Cardiovascular Reactivity During Marital Conflict in Laboratory and Naturalistic Settings: Differential Associations with Relationship and Individual Functioning Across Contexts BRIAN R. W. BAUCOM* KATHERINE J. W. BAUCOM* JASARA N. HOGAN* ALEXANDER O. CRENSHAW* STACIA V. BOURNE* SHEILA E. CROWELL* PANAYIOTIS GEORGIOU† MATTHEW S. GOODWIN‡
Cardiovascular reactivity during spousal conflict is considered to be one of the main pathways for relationship distress to impact physical, mental, and relationship health. However, the magnitude of association between cardiovascular reactivity during laboratory marital conflict and relationship functioning is small and inconsistent given the scope of its importance in theoretical models of intimate relationships. This study tests the possibility that cardiovascular data collected in laboratory settings downwardly bias the magnitude of these associations when compared to measures obtained in naturalistic settings. Ambulatory cardiovascular reactivity data were collected from 20 couples during two relationship conflicts in a research laboratory, two planned relationship conflicts at couples’ homes, and two spontaneous relationship conflicts during couples’ daily lives. Associations between self-report measures of relationship functioning, individual functioning, and cardiovascular reactivity across settings are tested using multilevel models. Cardiovascular reactivity was significantly larger during planned and spontaneous relationship conflicts in naturalistic settings than during planned relationship conflicts in the laboratory. Similarly, associations with relationship and individual functioning variables were statistically significantly larger for cardiovascular data collected in naturalistic settings than the same data collected in the laboratory. Our findings suggest that cardiovascular reactivity during spousal conflict in naturalistic settings is statistically significantly different from that elicited in laboratory settings both in magnitude and in the pattern of associations with a wide range of inter- and intrapersonal variables. These differences in findings across laboratory and naturalistic physiological responses highlight the value of testing physiological phenomena across interaction contexts in romantic relationships. Keywords: Heart Rate Reactivity; Romantic Relationships; Marital Conflict Fam Proc x:1–17, 2018 *Department of Psychology, University of Utah, Salt Lake City, UT. † Department of Electrical Engineering, University of Southern California, Los Angeles, CA. ‡ Department of Health Sciences, Northeastern University, Boston, MA.
Correspondence concerning this article should be addressed to Brian R. W. Baucom, Department of Psychology, University of Utah, 380 South 1350 East, BEHS 502, Salt Lake City, UT 84112. E-mail:
[email protected]. This manuscript was supported in part by start-up funding from the University of Utah and a Vice President for Research Seed Grant from the University of Utah awarded to Brian Baucom. 1
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C
ardiovascular reactivity is one of the major pathways by which marital conflict is thought to impact overall relationship functioning, mental health, and physical wellbeing (e.g., Robles & Kiecolt-Glaser, 2003; Whisman & Uebelacker, 2003). For example, faster heart rate (HR) during couple conflict is associated with lower levels of concurrent relationship satisfaction and greater longitudinal decline in relationship satisfaction (e.g., Levenson & Gottman, 1985), higher levels of negative communication behaviors (e.g., Newton & Sanford, 2003), and increased risk for hypertension and cardiovascular disease (e.g., Robles & Kiecolt-Glaser, 2003). These effects are generally understood to occur due to distress that partners experience during stressful couple interactions (e.g., Robles & Kiecolt-Glaser, 2003). However, empirical evidence has thus far failed to support the theorized magnitude of association between conflict-related cardiovascular reactivity and relationship functioning. For example, Robles and colleagues (Robles, Slatcher, Trombello, & McGinn, 2014) reported meta-analytic effect sizes of r = .10, .18, and .18 (ps < .001) for associations between relationship satisfaction and HR, diastolic blood pressure (DBP), and systolic blood pressure (SBP) reactivity, respectively. Existing research on cardiovascular reactivity during marital conflict is primarily based on physiological data collected in laboratory settings while spouses discuss an area of disagreement in their relationship (Robles et al., 2014). This methodology grew out of a behavior analytic tradition that emphasized the utility of directly observing spouses’ behavior during arguments recorded in laboratory settings (for a review see Heyman, 2001). However, empirical evidence suggests that while conflict behavior enacted in a laboratory is generally representative of how spouses typically behave, it tends to be less negative and more positive than conflict that occurs outside of a laboratory (e.g., at home; Gottman & Krokoff, 1989). Romantic relationship researchers have frequently explained this finding as reflecting a social desirability effect wherein spouses do not want to behave badly when being recorded (e.g., Heyman, 2001). While researchers have not viewed this behavioral discrepancy as an impediment to studying marital conflict in the laboratory generally, it may have a more pernicious effect when one seeks to identify representative psychophysiological processes associated with spousal conflict in naturalistic settings. Additional concern about the generalizability of laboratory-based psychophysiological reactivity during marital conflict arises from the literature examining the consistency of an individual’s cardiovascular reactivity during stress tasks across time and place. The fields of health psychology and behavioral medicine have long been concerned with the consistency of an individual’s physiological reactivity to stressors because stable individual differences in physiological reactivity are a key component of stress-related process models of disease. Early research into the stability of physiological reactivity to stress, and cardiovascular reactivity specifically, found that within-person correlations between cardiovascular reactivity during repeated laboratory stressors varies considerably from small (e.g., r = .15; Smith & O’Keeffe, 1988) to large (r = .68; Gerin et al., 1998). Larger correlations typically emerge between cardiovascular reactivity during identical stress tasks performed on different occasions than cardiovascular reactivity during different tasks performed either on the same or different occasions. Conversely, varying the context or location of a stress task has been associated with lower within-person correlations in cardiovascular reactivity relative to correlations that emerge for stress tasks that occur in the same place or similar settings. For example, Gerin et al. (1998) measured three indices of cardiovascular reactivity—HR,1 DBP, and SBP—during a serial subtraction task performed in the laboratory, a classroom, and 1 Correlations between measures of HR reactivity are not reported because they were all very small, and study authors suggest that they are unreliable because of measurement imprecision.
