Daily Mood and Sleep: Reciprocal Relations and Links with Adjustment Problems

Summary

Children’s sleep problems are common and associated with increased risk for adjustment problems. We examined daily links between children’s sleep and mood, using a daily diary method and actigraphy. We also tested children’s daily mood as a mediator of relations among sleep and children’s broader internalizing and externalizing symptoms. A community sample of 142 children (M age=10.69 years; 57% girls; 69% European American, 31% African American) and their parents participated. For one week, children wore actigraphs and parents completed a daily phone interview about their child’s mood. Following the week of actigraphy, mothers and fathers reported on their child’s adjustment. Multilevel models indicated within-person relations between children’s mood and subsequent sleep fragmentation (indicated by increased activity) and sleep latency, and between-person relations between sleep latency and subsequent mood on the next day. Significant indirect effects were found such that a more negative daily mood (aggregated across diary days) mediated relations between poor sleep efficiency and longer sleep latency and parent-reported internalizing and externalizing symptoms. Findings extend previous research by highlighting disruptions to children’s daily mood as a potential mechanism linking sleep problems to children’s mental health.

Sleep problems are common among children (Fricke-Oerlermann et al., 2008) and a public health concern given links to mental health (for reviews see Gregory & Sadeh, 2012 and Shochat et al., 2014). Short sleep duration and poor sleep quality are related to internalizing and externalizing problems cross-sectionally and longitudinally (El-Sheikh et al., 2007; Gregory & O’Conner, 2002; see also Astill et al., 2012 and Ivanenko et al., 2005), and these relations have been found in both community and clinical samples of children (Forbes, et al., 2008).

An important research direction is to understand the mechanisms linking sleep and children’s internalizing and externalizing symptoms (Gregory & Sadeh, 2012). Emotional processes have been implicated as one pathway linking poor sleep to children’s adjustment (Gregory & Sadeh, 2012). In this study, we posited that disruptions to daily mood may account for the relation between sleep problems and children’s broader adjustment. Supporting this proposition, direct links between sleep and mood have been observed (Pilcher & Huffcutt, 1996), and experimental studies with youth have found that short-term partial sleep deprivation and sleep restriction are related to lower levels of positive affect and greater tension and anger (Baum et al., in press; Talbot et al., 2010). Impairments in sleep likely predict a worse mood due to disruptions in brain processes, particularly those involving the amygdala and prefrontal cortex, which are important for the expression, experience, and regulation of emotion (Kahn et al., 2013).

The majority of studies with children have been correlational given concerns about sleep restriction. Diary studies, however, can provide information on the temporal relation between sleep and mood in children using the ecologically-valid context of the home. For example, Fuligni and Hardway (2006) found that ninth-graders who reported sleeping for less time reported more anxious mood (e.g., uneasy, nervous) the next day. Daily diary studies with children, however, remain a significant gap in the literature. Moreover, although reciprocal relations are plausible (e.g., Cousin et al., 2011), few studies have tested for bidirectional relations between daily mood and sleep in youth.

Repeated impairments to children’s daily mood can compound to affect their overall mental health. Children’s emotions are integral to theories of child psychopathology (Cicchetti et al., 1995) and research supports this theoretical view by demonstrating relations between children’s emotions and their overall adjustment (Olino et al., 2011). Empirical studies using experience sampling methods also show that daily mood predicts adolescents’ internalizing and externalizing problems (Schneiders et al., 2006; Silk et al., 2003). Notably, mood and child adjustment are related, yet conceptually distinct constructs. Whereas mood represents short-term emotional states, adjustment problems represent broader patterns of behaviors associated with impaired functioning.

The first aim of this study was to examine daily relations between sleep and mood. We used time-lagged analyses with daily diary data to explicate the temporal order between sleep and mood and examine both within- and between-person relations. We hypothesized that sleep problems would predict a worse mood on the next day (Hypothesis 1). Bidirectional relations were examined with the expectation that higher levels of negative mood would predict higher levels of sleep problems that night (Hypothesis 2). The second novel aim was to test daily mood as a mediator linking sleep problems to internalizing and externalizing symptoms. We hypothesized that sleep problems would predict higher levels of negative mood, which in turn, would predict higher levels of internalizing and externalizing symptoms (Hypothesis 3).

