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. 2022 Jan 23;3(2):370–382. doi: 10.1007/s42761-021-00094-2

Sleep and Negative Affect Across Toddlerhood in the Context of Stress

Jessica F Sperber 1,✉, Maureen E McQuillan 2, Caroline P Hoyniak 3, Angela D Staples 4, Kathleen M Rudasill 5, Victoria J Molfese 6, John E Bates 7
PMCID: PMC9382980  PMID: 36046005

Abstract

Negative affect is associated with both high stress and poor sleep, but questions remain about the direction of these associations across time and interactions between stress and sleep, especially in early childhood. The present study examined sleep deficits, family stress, and observed negative affect in a sample of toddlers at 30, 36, and 42 months (N = 504). Negative affect was observed during a parent–child free play task. Sleep was measured via actigraphy. Stress was measured using a cumulative risk index of socioeconomic status, single parent status, household chaos, role overload, parenting hassles, social support, and stressful events. Findings showed few associations between sleep and negative affect, except for toddlers experiencing high levels of family stress. Toddlers experiencing both high stress and poor sleep demonstrated the highest levels of negative affect in the lab at 30 months. Adequate sleep may serve as a protective factor for children in high-stress families.

Keywords: Negative affect, Sleep, Toddlers, Stress, Longitudinal

Individual differences in negative affect, as in fear, frustration, or anger (Bates et al., 2018), appear early in development, and high levels of negative affect can predict later internalizing and externalizing problems (Engle & McElwain, 2011). Individual differences in sleep can be measured in dimensions such as duration, activity, and night-to-night variability (Staples et al., 2019). Poor sleep on these dimensions can also predict later internalizing and externalizing problems (Sivertsen et al., 2015). Experimental work with toddlers shows that sleep deprivation can be a causal mechanism of increased negative affect (Berger et al., 2012), but it is unknown if this association occurs in natural settings. It could be the reverse. In natural contexts, there are also third variables that could influence how child sleep and affect are related. We are particularly interested in how family stress relates to—and potentially moderates—the linkage between sleep and affect. A child living in a stressful family context is more likely to show negative affect and poor sleep (De Stasio et al., 2020). Additionally, children experiencing high levels of stress also tend to experience poor sleep (Lee et al., 2019). It is unknown, though, how family stress and sleep combine to predict negative affect in young children. The present study examined the independent additive and interactive effects of sleep and family stress on toddlers’ observed negative affect across 1 year.

Negative Affect and Sleep

Negative affect is associated with poor sleep in young children (Davis et al., 2017), but this association may depend on individual or contextual factors. Palmer and Alfano (2017) found that greater amounts of slow wave sleep were associated with less negative affect in a group of clinically anxious children, but not in matched controls, suggesting that the link between negative affect and sleep may be dependent on child characteristics.

The research on negative affect and early childhood sleep is sparse, but a small experimental study found causal associations between sleep and affect. Toddlers (N = 10) who experienced nap restriction over five days displayed a 31% increase in negative emotionality on an unsolvable puzzle task compared to an earlier regulated nap condition with the same cohort (Berger et al., 2012). Much of the literature on child sleep and affect relies on parent report and cross-sectional designs, limiting our ability to evaluate causality. Longitudinal designs with actigraphic measures of sleep would strengthen our causal inferences (Dayyat et al., 2011).

Negative Affect and Stress

Negative affect is also associated with family stress. De Stasio and colleagues (2020) found that higher self-reported parenting stress was associated with increased negative affect in toddlers. They were not able to establish the directionality of this association. Additionally, Davis and colleagues (2017) found that high parenting stress was associated with high levels of externalizing and internalizing problems in preschool children, but only for those children high in negative emotionality. This association suggests that stressful contexts may increase the risk of poor adjustment outcomes for children high in negative affect, a possibility that might be tested with longitudinal designs.

Stress and Sleep

It is well-established that children in poverty are at greater risk for poor sleep (Bagley et al., 2018). Research also suggests that sleep moderates the link between low socioeconomic status (SES) and emotion regulation, a skill that modulates levels of negative affect in toddlers. In a low SES sample, Bocknek and colleagues (2008) found no positive effect of family routines on emotion regulation for toddlers who slept less than 11 h per night, suggesting the importance of sleep duration for children in high-risk contexts.

In addition to poverty, the daily challenges of family life that often accompany poverty, such as limited social support, stressful life events, role overload, parenting hassles, single parenthood, or a chaotic home environment, can add strain to a child’s environment. Higher parenting stress is associated with more toddler night wakings and longer sleep onset latency (De Stasio et al., 2020). The cumulative effect of environmental stressors exceeds the effect of any individual exposure (Evans et al., 2013), and may negatively influence children’s sleep and the occurrence of negative affect. Therefore, it is important to consider multiple kinds of environmental adversities when predicting outcomes, which can be done with a cumulative risk index (CRI; see Evans et al., 2013).