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participants’ homes. Reactivity scores were larger, though not statistically significantly so, in naturalistic settings than in laboratory settings. Furthermore, within-person correlations between (1) laboratory and classroom and (2) laboratory and home blood pressure reactivity (average r = .37) were smaller than those observed between classroom and home (average r = .51), which were in turn smaller than those observed between repeated laboratory stressors (average r = .65). Study authors interpreted these findings to indicate that correlations between repeated measurements of cardiovascular reactivity are highest when comparing measures obtained under similar circumstances (i.e., laboratory vs. naturalistic) even if the actual location varies. As may be expected from variability in the magnitude and rank ordering of cardiovascular reactivity across settings, the predictive utility of cardiovascular measures obtained in different settings is also widely variable. A prime example of this phenomenon is research examining the association between hypertension and risk for cardiovascular outcomes. Individuals who exhibit hypertension (i.e., elevated SBP) in both a clinic setting and during daily life are at significantly greater risk for cerebrovascular and coronary events relative to individuals who exhibit hypertension only in a clinic setting (e.g., Khattar, Senior, & Lahiri, 1998). Such “white coat” hypertension findings have been interpreted as evidence that SBP can be artificially elevated due to the stress of being in a clinic setting, rather than a true index of overall health. Extending this phenomenon to physiological assessments of marital conflict in the laboratory invites the possibility that cardiovascular reactivity in the laboratory may also be less strongly related to individual and relationship functioning than would be observed in the real world. One way spouses can behave less negatively in the laboratory than in more naturalistic environments is to inhibit impulses to engage in negative behaviors. Such a process could be described as “editing” from a relationship science perspective (Gottman, Notarius, Gonso, & Markman, 1976) or as “suppression” from an emotion regulation perspective (e.g., Gross & Levenson, 1993). Editing refers to refraining from engaging in negative behaviors and is viewed as a relationship enhancing process (Gottman et al., 1976), while suppression refers to inhibiting emotional expression and is linked to poorer interpersonal functioning (e.g., Gross & John, 2003). Of relevance to this study, editing should lead to lower cardiovascular reactivity during relationship conflict to the extent that it reduces overall level of negative behavior (e.g., Gottman & Krokoff, 1989), whereas suppression typically (e.g., Butler et al., 2003), but not always (e.g., Butler, Gross, & Barnard, 2014), leads to higher levels of cardiovascular reactivity. Building on the theory and findings presented above, the aims of this study were twofold. First, we sought to test mean level differences in cardiovascular reactivity during repeated marital conflict that occurs in and outside of a laboratory setting. Second, we sought to evaluate the magnitude of associations between individual and relationship functioning variables and cardiovascular reactivity during marital conflicts across laboratory and naturalistic settings. Consistent with the general pattern of mean level differences in cardiovascular reactivity observed in stress tasks completed in laboratory and naturalistic settings (e.g., Gerin et al., 1998), we hypothesized that cardiovascular reactivity occurring in the real-world environment would be larger in magnitude than cardiovascular reactivity elicited in the laboratory. We also hypothesized that cardiovascular reactivity in naturalistic settings would be more strongly related to individual and relationship functioning variables relative to cardiovascular reactivity recorded in the laboratory. Finally, exploratory analyses were conducted to examine differences in the magnitude and strength of associations involving cardiovascular reactivity during planned and spontaneous marital conflicts that took place in naturalistic settings (i.e., the home).
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METHODS Participants Participants were 20 married, heterosexual, community couples (N = 40 individuals) recruited for a study of couple communication. Inclusion criteria included being legally married for at least 1 year, living within 20 miles of the University, both spouses being between 18 and 65 years of age, reliable access to the Internet throughout the day, and ability to speak fluent English. Exclusion criteria included self-reports of moderate to severe physical aggression assessed using the Conflict Tactics Scale-Revised (Straus, Hamby, Boney-McCoy, & Sugarman, 1996), history of medical conditions that affect the cardiovascular system (e.g., Coronary Artery Disease), current or previous medication use associated with cardiovascular disease (e.g., angiotensin-converting enzyme inhibitors) or current medication use that affects cardiovascular functioning (e.g., Beta-blockers), current use of tobacco products, consuming an average of more than 10 alcoholic beverages per week, the wife being pregnant, and the presence of other family members (e.g., children or parents) or individuals (e.g., tenants or friends) living in the couple’s home and sharing the same living space. Finally, one spouse was required to report a score of 18 or less on the Couple Satisfaction Inventory, 4-item version (CSI4; Funk & Rogge, 2007) during the screening assessment to increase the likelihood of spontaneous conflict occurring during the week of daily data collection (described below). A score of 18 on the CSI4 is approximately 0.5 SD below the maximum possible score of 21 based on established norms for the scale (SD = 4.7, Funk & Rogge, 2007). Participants ranged from 22 to 64 years old, with a mean age for men of 29.3 (SD = 7.8) years and a mean age for women of 28.1 (SD = 9.0) years. They were, on average, college educated and earned a median annual household income of $32,400. Spouses largely selfidentified as Caucasian (80%), with 12.5% Asian or Pacific Islander, and 7.5% “Other.” Self-reported religious identity was 40% Church of Jesus Christ of Latter-Day Saints (LDS), 20% Atheist, 17.5% Spiritual/Agnostic, 5% Non-LDS Christian, and 17.5% chose not to answer.
