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. Author manuscript; available in PMC: 2024 Jun 1.
Published in final edited form as: J Soc Pers Relat. 2022 Oct 22;40(6):1920–1942. doi: 10.1177/02654075221135855

Sleep Efficiency and Naturalistically-Observed Social Behavior Following Marital Separation: The Critical Role of Contact with an Ex-partner

Andrea M Coppola 1, Matthias R Mehl 1, Allison M Tackman 1, Spencer C Dawson 2, Karey L O’Hara 3, David A Sbarra 1
PMCID: PMC10448982  NIHMSID: NIHMS1852426  PMID: 37637857

Abstract

Marital disruption is associated with increased risk for a range of poor health outcomes, including disturbed sleep. This report examines trajectories of actigraphy-assessed sleep efficiency following marital separation as well as the extent to which daily social behaviors and individual differences in attachment explain variability in these trajectories over time. One hundred twenty-two recently-separated adults (N = 122) were followed longitudinally for three assessment periods over five months. To objectively assess daily social behaviors and sleep efficiency, participants wore the Electronically Activated Recorder (EAR) during the day (for one weekend at each assessment period) and an actiwatch at night (for seven days at each assessment period). Greater time spent with an ex-partner, as assessed by the EAR, was associated with decreased sleep efficiency between participants (p = .003). Higher attachment anxiety was also associated with decreased sleep efficiency (p = .03), as was the EAR-observed measure of “television on.” The latter effect operated both between (p = .004) and within participants (p = .005). Finally, study timepoint moderated the association between EAR-observed measure of “television on” and sleep efficiency (p = .007). The current findings deepen our understanding of sleep disturbances following marital separation and point to contact with an ex-partner and time spent with the television on as behavioral markers of risk.

Keywords: divorce, health, longitudinal

Marital separation and divorce are significant life upheavals that have the potential to undermine psychological wellbeing and physical health (Malgaroli et al., 2017). Although most adults are resilient in the face of divorce and fare well over time (Sbarra et al., 2015), marital dissolution is associated with increased risk for a range of poor health outcomes, including early death (Sbarra et al., 2011). Mechanistic accounts that seek to explain the broad epidemiological association between divorce and ill health often focus on health behaviors (Sbarra & Coan, 2017), and disturbed sleep may play an important role linking the end of marriage to negative health outcomes. Subjective sleep complaints in the weeks after a marital separation presage increases in resting blood pressure three months later (Krietsch et al., 2014), and this effect is moderated by time since the separation: adults reporting ongoing sleep disturbances that persisted 10 weeks or more after their physical separation were more likely to show the increases in resting blood pressure (Krietsch et al., 2014). In the current report, we expand on this prior work by studying actigraphy-assessed sleep efficiency over time. Despite the fact that sleep disturbances are highly associated with negative health outcomes (Cappuccio et al., 2010; Cohen et al., 2009; Gangwisch et al., 2007) and are now considered a critical intermediary in modern stress-health models (Benham, 2010), relatively few studies in the literature include repeated actigraphy assessments as people adjust to and cope with a difficult life event. In addition, very few studies assess changes in actigraphy-assessed sleep efficiency as people recover from difficult life events, and little research addresses the social behaviors and individual differences that may be associated with these changes over time.

Sleep, Stress, and Marital Separation

Psychosocial stress is a robust predictor of sleep disturbance (Åkerstedt et al., 2007, 2012; Hall et al., 2015), and experimental manipulations of stress result in substantial changes in polysomnographic sleep architecture (Kim & Dimsdale, 2007). Sleep quality is highly associated with negative affectivity, especially for people who experience a high degree of interpersonal conflict (Fortunato & Harsh, 2006; Troxel, Robles, et al., 2007). O’Hara et al., (2022), recently reported that adults who have a tendency to become overinvolved in their psychological experiences—as reflected in greater first-person, present-focused language use when discussing a marital separation, evidence lower levels of sleep efficiency in the months following the dissolution of their relationship. It should come as no surprise that disturbed sleep now plays a major role in our understanding of the pathways linking psychosocial stress to downstream health outcomes (Prather, 2019), and recent work suggests that poor sleep amplifies the effect of perceived stress on lymphocyte telomere length, an important marker of cellular aging (Prather, Gurfein, et al., 2015). Despite considerable research focusing on the nexus of sleep and stress, we are aware of very few studies that track changes in sleep efficiency as people recover from stressful or difficult life circumstances. In many ways, marital separation provides an ideal model for studying stress adaptation and sleep disturbance (Sbarra et al., 2012).

Although the end of marriage is a significant upheaval and a source of stress, most people recover well over time and the course of psychological adaptation is characterized by steady linear improvements in subjective, separation-related emotional distress (O’Hara et al., 2020). Importantly, the average course of adaptation following marital separation is moderated by several key individual differences (Sbarra et al., 2015), and a recent report showed that naturalistically-observed time spent with an ex-partner slows the course of psychological recovery; the more time people spend with their ex-partners, the harder it is to emotionally recover from the divorce experience, and this is especially true for people who do not have children with their ex-partners (O’Hara et al., 2020). The current report extends this prior work in two primary ways. First, we investigate the trajectory of actigraphy-assessed sleep efficiency over time and evaluate whether divorcing adults also evidence steady improvements in sleep efficiency in the months after their separation. Second, we examine the extent to which two naturalistically-observed daily social behaviors—including contact with an ex-partner—are associated with sleep efficiency over time.