Method

Participants

Parents of children in third grade were contacted to participate in a longitudinal study (Child Regulation Study; see El-Sheikh et al., 2010 for more detail). To be eligible, children had to be living in a two-parent household, have no history of diagnosis of a sleep problem, attention deficit hyperactivity disorder, chronic or acute physical illness, or an intellectual or learning disability. The sample for the present study was the 142 families that participated at the second wave of data collection in 2006. On average, children were 10.69 years old (SD=.56) and 57% were female. The sample was diverse with regard to ethnicity and socioeconomic status (SES); 69% European American and 31% African American. Based on Hollingshead’s (1975) criteria, 21% of families were in Level 1 or 2 (unskilled workers), 45% were in Level 3 (skilled workers), and 34% were in Level 4 or 5 (professional).

Procedure

Researchers visited the family’s home and instructed parents on how to use the actigraphs. Parents placed the actigraphs on the child’s nondominant wrist at bedtime for seven consecutive nights. Parents (typically mothers) were called nightly and reported on their child’s bedtime and wake time that morning to validate the actigraphy measures of sleep. During this phone call, mothers reported on their child’s overall mood for that day. Sleep data were only collected during the school year, excluding holiday periods. Only data from medication-free nights (reported by mothers during phone interview) were used in analyses. Following actigraphic assessments, parents came to the laboratory and completed a measure of children’s adjustment. The study was approved by the university’s Institutional Review Board and informed consent and assent were obtained. Families were paid for their time.

Measures

Objective measures of sleep.

Objective sleep parameters were derived through Octagonal Basic Motionloggers (Amibulatory Monitoring Inc., Ardsley, NY), which measured motion in 1-min epochs using a zero crossing mode. Raw data were analyzed with ACTme software (Action W2, 2002 Ambulatory Monitoring Inc., Ardlsey, NY) and Sadeh’s scoring algorithm (Sadeh et al., 1994). The following sleep parameters were derived: (a) Sleep Minutes, total minutes scored as sleep between amount of lapsed time (minutes) between sleep onset and wake time; (b) Sleep Efficiency, percentage of epochs scored as sleep between sleep onset and wake time; a cutoff below 90% has been proposed for definition of poor sleep (Sadeh et al., 2000); (c) Sleep Activity, percentage of epochs with activity calculated from the moment of sleep onset; the Sadeh algorithm was used to calculate the intensity of movement above a precalculated threshold to determine activity; and (d) Sleep Latency, minutes between first attempting to fall asleep and sleep onset (see Saheh, 2011). Actigraphy provides reliable information when utilized for several consecutive nights (Acebo et al., 1999). The actigraph and analysis software have shown good reliability and validity when compared with polysomnography (Sadeh et al., 1994). In the pediatric sleep literature, inclusion of multiple sleep parameters is recommended (Sadeh et al., 2000), which we adopted in this study.

Most children (54.9%) had valid actigraphy data for all seven nights, 23.9% had data for six nights, 14.1% for five nights, 4.2% for 4 or 3 nights, and 2.8% did not have any actigraphy data. Children with 4 or fewer nights of actigraphy data (n=11) were removed from analyses (Acebo et al., 1999; Meltzer et al., 2012).

Daily mood.

Nightly, mothers reported on how their child felt, overall that day, using a 5-point semantic differential scale (e.g., Lori & Wunderlich, 1988). Responses on the following dimensions were averaged to create a daily mood score: happy-sad, calm-jittery, carefree-worried, easy going-irritable, even tempered-mood swings, and relaxed-tense. A positive mood was indicated by feeling happy, relaxed, even tempered, easy going, care free, and calm, whereas a negative or worse mood was characterized by feeling sad, tense, irritable, worried, jittery, and experiencing mood swings. Higher scores reflected a more positive mood, whereas lower scores reflected a more negative mood. Supporting this composite mood variable, within each day, these mood dimensions were significantly correlated (r range = .18 - .73) with two exceptions: on day 5, feeling even-tempered was not correlated with feeling relaxed or carefree. This composite had moderate-to-strong reliability within each day (Cronbach’s alpha range = .76 - .88). Two families did not complete the phone interview.

Children’s adjustment.

