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Psychol Sci. Author manuscript; available in PMC 2017 August 01. Published in final edited form as: Psychol Sci. 2016 August ; 27(8): 1123–1135. doi:10.1177/0956797616651972.

Heart Rate Variability Moderates the Association Between Separation-related Psychological Distress and Blood Pressure Reactivity Over Time Kyle J. Bourassa, Karen Hasselmo, and David A. Sbarra Department of Psychology, University of Arizona

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Abstract

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Divorce is a stressor associated with long-term health risk, though the mechanisms of this effect are poorly understood. Cardiovascular reactivity (CVR) is one biological pathway implicated in predicting poor long-term health after divorce. A sample of recently separated adults (N=138) were assessed over 7.5 months to explore whether individual differences in heart-rate variability (HRV) – assessed by respiratory sinus arrhythmia (RSA) – operate in combination with subjective reports of separation-related distress to predict prospective changes in CVR, as indexed by blood pressure reactivity (BPR). For people with low resting RSA at baseline, there was no association between divorce-related distress and BPR, whereas people with high RSA evidenced a positive association. In addition, within-person variation in RSA and between-person variation in separation-related distress interacted to predict BPR at each study occasion. Individual differences in HRV and subjective distress operate together to predict CVR, and may explain some of the long-term health risk associated with divorce.

Keywords Heart rate variability; respiratory sinus arrhythmia; cardiovascular reactivity; divorce; blood pressure reactivity

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Divorce is one of life’s most distressing events (Bloom, Asher, & White, 1978) and is tied to increased morbidity (Dahl, Hansen, & Vignes, 2015; Kiecolt-Glaser et al., 1987) and risk for early death (Shor, Roelfs, Bugyi, & Schwartz, 2012). These effects are well documented (Sbarra, Law, & Portley, 2011), but remain poorly understood (Sbarra, Hasselmo, & Bourassa, 2015). To establish a mechanistic account for how divorce affects health, divorce must be studied in relation to biologically plausible pathways that connect to diseaserelevant biological responses (cf. Miller, Chen, & Cole, 2009). One potential biological intermediary is cardiovascular reactivity (CVR), or change in one’s cardiac physiological state in response to a stressor (Manuck, Kasprowicz, Monroe, Larkin, & Kaplan, 1989). The recruitment of the cardiovascular system during stressful psychological tasks is hypothesized to reflect the mobilization of the sympathetic nervous system in preparation for changes in the metabolic demands of a situation (Sherwood, Allen, Obrist, & Langer, 1986). The ability

Correspondence can be directed to Kyle Bourassa, 1503 E. University Blvd., Bldg #68. Tucson, AZ 85721-0068, [email protected].

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to respond flexibly to environmental challenge indexes readiness for action in the face of a threat (Porges, 1995), but chronically elevated sympathetic activity can become pathological if maintained over time (Brook & Julius, 2000; Thayer & Lane, 2007).

Cardiovascular Reactivity (CVR) and Divorce

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The cardiovascular reactivity (CVR) hypothesis suggests that higher reactivity in response to stress increases risk of developing cardiovascular disease (Treiber et al., 2003). For example, CVR in laboratory stress tasks is predicts poorer cardiovascular health and greater risk for adverse cardiovascular outcomes (Chida & Steptoe, 2010; Treiber et al., 2003). One way to index CVR is blood pressure reactivity (BPR), which is predictive of left ventricular hypertrophy, anatomic carotid artery disease, and coronary heart disease (Devereux & Alderman, 1993; Smith & Ruiz, 2002). Higher BPR in response to reminders of divorce may reflect the potential for prolonged wear-and-tear on the body, ultimately leading to poorer cardiovascular health.

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Though the CVR hypothesis is well-established (Chida & Steptoe, 2010), the majority of this research is cross-sectional. To study how people adjust to a stressful event we should analyze changes in CVR over time (cf. Steptoe & Kivimäki, 2013). Meta-analyses reveal small but positive associations between greater CVR in response to laboratory stress and later cardiovascular risk (Chida & Steptoe, 2010; Treiber et al., 2003). Prospective studies demonstrate that low socioeconomic status and heightened CVR over four years predicts the progression of carotid atherosclerosis (Lynch, Everson, Kaplan, Salonen, & Salonen, 1998), though these findings are debated (see Carroll & Smith, 1999). Similarly, men with greater BPR who report high job stress evidence 46% greater atherosclerotic progression than less reactive men who report lower job stress (Everson et al., 1997). Beyond these studies, longitudinal assessment of CVR to a psychosocial stressor is rare. Studies linking separation-related stress to cardiovascular responding generally use data from single session investigations. For example, adults reporting more divorce-related emotional distress evidence higher resting BP (Sbarra, Law, Lee, & Mason, 2009). In the same data set, adults who are both high in attachment anxiety and who spoke about their separation using immersed language also exhibited greater BPR in a subsequent task (Lee, Sbarra, Mason, & Law, 2011). However, these findings have not been extended to longitudinal changes in CVR.

