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. Author manuscript; available in PMC: 2022 Nov 28.
Published in final edited form as: Dev Psychobiol. 2021 Dec;63(Suppl 1):e22220. doi: 10.1002/dev.22220

Longitudinal associations between adolescents’ sleep and adjustment: Respiratory sinus arrhythmia as a moderator

Lauren E Philbrook 1, Mina Shimizu 2, Stephen A Erath 2, J Benjamin Hinnant 2, Mona El-Sheikh 2
PMCID: PMC9704054  NIHMSID: NIHMS1849075  PMID: 34964495

Abstract

Sleep and autonomic nervous system functioning are important bioregulatory systems. Poor sleep and low baseline respiratory sinus arrhythmia (RSA), a measure of parasympathetic nervous system activity, are associated with externalizing behaviors and depressive symptoms in youth. Rarely, however, have measures of these systems been examined conjointly. The present study examined baseline RSA (RSA-B) as a moderator of longitudinal relations between adolescent sleep and adjustment. Participants were 256 adolescents (52% girls, 66% White/European American, 34% Black/African American) from small towns and surrounding rural communities in the southeastern United States. Sleep (minutes, efficiency, variability in minutes and efficiency) was assessed at age 15 via actigraphs across seven nights. RSA-B was derived from electrocardiogram data collected at rest. Adolescents self-reported externalizing problems and depressive symptoms at ages 15 and 17. Controlling for age 15 adjustment, findings generally demonstrated that sleep predicted age 17 adjustment particularly at higher (rather than lower) levels of RSA-B, such that adolescents with good sleep (more minutes and lower variability) and high RSA-B were at lowest risk for maladjustment. The results highlight the value of examining multiple bioregulatory processes conjointly and suggest that promoting good sleep habits and regulation of physiological arousal should support adolescent adjustment.

Keywords: adjustment, adolescence, respiratory sinus arrhythmia, sleep, sleep variability

1 |. INTRODUCTION

Sleep and autonomic nervous system (ANS) functioning contribute to regulation of affect and behavior. Poor sleep and low baseline respiratory sinus arrhythmia (RSA-B), an index of parasympathetic nervous system (PNS) activity, are risk factors for maladjustment, including externalizing behaviors and depressive symptoms (e.g., Beauchaine, 2015; Tarokh et al., 2016). Although sleep and PNS regulation are related to similar aspects of functioning in adolescence, measures of these systems have rarely been examined conjointly as predictors of youth adjustment. The present study assessed RSA-B as a moderator of longitudinal relations between adolescent sleep and both externalizing behaviors and depressive symptoms.

Fewer sleep minutes, inefficient sleep, and high levels of sleep problems have been linked to elevated externalizing behaviors (Meldrum & Restivo, 2014) as well as internalizing symptoms (Baglioni et al., 2010; Doane et al., 2015; Palmer et al., 2018) among adolescents. Reduced connectivity between the prefrontal cortex and amygdala areas of the brain has been detected following shortened or poor-quality sleep; this sleep-related disconnect may lead to diminished control over negative emotions (Telzer et al., 2013; Yoo et al., 2007) that increases risk for maladjustment.

Much of the literature on sleep and adjustment has incorporated measures of average sleep minutes and efficiency, though variability in such sleep parameters may also affect adjustment and is frequently experienced by adolescents (Becker et al., 2017). Bei et al. (2017) theorized that sleep variability may affect functioning via two specific mechanisms. First, sleep variability is associated with circadian misalignment, in which an individual sleeps at times outside of their optimal circadian phase. Circadian misalignment disrupts the rhythm of other physiological processes, such as cortisol patterning (Bei et al., 2017), which is related to mental health outcomes. Additionally, this misalignment is thought to be physically taxing on the body, causing wear and tear on multiple systems, including the cardiovascular and immune systems and hypothalamic–pituitary–adrenal (HPA) axis. Frequent changes in sleep patterning demand constant responses from these bodily systems, increasing allostatic load and in turn vulnerability for maladjustment. The empirical research concerning sleep variability and adjustment in adolescence is equivocal, however, with some studies detecting null results (Moore et al., 2009; Sally et al., 2015) and others finding significant relations between greater variability in sleep minutes and increased anxiety and depressive symptoms (Fuligni & Hardway, 2006; McHale et al., 2011). As further research on these associations is warranted, the present study assessed objective measures of sleep minutes and efficiency as well as variability in these parameters in relation to adolescent adjustment.

