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. Author manuscript; available in PMC: 2025 May 1.
Published in final edited form as: Psychosom Med. 2024 Mar 4;86(4):349–358. doi: 10.1097/PSY.0000000000001295

High heart rate variability buffers the effect of attachment insecurity on sleep quality

Jensine Paoletti 1, Daniel L Argueta 1, E Lydia Wu-Chung 1, Michelle A Chen 2, Ryan L Brown 3, Angie S LeRoy 4, Kyle W Murdock 5, Julian F Thayer 6, Christopher P Fagundes 1,7,8
PMCID: PMC11081832  NIHMSID: NIHMS1967469  PMID: 38446714

Abstract

Objective

Sleep quality is an important health-protective factor. Psychosocial factors, including attachment orientation, may be valuable for understanding who is at risk of poor sleep quality and associated adverse health outcomes. High attachment anxiety is reliably associated with adverse health outcomes, while high attachment avoidance is associated with adverse health outcomes when co-occurring with poor self-regulatory capacity, indexed by heart rate variability (HRV). We examined the associations between attachment anxiety, attachment avoidance, HRV, and sleep quality.

Methods

Using longitudinal data from a sample of 171 older adults measured four times over one year (M = 66.18 years old; 67.83% women), we separated the between-person variance (which we call “trait”) and within-person variance (which we call “state”) for attachment anxiety, attachment avoidance, and HRV (via the root mean square of successive differences). Sleep quality was measured with the Pittsburgh Sleep Quality Index.

Results

Higher trait attachment anxiety was associated with poorer global sleep quality (B = 0.22, p = .005). Higher state attachment avoidance was associated with poorer sleep quality (B = −0.13, p = .01), except for those with higher trait HRV. Higher state attachment anxiety was associated with poorer sleep quality (B = −0.15, p = .002), except for those with higher or mean trait HRV. Higher trait attachment anxiety was associated with poorer sleep quality (B = −0.31, p = .02), except for those with higher trait HRV.

Conclusions

High trait HRV mitigated the adverse effects of attachment insecurity on sleep quality. Our results suggest that people with high trait HRV had greater self-regulation capacity, which may be able them to enact emotion regulation strategies effectively.

Keywords: Healthy aging, self-regulatory capacity, sleep latency, habitual sleep efficiency

INTRODUCTION

High-quality sleep is a crucial component of health; sleep health is related to cardiovascular health and other physical and psychological health facets (1,2). Sleep quality is characterized by several distinct but related components, which include subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, sleeping medication usage, and daytime dysfunction. On average, older adults have poorer sleep quality than younger adults, and the age-related decline in sleep quality is associated with cognitive decline (3,4). However, there is considerable variability in sleep quality among older adults, even after adjusting for medications and comorbidities. Identifying non-medical factors associated with sleep quality in older adults may help us understand who is at risk for sleep problems and related downstream effects (e.g., higher chronic inflammation; 3–5). Psychological stress is a well-established non-medical factor that negatively alters sleep quality. Therefore, how people self-regulate when exposed to potentially stressful contexts should play a central role in sleep quality, as regulation strategies are associated with sleep quality (6,7). Self-regulation involves control over attention, thoughts, and affect in service of goal-directed behavior (8).

Because sleep is an evolutionarily vulnerable behavior, feeling safe and secure can optimize one’s sleep (9). Attachment theory is a valuable theoretical framework for understanding processes that govern feelings of security and processes like affect regulation. According to attachment theory, the quality of people’s close relationship experiences, starting early in life with one’s primary caregiver (i.e., primary attachment figure), shape their characteristic tendencies (i.e., working models) for responding to potentially stressful or threatening events. People with a history of unreliable, frustrating, or rejecting attachment figures develop insecure attachment orientations characterized by high levels of attachment anxiety and/or attachment avoidance (10). People who possess well-developed working models of helpful attachment relationships sustain a sense of confidence that they can manage potential threats and manage or regulate their emotions (10). Emotions, particularly negative affect, are a key pathway between attachment and sleep quality (11).

When confronted with potentially stressful or threatening daily events, those who report high levels of attachment anxiety are at risk for poor sleep quality via affective pathways (6,7,12–15). When faced with ambiguous social situations or other potential threats, those high on attachment anxiety are more likely to have negative interpretations and respond with maladaptive regulation strategies like hypervigilance and catastrophizing (16,17). Researchers theorize that the hypervigilance to perceived threats and the hyperactivating regulation strategies promote poor health behaviors, and put wear and tear on the body (16,18). Hypervigilance and hyperactivation are inconducive to sleep (9); higher attachment anxiety is associated with more sleep problems (19,20). For example, in couples where one partner is traveling, the non-traveling partner’s higher attachment anxiety was associated with more sleeping problems (21). The effect may not be limited to separations, as coupled people with high attachment anxiety have poorer sleep quality compared to those with high attachment avoidance or low attachment anxiety and low avoidance (22,23).

