Relationship Satisfaction, Feelings of Closeness and Annoyance, and Linkage in Electrodermal Activity

Abstract

Physiological linkage refers to moment-to-moment, time-linked coordination in physiological responses among people in close relationships. Although people in romantic relationships have been shown to evidence linkage in their physiological responses over time, it is still unclear how patterns of covariation relate to in-the-moment, as well as general levels of, relationship functioning. In the present study with data collected between 2014–2017, we capture linkage in electrodermal activity (EDA) in a diverse sample of young-adult couples, generally representative and generalizable to the Los Angeles community from which we sampled. We test how naturally occurring, shifting feelings of closeness with and annoyance toward one’s partner relate to concurrent changes in levels of physiological linkage over the course of one day. Additionally, we examine how linkage relates to overall relationship satisfaction. Results showed that couples evidenced significant covariation in their levels of physiological arousal in daily life. Further, physiological linkage increased during hours that participants felt close to their romantic partners but not during hours that participants felt annoyed with their partners. Finally, those participants with overall higher levels of relationship satisfaction showed lower levels of linkage over the day of data collection. These findings highlight how individuals respond in sync with their romantic partners and how this process ebbs and flows in conjunction with the shifting emotional tone of their relationships. The discussion focuses on how linkage might enhance closeness or, alternatively, contribute to conflict escalation and the potential of linkage processes to promote positive interpersonal relationships.

People in close relationships often exhibit similar behaviors and mannerisms, unconsciously and automatically mimicking one another or catching and experiencing each other’s emotional states (e.g., Gottman et al., 1977; Guéguen, 2009; Norscia & Palagi, 2011). Beyond mimicry, these interpersonal processes can, over time, become ingrained, such that people begin to regulate their emotions in the context of their relationships with other people (e.g., Anderson et al., 2003; Brown et al., 2021; Butner et al., 2007). This interpersonal phenomenon, referred to as “linkage,” “synchrony,” “attunement,” or “coregulation,” occurs when people evidence coordination in their levels of emotional and physiological responding across time (Butler, 2011; Butler & Randall, 2013; Sbarra & Hazan, 2008; Timmons et al., 2015). Studies focusing specifically on physiological linkage have consistently shown that romantic couples demonstrate coordination in their levels of physiological arousal across a wide range of response systems (e.g., cortisol, heart rate, respiratory sinus arrhythmia) and that this phenomenon is linked to various metrics of relationship functioning, such as relationship satisfaction and attachment style (see Timmons et al., 2015 for a review). However, it is still unclear how and under what conditions linkage in physiology benefits versus interferes with relationship functioning. The current study captures linkage in electrodermal activity (EDA) in couples’ daily lives and tests if naturally occurring, fluctuating relationship dynamics, as well as general levels of relationship satisfaction, are linked to amplified or dampened physiological linkage processes.

Relationships as Regulators of Individual Physiology

According to Family Systems Theory, individuals do not exist in isolation but rather make up interconnected systems that can either support or interfere with individual well-being (Cox & Paley, 1997), depending on the nature of the interpersonal interaction and the context in which the interaction occurs. In these systems, connected individuals are thought to engage in cross-person physiological, behavioral, and emotional feedback loops that either increase arousal (e.g., during conflict) or decrease arousal (e.g., stress buffering), thereby maintaining homeostasis or leading to dysregulation in emotional and physiological systems over time. For example, research shows that separation or loss of close others is linked to changes in sleep, eating, mood, and immune system function (Diamond et al., 2008; Field, 2012; Hofer, 1984, 1987, 1996) and that our stress systems are less active in the presence of other people (Beckes & Coan, 2011; Coan et al., 2006). Thus, via repeated interactions and conditioned responding, romantic couples may begin to evidence physiological linkage, manifested as time-linked, cross-person covariation in their levels of physiological arousal. A number of studies have shown that romantic couples do evidence similarity in their physiological responding both in the lab (Chen et al., 2021; Ferrer & Helm, 2013; Laurent & Powers, 2007; McAssey et al., 2013; Reed et al., 2013; Saxbe et al., 2014) and in naturalistic settings (Atzil et al., 2012; Berg & Wynne-Edwards, 2002; Liu et al., 2013; Papp et al., 2013; Saxbe & Repetti, 2010; Schreiber et al., 2006; Storey et al., 2000). Although the mediators of cross-person linkages in physiological states are not yet well understood, physiological linkage has been generally theorized to reflect interpersonal emotion processes and to occur during interpersonal interaction (Butler, 2011; Butler & Randall, 2013; Sbarra & Hazan, 2008; Timmons et al., 2015).

Physiological Linkage: Implications for Relationship Functioning

Despite clear evidence of linkage in physiological responses among people in close relationships, the implications of sharing in physiological states with others are still not well understood. On the one hand, physiological linkage may reflect connection and bonding between people in close relationships, who react sensitively to their partners’ emotional states, easily take others’ perspectives, and demonstrate high levels of empathy toward others. On the other hand, physiological linkage could amplify negative affect and contribute to the escalation of conflict if couples catch negative moods from each other or feed off each other’s stress levels, moving away from homeostasis and toward dysregulation. Linkage in physiological responses therefore may not be entirely positive or negative for relationships but instead may depend on various other factors, including the emotional context in which it occurs (e.g., if couples are arguing versus sharing vulnerable emotions), the specific physiological response system measured (e.g., cortisol versus vagal tone), and the way linkage is statistically modeled (e.g., concurrently versus time-lagged, levels changing versus staying constant over time). Various researchers studying linkage have thus proposed unifying frameworks to differentiate these different types of linkage processes, although these terms have not been consistently applied in the literature (e.g., Butler, 2011; Sbarra & Hazan, 2008). According to Butler (2011), linkage refers to general coordination in physiological responding between two people across time. Synchrony, in contrast, is a subcategory of linkage that reflects covariation in signals where partners move up and down together but where there is no change in the mean overall level of physiological arousal during an interaction. Finally, contagion or transmission refers to another subcategory of linkage that reflects covariation in signals where partners move up and down together and there is a change in the mean overall level of arousal over the course of an interaction.