RSA has also been associated with youth adjustment in numerous studies. RSA is measured as heart rate variability across the respiration cycle (Grossman & Taylor, 2007) and indexes the influence of the prefrontal cortex and PNS, specifically the vagus nerve, on the heart (Beauchaine, 2015). According to the Polvagal Theory, the vagus nerve modulates cardiac output to adapt emotion, behavior, and attention to environmental demands (Porges, 2007). The “vagal brake” reduces arousal in resting conditions, producing higher RSA. High RSA-B is theorized to indicate calmness and readiness to flexibly respond to challenges (Porges, 2007) and is generally associated with better adjustment outcomes for youth (Beauchaine, 2015), such as higher prosocial behavior and lower aggression (Chapman et al., 2010).

Sleep and RSA are both related to prefrontal cortex functioning (Beauchaine, 2015; Telzer et al., 2013; Yoo et al., 2007), which may mean that these bioregulatory systems act in concert to affect adolescent adjustment. However, to our knowledge only two studies have examined the conjoint influences of sleep and RSA-B on developmental outcomes. El-Sheikh et al. (2007)1 detected elevated risk among 8- to 9-year-olds with actigraphy-assessed poor sleep and lower RSA. For children with low RSA-B, more wake minutes, fewer sleep minutes, and lower sleep efficiency were associated with higher externalizing behaviors and depressive symptoms. Hamilton et al. (2019) found that for young adults with low RSA-B, a night of self-reported shorter sleep duration or more insomnia symptoms predicted more depressive symptoms the next day. For individuals with high RSA-B, by contrast, daily sleep duration and insomnia symptoms were not associated with depressive symptoms. This work indicates that short and inefficient sleep are associated with vulnerability to externalizing behaviors and internalizing symptoms at lower levels of RSA-B, perhaps because RSA is a marker of emotion regulation (Baum et al., 2014; O’Leary et al., 2017). Effective emotion regulation, as indicated by high RSA-B, may compensate for the potential negative effects of poor sleep, whereas the accumulated risks of sleep difficulties and emotion dysregulation may contribute to maladjustment (Dahl, 1996; El-Sheikh et al., 2007). This prior research highlights how examination of both sleep and RSA-B offers a more nuanced understanding of risk for maladjustment. The effects of sleep and RSA-B alone are not uniform, and assessment of multiple risk and protective factors in tandem improves prediction of adolescent adjustment.

The present study sought to advance the small existing literature concerning the joint influences of sleep and RSA-B on youth adjustment by examining such relations across 2 years in a socioeconomically and racially heterogeneous sample of adolescents. Adolescents generally have more autonomy over their sleep–wake behaviors in comparison to children, but not to the same degree as when they have left high school and begin to live independently as young adults. Prior work has shown that multiple aspects of sleep, including self-reported and objectively assessed duration and efficiency and self-reported sleep adequacy, decline across adolescence as well as young adulthood (Keyes et al., 2015; Park et al., 2019). Furthermore, previous research has also shown that RSA-B increases across childhood and plateaus around age 10 (Dollar et al., 2020). That sleep and RSA-B undergo normative developmental changes suggests that relations between sleep, RSA-B, and adjustment could vary depending on the age and developmental stage of assessment. To our knowledge, the present study is the first to longitudinally examine these associations in adolescence, and to incorporate measures of sleep variability in addition to sleep minutes and efficiency. Based on the findings of prior work (El-Sheikh et al., 2007; Hamilton et al., 2019), we hypothesized that poor sleep (fewer minutes, lower efficiency, higher variability) would be associated with more externalizing behaviors and depressive symptoms for youth with low but not high RSA-B.

2 |. METHODS

2.1 |. Participants

Data were drawn from the Family Stress and Youth Development Study, a longitudinal study of bioregulatory effects on youth adjustment across middle childhood and adolescence (see Hinnant et al., 2015 for more details). The present study utilized data for 256 adolescents at waves 4 (T4) and 6 (T6) of the parent study: 52% were female, 66% White/European American, and 34% Black/African American. T4 was used for analysis because it was the first wave collected in adolescence; T6 was selected (rather than T5) to allow for more variability in change in adjustment. Socioeconomic status (SES), derived from income-to-needs ratio (annual family income/poverty threshold with respect to family size; U.S. Department of Commerce; www.commerce.gov), indicated that 13%–15% of families were living in poverty (ratio ≤1), 23%–28% near the poverty line (ratio >1 and ≤2), 22%–23% lower middle class (ratio >2 and ≤3), and 35%–41% middle class or above (ratio ≥3) across waves. Mean participant age was 15.26 years (SD = 0.86) at T4 and 17.19 years (SD = 0.79) at T6. Of the T4 sample, 88% continued to participate at T6. Participants who dropped from the study did not differ from the T4 sample on demographic or primary study variables. For clarity, we refer to mean participant ages at each wave (ages 15 and 17) rather than wave number.