The Current Study

In the present study, we examined direct links between family stress, sleep, and negative affect across 1 year of toddlerhood, a period of rapid development in both sleep and affect regulation. Toddlers experience normative decreases in daytime sleep (Staples et al., 2015), night wakings (Byars et al., 2012), and total amount of sleep (Galland et al., 2012). Furthermore, toddlers grow in self-regulation (McClelland et al., 2010), which is related to the modulation of negative affect (Eisenberg et al., 2014). Across toddlerhood and beyond, differences in self-regulation become more stable and predictive of later adjustment (Carranza et al., 2013).

We also examined whether family stress and sleep interact to predict negative affect. Given the many ways families differ, we thought that sleep variables could relate to child negative affect in different ways in different families. Based on the robust literature showing interactions between stress and child temperament in predicting child adjustment (Schermerhorn et al., 2013), we thought that child sleep might be especially important for affect regulation in the context of family stress.

We expected that toddlers with poor sleep would demonstrate higher levels of concurrent, observed negative affect. We also expected that poor sleep would predict more negative affect across time. Given limited findings about the effects of sleep duration, variability, activity, onset latency, or timing on functioning, we considered all of these aspects, including daytime naps, and did not have specific hypotheses about particular domains. Finally, we anticipated that children with higher levels of family stress and sleep problems would show the most negative affect. Conversely, we expected that if toddlers experienced high quality sleep under a stressful context, their levels of negative affect would not be as elevated.

Methods

Participants

Data were from the Toddler Development Study, a multisite longitudinal study approved by local institutional review boards. Recruitment of community samples in two Midwestern locations occurred mostly through a database of county birth records and outreach at local institutions, such as daycare centers, the housing authority, and the farmer’s market. The sample excluded children with severe developmental delays. Compensation of $100 (~ $25 per hour) was provided, and transportation was offered. Initial assessment of toddlers began at age 30 months, with follow-up assessments occurring at ages 36 and 42 months (± 1 month). A sample of 504 children was analyzed at 30 months, 407 at 36 months, and 399 at 42 months. Reasons for missingness include failure to return questionnaire packets, toddler refusal to wear actigraphs, actigraphic equipment failure, or attrition across time points when a family moved or was unable to be contacted. Toddlers were primarily non-Hispanic White (88.2%). Family SES was calculated using the Hollingshead Four Factor Index (Hollingshead, 1975), an index based on the parents’ educational attainment and occupational prestige. For families with two employed parents, both parents’ education and occupational prestige scores equally informed estimates. SES scores ranged from 13 to 66, with an average score of 47.92 (SD = 13.30), indicating the sample was largely middle class. There was not a significant difference between toddlers with and without actigraphy data on family SES, t (504) =  − 0.88, p = 0.14. There was also not a significant difference between toddlers with and without lab observation of negative affect on SES, t (504) = 0.83, p = 0.54.

Procedure

All lab procedures and data collection processes occurred over the course of two weeks at all timepoints (30, 36, and 42 months). At an initial home visit, informed consent was established, and primary caregivers completed questionnaires on demographics and family stress. The child also received an actigraph at the home visit to wear for one week. To encourage child compliance, the actigraph was sewn into a cloth wristband for comfort, and the child received small daily gifts as a reward for wearing the device overnight. The actigraph was then replaced one week later at a lab visit for an additional week of data collection. During the lab visit, the parent–child dyads completed a series of video-recorded tasks that were later coded by research assistants.

Measures

Negative Affect

Negative affect was coded during a laboratory parent–child free play task. As in other developmental free play assessments (e.g., Wakschlag et al., 2008), toddlers and their primary caregivers were brought into a neutral room and instructed to play with toys on a table, but not to play with more attractive toys stored on a nearby shelf. The attractive toy shelf was placed several feet from where the dyads were playing and was accessible at the child’s height. The toys and room used in the free-play task were novel to the participants at the first visit, but the task was replicated at each time point with the same toys in the same room.

The five-minute interaction was recorded on video and later coded in 15-s intervals by trained research assistants using a 0–2 scale (0 = no negative affect; 1 = low negative affect, e.g., whining; 2 = high negative affect, e.g., crying). The research assistants used a manual which described the behaviors of interest, and during training they viewed a set of example videos with “master codes.” Twenty percent of all videos were double coded to 80% reliability.

A weighted composite was formed; the proportion of intervals that were coded as no negative affect was multiplied by 0, low negative affect multiplied by 1, and high negative affect multiplied by 2. These weights were then summed to create a composite with a theoretical range of 0–2, with higher scores reflecting a higher incidence of negative affect during the free play task.