Daily Social Behavior and Sleep in the Context of Marital Separation

Social relationships are vital for health and wellbeing and can serve as a protective factor in the face of stressful life events (Cohen & Wills, 1985; Holt-Lunstad, 2018), and new research highlights strong associations between sleep quality and social processes (Gordon et al., 2017), especially close relationships (Troxel, 2010; Troxel, Cyranowski, et al., 2007). In this report, we use the Electronically Activated Recorder (EAR; Mehl, 2017; Mehl et al., 2001) to objectively measure daily social behaviors in the months following a marital separation. The EAR is an ecological momentary assessment tool that operates via a smartphone app and is designed to capture brief snippets of ambient sound intermittently throughout the course of the day during participants’ waking hours. The EAR method is widely used across social and clinical psychology to capture observable behaviors (Robbins et al., 2011; Slatcher & Robles, 2012; Tobin et al., 2015); distinct from subjective reports on social experiences, the EAR method provides an objective (in the sense of traceable) record about observable social behaviors. What are people doing on a daily basis, and, in the case of this report, are these behaviors associated with the trajectory of sleep disturbance recovery? Using the EAR this way allows us not only to better understand social behaviors and sleep in the aftermath of a marital separation, but also to expand the study of sleep and interpersonal processes (Gordon et al., 2017) by examining naturalistically-observed social behaviors and moving beyond self-report alone in this area.

Using the EAR, we focus on two social behaviors that may be associated with sleep disturbances. First, time spent with an ex-partner is associated with worse outcomes following a breakup (Rhoades et al., 2011) and a divorce (Brown et al., 1980), yet many adults maintain a high degree of contact with their ex-partners (Fischer et al., 2005; Masheter, 1997). As noted above, data from a recent study indicates that EAR-assessed in-person contact with an ex-partner is associated with higher levels of separation-related (subjective) psychological distress two months later (O’Hara et al., 2020). A key goal for the present study is to extend this work to examine whether in-person contact with an ex-partner is also associated with actigraphy-assessed sleep efficiency. Second, considerable social theory suggests that television watching may be an indicator of social disengagement and decreased social capital (Eggermont & Vandebosch, 2001; Moy et al., 1999). As a potential marker of social disengagement, new data suggests that TV watching may play a causal role in the emergence of depression (Choi et al., 2020). Given this research, we also include EAR-assessed time when the television is on; importantly, the EAR method cannot discern active television watching per se, but it can reliably and objectively determine passive television exposure in the sense that the television was on in the ambient background, regardless of any other behaviors that may also have taken place.

Key Individual Differences: Attachment Orientations

A key idea in the divorce-health literature is that risk for poor outcomes is moderated by individual differences that are associated with poor emotion regulation (Sbarra et al., 2015). Attachment anxiety and attachment avoidance are both associated with a more pernicious course of recovery after marital separation (Bourassa, Hasselmo, et al., 2019; Lee et al., 2011). Recent work also finds that attachment insecurity is associated with greater sleep disturbance (Adams & McWilliams, 2015), although most of this work focuses on self-reported sleep disturbances. In the current report, we examine the idea that time spent with an ex-partner is most highly associated with sleep disturbances for people high in attachment anxiety (who engage in hyperactivating emotion regulatory strategies that may amplify emotional distress after interactions with an ex-partner) and/or avoidance (who engage in suppressive, or deactivating, emotion regulatory strategies that are cognitively and physiologically effortful). Because these emotion-regulatory strategies are maladaptive in many circumstances (Mikulincer & Shaver, 2019), we also expect people high in attachment insecurity who spend more time with their ex-partner to evidence greater sleep disturbances.

Present Study

The present study had two primary goals, and although our analyses were not preregistered (and thus should be considered more exploratory than confirmatory), the work was guided by a set of specific predictions. First, in a sample of 122 recently separated adults, we studied changes in actigraphy-assessed sleep efficiency following marital separation at three time points over five months; each assessment of sleep lasted one week. Consistent with prior work on trajectories of self-reported emotional adjustment (O’Hara et al., 2020), we expected to observe a positive linear increase in sleep efficiency over time as adults recovered from their separation over time. Second, also based on prior work focused on contact with an ex-partner and psychological distress (O’Hara et al., 2020), we predicted that greater EAR-assessed contact with an ex-partner following separation would correspond with decreased sleep efficiency—slower increases over time and, within-occasion, lower levels of sleep efficiency when contact with an ex-partner is relatively higher than participants’ average level of contact. In addition, we included attachment anxiety and avoidance as main effect predictors of sleep efficiency, and we examined whether the attachment orientation variables moderated the potential association between contact with an ex-partner and sleep efficiency. Finally, in addition to the effects outlined above, we predicted that participants who are observed to have the TV on for greater periods of time, which we conceived above as a potential marker of social disengagement or withdrawal, would evidence lower sleep efficiency at each study occasion.

