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Perceived Partner Responsiveness Predicts Better Sleep Quality Through Lower Anxiety

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The present study investigated whether perceived partner responsiveness—the extent to which individuals feel cared for, understood, and validated by their partner—predicted subjective sleep problems and objective (actigraph-based) sleep efficiency through lower anxiety and depression symptoms. A life span sample of 698 married or cohabiting adults (35–86 years old) completed measures of perceived partner responsiveness and subjective sleep problems. A subset of the sample (N = 219) completed a weeklong sleep study where actigraph-based measures of sleep efficiency were obtained. Perceived partner responsiveness predicted lower self-reported global sleep problems through lower anxiety and depression and greater actigraph-assessed sleep efficiency through lower anxiety. All indirect associations held after controlling for emotional support provision to the partner, agreeableness, and demographic and health covariates known to affect sleep quality. These findings are among the first to demonstrate how perceived partner responsiveness, a core aspect of romantic relationships, is linked to sleep behavior.
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Article
Perceived Partner Responsiveness
Predicts Better Sleep Quality
Through Lower Anxiety
Emre Selcuk
1
, Sarah C. E. Stanton
2
, Richard B. Slatcher
2
,
and Anthony D. Ong
3
Abstract
The present study investigated whether perceived partner responsiveness—the extent to which individuals feel cared for,
understood, and validated by their partner—predicted subjective sleep problems and objective (actigraph-based) sleep efficiency
through lower anxiety and depression symptoms. A life span sample of 698 married or cohabiting adults (35–86 years old)
completed measures of perceived partner responsiveness and subjective sleep problems. A subset of the sample (N¼219)
completed a weeklong sleep study where actigraph-based measures of sleep efficiency were obtained. Perceived partner
responsiveness predicted lower self-reported global sleep problems through lower anxiety and depression and greater actigraph-
assessed sleep efficiency through lower anxiety. All indirect associations held after controlling for emotional support provision to
the partner, agreeableness, and demographic and health covariates known to affect sleep quality. These findings are among the
first to demonstrate how perceived partner responsiveness, a core aspect of romantic relationships, is linked to sleep behavior.
Keywords
perceived partner responsiveness, sleep, marriage, well-being, anxiety
Sleep is a critical health behavior reducing the risk for morbid-
ity and mortality (e.g., Dew et al., 2003; Reid et al., 2006).
Given the well-established link between social relationships
and health (e.g., Holt-Lunstad, Smith, & Layton, 2010),
research has increasingly focused on the role of close relation-
ships in sleep. Although both sleep quality (Dew et al., 2003)
and total sleep duration (Shen, Wu, & Zhang, 2016) have been
linked to health outcomes, social relationships or lack thereof
have typically been found to be linked with sleep quality—for
instance, subjective evaluations of how well individuals sleep
or how much daytime dysfunction they experience, or objec-
tive assessments of sleep efficiency (the ratio of time spent
sleeping to the time spent in bed)—rather than total duration
(Bordeleau, Bernier, & Carrier, 2012; Cacioppo et al., 2002).
These findings suggest that social relationships are associated
with reduced nonrestorative sleep, which is defined as sleep
that is interrupted with frequent awakenings and not refreshing,
despite normal duration (Hawkley, Preacher, & Cacioppo,
2010). Restorative sleep depends on perceived absence of
threat in the environment and downregulation of arousal. Per-
sistent high arousal—a marker of anxiety—disrupts sleep by
increasing nightly awakenings and resulting in poorer daytime
functioning. Social relationships are thought to counteract this
process, as they are a potent source of safety and protection,
and they downregulate perceptions of threat (Eisenberger
et al., 2011) and physiological arousal (Slatcher, Selcuk, &
Ong, 2015).
Given that adult sleep is typically a shared activity between
romantic partners (National Sleep Foundation, 2013) and
romantic relationships have a unique capacity to influence the
quality of human health and well-being (Loving & Slatcher,
2013), the role of marital and cohabiting relationships in sleep
quality has received increased research attention (Troxel,
2010). Although studies have established that individuals’
sleep quality is closely linked to how happy (or unhappy) they
are in their relationship, the psychological processes through
which relationships affect sleep are still not well understood
(Troxel, 2010). Growing work, primarily led by social psychol-
ogists, aiming at explaining the psychological pathways by
which long-term romantic relationships are linked to physical
health demonstrates that relationship processes (e.g.,
1
Middle East Technical University, Ankara, Turkey
2
Wayne State University, Detroit, MI, USA
3
Cornell University, Ithaca, NY, USA
Corresponding Author:
Emre Selcuk, Middle East Technical University, B45 Human Sciences Building,
Ankara 06800, Turkey.
Email: semre@metu.edu.tr
Social Psychological and
Personality Science
1-10
ªThe Author(s) 2016
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DOI: 10.1177/1948550616662128
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responsiveness, support provision) predict psychological
symptoms (e.g., anxiety, depression) and well-being (e.g., life
satisfaction), which in turn predict physical health (for reviews,
see Slatcher, 2010; Slatcher & Selcuk, in press). In the few
studies taking this approach (e.g., the links between self-
disclosure to one’s partner and sleep quality; Kane, Slatcher,
Reynolds, Repetti, & Robles, 2014), the small sample sizes
limited the ability to detect between-person differences and
sleep quality was measured only with self-reports. However,
a multimethod approach to measuring sleep is important
because self-reported sleep quality is at best weakly correlated
with objective measures such as actigraphy (Grandner, Kripke,
Yoon, & Youngstedt, 2006), suggesting that the two types of
measures tap different aspects of sleep quality. Whereas self-
reports typically measure subjective (dis)satisfaction about
sleep quality, actigraphy provides indices of objective sleep
disruptions during a night’s interval. The two measures are also
differentially related to health and well-being (e.g., Lemola,
Ledermann, & Friedman, 2013; Liu et al., 2013). It may be the
case that romantic relationships may be associated with one
type of sleep measure but not the other, or they may predict
both subjective and objective sleep quality but through differ-
ent psychological mechanisms.
