Couples in Love

Abstract

Counselors may be unaware of the physiological underpinnings of couple relationships. Understanding emotions as physiological responses resulting from autonomic arousal, we measured couples’ heart rate across a series of typical conversations. Forty-nine heterosexual and one lesbian couple completed measures of emotional reactivity and dyadic adjustment. We used pulse oximetry to record individual heart rate through three 5-min conversations. Using multilevel dyadic growth models, we found emotional arousal and reactivity-predicted heart rate among women, and greater relationship length predicted heart rate among men. We additionally found couples synchronous with respect to relationship satisfaction and emotional reactivity but not to physiological responsivity. This study contributes to counselors’ understanding of women’s physiological reactivity, male responses in longer relationships, and how to support couples when there is potential for relationship conflict.

Keywords

couples relationship satisfaction heart rate emotions emotion regulation

James’ (1884) seminal work acknowledged the dual relationship between physiological arousal and emotions. Although James’ theory suggesting physiology precipitates and facilitates emotional experience has been called into question, his theory substantially changed how scholars understand emotions. Since James’ work, numerous scholars have investigated the relationship between physiological responses and emotions leading to current methodologies of measuring emotional responses via autonomic arousal, muscular activity, as well as neural responses (Barrett, 2016). Aside from individual subjective experiences of emotions, research recognizes the interpersonal role in emotions. From Bowlby’s (1969/1973) Attachment Theory stating that caregivers and their infants coregulate each other’s emotions to examining the emotional contagion between individuals (Buchanan, Bagley, Stansfield, & Preston, 2012), the understanding of emotions has evolved to include a social dimension.

Romantic partners may serve as emotion regulators (Butler, 2011; Butler & Randall, 2013; Laurent & Powers, 2007). Romantic partners affect emotions through bidirectional physiological transmission between partners (Butler, 2011; Butler & Randall, 2013; Helm, Sbarra, & Ferrer, 2012). A process that begins with the caregiver/infant attachment, caregivers provide emotional and physical regulation to help the child learn to self-regulate. Romantic partners later assume this role. Corresponding physiological states between romantic partners may demonstrate attunement with a romantic partner; thus, explaining a greater ability to regulate a partner’s emotions (Butler & Randall, 2013). Emotional responses during interactions reflect couple dynamics. Examining relationship satisfaction through physiological arousal may be an objective measure to understand couple dynamics (Levenson & Gottman, 1983, 1985). As such, the current study sought to examine the interactions among the romantic relationship, emotions, and heart rate as couples engage in a traditional couple interaction.

Couples’ Physiological Synchrony

Couple synchrony implies that both partners match their physiological and/or emotional states. Romantic relationships are organized dynamic systems that balance change and stability. As patterns of synchronization increase in frequency, the behaviors displayed between romantic partners become more automatic (Butler, 2011). Feedback loops in relationships either maintain stability or adapt to change. Synchrony may serve as a feedback loop that aids in development and emotion regulation. Different types of relational physiological synchrony, morphostatic and morphogenic, have been theorized (Butler, 2011). Morphostatic synchrony suggests partners share physiological states. Morphostatic synchrony is automatic and the presence of a romantic partner is sufficient to coordinate homeostatic physiology, whereas morphogenic synchrony is a jointly altered state that transfers between partners. Morphogenesis appears to likely transmit in distressed couples. When one partner becomes upset, the other partner matches the partner demonstrating covariance. Emotions and synchrony appear to be highly interdependent, and when couples adopt each other’s emotional state, it may promote relationship longevity (Helm et al., 2012).

Morphostatic synchrony denotes that romantic partners can serve as regulators of biological rhythms with direct, regular contact (Helm et al., 2012). Helm and colleagues (2012) sought to assess whether and how couples can synchronize physiological states from a morphostatic paradigm through measuring heart rate and respiratory output across specific tasks. Heart rate synchronized during the baseline activity. The authors noted the presence of a romantic partner was enough to elicit similar heart rates. In the first task, couples were directed to gaze into each other’s eyes and in the second task couples were instructed to match physiological state (e.g., heart rate or respiration). In support of synchrony, respiratory output matched on both tasks. During the gazing task, when females’ heart rate decreased, males’ heart rate was more likely to decrease. The males’ heart rate, however, did not correspond to their partners’ heart rate.

Physiological Responses and Emotional Reactivity

Emotions predict whether matched physiological responses are beneficial to couples’ present and long-term satisfaction (Levenson & Gottman, 1985). Effects of reciprocating emotions on relationship satisfaction differ by gender. When husbands do not reciprocate wives’ negative affect or wives reciprocate their husbands’ negative affect, the couple is more likely to be less satisfied in their relationship (Levenson & Gottman, 1985). Lack of reciprocation in positive affect by husbands also predicts reduced satisfaction (Levenson & Gottman, 1985). It cannot be ruled out that lack of reciprocity is related to emotional suppression which may increase cardiovascular responses (Ben-Naim, Hirschberger, Ein-Dor, & Mikulincer, 2013). Emotional suppression increases negative affect in the individual withholding emotions as well as decrease positive affect in the recipient (Ben-Naim et al., 2013).