Heart Rate Variability and Autonomic Flexibility Author Manuscript

One predictor of individual differences in CVR is flexible regulation of the cardiovascular system in response to environmental demands. According to Polyvagal Theory, the vagus nerve exerts inhibitory control of the myocardium (Porges, 1995). Vagal withdrawal in response to environmental challenge allows the sympathetic nervous system to make bodily resources available for energy expenditure (Porges, 1995). During recovery, balance shifts back toward parasympathetic bodily maintenance and inhibition of the sympathetic response. Heart rate variability (HRV) reflects variation in the heart’s beat-to-beat interval, and is believed to represent, in part, this parasympathetic regulation (Porges, 1995; Thayer,

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Ahs, Fredrikson, Sollers III, & Wager, 2012), also known as cardiac vagal control (Chambers & Allen, 2007). Low HRV at rest is hypothesized to reflect autonomic inflexibility and is linked with depression, anxiety, and poorer cardiovascular health (Beauchaine, 2001; Carney & Freeland, 2009; Dekker et al., 2000). One well-established measure of HRV is respiratory sinus arrhythmia (RSA; Beauchaine, 2001). RSA is a measure of changes in heart rate during the respiratory cycle, and individual differences in RSA are predictive of emotion regulatory ability (Beauchaine, 2015).

The Present Study

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To address the need for longitudinal studies of CVR and health-relevant biological intermediaries following marital separation/divorce, this paper examined blood pressure reactivity (BPR) in a sample of recently-separated adults (N=138) assessed at three laboratory visits across 7.5 months. We examined BPR at two temporal resolutions, first asking whether participants’ separation-related psychological distress and resting RSA at baseline predicted BPR 7.5 months later, then asking how these processes might interact to predict BPR within-occasion during the three laboratory visits. We hypothesized that if higher resting RSA reflects the capacity to effectively regulate physiological responses in potentially stressful situations, then initial separation-related distress and RSA would interact to predict later BPR reactivity during a separation-related mental activation task. We expected that participants who reported the highest levels of separation-related distress and the lowest resting RSA would evidence the greatest later BPR. Following this initial prospective analysis, we then evaluated whether the between- and within-person variance components for separation-related distress and RSA might interact to predict BRP at each study visit. We made no a priori hypotheses about whether and how the between-person and within-person variables might interact at each visit.

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Method Participants

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In the current study, 138 recently separated adults (n = 49 men) who reported recently experiencing a marital separation (mean months since separation = 3.9, SD = 2.4) were recruited from the local community (mean age = 40.65, SD = 9.76). These details are also reported in prior studies of either BP or separation-related distress drawn from this sample (Lee et al., 2011; Hasselmo, Sbarra, O’Connor, & Moreno, 2015; Sbarra et al., 2009). The length of participants’ prior relationship was 12.4 years (SD = 8.3) on average. Seventyeight percent of the sample reported they were Caucasian, 14% reported they were Hispanic, 1.5% reported they were Asian or African American, 0.8% were Native American, and 4.5% responded with “Other.” Approximately 52.2% of the sample reported making less than $30,000 a year. Of the 138 total participants, the first 29 participants were enrolled with a single expected assessment, but were included in analyses using full information maximum likelihood (FIML; see below for a more complete description). The remaining 109 participants were recruited for a longitudinal sample with three assessments, the second of which occurred 3months after their initial assessment. (We refer to these assessment occasions as Time 1

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(T1), T2, and T3, respectively, in the remainder of the paper.) For the third assessment they were randomly assigned to complete their follow-up at either 6 or 9-months from T1; this sampling procedure was part of a planned missingness design and is discussed in detail elsewhere (see Krietsch, Mason, & Sbarra, 2014). As a result, we report our results as evidencing estimates for 7.5 months of prospective assessment, on average. Of the 109 participants eligible for the follow-up assessments, 90 (82.6%) completed the first two assessments, whereas 79 (72.5%) completed all three assessments. Compared to people who completed all three assessments, people who did not complete all visits had higher selfreported separation related distress (Cohen’s d = 0.39). In contrast, people who did not complete the study were not significantly different in terms of their age (d = −0.12), sex (d = 0.01), length of prior relationship (d = −0.09), time since the separation (d = −0.14), HRV (RSA at rest during the first assessment, d = −0.17), systolic (d = 0.18) or diastolic BP (d = 0.12) at the initial assessment.

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Procedure

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Adult community members who experienced a recent marital separation were recruited from newspaper and listserv advertisements, as well as from a local “divorce recovery” support group. Eligible participants must have physically separated from their partner within the past five months and must have cohabitated with their former partner for at least 2 years. Eligible participants were aged 18 to 65 years, had never been diagnosed with a psychiatric disorder, reported good health, were not using blood pressure medications, and were not pregnant. Of 297 screened participants, 178 were eligible, and 138 completed our first study assessment. Common reasons for exclusion included having been separated for longer than 5 months (n = 68) and not having cohabitated for at least 2 years (n = 24). The University of Arizona Institutional Review Board approved the study protocol. All participants signed an informed consent form prior to study participation. Participants were informed the study’s purpose was to understand “how adults adjust to marital separation and the ways in which your body responds when you reflect on your separation experience.” Participants were mailed a questionnaire packet prior to their first laboratory visit, which included all self-report measures, and were asked to avoid consuming caffeine and tobacco for at least 4 hours prior to the laboratory visits. In the laboratory participants completed a series of tasks outlined in Figure 1. Participants were seated in a room that included physiological measurement devices, as well as a speaker and two cameras for communication between the participants and experimenters, located in an adjacent room.