Perceived partner responsiveness (i.e., the extent to which
individuals perceive their partner as caring, understanding, and
appreciative; Reis, 2007) may be one important process by
which romantic relationships affect sleep quality. In this study,
we investigated the associations between perceived partner
responsiveness and sleep quality in a large sample using both
self-report measures of sleep problems and an objective
actigraph-based measure of sleep efficiency. The large sample
size provided us with sufficient statistical power to investigate
the potential psychological mechanisms through which partner
responsiveness is associated with sleep. Specifically, we tested
two potential mediators of the link between perceived partner
responsiveness and sleep quality—anxiety and depressed affect
two classes of psychological symptoms that are among the
most common predictors of sleep disturbances (Koffel & Wat-
son, 2009)—hypothesizing that perceived partner responsive-
ness would positively predict sleep quality through lower
anxiety and depressive symptoms.
Perceived Partner Responsiveness and
Well-Being
Perceived partner responsiveness has been identified as a key
process that influences the extent to which romantic relation-
ships are satisfying and intimate. It focuses on partners’ posi-
tive responses to each other in contrast to negative responses
or indifference (Reis, 2007). When one perceives her or his
partner as caring, understanding, and appreciative, one is more
likely to self-disclose and also to react responsively to the part-
ner’s disclosures. When this process is enacted reciprocally and
mutually, it reinforces the development and maintenance of
intimacy in the relationship (Reis & Patrick, 1996).
Of particular relevance to the present study, a central func-
tion of perceived partner responsiveness involves downregulat-
ing anxiety and arousal and instilling a sense of security and
quiescence (Selcuk, Zayas, & Hazan, 2010). When individuals
encounter threats and stressors, the primary coping strategy for
most adults is to turn to their partners for safety and protection
(Mikulincer & Shaver, 2007). Responsive partner support dur-
ing these times alleviates distress and downregulates anxious
arousal. Indeed, when individuals were faced with an
anxiety-provoking experience in the laboratory (e.g., talking
about a stressful problem or anticipating giving a public
speech), their partner’s responsive support alleviated both
self-reported (Collins & Feeney, 2000) and observer-rated
(Simpson, Rholes, & Nelligan, 1992) anxiety. Repeated
responsive interactions with partners translate over time to a
long-term decline in anxiety, both psychologically and physio-
logically (e.g., endocrine functioning; Feeney & Collins,
2015). For instance, a recent daily experience study (Slatcher
et al., 2015) demonstrated that high partner responsiveness pre-
dicts a steeper decline in diurnal cortisol a decade later, sug-
gesting that the effect of responsiveness goes beyond the
immediate stressful context and may potentially be associated
with lower chronic levels of anxiety over the long term. This
finding is important in the present context also given that prior
work has linked steeper diurnal cortisol slopes to lower anxious
arousal (Doane et al., 2013), which would be expected to pre-
dict higher quality sleep.
Responsiveness (or lack thereof) is also thought to be one
key process that explains how social relationships affect
depression across the life span (Bowlby, 1980). More specif-
ically, relationships are argued to reduce an individual’s risk
for depression to the extent that a partner’s support (a) effec-
tively meets the demands of the stressful life situations and (b)
does not undermine the individual’s sense of autonomy
(Ibarra-Rovillard & Kuiper, 2011). Those who are perceived
as responsive are more likely to engage in support behaviors
that are appropriately contingent on the demands of the situ-
ation (Collins, Guichard, Ford, & Feeney, 2006) and, more-
over, their support also enhances the partner’s autonomy,
self-efficacy, and independent goal pursuit (Feeney, 2007).
Thus, it is not surprising that perceived partner responsiveness
is a strong predictor of lower symptoms of depression in daily
life (Fekete, Stephens, Mickelson, & Druley, 2007; Khan
et al., 2009).
Prior studies have illuminated consistent associations
between anxiety, depression, and sleep quality as well
(Alvaro, Roberts, & Harris, 2013; Koffel & Watson, 2009;
Magee & Carmin, 2010; Revenson, Marı´n-Chollom, Rundle,
Wisnivesky, & Neugut, 2016). Increasing research indicates
that disruptions in nighttime sleep is associated with indica-
tors of physiological hyperarousal such as increased meta-
bolic rates, heart rate variability, and cortisol output (for a
review, see Stepanski & Rybarczyk, 2006). Watson and
colleagues (1995) referred to these indicators as anxious
arousal, and in extensive work with both clinical and noncli-
nical samples of adults from different age-groups, they
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showed that these symptoms are specific to anxiety (and not
to depression). In a similar vein, they found that loss of
interest and low positive affect, which are collectively
referred to as anhedonic depression, are specific to depres-
sion (and not anxiety; Watson et al., 1995). Focusing on
these nonoverlapping aspects of anxiety and depression is
important to identify specifically through which symptoms
partner responsiveness would be linked to different aspects
of sleep quality.