The role of attachment avoidance on sleep behavior is more nuanced (18,24,25). People with high attachment avoidance have negative views of others and expect that relationship partners will not be available or responsive to their needs; rather, people high on attachment avoidance react to stressors by using deactivating strategies, such as minimizing or suppressing thoughts and feelings associated with distress (12–15,26). Although people with high attachment avoidance report hyper-independence, they still benefit from feeling close and secure in their relationships. Higher levels of sleep concordance (when both members in a couple go to sleep and wake up at similar times) were associated with higher sleep quality for women with high attachment avoidance (27). Being physically close to one’s partner while going to bed, sleeping, and waking benefited those high in attachment avoidance (27). Feeling emotionally close to one’s partner also affects sleep; when people high in attachment avoidance have negative interactions with their romantic partner, they have shorter sleep duration (28). There are some inconsistent findings in the literature on attachment avoidance and sleep. For instance, some studies show that couples with two highly avoidant partners have better sleep quality, while others show worse sleep quality (23,29). Deactivating strategies characteristic of attachment avoidance are effortful and require constant self-regulation to successfully manage the stressor (30), which may explain the inconsistent findings associated with attachment avoidance and adverse health outcomes. In particular, high self-regulatory capacity may be necessary to utilize deactivating regulation strategies effectively.

According to the Neurovisceral Integration Model, higher resting vagally-mediated heart rate variability (HRV) indicates a more active and integrated central autonomic network (8,31). The individual difference component of vagally-mediated HRV assessed at rest is an index of self-regulatory capacity (32–35). Lower HRV has been associated with a greater vulnerability to stress-related sleep disturbances (36); it may also be an important effect in conjunction with other variables. Higher HRV can buffer the adverse effects of higher attachment avoidance on loss adjustment, self-concept reorganization, quality of life, and general stress (30,37–39). The prior findings at the intersection of attachment avoidance and HRV occurred primarily in contexts characterized by high stress and/or loss when attachment systems are activated (e.g., samples of recently divorced people or breast cancer survivors).

In the present study, we replicate and extend prior work by examining attachment and HRV in a sample of healthy older adults, rather than environments characterized by loss and by our use of longitudinal design. Using longitudinal data, we disaggregate the individual difference component of HRV (which we call “trait HRV”); our operationalization of trait HRV is aligned with the definition vagally-mediated HRV as an index of self-regulatory capacity. Based on previous work showing that those high on attachment anxiety have poor health outcomes than those with low attachment anxiety, regardless of third variable influence (18,24,25), we expect that higher levels of attachment anxiety will be associated with poorer sleep quality, regardless of trait HRV (Hypothesis 1). In contrast, we expect that attachment avoidance will interact with trait HRV to predict sleep quality. We expect the association between attachment avoidance and sleep quality will be moderated by people’s self-regulatory capacity as indexed by trait HRV (Hypothesis 2). Finally, we explore the interaction between attachment anxiety and trait HRV (Research Question).

Methods

Raw data and all code for analyses will be made accessible on Open Science Framework (http://tinyurl.com/HRVAttachment).

Participants and Procedure

Data for the current project are from a larger study of biobehavioral health outcomes associated with widowhood. Participants in the present analyses were from the control group of the larger study only (i.e., healthy, not recently widowed) that were recruited to match the age, gender, and BMI distribution of the bereaved participants. Additional inclusion criteria required that participants spoke English. We excluded participants who experienced the loss of a loved one in the last year, were divorced in the last year, were undergoing cancer treatment, had a pacemaker, or had an autoimmune disease. The present sample (N = 171) represented older adults (M = 66.18 years; 67.83% women), most of whom were highly educated (14.04% had associate’s degrees, 13.45% bachelor’s degrees, 60.82% had advanced degrees) and in romantic partnerships (55% married or domestic partnerships, 25% single, separated, or divorced, 15% widowed, 5% missing data). See Table 1 for all other sample characteristics.

Table 1.