Procedures Couples completed a 3- to 4-hour laboratory assessment that included self-report questionnaires, physiological baselines, and four videotaped discussions. After consent, spouses were outfitted with physiological recording equipment that continuously recorded HR (described below) and then completed two 5-minute resting physiological baselines; one resting baseline was collected while spouses were in the same room and the other was collected while spouses were in separate rooms. The order of the two resting baselines was randomized and counterbalanced. While continuing to wear physiological recording equipment, spouses then completed a battery of self-report questionnaires that took approximately 45–90 minutes followed by four videotaped conversations consisting of a: (1) 5minute events of the day conversation; (2) 7-minutes relationship history conversation; and (3–4) two 10-minutes relationship conflict discussions. Each spouse determined the topic for one of the two relationship conflict discussions; the order of which spouse’s topic was discussed first was randomized and counterbalanced. In addition, all spouses completed the events of the day conversation first and the order of remaining types of discussions (i.e., relationship history vs. relationship conflict) were randomized and counterbalanced. Following completion of the laboratory procedures, participants completed 6–7 days of data collection during their daily lives. Data collection included continuous measurement of HR during all waking hours collected using the same equipment as used to collect HR in the laboratory as well as completion of daily diaries twice a day. Daily diaries assessed the www.FamilyProcess.org
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beginning and ending time of interpersonal conflict (including conflict with the spouse) and self-reported relationship functioning, mood, and stress, as well as physiological covariates (e.g., timing and duration of eating, drinking, and exercise); daily diaries were completed between 11:00 a.m. and 1:00 p.m. and between 8:00 p.m. and 10:00 p.m. each day. On the final day of data collection, participants were asked to spend the day with their spouse and to generate a continuous audio recording as well as a videotaped session repeating the two conflict discussions they had during the laboratory assessment. Participants were provided with a video-recorder, given a written reminder of the conflict topics discussed during the laboratory assessment, and asked to discuss the same two areas of conflict in reverse order. For example, if the wife’s topic was discussed first during the laboratory assessment, the husband’s topic was discussed first at home. The order of which spouse’s topic was discussed first was counterbalanced between laboratory and home settings to minimize any potential order effects. Participants were also asked to sit in an arrangement as similar as possible to the session in the laboratory, follow the same set of instructions as the laboratory conflict (e.g., discuss the same topic for 10 minutes, do not get up and move around, etc.), and video-record the entirety of their discussions. Finally, participants were provided with a digital timer to help them know when 10 minutes had elapsed. All procedures were approved by the Institutional Review Board at the University of Utah. Heart rate data for this study were based on the average of the two resting baselines, the two 10-minutes relationship conflict discussions from the laboratory assessment, the two 10-minutes relationship conflict discussions participants video-recorded in their homes, and the first 10 minutes of the first two spontaneous relationship conflicts participants reported in their daily diaries.2 We selected these periods of measurement for analysis because they maximize the similarity of the stressors by indexing the first 10 minutes of relationship conflict in all instances and included up to two assessments of conflict in each interaction context.3 HR during the two resting baselines were averaged to improve stability of the estimate by including a maximum amount of data; this decision was judged to be reasonable given that HR values were highly correlated across the two baselines, r = .93, p < .001. All self-report measures used in our analyses were collected during the laboratory assessment.
Measures Heart rate reactivity Continuous HR data during the laboratory assessment and daily life portions of the study were collected using ambulatory Actiheart biosensors (CamNtech Ltd U.K., 2014). The Actiheart is a two-lead, miniaturized biosensor validated against gold standard clinical/laboratory (3-lead, medical grade electrocardiogram [ECG] recordings) and ambulatory (e.g., Holter monitor) equipment at rest (Brage, Brage, Franks, Ekelund, & Wareham, 2005; Kristiansen et al., 2011), while exercising (Brage et al., 2005), and during activities of daily living (Kristiansen et al., 2011). The biosensor connects to standard ECG electrodes placed at midline and left ventral positions of V4. Waveform data were sampled at 2 As expected, not every couple reported having a spontaneous conflict. Twelve couples reported having at least one spontaneous conflict during the week of daily data collection. HR data from the planned home conflicts in two couples were missing because of equipment malfunction. In addition, one couple opted not to complete the daily data collection procedures; data for planned and spontaneous naturalistic conflict were not collected for this couple. 3 Including multiple measurements of physiological reactivity across contexts has been shown to increase the stability of reactivity within and across contexts and is the currently recommended method for studies comparing reactivity across contexts (e.g., Kamarck, Debski, & Manuck, 2000).