Methods

Participants

Participants for this study were taken from a community sample of 122 (87 women, 35 men) recently separated or divorced adults between the ages of 24 and 65 (Borelli et al., 2019; Hasselmo et al., 2018; Manvelian et al., 2018; Milek et al., 2018; O’Hara et al., 2020; Tackman et al., 2019). Participants were married for an average of 12.7 years (SD = 9 years) and separated for an average of 3.8 months (SD = 2.16) at the beginning of the study. The sample included participants who identified as non-Hispanic White (62.3%), Hispanic (22.1%), African American (4.9%), Native American (1.6%), and Asian (2.5%); the remaining participants identified their race/ethnicity as “Other.” Approximately 23.6% of participants made less than $11,999 per year, 32.8% made between $12,000 and $34,999, 28.5% made between $35,000 and $74,999, 6.4% made over $75,000 annually, and the remaining percentage did not report their annual income. Our sample of 122 was determined by selecting participants who had actigraphy data and usable EAR data for at least one measurement occasion. At Time 1, 114 participants completed the actigraphy assessment; at Time 2, 107 participants completed the actigraphy assessment, and at Time 3, 98 participants completed the actigraphy assessment, yielding a 20 percent reduction in compliance over the course of the study. We observed no mean differences in baseline sleep efficiency between participants who remained in the study relative to those who dropped out, suggesting that attrition did not operate as a function of early differences in sleep disturbance.

Procedures

Participants were recruited to enroll in a study investigating adults’ adjustment after marital separation or divorce through online and newspaper advertisements, divorce support groups, and family courts. Participants were eligible to participate if they had been married for at least three years and had lived with their partner for at least two years. All aspects of this study were approved by the Human Subjects Protection Program at the University of Arizona, and all participants provided informed consent for all aspects of the study. The parent study involved five waves of data collection over five months, and we assessed daily social behaviors (via the EAR) and sleep (via actigraphy) on three occasions during the odd numbered months; this report uses data from all three of those occasions. The measurement occasions were chosen to capture three assessments within the first six months after participants’ romantic separation, a period in which we expected to observe, based on prior literature (Sbarra et al., 2015), meaningful changes in psychological and social adjustment to the stress of marital dissolution. Each occasion included an actigraphy and EAR data collection; following standard conventions in the literature, actigraphy data was collected over seven days (Prather, Janicki-Deverts, et al., 2015), whereas the EAR data was collected over a weekend (Kaplan et al., 2020; beginning Friday evening until Sunday bedtime) that overlapped with the actigraphy assessment. The EAR recorded 30-second clips approximately every 12 minutes. The recording window for each participant included a 6-hour blackout period each night that was individually scheduled to occur during each participant’s habitual sleep time. After completion of the study, all sound files were transcribed and coded by trained research assistants. The research assistants used a standardized coding system (https://osf.io/4yb97/) that includes extensive use of the context preceding and following each sound file to enhance accuracy (the details are described below).

Sleep efficiency was objectively measured via actigraphy data collected through wrist actiwatch devices (Actiwatch 2, Phillips Respironics). Participants were instructed to wear the actiwatch from the time they got into bed until the time they got out of bed for one week (7 nights) during Times 1, 2, and 3. Participants were instructed to press the event marker button on the actiwatch 1) when they intended to fall asleep and 2) when they awoke for the final time at the end of their sleep period. These markers were used as lights-off and lights-on times. Sleep diaries (Carney et al., 2012) were collected concurrently as a subjective measure of sleep. Participants recorded the time they went to bed, the time they turned off the lights, the time they awoke in the morning, and the time they got out of bed in the morning. Although we primarily relied on event markers to set rest intervals, on occasions that participants did not push the event marker button, the rest interval was set using lights-off and lights-on times recorded in their sleep diaries.

Measures

Sleep efficiency.

The primary outcome in this study was sleep efficiency, calculated from actigraphy data and averaged across each 7-day assessment period. We provide detailed analyses with other sleep outcome metrics in our online Supplemental Materials. Sleep onset was defined as the beginning of the first five minutes of contiguous quiescence with less than 30 seconds of any recorded activity counts that occur after lights-off time. Once lights off, lights on, sleep onset, and sleep offset were established, and these four values were entered into a sleep-scoring algorithm. This algorithm was used to analyze sleep within the rest intervals, from which sleep parameters were derived, including time in bed, total sleep time, sleep onset latency, wake after sleep onset, and sleep efficiency. Sleep efficiency was calculated as total sleep time divided by the total time in bed. We multiplied all sleep efficiency scores by 100 to yield a percentage. Average sleep efficiency was 81.91% at occasion 1 (SD = 7.84), 81.61% at occasion 3 (SD = 8.90), and 82.34% at occasion 5 (SD = 7.97).

Naturalistically-observed daily social behaviors.