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Participants were first asked to sit without speaking and relax while watching a nature video in order to acclimate to the testing room, which provided baseline measurement. Following the video, participants engaged in a 5-minute serial subtraction math stressor task followed by a 1-minute recovery period (see Cacioppo et al., 1995). During the task, participants were asked to pick a number then continuously subtract another number from that number. A research assistant probed the participant to go faster during minutes 3, 4, and 5 of the serial subtraction task to increases the level of stress.

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Following the math stressor, participants were given a 3-minute recovery period, following which they engaged in a mundane events recall, which is described in more detail in Sbarra and Borelli (2013). Finally, participants completed a 7-minute divorce-related mental activation task (DMAT). Participants were asked to “spend some time thinking about yourself and your partner in a variety of situations.” Participants were then instructed to view a screen in the room and “concentrate on the question by letting any relevant thoughts, feelings, or images come to mind” for a 1-minute period for each questions. The questions during the DMAT were: (1) Please think about how you and your partner met; (2) Whose decision it was to end the relationship; (3) When did you first realize you and your partner were headed toward divorce. What was that time like?; (4) What do you remember about the separation itself, the actual time during which the two of you decided to stop seeing each other; (5) How do you think you’ve coped with this separation?; (6) How much have you seen your partner since the separation? What kind of contact have you had since ending your relationship?; and, (7) What’s been the worst part about this separation for you? After a 4minute recovery period, the physiological equipment was removed. Sbarra and Borelli (2013), using data from the sample discussed here, provided evidence that partners report that the mental reflection questions in the DMAT are highly similar to content they usually think about when reflecting on their separation and that the content of their separationrelated reflections in the laboratory are highly similar to what they think about while at home.

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Self-Report measures

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Demographics: Participants reported a variety of demographic information, include age and sex. In addition, they reported on the characteristics of their former relationship, including length of the prior relationship and the amount of time that had passed since their separation. Impact of Events Scale – Revised: The IES-R (Weiss & Marmar, 1997) assesses the degree to which people are experiencing ongoing avoidance, emotional intrusion, and somatic hyperarousal related to a specific stressful event. The scale has 22 questions using a five point Likert scale rating system with specific anchoring statements (0 = “Not at all,” 1 = “A little bit,” 2 = “Moderately,” 3 = “Quite a bit,” 4 = “Extremely”). The IES-R scale has demonstrated high internal consistency in a range of previous studies and showed high average internal consistency in the current study (α = 0.93). Physiological measures

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Respiratory sinus-arrhythmia (RSA): Electrocardiograph (ECG) data was collected using the Biopac MP100 system and ECG amplifier, and was recorded using a standard lead configuration, which included the right clavicle and pre-cordial site V6, using EL505AgAgCl electrodes (Biopac Technologies, Santa Barbara, CA). Signals were recorded on a computer running the Biopac Acqknowledge data collection software. Mindware Technologies HRV 2.60 application was used for post-processing artifact detection and cleaning of ECG interbeat interval signals (IBI). RSA was quantified using the natural log of the variance in the residual time series associated with respiration (0.12-0.40 Hz). This

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method for assessing RSA is a widely used and validated proxy measure for parasympathetic vagal influences on cardiac chronotropy (Allen, Chambers, & Towers, 2007; Sbarra & Borelli, 2013). In addition to RSA, it is accepted practice to also assess respiration to ensure changes in RSA across a task are not accounted for by simultaneous changes in respiration (Allen et al., 2007; Denver, Reed, & Porges, 2007). As a result, we also assessed respiration rate using the Biopac respiratory effort transducer and Mindware software HRV application to calculate respiratory rate (RR). Both RSA and RR were assessed over 1-minute epochs across a 4-minute baseline task.

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Blood Pressure (BP): BP was assessed using a noninvasive tonometry device on the wrist covering the radial artery. Research assistants placed the device on participants’ nondominant arm, which was placed on a table in front of the participant for the duration of the laboratory visit. The tonometry device produces systolic (SBP) and diastolic (DBP) BP, updating in real time during study tasks (Vasotrac AMP 205, Medwave Inc., Arden Hills, MN). SBP measures the peak pressure present in the arteries during the start of the cardiac cycle, whereas DBP measures the lowest pressure during the cycle. The Vasotrac system detects and displays arterial pressures using ongoing compression and decompression of the radial artery to detect zero-load states during which pressure signals are measured every 12 to 15 beats. The Vasotrac has excellent convergent validity with other measures of BP, accounting for 95% of the variation in SBP and DBP (Belani, Buckley, & Poliac, 1999). BP was scored using the Mindware Technology BP 2.6 postprocessing software. Minute-byminute mean scores were averaged across the math and divorce-specific stressors scores to produce mean levels of SBP and DBP during the individual tasks. We used scores on BP assessed over 1-minute epochs across the 7-minute DMAT and 5-minute math stressor task. We considered extreme BP scores (40 < DBP < 130; 80 < SBP < 200) as physiologically improbable and removed these values from the analysis. By regressing DMAT BP on math BP, we produced a BPR score that captured reactivity unique to the divorce task when accounting for reactivity to stressful tasks in general. We chose to use the math stressor task score to create our BPR score in the current study, rather than baseline BP, because we are most interested in the individual differences in how people respond to stressful tasks of different types. This approach conceptually matches a capability model approach to measurement and analysis (see Coan, Allen, & McKnight, 2006), and allows for greater variability in people’s physiological response profiles. Data Analysis