2.2 |. Procedure

The university’s Institutional Review Board approved study procedures. Parents consented and youth assented to participation. At age 15, youth wore actigraphs for 1 week to derive an objective measure of nighttime sleep parameters and visited the campus laboratory an average of 3.96 days (SD = 12.25) after actigraph data collection. During the lab visit, youth reported on externalizing problems and depressive symptoms, and RSA was obtained at rest (3 min), which followed a period of acclimation to the physiological equipment and laboratory during which youth sat quietly (3 min). At age 17, youth reported on their adjustment. At each time point, parents reported on their child’s race, sex, and age, as well as household income, and adolescents’ standardized body mass index (zBMI) was obtained in the lab using a Tanita weight scale (Model BC-418) and stadiometer.

2.3 |. Measures

2.3.1 |. Sleep

Adolescents wore Octagonal Basic Motionlogger actigraphs (Ambulatory Monitoring, Ardsley, NY) at home while sleeping for up to seven nights. Data were scored in Action-W2 using the Sadeh algorithm (Sadeh et al., 1994) to derive the number of 1-min epochs scored as sleep. Two sleep parameters were obtained: sleep minutes (number of epochs scored as asleep between sleep onset and wake time) and sleep efficiency (percentage of epochs scored as asleep between sleep onset and wake time). Variability in sleep minutes and in sleep efficiency were then created using the coefficient of variation statistic, which is calculated by dividing each person’s standard deviation for the sleep minutes and efficiency variables by their mean (Snedecor & Cochran, 1967). Prior work examining variability in sleep parameters has used this statistic (e.g., Hoffman et al., 2019; Moore et al., 2011).

These parameters were analyzed if participants had at least five nights of actigraphy data (see guidelines, Meltzer et al., 2012), excluding nights when they used medication. Eighty percent of the sample had actigraphy data for five or more nights. Otherwise, actigraph data were treated as missing, though participants with some missing data were not excluded from analyses.

2.3.2 |. Respiratory sinus arrhythmia

RSA-B was calculated using standard procedures and guidelines (Berntson et al., 1997). Electrocardiogram and thoracic impedance data were collected using BioNex 8-slot chassis and MW1000A acquisition system (Mindware Technologies, Inc., Gahanna, OH). Trained research assistants examined the electrocardiogram data for artifacts and manually edited missing or misplaced R-peaks. Respiration was derived from spectral analysis of the thoracic impedance data (Z0; Ernst et al., 1999). Mindware’s heart rate variability analysis software (HRV version 3.0.22) was used to obtain RSA values in 1-min epochs. RSA was calculated as the natural logarithm of the variance of the heart period within the frequency bandpass associated with respiration (0.15–0.40 Hz); the unit of measurement is the natural logarithm of milliseconds squared. RSA-B values were computed by averaging RSA across the three epochs (3 min).

2.3.3 |. Externalizing problems

Youth reported on their externalizing behavior problems over the prior 6 months by completing the well-validated Youth Self-Report (YSR; Achenbach & Rescorla, 2001). The externalizing scale is composed of 32 items (αs = .85–.92) that examine rule-breaking and aggressive behaviors such as alcohol use or destroying property. Each item was rated on a 3-point scale ranging from not true (0) to very true or often true (2). Youth reporting borderline or clinical levels of externalizing behavior problems (T-scores ≥60; Achenbach & Rescorla, 2001) ranged from 15% to 20% across study waves.

2.3.4 |. Depressive symptoms

Youth also reported on their depressive symptoms over the prior 2 weeks by completing the Child Depression Inventory (CDI; Kovacs, 1992). This measure is a widely used, well-established instrument for surveying adolescent depressive symptoms. It includes 27 items (αs = .88–.89) indicating the degree to which individuals experienced symptoms of depression, with each item rated on a 3-point scale ranging from definite symptom (2) to absence of symptom (0). Scores across the items were summed to create an overall depressive symptoms score (excluding two items pertaining to sleep disturbances). A small percentage of youth (5%–6% across waves) reported clinically elevated levels of symptoms (scores ≥20).

2.3.5 |. Covariates

Because of their potential associations with sleep and primary study variables, adolescent race, sex, age, zBMI, and family SES (income-to-needs ratio) were covaried in analyses. Family SES (r = .66, p < .001) and zBMI (r = .91, p < .001) were each strongly correlated across ages 15 and 17; thus, SES and zBMI were each averaged across the waves.