For each participant, approximately 90 sound files (45 minutes of audio recording) were collected per day via the EAR. For each sound file (from measurement occasions 1, 2, and 3), coders identified whether (1) the participant was with their ex-partner, and (2) the television was on (Milek et al., 2018). These binary codes were then aggregated within occasion to obtain an estimate of the proportion of time participants spent engaged in each behavior. The way coders go about coding these categories is by wholistically considering the voices of participants’ interaction partners, the content of their interactions, the context, and the “sound file history” (i.e., what they have heard so far and what they hear in the next few sound files). Realistically, coders may not “catch” that an interaction partner is the ex-partner at the first occasion. They might, based on the content of the conversation; or they might not, to the extent that the conversation or context does not (yet) reveal it. However, over the course of listening to more sound files, the coders find redundancy in voices and voices co-occurring with contexts. It is through this redundancy and covariation that it becomes typically fairly straightforward to determine who the main people are that a participant is talking with. Consistent with this experience across EAR studies, the agreement in this study between two independent coders for determining “with the ex-partner” at our first study occasion was high (ICC[1,2] = .90) and on par with (other) clearly discernable codings such as whether the participant was alone (ICC[1,2] = .91), whether the participant was laughing (ICC[1,2] = .93, and whether the participant was talking to their child/children (ICC[1,2] = .93). Note that the television variable is not interpreted as “time spent actively watching television,” only that the television is coded as “on” in the sound file, which reflects both active and passive television consumption. The intraclass correlation coefficients across all visits was 0.81 for time spent with an ex-partner and 0.98 for time with the television on. We observed positive skew and kurtosis in the distribution of WithEx raw scores but, in an effort to maintain an interpretable scale, chose not to transform this variable for our primary analyses. There are documented cautions against data transformations, including unstable and out-of-range parameter estimates (Feng et al., 2014; O’Hara & Kotze, 2010). The online Supplemental Materials include analyses conducted with an inverse transformation of the WithEx variable.

Attachment Orientations.

Each participant’s attachment orientation was assessed via the 12-item Experiences in Close Relationships Scale – Short Form (ECR-SF). This questionnaire assesses attachment on a 7-point scale from strongly disagree (1) to strongly agree (7) with higher scores indicating greater attachment insecurity. Attachment insecurity is operationalized as attachment anxiety (fear of rejection and abandonment) and attachment avoidance (discomfort with closeness and dependency on others) with two distinct 6-item subscales assessing each construct. People who report being high in attachment anxiety tend to use hyperactivating emotion regulatory strategies, many of which represent maladaptive attempts to promote reunion with an attachment figure (e.g., reassurance seeking, or expressed jealousy (Chris Fraley et al., 2006; Mikulincer et al., 2003; Mikulincer & Shaver, 2019); in contrast, people who report being high in attachment avoidance engage in deactivating emotion regulatory strategies, frequently observed as emotional suppression or hyper-self-reliance (Mikulincer et al., 2003; Mikulincer & Shaver, 2019). The subscale items in the current sample demonstrated adequate scale reliability (αanxiety=0.77, αavoidance=0.72).

Separation-related Psychological Distress.

We assessed separation-related psychological distress (SRPD) using four self-report questionnaires: Beck Depression Inventory (BDI; Beck et al., 1988), Impact of Events Scale (IES; Weiss, 2007), Loss-of-Self/Rediscovery-of-Self (LOSROS; Lewandowski & Bizzoco, 2007), and Inventory of Complicated Grief (ICG; Prigerson et al., 1995). The measures were rescaled using linear transformations to Percentage of Maximum Possible (POMP) scores on a scale of 0 to 100, and then combined into a standardized mean composite of SRPD with higher scores reflecting greater separation-related psychological distress. The SRPD is a reliable index of separation-related psychological distress that demonstrates strong construct validity (Bourassa, Tackman, et al., 2019; O’Hara et al., 2020). We used the SRPD composite as a covariate in all analyses.

Demographic and relationship covariates.

We included several other covariates in our models based on previous work suggesting these variables are associated with post-divorce adjustment (Mason & Sbarra, 2012; Sbarra & Emery, 2005; Wang & Amato, 2000). Demographic covariates included age and sex. Relationship covariates included time since separation (in months), length of relationship (in months), and perceived responsibility for the separation or divorce assessed on a 4-point Likert scale (1=participant was totally responsible, 4=ex-partner was totally responsible).

Data analysis

The data were analyzed in accord with the main study hypotheses using the nlme package in R, which allows for the specification of a mixed effect model that accounts for the nested nature of the data (Pinheiro et al., 2014). First, we tested a baseline model to examine the functional form of the change in sleep efficiency across study timepoints. We then added the between and within-person fixed and random effects of in-person contact with an ex-partner into the model, including age, sex, length of separation, relationship length, separation-related psychological distress (SRPD), and perceived responsibility for the separation/divorce as covariates (Table 1, Model 1). We decomposed all level-1 variables into their between- and within-person components, the former representing a grand-mean centered level-2 variable and the latter representing a person-centered level-1 deviation score (Curran et al., 2012). Attachment anxiety and attachment avoidance were then entered into the model; we examined these variables as main effects, as well as whether the two variables moderated the potential association between in-person contact with an ex-partner and sleep efficiency (Table 1, Model 2–4). Next, we added the EAR-assessed television on variable (Table 1, Model 5), and then included all significant variables of interest in a single model, including time with ex-partner, time with the television on, and attachment orientation (Table 1, Model 6). Finally, we tested 1) whether time moderated any of the significant associations between sleep efficiency and our variables of interest (Table 1, Model 7), and 2) whether having a child moderated the relationship between sleep efficiency and in-person contact with an ex-partner. All data and code from this study are available on the Open Science Framework (https://osf.io/8vd7k/?view_only=6fbb08ce51024e9392af17144c3cae85).