Although not entirely consistent across studies, research to date investigating the implications of physiological linkage on relationship functioning has generally found that heightened covariation in physiological signals is related to poorer couple functioning, such as lower relationship satisfaction, insecure attachment style, increased demand-withdraw behavior during conflict, lower levels of social support, greater negative affect reactivity, and increased inflammatory biomarkers (e.g., Chaspari et al., 2015; Coutinho et al., 2019; Ha et al., 2016; Levenson & Gottman, 1983; Liu et al., 2013; Phan et al., 2019; Saxbe & Repetti, 2010; Timmons, Baucom, et al., 2017; Wilson et al., 2018). It is possible that couples with lower levels of relationship satisfaction may in general exhibit heightened reactivity to their partners’ emotional states, leading to conflict escalation and increased fighting relative to more satisfied couples. Still, most research thus far has tested physiological linkage during laboratory-based marital conflict tasks or has specifically tested linkage in stress markers, such as cortisol. Paradoxically, other research in this domain has shown that linkage is also associated with heightened levels of empathy, perspective taking, and physical and emotional connectedness (e.g., Chatel-Goldman et al., 2014; Coutinho et al., 2019; Nelson et al., 2017; Papp et al., 2013; Reuf, 2001) and that the direction of these effects may depend on the specific conditions of each study, with some studies reporting positive associations between physiological linkage and adaptive relationship functioning (e.g., Helm et al., 2012; Helm et al., 2014).

Considering the Fluctuating Emotional Context

One important factor that might explain how and when physiological linkage helps versus harms relationships is consideration of the emotional context in which the linkage occurs. Limited prior research has begun to investigate this possibility by comparing physiological linkage across different types of laboratory tasks, such as discussing a conflictual topic (e.g., Helm et al., 2012; Nelson et al., 2017) or a personal loss (e.g., Chaspari et al., 2015). Another, complementary method, however, could be to track how levels of physiological linkage naturally change as a function of co-occurring and dynamically fluctuating dimensions of ongoing relationship processes. That is, rather than testing how linkage relates to static measures of relationship functioning across different experimental conditions, it is also possible to test how levels of physiological linkage change when romantic couples concurrently experience periods of naturally occurring relationship positivity and naturally occurring relationship distress. Such an approach would thus capture shifts within a couple (i.e., increases or decreases in physiological similarity during periods of positive versus negative couple interactions) in addition to differences across couples (i.e., differences in physiological linkage levels across people or couples reporting high versus low levels of relationship functioning). Importantly, examining linkage at both within-person and across-person levels of analysis could potentially yield different results. General reactivity to romantic partners, regardless of the concurrent relationship dynamic, could operate differently than an individual’s level of responsivity to ongoing, fluctuating relationship processes.

Capturing Linkage in EDA in Everyday Life

Beyond examining how linkage varies across emotional contexts, an important next step involves capturing linkage processes in real-life environments, rather than only during laboratory-based discussion tasks. Laboratory-based research, while informative, may provide a limited sample of how physiological linkage processes unfold in real time and real life. Measuring physiological linkage in everyday life, in contrast, may provide several key advantages in comparison to laboratory-based research, which includes the ability to capture naturalistically occurring interactions between romantic partners as they dynamically unfold on realistic time scales (Bolger et al., 2003; Laurenceau & Bolger, 2005). Moreover, existing research examining physiological linkage in daily life has thus far almost exclusively measured cortisol or other hormones (Berg & Wynne-Edwards, 2002; Liu et al., 2013; Papp et al., 2013; Saxbe & Repetti, 2010; Schreiber et al., 2006; see Timmons, Baucom, et al. (2017) for an exception). Measuring other physiological indices, such as EDA, in daily life could provide a more comprehensive picture of physiological linkage processes. EDA, which is a measure of sweat on the skin glands, is associated with sympathetic nervous system activity and is reflective of the fight or flight response, as well as general interest and attention, making EDA particularly valuable for capturing both positively and negatively valenced emotional processes (Benedek & Kaernbach, 2010; Dawson et al., 2007; Hugdahl, 1995). Further, EDA responds relatively quickly to environmental stimuli, especially in comparison to hormone measures, and can be passively monitored across extended time-frames via the use of mobile biosensors (Poh et al., 2012; Poh et al., 2010), allowing for greater temporal precision in measuring synchrony, particularly within naturalistic settings.

Current Study

The present study examines physiological linkage in dating couples’ EDA over the course of one day in a real-life setting to determine how naturally occurring, hour-to-hour, fluctuating feelings of closeness with and annoyance toward one’s dating partner relate to shifting levels of linkage. Further, we test whether general levels of relationship satisfaction are linked to heightened versus dampened levels of physiological linkage. Because we examine EDA over an entire day, during which different couples are engaging in a variety of different day-to-day activities, we do not expect to observe systematic changes in EDA levels in the sample. We thus model synchrony, or general levels of similarity in EDA, which involves capturing covariation in EDA signals, rather than change in overall mean levels. However, as a preliminary step, we do model linear, quadratic, and cubic trends in EDA to rule out the confounding effects of a diurnal trajectory and also examine a number of other potential confounding factors, including physical activity level, whether participants were together, interacting, with other people, communicated by phone, consumed alcohol, caffeine, tobacco, or other drugs, racial/ethnic status, employment status, student status, family income, age, relationship length, and if the couple was cohabitating.

Figure 1 presents an overview of the main study questions and hypotheses. We first tested if romantic partners evidence linkage in their EDA over the course of one day, hypothesizing that increased EDA in one person will be positively associated with increased EDA in one’s partner during the same hour (HO1). We next tested if levels of linkage in EDA vary when participants report feeling particularly emotionally close to or annoyed with their partners. Based on prior research indicating that linkage may increase as a function of interpersonal connectivity that can reflect both positive and negative relationship processes (e.g., Timmons et al., 2015), we hypothesized that physiological linkage would increase during hours that participants report high emotional closeness to their partners (HO2), as well as during hours that participants report high levels of annoyance with their partners (HO3).

Figure 1:

Hypothesized associations between hourly own EDA, hourly partner EDA, hourly own feelings of closeness, hourly own feelings of annoyance, and overall own relationship satisfaction

Finally, we examined overall levels of relationship satisfaction as a moderator of linkage processes. Consistent with prior work on linkage in sympathetic nervous system activity in couples (Chaspari et al., 2015; Coutinho et al., 2019; Ha et al., 2016; Levenson & Gottman, 1983; Liu et al., 2013; Phan et al., 2019; Saxbe & Repetti, 2010; Timmons, Baucom, et al., 2017; Wilson et al., 2018), we hypothesized that participants with overall higher relationship satisfaction would show generally lower levels of linkage over the course of the day (HO4). Couples with low levels of relationship satisfaction may in general show heightened responsivity to their partners’ emotional states, which if repeated over time, could negatively impact the quality of the relationship. Additionally, because the association between closeness, annoyance, and linkage might differ according to the general level of satisfaction in the relationship, we conduct follow-up 3-way interactions testing: (1) EDA, feelings of closeness, and feelings of annoyance, (2) EDA, feelings of closeness, and relationship satisfaction, and (3) EDA, feelings of annoyance, and relationship satisfaction.

