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. Author manuscript; available in PMC: 2020 Oct 1.
Published in final edited form as: Dev Psychol. 2019 Jul 25;55(10):2193–2202. doi: 10.1037/dev0000784

Interactive Effects of Family Instability and Adolescent Stress Reactivity on Socioemotional Functioning

Zhi Li 1, Melissa L Sturge-Apple 2, Meredith J Martin 3,4, Patrick T Davies 5
PMCID: PMC6932862  NIHMSID: NIHMS1037895  PMID: 31343228

Abstract

This study investigated whether adolescent vagal stress reactivity to parent-adolescent conflict moderates the effects of family instability on the development of adolescent behavioral problems. Participants were 192 adolescents (M age = 12.4) and their parents across two measurement occasions. Results indicated that the interaction between family instability and vagal stress reactivity significantly predicted change in externalizing problems. Greater family instability was associated with increases in externalizing problems, but only for adolescents showing greater vagal suppression (i.e., higher vagal reactivity) during a laboratory triadic family conflict discussion. Further tests suggested the interaction was consistent with diathesis stress, such that adolescents with higher vagal stress reactivity show higher increases in externalizing problems under high instability, but not lower increases in externalizing symptoms with low family instability. Findings indicate disruptions in the proximal rearing contexts may differentially influence development for adolescents, but the impact may differ as a function of stress reactivity.

Keywords: family instability, RSA reactivity, problem behavior, adolescents

Family instability, which refers to the cumulative occurrence of disruptive family events that interrupt continuity and cohesiveness within the proximal rearing environment (Ackerman, Kogos, Youngstrom, Schoff, & Izard, 1999; Forman & Davies, 2003), has been repeatedly linked to elevated problematic behavior and internalizing symptoms during childhood (Ackerman et al., 1999; Milan et al., 2006; Cavanagh & Huston, 2006; Sturge-Apple, Davies, Cicchetti, Hentges, & Coe, 2017) and adolescence (Forman & Davies, 2003; Marcynyszyn, Evans, & Eckenrode, 2008; Bakker, Ormel, Verhulst, & Oldehinkel, 2012). Whereas previous work has primarily documented the direct effects of family instability on child development, to our knowledge limited studies have investigated whether family instability functions differently across individuals. Guided by the diathesis stress (Zuckerman, 1999) and differential susceptibility frameworks (Belsky, 1997; 2004; Belsky & Pluess, 2009; biological sensitivity to context theory, Boyce & Ellis, 2005), the current inquiry sought to examine whether adolescent autonomic stress reactivity during a family conflict discussion moderated the effects of family instability on the development of externalizing and internalizing problems over the course of early adolescence.

Scholars interested in the role of experience on development have documented the effects of family contexts through examining indices of cumulative risks and associations with poorer developmental outcomes (e.g., Evans, Li, & Whipple, 2003). Towards building upon this body of work, recent conceptualizations derived from evolutionary developmental frameworks have advocated for examining specificity with respect to environmental effects. In particular, Ellis and associates (2009) identified that unpredictability may operate as a potent dimension of environmental adversity that may differentially shape development. According to Ellis et al. (2009), environmental unpredictability refers to stochastic variation in resources or mortality over time and space that cannot be predicted by the developing person. As such, exposure to unpredictability is theorized to shift developmental allocations of resources towards faster life-history strategies, which are usually reflected by compromised physical, behavioral and psychological well-being (Belsky, Steinberg, & Draper, 1991; Ellis et al., 2009; Sturge-Apple, et al., 2017).

Recent empirical work has operationalized environmental unpredictability as family instabilities such as paternal transitions, frequent changes in residential locations and paternal employment (e.g., Belsky, Schlomer, & Ellis, 2012; Simpson, Griskevicius, Kuo, Sung, & Collins, 2012). As expected, exposure to unpredictability was predictive of greater behavioral problems (e.g., Doom, Vanzomeren-Dohm, & Simpson, 2016) and risk-taking during adolescence (e.g., Belsky et al.,2012; Simpson et al., 2012). However, despite existing evidence on the effects of family instability, some studies have failed to link family instability with child well-being (e.g., Schoon, Jones, Cheng, & Mahghan, 2012), suggesting the existence of potential moderators for the process. Thus, here we sought to identify how biological stress system functioning, and in particular vagal regulation, may operate as an individual difference variable in these associations.

