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Exploring the Interdependence of Couples' Rest-Wake Cycles: An Actigraphic Study

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Abstract

Within western societies, it is commonplace for couples to share a bed. Yet there has been remarkably little research carried out on couples' sleep. This paper draws upon actigraphy, audio diary, and questionnaire data from both partners of 36 heterosexual couples (age 20-59 yrs) and aims to quantify the extent to which it is important to take into account the dyadic nature of sleep-wake cycles. It achieves this through two interrelated aims: to use hierarchical linear models to measure dyadic interdependence in actigraphically recorded variables, and to investigate how much of this dyadic interdependence truly results from couple dynamics. The variables with the most significant couple interdependency were actual bed time, sleep latency, light/dark ratio, and wake bouts. The paper concludes by suggesting that interdependence may be the defining feature of couples' sleep, and that we need to employ analytic approaches that both acknowledge this and are sensitive to the possibilities that not all aspects of sleep will behave in the same way.
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Exploring the interdependence of couples’ rest-wake cycles: an actigraphic
study
Robert Meadows1, Sara Arber1, Susan Venn1, Jenny Hislop2 and Neil Stanley3
1 Department of Sociology, University of Surrey, Guildford, Surrey GU2 7XH, UK
2 Institute of Life Course Studies, Keele University, Keele, Staffs ST5 5BG, UK
3 Clinical Research and Trials Unit, Norfolk and Norwich University Hospital, UK
This study was funded by UK Economic and Social Research Council grant RES-000-23-
0268.
Correspondence to:
Robert Meadows
Centre for Research on Ageing and Gender
Department of Sociology
School of Human Sciences
University of Surrey
Guildford
Surrey
Tel: 01483 689292
Fax: 01483 689090
Email: r.meadows@surrey.ac.uk
2
Abstract
Within western societies it is commonplace for couples to share a bed. Yet there has been
remarkably little research carried out on couples’ sleep. This paper draws upon actigraphy,
audio diary and questionnaire data from both partners in 36 heterosexual couples (age 20-59)
and aims to quantify the extent to which it is important to take the dyadic nature of sleep-
wake cycles into account. It achieves this through two interrelated aims: (i) to use
Hierarchical Linear Models to measure dyadic interdependence in actigraphically recorded
variables; and (ii) to investigate how much of this dyadic interdependence truly results from
couple dynamics. The variables with the most significant couple interdependency were
‘Actual bed time’, ‘Sleep latency’, ‘Light/Dark ratio’ and ‘Wake bouts’. The paper
concludes by suggesting that interdependence may be the defining feature of couples’ sleep
and that we need to employ analytic approaches which both acknowledge this and which are
sensitive to the possibilities that not all aspects of sleep will behave in the same way.
Key words: couples, sleep, actigraphy
3
Introduction
A paradox exists within sleep research. On the one hand, it has long been recognised that
the common practice of sharing a bed might be a significant contributing factor to daytime
sleepiness and chronic sleep loss’ (Kushida 2004, p. 133). Nearly forty years ago Monroe
(1969) highlighted how sleep architecture improves when bed partners sleep alone. In his
study of 14 couples, Monroe found that lone sleeping significantly increased the amount of
stage 4 sleep and reduced the number of awakenings by 60 per cent.
Yet, despite the existence of a nascent literature on couples’ sleep quality (Cartwright and
Knight 1987; Pankhurst and Horne 1994; Beninati et al 1999; Ulfberg et al 2000; Parish and
Lyng 2003; Strawbridge et al 2004; Rosenblatt 2006; Dittami et al 2007; Venn 2007),
discussions of couples’ sleep-wake remain largely confined to review articles (Troxel et al
2007), ‘brainstorms’ (Adams and Cromwell 1978, p. 11) or studies of marital quality (Larson
et al 1991). It is still the case that almost everything that has been published in the social and
behavioural sciences and in medicine about adult sleep has looked at adult sleep as an
individual phenomenon’ (Rosenblatt 2006, p. 1), with studies largely ignoring the potential
dyadic nature of bed partner’s sleep.
Given this contradiction, this paper aims to quantify the extent to which it is important to take
the dyadic nature of sleep-wake cycles into account. It achieves this through two interrelated
aims: (i) to measure dyadic interdependence in actigraphically recorded variables; and (ii) to
investigate how much of this dyadic interdependence truly results from couple dynamics.
Methods
Forty heterosexual couples were recruited in South East England in 2004-2005. Flyers were
hand delivered around housing estates in the South East of England (eliciting an average
response of 1 couple per 100 flyers). Participants were also invited to pass on details of the
study to family and friends. Each participating couple received £100 to compensate them for
their time and commitment.
Inclusion criteria were that the male in the couple was aged between 20 and 59 inclusive and
must be married or cohabiting. Female subjects could also not knowingly be pregnant at the
beginning of the project. All participants continued their normal sleep/wake routine
throughout a one week data collection period and no restrictions were placed on activities,
4
food or drink. The study was approved by the University of Surrey ethics committee and
conformed to international ethical standards (Touitou et al 2006).