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In the current study, we first evaluated whether RSA at T1 moderated the association between T1 self-reported separation-related distress and later BPR during the divorce-related mental-activation task (DMAT) at T3 in a hierarchical regression framework. BPR was calculated by residualizing participants’ blood pressure during the T3 math stressor task from their blood pressure during the DMAT; thus, higher scores on the outcome variable reflect greater BPR from the math stressor to the DMAT at the final (T3) assessment. We also included a measure of respiratory rate (RR) during the DMAT task at T1 to control for participants breathing rates when their RSA was measured (Allen et al., 2007; Denver et al., 2007). We evaluated the main effects of IES-R, RSA, RR, and our focal IES-R × RSA interaction predicting later BPR in a restricted model. Next, we included additional

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covariates of interest (sex, age, follow-up visit month, time since separation, length of relationship, body mass index, high blood pressure diagnosis, and self-rated health) to the model to explore whether these might account for the observed effect. We ran both models with SBP and DBP independently.

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Similar to initial regression analyses, we specified our multilevel models (MLMs) using BP responses to the DMAT at each occasion as our outcome of interest. We then accounted for the within-occasion math stressor values of BP, linear time (coded 0, 1, and 2 for the three occasions), then the between-person and person-centered versions of IES-R, RSA, and RR. We also included the interactions of between-person and person-centered variation in IES-R and RSA predicting BPR. As noted above, we expected to replicate the IES-R × RSA interaction, but we made no a priori hypotheses about the nature of this interaction; instead, we explored all four possible combinations of the two-way interaction predicting BPR (i.e., between-person IES-R interacting with between-person RSA; between-person IES-R interacting with person-centered RSA; person-centered IES-R interacting with personcentered RSA; and, finally person-centered IES-R interaction with between-person RSA). Finally, we added additional covariates of interest (sex, age, follow-up visit month, time since separation, length of relationship, body mass index, high blood pressure diagnosis, and self-rated health) to both models to explore whether these might account for the observed effects. We ran models for SBP and DBP independently.

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After evaluating initial (T1) IES-R and RSA values as predictors of later BPR, we explored whether within-occasion variation in these measures would be associated with BPR at each occasion using multilevel modeling. Because repeated, level-1 variables include both between- and within-person variance, we partitioned the variables of IES-R, RSA, and RR into their constituent between- and within-person components (see Bolger & Laurenceau, 2013), which yielded six total variables, three (level-2) between-person variables and three (level-1) person-centered variables that varied at each occasion. The former, level-2 variables represent where people score on each variable relative to everyone else in the sample, whereas the latter, level-1 variables represent where people score on a given variable with respect to their own mean across the three occasions. Those people who had only one within-occasion assessment were coded as missing to account for their lack of measured within-person variability.

Due to attrition, the original sample of 134 was reduced to 79 people with full data at T3. To account for issues with missingess, we used full likelihood maximum likelihood (FIML) estimation in our regression and maximum likelihood (ML) in our multilevel regression analyses. Due to possible issues with multicollinearity, we mean-centered both IES-R and RSA scores at T1, and used the robust ML estimation in MPLUS version 7.11 (Muthen & Muthen, 1998–2012) in our regression analyses. We ran the MLMs using the mixed analysis function in SPSS version 23. In both cases, we decomposed interactions using Preacher, Curran, and Bauer’s (2006) utility for probing interactions from multiple regression and multilevel models. Finally, we conducted additional post-hoc analyses examining whether extreme scores might have biased our model estimates. The results of these additional analyses are reported in an online supplement.

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Results Table 1 displays descriptive statistics and a correlation matrix of the main variables included in the study. Prospective Prediction of BP Reactivity

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Our first model included the focal predictors of interest (T1 IES-R, RSA, and IES-R × RSA), T1 DMAT BP scores, and necessary control variables (T1 RR, T3 Math BP) predicting the outcome of interest, T3 DMAT SBP. The analyses revealed significant main effects of IES-R, B = 0.25, p = .003, 95% CI [0.09, 0.41], but not RSA, B = −1.87, p = .257, 95% CI [-5.10, 1.35], predicting SBP at T3. In addition, the IES-R × RSA interaction significantly predicted SBP, B = −0.18, p = .012, 95% CI [0.04, 0.31]. The effects of interest, IES-R (β = 0.22), RSA (β = −0.12), and IES-R × RSA (β = 0.14), were moderate in size. We then included covariates of interest (sex, age, follow-up visit month, time since separation, and length of relationship, body mass index, high blood pressure diagnosis, and self-rated health) in the model. In addition, we included body mass index (BMI), diagnosis of high blood pressure and self-reported physical health as BP-relevant covariates to ensure our effects were not due to health status variables (see Sbarra, Law, Lee, & Mason, 2009). All substantive results predicting SBP were replicated when including these covariates.

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We next specified our model predicting T3 DMAT DBP. The analyses again revealed significant main effects of IES-R, B = 0.19, p < .001, 95% CI [0.10, 0.28], and RSA, B = −1.38, p = .004, 95% CI [-4.02, 1.26], predicting DBP. In addition, the focal IES-R × RSA interaction predicted DBP, B = 0.10, p = .001, 95% CI [0.04, 0.17]. The effects of interest, IES-R (β = 0.21), RSA (β = −0.12), and RSA×IES-R (β = 0.17), were again moderate in size. We then included covariates of interest in the model. All substantive results predicting DBP were replicated when including these covariates. The full results of both models predicting both SBP and DBP are presented in Table 2.