Changes in physiological state may produce emotional changes. When physiological arousal is attenuated, negative affect may decrease and positive affect may increase (Ben-Naim et al., 2013). For example, when women recall positive aspects of their relationship, both partner’s physiological arousal are inhibited. However, when couples are stressed, those whose physiology synchronizes are less happy in their relationship (Ben-Naim et al., 2013; Levenson & Gottman, 1983). Emotions occurring during synchrony predict couple effects.

Synchrony is thought to serve as a method for understanding others. An individual’s emotional dysregulation may indicate a higher need for interpersonal synchrony and connection (Crowell et al., 2014). When couples discuss emotionally negative topics, couples may be more likely to engage in unhealthy communication patterns. During emotionally negative discussions, couples increase blame, hostility, and demand/withdrawal patterns; thus, negative affect leads to detrimental behaviors (Tashiro & Frazier, 2007). If couples match negative affect during conflict, they experience reduced relationship satisfaction (Levenson & Gottman, 1985). However, couples who dissociate from their partners’ negativity during couple conflict tend to be happier in their relationships (Tashiro & Frazier, 2007). Although transference of a positive mood may not have an opposite effect (Nealy-Moore, Smith, Uchino, Hawkins, & Olson-Cerny, 2007; Tashiro & Frazier, 2007), synchrony in distressed relationships may provide a way to regulate and de-escalate the distressed person (Crowell et al., 2014). When another matches physiological or emotional state, the dysregulated individual may feel more interpersonally connected.

Relationship Satisfaction and Physiological Responses

The relationship between couple synchrony and relationship satisfaction is contradictory. Couples in distressed relationships are more likely to match physiological arousal; however, couples with matched physiological arousal are more likely to have higher marital satisfaction later in the relationship (Levenson & Gottman, 1983). This implies synchrony in a time of stress may be necessary for partners to understand and resolve stress. Ben-Naim et al. (2013) found that married couples whose physiological changes fluctuate in unison across positive and negative emotional states have greater relationship satisfaction suggesting that synchrony can serve as a restorative technique when the parasympathetic system is activated to relax and lower heart rate. Thus, allowing both partners to feel calm.

Along these lines, couples with congruent heart rates and skin conductance responses are happier in their relationship than couples lacking coherence (Coutinho, Silva, & Decety, 2014). Findings by Coutinho and colleagues indicate when couples are in conflict, synchrony may increase relationship satisfaction. Synchrony appears to measure the couples’ ability to empathize, critical for romantic relationships.

The Current Study

The relationship between physiological responses, emotions, relationship satisfaction, and couple interactions needs clarification. Synchrony appears to be beneficial to resolve stress, induces emotional regulation, and predicts longevity of relationship satisfaction. Yet, synchrony occurs in distressed couples, under stressful circumstances, and increases physiological arousal. Butler and Randall (2013) posited the best method for understanding coregulation between partners comes from examining a state of morphostatic not morphogenic processes, thus, giving credence to investigating a homeostatic or nonstressed state. Little research has examined couples in nondistressed states to test the theory of morphostatic synchrony to support the existence of coregulation between romantic partners (Butler & Randall, 2013) with one exception, Helm et al. (2012). More information is needed to assess whether emotions and relationship satisfaction predicts physiological morphostatic couple synchrony during routine couple interactions. As synchrony becomes more concrete over time (Butler, 2011), exploring the influence of length of the couple relationship is needed. Additionally, as synchrony may be a form of emotion regulation, it is necessary to measure whether couples synchronize emotion reactivity.

We sought to answer the following questions in a combined community and student sample of couples: (1) Do couples demonstrate heart rate synchrony during a typical couple interaction? (2) Are emotional reactivity, relationship satisfaction, and relationship length related to heart rate for male and female partners? and (3) Do couples demonstrate synchrony in self-report measures of emotional reactivity and relationship satisfaction? Based on the preceding questions, we predicted: (1) Couples’ heart rate would be synchronized during a typical couple interaction; (2) emotional reactivity, relationship length, and relationship satisfaction will be related to overall heart rate for male and female members of a couple; and (3) male and female partner self-report measures of relationship satisfaction and emotional reactivity will be related.

Method

Participants

A total of 50 couples participated in this study: 49 in opposite sex relationships and 1 lesbian couple. Participants ranged in age from 19 to 67 (M = 24.25, SD = 8.0). Income ranged from $0 to $400,000 per year (Mean = 55,794.27, Mode = 0, SD = 76,785.25, with 7 individuals not responding). The majority were heterosexual (n = 94), two individuals identified as lesbian, three individuals identified as bisexual, and one individual identified as queer. The sample was well educated with 64 holding a bachelor’s degree or beyond, 29 individuals had some college or an associate’s degree, three individuals held a high school diploma, and the remainder did not respond. The majority of the sample was White (n = 69), followed by Black/African American (n = 8), Latino (n = 8), and 10 individuals identified as multiracial or another race. Length of the relationship ranged from 4 months to over 10 years (124 months; M = 27.63 months, SD = 29.90).