Sample characteristics

Variable Count (%) M (SD)
Age (years) 66.18 (13.56)
Gender (women) 116 (67.84%)
Race and ethnicity
 White 120 (74.2%)
 Black 30 (17.5%)
 Asian 7 (4.1%)
 Other 11 (6.4%)
 Hispanic/Latino, any race 10 (5.8%)
Education (highest degree earned)
 High school diploma or GED 1 (0.58%)
 Associate’s degree 24 (14.04%)
 Bachelor’s degree 23 (13.45%)
 Master’s degree 48 (28.07%)
 Doctorate or professional degree 56 (32.75%)
Body mass index 28.61 (6.1)
Comorbidities 0.62 (3.30)
Medications 0.57 (0.50)
Attachment avoidance 19.09 (7.38)
Attachment anxiety 7.42 (4.76)
Log-transformed RMSSD 3.1 (0.77)
Global sleep quality 6.20 (3.46)
 Sleep latency 1.20 (0.98)
 Daytime sleep dysfunction 0.62 (0.67)
 Sleep disturbance 1.40 (0.60)
  Not in the past month 2 (1.2%)
  Less than once a week 103 (60.23%)
  More than once or twice a week 55 (32.16%)
  Three or more times a week 7 (4.1%)
 Sleep duration 0.53 (0.822)
  More than 7 hours 105 (61.4%)
  6–7 hours 39 (22.8%)
  5–6 hours 14 (8.19%)
  Less than 5 hours 7 (4.09%)
 Sleep efficiency 0.75 (0.99)
  Over 85% efficiency 90 (52.6%)
  75–84% efficiency 44 (25.73%)
  65–74% efficiency 14 (9.19%)
  Less than 65% efficiency 17 (9.94%)
 Use of sleep medication 0.81 (1.21)
  Not during past month 107 (62.57%)
  Less than once a week 17 (9.94%)
  Once or twice a week 10 (5.84%)
 Self-report sleep quality 0.90 (0.64)
  Very good 43 (25.15%)
  Fairly good 97 (56.73%)
  Fairly bad 27 (15.79%)
  Very bad 0 (0%)
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All study procedures were approved by Rice University’s Institutional Review Board. Participants came in for four visits during which they completed self-report questionnaires and measurements of HRV. Visit two was an average of 77.22 days (approximately 2.5 months) after visit one, visit three was an average of 134.30 days (approximately 4.5 months) after visit one, and visit four was an average of 306.5 days (approximately 10 months) after visit one. To minimize confounding effects on inflammation for the purposes of the larger study, participant visits were scheduled in the morning, and participants were instructed to avoid strenuous exercise 48 hours before their visit, reschedule visits if they were feeling symptoms of an acute illness, and avoid high-fat foods on the morning of their visit (e.g., butter). Participants were instructed to avoid caffeinated beverages prior to their visit, as caffeine can affect HRV measurement.

Measures

Attachment.

Attachment avoidance and anxiety was measured with the six-item and three-item subscales from the Experiences in Close Relationships Scale – Relationships Structure (ECR-RS), respectively (40). At each visit, participants answered questions about their experiences with close relationships in general on a scale of 1 (strongly disagree) to 7 (strongly agree). Alpha levels were α = .80 for attachment avoidance and α = .89 for attachment. The intraclass correlation (ICC) was .56 for attachment avoidance and was .37 for attachment anxiety, indicating that 56% of the variance in attachment avoidance and 37% of the variance in attachment anxiety was associated with the individuals over the repeated measurements.

Heart rate variability.

Heart rate variability was measured with the root mean square of successive differences (RMSSD) between heartbeats. Per recommendations, HRV was captured using the Polar s810 wristwatch connected with the Polar H10 heart rate sensor worn around participants’ chests (41). At each visit, participants rested in a sitting position for five minutes and data was collected with a 1000 Hz sampling rate. HRV data was processed in Kubios Premium software to remove artifacts and ectopic beats (42). Kubios provides the RMSSD values after corrections as a measure of vagally mediated, or parasympathetic, heart rate variability. RMSSD values were natural log-transformed to account for skewedness. The ICC was .44 for RMSSD.

Sleep.

Sleep quality was measured with the 20-item Pittsburgh Sleep Quality Index total score (43). At each visit, participants answered questions about their sleep habits for the last month. Items measure component scores of self-report sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbance, and daytime dysfunction. Those components are combined into the global sleep quality scale; higher scores represent poorer sleep quality. The alpha levels were calculated using component scores (α = .68), per recommendations (44) and was close to the range of alphas in prior studies (.70-.83; 27). The ICC is .74 for the global sleep score, .59 for sleep quality, .68 for sleep latency, .67 for sleep duration, .55 for habitual sleep efficiency, .60 for sleep disturbance, and .57 for daytime dysfunction.

Covariates.

Covariates were measured at each visit, except for age, gender, and education, which were measured at visit one only. Participants self-reported age and gender. Participants reported education (highest degree earned) as part of the Sociodemographic Questionnaire (46). Research assistants collected participants’ height and weight through in-person measurements to calculate body mass index (BMI). Comorbidities were measured with the Charlson Comorbidity Index, which assigns weights to 19 comorbid conditions based on their potential influence on one-year mortality (47). We included a binary inflammation-related medications variable (i.e., marking if participants were taking aspirin, statins, antihypertensive, and/or antidepressants) (48,49).