2.4 |. Statistical analysis

Multiple regression models were fit to examine whether RSA-B at age 15 moderated the predictive associations between sleep (sleep minutes, sleep efficiency, variability in sleep minutes, variability in sleep efficiency) at age 15 and externalizing behavior problems and depressive symptoms at age 17. Consistent with best practices in the field (e.g., Bernier et al., 2014; Chiang et al., 2016), separate models were run for each sleep parameter and the two outcome variables (eight total models). Variables were examined in separate models because we aimed to assess whether the findings converged upon a similar pattern of effects across predictors and outcomes rather than to determine the unique variance explained in each adjustment outcome by each sleep parameter or to identify which associations were strongest. In addition to the aforementioned covariates, adjustment problems at age 15 were statistically controlled to account for autoregressive effects; sleep minutes and efficiency were also controlled in the models involving variability in sleep minutes and in sleep efficiency, respectively, to ascertain the unique effects of sleep variability on adjustment problems, above and beyond the effects of average levels of these variables.2,3

Covariates, sleep parameters, and RSA-B were mean-centered to facilitate interpretation of the intercepts (Allison, 1999), and significantly related exogenous variables were allowed to covary. All models were fit in Amos 25, which uses full information maximum likelihood (FIML) estimation to handle missing data. FIML produces less biased estimates and lower Type I error rates than other imputation techniques (Enders & Bandalos, 2001; Raykov, 2005). Skewness values were less than 2.0 for all variables, suggesting that study variables were relatively normally distributed. Missingness ranged from 13% to 26% for primary study variables (20% for actigraphy, 13% for RSA-B, 18% for depressive symptoms, and 26% for externalizing behaviors), which is within the acceptable range for use of FIML with our sample size (McNeish, 2017). Acceptable model fit included at least two of the following three criteria: χ2/df < 2, comparative fit index (CFI) > 0.95, and root mean square error of approximation (RMSEA) < 0.06 (Hu & Bentler, 1999). All models in the current study met these criteria.

3 |. RESULTS

Table 1 presents correlations, means, and standard deviations for covariates, sleep parameters (predictors), RSA-B (moderator), and adjustment (outcomes). Identifying as Black was associated with fewer sleep minutes, more variability in sleep efficiency, and higher RSA-B. Lower SES was linked to fewer sleep minutes, lower sleep efficiency, greater variability in sleep minutes and sleep efficiency, more externalizing behaviors at ages 15 and 17, and more depressive symptoms at age 17. Identifying as male was also associated with fewer sleep minutes, lower sleep efficiency and greater variability in sleep efficiency, and fewer depressive symptoms at age 15. Older age at first assessment was correlated with fewer sleep minutes, and higher zBMI was linked to higher RSA-B.

TABLE 1.

Bivariate correlations, means, and standard deviations for covariates and main study variables

1 2 3 4 5 6 7 8 9 10 11 12 13 14
1. Race (AA) -
2. SES −.28** -
3. Sex (boys) −.06 .00 -
4. Age −.06 −.04 .23* -
5. zBMI .15* −.04 −.04 −.06 -
6. Minutes −.21* .17* −.25* −.15* −.05 -
7. Efficiency −.10 .16* −.19* .02 −.08 .60** -
8. Variability in minutes .12 −.21* −.05 .06 −.05 −.43** −.19* -
9. Variability in efficiency .21* −.23* .17* .00 .04 −.54** −.77** .23* -
10. RSA-B .14* .03 −.08 −.07 .27** .05 .05 .02 .02 -
11. Externalizing at age 15 −.01 −.13* −.03 .08 −.04 −.06 .01 .05 −.06 −.02 -
12. Depression at age 15 .03 −.05 −.16* −.02 −.05 −.14 .08 .17* −.02 −.02 .49** -
13. Externalizing at age 17 −.04 −.23* .06 .14 .00 −.17* −.06 .23* .06 −.04 .50** .65** -
14. Depression at age 17 .02 −.27* −.06 −.02 .02 −.20* −.10 .32** .12 −.06 .57** .35** .60** -
M - 2.48 - 15.26 0.84 406.37 90.86 0.15 0.05 6.91 10.43 6.86 9.42 7.21
SD - 1.43 - 0.86 1.02 54.84 6.83 0.07 0.04 1.15 7.82 5.95 6.87 6.73
Range (Minimum) - 0.14 - 14.00 −2.24 239.40 61.56 0.03 0.00 3.19 0.00 0.00 0.00 0.00
(Maximum) - 6.80 - 18.00 2.87 543.14 99.81 0.51 0.26 9.70 37.00 29.00 36.00 36.00
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Note: Race was coded as 0 = European American, 1 = African American (AA); sex was coded as 0 = Girls, 1 = Boys. Minutes = actigraphy-derived sleep minutes. Efficiency = actigraphy-derived sleep efficiency. Sleep minutes (Minutes): 406.37 min = 6 h 46 min.