Procedure

Approval for this study was obtained from the affiliated institutional review board. Couples were invited to participate in the research study using advertisements placed on the university newsletter, flyers placed across campus, word-of-mouth, and online advertisements. Participants contacted the principal investigator (PI) wherein they were prescreened by either the PI or a research team member to determine eligibility. Inclusion criteria: age of 18 or older, ability to speak and read English at a sixth-grade minimum level, exclusive romantic partnering for at least 3 months, and agreement of both members of the couple to participate in the study. Upon arrival at the laboratory, couples were provided the informed consent. Next, participants completed the questionnaires and once completed, couples were brought into a new room and seated opposite each other. Then, each individual was fitted with a pulse oximeter (Choice Medical MD300KI Handheld Pulse Oximeter, Hamburg, Germany). An alcohol swab was used to sterilize the sensor clip prior to application. Participants wore the clip for the duration of the session. Baseline heart rates were taken for approximately 2 minutes prior to the couples engaging in their conversation.

Couples were asked to engage in three conversations on specified topics (i.e., your day thus far, a frequent area of disagreement, and your weekend plans) lasting 5 minutes each. The purpose of the interaction was to create a typical couple conversation. Demonstrating lack of distress by the conversation, couples selected benign topics for the area of disagreement and heart rate did not vary across the three topics (see Figure 1). Therefore, heart rate across the conversation appears consistent with homeostatic heart rate. A member of the research team verbally conveyed instructions to the couple, left the room for the duration of each conversation, and returned to give new instructions for each session component. In the two intervals between conversations, individuals completed a neutral word search. Had the couples become highly emotional or physiologically aroused during any of the topics, the word search was included to help couples return to a resting heart rate. After the end of the final conversation, an investigator entered the room and removed the pulse oximeter clips simultaneously from the couples’ fingers. All participants’ conversations were video recorded.

Figure 1.
Spaghetti plot of average (thick) and subject-specific (thin) regression lines for female and male partners in 42 dyads.

Inventories

Emotion reactivity

Emotion reactivity was measured using the Emotion Reactivity Scale (ERS; Nock, Wedig, Holmberg, & Hooley, 2008). This self-report measure contains 21 items, rated on a 5-point Likert-type scale, that contribute to a total score as well as three subscales: “sensitivity,” “arousal/intensity,” and “persistence.” Internal consistency statistics ranged from excellent (total scale α = .94) to good (subscales α > .81). Internal consistency with this sample equaled .71 for the persistence subscale, .82 for the arousal/intensity subscale, and .82 for the sensitivity subscale. The original investigation of the scale found evidence for content and criterion validity (Nock et al., 2008).

Relationship satisfaction

Relationship satisfaction was measured using the 7-item short form of the Dyadic Adjustment Scale (DAS-7; Hunsley, Best, Lefebvre, & Vito, 2001). The DAS-7 is an abbreviated version of a widely used measure (Spanier, 1976) with acceptable reliability established by a meta-analysis covering 30 years of literature (Graham, Liu, & Jeziorski, 2006). Items were selected based on research (Sharpley & Rogers, 1984) that suggested a subset of 7 items best discriminated between well- and poorly adjusted marriages. The DAS-7 consists of 1-item rating overall marital satisfaction, along with 3 items each from the “consensus” and “cohesion” subscales of the original form. Internal consistency for the DAS-7 has been shown to be acceptable in a nonclinical sample (α = .79), indicating that the abbreviation of the scale does not affect its reliability. Criterion validity was similarly unaffected. Internal consistency with this sample equaled .77.

Pulse Oximetry

Pulse oximetry is a noninvasive method of measuring heart rate (Schermer et al., 2009). Pulse oximeters have been used to measure pulse rate as an indicator of individuals’ response to emotional stress or level of autonomic arousal (Elofsson, von Scheele, Theorell, & Sondergaard, 2008). Because this study required couples to enter into spontaneous verbal and nonverbal interaction, and hand gesticulation may compromise the accurate measurement of pulse rate by oximeters (Gehring, Hornberger, Matz, Konecny, & Schmucker, 2002), each participant was fitted with the oximeter on the nondominant hand and asked to keep only that hand still.

Data Recording and Processing

Data were downloaded onto a Windows PC from the oximeters using the MedView software package, version 2.2.5 Beta. Each individual yielded a data file; files from both members of the couple were combined into a single data set. Artifacts in recording due to participant movement were identified and removed.