Mothers and fathers reported on children’s internalizing and externalizing symptoms using the 280-item Personality Inventory for Children (PIC; Wirt et al., 1990). The internalizing subscale assesses depression, anxiety, fear, worry, and psychosomatic problems. The externalizing subscale assesses aggression, impulsivity, disruptive behavior, delinquency, and noncompliance. The PIC has good psychometric properties, including test-retest reliability, and discriminant and construct validity (Lachar & Gruber, 2001). In this sample, Cronbach’s alpha of mothers’ and fathers’ reports of internalizing symptoms were .91 and .85, and for externalizing symptoms were .94 and .90, respectively. There was no significant mean difference between mothers’ and fathers’ reports of their child’s internalizing (M_mother=48.72; M_father=48.42) or externalizing (M_mother=48.05; M_father=48.30) symptoms, and parents’ reports were significantly correlated for internalizing (r=.48, p< .001) and externalizing (r=.64, p< .001) symptoms. To provide a more robust measure of the constructs, a composite parent-report variable was created for children’s internalizing symptoms and externalizing symptoms by averaging mothers’ and fathers’ T-scores. Eight families did not complete the PIC. Fifteen children surpassed the clinical cutoff T score of ≥65 (Lachar & Gruber, 2005) for internalizing problems and 13 children surpassed the cutoff for externalizing problems based on either parent’s report.

Analysis Plan

Within- and between-person relations between daily mood and sleep were examined using hierarchical linear modeling (HLM; Raudenbush & Bryk, 2002). We followed centering guidelines in HLM for differentiating within- and between-person relations with diary data. We person-centered Level 1 predictors such that they represented fluctuations from one’s average level (within-person relation), and we included the average score across all diary days as a predictor of the intercept at Level 2 (between-person relation).

Preliminary analyses tested whether the sleep and mood variables significantly varied within and across participants, and whether sleep and mood scores systematically changed during the course of the week. Next, we ran time-lagged analyses, such that child sleep predicted mood on the subsequent day, controlling for the autoregressive effect of the previous day’s mood (Hypothesis 1). We also tested mood as a predictor of children’s sleep that night (Hypothesis 2). To provide a more cogent analysis of the relation between sleep and mood, we controlled for the potential confounding variables of children’s broader internalizing and externalizing symptoms. To minimize potential confounds, we also controlled for variables that are typically associated with sleep including weekend or weeknight and child age, sex, ethnicity, SES, and BMI (calculated from assessment of height and weight in our lab).

To test for the indirect effect of mood in the between-person relation between sleep and children’s adjustment (Hypothesis 3), we used path analysis and tested the indirect effect with the Sobel test. Internalizing and externalizing symptoms were included in the same model and were correlated. Given that sleep and mood were assessed concurrently, we also tested a post-hoc alternative model in which children’s mood predicted children’s adjustment through sleep.

Results

Preliminary Analyses

Descriptive statistics and correlations are presented in Supplemental Table 1. Intercept-only models were run in HLM to examine within- and between-person variability in mood and the sleep parameters across the 7 diary days. There was significant within-person (62%; σ²=.32, p<.01) and between-person (38%, τ²=.20, p<.01) variability in the mood ratings, indicating that the diary measure was valid in capturing fluctuations in daily mood. There was also significant within- and between-person variability in measures of sleep minutes (59.5% within, 40.5% between), efficiency (24.2% within, 75.8% between), activity (29.3% within, 70.7% between), and latency (67.2% within, 32.8% between).

Next, we added time to the model to test whether mood and sleep systematically changed during the course of the week, controlling for whether it was a weekend. Mood and the sleep parameters did not systematically change during the course of the week, nor were there any significant individual differences in change in mood or the sleep parameters as a function of time. That is, neither mood nor sleep became better or worse as a function of how many days children participated in the actigraphy phase of the study. Time, therefore, was excluded from analyses.

Sleep as a Predictor of Children’s Subsequent Mood

The four sleep parameters were individually tested as predictors of children’s mood on the next day (Table 1). There was a significant between-person relation between sleep latency and children’s mood on the next day, b= −.002, SE= .001, p< .001, indicating that children who, on average, take longer to fall asleep experience a decline in positive mood on the next day. The Level 2 proportional reduction in the mean squared prediction error (Snijders & Bosker, 1994) after adding sleep to the model was 1.1%¹. There were no significant within-person relations between sleep and children’s subsequent mood.

Table 1.