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We deconstructed the interaction of IES-R and RSA at T1 predicting T3 SBP. These effects are illustrated in Figure 2. The association of IES-R predicting SBP revealed significant positive slopes at the mean levels of RSA, B = 0.25, 95% CI [0.09, 0.42], p = .003, and 1 SD above the mean for RSA, B = 0.48, 95% CI [0.27, 0.69], p < .001. In contrast, those people with an RSA level 1 SD below the mean did not evidence significant associations between IES-R scores and their SBP reactivity, B = 0.03, 95% CI [-0.24, 0.29], p = .851. The simple slope became significant at RSA levels above 5.55. Finally, people differed on their SBP reactivity depending on their RSA levels when IES-R scores ranged from 0 to 19.99 points for SBP, and did not significantly differ at IES-R scores above 20. In short, people with low RSA had higher BPR regardless of their IES-R scores, but as people’s RSA level increased, relatively higher IES-R scores predicted greater BPR at T3. These substantive effects were replicated for DBP. Multilevel Analyses: Within-occasion Prediction of BP Reactivity In our multilevel models, which included all available measurements of RSA (including RR as a control variable), IES-R, and BP over the three time points of the study, analyses

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revealed significant main effects of between-person IES-R scores, B = 0.15, p = .031, 95% CI [0.01, 0.29], between-person RSA, B = −1.71, p = .018, 95% CI [-3.13, −0.29], and person-centered RSA, B = −2.08, p = .023, 95% CI [-3.87, −0.29], predicting SBP reactivity. In addition, the interaction of between-person IES-R × person-centered RSA significantly predicted SBP reactivity, B = −0.16, p = .038, 95% CI [-0.32, −0.01]. None of the other three combinations of between-person and person-centered IES-R and RSA scores significantly predicted SBP reactivity. We then included covariates of interest in the model (sex, age, follow-up visit month, time since separation, length of relationship, BMI, high blood pressure diagnosis, and self-rated health) as level 2 time-invariant covariates. The substantive results of the IES-R×RSA interaction and between-person IES-R predicting SBP held when including these alternative predictors, though both RSA main effects were no longer significant.

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The multilevel models predicting DBP revealed significant main effects of between-person IES-R, B = 0.13, p = .007, 95% CI [0.04, 0.22], and between-person RSA, B = −1.21, p = . 011, 95% CI [-2.15, −0.28], predicting DBP reactivity. In addition, the interaction of between-person IES-R × person-centered RSA predicted DBP reactivity, B = −0.10, p = . 060, 95% CI [-0.21, 0.00], though not at the .05 level. None of the other three interaction terms of between and person-centered IES-R and RSA scores predicted DBP reactivity. When including the level-2, time-invariant covariates of interest in the model, betweenperson IES-R continued to predict DBP, but between-person RSA no longer predicted DBP. In this model, the between-person IES-R × person-centered RSA interaction predicting DBP reactivity was stronger, B = −0.12, p = .020, 95% CI [-0.22, −0.02]. The full results of models predicting SBP and DBP, both with and without covariates, are presented in Table 3.

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We deconstructed the cross-level interaction of between-person IES-R and person-centered RSA predicting SBP reactivity, and the simple slopes for this analysis are displayed in Figure 3. There was no significant effect of time in the models (i.e., the linear trend across study occasion), and as a result the interaction effect visualized in Figure 3 was estimated to be equivalent at each time point. The simple slopes of between-person IES-R predicting SBP reactivity revealed significant positive associations at the mean levels of person-centered RSA, B = 0.15, 95% CI [0.01, 0.20], p = .031, and 1 SD below their own mean for RSA, B = 0.48, 95% CI [0.14, 0.52], p < .001. In contrast, those people with RSA levels 1 SD above their own mean evidenced weaker associations between IES-R scores and their reactivity for SBP, B = −0.17, 95% CI [-0.52, 0.15], p = .315. These substantive effects were replicated for DBP. These findings suggest that, in addition to a relatively large between-person effect for self-reported distress on BPR, on occasions when people evidence resting RSA scores at their own mean or one standard deviation below their own average RSA scores, people with greater separation-related distress have relatively higher BPR; said differently, mean to low person-centered RSA scores potentiated the association between subjective distress and BPR at each occasion. When people had higher RSA compared to their own average RSA, the association between separation-related distress and BPR was attenuated.

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Discussion In a sample of recently-separated adults, we examined the interaction of separation-related psychological distress and RSA predicting BP reactivity (BPR) at two temporal resolutions: first using baseline data to predict BPR 7.5 months later, and then within each of our three laboratory visits across this same period of time.