As a primary driver of the physiological stress system response, the autonomic nervous system (ANS) coordinates multiple internal organs (e.g., heart, lung) and maintains homeostasis in response to diverse challenges of internal and external environments. One branch of the ANS, the parasympathetic nervous system (PNS), is commonly known for promoting physiological recovery, restoration and the retaining of homeostasis after exposed to stress (Porges, 1992; 1995). Notably, PNS functioning through the vagus nerve is often indexed by respiratory sinus arrhythmia (RSA) or variability in heart rate that occurs along with respiratory cycle. As noted, vagal regulation in the form of RSA has been regarded as an index of one’s capacity to modulate responses to the challenges of social stress, and thus conceptualized as a marker of physiological stress reactivity (Porges, 2007).

With respect to the function of RSA as a moderator of the association between environmental context and individual development, two conceptual frameworks have been proposed to guide hypotheses. In particular, the diathesis stress framework (Zuckerman, 1999) and differential susceptibility theory (Belsky, 1997; 2005; Belsky & Pluess, 2009; Ellis, Boyce, Belsky, Bakermans-Kranenburg, & Van IJzendoorn, 2011) provide a comparative heuristic for examining how the biological stress system shapes individuals’ responsiveness to environmental influence. In particular, the diathesis stress model stipulates that some individuals are more vulnerable than others to the negative effects of contextual risks (e.g., poverty, family instability) and thus develop especially poorly under risky environments. Alternatively, the differential susceptibility model postulates that individuals with heightened environmental susceptibility not only function worse under adversity, but also benefit more and function better under supportive and nurturing environment (i.e., for better and for worse).

A variety of studies have demonstrated that RSA reactivity operates as a biological indicator for heightened environmental responsiveness in moderating associations between various environmental factors and child (e.g., Obradovic et al., 2010; Obradovic, Bush, & Boyce, 2011) and adolescent well-being (Diamond & Cribbet, 2013).Specifically, some research has provided support that RSA reactivity interacts with environment in the diathesis-stress pattern (e.g., Hagan, Roubinov, Adler, Boyce, & Bush, 2016; Wolff, Wadsworth, Wilhelm, & Mauss, 2012). However, other studies have demonstrated that RSA reactivity operates as a plasticity factor. In particular, Obradovic and colleagues (2010) investigated the interactive effects of child RSA reactivity towards laboratory stressors and the parent-report of family adversity (e.g., marital conflict) in five-to-six-year-olds. For children showing high RSA reactivity (i.e., greater vagal suppression to stress), family adversities predicted more behavioral problems and poorer social and academic competence. In contrast, the association between family adversity and child adjustment was not (or less) significant for children with low RSA reactivity. Furthermore, the detected interaction seemed consistent with differential susceptibility for academic competence (i.e., children with high RSA reactivity also demonstrated greater school engagement with lower family adversity). In accord with these findings, the current inquiry sought to investigate whether and how RSA stress reactivity during the parent-teen conflict discussion moderated the effects of family instability in shaping adolescent externalizing and internalizing behaviors over time.

Early adolescence is a critical developmental period, as adolescents start to gain greater autonomy and independence and expand their social landscape to the peer group (Eccles, 1999). However, family and parents still serve as a secure base for navigating challenges during early adolescence (e.g., Nickerson & Nagle, 2005) and frequent disruptions within the family may undermine parental availability and accessibility. Moreover, during early adolescence, teens encounter rapid pubertal transitions in physical and psychological domains (Susman & Rogol, 2013) that may make them particularly vulnerable to negative family events (e.g., Hetherington, Bridges, & Insabella, 1998). Although RSA reactivity has been demonstrated to be a biological marker of heightened reactivity to diverse environmental factors, to our knowledge no existing work has assessed whether RSA reactivity moderates the effects of family instability. Building on previous literature (e.g., Obradovic et al., 2010), we expect greater RSA reactivity to potentiate the adverse effects of family instability on adolescent functioning, as RSA reactivity reflects the biological disposition for environmental responsiveness. Unlike previous work that relied on laboratory stressors, we are interested in assessing the RSA reactivity within the conflict discussion paradigm (e.g., Saxbe, Margolin, Shapiro, Baucom, 2012; Sturge-Apple, Li, Martin, Jones-Gordils, & Davies, 2019). Thus, RSA reactivity was operationalized as the changes in RSA during the parent-teen triadic conflict discussion. Such an approach captures stress responses during real family interactions compared to responses to lab stressors. Finally, the longitudinal design of the present study allowed us to prospectively test whether the interactive effects shape changes of adolescent problematic behavior over time.