Sleep

Sleep Diaries

Primary caregivers completed a daily sleep diary to record child naps, bedtimes, night wakings, and morning rise times. These data were used to assist in actigraphy scoring.

Actigraphy

Child sleep data were collected using the MicroMini Motionlogger (actigraph) from Ambulatory Monitoring Inc. (AMI; Ardsley, NY). The actigraph records minute-by-minute epochs of movement which were then scored with the Motionlogger Analysis Software Package Action W-2 software (Version 2.6.92 [AW2]). Actigraphy data were processed using the Sadeh algorithm (Sadeh et al., 1994), which has been validated for use with young children and shown to provide reliable estimates when averaged over seven days (Acebo et al., 1999). Children provided, on average, 9.58 days of actigraphic data (SD = 3.81 days). Minutes asleep in bed were calculated using primary caregiver-reported bedtimes (i.e., sleep diaries) and actigraphy-determined start and end of sleep (time of awakening in the morning). Variables concerning activity and awakenings after sleep onset were based on motion recorded by the actigraph using the zero-crossing mode and a moderate sensitivity threshold (Meltzer et al., 2012). A night waking was determined when the activity count was above threshold (50 crossings) for more than five minutes.

Sleep Composites

Based on previous principal component analysis (Staples et al., 2019), the large number of AW-2 actigraph variables were summarized into four composite indexes. Composites were formed by standardizing the actigraphy variables and averaging them at each age. These four composites—sleep duration, sleep timing, sleep variability, and sleep activity—have been found to account for 82% of the variance in a set of 17 actigraphy variables (Staples et al., 2019), and they represent broad dimensions of actigraphy that are often examined in the child sleep literature (Meltzer et al., 2012). These composites have been used in previous studies (Hoyniak et al., 2018, 2020; McQuillan et al., 2019, 2021; Staples et al., 2020) and have been replicated with infants by another research team (Schoch et al., 2020). The specific actigraphy variables included in each composite are listed in Table 1. Of note, the only diary reported variables included in the sleep composites were mother-reported bedtime and the standard deviation of bedtime.

Table 1.

Actigraphy variables included in composites. Note. *Bedtime variable was based on parent report of time in bed on the daily diary

Sleep composite Variable descriptions
Sleep duration Average sleep period (minutes)
Average duration of time in bed (minutes)
Average minutes asleep in bed
Sleep timing Average time of midsleep (HH:MM in 24-h time)
Average time of sleep onset (HH:MM in 24-h time)
Average bedtime (HH:MM in 24-h time)*
Sleep variability SD of time of sleep onset
SD of duration of time in bed
SD of duration of sleep period
SD of time of midsleep
SD of bedtime*
SD of minutes asleep in bed
Sleep activity Average time (minutes) awake after sleep onset
SD of average minute to minute activity levels
Average number of awakenings (lasting 5 min or more)

Average duration (minutes) of longest wake episode (after sleep onset)

Average percent of active epochs (after sleep onset)
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Sleep onset latency, a single variable reflecting the amount of time between diary-reported bedtime and actigraphically determined true sleep, was used as an additional sleep index. A sixth, separate actigraphy variable that we examined was nap sleep minutes, which indexes the number of minutes scored as sleep during the period between the diary-reported nap time and the first epoch after a period of sleep for which the activity count reached 50 and remained above that threshold until the next sleep interval. The average total nap minutes per day across the two weeks of data collection was used in all analyses.

Stress

The following measures of family stress were also collected at all timepoints.

Household Chaos

Primary caregivers completed the Confusion, Hubbub, and Order Scale to measure household confusion and disorganization (CHAOS; Matheny et al., 1995). Primary caregivers responded to 12 forced-choice items (0 = no, 1 = yes), such as “first thing in the day, we have a regular routine at home” and “it’s a real zoo in our home.” The positively stated items were reverse coded before all items were summed to create a total chaos score, with higher scores indicating greater household chaos.

Role Overload

Perceived role overload was measured with the revised Reilly Role Overload Scale (Thiagarajan et al., 2006). Primary caregivers responded to six items on a 7-point Likert scale (1 = never, 7 = always), including “I need more hours in the day to do all the things that are expected of me” and “There are times when I cannot meet everyone’s expectations.” The average per-item rating was used in subsequent analyses.

Parenting Daily Events

Primary caregivers also completed the Parenting Daily Hassles Scale (Crnic & Greenberg, 1990) to report the frequency (1 = never to 5 = constantly) and intensity (1 = no hassle to 5 = big hassle) of 8 common parenting hassles, such as sibling conflicts and errands. The sum of the 16 parenting tasks subscale items was used.

Social Support

The Social Support Scale (Procidano & Heller, 1983) measured the perceived support primary caregivers reportedly receive from friends and families on a 5-point Likert scale (0 = never, 4 = often). The 11-item Lack of Support subscale was used to assess primary caregivers’ experiences with unsolicited advice, social rejection, others’ insensitive behavior, and failure to provide help. The average lack of support per item was used in all analyses.