Abbreviations: M, mean; RSA-B, baseline respiratory sinus arrhythmia; SD, standard deviation; SES, socioeconomic status (income-to-needs ratio); zBMI, standardized body mass index.

*

p < 05

**

p < .001

***

p < .001.

Small to strong associations also emerged among the sleep parameters. Shorter sleep and greater variability in sleep minutes at age 15 were associated with higher levels of externalizing problems and depressive symptoms at age 17. Sleep efficiency and variability in sleep efficiency at age 15 were not related to any of the outcomes variables at age 17. None of the sleep parameters or the adjustment outcomes were associated with RSA-B.

3.1 |. Sleep minutes

The first set of regression models examined whether RSA-B moderated the associations between sleep minutes and adolescents’ externalizing problems and depressive symptoms. Significant sleep minutes × RSA-B interactions emerged for both adjustment outcomes (Table 2, panel A). The models explained 53% of the total variance in externalizing problems and 44% in depressive symptoms, with the interaction accounting for 4% of unique variance in externalizing problems and 5% in depressive symptoms. Simple slope analyses indicated that longer sleep at age 15 was associated with fewer externalizing problems and depressive symptoms at age 17 (ps < .001) only for adolescents with higher RSA-B (Figure 1a). Adolescents with lower RSA-B had similar levels of externalizing problems and depressive symptoms to the sample mean, regardless of sleep minutes.

TABLE 2.

Estimates for regression models showing effects of the sleep × RSA-B interaction (age 15) on adolescent adjustment (age 17)

A. Sleep minutes
B. Sleep efficiency
Externalizing at age 17 Depression at age 17 Externalizing at age 17 Depression at age 17
Estimate (SE) Estimate (SE) Estimate (SE) Estimate (SE)
Intercept 9.69*** (0.36) 7.16*** (0.36) Intercept 9.55*** (0.37) 6.98*** (0.35)
Race (AA) −0.76 (0.84) −0.74 (0.84) Race (AA) −0.85 (0.82) −0.63 (0.79)
SES −0.87*** (0.27) −1.07*** (0.26) SES −0.86** (0.28) −0.97*** (0.26)
Sex −0.45 (0.76) −0.57 (0.78) Sex −0.34 (0.77) −0.59 (0.75)
Age 0.42 (0.43) −0.67 (0.43) Age 0.60 (0.44) −0.37 (0.43)
zBMI 0.28 (0.36) 0.53 (0.37) zBMI 0.13 (0.38) 0.28 (0.37)
Outcome at age 15 0.57*** (0.05) 0.62*** (0.06) Outcome at age 15 0.57*** (0.05) 0.61*** (0.06)
Minutes −0.01 (0.01) −0.02 (0.01) Efficiency −0.02 (0.06) −0.06 (0.06)
RSA-B −0.44 (0.33) −0.31 (0.34) RSA-B −0.47 (0.34) −0.30 (0.34)
Minutes × RSA-B −0.02*** (0.01) −0.02*** (0.01) Efficiency × RSA-B 0.12 (0.07) 0.26*** (0.06)
R 2 .53 .44 R 2 .50 .44
R2 explained by interaction .04 .05 R2 explained by interaction .00 .03
C. Variability in sleep minutes
D. Variability in sleep efficiency
Externalizing at age 17 Depression at age 17 Externalizing at age 17 Depression at age 17
Estimate (SE) Estimate (SE) Estimate (SE) Estimate (SE)
Intercept 9.58*** (0.36) 6.97*** (0.35) Intercept 9.63*** (0.37) 7.13*** (0.36)
Race (AA) −1.10 (0.83) −1.09 (0.80) Race (AA) −1.23 (0.84) −1.20 (0.83)
SES −0.65* (0.27) −0.82*** (0.26) SES −0.79** (0.28) −0.93*** (0.27)
Sex −0.25 (0.78) −0.29 (0.76) Sex −0.26 (0.77) −0.45 (0.77)
Age 0.43 (0.44) −0.69 (0.43) Age 0.56 (0.44) −0.36 (0.43)
zBMI 0.21 (0.37) 0.40 (0.36) zBMI 0.22 (0.37) 0.48 (0.38)
Outcome at age 15 0.58*** (0.05) 0.59*** (0.06) Outcome at age 15 0.58*** (0.05) 0.64*** (0.06)
Minutes 0.00 (0.01) −0.01 (0.01) Efficiency 0.04 (0.09) −0.05 (0.09)
Variability in minutes 18.57** (6.65) 18.10** (6.41) Variability in efficiency 24.17 (16.56) 23.02 (16.39)
RSA-B −0.39 (0.35) −0.04 (0.34) RSA-B −0.43 (0.34) −0.30 (0.34)
Variability in minutes × RSA-B 5.50 (5.72) 16.30** (5.56) Variability in efficiency × RSA-B 17.06* (8.12) 20.15** (8.17)
R 2 .53 .46 R 2 .52 .44
R2 explained byinteraction .00 .02 R2 explained by interaction .02 .03
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Note: Unstandardized estimates. Race was coded as 0 = European American, 1 = African American (AA); sex was coded as 0 = Girls, 1 = Boys. Minutes = actigraphy-derived sleep minutes. Efficiency = actigraphy-derived sleep efficiency. Externalizing = externalizing behavior problems derived with YSR. Depression = depressive symptoms derived with CDI. Sleep minutes and sleep efficiency were controlled in the models involving variability in sleep minutes and in sleep efficiency, respectively.