Method

Participants

Participants were 192 families from a mid-sized city in the Northeast. Families were recruited broadly through school districts, family-centered internet sites, and flyers, and were accepted into the study if fulfilling the following criteria: (a) having an adolescent between the ages of 12 to 14, (b) the target adolescent had been living with the two parental figures for at least the previous three years, (c) at least one of the parental figures was the biological or adoptive parent, (d) all participants are fluent in English, and (e) the target adolescent did not have significant cognitive impairment. Families were followed for two waves scheduled one year apart. The retention rate for Wave 2 was 91.14% (i.e., 175 families). Mean age for adolescents were 12.4 years at Wave 1, and 50% were female (N = 96). 76.6% of the adolescents were identified as White, followed by African-American (10.9%) and mixed race (10.4%), and a number identified as Hispanic or Latino ethnicity (12.0%). The median household income fell in the range of $55,000 to $ 74,999, with 11% of the families reporting an annual household income below $23,000. Mean time for the two parental figures living together was 15.6 years. This study was approved by the Institutional Review Board of the University of Rochester (title of study: Family Relationships in Early Adolescence, case number: RSRB00030791).

Procedures

Baseline RSA

Father, mother, and adolescent sat silently in the room for two minutes and each worked on a questionnaire during which baseline EKG was recorded. Talking and gross movement was prohibited during the two minutes.

Family conflict discussion

Fathers, mothers and adolescents participated in a triadic conflict discussion task during the first measurement occasion (e.g., Saxbe et al., 2012; Sturge-Apple et al., 2019). Before the task, family members were instructed to come up with a topic that they commonly disagree about and discuss the chosen topic for seven minutes as they normally would and attempt to get their point across. The most common disagreement topics included chores (33.3%), use of electronics (e.g., computer) (14.1%), and fighting with siblings (10.9 %). Post-discussion questionnaires indicated that family members tended to endorse that the lab discussion was similar to the ones at home (43.8%, 48.4% and 50.5% for father, mother, and adolescent, respectively).

During baseline and family conflict discussion task, adolescent EKG was recorded by BioGraph Infiniti software with a precordial, two-pole electrocardiogram lead. The EKG signals were sampled at 300 Hz with a voltage ranging from −2.5 V to 2.5 V. The data obtained in the leads were then transmitted and stored in a portable unit with SD card and saved upon completion of each visit. The baseline EKG data was processed by MindWare HRV analysis software 3.3.1.and the family conflict discussion EKG data was processed by the CardioPro Infiniti’s HRV analysis module. All EKG signals were examined and corrected for the artifactual detection of the R-wave occurrence before saved for further analyses.

Measures

Family instability

During the first measurement occasion, both mothers and fathers completed the revised version of the Family Instability Questionnaire (FIQ; Ackerman et al., 1999; Forman & Davies, 2011). The questionnaire consisted of 8 items, asking caregivers to report the frequency to which various disruptive family events occurred during the past five years. Questions included: (a) family member job loss; (b) family member sickness or death; (c) parent intimate relationship changes (e.g., breaking up with a romantic partner); (d) residential changes; and (e) changes in child’s primary caregiver. Answers to each item (i.e., the frequency for each disruptive event) were summed together and averaged across mother and father reports (father & mother agreement: r = 0.51, p < .01) to create a composited frequency of family instability. The most commonly endorsed items (i.e., occurring more than zero) were family member job loss (i.e., mother: 36.70%, father: 40.84%), family member sickness (i.e., mother: 48.40%, father: 51.85%) and death (mother: 51.32%, father: 55.50%) for both mother- and father-report. Note that the FIQ was based on frequency measures, typical internal consistency measures such as Cronbach alpha was not appropriate. The FIQ has been widely used to measure family instability (e.g., Forman & Davies, 2011; Sturge-Apple et al., 2017) and that the theoretical and conceptual soundness of FIQ was well supported by its strong association with child well-being (Forman & Davies, 2011). In addition, extreme values of the composite score (> 3SD) were removed, resulting in four missing values for the final family instability score. Upon further inspection, the distribution of family instability was highly skewed (skewness = 1.04, Z = 5.10, p < .01), with the lowest possible range being only 1.22 SD below the mean. Given the high skewness and that Roisman et al. (2012) recommended to probe interaction within [−2, 2] SD, we performed natural log transformation to family instability after adding a constant (i.e., 1) to all cases, resulting in a more normalized distribution (see Table 1 for descriptive information).

Table 1.

Mean, Standard Deviations, and the Bivariate Correlations for the Primary Variables.