Method

Participants

Two hundred and eighteen people consisting of 109 dating couples (106 opposite-sex couples and 3 female same-sex couples) participated in the study between 2014–2017. Participants were recruited through flyers and advertisements posted online and in the community for a study examining “how young dating couples talk to each other, what types of physiological reactions couples have when having such discussions, and whether experiences in one’s family when growing up play a role in young-adult relationships.” Twenty-three couples were recruited from a longitudinal study investigating family aggression and development.

We conducted tests comparing age, income, ethnicity, and relationship length between the new sample and those recruited from the longitudinal sample and found one significant difference: Men in the newly recruited sample were more likely to be Caucasian than were males in the longitudinal sample: χ²(1) = 10.42, p < .01. To participate, we required that participants were 18–25 years old (M age = 23.1; SD = 3.0), able to read and write in English, and that the dating partners were in a relationship for at least a 2-month period prior to beginning of the study (M relationship length = 32.2 months; SD = 26.8). The ethnic/racial composition of the sample consisted of 27.5% White Non-Hispanic/Non-Latinx, 23.9% White Hispanic/Latinx, 16.1% African American/Black, 12.8% Asian, 0.5% Native Hawaiian or Pacific Islander, 15.6% multiracial, and 3.7% other. Data on sex, immigration history, and clinical diagnoses were not collected. In total, 44.0% of the couples lived together; 54.1% were enrolled as either part-time or full-time students; and 73.5% were employed on a part-time or full-time basis.

Procedures

Participants who responded to study advertisements first underwent a phone-based eligibility screening and were then invited to participate in a laboratory visit. While at the laboratory, participants separately completed questionnaires assessing satisfaction in their current relationship using computers with privacy screens. This visit also included several other study procedures, such as having a series of discussions about their relationship, which were unrelated to the current study. Couples were then invited to participate in the at-home procedures, which were scheduled for a separate day to begin at 10:00 am. At this second visit, couples returned to the laboratory, were outfitted with a wireless biosensor on their wrist, and were lent smartphones to be used to provide hourly reports on the quality of their interactions until 3:00 am that night, or until participants went to bed, with bedtime potentially differing between partners within a couple. To practice using the phones, couples completed the first survey while in the laboratory for the prior hour with the help of an experimenter. The participants were asked to fill out these questions separately and to not discuss the survey answers with one another. Couples were then instructed to leave the laboratory and spend at least 5 total hours of the day together, which could be broken into multiple time periods if desired. Couples returned to the laboratory the next day to complete a questionnaire and interview about their reactions to participating in the study and their activities for each hour of data collection. Questionnaires and phone surveys were uploaded to an encrypted server. Each participant received $100 for completing the home-based segment of the study. All study procedures were approved by the University of Southern California IRB. Further details on the procedures are provided in Timmons, Baucom, et al., 2017.

2.3. Equipment

Smartphones.

Nexus 5 Android smartphones were used to send alarms notifying participants to complete surveys once per hour. We password protected all applications on the smartphones so that the participants could not change the phone settings or leave additional, unsolicited data on the phones (e.g., pictures or videos). After each use, we cleared data from the smartphones to prevent couples from accessing data left by prior participants.

Q sensor.

EDA was collected using the Q sensor, a small, wireless biosensor worn on the inside of the wrist (Poh et al., 2010). Although less sensitive than measurements taken on the fingertips or palms, measures of EDA taken from the wrist have been shown to correlate with gold-standard lab-based measures and have been linked to psychological and physiological functioning measured in naturalistic settings (e.g., Poh et al., 2012; Poh et al., 2010; Timmons, Baucom, et al., 2017; Timmons, Chaspari, et al., 2017). To reduce movement artifacts, we applied the Q sensor to the non-dominant hand. We set the sampling rate to 8 hertz to ensure sufficient battery life and data storage over the relatively long sampling period, consistent with recommendations for use with similar, real-life EDA sensors (e.g., Poh et al., 2012; Poh et al., 2010).

Measures

Hourly feelings of closeness and annoyance.

Participants completed short hourly surveys on the smartphones via the application Survelytics to assess feelings of closeness with and annoyance toward their dating partners (average time to complete each survey = 1 minute and 56 seconds). Each participant individually rated the extent to which he or she felt “close and connected with my romantic partner” and “annoyed or irritated toward my romantic partner” over the last hour on a 0 (not at all) to 100 (extremely) scale. These hourly surveys also included other items to be used as covariates, including if participants engaged in physical activity, communicated by phone, or consumed alcohol, caffeine, tobacco, or other drugs. Other items assessed in the hourly surveys included hourly mood states, such as stress, happiness, sadness, anxiety, and anger. Because the current study aims to test how physiological linkage varies in accordance with relationship-related mood states, we focus here on closeness or annoyance toward one’s romantic partner, though as follow-up analyses, we do examine the moderating effects of other moods.

Hourly EDA.

Skin conductance level measured in microsiemens was downloaded from the Q sensors and exported to MATLAB (The Mathworks Inc., 2019) to be processed via computer scripts that applied a low pass filter and then automatically identified and removed movement artifacts from the EDA signals. Prior to finalizing the EDA data, research assistants visually inspected the signals and revised Matlab-identified artifacts as needed. Time periods marked for deletion were then coded as missing data. The MATLAB script used to process EDA signal are included in the supplemental materials. Minimum and maximum EDA values and distributions of EDA were examined to ensure that values were within expected ranges, considering increased variability of signals obtained in ambulatory methods and the influence of external factors (e.g., temperature or physical activity) that can influence EDA outside of the laboratory setting. Values outside the upper expected value of 60 microsiemens based on prior work (Braithwaite et al., 2013; Venables & Christie, 1980) were removed from the dataset prior to analysis. Couples were instructed to always wear the EDA sensor unless showering, bathing or engaging in other activities that might damage the equipment. Occasionally, participants removed the sensor for other reasons (e.g., felt embarrassed wearing the sensor; the sensor was itchy or uncomfortable). In total, couples wore the sensors 85.0% of the waking hours sampled, indicating high overall adherence to the study protocol. Periods during which couples took off the sensors were identified via an exit questionnaire for which participants listed all times they removed the sensor and their reasons for removing it. Periods of time during which participants were not wearing the sensors were coded as missing data. Obtained EDA signals were then averaged per hour to match the sampling interval of the hourly survey reports of closeness and annoyance between dating partners.

Overall relationship satisfaction.

General levels of satisfaction in the current relationship were measured using the Quality of Marriage Index (QMI; Norton, 1983), a commonly used measure of general marital quality that has demonstrated good psychometric properties across a range of samples (e.g., Johnson et al., 1986; Maroufizadeh et al., 2019; Zimmermann et al., 2019). For the current study, items were reworded to be applicable to dating couples. The questionnaire consists of 6 items, with the first 5 items measured on a 1 to 7 scale ranging from strongly disagree to strongly agree, and the final item measured on a 1 to 10 scale ranging from extremely low to extremely high, intentionally designed to weigh more than the other items. Example items include: “My relationship with my partner makes me happy,” “We have a good relationship,” and “My relationship with my partner is very stable.” To account for missing values that would yield inaccurate totals if summed, scores for relationship satisfaction were calculated by taking the average score across 6 items, resulting in possible values ranging from 1 to 7.5. Cronbach’s alpha was .94 for women and .94 for men.