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In the first set of prospective analyses, higher initial levels of separation-related distress predicted greater BPR during a divorce-specific mental activation task (DMAT) 7.5 months later. The interaction between the IES-R and RSA also revealed a significant association with later BPR. In one respect, our hypothesis was confirmed: People reporting both high separation-related distress with low RSA at rest evidenced high levels of BPR during the DMAT 7.5 months later. The results presented a more complex picture, however, with low RSA predicting high BPR regardless of participants’ separation-related distress.1 As RSA increased, there was an increasingly positive association between subjective distress and later BPR, suggesting that the lowest levels of BPR were observed among people with low subjective distress and high RSA at rest. This interaction effect can also be interpreted in the opposite direction: RSA was only associated with BPR at lower levels of self-reported separation distress. These results suggest that both low levels of separation-related distress and relatively high RSA buffer against BPR following divorce, whereas people with low RSA show an exaggerated BPR profile 7.5 months later. The size of this effect was such that a 1 SD increase in a person’s separation-related distress translated to a difference of 7.9 millimeters mercury (mm HG) of SBP for people with relatively high RSA.

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In our second set of analyses, we explored the association between separation-related distress, RSA, and BPR at each of the three study occasions. People who reported higher overall separation-related distress had higher BPR across the entire study period (i.e., at each occasion). In addition, this between-person difference interacted with person-centered RSA at each occasion. On occasions in which people were at or below their own resting RSA levels across the entire study, separation-distress was positively correlated with BPR. At each visit, people reporting high separation-related distress and with low levels of personcentered RSA had the highest overall levels of BPR. On occasions when participants’ RSA was 1 SD above their own average resting RSA, however, the association between separation-related distress and BPR was attenuated. One way to interpret this pattern of association is that on occasions when a person’s general cardiac vagal capacity is diminished, the association between their separation-related distress and BPR is potentiated.

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As one of the first prospective studies to explore BPR longitudinally and following marital separation specifically, these results have clear theoretical and clinical implications. First, the findings illuminate one pathophysiological route through which divorce-related psychological distress may affect long-term health. Although the effects of divorce on health are well-established (Kiecolt-Glaser et al., 1987; Sbarra et al., 2011), the biologicallyplausible pathways that transmit this risk remain poorly understood (Sbarra et al., 2015).

1We also note that there is not a main effect of RSA predicting BPR when excluding the interaction term from our models. This is described in more detail in our online supplement (S1).

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Our findings suggest that cardiovascular reactivity is a pathway through which divorce may transmit health risk, particularly for people with high levels of psychological distress. People with greater divorce-related distress continued to demonstrate heightened BPR nearly 8 months later. These results are notable for two reasons. First, the current study’s results were above and beyond the association of a math stressor task completed prior to the DMAT. As a result, these models are related to divorce-specific BPR, rather than BPR to a general stressor. Second, this study is one of the first investigations of person-centered variation in RSA and its relation to BPR. The results suggets that variations in RSA within people over time predict differences in BPR, and that future investigations into RSA should acknowledge and explore meaningful ways in which RSA might vary from occasion to occasion and how deviations from a person’s own mean can predict health-relevant biological outcomes.

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In addition, the size of the study effects may be clinically meaningful. People with high RSA evidenced a difference of 7.9 mm Hg SBP per SD change in divorce-specific distress scores, and the reactivity of people with low RSA best matched those people at the highest levels of distress. An increase in 20 mm Hg in SBP is associated with a doubling of the risk for mortality as a result of cardiovascular causes (Prospective Studies Collaboration, 2002), so this difference – if maintained over the long term – would translate to an increase of between 20% and 25% in mortality due to a cardiovascular event. Although it is unlikely that recently-separated people would show this large a difference in BP consistently over time and a 7.9 mm Hg increase in BP may affect people differently across the range of possible BP scores, frequent ongoing reminders in of people’s separation could result in increased risk for cardiovascular pathology as a result of CVR.

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The study’s results should be understood in light of its limitations. First, the study had a moderate level of dropout (27.5%) over three study occasions. Added to this was the inclusion of 29 participants originally recruited before the longitudinal design was implemented, resulting in 79 participants who completed all study assessments (57%). Although we used FIML to account for missing data, it is possible that differential attrition may have biased the study’s results. Second, although this study was one of the first longitudinal studies of CVR after a stressful event, we have no assessments of pre-clinical disease states (; e.g., Smith et al., 2011); longer-term studies are needed to link these disease-relevant outcomes to separation-related distress through CVR. Nevertheless, the study of BPR has clear end-point health relevance (Chida & Steptoe, 2010; Treiber et al., 2003).

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Conclusion Recently separated adults’ initial separation-related psychological distress and RSA interacted to predict their BP reactivity (BPR) 7.5 months later. In addition, person-centered RSA interacted with differences in between-person separation-related distress to predict BPR at each study visit. At any occasion, people evidencing resting RSA scores at or below their own RSA mean (across all study visits) demonstrated a significant positive association between separation-related distress and BPR, suggesting that person-centered differences in

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RSA may reflect diminished autonomic flexibility (cf. Thayer & Lane, 2000). Taken together, these results suggest that BPR is one biologically-plausible mechanism that may link marital separation to distal health outcomes. Importantly, this risk depends on individual differences in RSA and participants’ distress regarding the separation experience.

Supplementary Material Refer to Web version on PubMed Central for supplementary material.

Acknowledgments The third author’s work on this paper was supported by a grant from the National Institute of Child Health and Human Development (HD069498), and the overall project was funded by grants from the National Institute of Mental Health (MH074637) and the National Institute on Aging (AG028454 and AG036895).

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Author Manuscript Figure 1.

The laboratory procedure participants completed at all visits. DMAT = Divorce-related mental activation task.