Stressful Life Events

Lastly, primary caregivers completed the Changes and Adjustments Questionnaire (CAQ; Dodge et al., 1994). The CAQ lists 16 stressful life events that primary caregivers may have experienced within the last year, such as a move, divorce, or death in the family (0 = did not happen, 1 = happened and had a minor effect on the family, or 2 = happened and had a major effect on the family). These items are based on previous questionnaires with an emphasis on events that may be most salient for families with young children (e.g., Johnson & McCutcheon, 1980). The sum was used in all analyses.

Cumulative Risk Index (CRI)

To create a more robust measure of stress, a Cumulative Risk Index (CRI) was formed based on previous research (McQuillan et al., 2019). The following variables were standardized, averaged, and re-standardized: SES (Hollingshead reversed), single parent status (coded 0 = partnered, 1 = single), household chaos, role overload, parenting hassles, lack of social support, and stressful life events. The CRI was calculated for each timepoint and used in subsequent analyses.

Analysis Plan

First, concurrent, bivariate correlations between negative affect, sleep, and stress were analyzed at each age. Next, we examined lagged correlations between negative affect and the sleep indexes across toddlerhood to determine whether poor sleep predicted negative affect more so than the reverse. Any significant lagged correlations were then tested further in partial correlations to control for prior levels of the relevant variable. Lagged correlations between stress and affect, as well as stress and sleep, were also examined. Finally, to evaluate if sleep and stress interacted to predict negative affect, multiple regression analyses were conducted, with six separate models tested at each wave for each of the six sleep indexes. To interpret significant interactions, we did post-hoc probing using estimation of simple slopes at the mean and at 1 SD above and below the sample mean of stress using the CRI. Plots were produced for visual inspection of the interaction effects. Data were analyzed in SPSS version 26 (IBM Corp., 2019). Multiple regressions were conducted using the PROCESS Macro toolbox (Hayes, 2012), and the interactions were tested with 1000 bootstrap resampling for bias correction.

Results

Descriptive statistics for all variables across all ages are presented in Table 2.

Table 2.

Descriptive statistics

Domain Variable 30 months 36 months 42 months
N M SD Min Max N M SD Min Max N M SD Min Max
Sleep Sleep duration (z) 510  − .01 .87  − 2.97 2.80 414 .05 .92  − 2.82 2.51 318 .05 .84  − 3.07 2.01
Sleep variability (z) 510 .00 .82  − 1.40 6.81 412 .03 .88  − 1.37 8.34 317  − .02 .77  − 1.40 3.53
Sleep activity (z) 510 .19 .90  − 1.92 4.31 411  − .05 .80  − 2.08 2.83 318  − .15 .79  − 1.79 2.89
Sleep timing (z) 510  − .03 .91  − 2.08 3.17 411 .02 .99  − 2.28 5.27 318  − .03 .96  − 2.22 4.73
Sleep onset latency 506 39.29 24.21 .00 181.60 410 39.26 24.60 .00 193.75 317 36.03 21.83 .00 123.50
Nap—minutes asleep 467 87.76 30.11 .00 299.00 360 84.92 32.12 .00 193.00 258 78.70 31.43 .00 231.00
Stress Cumulative Risk Index 634  − .05 .51  − 1.18 1.96 625  − .05 .51  − 1.37 1.70 697  − .09 .50  − 1.23 1.60
Affect Observed negative affect 504 .08 .18 .00 1.48 407 .06 .18 .00 2.00 399 .04 .11 .00 .95
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Correlations Between Negative Affect, Sleep, and Stress

Bivariate correlations between negative affect and the six sleep indexes are presented in Table 3. Of the 18 concurrent correlations tested, only three were significant. All three correlations occurred at 30 months and were weak in magnitude. Higher levels of observed negative affect during free play were significantly associated with more fragmented sleep (as indexed by sleep activity; r = 0.14, p < 0.05), more variable sleep (r = 0.11, p < 0.05), and shorter nighttime sleep durations (r =  − 0.13, p < 0.05).

Table 3.