Abbreviations: RSA-B, baseline respiratory sinus arrhythmia; SES, socioeconomic status (income-to-needs ratio); zBMI, standardized body mass index.

*

p < 05

**

p ≤ .001

***

p < .001.

FIGURE 1.

FIGURE 1

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Baseline respiratory sinus arrhythmia (RSA-B) as a moderator of the associations between (a) sleep minutes, (b) sleep efficiency, (c) variability in sleep minutes, and (d) variability in sleep efficiency at age 15 and adolescent adjustment outcomes at age 17

In addition, compared to adolescents with lower RSA-B, those with higher RSA-B showed fewer externalizing problems (0.56 SD difference; Figure 1a) and depressive symptoms (0.61 SD difference; Figure 1a) at high levels of sleep minutes. However, at low levels of sleep minutes, the differences in the levels of externalizing problems (0.25 SD difference) and depressive symptom (0.34 SD difference) were much smaller between youth who had higher and lower RSA-B.

3.2 |. Sleep efficiency

The second set of regression models examined whether RSA-B moderated the relations between sleep efficiency and adolescents’ adjustment. The sleep efficiency × RSA-B interaction was significant for depressive symptoms but not externalizing problems (Table 2, panel B). The model accounted for 44% of the total variance in depressive symptoms, with the interaction accounting for 3% of unique variance. Simple slope analyses showed that the magnitude of the associations between sleep efficiency and depressive symptoms was stronger for adolescents with lower RSA-B: lower efficiency was associated with greater depressive symptoms for adolescents with lower RSA-B (p < .001) but was associated with fewer depressive symptoms for those with higher RSA-B (p < .05; Figure 1b). Differences in the levels of depressive symptoms between adolescents who had higher and lower RSA-B were larger at lower levels of sleep efficiency (0.76 SD difference; Figure 1b) compared to higher levels of sleep efficiency (0.54 SD difference).

3.3 |. Variability in sleep minutes

The third set of regression models examined whether RSA-B moderated the associations between variability in sleep minutes and adolescents’ adjustment over time. A significant variability in sleep minutes × RSA-B interaction was detected only for depressive symptoms (Table 2, panel C). The model explained 46% of the total variance in depressive symptoms, with the interaction accounting for 2% of unique variance. Simple slope analyses indicated a positive association between variability in sleep minutes and depressive symptoms only for adolescents with higher RSA-B (p < .001; Figure 1c). Youth with lower RSA-B had similar levels of depressive symptoms to the sample mean regardless of the levels of variability in sleep minutes.

Furthermore, compared to adolescents with lower RSA-B, those with higher RSA-B showed fewer depressive symptoms (0.39 SD difference; Figure 1c) at lower levels of variability in sleep minutes. However, adolescents with higher RSA-B had higher levels of depressive symptoms than those with lower RSA-B at higher levels of variability in sleep minutes (0.38 SD difference).