Table 1.

Unstandardized Regression Coefficients from Models Predicting Sleep Efficiency

Model 1: Baseline In-Person Contact Model b SE 95% CI
Intercept 84.59 4.58 75.66, 93.53
Between-Person (grand-mean centered) EAR With Ex −59.92*** 14.96 −89.20, −30.63
Within-Person (person-mean centered) EAR With Ex −37.38 21.01 −78.34, 3.59
Age −0.04 0.06 −0.16, 0.08
Gender 0.28 1.47 −2.60, 3.16
Separation Length 0.003 0.31 −0.60, 0.61
Perceived Responsibility for Separation −0.53 0.51 −1.54, 0.48
Model 2: Baseline In-Person Contact with All Covariates b SE 95% CI
Intercept 94.22 5.63 83.30, 105.14
Between-Person (grand-mean centered) EAR With Ex −53.54** 14.94 −82.62, −24.46
Within-Person (person-mean centered) EAR With Ex −40.28 20.53 −80.08, −0.48
Attachment Anxiety −1.45* 0.58 −2.58, −0.32
Attachment Avoidance −1.08 0.66 −2.36, 0.20
Separation-Related Psychological Distress 0.01 0.03 −0.05, 0.08
Age −0.06 0.06 −0.19, 0.06
Gender −0.05 1.49 −2.85, 2.94
Separation Length 0.13 0.31 −0.48, 0.74
Perceived Responsibility for Separation −0.69 0.53 −1.71, 0.34
Model 3: Interaction with Attachment Anxiety b SE 95% CI
Intercept 89.33 2.80 83.88, 94.78
Between-Person (grand-mean centered) EAR With Ex −67.22 54.92 −174.72, 40.27
Within-Person (person-mean centered) EAR With Ex −41.41* 19.78 −79.99, 2.82
Attachment Anxiety −1.23* 0.55 −2.31, −0.16
Attachment Avoidance −0.88 0.62 −2.10, 0.35
Separation-Related Psychological Distress −0.02 0.03 −0.08, 0.05
Between-Person With Ex * Attachment Anxiety 5.23 11.86 −18.00, 28.46
Model 4: Interaction with Attachment Avoidance b SE 95% CI
Intercept 89.61 2.89 83.97, 95.25
Between-Person (grand-mean centered) EAR With Ex −32.15 70.64 −170.40, 106.10
Within-Person (person-mean centered) EAR With Ex −40.77* 20.23 −80.23, −1.31
Attachment Anxiety −1.26* 0.55 −2.36, −0.18
Attachment Avoidance −0.91 0.64 −2.16, 0.34
Separation-Related Psychological Distress −0.02 0.03 −0.08, 0.05
Between-Person With Ex * Attachment Avoidance −3.17 19.04 −40.43, 34.09
Model 5: Including EAR TV On b SE 95% CI
Intercept 94.76 5.41 84.28, 105.24
Between-Person (grand-mean centered) EAR With Ex −46.19** 14.40 −74.18, −18.20
Within-Person (person-mean centered) EAR With Ex −40.21 20.64 −80.17, −0.26
Attachment Anxiety −1.27* 0.56 −2.36, −0.19
Attachment Avoidance −1.08 0.63 −2.31, 0.15
EAR Television −7.91*** 1.91 −11.62, −4.20
Separation-Related Psychological Distress 0.03 0.03 −0.04, 0.09
Age −0.03 0.06 −0.15, 0.09
Gender −0.03 1.43 −2.81, 2.75
Separation Length 0.12 0.30 −0.46, 0.71
Perceived Responsibility for Separation −0.75 0.51 −1.74, 0.23
Model 6: Model with All Primary Variables of Interest b SE 95% CI
Intercept 91.81 5.46 81.26, 102.37
Between-Person (grand-mean centered) EAR With Ex −44.71** 14.68 −73.18, −16.23
Within-Person (person-mean centered) EAR With Ex −40.03 20.79 −80.19, 0.13
Attachment Anxiety −1.25* 0.56 −2.34, −0.16
Attachment Avoidance −1.07 0.63 −2.29, 0.16
Between-Person (grand-mean centered) EAR TV −9.40** 3.17 −15.55, −3.25
Within-Person (person-mean centered) EAR TV −7.02** 2.45 −11.74, −2.29
Separation-Related Psychological Distress 0.03 0.03 −0.04, 0.09
Age −0.02 0.06 −0.15, 0.10
Gender −0.11 1.44 −2.90, 2.68
Separation Length 0.13 0.30 −0.46, 0.71
Perceived Responsibility for Separation −0.76 0.51 −1.74, 0.23
Model 7: Study Timepoint*Within-Person TV Interaction b SE 95% CI
Intercept
Study Timepoint −0.04 0.37 −0.75, 0.68
Within-Person (person-mean centered) EAR TV 5.44 4.82 −3.84, 14.72
Between-Person (grand-mean centered) EAR TV 10.89* 3.25 −17.17, −4.61
Within-Person (person-mean centered) EAR With Ex −20.49 12.57 −44.71, 3.71
Between-Person (grand-mean centered) EAR With Ex −34.37** 16.31 −65.91, −2.83
Attachment Anxiety −1.14* 0.58 −2.25, −0.03
Attachment Avoidance −0.83 0.64 −2.07, 0.41
Separation-Related Psychological Distress 0.01 0.04 −0.06, 0.08
Age −0.03 0.06 −0.16, 0.09
Gender −0.41 1.46 −3.23, 2.42
Separation Length 0.18 0.31 −0.41, 0.78
Perceived Responsibility for Separation −0.66 0.52 −1.66, 0.34
Study Timepoint*Within-Person EAR TV −11.42** 4.15 −19.42, −3.43
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Note: b = unstandardized regression coefficient, SE = standard error, CI = confidence interval.

