −.06 .53***
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***

p<.001,

**

p<.01,

*

p<.05

Self-Regulation, z-BMI, and Mother-Reported General Health

Results from the structural equation model examining the simultaneous effects of SR on z-BMI scores and mother-reported general health at age 8 are presented in Figure 1a. Recall that this model controls for children’s z-BMI scores and mother-reported general health at 4.5. Children with better SR demonstrated significantly smaller increases in z-BMI between 4.5 and 8 years old. Similarly, controlling for their concurrent z-BMI scores and a set of child and family factors, mothers reported greater improvements in general health for children with better SR than for children with poorer SR. Effect sizes varied by outcome, with SR accounting for 3.4% (p < .05) of the variance in z-BMI beyond control variables. For mother-reported general health, SR explained 10.7% (p < .001) of the total variance above and beyond control variables. The model fit the data well (see Figure 1a).

Figure 1.

Figure 1

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a–c. Structural equation models depicting the associations between self-regulatory skills at 4.5 years old and auto-regressive changes in z-BMI and mother-reported general health from 4.5 to 8 years old (1a), 4.5 to 11 years old (1b), and 4.5 to 15 years old (1c). Standardized coefficients for key relations are presented in parentheses. Model includes the following controls on self-regulation: Male, African-American status, temperament, chronic working poverty status, and maternal education, as well as nine site dummy variables. *** p<.001, ** p<.01, * p<.05

To investigate how long the benefits of SR for children’s health last, we replaced the age 8 z-BMI scores and mother-reported general health variable first with the age 11 measures (Figure 1b) and then with the age 15 measures (Figure 1c). Results suggest that children with better SR skills at age 4.5 had lower z-BMI scores at age 11, controlling for their z-BMI at age 4.5. These children also demonstrated greater gains in mother-reported general health between 4.5 and 11 years old. Model fit was excellent and SR accounted for a bit more of the total variance in z-BMI (R2 = .063, p < .01) than it did at age 8, although it accounted for considerably less of the total variance in mother-reported general health (R2 = .026, p = .07). The pattern of findings at age 15 was very similar (see Figure 1c), with SR accounting for 8.7% of the variance in z-BMI (p < .01) but only 2.4% of the variance in mother-reported general health (p = .09).

Self-Regulation, Sleep Problems, and Sleep Duration

Results from the structural equation model investigating the simultaneous effects of SR at age 4.5 on night wakings and daytime sleepiness at age 8 are presented in Figure 2a. Instruments assessing frequency of night waking and sleep problems at age 3 were included as covariates and thus the results account for chronic sleep problems on the outcomes. Children with poorer SR at age 4.5 were reported by their mothers to experience more night wakings and greater daytime sleepiness at age 8. Self-regulation accounted for only a small amount of variance in night wakings (R2 = .032, p < .05) and daytime sleepiness (R2 = .087, p < .001) beyond the control variables. Fit statistics provided mixed evidence in support of this model (see Figure 2a).

Figure 2.

Figure 2

Figure 2

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a–c. Structural equation models depicting the associations between self-regulatory skills at 4.5 years old and (2a) sleep problems at age 8, (2b) sleep problems at age 11, and (2c) sleep problems at age 15, controlling for sleep problems at age 3. Standardized coefficients for key relations are presented in parentheses. Model includes the following controls on self-regulation (standardized coefficients): Male, African-American status, temperament, chronic working poverty status, and maternal education, as well as nine site dummy variables. *** p<.001, ** p<.01, * p<.05

We again wanted to explore whether the benefits of SR for children’s sleep lasted through childhood and adolescence and thus we re-fit the model in Figure 2a by replacing the age 8 sleep variables with a set of sleep variables at age 11 (Figure 2b) and again at age 15 (Figure 2c). Unlike the z-BMI and mother-reported general health variables, however, we do not have identical measures across time and thus each model varies somewhat. Despite these model differences, our overall goal of examining longitudinal associations between SR and sleep problems was retained. Controlling for age 3 sleep problems, SR was a negative but relatively weak predictor of mother reported night wakings and child reported general sleep problems at age 11 but not mother reported daytime sleepiness. Effect sizes were small for all three outcomes (night wakings: R2 = .018, p = .15; daytime sleepiness: R2 = .035, p < .05; general sleep problems R2 = .027, p = .07). There was no evidence that SR was related to adolescent reported general sleep problems or preference for mornings, suggesting the direct benefits of self-regulation for sleep may fade by adolescence or that the processes linking SR and sleep may be more complex among this age group.