Parameter Estimates from HLM Analyses Examining Daily Relations between Sleep and Subsequent Mood

	Sleep Parameter:
	Sleep Minutes	Sleep Efficiency	Sleep Activity	Sleep Latency
	Fixed Effects
Level 1	b (SE)	b (SE)	b (SE)	b (SE)
Intercept	4.45 (.01)**	4.45 (.01)**	4.45 (.01)	4.45 (.01)**
Sleept-1	−.00 (.00)	.003 (.005)	.000 (.003)	.002 (.002)
Moodt-1	−.17 (.04)**	−.15 (.04)**	−.16 (.04)**	−.16 (.04)**
Weekend	.02 (.05)	.02 (.05)	.01 (.05)	.01 (.05)
Level 2
Averaged Diary Sleep Score	.00 (.00)	.001 (.001)	.00 (.001)	−.002 (.001)**
Averaged Diary Mood Rating	1.08 (.03)**	1.08 (.03)**	1.09 (.03)**	1.07 (.03)**
Age	.002 (.002)	.002 (.002)	.002 (.002)	.002 (.002)
Sex	−.01 (.02)	−.01 (.02)	−.01 (.02)	−.004 (.02)
Ethnicity	.02 (.02)	.02 (.02)	.02 (.02)	.02 (.02)
BMI	.001 (.002)	.001 (.002)	.001 (.002)	.002 (.002)
SES Level	.01 (.01)	.01 (.01)	.01 (.01)	.01 (.01)
Parent-reported Internalizing Symptoms	.004 (.002)†	.004 (.002)†	.004 (.002) †	.004 (.002)
Parent-reported Externalizing Symptoms	−.002 (.002)	−.002 (.002)	−.002 (.002)	−.002 (.002)
	Random Effects (Variance Estimates)
Level 2
Intercept	.00	.0003	.0002	.00
Sleep	.00**	.0002**	.00009**	.00002**
Mood	.001**	.002	.003	.002
Weekend	.07**	.09**	.08**	.08*

Note. Estimates with robust standard errors reported. Mood and sleep ratings at Level 1 were person-centered, and the person-mean across the diary days for each was included at Level 2. Significant within-person relations indicated in bold. The following variables were coded as: weekend: 0 = no, 1 = yes; Sex: −.05 = female, .05 = male; Ethnicity: −.05 = White, .05 = African American.

^†p < .10

^*p < .05

^**p < .01

Mood as a Predictor of Children’s Subsequent Sleep

To test for bidirectional relations between mood and sleep, we also tested children’s mood during the day as a predictor of children’s sleep that night (Table 2). There was a significant within-person relation between mood and children’s sleep activity, b= −1.01, SE= .49, p=.04, and sleep latency, b= −3.47, SE= 1.62, p=.03, indicating that within-person fluctuations in mood (i.e., worse mood than usual) predicted higher levels of sleep activity and longer time to fall asleep that night. The Level 1 proportional reduction in the mean squared prediction error after adding mood to the model was .6% for activity and 13.2% for latency². There were no significant between-person relations between mood and children’s sleep.

Table 2.

Parameter Estimates from HLM Analyses Examining Daily Relations between Mood and Subsequent Sleep

	Sleep Minutes	Sleep Efficiency	Sleep Activity	Sleep Latency
	Fixed Effects
Level 1	b (SE)	b (SE)	b (SE)	b (SE)
Intercept	445.81 (4.05)**	89.00 (.83)**	39.06 (1.00)**	15.18 (1.05)**
Moodpc	−.70 (3.79)	−.06 (.32)	−1.01 (.49)*	−3.48 (1.62)*
Weekend	10.51 (5.67)†	−1.40 (.50)**	3.14 (.69)**	1.84 (1.48)
Level 2
Average Diary Mood Rating	11.02 (11.90)	5.90 (4.72)	.55 (2.82)	−9.58 (5.58)†
Age	−.75 (.70)	.03 (.12)	-.25 (.13)†	.31 (.15)*
Sex	−18.52 (8.92)*	−2.76 (1.84)	4.01 (2.30)†	2.23 (2.07)
Ethnicity	−28.09 (9.82)**	.37 (1.58)	−7.13 (2.49)**	−.09 (2.02)
BMI	−1.67 (.66)*	−.28 (.13)*	.31 (.18)	.33 (.14)*
SES Level	3.92 (4.48)	−.32 (.80)	.27 (1.27)	.16 (.99)
Parent-reported Internalizing Symptoms	−.24 (.77)	.25 (.19)	−.03 (.21)	−.29 (.21)
Parent-reported Externalizing Symptoms	−.60 (.72)	−.02 (.16)	.10 (.19)	−.12 (.14)
	Random Effects (Variance Estimates)
Level 2
Intercept	1732.11**	85.63**	123.22**	108.04**
Mood	136.75†	.50	.54	114.17**
Weekend	1746.44**	6.74**	14.05**	82.64**

Note. Estimates with robust standard errors reported. pc = Mood rating at Level 1 was person-centered, and the person-mean across the diary days was included at Level 2. The following variables were coded as: weekend: 0 = no, 1 = yes; Sex: −.05 = female, .05 = male; Ethnicity: −.05 = White, .05 = African American.