Bivariate correlations between negative affect and sleep variables

Age Variable Negative affect (30 months) Negative affect (36 months) Negative affect (42 months)
30 months Sleep activity .14**  − .04 .09
Sleep variability .11* .04  − .01
Sleep duration  − .13*  − .06  − .10
Sleep timing .05 .07 .08
Sleep onset latency .04  − .02 .14*a
Nap—minutes asleep .09 .00 .08
36 months Sleep activity  − .03 .05 .01
Sleep variability  − .13*  − .01  − .05
Sleep duration  − .12* .02  − .06
Sleep timing .04  − .01  − .02
Sleep onset latency .03 .01 .13*
Nap—minutes asleep .04 .03 .07
42 months Sleep activity  − .03  − .01  − .01
Sleep variability  − .05 .04  − .03
Sleep duration  − .10  − .06  − .06
Sleep timing .05 .05 .04
Sleep onset latency  − .10 .11 .09
Nap—minutes asleep .08 .06  − .01
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Note. **Correlation is significant at the 0.01 level (2-tailed). *Correlation is significant at the 0.05 level (2-tailed). Significant associations are bolded. Concurrent correlations are highlighted in gray

aAssociation remained significant when controlling for prior level of negative affect at 30 months

Table 3 also presents the lagged associations between sleep and negative affect. Four of these were statistically significant, with three associations indicating that higher negative affect was associated with later sleep problems and one indicating the reverse. Higher levels of negative affect observed at 30 months were associated with shorter sleep duration at 36 months (r =  − 0.12, p < 0.05), and longer sleep onset latencies at 30 and 36 months were associated with more negative affect observed in the lab at 42 months (r = 0.14 and 0.13, respectively, p < 0.05). Surprisingly, higher negative affect at 30 months was also associated with less variable sleep at 36 months (r =  − 0.13, p < 0.05). Notably, only one of these lagged associations remained statistically significant when controlling for prior levels in partial correlations. Sleep onset latency at 30 months was significantly, positively associated with negative affect at 42 months, even when controlling for negative affect at 30 months (β = 0.13, 95% CI [0.12, 0.14], p = 0.02).

Table 4 presents the bivariate correlations between negative affect and the CRI measure of family stress within and across time. There were no significant associations between family stress and observed child negative affect at any age. Table 5 presents the correlations between sleep and stress within and across time. Of the 18 concurrent correlations tested, four were statistically significant. More variable sleep was associated with higher stress at 30 months (r = 0.23, p < 0.05) and at 42 months (r = 0.13, p < 0.05). Shorter nighttime sleep duration was associated with higher stress at 30 months (r =  − 0.14, p < 0.05), and later sleep timing was also associated with higher stress at 30 months (r = 0.16, p < 0.05). Across time, early stress predicted later sleep timing and more variable sleep (0.12 ≤ r ≤ 0.16, p < 0.05), and some early sleep measures predicted later family stress, including sleep variability, sleep duration, and sleep timing, all in the expected direction, − 0.13 ≤ r ≤ 0.21, p < 0.05. No lagged correlations between stress and sleep remained significant when controlling for prior levels.

Table 4.

Bivariate correlations between negative affect and Cumulative Risk Index (CRI)

Variable 1 2 3 4 5 6
1. CRI 30 months 1.00
2. CRI 36 months .74** 1.00
3. CRI 42 months .65** .74** 1.00
4. Negative affect 30 months .07 .05 .06 1.00
5. Negative affect 36 months  − .01 .06 .04 .20** 1.00
6. Negative affect 42 months .03 .04 .01 .12* .24** 1.00
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Note. **Correlation is significant at the 0.01 level (2-tailed). *Correlation is significant at the 0.05 level (2-tailed). Significant associations are bolded

Table 5.

Bivariate correlations between Cumulative Risk Index (CRI) and sleep variables

Age Variable CRI (30 months) CRI (36 months) CRI (42 months)
30 months Sleep activity .07 .05 .06
Sleep variability .23** .18** .21**
Sleep duration  − .14**  − .13**  − .10*
Sleep timing .16** .13** .13**
Sleep onset latency .03 .02 .01
Nap—minutes asleep .06 .02 .03
36 months Sleep activity .04 .03  − .04
Sleep variability .09 .07 .08
Sleep duration  − .02 .01  − .02
Sleep timing .13** .08 .10
Sleep onset latency  − .02  − .01  − .04
Nap—minutes asleep .05  − .05 .01
42 months Sleep activity .03 .04 .05
Sleep variability .12* .16** .13*
Sleep duration  − .05  − .01  − .03
Sleep timing .15** .09 .05
Sleep onset latency .06 .04 .02
Nap—minutes asleep  − .06  − .08  − .11
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Note. **Correlation is significant at the 0.01 level (2-tailed) *Correlation is significant at the 0.05 level (2-tailed). Significant associations are bolded. Concurrent correlations are highlighted in gray

Interactions Between Stress and Sleep to Predict Negative Affect

Finally, to examine if sleep and stress interacted to predict negative affect, multiple regressions were conducted for each of the six sleep indexes at each time point, resulting in 18 total interaction models. In all models child negative affect was the dependent variable, and in the various models, each sleep index was entered as an independent variable, and stress at each time point served as the moderator. At 30 months, each sleep index (except for nap) significantly interacted with stress to predict negative affect (see Figs. 1a–d and 2a), thus 83% of the models tested at this age were statistically significant, far surpassing what would be expected by chance. Table 6 presents the interaction results for the five significant sleep domains at 30 months.