The Present Research
The present study aimed to further extend our conceptual
understanding of how partner responsiveness is linked to health
and well-being by investigating the role of perceived partner
responsiveness in sleep. Importantly, we tested the associations
of perceived partner responsiveness with both self-report mea-
sures of sleep problems and an objective actigraph-based
assessment of sleep efficiency—that is, the ratio of total sleep
time to the total time spent in bed. Based on prior theorizing
and empirical work on social relationships and sleep (Borde-
leau et al., 2012; Cacioppo et al., 2002; Hawkley et al.,
2010), we prioritized testing the links between partner respon-
siveness and sleep quality, although we included sleep duration
in analyses as well.
Moreover, we investigated the potential mechanisms by
which perceived partner responsiveness was linked to sleep.
Specifically, we chose anxiety and depression, given their
well-established links to both perceived partner responsive-
ness (Selcuk et al., 2010; Slatcher et al., 2015) and sleep
quality (Alvaro et al., 2013; Koffel & Watson, 2009;
Revenson et al., 2016). Given the theoretical function of
partner responsiveness in downregulating arousal and anxi-
ety (Selcuk et al., 2010) and prior empirical work docu-
menting that perceived partner responsiveness predicts
lower depression and anxiety (e.g., Fekete et al., 2007;
Simpson et al., 1992), we expected that perceived partner
responsiveness would be meaningfully linked to sleep qual-
ity via its associations with those two symptomologies, a
hypothesis hitherto unexplored in the relationships and
health literatures. Previous research indicated that individu-
als project their own support provision to their partner, that
is, individuals who provide more support to their partner
are more likely to perceive their partner as responsive
(Lemay, Clark, & Feeney, 2007). Moreover, individuals
who perceive their partner as responsive may be more
agreeable people in general. Thus, following prior work
on partner responsiveness (Slatcher et al., 2015), we con-
trolled for emotional support provision to the partner and
agreeableness in the analyses. In addition, our analysis con-
trolled for demographic factors (age, gender, race, and edu-
cation) and physical health factors (perceived health, health
symptoms, and body mass index [BMI]) known to affect
sleep quality (Mezick, Wing, & McCaffery, 2014; Ong
et al., 2013).
Method
Sample and Procedure
The data for the present study came from the National Survey
of Midlife Development in the United States II (MIDUS II), a
study on health and aging conducted in 2004–2006 (N¼4,963;
age range ¼32–84). The MIDUS II survey consisted of a
phone interview and a self-administered questionnaire (Ryff
et al., 2007). Upon completion of MIDUS II, a subset of
respondents (N¼1,255) participated in the Biomarkers Study
(Dienberg Love, Seeman, Weinstein, & Ryff, 2010), which
included sleep assessments. Mean time lag between the
MIDUS II self-administered questionnaire and the Biomarkers
Study was 25 months (SD ¼14 months). The current sample
consisted of 698 married or cohabiting adults (mean age ¼
57 years, range ¼35–86 years) who completed the perceived
partner responsiveness measure, all covariates, and at least one
of the sleep measures (self-reported global quality or objective
sleep efficiency) and reported still being together with their
partner over the course of data collection (i.e., between the
MIDUS II phone interview and the Biomarkers Study). Of
these participants, 50%were female and 50%male; 94%were
White and 6%were from other racial backgrounds; 24%grad-
uated from high school or less and 76%had some college edu-
cation or more. In the final sample, 479 participants completed
only the self-reported global sleep quality measure, 16 com-
pleted only the objective sleep efficiency measure, and 203
completed both measures. Thus, analyses testing the associa-
tions of perceived partner responsiveness with global sleep
quality and objective sleep efficiency were based on 682 and
219 adults, respectively. Participants who had data for objec-
tive sleep efficiency were slightly younger (M¼56.22, SD
¼10.86 vs. M¼58.03, SD ¼11.28, p¼.048, d¼.16, 95%
CI [0.02, 3.59]) and scored slightly lower on anxiety (M¼
20.84, SD ¼4.20 vs. M¼21.54, SD ¼4.41, p¼.047, d¼
.16, 95%CI [0.01, 1.40]). The two groups, however, did not
differ on other variables of interest including perceived partner
responsiveness, global sleep quality, depression, or any of the
covariates (ps > .071).
Measures
Perceived partner responsiveness. Following prior work (Selcuk,
Gunaydin, Ong, & Almeida, 2016; Selcuk & Ong, 2013;
Slatcher et al., 2015), perceived partner responsiveness was
measured with 3 items in the MIDUS II self-administered ques-
tionnaire. Participants indicated the extent to which their part-
ner or spouse cares about them, understands the way they feel
about things, and appreciates them (1 ¼alotto 4 ¼not at all,
a¼.82). Responses were reverse scored, so that higher scores
reflected greater partner responsiveness.