Analysis Plan

All analyses were conducted using SAS v9.4. Paired-samples t-tests for continuous variables and McNemar’s tests for dichotomous variables examined male–female partner differences in age, overall average level of heart rate, ERS subscales, and relationship satisfaction. Pearson’s correlation coefficients also assessed whether male–female partner emotional reactivity and satisfaction were related. Spaghetti plots were assessed separately for male and female partners (Figure 1) to examine the variability across male and female partners in initial heart rate (intercept) and change over time (slope). Thin gray lines show individual change and thick black lines show average change.

The mixed procedure (PROC MIXED) in SAS v9.4 estimated multilevel dyadic growth models to examine heart rate across time for male and female partners (Bolger & Shrout, 2007; Laurenceau & Bolger, 2012; Raudenbush & Bryk, 2002; Raudenbush, Brennan, & Barnett, 1995). Dyadic growth models treat three levels of data (e.g., time within individuals within couples) as two levels of random variation by simultaneously estimating two individual growth curves for each partner, controlling for dependency between partners’ heart rates. Level 1 represents variability due to within-person repeated measures for male and female partners, captured as distinct residual error variances for males and females. Also, temporal dependencies in residuals are modeled as residual autocorrelation of lag 1 across time epochs within males and females. Level 2 represents between couple variation across male partners and across female partners. Each heart rate data point is representative of the average of a 28-second time period. Time was modeled as a continuous variable from 0 to 68, representing each 28-second time interval since baseline. Random effects of heart rate intercepts and time slopes were included in the growth models. Correlations were calculated for the association of male–female random intercepts and male–female random time slopes and tested using Student’s t-test. To address whether there was a relationship of heart rate with relationship satisfaction, emotional reactivity, and length of relationship, separate, bivariate regression analyses with means-as-outcomes random intercept effect models were assessed. These models were identical to dyadic growth models but did not include time fixed or random effects. These models assessed whether males and females with higher emotional reactivity or relationship satisfaction and length also had higher heart rates.

Results

The sample included 50 couples: 49 heterosexual and 1 same-sex couple. Since analyses required distinguishable partners, only heterosexual couples were examined (n = 49). There were a total possible 69 heart rates (69 time epochs where heart rate averaged over 28-second time interval) available for analysis. Individuals had to have at least one valid heart rate reading to be included in analyses. There were 14 individuals (7 couples) excluded due to lack of heart rate data, leaving 42 heterosexual couples (84 individuals) for analysis. The analysis data set consisted of 42 couples × 2 persons × 69 possible heart rates = 5,796 possible observations. Not all couples/individuals had all 69 total heart rates so there was a total of 4,406 heart rates available for analysis. Using an individual’s heart rates, any outliers (greater than +/− 2.5 z-scores from an individual’s own mean) were also removed leaving 4,261 remaining heart rates for analysis (heart rate range = 33–121).

Table 1 shows dyadic level associations between age, overall average heart rate, ERS subscales, relationship length, and relationship satisfaction. Results showed that at the level of the dyad, males tended to be older (25.28 male vs. 24.21 female). There were no differences between male–female relationship satisfaction and overall average heart rate. However, it was found that females were higher than their male partners on all ERS subscales (sensitivity, arousal, and persistence). A Pearson’s correlation assessed the relationship of ERS subscales and relationship satisfaction within male–female dyads. Results showed positive relationships between male–female ERS sensitivity (r = 0.43, p < .01), ERS arousal (r = 0.33, p < .05), and relationship satisfaction (r = 0.50, p < .0001).

Table 1.
Male and Female Partner Differences in Age, Heart Rate, ERS, and Relationship Satisfaction (n = 42, Male–Female Couples).

Variables, Mean (SD) Overall Female Male p Value^a Pearson’s r ^b

Age, mean (SD) 24.75 (8.54) 24.21 (7.90) 25.28 (9.20) .015 -

Relationship length (months) 28.23 (31.12) - - - -

Overall average heart rate 79.37 (9.26) 78.18 (9.57) 80.57 (8.88) .194 0.19

ERS sensitivity 9.86 (6.26) 11.28 (5.97) 8.43 (6.30) .007 0.43**

ERS arousal 9.08 (5.28) 10.48 (4.79) 7.69 (5.44) .004 0.33*

ERS persistence 4.95 (3.35) 6.24 (3.40) 3.69 (2.81) <.001 0.18

Relationship satisfaction 5.04 (1.15) 5.00 (1.36) 5.07 (0.91) .797 0.50***

Note. ERS = Emotion Reactivity Scale.

^a p Value is for paired-samples t-test or McNemar’s test comparing males and females.

^bMale–female correlation.

p < .05. p < .01. p < .001.

Table 2 shows separate bivariate dyadic means-as-outcomes models for the relationship of ERS subscales and relationship satisfaction with heart rate. For females, on average, a unit increase in ERS sensitivity and arousal are associated with a 0.53 (SE = 0.24) and 0.74 (SE = 0.29) unit higher heart rate, respectively. ERS persistence and relationship satisfaction were unrelated to heart rate for females. For males, there were no associations with ERS subscales and relationship satisfaction with heart rate. However, for males, relationship length was positively associated with overall heart rate.