Transparency and openness.

We report how we determined our sample size, all data exclusions and manipulations, missing data, and all relevant measures in the study following the Journal Article Reporting Standards (JARS; Kazak, 2018). Data, analysis code, and research materials are publicly available and can be accessed at: https://colliga.io/data-repository/ or by emailing the corresponding author. This study’s design, hypotheses, and analyses were not pre-registered.

Overview of Analyses

Data were analyzed in IBM SPSS Version 25 (IBM Corp., 2017), with study hypotheses tested using three-level models, where one’s own EDA, own feelings of closeness and annoyance, and own relationship satisfaction were used as predictors of partner EDA levels. Specifically, we use Pairwise Data Structure, also known as Double-Entry Structure, which is created by copying the data for Partner 1 and pasting it for Partner 2 and vice versa (as described in Kenny et al., 2006). This structure allows for irregular lengths of data collection across partners within a couple and across different couples (van Buuren, 2018) and allows for the direct testing of gender effects, rather than specifying female EDA as a predictor of male EDA or vice versa. It further allows same-sex couples to be included in the sample because it does not require separate variables for male and female EDA to be specified. Although, for the purposes of model specification, we entered own EDA as the predictor variable and partner EDA as the outcome variable, we test synchrony by modeling covariation in EDA within the same time point; we thus conceptualize linkage as occurring concurrently, rather than as one person impacting the other person or vice versa. All models included three levels, with repeated observations and people nested in couples. Sample size was determined based on a priori power analyses. Power for within-level effects was conducted via a Monte Carlo simulation in Mplus Version 7 (Muthén & Muthén, 2012) and for cross-level interactions in R Version 3.1 (simulation code obtained from Mathieu et al., 2012), with within-level effects and cross-level interactions showing empirical power of .80 or greater for small effects. Prior to presenting our main hypothesized models, we first (1) provide descriptive statistics and (2) present results of preliminary multilevel models testing direct associations between the main study variables.

The first hypothesis testing whether romantic partners show linkage in their EDA levels in daily life was examined by adding hourly own EDA as a level-1 predictor of hourly partner EDA, as shown in the following equation:

$$\text{Level}\hspace{0.17em}\hspace{0.17em}\text{1:}\hspace{0.17em}Hourly\hspace{0.17em}Partner\hspace{0.17em}ED{A}_{ijk}={\beta }_{0jk}+{\beta }_{1jk}\left(Hourly\hspace{0.17em}Own\hspace{0.17em}ED{A}_{1ijk}\right)=e_{ijk}$$

$$\begin{gathered} \text{Level}\hspace{0.17em}\hspace{0.17em}\text{2:}\hspace{0.17em}\hspace{0.17em}{\beta }_{0jk}={\gamma }_{00k}+u_{0jk} \\ \hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}{\beta }_{1jk}={\gamma }_{10k} \end{gathered}$$

$$\begin{gathered} \text{Level}\hspace{0.17em}\hspace{0.17em}\text{3:}\hspace{0.17em}\hspace{0.17em}{\gamma }_{00k}={\lambda }_{000}+r_{00k} \\ \hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}{\gamma }_{10k}={\lambda }_{100} \end{gathered}$$

Our second hypothesis that linkage in EDA would be heightened during hours participants reported increased closeness toward their dating partners was tested by adding hourly own EDA, hourly own closeness, and hourly own EDA × hourly own closeness as level-1 predictors of level-1 hourly partner EDA. This model is depicted in the following three-level equation:

$$\begin{gathered} \text{Level}\hspace{0.17em}\hspace{0.17em}\text{1:}\hspace{0.17em}\hspace{0.17em}Hourly\hspace{0.17em}Partner\hspace{0.17em}ED{A}_{ijk}={\beta }_{0jk}+{\beta }_{1jk}\left(Hourly\hspace{0.17em}Own\hspace{0.17em}ED{A}_{1ijk}\right) \\ \hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}+{\beta }_{2jk}\left(Hourly\hspace{0.17em}Own\hspace{0.17em}Closenes{s}_{2ijk}\right) \\ \hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}+{\beta }_{3jk}\left(Hourly\hspace{0.17em}Own\hspace{0.17em}EDA\times Hourly\hspace{0.17em}Own\hspace{0.17em}Closenes{s}_{3ijk}\right)+e_{ijk} \end{gathered}$$

$$\begin{gathered} \text{Level}\hspace{0.17em}\hspace{0.17em}\text{2:}\hspace{0.17em}\hspace{0.17em}{\beta }_{0jk}={\gamma }_{00k}+u_{0jk} \\ \hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}{\beta }_{1jk}={\gamma }_{10k} \\ \hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}{\beta }_{2jk}={\gamma }_{20k} \\ \hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}{\beta }_{3jk}={\gamma }_{30k} \end{gathered}$$

$$\begin{gathered} \text{Level}\hspace{0.17em}\hspace{0.17em}\text{3:}\hspace{0.17em}\hspace{0.17em}{\gamma }_{00k}={\lambda }_{000}+r_{00k} \\ \hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}{\gamma }_{10k}={\lambda }_{100} \\ \hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}{\gamma }_{20k}={\lambda }_{200} \\ \hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}{\gamma }_{30k}={\lambda }_{300} \end{gathered}$$

A similar equation was used to test our third hypothesis that linkage would increase during hours of increased annoyance between partners. Our fourth hypothesis tested whether physiological linkage in dating couples’ daily lives is moderated by relationship satisfaction. To test this hypothesis, we entered hourly own EDA at level 1, overall own relationship satisfaction at level 2, and hourly own EDA × overall own relationship satisfaction as a cross-level predictor of level-1 hourly partner EDA, as depicted here:

$$\text{Level}\hspace{0.17em}\hspace{0.17em}\text{1:}\hspace{0.17em}Hourly\hspace{0.17em}Partner\hspace{0.17em}ED{A}_{ijk}={\beta }_{0jk}+{\beta }_{1jk}\left(Hourly\hspace{0.17em}Own\hspace{0.17em}ED{A}_{1ijk}\right)=e_{ijk}$$

$$\begin{gathered} \text{Level}\hspace{0.17em}\hspace{0.17em}\text{2:}\hspace{0.17em}\hspace{0.17em}{\beta }_{0jk}={\gamma }_{00k}+{\gamma }_{01k}\left(Overall\hspace{0.17em}Own\hspace{0.17em}\text{Re}lationship\hspace{0.17em}Satisfactio{n}_{1jk}\right)+u_{0jk} \\ \hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}{\beta }_{1jk}={\gamma }_{10k}+{\gamma }_{11k}\left(Overall\hspace{0.17em}Own\hspace{0.17em}\text{Re}lationship\hspace{0.17em}Satisfactio{n}_{1jk}\right) \end{gathered}$$

$$\begin{gathered} \text{Level}\hspace{0.17em}\hspace{0.17em}\text{3:}\hspace{0.17em}\hspace{0.17em}{\gamma }_{00k}={\lambda }_{000}+r_{00k} \\ \hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}{\gamma }_{01k}={\lambda }_{010} \\ \hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}{\gamma }_{10k}={\lambda }_{100} \\ \hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}\hspace{0.17em}{\gamma }_{11k}={\lambda }_{110} \end{gathered}$$