1 2 3 4 5 6 7 8 9 10 11
1. Family Instabilitya -
2. RSA Reactivity .03 -
3. Parent Hostile Conflict Behavior .05 −.11 -
4. RSA Reactivity-x- Family Instability Interaction −.05 −.01 .13 -
5. Wave 1 Externalizing Problems .21** −.14† .52** .09 -
6. Wave 2 Externalizing Problems .16* −.13† .34** −.08 .64** -
7. Wave 1 Internalizing Problems .11 −.20** .41** −.01 .63** .37** -
8. Wave 2 Internalizing Problems .16* −.17* .13† −.08 .31** .51** .46** -
9. Age −.13† −.01 .13† .01 .07 .06 .09 .09 -
10. Diagnosisc .15† .03 −.18* −.17* −.03 −.03 −.01 −.03 −.15* -
11. Gender d −.06 .002 −.18* −.02 −.11 −.005 −.10 −.12 .02 .10 -
Mean 1.23 0 0.13 - 0.24 0.28 0.32 0.40 12.35 0.36 0.50
SD 0.64 15.73 0.15 - 0.20 0.24 0.23 0.32 0.53 0.48 0.50
Min 0 −67.70 0 - 0 0 0 0 12.00 0 0
Max 2.53 75.96 0.73 - 0.86 1.45 1.18 1.66 14.00 1 1
N 188 173 182 170 185 175 188 175 192 181 192
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Note.

†:

p < .10

*:

p < .05

**:

p < .01.

a.

Due to high (right) skewness, family instability was log transformed (after removing extreme values beyond +3SD and adding the constant 1 to every case).

b:

the interaction term was created based on the standardized family instability and RSA reactivity, with a mean of 0.03, standard deviation of 0.81, and within the minimum to maximum range of [−3.38, 3.58].

c.

Diagnosis: whether the adolescent has been diagnosed with any psychological or physical problems by a health professional in the past (0=No, 1= Yes).

d.

Adolescent gender: 0= Girls (N = 96), 1= Boys. Ns for bivariate correlation ranged from 154 to 192.

Externalizing problems

The 30-item subscale of the 88-item Youth Self Report (YSR) (Achenbach & Rescorla, 2001) was used to index externalizing behavior in the first and second measurement occasions. The YSR reliably measures behavioral problems (Verhulst, Van der Ende, & Koot, 1997) and adolescent self-reports might more accurately capture behavioral problems during adolescence compared to parent report (e.g., Achenbach, 2006; Renk, Roberts, Klein, Rojas-Vilches, & Sieger, 2005). Adolescents reported on a 3-point scale (“0” = Not True to “2” = Very True or Often True) the extent to which their behavior matched a series of statements (e.g., “I physically attack people”), with higher scores indicating more externalizing problems (αWave1 = 0.86; αWave2= 0.89).

Internalizing problems

Internalizing problems were measured by the 21-item subscale of the 88-item Youth Self Report (YSR, Achenbach & Rescorla, 2001) at the first and second measurement occasions. Responses range from “0” (Not True) to “2” (Very True of Often True), with higher score reflecting more internalizing problems (e.g., “I feel worthless or inferior”) (αWave1 =0.80; αWave2= 0.88).

Parent hostile conflict behavior

In Wave 1, teens reported the degree of the conflict between themselves with both parents on the 20-item (“1” = True, “0” = False) Conflict Behavior Questionnaire (CBQ) (e.g., “At least once a day we get angry at each other”, Prinz, Foster, Kent, & O’Leary, 1979). A higher score indicates more conflicts and thus poorer relationship quality. Mean score for mothers (α = 0.87) and fathers (α = 0.89) were moderately correlated (r = 0.29, p < .001). Given the conflict discussion involved both parents, an average score (between mother and father) was created to indicate the overall parent-child hostile conflict.