3.4 |. Variability in sleep efficiency

The final set of regression models examined whether RSA-B moderated the associations between variability in sleep efficiency and adolescents’ adjustment. Significant variability in sleep efficiency × RSA-B interactions was observed for both adjustment outcomes (Table 2, panel D). The models explained 52% of the total variance in externalizing problems and 44% in depressive symptoms, with the interaction accounting for 2% of unique variance in externalizing problems and 3% in depressive symptoms. Simple slope analyses showed a positive association between variability in sleep efficiency and both externalizing problems and depressive symptoms only for adolescents with higher RSA-B (ps < .05; Figure 1d). Youth with lower RSA-B had similar levels of such adjustment problems to the sample mean, regardless of variability in their sleep efficiency.

In addition, the plots illustrate that adolescents with higher RSA-B showed fewer externalizing problems (0.43 SD difference; Figure 1d) and depressive symptoms (0.41 SD difference; Figure 1d) than those with lower RSA-B at lower levels of variability in sleep efficiency. However, differences in the levels of externalizing problems (0.14 SD difference) and depressive symptoms (0.19 SD difference) were much smaller between youth who had higher and lower RSA-B at higher levels of variability in sleep efficiency.

4 |. DISCUSSION

The present investigation assessed RSA-B as a moderator of the longitudinal relations between several objective sleep parameters and adolescent adjustment outcomes. The findings generally demonstrated that better sleep at age 15 predicted better adjustment at age 17 at higher levels of RSA-B. These novel results contribute to the scant literature concerning the conjoint influences of sleep and RSA on youth functioning, further specifying the risk and protection conferred by these bioregulatory variables.

Based on limited prior work (El-Sheikh et al., 2007; Hamilton et al., 2019), we hypothesized that poor sleep (fewer minutes, lower efficiency, higher variability) would be associated with more externalizing behaviors and depressive symptoms for adolescents with low but not high RSA-B. The results (four of six significant interactions) suggested a different pattern of effects: More sleep minutes and less variability in sleep efficiency predicted fewer externalizing behaviors and depressive symptoms at higher levels of RSA-B. Youth with low RSA-B, in contrast, had similar externalizing behaviors and depressive symptoms regardless of sleep. These findings may have been distinct from those of prior cross-sectional work because the analyses were longitudinal and therefore examined change in adjustment over time. Furthermore, the present study was focused on adolescents, whereas prior research assessed samples of children and young adults.

The results are consistent with a dual-protection or protective-reactive pattern of effects, in which a factor is beneficial in low- but not high-risk contexts (Luthar et al., 2000). Prior empirical work found that longer caregiver-reported sleep duration was associated with fewer internalizing symptoms for toddlers who showed a greater decrease in RSA in response to challenge (Cho et al., 2017). A decrease in RSA in this context may facilitate adaptive emotional and behavioral responses (Cui et al., 2015; Porges, 2007). Together with the results of the present study, this work suggests that the combination of good sleep and adaptive RSA (i.e., high RSA-B or decreased RSA in response to challenge) may underlie emotion regulation and healthy stress and coping responses that reduce maladjustment. High stability of adjustment in adolescence (Snyder et al., 2017) may mean that adaptive functioning of two bioregulatory systems that facilitate emotion regulation, rather than one alone, is needed to detect improvement. The findings could be interpreted to suggest that sleep interventions that include a component that promotes higher RSA-B, such as through biofeedback (e.g., De Witte et al., 2019; Dormal et al., 2021; Wheat & Larkin, 2010), may be particularly effective for facilitating adolescent adjustment. Of note, we conceptualized the sleep parameters as the predictor variables and RSA-B as the moderator in keeping with prior literature, but using the dual-protection framework, the findings could be interpreted with the predictor and moderator reversed as well.

There were two exceptions to this pattern of effects. One significant interaction demonstrated that lower sleep efficiency was associated with more depressive symptoms 2 years later at lower levels of RSA-B. This result is consistent with prior work (El-Sheikh et al., 2007; Hamilton et al., 2019) and study hypotheses. For adolescents with difficulty both sleeping well and regulating physiological arousal, risk may accumulate to predict depressive symptoms. However, a positive relation between sleep efficiency and depressive symptoms at higher levels of RSA-B was also detected within this interaction. This effect is difficult to interpret and requires replication before conclusions can be drawn. A second interaction showed that variability in sleep minutes was positively associated with depressive symptoms at higher levels of RSA-B. However, depressive symptoms at low variability in sleep minutes and high RSA-B were lower than depressive symptoms at low variability in sleep minutes and low RSA-B. This finding is consistent with theory and some empirical work demonstrating that individuals with high RSA-B may be particularly sensitive to context (Ellis et al., 2011; Peltola et al., 2017), such that they are especially likely to have positive outcomes in favorable circumstances (e.g., good sleep) but poor outcomes in adverse contexts (e.g., poor sleep). Additionally, work with children has found that moderate levels of RSA-B are associated with the fewest internalizing symptoms and externalizing behaviors, providing further evidence that in some contexts high RSA-B is associated with elevated risk (Ugarte et al., 2021). The authors suggest that youth with high RSA-B may be more likely to suppress emotions without effectively coping with them. These interpretations of our results are speculative and require further replication. As in the present study, El-Sheikh et al. (2007) also detected mixed effects in their examination of cross-sectional interactions between sleep parameters and RSA-B predicting children’s adjustment.