Fig. 1.

Fig. 1

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a–d Sleep indexes and negative affect at 30 months by Cumulative Risk Index (CRI). Observed negative affect was measured during parent–child free play in the lab. Stress was measured using a Cumulative Risk Index (CRI). Low/high stress defined as − / + 1 SD from the mean. a Two-way interaction between child sleep duration (z-scored) and stress predicting negative affect. b Two-way interaction between child sleep variability (z-scored) and stress predicting negative affect. c Two-way interaction between child sleep timing (z-scored) and stress predicting negative affect. d Two-way interaction between child sleep activity (z-scored) and stress predicting negative affect

Fig. 2.

Fig. 2

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a, b Sleep onset latency and negative affect by Cumulative Risk Index (CRI). Observed negative affect was measured during parent–child free play in the lab. Stress was measured using the Cumulative Risk Index (CRI). Low/High stress defined as − / + 1 SD from the mean. a Two-way interaction between child sleep onset latency and stress predicting negative affect at 30 months. b Two-way interaction between child sleep onset latency and stress predicting negative affect at 36 months

Table 6.

Hierarchical multiple regression equations predicting negative affect at 30 months

Sleep index tested Predictor β 95% CI t p
Sleep variability CRI .00  − .02, .01  − .21 .84
Sleep .01  − .01, .03 1.17 .24
Sleep × CRI .08 .06, .09 2.48 .01
Sleep duration CRI .00  − .01, .02 .58 .57
Sleep  − .02  − .04, .00  − 2.25 .02
Sleep × CRI  − .08  − .09, − .06  − 2.16 .03
Sleep timing CRI .00  − .01, .02 .43 .67
Sleep .01  − .01, .02 .82 .41
Sleep × CRI .07 .05, .08 2.87 .00
Sleep activity CRI .00  − .01, .02 .49 .62
Sleep .02 .00, .04 2.38 .02
Sleep × CRI .07 .06, .09 2.55 .01
Sleep onset latency CRI .01  − .01, .02 .72 .47
Sleep .00 .00, .00  − .12 .91
Sleep × CRI .08 .06, .09 3.51 .00
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Note. CRI refers to the Cumulative Risk Index of stress. Beta values represent standardized estimates

Figure 1a shows the interaction between sleep duration and stress at 30 months. For toddlers experiencing low levels of family stress, sleep duration and negative affect were not significantly linked. However, for children experiencing high levels of stress (+ 1SD), there was a significant, inverse association between sleep duration and negative affect, such that stressed toddlers who experienced shorter sleep also showed the most negative affect during free play (β =  − 0.13, 95% CI [− 0.15, − 0.12], p < 0.05, R2 = 0.05).

Figure 1b shows the interaction for sleep variability at 30 months. For toddlers experiencing low levels of family stress, sleep variability and negative affect were not associated. However, among toddlers experiencing high levels of stress, higher levels of sleep variability significantly predicted higher levels of negative affect (β = 0.08, 95% CI [0.06, 0.09], p < 0.05, R2 = 0.03).

Figure 1c shows the interaction for sleep timing at 30 months. Again, toddlers who experienced high levels of family stress showed a positive association between sleep timing and negative affect such that toddlers who experienced high levels of family stress and later sleep timing also showed higher negative affect (β = 0.09, 95% CI [0.07, 0.10], p < 0.05, R2 = 0.02). This was not true for toddlers with average to low levels of stress.

Figure 1d shows the interaction for sleep activity at 30 months. For toddlers experiencing low levels of stress, there was no association between sleep activity and negative affect. However, for toddlers experiencing high levels of family stress, greater sleep activity positively predicted greater negative affect (β = 0.08, 95% CI [0.06, 0.09], p < 0.05, R2 = 0.04).

Figure 2a shows the interaction for sleep onset latency at 30 months. Among toddlers experiencing low levels of family stress, there was no association between sleep and affect. However, for toddlers experiencing average to high levels of stress, the longer the child took to fall asleep, the more negative affect they displayed in the lab (β = 0.08, 95% CI [0.07, 0.09], p < 0.05, R2 = 0.03).

To examine the replicability of these interaction effects across time, the same regression equations were fit at 36 and 42 months using sleep, stress, and negative affect measured at those ages. Only one interaction remained significant: stress and sleep onset latency again significantly predicted negative affect at 36 months (see Fig. 2b). Toddlers experiencing high levels of stress and longer sleep onset latency were more likely to demonstrate negative affect at 36 months (β = 0.13, 95% CI [0.09, 0.42], p < 0.01, R2 = 0.03).