Anxiety and depression symptoms. Anxiety and depression were
assessed in the Biomarker Study using the Anxious Arousal
subscale of the Mood and Symptom Questionnaire (MASQ;
Watson et al., 1995), measuring specific anxiety symptoms
Selcuk et al. 3
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(somatic tension and hyperarousal) that are critical for sleep
quality (Stepanski & Rybarczyk, 2006), and the Anhedonic
Depression subscale measuring specific depression symptoms
(low positive affect and loss of interest). Prior work showed
that these subscales are less correlated with each other (r¼
.369, p< .001 in the current sample) and show higher discrimi-
nant validity compared to other measures of anxiety and
depression while maintaining convergent validity, in both clin-
ical and nonclinical samples (Watson et al., 1995). Participants
indicated how much they experienced each symptom during
the past week (1 ¼not at all to 5 ¼extremely). The Anxious
Arousal subscale consisted of 17 items (e.g., ‘‘heart was racing
or pounding’’) and the Anhedonic Depression subscale con-
sisted of 22 items (e.g., ‘‘felt nothing was very enjoyable’’).
Anxiety and depression scores were computed by summing
across all items for participants who had no missing value (for
participants who had a missing value for only 1 item, mean sub-
stitution was used for the item), a¼.73 for anxiety and .93 for
depression.
Sleep outcomes. All sleep assessments were obtained in the Bio-
marker Study. Global sleep quality was measured with the
widely used Pittsburgh Sleep Quality Index (PSQI; Buysse,
Reynolds, Monk, Berman, & Kupfer, 1989). The PSQI
includes subjective assessments of seven sleep components:
sleep quality (overall assessment of sleep quality), sleep
latency (time and difficulty to fall asleep at night), sleep dura-
tion (hours of sleep gotten at night), habitual sleep efficiency
(the ratio of actual sleep to the time spent in bed), sleep distur-
bance (trouble staying asleep), use of sleeping medication, and
daytime dysfunction (trouble staying awake during daytime).
Each category receives a score between 0 and 3, with higher
scores reflecting worse sleep quality. Although the components
measure different aspects of sleep, the PSQI is typically ana-
lyzed using a global score, especially given that all components
reflect an underlying subjective (dis)satisfaction with sleep
(e.g., Grandner et al., 2006). Thus, in line with prior work using
the PSQI, a global sleep problems index was computed by sum-
ming the seven sleep components for each participant with
complete data (a¼.69; see Online Supplemental Material for
supplemental analyses using the component scores separately).
Objective sleep outcomes were measured by collecting acti-
graphy data. Participants wore a Mini Mitter Actiwatch1-64
activity monitor on their nondominant wrist for 7 consecutive
days and nights starting on a Tuesday morning at 7:00 a.m. and
ending the next Tuesday morning. Using a built-in sensor, the
monitor detects the number of movements made by the wearer.
The start and end times of actigraphic records were determined
using diary logs in which participants entered their bedtime and
risetime. Activity counts within 30-s epochs were used to esti-
mate sleep statistics. Whether participants were asleep or
awake was estimated by comparing activity counts in each
epoch and the epochs surrounding it to a predetermined thresh-
old value. Sleep duration was computed by summing the
epochs, in minutes, marked as sleep during a night’s interval
(the difference between the start and end times logged in the
diary). Sleep efficiency was computed as the percentage ratio
of total sleep time to the total time spent in bed. Sleep effi-
ciency may suffer due to two reasons: difficulty to fall asleep
or difficulty to stay asleep. Therefore, sleep onset (a measure
of difficulty falling asleep) and wake after sleep onset (a mea-
sure of difficulty staying asleep) were also included in the anal-
yses to figure out which aspects of sleep efficiency were linked
with partner responsiveness. Sleep onset corresponded to the
time required, in minutes, for the onset of sleep after attempting
to get to sleep. Finally, wake after sleep onset corresponded to
the total time of awakenings during the night’s interval after
falling asleep.
Covariates
Demographic covariates. Demographic covariates included age
at completion of the Biomarker study, and gender (0 ¼male,
1¼female), race (0 ¼White,1¼Non-White), and education
(1 ¼no school/some grade school to 12 ¼doctoral degree)
assessed at MIDUS II.
Physical health covariates. We controlled for three physical health
predictors of sleep quality: perceived physical health, health
symptoms, and BMI. Perceived physical health was measured
in the MIDUS II phone interview via a single item asking par-
ticipants to evaluate their physical health (1 ¼excellent to 5 ¼
poor). Participants also completed a health symptoms checklist
(e.g., ‘‘ever had heart disease?’’ ‘‘ever had cancer?’’) in the
Biomarker Study. The total number of health symptoms ever
experienced was included in the analyses. Finally, the BMI was
computed by dividing weight in kilograms by height squared in
meters. These measurements were obtained by clinical staff
during a physical exam as part of the Biomarker Study.
Relationship covariates. Emotional support provision to the part-
ner was measured by a single item asking how many hours per
month participants give emotional support to their partner (e.g.,
comforting, lending a listening ear, giving advice; Rossi,
2001). Given the open-ended nature of the item, there were
some outliers with very high values on this variable. Responses
higher than 2.5 standard deviations of the mean were recoded
to the highest value below 2.5 standard deviations to reduce the
influence of the outliers on the results.
Agreeableness was measured by asking participants the
extent to which each of five adjectives (helpful, warm, caring,
softhearted, sympathetic) described them (1 ¼alotto 4 ¼not
at all; Rossi, 2001). Responses were reverse coded, so that
higher scores indicated greater agreeableness (a¼.81).
Results
Table 1 provides the correlations among variables of interest.