Consistent with recommendations for testing level-1 effects and cross-level interactions, level-1 predictors were group mean centered while level-2 and 3 predictors were grand mean centered (Enders & Tofighi, 2007; Hofmann & Gavin, 1998; Peugh, 2010). To increase parsimony in our models and to avoid convergence problems, intercepts were set as random, while all other effects were fixed (Raudenbush & Bryk, 2002; Singer et al., 2003). Effect sizes are reported as the Snijders and Bosker percent variance explained (R²[S&B]; LaHuis et al., 2014; Snijders & Bosker, 1994). As a preliminary step, we tested if EDA showed a significant diurnal trajectory by adding linear, quadratic, and cubic terms representing time as predictors of EDA levels. Results showed significant linear, quadratic, and cubic trends. We next examined if EDA was associated with other, possible confounding factors by entering the following variables as predictors of EDA in separate multilevel models: physical activity level, if the participants were together, interacting with each other, with other people, communicated by phone, and consumed alcohol, caffeine, tobacco, or other drugs at level 1; employment status, student status, racial/ethnic status, family income, and age at level 2; and relationship length and if the couple lived together at level 3. The results indicated that being together, interacting with one another, being with others, communicating by phone, physical activity, and alcohol consumption were significantly associated with EDA.

We next ran hypothesized models with and without time variables and the significant covariates included, which were entered simultaneously. Additionally, because linkage may be related to overall sympathetic nervous system activity, we ran all hypothesized models with average EDA included as a covariate. The inclusion of these variables did not alter the size, significance, or direction of findings; thus, for parsimony and to avoid convergence problems due to overly complex models, results are reported without covariates included. We ran all models with and without same-sex couples included in the sample; the size, significance, or direction of findings did not differ according to whether same-sex couples were included; we therefore present models that include both opposite-sex and same-sex couples. As follow-up analyses, we tested gender as a moderator of all the hypothesized effects. Also, as follow-up exploratory analyses, we tested whether other mood states, including stress, happiness, sadness, nervousness, and anger were linked to the degree of linkage observed. In our final set of exploratory follow-up analyses, we tested models that included three-way interactions for (1) hourly own EDA, hourly own feelings of closeness, and hourly own feelings of annoyance, (2) hourly own EDA, hourly own feelings of closeness, and overall own relationship satisfaction and (3) hourly own EDA, hourly own feelings of annoyance, and overall own relationship satisfaction.

Results

Descriptive Statistics

Table 1 presents descriptive statistics for the main variables. Across both women and men, we followed participants for 3,118 total waking hours (M = 14.4 hours; SD = 1. 48). Across this time frame, women and men completed 87.3% and 86.3% of the hourly phone surveys, respectively, with 92.0% of completed surveys started within 15 minutes of the phone alarm notification. Further, we obtained usable EDA data for 87.8% of hours for women and 82.0% for men. The number of hours with complete data on both partner’s EDA and survey data was 2,099. Participants within the same couple went to bed at different times in 20% of observed cases. In total, 89.3% of women and 80.2% of men reported feeling annoyed with their dating partners at least once during the day. Conversely, 100% of women and 100% of men reported feeling close with their dating partners at least once. On average, couples were together 11.8 hours out of 14.4 waking hours captured (together 81.9% of waking hours). Results of paired-sample t-tests using scores averaged over the day of data collection showed only one significant gender difference: men (M = 6.79; SD = 6.56) had higher levels of EDA compared to women (M = 4.54; SD = 5.72), t(108) = 2.40, p = .02; CI[0.33, 3.48]. No significant differences in general EDA levels for ethnic/racial status were observed. Between-person correlation analyses using averaged daily scores per person indicated several significant associations (see Table 1). In both women and men, feelings of closeness were negatively associated with feelings of annoyance, and feelings of closeness were positively associated with relationship satisfaction. In women, feelings of annoyance were linked to lower levels of relationship satisfaction, and EDA was marginally related to greater feelings of closeness with partners. In preliminary multilevel models, we also tested whether participants evidenced shifts in their EDA during hours they felt annoyed or close with their partners, whether feelings of closeness and annoyance were linked to each other on an hourly basis, and whether general levels of relationship satisfaction were linked to hourly EDA. Results showed one significant effect: during hours of increased annoyance with their romantic partners, participants simultaneously reported decreased feelings of closeness (b = −.47, p < .001; CI[−0.52, −0.42]; Level 2 ICC = .28; Level 3 ICC = .23; R² [S&B] = 27.61%). To provide an illustration of high versus low linkage, Figure 2 depicts example data for two participating couples. Couple 1 shows high linkage in EDA (Panel A), closeness (Panel B), and annoyance (Panel C); Couple 2 shows low linkage in EDA (Panel D), closeness (Panel E), and annoyance (Panel F).”

Table 1.

Descriptive Statistics for the Main Study Variables

	Women			Men
	M	SD	Min-Max	M	SD	Min-Max	1.	2.	3.	4.
1. Hourly electrodermal activity1	4.54	5.72	0.00–28.93	6.79	6.56	0.07–32.21	--	.16†	−.02	−.06
2. Hourly feelings of closeness1	68.78	22.54	4.82–100.0	70.97	22.37	0.09–100.0	.06	--	−.34**	.42**
3. Hourly feelings of annoyance1	8.35	10.01	0.00–59.73	7.12	10.63	0.00–90.18	−.08	−.41**	--	−.26**
4. Overall relationship satisfaction	6.40	1.14	2.33–7.50	6.67	0.86	2.33–7.50	.01	.25**	−.06	--

Note.

¹Hourly scores averaged over the day of data collection per person; Top diagonal = women; bottom diagonal = men

^*p < .05

^**p < .01

^†marginally significant at p < .10. Same-sex couples are included in the descriptive statistics for Ms, SDs, and Min-Max values. To avoid violation of the assumption of independence of observations, only opposite-sex couples are included in the correlations analyses presented above (same-sex couples are included in all multilevel model results). Correlation results did not change with inclusion or exclusion of same-sex couples.