Analyses

All analyses were conducted in R using the lme4 package, which uses listwise deletion. Our sample was adequate for the models tested, with power over .80. We conducted linear mixed-effect models that accounted for multiple observations for each participant for each variable. Prior to running the analyses, we found that our variables had ICCs ranging from .37-.74, indicating that a minimum of 37% of the variance in the data was associated with the participant. Therefore, a within-person analysis was a good fit for the data. Next, we disaggregated the within-person and between-person effects, following suggestions for longitudinal data (50,51). Please note, we centered on the control group only (present sample), not the bereaved plus control group from the overall study. We person-mean centered the attachment and HRV variables and included each person’s mean in analyses. Therefore, for attachment avoidance, attachment anxiety, and HRV, we created a “trait” variable, which captures each person’s mean score across the four visits relative to the rest of the participants (i.e., the grand-mean centered person mean). We also created a “state” variable, which captures participant’s deviance from their mean score at each visit (i.e., the person-mean centered variables). This statistical approach allows us to explain more variance than simply examining the person-mean-centered variables alone. In particular, using this approach allows us to explain level 1 variance (time or situational variance) and level 2 variance (person-related variance) by reducing covariance between the level 1 and level 2 (50,51).

Additionally, we ran model comparisons without fixed effects to determine the optimal random effects, prior to testing the hypotheses. The model with one random intercept was the best fit according to Akaike information criterion (AIC). The lowest AIC value represents the lower relative mean squared error of the estimate (52). Therefore, we included random intercepts for participant identifier. Please note, we also tested a model with a random intercept of participant identifier and visit number, but that model was not significantly better than a model with a random intercept of participant identifier only. We ran models adjusting for relevant demographic and health-related covariates (53). Covariates included age, gender, BMI, education, comorbidities, and medications; attachment avoidance was included as a covariate in models testing attachment anxiety and attachment anxiety was included as a covariate in models testing attachment avoidance. All models were estimated using restricted maximum likelihood estimations.

Results

Sample characteristics are in Table 1. Results for the regression analyses are in Table 2 and the Supplemental Digital Content materials. Variable means, standard deviations, and intercorrelations are also in the supplemental materials.

Table 2.

Attachment Insecurity and Heart Rate Variability on Sleep

Global Sleep Quality Sleep Duration Sleep Latency Sleep Efficiency
Predictors B (SE B) B (SE B) B (SE B) B (SE B)
 (Intercept) 8.30*** (2.05) 1.66*** (0.45) 1.73** (0.60) 2.06*** (0.55)
Age −0.05* (0.02) −0.01*** (0.00) −0.01 (0.01) −0.02*** (0.01)
Gender1 0.78 (0.56) −0.04 (0.12) 0.23 (0.16) −0.08 (0.14)
Education −0.25 (0.23) −0.09 (0.05) −0.11 (0.07) −0.14* (0.06)
Body mass index 0.04 (0.04) 0.00 (0.01) −0.00 (0.01) 0.01 (0.01)
Comorbidities 0.04 (0.05) 0.00 (0.01) −0.02 (0.01) −0.00 (0.02)
Medications −1.23 (0.89) −0.36 (0.21) −0.40 (0.28) −0.33 (0.27)
Trait attachment avoidance −0.02 (0.04) −0.00 (0.01) 0.01 (0.01) 0.01 (0.01)
State attachment avoidance 0.06* (0.03) 0.01 (0.01) 0.01 (0.01) 0.03** (0.01)
Trait attachment anxiety 0.20** (0.07) 0.02 (0.02) 0.01 (0.02) 0.01 (0.02)
State attachment anxiety −0.01 (0.03) −0.01 (0.01) 0.01 (0.01) −0.00 (0.01)
Trait HRV 0.70 (0.37) −0.12 (0.08) −0.26** (0.11) −0.27** (0.10)
State HRV −0.18 (0.19) −0.00 (0.05) −0.03 (0.06) −0.02 (0.06)
State attachment avoidance × trait HRV −0.13** (0.05) −0.01 (0.01) −0.05** (0.02) −0.04* (0.02)
Trait attachment avoidance × trait HRV 0.14* (0.06) −0.02† (0.01) 0.04* (0.02) 0.02 (0.02)
State attachment anxiety × trait HRV −0.15** (0.05) −0.03 (0.01) −0.04* (0.02) −0.04* (0.02)
Trait attachment anxiety × trait HRV −0.31* (0.13) −0.04 (0.03) −0.11** (0.04) 0.03 (0.03)
Random Effects
 σ2 3.14 0.21 0.33 0.38
 τ00 7.75 People 0.32 People 0.62 People 0.44 People
 ICC 0.71 0.60 0.65 0.54
 N 150 People 150 People 150 People 150 People
 Observations 400 401 404 400
Marginal R2 .17 .14 .12 .17
Conditional R2 .76 .66 .70 .62
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Note.