Finally, to investigate the extent to which SR predicts sleep duration, we re-fit the models presented in Figures 2a (age 8) and 2b (age 11) and included sleep duration. Results suggest that SR is a positive predictor of sleep duration at ages 8 (β = .228, p < .001) and 11 (β = .222, p < .001), even after accounting for the links between SR and sleep problems. Model fit was relatively similar to the previous models at ages 8 (χ2 = 147.85, p < .001; CFI = .822; RMSEA = .049, n.s.) and 11 (χ2 = 103.80, p < .001; CFI = .905; RMSEA = .035, n.s.).

Discussion

Recent increases in childhood health problems, including higher rates of pediatric obesity (Ogden et al., 2014) and pre-diabetes (Li et al., 2009), as well as declines in health behaviors such as sleep (Singh & Kenney, 2013) have been documented. As such, the purpose of the current study was to examine whether SR skills during early childhood predicted health-related outcomes in later childhood and adolescence. Consistent with existing research illustrating that better SR is a protective factor against rapid weight gain and subsequent obesity (Evans, Fuller-Rowell, & Doan, 2012; Graziano, Kelleher, Calkins, Keane, & Brien, 2013), our study findings show that SR is also associated with maintaining general health as well as fewer sleep problems across childhood and adolescence. To our knowledge, this is the first study to show that early SR, marked by a comprehensive set of skills and behaviors, predicts subsequent sleep problems throughout childhood. These findings are of particular relevance because of mounting evidence suggesting that even mild sleep restriction (e.g., 1 hour) is associated with a range of poor health outcomes (El-Sheikh, Buckhalt, Cummings, Keller, & Acebo, 2007). Although researchers have suggested that inadequate sleep is perhaps the most preventable of all threats to child development, improving children’s sleep has not been incorporated into contemporary prevention and intervention efforts (Buckhalt, Wolfson, & El-Sheikh, 2009). Our study provides much-needed evidence of individual-level antecedents to sleep problems. Because SR has been found to be malleable in children and adults (Raver et al., 2011; Stadler et al., 2010), programs designed to foster these skills may offer avenues for reducing sleep deficits across childhood.

Our findings are also consistent with supplementary analyses reported in Moffitt et al. (2011) but we extend their work, and the work of others (e.g., Riggs et al., 2007), by offering evidence that the links between SR and health-related outcomes are not just limited to one’s ability to delay gratification. Instead, a broad spectrum of cognitive (e.g., attention focusing, sustained attention) and behavioral (e.g., delay of gratification, inhibitory control) SR skills may benefit children’s healthy development. For example, Graziano and colleagues (2013) reported that better SR skills including emotion regulation, sustained attention, and delay of gratification in toddlerhood were associated with fewer eating concerns and lower pediatric obesity among 10 year olds. Appleton and colleagues (2011; 2013) found that poorer attention and behavioral inhibition as well as inappropriate self-regulation were associated with greater risk of cardiovascular disease and higher levels of systematic inflammation (marked by C-reactive protein). Working memory has also been found to be associated with healthier eating among youth (Davidson, Kanoski, Walls, & Jarrard, 2005) and greater inhibitory control is associated with lower BMI and fat mass in preadolescence (Kamijo et al., 2012). These studies suggest that consideration of a broader spectrum of SR capacities is important for understanding healthy development. Although they have been examined as complementary but overlapping indicators of SR for social and academic outcomes (Raver et al., 2011; Liew, 2012), few studies have investigated how they might work in combination to support positive health-related outcomes – a gap which we begin to address here.

It is worth noting that the relationship between SR and health-related outcomes is likely bi-directional. Research has shown that engagement in physical activity increases pre-frontal cortex activity (Chaddock, Pontifex, Hillman, & Kramer, 2011) and predicts better SR skills (Davis et al., 2011). Similarly, there is some evidence linking sleep problems with poorer SR skills (Touchette, Petit, Seguin, Boivin, Tremblay, & Montplaisir, 2007). If studies seeking to identify the antecedents of poor health among young children are to be informative, research is needed to determine whether SR offers an important foundation for healthy living or whether positive health behaviors foster SR capacities necessary for a healthy and successful life.