Figure 2:

Illustration of high linkage in EDA (Panel A), closeness (Panel B), and annoyance (Panel C) for example participating Couple 1 and low linkage in EDA (Panel D), closeness (Panel E), and annoyance (Panel F) for example participating Couple 2 across the day of data collection

HO1: Linkage in EDA in Dating Partners’ Daily Lives

We first tested whether dating partners exhibit linkage in EDA in everyday life by entering level-1 hourly own EDA as a predictor of level-1 hourly partner EDA in a three-level model. Results showed a significant association, such that hour-to-hour fluctuations in one’s own EDA were significantly and positively associated with hour-to-hour fluctuations in partner EDA (b = .28, p < .001; CI[0.24, 0.32]; Level 2 ICC = .39; Level 3 ICC = .22; R² [S&B] = 5.74%). The strength of physiological linkage between own and partner EDA was not significantly moderated by gender. As follow-up analyses, we also tested whether physical proximity and communicating via phone or text impacted physiological linkage processes. Results showed significant interaction effect (b = .12, p = .03), such that couples evidenced linkage when physically together (b = .29, p < .001; CI[0.25, 0.34]; Level 2 ICC = .23; Level 3 ICC = .37; R² [S&B] = 7.59%) but not when physically apart. Communicating via phone or text did not significantly moderate the degree of physiological linkage between couples.

HO2 and HO3: Linkage in EDA in Dating Partners’ Daily Lives Moderated by Feelings of Closeness and Annoyance

We investigated whether feelings of closeness were associated with increased linkage in EDA in romantic partners’ daily lives by entering hourly own EDA, hourly own feelings of closeness, and hourly own EDA × hourly own feelings of closeness as level-1 predictors of level-1 hourly partner EDA. Results showed a significant moderation effects for feelings of closeness (b = .03, p < .01, CI[0.01, 0.06]; Level 2 ICC = .37; Level 3 ICC = .23; R² [S&B] = 10.52%). See Table 2 for the complete model results. As depicted in Figure 3, participants exhibited heightened linkage in their EDA levels during hours they reported feeling especially close with their romantic partners. Simple slopes analysis showed that the association between own and partner EDA became significant at levels of closeness M – 2.26 SD (b = .12, p = .05) and that the strength of linkage increased as closeness increased (closeness at M + 2.00 SD: b = .42, p < .001). We next tested if feelings of annoyance were associated with increased linkage in EDA by entering hourly own EDA, hourly own feelings of annoyance, and hourly own EDA × hourly own feelings of annoyance as level-1 predictors of level-1 hourly partner EDA. The moderation effect for annoyance was not significant. Moderation effects for closeness and annoyance did not significantly differ by gender.

Table 2.

Multilevel Model of Linkage in Hourly EDA Moderated by Hourly Own Feelings of Closeness toward Dating Partners

Variables	b	SE	p	CI
Fixed Effects
Intercept (γ00)	5.48	0.48	< .001	[4.53, 6.42]
Hourly own EDA (γ10k)	0.29	0.02	< .001	[0.24, 0.33]
Hourly own feelings of closeness (γ20k)	−0.09	0.05	.07	[−0.19, 0.01]
Hourly own EDA × hourly own feelings of closeness (γ30k)	0.03	0.01	< .01	[0.01, 0.06]
Random Effects
Residual (eijk)	21.90	0.71
Between person intercept (u0jk)	20.53	3.16
Between couple intercept (r00k)	12.81	3.68

Note. Bold = significant hypothesized effect. EDA = electrodermal activity; ICC = intraclass correlation coefficient; R² (S&B) = Snijders & Bosker percent variance explained; Level 2 ICC = .37; Level 3 ICC = .23; R² [S&B] = 10.52%).

Figure 3:

The association between hourly own EDA and hourly partner EDA moderated by hourly own feelings of closeness. Values are plotted at the region of significance (M 2.26 SD: b = .12, p = .05 and M + 2 SD: b = .42, p < .001). Linkage between own and partner EDA is significant at values of own closeness 2.26 SD below the mean and above. EDA = electrodermal activity; µS = microsiemens.

We next conducted follow-up analyses to determine if mood (including stress, happiness, sadness, nervousness, and anger) moderates the degree of physiological linkage in couples. We examined these mood states separately, given research that suggests that specific emotional experiences may relate to linkage differently (e.g., see Timmons, Margolin, & Saxbe, 2015 for a review of the literature). Results (see Supplementary Tables 1–2 and Supplementary Figures 1–2) showed significant interaction effects for happiness (b = −.004, p < .001; CI[−0.01, −.002]; Level 2 ICC = .22; Level 3 ICC = .39; R² [S&B] = 6.03%) and sadness (b = .01, p < .001; CI[.002, .01]; Level 2 ICC = .22; Level 3 ICC = .40; R² [S&B] = 6.28%) but not for stress, nervousness, or anger. Simple slopes analysis showed that association between self and partner EDA was generally significant across all values of happiness, but the degree of linkage evidenced increased as happiness decreased (happiness M – 1 SD: b = .36, p < .001; M + 1 SD: b = .20, p < .001). For sadness, the opposite pattern emerged, with linkage generally significant across all values of sadness and increasing as sadness increased (sadness M – 1 SD: b = .21, p < .001; M + 1 SD: b = .35, p < .001). As additional exploratory analyses, we tested a 3-way interaction between own EDA, own closeness, and own annoyance. We further tested whether linkage was amplified when both partners felt annoyed or when both partners felt close at the same time. No significant effects were found. Finally, follow-up analyses indicated that when analyzing only hours during which couples were physically apart, no significant moderation effects for either closeness or annoyance were observed.

HO4: Linkage in EDA in Dating Partners’ Daily Lives Moderated by Relationship Satisfaction

In our final set of analyses, we examined if the association between own and partner EDA was moderated by general levels of relationship satisfaction by adding hourly own EDA as a predictor at level 1, overall own relationship satisfaction as a predictor at level 2, and hourly own EDA × overall own relationship satisfaction as a cross-level predictor of level-1 partner EDA in a three-level model. Table 3 provides the complete results for this model. Findings showed a significant cross-level interaction (b = −.07, p < .001; CI[−.10, −.03] Level 2 ICC = .38; Level 3 ICC = .24; R² [S&B] = 3.00%), such that linkage in own and partner EDA was greater for participants reporting generally low levels of relationship satisfaction. See Figure 4 for a depiction of this moderation result. Simple slopes analysis showed that the association between own and partner EDA became significant at levels of relationship satisfaction M + 8.13 SD (b = .13, p = .05) and that strength of linkage increased as relationship satisfaction decreased (relationship satisfaction at M – 2 SD: b = .85, p < .001). This moderation effect did not significantly differ according to gender. As follow-up analyses, we also tested three-way interactions for (1) hourly own EDA, hourly own feelings of closeness, and overall own relationship satisfaction and (2) hourly own EDA, hourly own feelings of annoyance, and overall own relationship satisfaction. Neither of these three-way interaction terms was significant. Follow-up analyses further showed that when analyzing only hours during which couples were physically apart, the moderation effect became non-significant.