1

Gender is coded so 1 = man and 2 = woman. For all outcomes, higher scores indicate poorer sleep. σ2 = indicates the level 1 residual variance; τ00 = indicates the level 2 residual variance.

†

indicates p < 0.1

*

indicates p < 0.05

**

indicates p < 0.01

***

indicates p < 0.001

Global Sleep Quality

In model testing Hypothesis 1 (the association between attachment anxiety and sleep quality), we adjusted for covariates of age, gender education, BMI, comorbidities, and medications. There was a significant association between higher trait attachment anxiety and poorer sleep quality (B = 0.21, SE = 0.07, p = .006). There were no significant associations between state attachment anxiety and sleep quality, trait attachment avoidance and sleep quality, nor state attachment avoidance and sleep quality. The results provide some support for Hypothesis 1.

In the model testing Hypothesis 2 (the association between attachment avoidance and sleep quality moderated by trait HRV) and the Research Question (the association between attachment anxiety and sleep quality moderated by trait HRV), we adjusted for the same covariates as in Hypothesis 1. The interaction term representing state attachment avoidance and trait HRV was significantly associated with sleep quality (B = −0.13, SE = 0.05, p = .01), as was the interaction term representing trait attachment avoidance and trait HRV (B = 0.14, SE = 0.06, p = .03). The interaction term representing state anxiety and trait HRV was also associated with sleep quality (B = −0.15, SE = 0.05, p = .002), as well as the interaction term representing trait attachment anxiety and trait HRV (B = −0.31, SE = 0.13, p = .02). There were no significant three-way interactions.

State attachment avoidance interaction.

We examined the interaction of state attachment avoidance and trait HRV using simple slopes. There was no association between state attachment avoidance and sleep quality (B = −0.03, SE = 0.04, p = .40) at high trait HRV (i.e., one standard deviation above the mean). However, at mean levels (B = 0.06, SE = 0.03, p = .02) and low (one standard deviation below the mean; B = 0.16, SE = 0.05, p = .003) levels of trait HRV, higher state attachment avoidance was associated with poorer sleep quality (Figure 1).

Figure 1.

Figure 1.

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Interaction between state attachment anxiety and trait heart rate variability on global sleep quality. High and low levels represent one standard deviation above and below the mean, respectively. Shading represents the 95% confidence intervals. The slope representing high trait HRV was not significantly different than zero.

Trait attachment avoidance interaction.

We examined the interaction between trait attachment avoidance and trait HRV using simple slopes, but there were no significant effects at the mean trait HRV, nor at one standard deviation above or below the mean level trait HRV. Combined with the results for state attachment avoidance, the trait attachment avoidance results provide some support for Hypothesis 2.

State attachment anxiety interaction.

In the simple slopes examining the interaction of state attachment anxiety and trait HRV, there was no association between state attachment anxiety and sleep quality at mean levels of trait HRV (B = 0.01, SE = 0.03, p = .84). At low levels of trait HRV, higher levels of state attachment anxiety were associated with poorer sleep quality (B = 0.11, SE = 0.05, p = .03); at high levels of trait HRV, higher levels of sate attachment anxiety were associated with higher sleep quality (B = −0.10, SE = 0.04, p = .02; Figure 2).

Figure 2.

Figure 2.

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Interaction between state attachment anxiety and trait heart rate variability on global sleep quality. High and low levels represent one standard deviation above and below the mean, respectively. Shading represents the 95% confidence intervals. The slope representing mean trait HRV was not significantly different than zero.

Trait attachment anxiety interaction.

In the simple slopes examining the interaction of trait attachment anxiety and trait HRV, there was no association between trait attachment anxiety and sleep quality at high levels of trait HRV (B = −0.02, SE = 0.12, p = .86). However, at mean levels (B = 0.20, SE = 0.07, p = .007) and low levels (B = 0.42, SE = 0.12, p < .001) of trait HRV, higher trait attachment anxiety was associated with poorer sleep quality (Figure 3). Combined with the results for state attachment anxiety, the trait attachment anxiety results provide evidence to answer our Research Question.

Figure 3.

Figure 3.

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Interaction between trait attachment anxiety and trait heart rate variability on global sleep quality. High and low levels represent one standard deviation above and below the mean, respectively. Shading represents the 95% confidence intervals. The slope representing high trait HRV was not significantly different than zero.