Table 2.
Bivariate Parameter Estimates for Relationship of Male and Female Partner Characteristics With Heart Rate, Separate Dyadic Means-As-Outcomes Random Intercept Mixed Models (n = 42, Male–Female Couples With 4,261 Heart Rates).

Female Male

Variable Intercept (SE)^a B (SE) Intercept (SE)^a B (SE)

Relationship length (months) 77.16 (2.02) 0.031 (0.048) 77.51 (1.83) 0.10 (0.044)*

ERS sensitivity 72.01 (3.03) 0.53 (0.24)* 77.61 (2.35) 0.31 (0.22)

ERS arousal 70.33 (3.35) 0.74 (0.29)* 81.04 (2.49) −0.10 (0.26)

ERS persistence 75.76 (3.17) 0.35 (0.44) 81.10 (2.35) −0.23 (0.51)

Relationship satisfaction 75.84 (5.64) 0.44 (1.09) 87.26 (8.15) −1.33 (1.58)

Note. ERS = Emotion Reactivity Scale.

^aAll intercepts are significantly different from 0 (all ps < .0001).

*p < .05.

Table 3 shows results for simultaneous dyadic growth models for males and females. Fixed effects show that average heart rate during the initial time period for males and females was similar (80.46 vs. 79.07, p = .487). For females, heart rate significantly dropped .04 units per 28 second interval. However, for males, there was no significant change in heart rate over time. Although females showed a time trend for heart rate while males did not, there was no difference in average slope for males and females.

Table 3.
Parameter Estimates for Dyadic Growth Models Predicting Heart Rate Over Time (n = 42, Male–Female Couples With 4,261 Heart Rates).

Fixed Effects (Intercepts, Slopes) Estimate (B) 95% CI p Value

Intercepts

Female 79.07 [75.78, 82.36] <.0001

Male 80.46 [77.46, 83.45] <.0001

Female vs. male intercept (contrast) −1.39 [−5.38, 2.61] .487

Time slope

Female −0.039 [−0.067, −0.011] .008

Male −0.0066 [−0.035, 0.022] .643

Female vs. male slope (contrast) −0.032 [−0.070, 0.0062[ .098

Random Effects ([co]variances) Estimate (B) 95% CI p Value

Level 2 (between couples)

Female intercept 107.33 [71.47, 178.06] <.0001

Male intercept 88.23 [58.80, 147.03] <.0001

Female time slope 0.0039 [0.0020, 0.012] .011

Male time slope 0.0043 [0.0021, 0.013] .013

Female–male intercept covariance 19.46 [−12.10, 51.03] .227

Correlation of male–female intercept 0.20 [−0.10, 0.50] .194

Female–male time slope covariance 0.00069 [−0.0018, 0.0032] .586

Correlation of male–female slope 0.17 [−0.13, 0.47] .282

Level 1 (within couples)

Female residual 25.93 [24.28, 27.75] <.0001

Male residual 25.76 [24.09, 27.62] <.0001

Female–male residual covariance 0.59 [−0.56, 1.74] .317

First-order autocorrelation 0.35 [0.32, 0.39] <.0001

Discussion

The present study investigated couple morphostatic synchrony, relationship dynamics, and emotion reactivity. Results did not support physiological morphostatic synchrony; however, results did support similar relationship satisfaction and emotional reactivity responses between partners. Relationship satisfaction did not contribute to changes in heart rate. For women partners only, emotional arousal and emotional sensitivity reactivity were positively associated with heart rate. Women also demonstrated a trend of decreasing heart rate across the intervention. For men, length of the couple relationship was positively associated with heart rate.

Our findings that emotional arousal and sensitivity reactivity predicted female’s heart rate is well documented in the literature (Ben-Naim et al., 2013; Levenson & Gottman, 1983, 1985). Heart rate is particularly sensitive to changes in emotional state, so perhaps it is more surprising that men’s emotional reactivity was not associated with heart rate. Women’s heart rate decreased across the conversations suggesting higher heart rates earlier in the conversation were indicative of novelty of the experience. This may directly reflect our findings related to emotional sensitivity and arousal predicting heart rate. As women became habituated to the heart rate monitor, their emotional sensitivity and arousal decreased resulting in lower heart rate. Our findings related to men having higher heart rates indicate men may become more physiologically aroused with their partner the longer the couple relationship. Perhaps, this reflects that men respond to their partners based on previous experiences. The expectation of relationship patterns may lead men to become more emotionally flooded in response to anticipated partner behaviors (Butler, 2011). Fundamental to social interactions is the ability to anticipate another’s behaviors. If individuals can anticipate stranger’s behaviors then the potential for a programmed response in couple relationships should be stronger.