^†p < .10

^*p < .05

^**p < .01

Daily Mood as a Mediator of the Relation between Sleep and Children’s Adjustment

We tested children’s daily mood (averaged across the 7 days) as a mediator of the relation between children’s sleep (averaged across the 7 nights) and children’s broader adjustment. The model with sleep efficiency provided a good fit to the data, χ²(9)=14.54, p= .11, χ²/df = 1.62, CFI= .96, RMSEA= .07 (Figure 1). Lower sleep efficiency predicted higher levels of negative mood, b=.01, SE=.004, B=.18, p=.03, and negative mood predicted higher levels of internalizing, b= −10.13, SE=1.22, B=−.59, p<.001, and externalizing, b= −8.21, SE= 1.46, B=−.45, p<.001, symptoms. The Sobel tests for the indirect effect on internalizing (z= 2.17, p= .03) and externalizing (z= 2.09, p=.04) symptoms were significant.

Figure 1.

Daily mood as a mediator of relation between children’s sleep efficiency and broader adjustment. Note. Standardized estimates reported. Sleep efficiency and daily mood represent average scores across the 7 diary days. Model included the following inter-correlated covariates: ethnicity, puberty, and SES level on daily mood and child internalizing and externalizing symptoms, and age and sex on daily mood. Model fit: (9) = 14.54, p = .11, x²df = 1.62, CFI = .96, RMSEA = .07. R^- Internalizing = .39; R² Externalizing = .24. *p < .05, **p < .01

The model testing sleep latency as a predictor also provided an acceptable fit to the sample data, χ²(9)=19.15, p= .02, χ²/df= 2.13, CFI= .94, RMSEA= .09 (Figure 2). Longer latency was related to higher levels of negative mood, b= −.01, SE= .003, B=−.21, p=.02, which was related to higher levels of concurrent internalizing, b= −10.13, SE= 1.23, B=−.59, p<.01, and externalizing, b= −8.29, SE= 1.46, B=−.46, p<.001, symptoms. The Sobel tests for both indirect effects were significant (internalizing: z= 2.54, p=.01; externalizing: z= 2.41, p=.02)³.

Figure 2.

Standardized parameter estimates reported. Daily mood as a mediator of relation between children’s sleep latency and broader adjustment.

Note. Sleep latency and daily mood represent average scores across the 7 diary days. Model included the following inter-correlated covariates: ethnicity, puberty, and SES level on daily mood and child internalizing and externalizing symptoms, and age and sex on daily mood. Model fit: x²(9) = 19.15, p = .024, x²/df= 2.13, CFI = .94, RMSEA = .09. R² Internalizing = .39; R² Externalizing = .24. *p < .05, **p < .01

Although the models with sleep minutes and sleep activity provided acceptable model fit, neither sleep parameter was significantly related to average daily mood.

Alternative model.

Given that children’s averaged sleep and mood were concurrently assessed, and within-person relations were found between mood and subsequent sleep, we tested an alternative model to examine whether children’s sleep mediated between-person relations between mood and children’s adjustment. Sleep did not emerge as a significant mediator in any of the four models.

Discussion

We examined daily relations between children’s sleep and mood, using actigraphy-derived measures of children’s sleep duration and quality. To our knowledge, this is the first study to explicitly test daily mood as a mechanism by which sleep impairments predict children’s broader adjustment. Two important findings emerged. First, daily relations between sleep and mood were found; however, the direction of these relations differed at the between- and within-person levels. Within-person relations were found between mood and subsequent sleep activity and latency, indicating that when a child’s mood is worse than their typical mood, his or her sleep quality that night is negatively affected (more activity, longer latency). Between-person relations revealed that, on average, children who take longer to fall asleep have a worse mood from one day to the next. These findings highlight that fluctuations in typical sleep and mood functioning can have an immediate impact on one another on a daily basis.