Component Measures of the Sleep Quality Index

We followed up our finding on global sleep quality by examining the main effects and interactions of state attachment avoidance, state attachment anxiety, trait attachment avoidance, and trait attachment anxiety with trait HRV on the component measures of the sleep quality index. We found a significant association between higher trait attachment anxiety and lower self-reported sleep quality (B = 0.03, SE = 0.01, p = .02), higher sleep disturbance (B = 0.04, SE = 0.01, p = .001), and higher daytime dysfunction (B = 0.07, SE = 0.01, p < .001). We also found an association between higher trait HRV and higher sleep efficiency (B = −0.25, SE = 0.09, p = .008). The full results table for main effects can be found in the Supplemental Digital Content materials. Next, we found significant interactions on sleep disturbance, sleep latency and habitual sleep efficiency, detailed below and in Table 2. No significant interactions were found on self-reported sleep quality, use of sleep medications, sleep disturbance, or daytime dysfunction.

State attachment avoidance interactions.

The interaction term representing state attachment avoidance and trait HRV was significantly associated with two components of global sleep quality, sleep latency (B = −0.05, SE = 0.02, p = .001) and habitual sleep efficiency (B = −0.04, SE = 0.02, p = .04). In the simple slopes examining the interaction between state attachment avoidance and trait HRV on sleep latency, we found no association between state attachment avoidance and sleep latency at mean levels of trait HRV (B = 0.01, SE = 0.01, p = .19). At high levels of trait HRV, higher state attachment avoidance was associated with shorter sleep latency (B = −0.03, SE = 0.01, p = .03). At low levels of trait HRV, higher state attachment avoidance was associated with longer sleep latency (B = 0.05, SE = 0.02, p = .003; Figure S1, Supplemental Digital Content). In the simple slopes examining the interaction between state attachment avoidance and trait HRV on habitual sleep efficiency, we found no association between state attachment avoidance and sleep efficiency at high levels of trait HRV (B = 0.00, SE = 0.01, p = .77). At mean levels (B = 0.03, SE = 0.01, p = .002) and low levels (B = 0.05, SE = 0.02 p = .002) of trait HRV, higher state attachment avoidance was associated with poorer sleep efficiency (Figure S2).

Trait attachment avoidance interactions.

The interaction term representing trait attachment avoidance and trait HRV was significantly associated with two components of global sleep quality, sleep duration (B = 0.03, SE = 0.01, p = .05) and sleep latency (B = 0.04, SE = 0.02, p = .03). In the simple slopes examining the interaction between trait attachment avoidance and trait HRV on sleep duration, we found no association between trait attachment avoidance and sleep duration at low, mean, or high levels of trait HRV. Likewise, in the simple slopes examining the interaction between trait attachment avoidance and trait HRV on sleep latency, we found no association between trait attachment avoidance and sleep latency at low, mean, or high levels of trait HRV.

State attachment anxiety interactions.

The interaction term representing state attachment anxiety and trait HRV was significantly associated with two components of global sleep quality, sleep latency (B = −0.04, SE = 0.02, p = .02) and habitual sleep efficiency (B = −0.04, SE = 0.02, p = .02). In the simple slopes examining the interaction between state attachment anxiety and trait HRV on sleep latency, we found no association between state attachment anxiety and sleep latency at high levels (B = −0.02, SE = 0.01, p = .24) or mean levels (B = 0.01, SE = 0.01, p = .34) of trait HRV. At low levels of HRV, higher state attachment anxiety was associated with longer sleep latency (B = 0.04, SE = 0.02, p = .03; Figure S3, Supplemental Digital Content). In the simple slopes examining the interaction between state attachment anxiety and trait HRV on habitual sleep efficiency, we found no association between state attachment anxiety and sleep efficiency at low, mean, or high levels of trait HRV.

Trait attachment anxiety interactions.

The interaction term representing trait attachment anxiety and trait HRV was significantly associated with one component of global sleep quality, sleep latency (B = −0.11, SE = 0.04, p = .003). In the simple slopes examining the interaction between trait attachment anxiety and trait HRV on sleep latency, we found no association between trait attachment anxiety and sleep latency at mean levels of trait HRV (B = 0.01, SE = 0.02, p = .63). At high levels of HRV, higher trait attachment anxiety was associated with shorter sleep latency (B = −0.07, SE = 0.03, p = .04). At low levels of trait HRV, higher state attachment avoidance was associated with longer sleep latency (B = 0.09, SE = 0.03, p = .01; Figure S4, Supplemental Digital Content).