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128 Hz and processed in real-time to generate 15-seconds epochs of HR values, which were manually inspected for outliers and poor signal quality prior to analysis. Standard regression diagnostics were used to test for outliers, and signal quality was assessed using the percent of missing data for each epoch provided by the Actiheart software. Signal quality was considered to be acceptable if 10% or fewer data points were missing in any epoch. No HR values were identified as outliers or poor quality using these methods. To account for baseline differences in cardiovascular functioning across settings, average HR during the two resting baselines was subtracted from the average HR during all relationship conflicts prior to analysis to create change scores. We decided to use the laboratory baseline to calculate heart rate reactivity (HRR) in all relationship conflicts because there are no currently established methods for selecting baselines for naturally occurring stressors, and cardiovascular baselines have been shown to be stable across laboratory and naturalistic settings (e.g., average within-subject correlation: for SBP and DPB, r = .73; Gerin et al., 1998; for HR, SBP, and DPB, r = .70; Cohen et al., 2000). While this decision makes interpretation of cardiovascular reactivity scores during the naturalistic stressors less precise, it allowed HRR values across settings to be maximally comparable to one another because they were all calculated relative to the same resting baseline value. Relationship functioning variables Relationship satisfaction was assessed using the total score on the Couples Satisfaction Index, 4-item version (CSI4; Funk & Rogge, 2007), where higher scores indicate greater relationship satisfaction. Perceived emotional flooding during relationship conflict was assessed using the total score on the 10-item Flooding questionnaire (Gottman, 1999), where higher scores indicate feeling more overwhelmed by emotion during relationship conflict. Individual functioning variables Emotion dysregulation was assessed using the total score on the 36-item Difficulties in Emotion Regulation Scale (DERS; Gratz & Roemer, 2004), where higher scores indicate greater emotion dysregulation. Emotion regulation strategies were assessed using the Reappraisal (six items) and Suppression (four items) scales of the Emotion Regulation Questionnaire (Gross & John, 2003), where higher scores on both scales indicate a stronger tendency to use that emotion regulation strategy. Psychological distress was assessed using the total score on the 21-item version of the Depression, Anxiety, and Stress Scale (DASS; Antony, Bieling, Cox, Enns, & Swinson, 1998), where higher scores indicate greater psychological distress. Positive and negative mood were assessed using the Positive (10 items) and Negative (10 items) scales of the 20-item version of the Positive and Negative Affect Schedule (PANAS; Watson, Clark, & Tellegen, 1988), where higher scores indicate a stronger general experience of the respective mood. Body Mass Index Body Mass Index was computed as BMI = 703 * weight in lb./height in inches2. Height was measured using a stadiometer, and weight was measured using a beam scale.
RESULTS Descriptive Statistics Table 1 presents descriptive statistics for and correlations between all study variables. Of particular note, HRR during laboratory conflict and during planned conflict at www.FamilyProcess.org
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.70*** .17 .05 .04 .01 .22 .22 .08 .12 .02 .34* 0.44 6.24 —
—
1
.34 .29 .20 .06 .21 .30 .07 .21 .02 .03 8.17 9.87 —
—
2
.07 .66** .44* .31 .05 .45* .40* .19 .20 9.05 8.16 —
—
3
— .14 .55*** .26 .22 .42** .47** .57*** .05 17.09 3.19 .92
4
.38* .38* .22 .05 .08 .00 .08 1.29 9.33 .86
—
5
— .23 .54* .41** .35* .49* .08 74.18 16.94 .89
6
— .02 .09 .39* .14 .09 32.80 4.96 .71
7
— .21 .10 .27 .19 13.91 5.49 .78
8
— .19 .58*** .04 20.87 13.57 .89
9
— .31 .20 38.02 5.67 .83
10
.19 19.70 6.39 .85
—
11
26.21 5.25 —
12
Notes. 1—HRRlab; 2—HRRat-home planned; 3—HRRat-home spontaneous; 4—CSI; 5—flooding; 6—DERS; 7—reappraisal; 8—suppression; 9—DASS; 10—PANAS positive; 11—PANAS negative; 12—BMI. *p < .05; **p < .01; ***p < .001.
1 2 3 4 5 6 7 8 9 10 11 12 Mean SD Alpha
Variable
Correlations
TABLE 1 Descriptive Statistics for and Correlations Between All Study Variables
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8.17 11.54 33.86 9.65
0.44 11.16 16.43 6.43 9.05 14.64 31.16 8.98
Home—spontaneous 3.71 20.85 23.05 4.23
1 2.45 — — —
2 1.37 — — —
3
1.21 — — —
4
2.2 — — —
5
Other studies of marital conflict
3.6 — — 0.18
6
30.63 — — —
7
19.47 — — —
8
Trier studies
Note. 1—Gottman et al. (1995); 2—Levenson and Gottman (1985); 3—Levenson et al. (1994); 4—Denton, Burleson, Hobbs, Von Stein, and Rodriguez (2001); 5—Nealey-Moore et al. (2007); 6—Smith et al. (2009); 7—Ditzen et al. (2007); 8—Larson et al. (2001).
Mean Minimum Maximum SD
Home—planned
Laboratory
This study
TABLE 2 Heart Rate Reactivity in the This Study, Other Studies of Marital Conflict, and Studies of Other Social Stressors
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home was statistically significantly and positively associated (r = .7, p < .001). However, correlations between HRR during laboratory conflict and spontaneous conflict at home and between planned and spontaneous conflict at home were nonsignificant (ps > .05). To better contextualize mean levels of HRR presented in Tables 1 and 2 presents descriptive statistics from other published studies of laboratory marital conflict that provide descriptive statistics for HRR and of the Trier Social Stress Task (TSST; Kirschbaum et al., 1993), considered to be a gold standard laboratory social stress task that reliably provokes a cardiovascular response (Larson, Ader, & Moynihan, 2001). Descriptive statistics for the TSST are taken from a meta-analysis of the TSST (Larson et al., 2001) and from the only existing study of the TSST that recruited couples and reported HRR (Ditzen et al., 2007). The mean level of HRR during laboratory conflict observed in this study was somewhat lower than, though within 1 SD of, HRR reported in other studies of marital conflict. In addition, the minimum and maximum levels of HRR obtained in this study were consistent with those in the only study that reported these values (Gottman, Jacobson, Rushe, & Shortt, 1995). Mean levels of HRR during planned and spontaneous conflict in this study were also between mean HRR values reported in previous studies of laboratory marital conflict and laboratory studies of the TSST (Ditzen et al., 2007; Gottman et al., 1995; Larson et al., 2001; Levenson, Carstensen, & Gottman, 1994; Levenson & Gottman, 1985; Nealey-Moore, Smith, Uchino, Hawkins, & Olson-Cerny, 2007; Smith et al., 2009). Finally, maximum HRR values during marital conflict in naturalistic settings in this study were consistent with mean HRR values during TSST reported in Ditzen et al. (2007). Taken together, the consistency of HRR values in this study and those reported in previous work on social stress indicates that HRR during laboratory and naturalistic conflicts is consistent with values that would be expected based on similar previous work.