Significance levels:

*

p < .05,

**

p < .01,

***

p < .001

Results

Table 2 shows descriptive statistics of and correlations among all study variables.

Table 2.

Means, standard deviations, and correlations of study variables

Variable 1 2 3 4 5 6 7 8 9 10 M SD
1. Time 1.00 0.82
2. Sleep Efficiency .01 82.06 8.20
3. Attachment Anxiety .00 −.20** 3.86 1.23
4. Attachment Avoidance .00 −.15** .06 2.86 1.07
5. WithEx .03 −.29** .15** .13* 0.01 0.05
6. TV −.02 −.28** .12* .08 .09 0.34 0.24
7. SRPD −.26** - 17** .36** 17** .07 .23** 24.87 15.88
8. Age .00 −.05 −.22** .07 −.05 .16** .05 43.74 10.56
9. Gender .00 .06 −.08 −.09 .02 −.12* −.22** −.12* 1.71 0.45
10. Relationship Length .00 .12* −.21** −.04 −.15** .01 −.10 .62** −.10* 187.98 112.35
11. Separation Length .00 .02 .10 .05 −.16** .00 .07 .00 −.00 .05 3.76 2.16
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Note. M and SD are used to represent mean and standard deviation, respectively. WithEx = In-person contact with ex-partner. SRPD = Separation Related Personal Distress. Time = study timepoint (1, 2, 3). Gender was coded as 0=female, 1=male.

*

indicates p < .05.

**

indicates p < .01.

We first examined changes in actigraphy-assessed sleep efficiency across the five-month study period and, contrary to our main hypothesis, found no effect of study timepoint on sleep efficiency in this sample (b = −0.35, SE = 0.32, 95% CI = [−0.98, 0.29], p = .28); sleep efficiency remained stable across the three measurement occasions. Across all timepoints, mean sleep efficiency was 82.10%, and the random effect on the intercept was reliably different from zero, indicating significant variability around this mean level over time.

We next examined whether contact with an ex-partner was associated with sleep efficiency. In the first series of analyses controlling for participants’ age, sex, time since separation and perceived responsibility for the separation (Table 1, Model 1), the between-person (grand mean-centered) effect of in-person contact with an ex-partner was significantly associated with sleep efficiency (p < .001). This effect was in the hypothesized direction: participants who spent more time with an ex-partner following separation had lower overall sleep efficiency. The within-person (person mean-centered) effect of in-person contact with an ex-partner was not significantly associated with sleep efficiency (p = .08). (In conducting these analyses, an obvious question is whether the associations of interest operate bidirectionally. Thus, we tested the same basic model specification as reported in Table 1 but switched the primary dependent and predictor variables. Using the same set of control variables, we find no evidence that sleep efficiency predicts greater contact with an ex-partner, b = −0.0006, SE = 0.0006, p = .27. We also explored whether adding the lagged-1 sleep efficiency variable to this model would predict greater contact with an ex-partner at the next occasion of measurement; as with the concurrent association, the lagged effect was non-significant, b = −0.0004, SE = 0.00065 p = .37.)

Having examined these effects, we added attachment anxiety and avoidance, as well as the SRPD variable, into the model (Table 1, Model 2). As shown, the between-person contact remained a significant predictor of sleep efficiency (p < .001). We found a main effect of attachment anxiety (p = .01), indicating that participants who reported higher levels of attachment anxiety had lower sleep efficiency across all measurement occasions. There was no main effect of attachment avoidance on sleep efficiency (p = .11). We then examined (Table 1, Model 3–4) whether the effect of in-person contact with an ex-partner on sleep efficiency was moderated by attachment insecurity (anxiety, avoidance). We found no evidence of a moderation by attachment anxiety (p = .66), nor moderation by attachment avoidance (p = .87).

The next model examined whether the EAR television on variable was associated with sleep efficiency. We found a main effect of time with the television on on sleep efficiency (p < .001, Table 1, Model 5). When decomposed (Table 1, Model 6), we found that both the between-person effect (p = .004) and within-person effect (p = .005) for time with the television on were associated with sleep efficiency in a manner that reliably differed from zero. The effect was in the expected direction: people who had the television on more in general evidenced lower sleep efficiency and on occasions when people had the television on (more than they do on average), we also observed decreased sleep efficiency. Table 1 (Model 6) displays the results of the significant effects across all of the prior analyses, and the overall results from this model are highlighted in Figure 1, which displays the unique effects of the significant predictor variables on sleep efficiency across the study period.