Discussion

Sleep is prognostic for an array of long-term physical health outcomes and helpful for identifying those most at risk for sleep problems. We investigated how individual differences (i.e., trait attachment) and situational variation in attachment orientations (i.e., state attachment) interact with one’s physiological capacity to self-regulate to affect sleep quality among relatively healthy older adults. We found that (Hypothesis 1) higher trait attachment anxiety was associated with poorer sleep quality; we also found that (Hypothesis 2) higher state attachment avoidance was associated with poorer sleep quality, except among those with high trait HRV. Next, we found that (Research Question) higher state attachment anxiety was associated with poorer sleep quality among those with low trait HRV; finally, higher trait attachment anxiety was associated with poorer sleep quality, except among those with high trait HRV. Altogether, our results suggest that self-regulatory capacity (as indexed by higher trait HRV) may have a buffering effect when adverse events arise.

By studying within and between person relationships between attachment orientations, HRV, and sleep in older adults, this study makes several novel contributions to the literature; here, we highlight two. First, prior research has demonstrated the moderating role of HRV for attachment avoidance on health outcomes in settings characterized by loss, when attachment systems are more likely activated (30,37–39); we extend this research by demonstrating the same patterns in healthy older adulthood. Second, due to our longitudinal, repeated measures data, we disaggregate within-person and between-person effects to examine the ‘trait’ effects (i.e., one’s mean level across measurements) and the ‘state’ effects (i.e., one’s deviance from one’s mean). Within-person approaches are used less often but can elucidate time-varying experiences.

In the adult attachment literature, attachment insecurity is conceptualized across two orthogonal dimensions of attachment anxiety and avoidance. Although the degree to which one is high on attachment anxiety and avoidance is relatively stable, it does fluctuate around this common stable value (i.e., ‘prototype value’), depending on one’s current context (54,55). For example, when confronted with a stressful or threatening context, especially if relational in nature, attachment insecurity (anxiety and avoidance) temporarily increases, relative to one’s average level of attachment anxiety and avoidance. These experiences illustrate within-person variation in attachment (56,57). Examining the within-person variance in attachment avoidance and attachment anxiety explains one’s breadth of situational responses to stress. Examining the between-person variance in attachment avoidance and attachment anxiety explains how one typically responds to stress, relative to others. For example, we found that higher trait attachment anxiety was associated with poorer sleep quality – meaning that people who use hyperactivating coping strategies more often than other people tend to have worse quality sleep. Hypothetically, if we had found higher state attachment anxiety was associated with poorer sleep quality, that would suggest that the times one uses hyperactivating coping strategies more than one’s normal amount are times when one has poorer quality sleep.

Attachment Avoidance

We found that state attachment avoidance interacted with trait HRV to predict global sleep quality. Upon examining the simple slopes, we found that high levels of trait HRV buffered the effect of state attachment avoidance on global sleep quality; but at mean and low levels of trait HRV, higher state attachment avoidance is associated with poorer sleep quality. That is, people with average or low trait HRV tended to have poorer sleep quality when they also had higher attachment avoidance than their normal amount. People with high trait HRV had higher avoidance than normal without also having poorer sleep. Recall that higher temporary levels of attachment avoidance (and attachment anxiety) may occur when experiencing a stressor. When someone is experiencing more attachment avoidance than typical, perhaps they are experiencing a stressor and reacting by using deactivating emotion regulation strategies. This finding suggests that people who have greater self-regulatory capacity than the average person can effectively enact deactivating regulation strategies such that their sleep quality is not affected. However, caution is warranted, as there may be alternative explanations and other relevant phenomena. For instance, depression is associated with poor sleep quality and low HRV (58–61). For people with romantic partners, their partner’s attachment orientation and relationship quality may also matter for sleep (28,29)

These findings replicate and extend previous work showing that the relationship between attachment avoidance and health outcomes largely depends upon their physiological capacity for self-regulation (as indexed by vagally mediated HRV at rest or in response to an experimental stressor). Two papers based on secondary data analysis from the Pittsburgh Cold Study 3 showed that adults with higher attachment anxiety reported more stress than those who reported less attachment anxiety (39,62), while the relationship between attachment avoidance and stress was moderated by HRV such that avoidance was only associated with stress levels among those with higher HRV (39). In a prospective study evaluating risk factors for poor loss adjustment among adolescents, those who reported more attachment anxiety at age 14 were more likely to report poorer adjustment to a future loss than adolescents who reported less attachment anxiety; furthermore, the association between attachment avoidance and loss adjustment was moderated by an adolescent’s physiological capacity for self-regulation (as indexed by stress-induced HRV; 28). Recently divorced adults who showed signs of poorer adjusted to the loss (indexed by poorer self-concept) showed a similar pattern of results (30). Finally, in a study examining predictors of quality of life in breast cancer survivors, those who reported more attachment anxiety showed poorer quality of life than those who reported less attachment anxiety; whereas the association between attachment avoidance and quality of life depended on their physiological capacity for self-regulation, as indexed by HRV (38). These studies demonstrate a pattern – higher attachment avoidance is associated with adverse outcomes, but only when accompanied by lower HRV. Our findings extend and replicate this pattern by clarifying that trait HRV buffers state attachment avoidance.