Discussion

This study investigated the associations between child sleep patterns, negative affect in a 5-min parent–child lab task, and family stress over 1 year of toddlerhood. The study used actigraphic measures of sleep, observed negative affect coded in 15-s intervals, and a cumulative risk index of family stressors. We examined bidirectional links between sleep and negative affect, and whether sleep and family stress interacted to predict negative affect. Sleep and negative affect were not strongly related, either concurrently or across time. However, family stress moderated the association between sleep and negative affect. Poor and insufficient sleep on nearly all the distinct indexes predicted higher negative affect for children in high-stress families at 30 months, and did not predict negative affect in the lab task for children in low stress families. This strong pattern of interactions at 30 months was not found at the two later ages (36 and 42 months).

Our first hypothesis that poorly rested toddlers would demonstrate higher levels of negative affect concurrently was minimally supported, with three weak significant correlations. The few associations we found align with previous work using actigraphy with preschool children showing weak concurrent correlations among parent-reported negative affect and longer sleep onset latency (Cremone et al., 2018).

The present study built on previous cross-sectional work by examining links between sleep and negative affect across time. Again, we found minimal support for bidirectional links among sleep and affect, with four significant associations, only one of which remained significant when accounting for prior levels. Sleep onset latency at 30 months positively predicted higher levels of negative affect 1 year later, even when controlling for earlier negative affect. This finding is similar to previous longitudinal research showing that parent-reports of sleep in toddlerhood predicted emotional problems in preschool (Troxel et al., 2013), an outcome that is associated with toddler negative affect (Cremone et al., 2018). Although we found few associations between sleep and affect, actigraphic measures of sleep are considered more accurate than parent-report (Dayyat et al., 2011), which may partially account for the difference between our work and prior work. Cremone and colleagues (2018) proposed that when a toddler experiences prolonged sleep onset latency, they may in turn initiate sleep outside of the optimal circadian phase, limiting the restorative qualities of sleep and potentially resulting in the development of more negative emotionality, as observed here.

Our third aim was to examine interactions between sleep and family stress predicting negative affect. We predicted that toddlers with high family stress and poor sleep would show the most negative affect. Five of our sleep indexes at 30 months significantly predicted negative affect in the lab task in the context of family stress, supporting our hypothesis. Toddlers from high stress environments who also experienced short, late, variable, or fragmented sleep, or who took longer to fall asleep, demonstrated the highest levels of negative affect in the lab at 30 months. At 36 months, this moderation by stress effect was replicated for sleep onset latency. No interactions were significant at 42 months. Although sleep and affect do not appear strongly linked for toddlers from low stress environments, sleep may be a critical buffer for the effects of stress on negative affect for the youngest toddlers.

Evidence suggests that negative emotionality and adverse environments interact to increase the risk of adjustment issues in childhood (e.g., Shaw et al., 2001). For example, children with high negative affect are especially prone to behavioral issues when exposed to poor parenting quality (Putnam et al., 2002). The vulnerability model posits that an individual’s temperament and environment interact to influence the development of disorders (Rothbart & Bates, 2006). Stressful home environments may be another adversity through which temperamental vulnerabilities are exacerbated in young children. The specific mechanism through which this relationship occurs in toddlers is unknown. Bagley et al. (2015) found that pre-sleep worries mediated the relationship between SES and sleep/wake problems in older children. Children high in negative affect are prone to experience concurrent internalizing issues (Cremone et al., 2018), with worrying a key symptom. Like older children, toddlers in stressful contexts may also experience pre-sleep insecurity, thus reducing sleep quality. Furthermore, toddler sleep may be particularly sensitive to parental distress. De Stasio et al. (2020) found parenting stress to predict toddler night wakings, sleep duration, and emotion regulation. It is theorized that parenting stress influences child adjustment via poor parenting strategies (Deater-Deckard, 1998). Through similar mechanisms, stressed parents may create less secure or consistent bedtime routines, which may result in poor sleep quality (Hoyniak et al., 2020; Prokasky et al., 2019), resulting in cascading effects on child behavior. Our discussion has emphasized the possible mechanisms of stress effects upon the child. However, our work is based in systems models of development, so we also recognize the important possibility that young children who are sleeping poorly and are expressing negative affect in a play task with the parent could also be contributing to the parent’s sense of stress, as has been shown in prior research (e.g., Mindell & Durand, 1993).

Sleep may be an important protective factor for children in vulnerable contexts. Dahl (1996) proposed that sleep deprivation may exacerbate problems in affective regulation for some children. For toddlers experiencing high stress, good sleep may buffer the effects of the parent’s stress on the child’s negative affect in a task with potential for both enjoyment (playing with simple toys with the parent) and conflict (delayed access to more attractive toys). Although more research is needed to confirm this hypothesis, these findings may be important for intervention purposes. A focus on improving sleep especially for children in stressful environments may reduce the development of externalizing and internalizing disorders, both of which are associated with negative affect (NICHD, 2004).