Differences in the Magnitude of HRR Across Settings A 3-level, multilevel model (MLM) was used to test differences in the magnitude of HRR during conversations in the laboratory, planned conversations at home, and spontaneous conflicts outside of the laboratory, where HRR was regressed onto dummy-coded variables indicating planned conversations at home (0 = laboratory conflict, 1 = planned conversations at home) and spontaneous conflicts outside of the laboratory (0 = laboratory conflict, 1 = spontaneous conflict outside of the laboratory), an effect-coded spouse variable (0.5 = husband, 0.5 = wife), and grand-mean centered BMI as a covariate,4 as illustrated by the following series of equations: Level-1: Y ðHRRÞijk ¼ p0jk þ p1jk ðPlannedijk Þ þ p2jk ðSpontaneousijk Þ þ eijk Level-2: p0jk ¼ b00k þ b01k ðSpousejk Þ þ b02k ðBMIjk Þ þ r0jk for i ¼ 1; 2: pijk ¼ bi0k þ bi1k ðSpousejk Þ þ bi2k ðBMIjk Þ Level-3: b00k ¼ c000 þ u00k for i ¼ 0 to 2; j ¼ 0; 1: bijk ¼ cij0 where i indexes conversations, j indexes spouses, and k indexes couples.
4 BMI was included in models to statistically control for variability in baseline heart rate associated with obesity in young adults (e.g., Rossi et al., 2015).
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Consistent with our hypotheses, HRR during planned conflicts and spontaneous conflicts outside of the laboratory were statistically significantly greater than HRR during laboratory conflicts (B = 8.18, p < .001; B = 8.14, p < .001, respectively). HRR was not statistically significantly different during planned conflicts outside of the laboratory and spontaneous conflicts outside of the laboratory (B = .04, p > .50). As such, we refer to conflict in the naturalistic setting as “naturalistic conflict” below.
Differences in the Strength of Association with HRR Across Settings The 3-level MLM used to test differences in the magnitude of HRR was expanded by adding predictors at levels 2 and 3 to test differences in the strength of association with cardiovascular reactivity across settings (represented by the cross-level interactions between predictor and planned/spontaneous dummy-coded variables5) as illustrated by the following series of equations: Level-1: Y ðHRRÞijk ¼ p0jk þ p1jk ðPlannedijk Þ þ p2jk ðSpontaneousijk Þ þ eijk Level-2:p0jk ¼ b00k þ b01k ðSpousejk Þ þ b02k ðBMIjk Þ þ b03k ðPredictorcouple centered; jk Þþ r0jk fori ¼ 1;2:pijk ¼ bi0k þ bi1k ðSpousejk Þþ bi2k ðBMIjk Þþ bi3k ðPredictorcouple centered; jk Þ Level-3: b00k ¼ c000 þ c001 ðPredictorgrand centered; jk Þ þ u00k for i ¼ 1; 2; j ¼ 0: bijk ¼ cij0 þ ci01 ðPredictorgrand centered; jk Þ for i ¼ 0 to 2; j ¼ 1; 2; 3: bijk ¼ cij0 where i indexes conversations, j indexes spouses, and k indexes couples. Separate models were run for each variable resulting in eight total models. Again consistent with our hypotheses, there were statistically significant interactions between measurement context and most predictors in predicting HRR; differences in the strength of associations between HRR during laboratory conflict and HRR during naturalistic conflict emerged for relationship satisfaction, flooding, suppression, difficulties with emotion regulation, positive affect, and negative affect (all ps < .034); trends for differences in the strength of associations emerged for psychological distress (see Table 3). In all cases, associations between HRR and predictor variables were larger in magnitude for HRR during naturalistic conflict than during laboratory conflict. In addition, decomposing these interactions revealed that simple slopes for the effects of naturalistic conflict HRR were statistically significant in eight of nine interactions (and 10 of 13 statistically significant interactions and nonsignificant, trend-level interactions considered jointly), indicating that HRR measured in naturalistic contexts was significantly associated with most expected predictors. None of the associations between any predictor variable and HRR during laboratory conflict (i.e., c001 and c031) were statistically significant, indicating HRR measured in the laboratory context was not significantly associated with expected predictors. 5 The coefficients for these cross-level interactions indicate the magnitude and significance of the fixed effect regression coefficients in the planned/spontaneous at-home relative to the same association in the laboratory conflict. For example, a significant cross-level interaction between a couple centered predictor and the dummy code for planned at home conflict (b13k) would indicate that the association between the predictor and HRR was significantly different for HRR during planned laboratory conflict and planned athome conflict; the sign of this interaction coefficient indicates the relative magnitude of the association across settings. If the interaction term was positive, it would indicate that the association between the predictor and HRR was more positive/less negative for planned conflict at home relative to planned conflict in the laboratory.