As in prior work (Grandner et al., 2006), the PSQI global score
showed weak correlations with the actigraph assessments (all
rs < .24), suggesting that the two measures tap different aspects
of sleep quality. Looking at the specific PSQI components that
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Table 1. Descriptive Statistics and Correlations Among Variables.
Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1. Partner
responsiveness
2. Sleep problems
(PSQI)
.144*** –
3. Sleep efficiency
(actigraph)
.015 .159* –
4. WASO (actigraph) .118 .242*** .608*** –
5. Sleep onset
(actigraph)
.039 .089 .728*** .197**
6. Sleep duration
(actigraph)
.036 .068 .549*** .032 .358*** –
7. Anxiety .148*** .359*** .186** .294*** .053 .008 –
8. Depression .212*** .401*** .070 .054 .042 .042 .369*** –
9. Agreeableness .110** .005 .097 .026 .168* .035 .071 .212*** –
10. Provision of
emotional support
.142*** .046 .092 .041 .067 .134* .002 .045 .106** –
11. Poor perceived
health
.049 .256*** .195** .218** .153* .009 .340*** .346*** .044 .039 –
12. Health symptoms .001 .254*** .131 .193** .175** .070 .308*** .164*** .076* .056 .345***
13. BMI .069 .062 .176** .114 .151* .147* .104** .092* .014 .048 .240*** .151***
14. Age .137*** .073 .138
*
.150* .086 .002 .021 .172*** .086* .069 .022 .355*** .004
15. Education .028 .033 .011 .071 .012 .077 .124*** .066 .094* .110** .119** .006 .059 .059 –
16. Race
a
.046 .015 .031 .035 .035 .068 .038 .067 .031 .029 .162*** .014 .080* .108** .031 –
17. Gender
b
.151*** .162*** .313*** .126 .230*** .316*** .096* .012 .221*** .101** .034 .068 .100** .167*** .049 .014
M3.592 5.640 82.975 42.522 24.001 384.913 21.321 50.534 3.420 27.546 2.280 3.930 29.092 57.460 7.830
SD 0.538 3.357 7.887 18.391 20.921 62.514 4.356 12.338 0.496 38.758 0.909 2.807 5.565 11.174 2.449
Note. PSQI ¼Pittsburgh Sleep Quality Index; WASO ¼wake after sleep onset; BMI ¼body mass index. The sample size was 682 for estimates including the PSQI, 219 for estimates including actigraph assessments, and 698
for the remaining estimates.
a
0¼White,1¼non-White.
b
0¼male,1¼female.
*p< .05. **p< .01. ***p< .001.
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map onto the actigraph-based measures, PSQI sleep efficiency
was unrelated to the actigraph sleep efficiency (r¼.104,
p¼.140) and the PSQI sleep duration was moderately related
to actigraph sleep duration (r¼.379, p< .001; see Table S1 in
Online Supplemental Materials for all pairs of correlations
between the actigraphy indices and the PSQI components).
Perceived Partner Responsiveness and Self-Reported
Sleep Problems
Participants who perceived their partner as responsive reported
lower sleep problems as measured by the global PSQI score
(B¼.901, SE ¼.237, p< .001). Partner responsiveness also
indirectly predicted lower global sleep problems through lower
anxiety (indirect association [IA] ¼.223, 95%CI: [0.426,
0.081]) and depression (IA ¼.406, 95%CI: [0.620,
0.251]; Figure 1A; see Online Supplemental Materials for
complete details on the data analytic approach for testing indi-
rect associations). Once anxiety and depression were included
in the model, the direct association between partner responsive-
ness and self-reported sleep problems was not significant
(although the effect size was similar to that of the IA through
anxiety, B¼.272, SE ¼.218, p¼.213). The indirect asso-
ciations between perceived partner responsiveness and global
sleep problems held even when the analyses were repeated
by controlling for emotional support provision to the partner,
agreeableness, demographic factors, and physical health fac-
tors (IA ¼.129, 95%CI [0.297, 0.027] for anxiety and
IA ¼.280, 95%CI [0.465, 0.143] for depression; see
Table S2 in Online Supplemental Materials for all direct and
indirect associations between partner responsiveness and the
PSQI subcomponents).
Perceived Partner Responsiveness and Actigraph-
Assessed Sleep Efficiency
Partner responsiveness was not directly associated with
actigraph-assessed sleep efficiency or sleep duration (Table
1). However, partner responsiveness indirectly predicted
greater objective sleep efficiency via lower anxiety (IA ¼
.678, 95%CI [0.100, 2.014]) but not depression (IA ¼.052,
95%CI [0.398, 0.504]; Figure 1B). Improved sleep
Figure 1. The indirect associations of perceived partner responsiveness with global sleep problems (Panel A) and actigraph-assessed sleep
efficiency (Panel B) through anxiety and depression. Numbers outside the parentheses are unstandardized regression coefficients and numbers
inside the parentheses are standard errors. The sample size was 682 in analyses predicting global sleep problems and 219 in analyses predicting
sleep efficiency. PSQI ¼Pittsburgh Sleep Quality Index. **p<.01. ***p< .001.
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efficiency could be due to faster sleep onset or lower wake after
sleep onset. Our findings supported the latter possibility.