Our findings emphasize the importance of context when studying sleep and childhood affect. A notable finding is the lack of significant interactions between the variables of interest at 36 and 42 months. This may be due to the maturation of sleep during toddlerhood. Indeed, previous research has shown that toddler sleep tends to improve with age, with decreases in daytime sleep (Staples et al., 2015), night wakings (Byars et al., 2012), and the total amount of sleep (Galland et al., 2012). More research is needed to identify how and when sleep may buffer children against negative affect associated with family stress. At the same time, there may have been increases in the child’s ability to manage the possible conflict of being asked to play with the parent at the table while having to wait to play with more stimulating toys. Part of this would be due to growth in self-regulatory skills. At later ages, the child may have had sufficient reserves of self-regulation to manage any negative emotion even if working with a relative sleep deficit. Part of it may be due to increased awareness of social rules and memory of how this one lab task is followed by others.

Limitations

Limitations of this study include our measure of negative affect, which was gathered during play with the mother in a laboratory. Toddlers’ levels of negative affect in this task tended to be quite low, so variability was limited (averages ranged between 0.04 and 0.08 out of a maximum possible score of 2.00). A potential task that may provide greater variation in observed negative affect is a frustration task: a previous experimental study found that toddlers who experienced nap restriction demonstrated higher levels of negative affect during an unsolvable puzzle task compared to a control condition (Berger et al., 2012). Further research should investigate these associations in settings with greater variation in observed negative affect.

Another notable limitation is that our sample consisted of predominately White, middle class families with typically developing children. Although our community sample did have a meaningful range of stress, it was not a sample of highly stressed families. Future research conducted with more racially, ethnically, socioeconomically, and developmentally diverse families may add importantly to understanding the connections between stress, sleep, and affect in early childhood. Furthermore, children with developmental delays such as Autism Spectrum Disorder tend to have a higher incidence of reported sleep problems (Souders et al., 2017) and caregiver stress (Hayes & Watson, 2013). Although children with severe developmental delays were excluded from the study, toddlers diagnosed with mild to moderate delays (or those that will eventually be diagnosed with such delays) may confound these associations.

Additionally, although we accounted for associations between sleep and negative affect in the context of family stress, we did not account for parent behavior, which has been shown to be associated with family stress (McQuillan et al., 2019), child sleep (Hoyniak et al., 2020), and child negative affect (Lagacé‐Séguin & d’Entremont, 2006). Previous research finds that high levels of supportive parenting in early childhood mitigate the effects of family stress on later behavioral problems (Pettit et al., 1997). Although it is beyond the scope of this investigation, future research could consider the observed warmth and control style of the parent during play with the child as a moderator for the impact of family stress on children’s negative affect.

Lastly, there was no standardized time at which the lab tasks were administered across participants. Children could have been assessed at any time of the day, based on family preference. Therefore, variations in child alertness or sleep inertia may have affected task performance. Prior research has shown that sleep deprived children are more likely to have poor cognitive performance when tested in the early morning as compared to afternoon (Sadeh et al., 2002). Future research accounting for either the time since last sleep period or the time of day when negative affect was measured would advance the present findings.

Conclusion

The present study emphasizes the importance of family context when considering the associations between sleep and negative affect in toddlerhood. This study, as with previous literature (e.g., Cremone et al., 2018), finds weak concurrent or longitudinal associations between toddler sleep and negative affect. However, at the earliest age, in the context of family stress, a significant and consistent interaction emerged across five sleep indices: poor and insufficient sleep predicted greater negative affect in the context of high family stress. Future research is needed to confirm and extend this robust pattern. The present findings may prove useful to clinical practitioners who find ways to reduce children’s negative emotionality in stress-filled families by helping the family to improve child sleep.

Additional Information

Funding

The Toddler Development Study has been funded by grants MH099437 from the National Institute of Mental Health and HD073202 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development.

Code and Data Availability

All data and syntax are publicly available on Open Science Framework (https://osf.io/zgv5f/).

Ethical Approval

This study was approved by local institutional review boards (Protocol 0,811,000,120) and was performed to ethical standards as laid down in the 1964 Declaration of Helsinki. All participants provided informed consent.

Conflicts of Interest

The authors declare no competing interests.

Informed Consent

Not applicable.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42761-021-00094-2.

Author Contributions

J.S. conceived of the theoretical framework, interpreted statistical analyses, and drafted the manuscript. M.M. performed data analyses and made substantial contributions to manuscript drafts. C.H. and A.S. contributed in critical manuscript revisions. K.R., V.M., and J.B. acquired funding, supervised data collection, and revised the manuscript. J.B. supervised project execution. All authors reviewed and approved of final manuscript.

Footnotes

Handling Editor: Aric Prather

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