RSA reactivity

Adolescent RSA reactivity was measured by the residualized change score in heart-rate variability (HRV) during the conflict discussion, after controlling for baseline RSA. More specifically, the root mean square of successive differences (RMSSD) of the interbeat interval —a well-established time-domain indicator for vagal activities — was treated as the indicator of RSA during the 2-min baseline and each minute during the family conflict discussion (e.g., Laborde, Mosley, & Thayer, 2017). RMSSD beyond +/−3SD were treated as missing values. In addition, because the family discussion varied in length and many families did not have enough data to reliably estimate RMSSD during the last minute (N (not missing) = 88), the seventh minute was thus not considered. For purposes of the analyses, we were interested in parameterizing RSA reactivity within the family conflict discussion for each adolescent. We created a residualized change score of RSA for every minute/epoch vs. the beginning of the task (i.e., the first minute) by regressing the RSA for minute/epoch two to six on the first minute/epoch (See descriptive information in Supplemental material, Table S1). This residualized reactivity score represents the RSA reactivity for a specific epoch/minute after controlling for RSA at the beginning of the task. The unstandardized residual scores were saved for each epoch/minute (e.g., epoch/minute six vs. one) for further analyses. A greater value of the residualized change score reflects dampened suppression/withdrawal of RSA (i.e., lower RSA reactivity) for the corresponding minute/epoch during the conflict discussion compared to the rest of sample, whereas a lower value indicates greater RSA suppression (i.e., greater RSA reactivity). Furthermore, as the discussion may differ in time course for different families, we selected the minimum value for the residualized change scores among all epochs/minutes—reflecting greatest RSA withdrawal anytime during the discussion—for each individual as the indicator for RSA reactivity in the conflict discussion task. This score captures the highest RSA reactivity during any epoch/minute across the whole conflict discussion (landmark registration, e.g., Lopez-Duran, Mayer, & Abelson, 2014; Ramsay & Li, 1998) for each adolescent, as individuals may peak in their RSA reactivity at different time. Finally, to control for baseline levels of RSA, we regressed the reactivity score derived from the last step on the baseline RSA and saved the unstandardized residual as the final indicator for RSA reactivity. Therefore, the final RSA reactivity reflects the maximum amount of RSA reactivity for each individual during the conflict discussion task, regardless of their baseline RSA levels. Higher scores reflect dampened RSA suppression to the stressor of family conflict compared to the rest of the sample, and lower scores represent greater RSA suppression.

Data Analysis Plan

Data analyses involved testing whether adolescent RSA reactivity moderated the effect of family instability in shaping the growth of adolescent externalizing and internalizing problems. The latent difference score model (LDS) was used to parameterize intraindividual changes in externalizing and internalizing problems from Wave 1 to 2 (McArdle, & Hamagami, 2001). Then, family instability, RSA reactivity, and parent hostile conflict were standardized to avoid multicollinearity and to ensure variables were on relatively similar scales prior to analyses (Aiken & West, 1991). After creating the instability-x- RSA reactivity interaction term, family instability, RSA reactivity, and the instability-x- RSA reactivity interaction were specified as potential predictors for the LDS scores of adolescent externalizing and internalizing problems. Since adolescent stress reactivity was measured during the parent-child conflict discussion, adolescent-reported parent hostile conflict behavior was specified as an exogenous covariate to control for the overall conflicts in the family. Adolescent age, gender, and maternal report on whether the adolescent was diagnosed with any psychological or physical problems by a health profession in the past were included as exogenous covariates in the model as well. In addition, covariances were specified among all exogenous predictors and between the two LDS scores (i.e., W1-2 Δ externalizing and Δ internalizing problems). Analyses were performed in Mplus 8 (Muthén & Muthén, 1998–2011) using the maximum likelihood estimation with robust standard error (MLR). Missing data were treated according to the full information maximum likelihood procedure (FIML) (Enders & Bandalos, 2001).

Results

Mean, standard deviations and the bivariate correlations of the primary variables are presented in Table 1. As expected, parent hostile conflict behavior was (at least marginally) associated with more behavioral problems at both measurement occasions. Greater family instability was linked to significantly more externalizing problems at both waves, and more internalizing at wave 2. Greater RSA reactivity (i.e., lower value) was significantly associated with more internalizing problems at both waves. The overall model achieved almost perfect fit, X2(2) = 1.74, p = 0.42, X2/df= 0.87, RMSEA = 0.00, CFI = 1.00, TLI = 1.00, SRMR = 0.01. As shown in Figure 1 (also see Table 2 for pathway estimates), neither family instability nor RSA reactivity were significantly associated with changes in externalizing or internalizing problems between the two measurement occasions. Notably, however, the Family Instability-x- RSA reactivity interaction significantly predicted change in externalizing problems over the year. Simple slope analyses indicated that family instability predicted increases in externalizing problems over the year for adolescents with high RSA reactivity (i.e., −1SD for the residualized change score, reflecting greater vagal suppression), B = 0.04, p < .05. The same association was not significant for teens with low RSA reactivity (i.e., +1SD for the residualized change score, reflecting dampened vagal suppression), B = −0.02, p = .29 (Figure 2).