Whereas perceived partner responsiveness indirectly predicted
lower wake after sleep onset through lower anxiety (IA ¼
2.495, 95%CI [5.650, 0.728]), it was not associated with
sleep onset (IA ¼.529, 95%CI [03.052, 0.630]). The indi-
rect associations between perceived partner responsiveness and
objective sleep quality through anxiety held, even after adjust-
ing for emotional support provision to the partner, agreeable-
ness, demographic factors, and physical health covariates (IA
¼.566, 95%CI [0.013, 1.856] for sleep efficiency and IA ¼
1.796, 95%CI [4.656, 0.140] for wake after sleep onset).
After adjusting for covariates, partner responsiveness was not
associated with actigraph-assessed sleep duration through anxi-
ety (IA ¼2.195, 95%CI [.719, 10.389]) or depression (IA ¼
1.148, 95%CI [1.113, 5.691]).
Discussion
These findings are the first to demonstrate how perceived part-
ner responsiveness is linked to subjective and objective sleep
quality. Perceived partner responsiveness predicted lower glo-
bal sleep problems through lower anxiety and depression.
Importantly, perceived partner responsiveness was also associ-
ated with actigraph-assessed sleep efficiency through lower
anxiety (but not depression). These indirect associations
remained significant after we statistically controlled for emo-
tional support provision to the partner, agreeableness, and
demographic (age, gender, race, and education) and health cov-
ariates (perceived health, health symptoms, and BMI) that
could have potentially accounted for the findings.
An important strength of the present study was using a com-
bination of subjective (the PSQI) and objective (actigraph)
sleep measures. Past work showed that the PSQI, the most
widely used subjective sleep quality measure, and actigraph
assessments are not substitutes for each other but rather mea-
sure distinct aspects of sleep quality (Grandner et al., 2006;
Landry, Best, & Liu-Ambrose, 2015). The low correlations
between the PSQI and actigraphy assessments (also replicated
in the present work) have led researchers to suggest that both
measures should be included in sleep studies whenever possi-
ble (Landry et al., 2015). Using the two measures in the same
study enabled us to document the distinct pathways by which
perceived partner responsiveness is associated with sleep.
We found a direct association between partner responsive-
ness and sleep only for the PSQI but not for the actigraph-
assessed sleep quality. Indirect associations were much more
pronounced across both subjective and objective sleep mea-
sures. The more consistent pattern with indirect (vs. direct)
associations is in line with theoretical models explaining how
romantic relationships are associated with physical health
(Burman & Margolin; 1992; Kiecolt-Glaser & Newton, 2001;
Slatcher, 2010; Slatcher & Selcuk, in press). These models sug-
gest that romantic relationship processes are more likely to be
linked to physical health through psychological mechanisms
(e.g., psychological symptoms, well-being) rather than having
a direct effect. The present findings are in line with this theo-
rizing by showing that partner responsiveness is mainly linked
to sleep through psychological symptoms—particularly anx-
ious arousal but also anhedonic depression.
Although partner responsiveness predicted better actigraph-
assessed sleep efficiency through lower anxiety, there was no
such indirect association between partner responsiveness and
actigraph-assessed sleep duration. A prior study on parental
responsiveness and child sleep reached a similar conclusion,
with parental responsiveness predicting parent reports of child
sleep quality but not duration, although that study only focused
on the direct links (Bordeleau et al., 2012). Taken together with
the present findings, it seems that sleep duration is unrelated
to responsiveness of partners or caregivers, but individuals
with less responsive close others experience more disrupted
sleep. This qualitative difference between individuals who
have responsive versus unresponsive partners is important,
as chronic disruptions—that is, inefficient sleep—predicts
important physical health outcomes, including mortality
(Dew et al., 2003).
Depression also mediated the partner responsiveness–sleep
association but only for subjective sleep problems. This finding
replicates prior work on the association between depression
and the PSQI and extends a recent finding that depression med-
iates the association between quality of general social ties and
the global PSQI score (Kent, Uchino, Cribbet, Bowen, &
Smith, 2015). Given our finding that subjective evaluations
of sleep quality do not reflect actual sleep efficiency, depressed
individuals may be negatively biased in their perceptions of
their psychological and physiological states, which may extend
to sleep (Grandner et al., 2006). This is not to say that the PSQI
assessments are irrelevant to sleep quality; on the contrary, sub-
jective sleep quality does predict important health and well-
being outcomes (Lemola et al., 2013; Martin et al., 2011).
Rather, the present findings show the importance of using mul-
tiple measures to study the links between close relationships
and sleep, as the nature of the associations and the mediating
psychological mechanisms may be different across measures.
The present findings also dovetail with and extend past work
investigating the role of romantic attachment orientations in sleep
quality. Insecure (i.e., anxious or avoidant) attachment, which is
thought to result from closeothers’ failure to behave responsively,
has been linked to poor self-reported sleep quality and sleep dis-
turbances such as difficulty falling asleep and staying asleep
(e.g., Adams & McWilliams, 2015; Carmichael & Reis, 2005;
see also Adams, Stoops, & Skomro, 2014, for a review). There
was a significant direct association between perceived partner
responsiveness and subjectivesleep quality in the present sample
as well. In addition, the present study extended and complemen-
ted prior findings by documenting psychological mechanisms
through which partner responsiveness is linked to both self-
reported sleep problems and objectively assessed sleep efficiency.