Table 3.

Multilevel Model of Linkage in Hourly EDA Moderated by Overall Own Relationship Satisfaction

Variables	b	SE	p	CI
Fixed Effects
Intercept (γ00)	5.68	0.51	< .001	[4.68, 6.69]
Hourly own EDA (γ10k)	0.71	0.13	< .001	[0.46, 0.96]
Overall own relationship satisfaction (γ01k)	−0.56	0.43	.19	[−1.41, 0.29]
Hourly own EDA × overall own relationship satisfaction (γ11k)	−0.07	0.02	< .001	[−0.10, −0.03]
Random Effects
Residual (eijk)	23.11	0.72
Between person intercept (u0jk)	22.56	3.51
Between couple intercept (r00k)	14.18	4.10

Note. Bold = significant hypothesized effect; EDA = electrodermal activity; ICC = intraclass correlation coefficient; R² (S&B) = Snijders & Bosker percent variance explained; Level 2 ICC = .38, Level 3 ICC = .24; R² (S&B) = 3.00%.

Figure 4:

The association between hourly own EDA and hourly partner EDA moderated by overall own relationship satisfaction. Values are plotted at the region of significance (M 8.13 SD: b = .13, p = .05 and M 2 SD: b = .85, p < .001). Linkage between own and partner EDA is significant at values of own relationship satisfaction 8.13 SD above the mean and below. EDA = electrodermal activity; µS = microsiemens

Discussion

The present study investigated physiological linkage in EDA in romantic couples’ everyday lives, tested how naturalistically occurring fluctuations in feelings of closeness and annoyance toward dating partners relate to concurrent changes in physiological linkage and tested overall relationship satisfaction as a moderator of general levels of linkage. Our first hypothesis that romantic couples would evidence significant covariation in their levels of EDA over the course of one day was supported (HO1). Moreover, we found that levels of physiological linkage increased during hours that participants reported feeling especially close to (HO2) their romantic partners but not when feeling annoyed at (HO3) their romantic partners. Finally, consistent with hypothesis four, we found that physiological linkage was lower among participants reporting higher general levels of relationship satisfaction (HO4). The present study is the first to our knowledge to investigate how linkage in EDA in everyday life covaries with fluctuating, real-life relationship phenomena and relates to general levels of relationship satisfaction.

Physiological Linkage in EDA in Daily Life

Findings from our study provide new evidence that young adult dating couples demonstrate similarity in their EDA signals in daily life. This work thus adds to a small number of studies reporting linkage in couples’ physiology in real-life rather than in laboratory environments, although such studies to date have typically measured cortisol or other hormones (Berg & Wynne-Edwards, 2002; Liu et al., 2013; Papp et al., 2013; Saxbe & Repetti, 2010; Schreiber et al., 2006). Importantly, we examined multiple potential confounding factors that might explain observed covariation in EDA levels, including diurnal trajectories of EDA, as well as physical activity levels, whether the participants were together, interacting with each other, with other people, communicated by phone, consumed alcohol, caffeine, tobacco, or other drugs, employment status, student status, racial/ethnic status, income, age, relationship length, and if the couple lived together. Although levels of EDA were significantly associated with a number of these factors, the inclusion of the time variables and significant covariates in our models did not impact the general size, direction, or significance of the findings, suggesting that linkage in couples’ EDA is a robust effect. Follow-up analyses showed that linkage only occurred when couples were physically together. Although the number of hours that couples were not together was small (n = 333) in comparison to the number of total hours captured (N = 3,118), this finding provides initial evidence that linkage is associated with couple interaction and does not occur when couples are apart. This result is consistent with other recent research showing that partner presence is associated with decreased EDA levels in daily life (Han et al., 2021). Further, we found evidence that physiological linkage in EDA in daily life was significantly associated with both concurrent fluctuations in, as well as general levels of, relationship functioning, suggesting that linkage is indicative of meaningful interpersonal processes and not a statistical artifact.

Physiological Linkage in EDA and the Shifting Emotional Context

Interestingly, the results of our analyses showed that feelings of closeness to one’s romantic partner were associated with concurrent increases in physiological linkage. Although we cannot ultimately determine whether this linkage is driven by relationship processes, shared experience, or an unmeasured third variable, our findings for closeness are consistent with other work reporting associations between linkage and empathy, emotional and physical connection, and perspective taking (e.g., Chatel-Goldman et al., 2014; Coutinho et al., 2019; Nelson et al., 2017; Papp et al., 2013; Reuf, 2001). This converging evidence lends support to the idea that physiological linkage is reflective of concurrent shifts toward greater interpersonal connectedness specifically. Perhaps moments of connection are characterized by increased awareness, permeability, receptiveness, and reactivity to others’ emotional states. In moments of emotional closeness, physiological connectivity might facilitate empathic responding and emotional bonding between romantic partners.

In contrast, moments of annoyance with one’s partner were not significantly associated with linkage in EDA. It is possible that we did not obtain enough negative interpersonal events within our day-long data collection procedures to detect a significant within-person effect. Alternatively, it is possible that the implications of physiological linkage vary across levels of analysis, with couples showing increased linkage relative to their own baseline when feeling connected but that couples with higher linkage generally show decreased relationship satisfaction on an aggregate level. Another possibility is that linkage processes during conflict are better suited to analytic frameworks that test contagion rather than synchrony or are better modeled on micro-process, second-by-second time scales. Future work with longer data collection periods or that focus on capturing and modeling couple conflicts at different time intervals could help to elucidate these associations.

Follow-up exploratory analyses examining stress, happiness, sadness, nervousness, and anger as moderators of linkage in couples resulted in significant effects for happy and sad mood. Specifically, as happy mood increased, physiological linkage decreased. In contrast for sad mood, as sad mood increased, physiological linkage also increased. These results suggest that negative emotional states may be more readily linked to increased linkage than positive mood states, though, notably, our findings for relationship closeness did not fit this pattern. This may suggest that positive emotions specifically related to the relationship may be related to increased linkage whereas general positive mood states, perhaps related to circumstances outside the relationship, are not. Additional follow-up exploratory analyses showed no evidence of a significant 3-way interaction between own EDA, own closeness, and own annoyance or when both partners felt annoyed or when both partners felt close at the same time. It is possible these models were underpowered to find significant 3-way effects. Alternatively, it is possible that one’s own individual relationship-relevant mood states are more directly related to linkage than the interaction of multiple moods or the mood states occurring across partners.