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— — — — 33.29 13.41*** 13.17***
1
0.44 — — 8.18*** — — 8.14*** — —
0
30.09 13.45*** 8.85**
— 0.68
— 0.36**
0.44 0.03 0.96 8.15*** 0.2 1.02 8.27*** 0.4*** 1.64*
2
32.48 12.65*** 11.29**
— —
— —
0.44 0.25 0.32 8.39*** 0.37 0.26 7.97*** 0.13 0.24
3
31.18 14.03*** 6.00*
— —
1.20** —
0.44 0.53 0.01 8.20*** 0.67* 0.44 8.37*** 0.31 0.39
4
27.07 14.34*** 13.48***
0.24* —
— 0.41***
0.44 0.02 0.00 8.11*** 0.01 0.23* 7.07*** 0.43*** 0.04
5
36.03 4.08** 14.18***
— 0.21*
— 0.56†
0.44 0.05 0.02 8.42*** 0.1 0.16 9.9*** 0.53† 0.19†
6
28.78 15.86*** 8.86*
— 0.58*
0.71* —
0.44 0.21 0.37 8.62*** 0.49† 0.43 7.37*** 0.41 0.95***
7
Notes. 0—Baseline model; 1—CSI; 2—Flooding; 3—Reappraisal; 4—Suppression; 5—DERS; 6—DASS; 7—PANAS positive; 8—PANAS negative. a Parameter estimates for simple slopes are unstandardized regression coefficients. † p < .10; *p < .05; **p < .01; ***p < .001.
Predictor Intercept PredictorBetween-couple PredictorWithin-couple At-home planned At-home planned 9 PredictorBetween-couple At-home planned 9 PredictorWithin-couple At-home spontaneous At-home spontaneous 9 PredictorBetween-couple At-home spontaneous 9 PredictorWithin-couple Simple slopesa Between-couple At-home planned At-home spontaneous Within-couple At-home planned At-home spontaneous Variance components Level-1 Level-2 Level-3
Variable
29.88 12.83*** 14.55***
— 0.49*
0.34 1.02*
0.44 0.14 0.05 8.28*** 0.47† 0.27 6.63*** 1.14*** 0.54*
8
TABLE 3 Parameter Estimates of Multilevel Models Regressing HRR onto Relationship Functioning, Emotion Regulation, Psychological Well-Being, and Mood Variables
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Taken together, our collection of findings indicates that HRR during marital conflict in daily life is robustly associated in expected directions with a wide range of relationship and individual functioning variables, that these associations are statistically significantly greater than those involving HRR during laboratory conflict. Moreover, none of the associations involving HRR during laboratory conflict are predictive of individual or relationship functioning variables,6 which may indicate that laboratory associations are downwardly biased.
DISCUSSION This study examined differences in the magnitude of HRR during relationship conflict in- and outside of the laboratory and differences in the magnitude of associations of HRR, individual functioning, and relationship functioning variables across laboratory and naturalistic contexts. Consistent with our hypotheses, there were significant interactions between measurement context and individual and relationship functioning on HRR such that HRR during naturalistic conflict was larger in magnitude and more strongly associated with both individual and relationship functioning variables relative to HRR during laboratory conflict. Moreover, when decomposing these interactions, statistically significant associations emerged in expected directions between seven of eight variables and HRR during naturalistic conflict, but no statistically significant associations emerged between any variable and HRR during laboratory conflict. This collection of findings has both conceptual and methodological implications for the study of psychophysiology, behavior, emotion, and psychological distress in romantic relationships. In contrast to meta-analytic findings documenting weak and inconsistent associations between HRR and relationship adjustment, this study found strong and consistent associations between HRR during relationship conflict, relationship functioning variables, and individual functioning variables—but only when HRR was measured in naturalistic settings. These findings for HRR in naturalistic settings are consistent with theoretical models that consider physiological reactivity during conflict to be a primary mechanism by which romantic relationships impact overall well-being (e.g., Robles & Kiecolt-Glaser, 2003) and support the notion that HRR during marital conflict is robustly associated with a wide variety of individual and relationship functioning domains. In addition to supporting the general premise of theoretical models (e.g., Robles & Kiecolt-Glaser, 2003; Robles et al., 2014), the findings in this study suggest that such theoretical models would benefit by incorporating context as a moderator of associations. Statistically significant effects only emerged in HRR data collected during relationship conflict in daily life. This pronounced difference in the strength of association involving a wide range of individual and relationship variables suggests that HRR is not associated with individual and relationship functioning variables in all interaction contexts. Future research on HRR, and autonomic and endocrine reactivity more broadly, during conjoint couple therapy is an area of particular need (e.g., Karvonen, Kykyri, Kaartinen, Penttonen, & Seikkula, 2016). Spontaneous and planned (e.g., re-enactments of recent arguments) couple conflict during couple therapy are both assumed to be representative of couple conflict that occurs outside of the therapy room (e.g., Epstein & Baucom, 2002). This assumed similarity includes form (i.e., how the couple behaves during and experiences conflict) and functional implications (i.e., associations with other aspects of relationship adjustment). 6
Because of the dummy coding scheme used to test differences in associations across interaction contexts, the conditional main effects in the MLMs represent the associations between each predictor and HRR in planned laboratory conflicts.