Due to non-compliance or missing data the analysis set included 36 couples. The age range
for the men was 21 years to 59 years (mean= 39.83; sem=1.92). The women ranged from 22
years to 58 years (mean=38.64; sem=1.86). Most partners were similar in age; the age
difference ranged from 0 years to 12 years (mean difference=2.83 years; sem=0.41).
Nineteen couples had no children living at home. Seventeen couples had children living at
home; 11 of which had at least one child under the age of 10 co-residing. All of the men
were in fulltime paid employment, meaning that, for the large part, the actigraphy data
included 5 paid employment days and 2 ‘rest’ days. Six of the women described themselves
as a ‘housewife’ or ‘not in employment’, three were full-time students and the remainder
were employed in a wide range of full or part-time jobs. The mean difference in Horne and
Östberg score (Horne and Östberg 1976), a measure of diurnal preference, was 7.82
(s.d.=6.42). This suggests that individuals were not selecting partners with similar diurnal
patterns.
Actigraphy
As well as completing the Horne and Östberg questionnaire and the Pittsburgh Sleep Quality
Index questionnaire (Buysse et al 1989), each partner wore an actigraph for 1 week.
Actigraphically recorded measurement data can, with high reliability, be translated into
information about a person’s sleep (Webster et al 1982; Cole et al 1992; Sadeh et al 1994;
Sadeh et al 1995; Blood et al 1997). Variables such as ‘sleep latency’, ‘sleep efficiency’,
‘number of awakenings’ and ‘sleep fragmentation’ are now routinely derived from actigraphy
data (Knutson et al 2007).
Within the present study, actiwatches (Cambridge Neurotechnology Ltd, [CNT] Cambridge,
UK) were set to collect data at 1 minute epochs. All watches were calibrated using
standardized equipment from CNT prior to use. Actigraphy files were downloaded and
summarised using inbuilt algorithms within the manufacturer’s software ‘Sleep Analysis 5’
(Cambridge Neurotechnology Ltd).
Following Morgenthaler et al’s (2007) recommendation that careful attention be paid to start
and stop times, three steps were taken to ensure the accuracy of our analysis: first, as all of
5
the sleep variables are calculated in relation to ‘bed time’ and ‘get up time’, participants were
asked to press an ‘event button’ (positioned on the surface of the watch) when they were in
bed ready to go to sleep and when they awoke in the morning. This event press was recorded
within the actigraphs memory. Second, two experienced actigraphy scorers independently
analysed the files. Results were then compared, and any discrepancies between the two led
to further investigation. Particular attention was paid to those files where the subject did not
comply fully with marker press instructions and the person analysing was required to
estimate ‘bed time’ and/or ‘get up time’ (n=87 nights out of a total of 473 nights). Third,
audio diaries were referred to throughout the analysis and especially in cases where the ‘bed
time’ or ‘get up time’ were ambiguous. Although Lockley et al (1999) have shown that
subjective written diaries and actigraphy are poorly correlated in relation to number and
duration of awakenings the audio diaries within this study were differentiated from earlier
research, especially because bed partners were able to provide extensive data about each
night.
Audio Diaries
Audio diaries (Sony Dictaphones) were given to each partner with the instruction that, for the
following 7 days, they should record, after awakening in the morning, any information they
wished about their sleep or wake during the previous night. Written instructions to this effect
were provided, which asked them specifically to record bed times, any disturbances
throughout the night, and number and times of awakening. Subjects were also instructed that
they could make multiple entries and return to the audio diary on as many occasions as they
wished throughout the day. The completed audio sleep diary recordings were transcribed in
full. A detailed discussion of this method can be found in Hislop et al (2005).
Measures
Twelve variables were calculated for each bed partner. These variables measure aspects of
sleep timing, sleep quality and circadian rhythmicity. The sleep timing variables included
‘Preferred bed time’ (taken from questionnaire items which asked about the time that partners
would like to go to bed), ‘Actual bed time’ and ‘Get up time’ (both of which were calculated
from marker presses within the actigraphy) and ‘Difference between preferred and actual
bedtime’. Sleep quality variables were all calculated from the actigraphy data and included
‘Sleep latency’, ‘Actual sleep time’, ‘Sleep efficiency’, ‘Wake bouts’ (number of times the
subject woke during the night), ‘Light/Dark ratio’ (the difference between amount of daytime
6
movement and nighttime movement) and ‘Fragmentation index’. As an indication of
circadian rhythmicity the ‘interdaily stability’ and ‘intradaily variability’ were calculated.
The ‘interdaily stability’ (IS) gives an indication of the strength of the coupling between the
rest-activity rhythm and Zeitgebers’ (Van Someren et al 1997, p. 957). The ‘intradaily
variability’ (IV) gives an indication of the fragmentation of the rhythm’ (Van Someren et al
1997, p. 957). Both variables were created following the method described in Van Someren
et al (1997).
Statistical approaches to examining the interdependence of couple data
There is debate surrounding how the interdependence of couples’ sleep should be measured.