Figure 1. The structural equation model that examined the interaction of Family instability-X-RSA reactivity forecasting the growth of adolescent externalizing and internalizing problems.

Figure 1.

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Note. Figure presented the standard pathway coefficient estimates. **: p < .01, *: p < .05.

Table 2.

Pathway Coefficient Estimates for Model Predicting Adolescent Functioning (N = 192).

B SE β Z 95% CI for B p
Change in Externalizing Problems from Wave 1 to 2
 Wave 1 Externalizing Problems −0.27 0.08 −0.28 −3.33 [−0.42, −0.11] .001
 Family Instability 0.01 0.02 0.04 0.45 [−0.02, 0.04] .65
 RSA Reactivity −0.02 0.01 −0.12 −1.75 [−0.05, 0.003] .08
 RSA Reactivity-x- Family Instability Interaction −0.03 0.01 −0.13 −2.19 [−0.06, −0.003] .03
 Parent Hostile Conflict Behavior 0.01 0.02 0.04 0.48 [−0.03, 0.04] .63
 Age 0.01 0.03 0.02 0.27 [−0.05, 0.06] .79
 Psychological or Physical Problem Diagnosis −0.01 0.03 −0.01 −0.20 [−0.06, 0.05] .84
 Gender 0.03 0.03 0.09 1.24 [−0.02, 0.09] .22
Change in Internalizing Problems from Wave 1 to 2
 Wave 1 Internalizing Problems −0.35 0.09 −0.27 −4.03 [−0.52, −0.18] .00
 Family Instability 0.03 0.03 0.11 1.23 [−0.02, 0.08] .22
 RSA Reactivity −0.03 0.02 −0.11 −1.62 [−0.07, 0.007] .11
 RSA Reactivity-x- Family Instability Interaction −0.01 0.03 −0.03 −0.48 [−0.06, 0.04] .63
 Parent Hostile Conflict Behavior −0.02 0.02 −0.08 −1.28 [−0.06, 0.01] .20
 Age 0.04 0.04 0.08 1.09 [−0.03, 0.12] .28
 Psychological or Physical Problem Diagnosis −0.01 0.05 −0.02 −0.25 [−0.11, 0.09] .80
 Gender −0.05 0.04 −0.09 −1.21 [−0.14, 0.03] .23
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Figure 2. Family Instability-x-RSA Reactivity Interaction on Latent Change of Adolescent Externalizing Problems.

Figure 2.

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Note. Parameters presented were unstandardized coefficients. High RSA reactivity (solid line): −1 SD residualized RSA score (i.e., greater vagal suppression in response to stressor); low RSA reactivity (dotted line): +1 SD residualized RSA score (i.e., dampened vagal suppression in response to stressor). Family instability was plotted within [−2, +2] SD range. Gray shaded area (X > 0.095) represents regions of significance (RoS) on X (i.e., environmental predictor). Simple slope for High RSA reactivity (i.e., −1SD residualized RSA score) achieved significance. W1-2 LDS Externalizing Problems: The latent changes of externalizing problems between Wave one and two.

Further analyses were conducted following Roisman et al.’s (2012) recommendations and Del Giudice’s (2017) revision to rigorously probe the interaction pattern. More specifically, Regions of Significance test (RoS) indicated that relation between changes in externalizing problems and RSA reactivity was significant above the standardized family instability X = 0.10 (i.e., high family instability) and below X= −10.83 (i.e., low family instability) (see Figure 2 shaded area). As the lower threshold of RoS on X was beyond the −2 SD range (i.e., lower family instability), the RoS on X test supported diathesis stress. We next tested the Proportion of Interaction (PoI), which is expected to be close to 0.50, and between the range of 0.20 to 0.80 to support for differential susceptibility (Del Giudice, 2017). Results demonstrated that the PoI was 0.16 within the range of +/−2 SD, again supporting diathesis stress. Third, the Percent Above (PA) test investigates the proportion of cases showing “for better” pattern (i.e., lower family instability ➔ lower increases in externalizing problems). According to Roisman et al. (2012), the proportion should be greater than 16% to quality for differential susceptibility. When this was done, PA = 0.22, partially supporting differential susceptibility. Taken together, even though PA indicated differential susceptibility, the two most effective tests in distinguishing diathesis stress vs. differential susceptibility (i.e., RoS and PoI), highlighted by Del Giudice (2017), supported diathesis stress.