Physiological Linkage in EDA and General Relationship Satisfaction

Results of our analyses testing how physiological linkage relates to general levels of relationship functioning showed that less satisfied couples evidenced higher levels of linkage in their physiological responding. These data are consistent with extant work examining associations between linkage and relationship functioning across a range of physiological measures and contexts, generally showing that similarity in physiological signals is associated with decreased relationship functioning (e.g., Chaspari et al., 2015; Coutinho et al., 2019; Ha et al., 2016; Levenson & Gottman, 1983; Liu et al., 2013; Phan et al., 2019; Saxbe & Repetti, 2010; Timmons, Baucom, et al., 2017; Wilson et al., 2018). It is possible that couples with lower relationship satisfaction are more responsive to one another’s negative emotional states, causing them to easily catch their partners’ stress and ultimately contributing to physiological dysregulation and disruption in homeostasis. Importantly, our contrasting results for the hourly data highlight the importance of modeling how physiological linkage relates to relationship functioning across multiple levels of analyses. Less satisfied couples may exhibit increased linkage overall but may not evidence dynamic shifts in linkage in response to the fluctuating emotional tone of the relationship. Perhaps less satisfied couples are less attuned, sensitive, and responsive to shifting dynamics and instead evidence high levels of reactivity, regardless of the concurrent interactional quality. Our exploratory analyses showed no evidence of 3-way interactions between either (1) hourly own EDA, hourly own feelings of closeness, and overall own relationship satisfaction and (2) hourly own EDA, hourly own feelings of annoyance, and overall own relationship satisfaction. These findings suggest that changes in linkage during relationship-relevant mood states were not dependent on the overall level of relationship satisfaction. Alternatively, these exploratory analyses may have been underpowered for detecting significant 3-way interaction effects.

Strengths, Limitations, and Future Directions

The present study has several strengths, including using innovative methods to investigate physiological linkage in EDA in romantic partners’ everyday lives using mobile biosensors; capturing naturally occurring, real-life shifts in relationship functioning; and modeling the association between physiological linkage and relationship functioning across multiple levels of analysis. As part of our inclusion efforts, we recruited an ethnically, racially, and economically diverse sample. We recruited our participants to be representative of the diversity of the Los Angeles community from which we sampled to increase the generalizability of our work. Moreover, by combining mobile physiological data with ecological momentary assessments of interpersonal states, we were able to examine time-linked covariation across multiple modalities of measurement. Our detailed measurement procedures further allowed us to investigate patterns of EDA linkage across an entire day while also testing for and considering the effects of confounding variables. In total, these data provide evidence of the importance of considering the emotional context in the measurement of linkage processes and suggest that permeability toward others’ physiological states might confer both emotional risk and benefit.

Constraints on generality.

Despite these strengths, the results of our study must be interpreted in light of several important limitations. First, we measured linkage in EDA over a single day; couples may not have had sufficient time to habituate to the study procedures, though reactivity to at-home procedures was likely significantly lower than that of laboratory-based studies, which generally take place over even shorter time frames. Future studies should measure physiological linkage processes over days or weeks. Relatedly, given the one-day study duration, couples exhibited generally low levels of relationship conflict. Our findings may not generalize to larger-scale conflict episodes. Still, our results for closeness suggest that even small-scale relationship processes may be meaningfully associated with linkage, indicating that seemingly mundane interactions may hold significance for physiological functioning. Second, we investigated these processes in young couples—patterns of effects may differ among people in longer-term relationships who may change their physiological responsiveness over time; some people may increasingly “tune out” their partner while others may become more attached and emotionally attuned. Third, our models had relatively small effect sizes, explaining between 3 and 10 percent of the variance in EDA levels. Although small, it is important to note that small-scale events and micro-processes may have important implications for cumulative, macro-level variables, especially for repeated and chronic physiological processes, which may impact later health outcomes.

Fourth, our study focused here on physiological synchrony, or concurrent covariation in general levels of in physiological responding. We cannot tell from these analyses the directionality of effects or who was reacting to whom. An important next step is to conduct time-lagged analyses to learn how people react to their partners’ emotional states over time and to test emotional experiences as mediators of observed associations between physiological linkage and relationship quality. Fifth, it is possible that linkage in EDA is related to overall levels of sympathetic nervous system activation rather than specific emotional states, though, notably, adding average EDA as a covariate did not impact the size, significance, or direction of the model results. Moreover, results of follow-up analyses showed that linkage decreased when feeling happy, suggesting that some emotional contexts are linked to greater linkage, whereas others are linked to less. Finally, it is important to note that our analyses aggregated EDA over an hour-long span to conduct analyses linking EDA to self-reported relationship-related mood states. EDA is a fast-moving physiological measure with a response latency of 1–2 seconds but also shows general changes in level across the course of the day that correlate with various activities, general arousal, and mood (Picard et al., 2015, Poh et al., 2010, Poh et al., 2012). In our study, aggregating the EDA signals resulted in a loss of information about fine-grained relationship dynamics. Still, results aggregated over the hour showed evidence of physiological linkage that was associated with both concurrent and global measures of relationship functioning, suggesting that EDA linkage manifests over multiple timescales. Research testing linkage during micro-process, second-by-second interactions in daily life will be an informative next step for future work.

Implications and Conclusion

The results of our study have important implications for understanding how couples relate to each other, respond physiologically to one another’s moods and behaviors, and how such processes may affect relationship functioning. Connection to other people is imperative for decreasing stress responding and promoting wellbeing; however, interpersonal connection, if characterized by high levels of conflict, can impair wellbeing and contribute to negative mental and physical health outcomes over time (Beckes & Coan, 2011; Burman & Margolin, 1992; Repetti et al., 2011; Robles & Kiecolt-Glaser, 2003). Findings of this study highlight the double-sided nature of interpersonal connectivity, suggesting that generally heightened permeability and reactivity to others’ physiological states and emotions may be beneficial in some circumstances while detrimental in others. Still, important questions about the nature of linkage remain. Is linkage an outcome of relationship functioning or a driver? Can linkage be consciously controlled or manipulated? If so, interventions designed to improve relationship health could potentially alter linkage processes. Such interventions might focus on positively harnessing interpersonal connectivity, where couples understand and respond with sensitivity to each other’s emotions and perspectives without catching and amplifying negative emotionality or stress. Promoting connectivity with and responsivity to close others in adaptive contexts could thus be an effective mechanism for improving relationship functioning and may also have positive downstream impacts on individual mental and physical health.