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Study of autonomic and endocrine reactivity during conjoint couple therapy would be beneficial not only because it would provide a further test of contextually dependent physiological activation during couple conflict but also because it could provide practitioners with information that could be used to guide treatment decisions. For example, if assumptions that conflicts during couple therapy are representative of how the couple normally argues during daily life are correct, it is possible that physiological reactivity during psychotherapy sessions may be associated with domains of emotional functioning similar to those that were associated with at home HRR in this study (e.g., flooding, emotional suppression, difficulties with emotion regulation). High levels of emotional reactivity during couple conflict have been linked to poor treatment outcomes in couple therapy (Baucom, Atkins, Simpson, & Christensen, 2009; Baucom, Weusthoff, Atkins, & Hahlweg, 2012) and an improved understanding of the physiological underpinnings of these associations could help therapists decide when and how to heighten versus dampen emotion during couple therapy sessions (e.g., Baucom & Crenshaw, in press). It is important to note that the individual and relationship functioning variables included in this study were global assessments of what typically occurs in a relationship, enduring psychological states that persisted over at least the previous week, and stable individual differences in affectivity. While these findings demonstrate that HRR during marital conflict in naturalistic settings is more consistently associated with such variables, it is unknown whether these findings will extend to other more proximal measures, such as behavior during (or perceptions of) an instance of marital conflict. Examination of differential patterns of associations involving HRR and proximal measures during marital conflict in laboratory and naturalistic settings should be explored in future research. The observed differences in the statistical significance and magnitude of associations involving HRR during planned naturalistic conflict and those involving laboratory conflict is particularly noteworthy given that couples were videotaped discussing the same two topics for the same amount of time in similar physical settings during both data collection settings. Given these similarities, it is not surprising that HRR values during laboratory and planned naturalistic conflict were strongly correlated, r = .7, but it is surprising that the statistical significance and magnitude of associations with individual and relationship functioning variables were so different across the two contexts. One possible explanation for these differences is that, even though couples were aware they were being videotaped at home, there were fewer cues to remind them that they were participating in a study during their home conversations. It is also possible that being at home contributed to spouses behaving more naturalistically because they were in a more familiar environment. While such explanations are plausible, future research would benefit from including a self-report measure of the representativeness of interactions in both settings to directly test these possibilities. Planned and spontaneous naturalistic conflict produced similar levels of HRR when averaged across participants as a group, but HRR values during the two types of conflicts were only modestly correlated, r = .34, and associations involving individual and relationship functioning variables were inconsistent across the two types of conflict. No hypotheses were made regarding differences in the strength of association across the two types of conflict, and it is not immediately clear why such differences emerged. One possibility is that HRR during spontaneous conflict may be considerably more variable than HRR during planned conflict, and may be differentially associated with variables as a result. For example, the topics discussed during planned conflicts both in and outside of the laboratory are topics that one or both spouses identified as being among the most upsetting in their relationship. In contrast, spontaneous conflicts could have involved topic areas of a wide range of importance to spouses.
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The patterns of associations with HRR across interaction contexts have the potential to create valuable new avenues for relationship scientists to study marital conflict. The field’s original goal of studying behavior and physiological reactivity during marital conflict in laboratory settings was to improve the quality of such data through tight experimental control of the topics being discussed and the environments in which they occurred. The results of this study suggest that studying HRR during marital conflict as it naturally occurs during daily life with few or no constraints placed on topics, timing, or location of discussions offers valuable insights into well-being. This pattern of findings has implications for a wide range of important domains of functioning (e.g., relationship satisfaction, emotion regulation, and mood) that cannot be similarly assessed using laboratory conflict data. As passive data acquisition technologies (i.e., methods for collecting data from participants that involve little or no direct effort from participants themselves; e.g., Atkins & Baucom, 2016; Goodwin, Velicer, & Intille, 2008) continue to develop, relationship scientists will have an increasingly large number of options for collecting high quality data about marital conflict using designs that maximize ecological validity (e.g., Hogan & Baucom, 2016; Walls, H€ oppner, & Goodwin, 2007). There are a number of limitations to keep in mind when considering this study’s results. First, the sample size of couples in this study was small by current standards in relationship science. The repeated measurement of HRR during conflict across contexts reduces this concern because of the increase in precision of HRR estimates generated from repeated measures. However, it is possible that this sample size of couples resulted in Type II error in failing to find relationships between HRR in the laboratory and self-report measures. Second, the timing of spontaneous naturalistic conflicts was based on selfreports. The timing of the daily diaries was designed to assess potential relationship conflicts shortly after participants typically parted in the mornings and again after participants reunited in the evening, thereby minimizing reporting inaccuracies. Imprecision in the reported timing of spontaneous conflicts could result in the inclusion of HR values outside of conflict and such data would likely reduce the magnitude of HRR and their strength of associations with individual and relationship variables. Thus, while imprecision in the timing of reported spontaneous conflicts may have occurred, concerns about its impact on findings are reduced given the statistical significance and consistency of findings across multiple individual and relationship variables. Third, the planned conflict discussions at home always occurred after the planned conflict discussions in the laboratory and it is therefore not possible to disentangle order effects from locations effects in tests of laboratory versus planned at home data. It is possible that HRR was higher during the planned at home discussions simply because it was the second time that partners discussed the same topics within a week’s time. Significantly greater HRR during spontaneous conflicts relative to laboratory conflicts and nonsignificant differences in HRR between planned at home and spontaneous conflicts mitigates this concern. Replication of these findings with data from a fully factorial, order (1st vs. 2nd) by location (laboratory vs. at home) design is an important direction for future research. Fourth, equipment malfunction resulted in the unavailability of HRR data during planned conflicts for two couples. These malfunctions appeared to be random, but it is possible that these missing data biased results. Finally, participants were largely Caucasian and did not have children. In addition, the average age of men (M = 29.3) in the sample was below that for first time fathers in the United States (M = 30.9; Khandwala, Zhang, Lu, & Eisenberg, 2017), but the average age of women in the sample (M = 28.1) was above that for first time mothers in the United States (M = 26.4; CIA, 2017). These demographic characteristics of the sample may limit its generalizability and highlight the importance of replicating study findings in a larger and more diverse sample of couples.