Figure 1. Significant Predictors of Sleep Efficiency.

Figure 1.

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Fixed-effect estimates predicting sleep efficiency (from Model 6, Table 1). Horizontal line represents mean sleep efficiency in the sample (82.09%; SD = 8.34%). The estimate within any one variable category represents the differences associated with being 1 SD above/below the mean for the given variable with all other variables in the model estimated at their mean values. Thus, the figure represents the unique and independent effects of each predictor on sleep efficiency.

Finally, in a series of exploratory analyses, we tested whether study timepoint moderated any of the observed effects (i.e., those reported in Table 1, Model 6). We tested five interaction models and found that study timepoint interacted with the EAR television on variable (Table 1, Model 7; Figure 2). At Time 1, time with the television on was not significantly associated with sleep efficiency (b = 3.35, SE = 4.19, p = .42). At Time 2 (b = −5.99, SE = 2.52, p = .02) and Time 3 (b = −15.33, SE = 4.27, p < .001), time with the television on was negatively associated with sleep efficiency. Thus, after the third month in the study, the association between EAR-assessed time with the television on and sleep efficiency is negative and significantly different from zero.

Figure 2. Association between Television On and Sleep Efficiency Moderated by Study Timepoint.

Figure 2.

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Moderation of the association between time with the television on and sleep efficiency by study timepoint (from Model 7, Table 1). Asterisks indicate significance levels determined by simple slope decompositions.

Discussion

In a sample of 122 recently separated adults, the current study examined the association between actigraphy-assessed sleep efficiency and two naturalistically-observed daily social behaviors: time spent with an ex-partner and time with the television on. Contrary to our main study hypothesis, we observed no significant improvements in sleep efficiency over time. Participants averaged 82% sleep efficiency across the five months of the study; despite the absence of a significant linear increase over time, the random variance component of the intercept was reliably different from zero, which points to considerable variability in mean-level sleep efficiency at each occasion. Consistent with our second hypothesis, in-person contact with an ex-partner, assessed using the EAR, was a significant predictor of sleep efficiency—greater contact with an ex-partner in the months after marital separation was associated with lower sleep efficiency. We found this effect operated between but not within subjects, indicating that people who spend more time with their ex-partner overall (following marital separation) evidence lower sleep efficiency. Importantly, this effect remained consistent and significant after accounting for a number of relevant covariates, including participants’ age, gender, time since the separation, perceived responsibility for the separation, individual differences in attachment insecurity, separation-related psychological distress, and EAR-assessed time with the television on. From our vantage point, it is especially notable that independent of adults’ subjective emotional distress about their separation, the degree to which they spend time with their partner after the separation is associated with lower sleep efficiency. This effect mirrors the established association between contact with an ex-partner and separation-related psychological distress following the end of marriage (O’Hara et al., 2020).

In the general population, non-clinical sleep efficiency is close to 85% (Cespedes Feliciano et al., 2017), which is consistent with what we observed among our recently-separated participants. On one hand, given our initial hypothesis for lower sleep efficiency followed by a gradual but systematic improvement, the average level sleep efficiency among our participants may seem surprisingly high. On the other hand, any group of divorcing participants includes some people who are faring quite well and some who are struggling quite a lot (Sbarra et al., 2015). On average, people who engage in one standard deviation more contact with their ex-partner (that is, people who spend an average of 4.8% more time with their ex-partner) evidence 2.15% lower sleep efficiency. One way to understand this effect size better is to calibrate it against existing changes in actigraphy (cf. Funder & Ozer, 2019). Empirically, there is strong evidence that sleep efficiency decreased as we age—e.g., meta-analytic findings indicate that between the fifth and sixth decade of life, actigraphic sleep efficiency drops from 86.77% to 85.00%, which is a change of 1.77% (Evans et al., 2021). In our sample, we see a larger change over four months for those who evidence more time spent with their ex-partner. Prior literature suggests that sleep-disturbances after marital separation can presage future increases in resting blood pressure, and it is well documented that interpersonal stressors negatively impact sleep quality (Gordon et al., 2017). The results of this study provide new evidence for how these two sets of findings may fit together. In-person contact with an ex-partner may be a form of social stress that heightens risk for sleep efficiency. Beyond typical social stress, a high degree of time spent with an ex-partner may be a unique stressor in many ways, including serving as a reminder of the lost relationship (O’Hara et al., 2020).