Recent studies have shown that perceived partner respon-
siveness has relevance for health outcomes including
all-cause mortality (Selcuk & Ong, 2013). There is increasing
evidence that partner responsiveness predicts potential
Selcuk et al. 7
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mechanisms, including affective reactivity to stressors, trait
negative affect, depression, psychological well-being, and diur-
nal cortisol (Fekete et al., 2007; Selcuk et al., 2016; Selcuk,
Zayas, Gunaydin, Hazan, & Kross, 2012; Slatcher et al.,
2015), that may ultimately affect adult morbidity and mortality.
By showing that perceived partner responsiveness predicts self-
reported sleep problems through lower anxiety and depression,
and objective sleep efficiency through lower anxiety, the pres-
ent study extends the set of processes by which perceived part-
ner responsiveness potentially affects physical health.
The findings also have implications for therapy and inter-
vention design. The inherently interdependent nature of adult
romantic relationships means that romantic partners, as well
as perceptions of one’s romantic partner, play a meaningful
role in promoting better health and well-being. Our findings
suggest that enhancing perceived partner responsiveness has
the potential to increase the effectiveness of interventions
designed to reduce sleep disturbances in particular and improve
individual well-being in general.
Before concluding, we acknowledge some limitations of the
present research. These data are correlational, meaning that we
are unable to make claims about the causal direction of the asso-
ciations between partner responsiveness, anxiety and depression,
and sleep. For example, it is possible that partner responsiveness
may be linked to anxiety, depression, and sleep simultaneously,
or poor sleep could have affected scores on the MASQ as well as
individuals’ perceptions of their partner’s responsiveness. The
existing literature, however, makes a stronger theoretical case
for individuals who experience higher partner responsiveness
to have better sleep outcomes, rather than the other way around
(cf. Carmichael & Reis, 2005; Selcuk et al., 2015; Troxel,
Buysse, Hall, & Matthews, 2009). Moreover, the fact that part-
ner responsiveness was assessed on average 25 months before
both the mediating and outcome variables supports this possi-
bility, although we should note that we were not able to model
change in sleep behavior. Related to this point, it may be pos-
sible to observe stronger associations between partner respon-
siveness and sleep in a design with a smaller time interval
between the measurements. For instance, future daily experi-
ence studies may investigate whether daily perceptions of
responsiveness predicts sleep, especially on days participants
experience stressors and exhibit greater anxiety.
A second limitation of the current study involves the diver-
sity of the sample. The MIDUS sample is not racially diverse,
limiting our ability to generalize our findings to non-White
individuals. Furthermore, although the PSQI was administered
at three different study sites (University of California Los
Angeles, University of Wisconsin, and Georgetown Univer-
sity), the subset of participants who provided actigraph data
of sleep quality completed the study at only one site (University
of Wisconsin). Finally, participants were in their middle and
late adulthood, leaving the question of whether the results
would look similar for younger individuals open for future
research. Regardless, the emergence of the partner responsive-
ness–sleep link through lower anxiety in analyses that included
potentially meaningful covariates bolsters our confidence in
the findings. Future studies would benefit from replicating
these findings in a more heterogeneous sample.
In sum, the present study demonstrated the role of perceived
partner responsiveness in subjective and objective assessments
of sleep quality through lower anxiety and depression. Future
research should further elucidate the mechanisms by which
higher partner responsiveness exerts a salutary influence on
health and well-being.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to
the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the
research, authorship, and/or publication of this article: The MIDUS II
research was supported by a grant from the National Institute on Aging
(P01-AG020166). The Biomarker Project was further supported by the
following grants: M01-RR023942 (Georgetown), M01-RR00865
(UCLA) from the General Clinical Research Centers Program and
UL1TR000427 (UW) from the National Center for Advancing Transla-
tional Sciences (NCATS), National Institutes of Health.
Supplemental Material
The online data supplements are available at http://spps.sagepub.com/
supplemental.
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Author Biographies
Emre Selcuk is an assistant professor in the Department of Psychol-
ogy at Middle East Technical University, Turkey. His research focuses
on the formation, maintenance, and functions of attachment relation-
ships across the life span.
Sarah C. E. Stanton is a postdoctoral fellow at Wayne State Univer-
sity. She uses a social psychological approach to understand the cog-
nitive and affective aspects of close relationships and their effects on
behavior, physiology, and health and well-being.
Richard B. Slatcher is an associate professor of social psychology at
Wayne State University. His research has two main facets: basic
research on close relationship processes and investigations of the links
between close relationships, biological processes, and physical health.
Anthony D. Ong is an associate professor of human development at
Cornell University and an associate professor of gerontology in med-
icine at Weill Cornell Medical College. His research focuses on the
dynamic processes that underlie expressions of vulnerability and
adaptation across the life span.
Handling Editor: Nickola Overall
10 Social Psychological and Personality Science
by guest on August 18, 2016spp.sagepub.comDownloaded from
... In non-patient samples, perceived partner responsiveness has been associated with health outcomes including all-cause mortality [23], diurnal cortisol profiles [24], and sleep quality [25]. This relationship between perceived partner responsiveness and sleep quality may be due in part to a reduction in anxious arousal when one feels understood, validated, and cared for thus creating an optimal environment for sleep [25]. ...
... In non-patient samples, perceived partner responsiveness has been associated with health outcomes including all-cause mortality [23], diurnal cortisol profiles [24], and sleep quality [25]. This relationship between perceived partner responsiveness and sleep quality may be due in part to a reduction in anxious arousal when one feels understood, validated, and cared for thus creating an optimal environment for sleep [25]. Moreover, research suggests that partner responsiveness may also benefit the responding partner (attempted partner responsiveness), such that compassionate acts like demonstrating understanding and acceptance are associated with greater emotional well-being for both the provider and recipient [26]. ...