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Despite these limitations, the results of this study suggest that assessing HRR—and potentially physiological reactivity more broadly— during relationship conflict outside of the laboratory may improve relationship scientists’ ability to understand various ways that physiological reactivity during conflict can differentially associate with individual and relational functioning. Assessing physiological reactivity outside of the laboratory can involve loss of experimental control, decrease measurement precision, and add noise to data. However, these drawbacks appear to be outweighed by the unique associations with multiple variables provided by data acquired outside of laboratory settings. Studying associations involving physiological reactivity across multiple contexts appears to be a fruitful direction for future research, and relationship scientists interested in physiological mechanisms are encouraged to consider incorporating ambulatory methods to complement laboratory measurement. REFERENCES Antony, M. M., Bieling, P. J., Cox, B. J., Enns, M. W., & Swinson, R. P. (1998). Psychometric properties of the 42item and 21-item versions of the Depression Anxiety Stress Scales in clinical groups and a community sample. Psychological Assessment, 10, 176–181. https://doi.org/10.1037/1040-3590.10.2.176. Atkins, D. A., & Baucom, B. R. (2016). Emerging methodological and statistical techniques in couple research. In E. Lawrence & K. Sullivan (Eds.), Oxford handbook of relationship science and couple interventions (pp. 148– 163). New York: Oxford University Press. Baucom, B. R., Atkins, D., Simpson, L., & Christensen, A. (2009). Prediction of response to treatment in a randomized clinical trial of couple therapy: A 2-year follow-up. Journal of Consulting and Clinical Psychology, 77, 160–173. https://doi.org/10.1037/a0014405. Baucom, B. R. W., & Crenshaw, A. O. (in press). Evaluating the efficacy of couple and family therapy. In B. Fiese, M. Whisman, M. Celano, K. Deater-Deckard, & E. Jouriles (Eds.), APA handbook of contemporary family psychology. Washington, DC: American Psychological Association. Baucom, B. R., Weusthoff, S., Atkins, D., & Hahlweg, K. (2012). Greater emotional arousal predicts poorer longterm memory of communication skills in couples. Behaviour Research and Therapy, 50, 442–447. https://doi. org/10.1016/j.brat.2012.03.010. Brage, S., Brage, N., Franks, P., Ekelund, U., & Wareham, N. (2005). Reliability and validity of the combined heart rate and movement sensor Actiheart. European Journal of Clinical Nutrition, 59, 561–570. https://doi. org/10.1038/sj.ejcn.1602118. Butler, E. A., Egloff, B., Wilhelm, F. H., Smith, N. C., Erickson, E. A., & Gross, J. J. (2003). The social consequences of expressive suppression. Emotion, 3, 48–67. https://doi.org/10.1037/1528-3542.3.1.48. Butler, E. A., Gross, J. J., & Barnard, K. (2014). Testing the effects of suppression and reappraisal on emotional concordance using a multivariate multilevel model. Biological Psychology, 98, 6–18. https://doi.org/10.1016/j. biopsycho.2013.09.003. CamNTech Inc. (2014). Actiheart [equipment]. Boerne, TX: CamNTech Inc. Central Intelligence Agency (2017). The World Factbook. Retrieved from https://www.cia.gov/library/publi cations/the-world-factbook/fields/2256.html Cohen, S., Hamrick, N. M., Rodriguez, M. S., Feldman, P. J., Rabin, B. S., & Manuck, S. B. (2000). The stability of and intercorrelations among cardiovascular, immune, endocrine, and psychological reactivity. Annals of Behavioral Medicine, 22, 171–179. https://doi.org/10.1007/BF02895111 Denton, W. H., Burleson, B. R., Hobbs, B. V., Von Stein, M., & Rodriguez, C. P. (2001). Cardiovascular reactivity and initiate/avoid patterns of marital communication: A test of Gottman’s psychophysiologic model of marital interaction. Journal of Behavioral Medicine, 24, 401–421. https://doi.org/10.1023/a:1012278209577. Ditzen, B., Neumann, I. D., Bodenmann, G., von Dawans, B., Turner, R. A., Ehlert, U. et al. (2007). Effects of different kinds of couple interaction on cortisol and heart rate responses to stress in women. Psychoneuroendocrinology, 32, 565–574. https://doi.org/10.1016/j.psyneuen.2007.03.011. Epstein, N. B., & Baucom, D. H. (2002). Enhanced cognitive-behavioral therapy for couples: A contextual approach. Washington, DC: American Psychological Association. https://doi.org/10.1037/10481-000 Funk, J. L., & Rogge, R. D. (2007). Testing the ruler with item response theory: Increasing precision of measurement for relationship satisfaction with the Couples Satisfaction Index. Journal of Family Psychology, 21, 572– 583. https://doi.org/10.1037/0893-3200.21.4.572. Gerin, W., Christenfeld, N., Pieper, C., Derafael, D. A., Su, O., Stroessner, S. J. et al. (1998). The generalizability of cardiovascular responses across settings. Journal of Psychosomatic Research, 44, 209–218. https://doi.org/ 10.1016/S0022-3999(97)00207-9
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