Second, children’s daily mood mediated relations between poor sleep quality (activity and latency) and internalizing and externalizing symptoms. This supports our proposition that repeated disruptions to children’s daily mood due to poor sleep may be cumulative and compound to eventually develop into emotional and behavioral difficulties. Both paths in this mediation model are also consistent with previous research and theory. Sleep impairments have been shown to disrupt emotional processes involving the amygdala and prefrontal cortex (Dahl, 1996; Silk et al., 2007; Talbot et al., 2010). The effects of sleep disruptions on emotional processes are pervasive, affecting reactivity and regulation of emotions (Kahn et al., 2013). For example, when sleep deprived, young adults are more likely to label neutral pictures as negative, are more reactive to negative stimuli, and are less able to inhibit negative emotions (Anderson & Platten, 2011; Tempesta et al., 2010). Soffer-Dudek et al. (2011) found that even minor disruptions in children’s sleep (night wakings, less sleep efficiency) impaired performance on an emotional face processing task. Impairments in emotional processes, in turn, have been implicated in theoretical models of children’s adjustment problems (Cicchetti et al., 1995). Experiencing states of unpleasant affect, in particular, is related to both internalizing and externalizing disorders (Kring, 2008). The findings are also consistent with previous research with children showing a particularly robust link between sleep latency and children’s internalizing problems (Ivanenko et al., 2005).

Given the current study was correlational, the question remains whether mood and sleep disruptions are a cause or consequence of adjustment problems. Reciprocal relations between sleep and adjustment problems is likely; however, evidence for bidirectional relations in adults is mixed and there is more consistent evidence for unidirectional relations between sleep and children’s internalizing symptoms (see Alvaro et al., 2013; Gregory & Sadeh, 2012). For example, Gregory and colleagues (2009) found that sleep problems at age 8 predicted depressive symptoms two years later but the opposite direction of effects was not supported. Explication of the temporal order of sleep and adjustment problems, and conditions under which the two may develop into a negative cyclical pattern, is warranted

Although findings provide evidence supporting daily negative mood as a mechanism linking sleep to internalizing and externalizing symptoms, there is likely individual variability in this mediational pathway. It is important to consider children’s ability to regulate their negative mood in order to fully understand the links between sleep and children’s broader adjustment. Silk et al.’s (2003) study found that greater use of disengagement and involuntary engagement coping with negative emotions predicted higher levels of adjustment problems. In addition to emotional regulation, children’s physiological regulation of stress is also an important predictor of which children are most vulnerable for adjustment problems in the context of poor sleep (El-Sheikh et al., 2007).

Consistent with recommendations, we used multiple parameters of child sleep (Sadeh et al., 2000) and included separate measures of sleep quality and duration. Relations among sleep quality, mood, and adjustment were evident; however, relations were not found when considering sleep minutes. Although both sleep quality and duration are associated with child adjustment, our findings are consistent with previous research showing that sleep quality may be associated more robustly with environmental stress and negative outcomes than sleep duration (Kelly et al., in press; Sadeh et al., 2000; Soffer-Dudek et al., 2011).

Daily mood mediated relations between sleep and both internalizing and externalizing symptoms; thus, sleep and negative mood may be nonspecific predictors of children’s adjustment. Future research should aim to explicate under what conditions children develop internalizing versus externalizing versus comorbid symptoms. Of note, however, we controlled for internalizing symptoms when examining externalizing symptoms and vice versa. Thus, study findings indicate that mood disruptions associated with poor sleep are uniquely related to both internalizing and externalizing symptoms.

When interpreting the findings, the study’s context and methodology need to be considered. First, a diverse community sample in terms of SES and ethnicity participated and was limited to those in late childhood. Although this may constitute a strength of the design in the context of the scant literature, results may be less or more pronounced for children of other ages or those with clinically significant levels of sleep or adjustment problems. Second, actigraphy has many advantages including the provision of multiple objective measures of sleep duration and quality in the home environment (Sadeh, 2011). Nevertheless, it does not allow for the assessment of sleep architecture and stages related to mood and adjustment (Kahn et al., 2013). Third, positive or negative mood was assessed using a semantic differential scale; therefore, we did not assess positive and negative mood along separate, orthogonal dimensions. Fourth, parents reported on children’s mood and it is plausible that children may have been better reporters of this construct; this study may only have tapped relations with observable behaviors related to mood, rather than subjective feelings. Additionally, given parents reported on their child’s mood, children’s mood while at school may not have been captured. Fifth, sleep and mood were averaged across the 7 nights and therefore were assessed concurrently in the mediation models. Nonetheless, the post-hoc alternative models support our conclusion that disruptions to mood (and not sleep) are the key mediating process predicting children’s adjustment. Sixth, the data were correlational and there may be third variables (see Astill et al., 2012) that may account for relations between sleep, mood, and adjustment. Finally, there are also likely other pathways by which sleep and adjustment problems are related, such as disruptions to cognitive processes and psychophysiological responses to stress (Williams et al., 2013). Examining other potential mechanisms, using longitudinal designs, is an important direction for future research.