To fully interpret our findings, it may be important to understand the nature of the in-person contact with the ex-partner. A person may maintain contact with their ex-partner or encounter them unexpectedly for a variety of reasons (e.g., co-parenting, mutual friends; O’Hara et al., 2020) that may differentially impact sleep efficiency. In our sample, having a child did not moderate the association between contact with an ex-partner and sleep efficiency. Unfortunately, additional details of the contact between ex-partners are not available in the current study; therefore, these questions will require further investigation. In addition, it is important to remember that our study assessed contact and sleep every other month over three occasions. It is likely the case that, to the extent that the observed effects are causal, contact is associated with sleep efficiency at the daily or weekly level. Thus, the measurement resolution of this study may not be ideal for identifying within-person processes that may unfold over days and weeks. Thus, using a different timescale, we cannot rule out the possibility that the associations of interest operate at a within-person level. We readily acknowledge that, if assessed at a different resolution, decreased sleep efficiency may be associated with an increased likelihood of future contact with an ex-partner. As reported in the results, we tested such a model and find no support for evidence of effects from sleep efficiency (concurrently or at the occasions before) to contact with an ex-partner over the course of our bi-monthly assessments.

In addition to the effects reported above, we also found that more time spent with the television on was associated with significantly lower sleep efficiency, and this effect was observed both between people and within occasions (when people evidenced greater time with the television on than they do on average). Further, the association of the EAR within-person time with the television on variable and sleep efficiency interacted with time in the study such that at the second and third assessments, but not the first, greater percentages of time with the television on were associated with lower sleep efficiency. This suggests that later in the recovery trajectory, when people evidence greater time with the television on when they do on average, they also evidence lower sleep efficiency. We speculate that as time moves forward after a separation experience, time with the television on may serve as a critical marker of social disengagement; early in the separation period, having the TV on in the background may not be associated with worsened sleep, but as time persists, this behavior may reflect continued separation-related grief. Previous research links television with poor sleep outcomes (Gradisar et al., 2013). Here we provide an objective measure of how long the television is on during the day (range from 0% to 98.38% of time across the sample) and show that this variability is highly associated with actigraphy-assessed sleep efficiency. As noted above, although the EAR is able to capture the sound of the television, we cannot determine from these files whether the participant is actively watching television. Of course, even with the prospective design of this study, it is difficult to discern the direction of this effect. It is possible that people who have difficulty sleeping have the television on more regularly; it is possible that television watching contributes directly to poor sleep; and, finally, it is possible that having the television on is a proxy variable for sedentary time, emotional avoidance, or depressed mood not captured by our separation-related distress measure, any of which may predict disturbed sleep.

We also observed that people reporting greater attachment anxiety had lower sleep efficiency, which is consistent with prior findings reported in the literature (Haydon & Moss, 2021; Troxel & Germain, 2011). Attachment anxiety is associated with a range of negative health outcomes (McWilliams & Bailey, 2010). Individuals high in attachment anxiety show stronger physiological reactivity to stress, which is likely to impair sleep quality (Dozier & Kobak, 1992; Quirin et al., 2008). Indeed, attachment anxiety is negatively associated with subjective sleep efficiency (Gouin et al., 2009) and is related to sleep disturbance independent of health conditions and concurrent psychiatric disorders (Adams & McWilliams, 2015; Troxel & Germain, 2011). Our findings extend prior work and show that higher attachment anxiety is associated with lower actigraphy-assessed sleep efficiency in adults following marital separation.

The findings reported here should be understood in terms of the study’s limitations. First, although we report that participants evidenced a mean of 1.5% of their EAR-file time with their ex-partners, we observed positive skew and kurtosis in the distribution of raw scores; the majority of our sample did not spend any time with their ex-partner over the weekend as assessed by the EAR. Moreover, we have no information about the nature of the contact between ex-partners. In this respect, these findings cannot speak to the valence of the effects. Future research will be needed to determine if all types of contact with an ex-partner are associated with decreased sleep efficiency or whether this effect is eliminated when contact is agreeable and positive. Second, participants wore the EAR device on weekends only. Thus, we have no assessment of daily behavior during weekdays. In-person contact with an ex-partner and time spent with the television on could be different on weekdays compared to weekend days. Finally, the sample is predominantly White (62%; 22% Hispanic) and includes relatively few men (n = 35), and we do not have data regarding sexual orientation or disability status. This precludes understanding whether ethnicity, gender, sexual orientation, and/or disability status moderates any of the key effects. Given that men experience worse physical health outcomes than women when a relationship ends (Sbarra et al., 2011), future research examining gender differences in actigraphy-assessed sleep efficiency would make a large contribution to the literature.

Conclusion

This work provides important insight into the dynamic interplay between sleep and recovery from divorce. A major strength of this study was its multi-method assessments, and we showed that naturalistically-observed daily social behaviors, especially contact with an ex-partner, are associated with lower sleep efficiency. Additionally, attachment anxiety and time spent watching television are inversely associated with sleep efficiency. These findings highlight the influence of social relationships on health behaviors and suggest potential intervention targets for adults recovering from the end of a marriage.

Supplementary Material

1

Acknowledgments

This paper is dedicated to the memory of Richard R. Bootzin, Ph.D., who was a collaborator on this project before his untimely death in 2014.

Role of Funding Source

This research was supported by a grant from the National Institute of Child Health and Human Development (R01HD069498). Karey L. O’Hara’s work on this paper was supported by a career development award provided by the National Institute of Mental Health (K01MK120321).

Footnotes

Declaration of Conflicting Interests

The authors declare no conflicts of interest with respect to their authorship or the publication of this article.

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