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Figure 2.

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The interaction of IES-R scores and respiratory sinus-arrhythmia (RSA) levels predicting blood pressure reactivity (BPR) 7.5 months later during a divorce-specific activation task when controlling for BP during a prior general stressor task. The effects of covariates (T3 math BP, T1 DMAT BP, T1 DMAT respiratory rate, which would add 118.99 to any given BP score) were removed from the y-axis to illustrate a BPR score. For people with low levels of RSA, IES-R scores do not predict BPR, whereas for people who exhibit high levels of RSA, IES-R scores are positively associated with BPR 7.5 months later, such that when people are below 20 points on the IES-R, their BP differs significantly depending on their RSA scores. The shaded portion of the figure indicates this area of significance.

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Figure 3.

Author Manuscript

The interaction of between-person IES-R scores and person-centered respiratory sinusarrhythmia (RSA) scores predicting blood pressure reactivity (BPR) at each occasion during a divorce-specific activation task when controlling for BP during a prior general stressor task. The effect of the intercept was removed from the y-axis to illustrate a BPR score. On occasions when people were higher in RSA compared to their own average RSA, IES-R did not predict BPR, whereas at occasions when people are lower in RSA compared to their own mean, IES-R scores were positively associated with BPR, such that when people are above 22.34 points on the IES-R, their BP differs significantly depending on person-centered RSA scores. The shaded portion of the figure indicates this area of significance.

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Author Manuscript −0.11

−0.09 0.23

T1 RSA(2)

T1 RR (3)

T3 DMAT SBP (4)

−0.35 0.06

0.18 0.04 0.01 −0.01 −0.03 31.65 16.43

T3 Math DBP (7)

Age (8)

Sex (9)

Rel. length (10)

Mean

SD 3.69

12.05

0.03

−0.06

−0.14

−0.01

−0.00

−0.05

−0.03

1.0

3

19.32

141.71

0.40

0.12

0.34

0.73

0.92

0.81

1.0

4

20.26

142.43

0.16

0.16

0.23

0.95

0.75

1.0

5

14.07

81.53

0.22

0.18

0.18

0.78

1.0

6

13.83

82.53

0.06

0.18

0.12

1.0

7

9.71

40.65

0.62

−0.00

1.0

8

-

-

0.05

1.0

9

99.83

163.09

1.0

10

Note: All values used full-information maximum likelihood for missing data. IES-R = Impact of events scale – revised, RSA = respiratory sinus-arrhythmia, DMAT = Divorce-related mental activation task, BP = blood pressure, RR = respiratory rate, Rel. = relationship. RSA and RR were measured during a resting baseline assessment task. Rel. length was assessed in months.

1.25

5.91

−0.21

−0.35

−0.33

0.10

T3 Math SBP (5)

T3 DMAT DBP (6)

−0.40

−0.43

1.0

1.0 0.07

T1 IES-R (1)

2

1

Variables

Author Manuscript

Demographics and Correlation Table for Primary Study Variables

Author Manuscript

Table 1 Bourassa et al. Page 18

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Table 2

Author Manuscript

Unstandardized Coefficients from Regression Models Predicting BP Reactivity B

95% CI

B

95% CI

Intercept

17.55

[−15.04, 50.13]

36.33*

[0.17, 72.49]

T1 DMAT BP

0.15*

[0.02, 0.31]

0.09

[−0.10, 0.28]

T3 Math BP

0.70**

[0.49, 0.92]

0.68**

[0.49, 0.87]

T1 RR

0.26

[−0.40, 0.91]

0.15

[−0.53, 0.82]

T1 RSA

−1.87

[−5.10, 1.35]

−1.90

[−5.57, 1.78]

T1 IES

0.25**

[0.09, 0.41]

0.20**

[0.05, 0.35]

T1 RSA × T1 IES

0.18*

[0.04, 0.31]

0.14*

[0.03, 0.24]

Sex

0.67

[−1.98, 3.32]

Outcome: DBP

Author Manuscript

Age

−0.34

[−0.81, 0.13]

Time sep.

−0.14

[−1.47, 1.92]

Rel. length

0.07**

[0.03, 0.11]

T3 Group

−3.63

[−8.63, 1.37]

BMI

0.27

[−0.10, 0.64]

Hypertension Dx

6.49

[−2.50, 15.57]

Self-reported Health

−1.01

[−3.70, 1.69]

B

95% CI

B

95% CI

Intercept

9.85

[−3.33, 23.04]

20.37

[−6.21, 49.94]

T1 DMAT BP

0.05

[−0.13, 0.23]

0.05

[−0.16, 0.26]

0.79**

[0.60, 0.98]

0.75**

[0.09, 0.98]

T1 RR

0.18

[−0.29, 0.64]

0.15

[−0.36, 0.67]

Outcome: DBP

T3 Math BP

Author Manuscript

T1 RSA

−1.34

[−4.02, 1.26]

−2.11

[−6.22, 2.00]

T1 IES-R

0.19**

[0.10, 0.28]

0.19**

[0.09, 0.29]

T1 RSA × T1 IES-R

0.11**

[0.04, 0.17]

0.11*

[0.03, 0.20]

Sex

0.53

[−1.74, 2.81]

Age

−0.12

[−0.50, 0.27]

Time since sep.