... The observed actor effects are consistent with the limited body of research in non-patient samples on partner responsiveness and sleep, suggesting that greater perceived partner responsiveness is associated with better sleep outcomes [25]. In the current study, the degree to which the survivor or partner went to bed feeling validated, understood, or cared for was associated with better subjective sleep that same night. ...
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... Interpersonal security contributes to psychophysiological responses that could impact sleep onset and quality (Palagini et al., 2018). For instance, partner responsiveness, as a characteristic of interpersonal security, predicts lower arousal and consequently contributes to better sleep outcomes (Selcuk et al., 2017). Additionally, for most adults, sleep is a dyadic behavior. ...
... The role of social factors in sleep outcomes among older adults has also been explored. These studies show that social support such as partner responsiveness predicts better sleep outcomes (Selcuk et al., 2017) and social strain is associated with poorer sleep (Chung, 2017). Although sleep has been characterized in numerous publications in MIDUS datasets, the moderating role of gender in the association between quality of social relationships and sleep is lacking. ...
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... Extensive research has demonstrated the relevance of perceived partner responsiveness to relationship well-being. For example, perceived partner responsiveness has been shown to underlie effective social support (Maisel & Gable, 2009) and its role in lowering all-cause mortality (Selcuk & Ong, 2013), while promoting gratitude (Algoe & Zhaoyang, 2016), caregiving (Canevello & Crocker, 2010;Lemay & Clark, 2008), attraction to potential romantic partners (Birnbaum & Reis, 2012;Reis, 2020), constructive conflict resolution (Overall & McNulty, 2017), beneficial outcomes of sharing personal good news (Gable, Gosnell, & Maisel, 13 Sociability Matters & Strachman, 2012;Reis et al., 2010), sexual satisfaction and desire (Birnbaum et al., 2016), forgiveness (Pansera & La Guardia, 2012), security-enhancing interactions (Simpson, Rholes, Oriña, & Grich, 2002), effective partner support of goal-directed activity (Rusbult, Kumashiro, & Reis, 2010), sleep quality (Selcuk, Stanton, Slatcher, & Ong, 2017), pain management (O'Neill, Mohr, Bodner, & Hammer, 2020), marital conversations about end-of-life care (Moorman, 2011), and coping with COVID-related stress (Slatcher et al., 2021). Partner responsiveness is likely to be a key variable not only in personal relationships, but also in all forms of human sociability in general. ...
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Extensive research has documented people’s desire for social partners who are responsive to their needs and preferences, and that when they perceive that others have been responsive, they and their relationships typically thrive. For these reasons, perceived partner responsiveness is well-positioned as a core organizing theme for the study of sociability in general, and close relationships in particular. Research has less often addressed the downstream consequences of perceived partner responsiveness for cognitive and affective processes. This gap in research is important, because relationships provide a central focus and theme for many, if not most, of the behaviors studied by social psychologists. This chapter begins with an overview of the construct of perceived partner responsiveness and its centrality for relationships. We then review programs of research demonstrating how perceived partner responsiveness influences three core social-psychological processes: self-enhancing social cognitions, attitude structure, and emotion regulation. The chapter concludes with a brief overview of how deeper incorporation of relationship processes can enhance the informativeness and completeness of social psychological theories.
... It may be that regular relationships are associated with greater comfort and partner responsiveness (i.e. feeling understood, cared for, and validated), which leads to more positive sleep outcomes 32 . Further, shorter sleep latency may result from reduced physiological arousal, as sleeping next to a regular partner can improve physical and emotional security, causing a down regulation of arousal levels and an increase in sleep quality and duration 24 . ...
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... Perceived partner responsiveness has been shown to predict numerous beneficial outcomes, such as intimacy (Laurenceau et al., 2004), emotional well-being and happiness (Selcuk & Karagobek, 2018), emotional openness , personal growth and sleep efficiency (Selcuk, Stanton, Slatcher, & Ong, 2017), self-esteem (Cortes & Wood, 2018;Murray et al., 2000), and even lower mortality risk (Selcuk & Ong, 2013). In this vein, and supporting our proposal about the relevance of responsiveness to interpersonal emotion dynamics, research shows that perceived partner responsiveness to emotional self-disclosures are more strongly tied to important relational outcomes such as intimacy than to factual selfdisclosures (Laurenceau, Barrett, & Pietromonaco, 1998). ...
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Emotions are not only fundamentally dynamic in nature in the sense of varying across time, but they are also fundamentally social, originating in and shaping our interpersonal processes. Interpersonal emotion dynamics refer to the ways in which emotions and emotional self-regulation are dynamically influenced by interactional partners, given the interdependence that exists between them. We begin this chapter by describing the premise for interpersonal emotion dynamics in intimate relationships, what interpersonal emotion dynamics constitute, and the state of the art in the fields of emotion science, relationship science, and interpersonal emotion dynamics. Next, we discuss two key themes that we believe promote theoretical integration among seemingly disparate strands of research (in emotion and relationship research), emphasizing the importance of interdependence and perceived partner responsiveness in the interpersonal emotion dynamics that characterize intimate relationships. The chapter concludes with recommendations for future research in this promising area.
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