Given inconsistencies in test results, we utilized Widaman et al.’s (2012) approach to test for differential susceptibility. Specifically, this approach examines the cross over point (i.e., C) on the environmental predictor (i.e., family instability) at which slopes for different hypothesized levels of moderator (i.e., RSA reactivity) cross. Importantly, Widaman et al.’s (2012) approach is considered a confirmatory approach that is mutually informative with the explorative regression analyses of the Roisman approach (Widaman et al., 2012; Belsky, Pluess, & Widaman, 2013). The cross-over point (C = −b2/b3, b2: regression coefficient for RSA reactivity, b3: regression coefficient for the family instability-x- RSA reactivity interaction, see more details in Widaman et al., 2012) was C= −0.79, 95% Confidence Interval (CI): [−2.00, 0.43]. As the lower bound of the 95% CI fell outside the range of the family instability (i.e., lowest value for the standardized family instability: −1.91), the Widaman et al. (2012) test was more consistent with diathesis stress. In summary, multiple tests suggested the detected family instability-x-RSA reactivity interaction to be generally consistent with diathesis stress. That is, even though highly reactive adolescents demonstrated greater increases in externalizing problems under high family instability, they did not show greater decreases in externalizing problems with low instability.

Discussion

In the present study, we investigated whether and how instability within the proximal rearing context may be associated with the development of adolescent adjustment over time. Furthermore, we tested whether adolescent RSA reactivity during a conflict discussion with their parents, interacts with family instability to predict the development of problem behavior. Findings suggest that family instability is not directly linked to either externalizing or internalizing problems but associated with externalizing symptoms through interactions with RSA reactivity within emotionally charged family context. Our findings extend previous work documenting that the impact of family instability differs across adolescents depending on their vagal stress reactivity during the parent-adolescent conflict discussion.

Consistent with previous empirical findings (e.g., Forman & Davies, 2003; Marcynyszyn, 2008), the present study revealed (at least marginally) significant bivariate correlations between (greater) family instability and more problematic functioning (i.e., greater externalizing problems at both waves, and more internalizing problems at the second wave). Our findings are also in line with evolutionary perspectives which propose that individuals who experience more unpredictable family environments are expected to show undermined health and well-being (e.g., Belsky et al., 2012; Simpson et al., 2012; Doom et al., 2016). Turning to the changes in adolescent functioning, even though family instability tended to be associated with greater increases in both externalizing and internalizing problems, neither of the direct effects achieved statistical significance. Such discrepancies might be attributed to our focus on the rate of the changes in problematic behavior while previous research primarily assessed level of functioning. Specifically, Forman and Davies (2003) relied on cross-sectional design whereas Marcynyzyn and associates (2008) did not control for initial problematic behavior. More importantly, the effects of family instability on changes of externalizing problems were observed within its interactive nature of stress reactivity during family conflict discussion. As noted before, adolescence is a time of profound physical and social changes (Graber & Brooks-Gunn, 1996), including the maturation of parasympathetic regulation (e.g., Goto et al., 1997; Silvetti, Drago, & Ragonese, 2001). As the physiological system matures, the effects of family instability might diminish and/or be affected by one’s physiological regulation. In other words, the “universal” effects of family instability might actually emerge differently among adolescents, depending on their levels of physiological responsiveness to the contexts. Furthermore, the specificity of the family instability-x-RSA reactivity interaction might be accounted by life history theory. It has been proposed that family instability is a potent dimension of environmental adversity that shifts allocation of resources towards faster life history strategies, which is usually indicated by accelerated growth and reproduction, and greater risk-taking and problematic behavior (e.g., Belsky et al., 1991; Ellis et al., 2009; Del Giudice, Gangestad, & Kaplan, 2015). Certainly consistent with this claim is evidence that exposure to early-life unpredictability was primarily associated with greater externalizing problems during adolescence (Doom et al., 2015).