We found that couples matched self-report measures of emotional reactivity for sensitivity and arousal. A possible explanation for similar self-report measures of emotional reactivity is the principal that individuals are attracted to others who are like themselves (Byrne & Nelson, 1965). Couples also displayed similar self-reported relationship satisfaction demonstrating couples have similar relationship perceptions. The couple relationship is a systemic unit, thus it is important to gain an understanding of the relationship by asking both partners about their experiences. However, because research examining relationships may only utilize one partner, this study contributes to the literature by showing that responses from one member of the romantic partnership may be sufficient for determining relationship quality.

Limitations

Our surprising lack of support for a morphostatic physiological synchrony between couples could reflect methodological limitations in the study. First, we do not know how many couples lived together and as some couples had only been dating for as little as 4 months, we suspect many couples were not living together. As research suggested that physiological synchrony may be dependent on time spent together (Papp, Pendry, Simon, & Adams, 2013; Saxbe & Repetti, 2010), this may not have been the best sample to measure physiological synchrony. Additionally, despite our goal to measure naturalistic conversations, it cannot be ruled out that the lack of support for synchrony is attributed to executing the study in the laboratory setting. The couples may have been preoccupied by the novelty of the environment and this precipitated the automatic physiological responses experienced in the presence of their romantic partner. Finally, as most of the previous research demonstrated synchrony present during arguments, our method of using more benign conversations may have merely captured cardiovascular health of the participants.

Future Research

Future research should recreate this study with a more diverse sample to examine applicability of the findings. Future research may want to include additional physiological measures to more accurately capture autonomic responses. Heart rate may not be as sensitive of a measure of arousal as other measures (e.g., skin conductance). It may also be beneficial for the couples to engage in longer conversations beyond 5 minutes per topic to explore each topic in depth to allow physiological changes to emerge. Topics may want to be more emotional in nature to assess how processing more intense emotions may alter changes in heart rate across couples. Finally, our heart rate baselines at the beginning of the intervention tended to be slightly higher than most of the heart rates across the conversation. Future research may want to include a longer initial baseline to allow the participants to become habituated to the heart rate monitors.

Therapeutic Implications

The findings help to inform clinical practice with couples across presenting problems. Clinicians need to recognize the patterns between emotional and physiological reactivity for women. We found that women who experience more emotional reactivity maintained a higher heart rate as their homeostatic state. This finding may suggest a different emotional responsive threshold for women with naturally higher heart rates, perhaps leading to emotional flooding earlier in an interaction. Counselors may assume that clients will display visual indicators of their emotional state; however, how emotions may affect women may not be evident. As partners within couples correlated on emotional reactivity, this has implications for both members. Assessing heart rate and emotion regulation strategies and abilities early in counseling may be necessary to determine how counselors need to approach the therapeutic process. For example, if individuals are predisposed to emotional flooding, easily investigating the potential for flooding without a traditional buildup will be important to determine before asking couples to interact.

There are a variety of treatment modalities and techniques that assist a couples’ ability to communicate and understand their emotions. Many physiological changes can be minute and unnoticed by partners; however, when changes reach a threshold, they become overwhelming. Our findings imply established patterns may be physiologically arousing for men. This is an opportunity for the clinician to help partners identify and label their emotional responses. For examples, emotion-focused therapy (EFT) encourages emotional awareness and intelligence among partners (Johnson, 2004). EFT works from an attachment model of emotional regulation and using partners to self-regulate and soothe each other to decrease stress responses and succeed as a couple. The clinician can use techniques to encourage emotional regulation, physiological calmness, and attempt to pace the interventions comfortably for both partners.

Variables, Mean (SD)	Overall	Female	Male	p Value^a	Pearson’s r ^b
Age, mean (SD)	24.75 (8.54)	24.21 (7.90)	25.28 (9.20)	.015	-
Relationship length (months)	28.23 (31.12)	-	-	-	-
Overall average heart rate	79.37 (9.26)	78.18 (9.57)	80.57 (8.88)	.194	0.19
ERS sensitivity	9.86 (6.26)	11.28 (5.97)	8.43 (6.30)	.007	0.43**
ERS arousal	9.08 (5.28)	10.48 (4.79)	7.69 (5.44)	.004	0.33*
ERS persistence	4.95 (3.35)	6.24 (3.40)	3.69 (2.81)	<.001	0.18
Relationship satisfaction	5.04 (1.15)	5.00 (1.36)	5.07 (0.91)	.797	0.50***

	Female	Male
Relationship length (months)	77.16 (2.02)	0.031 (0.048)	77.51 (1.83)	0.10 (0.044)*
ERS sensitivity	72.01 (3.03)	0.53 (0.24)*	77.61 (2.35)	0.31 (0.22)
ERS arousal	70.33 (3.35)	0.74 (0.29)*	81.04 (2.49)	−0.10 (0.26)
ERS persistence	75.76 (3.17)	0.35 (0.44)	81.10 (2.35)	−0.23 (0.51)
Relationship satisfaction	75.84 (5.64)	0.44 (1.09)	87.26 (8.15)	−1.33 (1.58)