Implications for Prevention

Our findings add to the growing body of evidence showing that the long-term benefits of SR extend beyond those commonly identified for academic and social/behavioral skills, to numerous health-related outcomes. Although additional studies are needed that employ more diverse samples as well as comprehensive assessments of SR and health-related outcomes, mounting evidence linking these constructs offers some indication that programs designed to improve young children’s regulatory capacities may have subsequent benefits for health outcomes across the lifespan. Indeed, Verbeken and colleagues (2013) have shown that obese 8 to 14 year old children who participated in a program targeting executive functioning skills were able to maintain their weight loss 8-weeks post treatment. Similarly, Riggs and colleagues (2007) reported that 5th graders in a SR intervention made better food choices following the intervention than did their peers who were not participating. These findings offer critical support for the potential of SR interventions to reduce poor health-related outcomes but they have commonly focused on older children or adults rather than young children. Yet, SR skills develop rapidly between the ages of 2 and 5 (McClelland & Morrison, 2003) and thus early childhood may be an especially salient period in which to intervene on these skills. It is also during early childhood that critical lifestyle and health behaviors are being established and shaped that will then contribute to health behaviors and health status during adulthood (Craigie, Lake, Kelly, Adamson, & Mathers, 2011).

Limitations and Future Directions

The current study advances our understanding of the essential role that SR skills play in fostering positive health-related outcomes. Nevertheless, several limitations are noteworthy. First, our measure of general health was coarse (i.e., a single item reported by mothers). Thus, future studies should also assess factors like cardiometabolic indicators and inflammatory markers. Second, our measures of sleep problems and duration were subjective rather than objective, not collected consistently across time, and not always developmentally appropriate, although this is not uncommon in large-scale, longitudinal datasets. For example, sleep duration was parent reported rather than child reported but research suggests that as parents become less involved in sleep routines, they may also be less aware of what happens between bedtime and wake-time, making them less reliable reporters of sleep duration and sleep problems (Owens et al., 2000; Sadeh, Raviv, & Gruber, 2000). Future studies should include both objective (i.e., actigraphy) and subjective (i.e., parent and self-reported) measures of sleep in order to offer a more comprehensive understanding of the potential impact that SR can have in supporting healthy development. The inclusion of objective measures of sleep may be particularly important during adolescence, when there are considerable developmental shifts in the circadian rhythm associated with puberty (Carskadon, Acebo, & Jenni, 2004) that are likely perceived by adolescents (and their parents) as normative and thus not reported as sleep problems.

Third, although participants were representative of the catchment areas from which they were recruited in 1991, the sample is not nationally representative and thus our findings cannot be generalized to the broader population. Given that poor health and sleep problems are more prevalent among ethnic minority and low-income populations (Buckhalt, El-Sheikh, & Keller, 2007; Ogden et al., 2014), future studies should focus on recruiting a diverse sample of children and adolescents if our work is to offer insight into how we can reduce rising health disparities. Finally, our study is an investigation of the direct effects of SR on health-related outcomes; we did not investigate the mechanisms by which SR might affect health-related outcomes. Only when we have identified key antecedents to positive health outcomes can we begin to explore how those antecedents affect our outcomes. Further, we did not aim to determine whether changes in SR (either developmental or via intervention) were related to improved health-related outcomes. As the field continues to amass evidence linking SR and health, however, the next critical step will be to investigate the mechanisms by which SR impacts health-related outcomes as well as to determine whether changes in SR predict improvements in these outcomes.

Acknowledgments

The authors would like to thank the investigators in the NICHD Early Child Care Research Network for the dataset, the site coordinators and research assistants for their data collection efforts, and the children and their families for their participation in this longitudinal study. The current study was funded by a grant awarded to the first author by the National Institutes of Child Health and Human Development (1R03HD061565-01).

Contributor Information

Kristen L. Bub, Department of Educational Psychology, University of Illinois at Urbana-Champaign

Leah E. Robinson, School of Kinesiology, University of Michigan

David Curtis, Department of Human Development and Family Studies, Auburn University.

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