Notably, the present study revealed the interactive nature of the family instability and adolescent stress reactivity to emotionally-charged family contexts on the development of externalizing behaviors over time. When examined, the pattern of the interaction was more consistent with the diathesis stress pattern (Zuckerman, 1999). In particular, highly reactive adolescents demonstrated greater increases in externalizing problems in the context of greater instability, but did not show lower increases or greater decrease in externalizing problems with low family instability. In contrast, family instability was not associated with changes in externalizing problems for low reactive adolescents. Our findings are in accordance with previous empirical work (e.g., Obradovic et al., 2010; Obradovic et al., 2011) demonstrating that greater vagal suppression during conflict discussions may serve as a marker for greater reactivity (i.e., vulnerability) to the influences of family instability. After all, greater vagal reactivity in the form of RSA suppression was conceptualized as a marker for heightened responsiveness to environmental influences (Belsky & Pluess, 2009). It is important to note that we did not observe an interaction consistent with differential susceptibility (i.e., highly reactive individuals also function better than non-reactive individuals under supportive environment). We suggest that this might be due to our assessment of the environment factors. Tests of differential susceptibility require the assessment of a sufficient range of environmental context that captures both supportive and unsupportive experiences (Belsky & Pluess, 2009). In the present study, even though low family instability signified the absence of adversity, it did not capture the extremely positive and nurturing end of the environment (i.e., high stability). Thus, our conclusions regarding the nature of the moderating effect are tentative until future studies capture the full range of family stability/instability.

Taken together, the findings from the present study are inconsistent with conceptualizations of PNS reactivity as an index of active coping and self-regulation (Porges, 2007; Beauchaine, 2001), which functions as a protective factor that buffers against environmental risks (e.g., El-Sheikh, Harger & Whitson, 2001; McLaughlin, Alves, & Sheridan, 2014). There are two potential explanations for this inconsistency. From a conceptual standpoint, recent evolutionary accounts of the functional nature of the stress response system put forth in differential susceptibility theory propose that greater stress reactivity may reflect a biological disposition for greater responsiveness to proximal rearing contexts and environmental inputs (Boyce & Ellis, 2005). As such, greater vagal withdrawal during parent-adolescent conflicts in the context of unstable and chaotic rearing environments may reflect increased engagement, difficulty in self-regulation, more involvement in toxic conflicts, and greater frustration during family interactions. In turn, this may result in greater difficulties in behavioral regulation over time through increased aggression and deviant behavior. From a methodological standpoint, inconsistent functioning of RSA reactivity may be the result of the nature of the context in which the stress response system is activated (e.g., Hastings & Miller, 2014). Different groups might appear to be more susceptible to the environmental influences depending on the types of task used to evoke RSA reactivity (e.g., Obradovic, et al., 2011), or whether the stressors faced by individuals require executive functioning (Laborde et al., 2017). In the current study, adolescent RSA reactivity in the context of heightened family instability was assessed during a parent-child conflict discussion task, whereas other research has examined reactivity in social stress tasks which are devoid of family context (e.g., trier social stress task; McLaughlin et al., 2014; speech task and the cognitively challenging math task, Fletcher et al., 2017).

Results of the current work need to be considered in light of several limitations. First, the study sample consisted primarily of white, two-parent families and caution is called for generalizing these findings. Second, even though the current work relied on multiple informants (i.e., father, mother, and adolescents), family instability and adolescent problematic behavior were both based on self-reports. Third, whereas the evolutionary perspective highlighted the role of experiences in the first five to seven years (e.g., Belsky et al., 1991), our measure only involved family instability happened in the past five years for adolescents. Thereby, we mostly captured instability that occurred around middle childhood, even though there has been work suggesting that this stage may be another critical period for adjusting life history strategies (e.g., Del Giudice, 2018). Fourth, evolutionary perspectives highlight the effects of early unpredictability on more direct markers of life history strategy (e.g., sexual risk taking, pubertal development), however these constructs were not available in our data. As such, further studies are encouraged to evaluate whether our findings hold on other life-history-related outcomes.

These limitations notwithstanding, the present work presents an advance to the literature in several important ways. In the context of very limited work documenting the effect of family instability on behavioral problems during early adolescence, our findings suggest that family instability during this period of developmental transition may have important ramifications for adolescent functioning, particularly for those with higher RSA reactivity during family conflict. Furthermore, we demonstrated that RSA reactivity during parent-adolescent conflict discussion operated as a vulnerability factor between family instability and changes in externalizing problems. Taken together, our findings have important implications for identifying the most vulnerable targets and thus alleviating the impact of unstable and chaotic rearing contexts for adolescents.

Supplementary Material

Supplemental Material

Acknowledgments

This research was supported by the National Institute of Mental Health awarded to Melissa L. Sturge-Apple and Patrick T. Davies (5R01HD06078905). We are grateful to the teens and parents who participated in this project. Our gratitude is expressed to the staff on the project at Mt. Hope Family Center and the graduate and undergraduate students at the University of Rochester.

Contributor Information

Zhi Li, University of Rochester.

Melissa L. Sturge-Apple, University of Rochester

Meredith J. Martin, University of Nebraska-Lincoln .

Patrick T. Davies, University of Rochester

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