Fixed Effects (Intercepts, Slopes)	Estimate (B)	95% CI	p Value
Intercepts
Female	79.07	[75.78, 82.36]	<.0001
Male	80.46	[77.46, 83.45]	<.0001
Female vs. male intercept (contrast)	−1.39	[−5.38, 2.61]	.487
Time slope
Female	−0.039	[−0.067, −0.011]	.008
Male	−0.0066	[−0.035, 0.022]	.643
Female vs. male slope (contrast)	−0.032	[−0.070, 0.0062[	.098
Random Effects ([co]variances)	Estimate (B)	95% CI	p Value
Level 2 (between couples)
Female intercept	107.33	[71.47, 178.06]	<.0001
Male intercept	88.23	[58.80, 147.03]	<.0001
Female time slope	0.0039	[0.0020, 0.012]	.011
Male time slope	0.0043	[0.0021, 0.013]	.013
Female–male intercept covariance	19.46	[−12.10, 51.03]	.227
Correlation of male–female intercept	0.20	[−0.10, 0.50]	.194
Female–male time slope covariance	0.00069	[−0.0018, 0.0032]	.586
Correlation of male–female slope	0.17	[−0.13, 0.47]	.282
Level 1 (within couples)
Female residual	25.93	[24.28, 27.75]	<.0001
Male residual	25.76	[24.09, 27.62]	<.0001
Female–male residual covariance	0.59	[−0.56, 1.74]	.317
First-order autocorrelation	0.35	[0.32, 0.39]	<.0001

Footnotes

Authors’ Note

This research has been presented at the Missouri Association for Marriage and Family Therapists annual conference in Saint Louis Missouri in April 2016.

Acknowledgments

The authors thank Dr. Tony Buchanan for his assistance with this project.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by faculty start-up funding received by Dr. Dixie Meyer upon joining Saint Louis University.

ORCID iD

Dixie Meyer

References

Barrett

L. F.

(2016). Navigating the science of emotion. In Meiselman

H. L.

(Ed.), Emotion measurement (pp. 31–63). Boston, MA: Elsevier.

Ben-Naim

Hirschberger

Ein-Dor

Mikulincer

(2013). An experimental study of emotion regulation during relationship conflict interactions: The moderating role of attachment orientations. Emotion, 13, 506–519. doi:10.1037/a0031473

Bolger

Shrout

P. E.

(2007). Accounting for statistical dependency in longitudinal data on dyads. In Little

T. D.

Bovaird

J. A.

Card

N. A.

(Eds.), Modeling contextual effects in longitudinal studies (pp. 285–298). Mahwah, NJ: Lawrence Erlbaum Associates.

Bowlby

(1969). Attachment and loss (Vol. 1). New York: Basic Books.

Bowlby

(1973). Attachment and loss (Vol. 2). New York: Basic Books.

Buchanan

Bagley

Stansfield

Preston

. (2012). The empathic, physiological resonance of stress. Social Neuroscience, 7, 191–201. doi:10.1080/17470919.2011.588723

Butler

E. A.

(2011). Temporal interpersonal emotion systems: The “TIES” that form relationships. Personality & Social Psychology Review, 15, 367–393. doi:10.1177/1088868311411164

Butler

E. A.

Randall

A. K

. (2013). Emotional coregulation in close relationships. Emotion Review, 5, 202–210. doi:10.1177/1754073912451630

Byrne

Nelson

. (1965). Attraction as a linear function of proportion of positive reinforcements. Journal of Personality and Social Psychology, 1, 659–663. doi:10.1037/h0022073

10.

Coutinho

J. F.

Silva

P. O.

Decety

. (2014). Neurosciences, empathy, and healthy interpersonal relationships: Recent findings and implications for counseling psychology. Journal of Counseling Psychology, 61, 541–548. doi:10.1037/cou0000021

11.

Crowell

S. E.

Baucom

B. R.

Yaptangco

Bride

Hsiao

McCauley

Beauchaine

T. P

. (2014). Emotion dysregulation and dyadic conflict in depressed and typical adolescents: Evaluating concordance across psychophysiological and observational measures. Biological Psychology, 98, 50–58. doi:10.1016/j.biopsycho.2014.02.009

12.

Elofsson

U. O.

von Scheele

Theorell

Sondergaard

H. P.

(2008). Physiological correlates of eye movement desensitization and reprocessing. Journal of Anxiety Disorders, 22, 622–634. doi:10.1016/j.janxdis.2007.05.012

13.

Gehring

Hornberger

Matz

Konecny

Schmucker

(2002). The effects of motion artifact and low perfusion on the performance of a new generation of pulse oximeters in volunteers undergoing hypoxemia. Respiratory Care, 47, 48–60.

14.