Attachment Anxiety

We found that higher trait attachment anxiety was related to poorer global sleep quality. Although this finding aligns with extant research demonstrating a consistent link between high attachment anxiety and adverse health outcomes (e.g., 15,16), we also found an interaction between trait HRV and state anxiety, as well as an interaction between trait HRV and trait anxiety. First, we found that higher state attachment anxiety was associated with poorer sleep quality for people with low trait HRV, but not for people with mean or high trait HRV. Unexpectedly, for people with high trait HRV, higher state anxiety was associated with better sleep quality. Next, we found an interaction between trait anxiety and trait HRV; higher trait attachment anxiety was associated with poorer sleep quality for people with low and mean trait HRV, but not people with high trait HRV. These findings might mean that people with low self-regulatory capacity (as indexed by higher trait HRV) experienced worse sleep during times when they coped with stressors by using hyperactivating strategies, while the use of hyperactivating strategies did not adversely affect sleep for those with higher trait HRV. Findings from the literature may help further contextualize our results.

State attachment anxiety.

Within close relationships, physiological coregulation can occur (63). In a dyadic study, researchers primed state attachment insecurity with a 10-minute discussion between members of a couple on an area of disagreement (64). They found that higher state attachment anxiety was associated with higher physiological coregulation, as indexed by a participant’s heart rate becoming aligned to their partner’s heart rate (64). The authors explain this association by noting that hyperactivating strategies characteristic of attachment anxiety often require coregulation with a partner (64). Higher state attachment anxiety may be associated with effective threat management via coregulation (64–67). Our results indicate that people with high trait HRV experienced better sleep quality when they had higher state attachment anxiety. Interpreted in the context of extant findings, perhaps the association between higher state anxiety and better sleep quality is due to an increase in coregulation. People with greater self-regulatory capacity, as indexed by higher trait HRV, may be capable of using hyperactivating strategies alone or through coregulating with a partner.

Trait attachment anxiety.

In an attachment priming study with recently separated participants, researchers primed attachment insecurity by having participants describe their marital separation (68). Participants’ descriptions of marital separation were coded as high (versus low) use of personal and involved language (68). Personal language use moderated the association between higher trait attachment anxiety and higher systolic and diastolic blood pressure (68). For those who used low levels of personal language when describing their marital separation, trait attachment anxiety was unrelated to blood pressure. For those who used high levels of personal language when describing their marital separation, higher trait attachment anxiety was associated with higher systolic and diastolic blood pressure. In the context of our findings, perhaps lower personal language use is a sign that one is exhibiting greater self-regulation. If so, our finding on the interaction between trait attachment anxiety and trait HRV is aligned with the literature – high self-regulatory capacity (indexed by high trait HRV) may buffer the adverse outcomes associated with higher trait attachment anxiety.

Strengths, Limitations, and Future Directions

The study has numerous strengths; namely, the study hypotheses are theoretically grounded and build upon prior work biobehavioral research on attachment, self-regulation, HRV, and health outcomes. It replicates and extends prior work linking attachment orientations to sleep quality by showing that the impact of attachment on sleep patterns largely depends upon people’s physiological self-regulatory capacity. Nevertheless, the study has limitations that should be addressed in future work. First, participants for this study were predominately white, middle- to high-SES women; future work may examine within-person and between-person variance in attachment and HRV with a dyadic approach. Second, HRV was only assessed at rest, and it would be interesting for future work to examine both resting HRV and stress-induced HRV as was the case in some of the prior studies described above. Finally, because the study consists of relatively healthy people without severe sleep problems, future work should investigate these findings in clinical populations.

Conclusion

Attachment theory is a valuable theoretical framework for understanding non-medical factors that alter sleep quality. The findings extend our understanding of how attachment orientations and people’s physiological capacity for self-regulation influence people’s sleep quality. Altogether, findings indicate that greater self-regulatory capacity (as indexed by higher trait HRV) may buffer stressors.

Supplementary Material

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Footnotes

Conflicts of Interest and Source of Funding: This project was supported by funding from the National Institute of Health (NIH, R01HL127260, PI Fagundes). JP (F32AG079624), LWC (F31AG074648), MC (F32HL164050), AL (K01AG073824), and CP (R01AG062690, RF1AG075946, R21AG061597-02) are funded by the National Institutes of Health.

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