The results from the present study underscore the utility of assessing variability in sleep parameters in addition to averages. Three of the six significant interactions included variability in a sleep parameter (minutes, efficiency) as a predictor. Variability in sleep minutes and efficiency is common in adolescence as youth undergo normative circadian rhythm shifts and navigate school, peer, and family demands (Becker et al., 2017). Greater variability in sleep may contribute to the experience of social jet lag, in which awake times are misaligned with the biological circadian rhythm, resulting in decreased alertness as well as dysregulation and wear and tear on other physiological systems as they adapt to frequent changes in sleep patterning (Bei et al., 2017). The novel findings from the present study add to this growing literature by demonstrating that high RSA-B may accentuate the benefits of consistency in sleep minutes and efficiency.

The majority of the sample reported subclinical levels of mental health symptoms and therefore the present work is not able to speak to the associations between sleep, RSA, and adjustment among youth with clinical symptomology. Our analytic approach was also not able to parse between-person effects from within-person effects and artifacts. Furthermore, variability in sleep may be less consistent over time in comparison to average sleep minutes and efficiency, and therefore variability assessed by actigraphy at age 15 may not necessarily capture the degree of sleep variability experienced at age 17 when the adjustment outcomes were measured. However, study strengths include the longitudinal design and autoregressive covariates, which allowed for statistical prediction of change in adjustment over time, as well as incorporation of multiple objective sleep parameters, consistent with recommended practices (Sadeh, 2015). The similarity in effects that emerged across interactions also runs counter to the possibility that the results represent artifacts. Further, although limited to Black and White youth living in rural towns and surrounding communities, the racially and socioeconomically diverse sample enhances generalizability of the findings to a wide population of adolescents. This is critical given documented racial and socioeconomic disparities in sleep (Guglielmo et al., 2018; Schmeer et al., 2019) and adjustment (Alegría et al., 2015), as demonstrated within the present study as well. Prior research also indicates that adaptive patterns of RSA for youth functioning may vary by race, perhaps due to experiences of race-based stress (Graziano & Derefinko, 2013). Future work testing these interactions among youth from different racial and socioeconomic backgrounds could shed light on the circumstances under which disparities in adjustment are most likely to emerge. Following Hamilton et al. (2019), future studies could also incorporate a daily design to examine how fluctuations in sleep and RSA-B are related to the stress and coping responses that underlie adjustment outcomes.

Altogether, study findings suggest that adolescents with good sleep (more sleep minutes, low variability) and high RSA-B face lower risk for maladjustment. Adolescents with either good sleep or high RSA-B alone are less protected. This work highlights the utility of assessing measures of sleep variability and examining multiple bioregulatory processes in tandem. Interventions that promote both good sleep and more effective regulation of arousal, such as those incorporating biofeedback (Dormal et al., 2021), may enhance adolescent adjustment.

ACKNOWLEDGMENTS

This research was supported by grant R01-HD046795 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (PI El-Sheikh). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We thank our research laboratory staff, particularly lab coordinator Bridget Wingo, as well as the adolescents who participated.

Funding information

Eunice Kennedy Shriver National Institute of Child Health and Human Development, Grant/Award Number: R01-HD046795

Footnotes

CONFLICT OF INTEREST

The authors declare no conflict of interest.

1

Although conducted by some of the same research team, the El-Sheikh et al. (2007) study assessed an independent sample of 8-to 9-year-old children from the longitudinal adolescent sample examined in the present work. Therefore, there was no overlap in participants.

2

Sensitivity analyses tested whether the effects varied by SES via three-way interactions between the sleep parameters, RSA-B, and SES (income-to-needs ratio). All three-way interactions

3

Additional analyses examined how interactions between the sleep parameters and RSA-B predicted trajectories of adolescents’ depressive symptoms and externalizing behaviors. They did not corroborate the results from the present study

DATA AVAILABILITY STATEMENT

Research data are not shared at this time.

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Data Availability Statement

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