0.21

[−0.94, 1.36]

Rel. length

0.01

[−0.02, 0.04]

T3 Group

−3.88

[−8.98, 1.36]

BMI

0.12

[−0.18, 0.42]

Hypertension Dx

0.20

[−6.38, 6.77]

Self-reported Health

−0.43

[−2.58, 1.73]

Author Manuscript

Note: 95% CI = 95% confidence interval. T3 Group = membership in the 6 or 9 month T3 visit groups. Hypertension Dx = a self-reported diagnosis of high blood pressure. IES-R = Impact of events scale – revised, RSA = respiratory sinus-arrhythmia, Sep. = separation, DMAT = Divorce-related mental activation task, BP = blood pressure, RR = respiratory rate, Rel. = relationship. RSA and RR were measured during a resting baseline assessment task. Rel. length was assessed in months. *

p < .05.

**

p < .01.

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Table 3

Author Manuscript

Unstandardized Coefficients from Multilevel Models Predicting BP Reactivity Outcome: SBP Intercept Time

B

95% CI

B

95% CI

37.29**

[24.50, 50.07]

33.82**

[12.89, 54.77]

1.20

[−1.07, 3.47]

1.43

[−0.77, 3.63]

MATH SBP

0.71**

[0.62, 0.79]

0.72**

[0.63, 0.82]

BP IES

0.15*

[0.01, 0.29]

0.17*

[0.03, 0.31]

WP IES

0.01

[−0.16, 0.18]

−0.03

[−0.19, 0.14]

BP RSA

−1.71*

[−3.13, −0.29]

−0.63

[−2.36, 1.10]

WP RSA

−2.08*

[−3.87, −0.29]

−1.38

[−3.08, 0.31]

BP RR

0.18

[−0.26, 0.61]

0.28

[−0.18, 0.75]

Author Manuscript

WP RR

0.37

[−0.30, 1.04]

0.46

[−0.15, 1.08]

WP IES-R × WP RSA

0.05

[−0.14, 0.24]

0.02

[−0.15, 0.19]

WP IES-R × BP RSA

−0.04

[−0.16, 0.09]

−0.05

[−0.18, 0.07]

BP IES-R × BP RSA

−0.05

[−0.16, 0.06]

−0.03

[−0.14, 0.07]

BP IES-R × WP RSA

−0.16*

[−0.32, −0.01]

−0.20**

[−0.34, −0.05]

−0.65

[−2.45, 1.15]

Sex Age

0.17

[−0.08, 0.43]

Time sep.

−0.97

[−1.95, 0.01]

Rel. length

−0.02

[−0.00, 0.05]

T3 Group

0.17

[−2.06, 1.13]

BMI

0.27

[−0.03, 0.57]

Hypertension Dx

1.00

[−4.47, 6.46]

Self-report Health

−0.56

[−2.87, 1.76]

Author Manuscript

B

95% CI

B

95% CI

18.01**

[10.58, 25.45]

14.25*

[0.58, 27.92]

1.60

[−0.02, 3.22]

1.52

[−0.05, 3.10]

MATH DBP

0.73**

[0.64, 0.81]

0.75**

[0.65, 0.85]

BP IES

0.13**

[0.04, 0.22]

0.14*

[0.04, 0.23]

Outcome: DBP Intercept Time

Author Manuscript

WP IES

0.11

[−0.00, 0.23]

0.08

[−0.03, 0.20]

BP RSA

−1.22*

[−2.15, −0.28]

−0.97

[−2.20, 0.25]

WP RSA

−0.91*

[−2.15, 0.32]

−0.48

[0.04, 0.23]

BP RR

0.09

[−0.19, 0.37]

0.19

[−0.14, 0.51]

WP RR

0.18

[−0.29, 0.66]

0.23

[−0.21, 0.67]

WP IES-R × WP RSA

0.03

[−0.09, 0.16]

0.01

[−0.11, 0.13]

WP IES-R × BP RSA

−0.02

[−0.11, 0.07]

−0.06

[−0.15, 0.03]

BP IES-R × BP RSA

−0.05

[−0.12, 0.03]

−0.04

[−0.12, 0.03]

BP IES-R × WP RSA

−0.10

[−0.21, 0.00]

−0.12*

[−0.22, −0.02]

−0.17

[−1.42, 1.08]

Sex

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B

95% CI

Age

0.09

[−0.09, 0.27]

Time since sep.

−0.52

[−1.20, 0.17]

Rel. length

0.00

[−0.01, 0.02]

T3 Group

−0.22

[−1.33, 0.89]

Outcome: SBP

B

95% CI

Author Manuscript

BMI

0.17

[−0.04, 0.38]

Hypertension Dx

1.13

[−2.63, 4.90]

Self-report health

−0.10

[−1.71, 1.52]

Note: 95% CI = 95% confidence interval. WP = within-person, BP = between-person, T3 Group = membership in the 6 or 9 month T3 visit groups. Hypertension Dx = a self-reported diagnosis of high blood pressure. IES-R = Impact of events scale – revised, RSA = respiratory sinus-arrhythmia, Sep. = separation, DMAT = Divorce-related mental activation task, BP = blood pressure, RR = respiratory rate, Rel. = relationship. RSA and RR were measured during a resting baseline assessment task. Rel. length was assessed in months. *

p < .05.

**

Author Manuscript

p < .01.

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