Graham

J. M.

Liu

Y. J.

Jeziorski

J. L.

(2006). The Dyadic Adjustment Scale: A reliability generalization meta-analysis. Journal of Marriage and Family, 68, 701–717. doi:10.1111/j.1741-3737.2006.00284.x

15.

Helm

Sbarra

Ferrer

. (2012). Assessing cross-partner associations in physiological responses via coupled oscillator models. Emotion, 12, 748–762. doi:10.37/a0025036

16.

Hunsley

Best

Lefebvre

Vito

(2001). The seven-item short form of the Dyadic Adjustment Scale: Further evidence for construct validity. American Journal of Family Therapy, 29, 325–335. doi:10.1080/01926180126501

17.

James

(1884). What is emotion? Mind, 9, 188–205.

18.

Johnson

S. M

. (2004). The practice of emotionally focused couple therapy (2nd ed.) New York: Brunner-Routledge.

19.

Laurenceau

J. P.

Bolger

(2012). Analyzing diary and intensive longitudinal data from dyads. In Mehl

Conner

(Eds.), Handbook of research methods for studying daily life (pp. 407–422). New York: Guilford Press.

20.

Laurent

Powers

(2007). Emotion regulation in emerging adult couples: Temperament, attachment, and HPA response to conflict. Biological Psychology, 76, 61–71. doi: 10.1016/j.biopsycho.2007.06.002

21.

Levenson

R. W.

Gottman

J. M.

(1983). Martial interaction: Physiological linkage and affective exchange. Journal of Personality and Social Psychology, 45, 587–597. doi:10.1037/0022-3514.45.3.587

22.

Levenson

R. W.

Gottman

J. M.

(1985). Physiological and affective predictors of change in relationship satisfaction. Journal of Personality and Social Psychology, 49, 85–94. doi:10.1037/0022-3514.49.1.85

23.

Nealy-Moore

Smith

Uchino

Hawkins

Olson-Cherry

. (2007). Cardiovascular reactivity during positive and negative marital interactions. Journal of Behavioral Medicine, 30, 505–519. doi:10.1007/s10865-007-9124-5

24.

Nock

M. K.

Wedig

M. M.

Holmberg

E. B.

Hooley

J. M.

(2008). The Emotion Reactivity Scale: Development, evaluation, and relation to self-injurious thoughts and behaviors. Behavior Therapy, 39, 107–116. doi:10.1016/j.beth.2007.05.005

25.

Papp

Pendry

Simon

Adams

. (2013). Spouses’ cortisol associations and moderators: Testing physiological synchrony and connectedness in everyday life. Family Process, 52, 284–298. doi:10.1111/j.1545-5300.2012.01413.x

26.

Raudenbush

S. W.

Bryk

A. S.

(2002). Hierarchical linear models (2nd ed.). Thousand Oaks, CA: Sage.

27.

Raudenbush

S. W.

Brennan

R. T.

Barnett

R. C.

(1995). A multivariate hierarchical model for studying psychological change within married couples. Journal of Family Psychology, 9, 161–174. doi:10.1037/0893-3200.9.2.161

28.

Saxbe

D. E.

Repetti

R.L

. (2010). No place like home: Home tours correlate with daily patterns of mood and cortisol. Personality and Social Psychology Bulletin, 36, 71–81. doi:10.1177/0146167209352864

29.

Schermer

Leenders

in ‘t Veen

van den Bosch

Wissink

Smeele

Chavannes

. (2009). Pulse oximetry in family practice: Indications and clinical observations in patients with COPD. Family Practice, 26, 524–531. doi:10.1093/fampra/cmp063

30.

Sharpley

C. F.

Rogers

(1984). Preliminary validation of the abbreviated Spanier Dyadic Adjustment Scale: Some psychometric data regarding a screening test of marital adjustment. Educational and Psychological Measurement, 44, 1045–1049.

31.

Spanier

G. B.

(1976). Measuring dyadic adjustment: New scales for assessing the quality of marriage and similar dyads. Journal of Marriage and Family, 38, 15–28. doi:10.2307/350547

32.

Tashiro

Frazier

(2007). The causal effects of emotion on couples’ cognition and behavior. Journal of Counseling Psychology, 54, 409–422. doi:10.1037/0022-0167.54.4.409

	Female		Male
Variable	Intercept (SE)^a	B (SE)	Intercept (SE)^a	B (SE)
Relationship length (months)	77.16 (2.02)	0.031 (0.048)	77.51 (1.83)	0.10 (0.044)*
ERS sensitivity	72.01 (3.03)	0.53 (0.24)*	77.61 (2.35)	0.31 (0.22)
ERS arousal	70.33 (3.35)	0.74 (0.29)*	81.04 (2.49)	−0.10 (0.26)
ERS persistence	75.76 (3.17)	0.35 (0.44)	81.10 (2.35)	−0.23 (0.51)
Relationship satisfaction	75.84 (5.64)	0.44 (1.09)	87.26 (8.15)	−1.33 (1.58)