Standard correlation analysis techniques are often applied to data on couples (Kenny and
Cook 1999), in which sleep data is aggregated to give an average score for each individual
within the couple. Each spouses’ aggregated score would then be correlated with their
partner’s aggregated score. With heterosexual dyads these techniques quantify the extent to
which women who receive a high score on a variable, relative to other women, are matched
with men who receive a high score, relative to other men. However, this aggregation may
result in cross-level errors or level of analysis errors (Gonzalez and Griffin 1997) and ignore
potentially interesting autocorrelations between nights.
Researchers also disagree over the appropriateness of using Hierarchical Linear Modelling to
examine couples’ sleep. Some authors suggest that it is a useful method for identifying and
handling the interdependence of married/cohabiting couples’ data (DeLongis and Holtzman
2005, p. 5-6). While others, such as Laurenceau and Bolger (2005) and Kenny et al (2006),
criticise the use of Hierarchical Linear Modelling within research on couples. In their
discussion of multilevel models with binary outcomes, McMahon et al (2006) suggest that it
is often not feasible to estimate random effects for both the intercept and slope because there
are only two observations per cluster (i.e. per couple). Further to this, dyads in which both
members have identical responses do not contribute to the likelihood function.
Statistical analysis
In light of these debates, and the aims of the paper, analysis was undertaken in three stages:
First, we began by inspecting the validity of the data. The Kolmogorov-Smirnov (K-S) test
was applied to test for normality (using SPSS v141.) and reliability measures were obtained
(using HLM for windows, version 6.02a; Raudenbush and Bryk 2002). Reliability measures
7
indicate whether the sample mean is a reliable estimate of the true mean. Reliability scores
range from 0 to 1 and reliability is considered medium if greater than 0.5 or large if greater
than 0.7. Residuals were also plotted to identify the number of couples which differed
significantly from the average at the 5% level. Outliers were removed from the data set prior
to further analysis. As Langford and Lewis (1998) suggest, the word ‘outlier’ is used by
different writers in different senses. Here we use the term to describe a situation where: i)
significant difference between couples exists; and ii) this significant difference between
couples no longer exists when a single couple is removed from the model.
Second, we examined the shared variance between partners within couples using
unconditional hierarchical linear models (Raudenbush and Bryk 2002). For each dependent
variable, intraclass correlations (ICC) were calculated to give an indication of the proportion
of the total variation that is accounted for by higher level units. For the two level models
(which analyse IS, IV, ‘Preferred bedtime’) we calculated the proportion of the total variance
which occurs between individuals (Level 1) and between couples (Level 2). For the three
level models (which include ‘Sleep efficiency’, ‘Wake bouts’, ‘Fragmentation index’ and
‘Actual bed time’) we calculated the portion of the total variation which occurs between
nights (Level 1), between individuals (Level 2) and between couples (Level 3) (cf. Singer
1998).
Because the ICC indicates proportion of variance at the couple level, it can be high even
when the actual amount of variation is small. Therefore, tests were performed to calculate
whether the proportion of the variation at the couple level is statistically significant. Each
model was run in MLwiN (version 2.02; Rasbash et al 2005) with and without the couple
level. The likelihood ratio statistic was then obtained by subtracting the -2*loglikelihoods of
the two models and comparing the result to a chi-squared distribution on 1 degree of freedom
(Rasbash et al 2005).
Finally, we investigated how much of the variance component at the couple level truly results
from couple dynamics. Couples in this study tended to be of similar age and, as age can
affect sleep and rhythms, some of the couple ICC could be due to similar ages of partners and
not specifically related to dyadic interdependence, shared events or direct impacts2. Huang
et al (2002), for example, suggest that the IV of those aged 61 91 is significantly higher
than those aged 21-44 and Monk et al (2006) found that compared to younger adults older
8
adults have a highly regular lifestyle. Therefore, ‘age’ was added to the models and changes
in the proportion of variance at the couple level noted. ‘Presence of children’ was also added
and changes in couple level variance observed. Due to differing rates of compliance among
the sleep measures used, the total number of participants within the models discussed below
varies.
Results
Data was checked for validity. ‘Get up time’, ‘Sleep efficiency’ and ‘Difference between
preferred and actual bed time’ were not normally distributed. This was not resolved by log
transformation and no further analysis was performed on these three variables.
Figure 1 shows the number of couples which differ significantly from the average for the
remaining nine sleep variables. As Figure 1 illustrates, with interdaily stability (IS) only one
couple differed significantly from the average at the 5% level. Similarly, with intradaily
variability (IV) only 1 couple were significantly different from the average at the 5% level.
When these two couples were removed from the analysis there were no significant
differences between couples and a large proportion of the couple level variation was
removed. Therefore these two couples were considered outliers and removed from further
analysis of interdaily stability and intradaily variability.
Insert Figure 1 about here
Figure 1 also suggests that there is little couple level variation for ‘Actual sleep time’ and
‘Fragmentation index’ (as d=0). The residual plots in Figure 1 do indicate the existence of
some couple level variation in ‘Preferred bed time’ (d=3), ‘Sleep latency’ (d=4), ‘Light/Dark
ratio’ (d=4), and a high degree of couple level variation in Wake bouts (d=8) and ‘Actual
bed time (d=17). This was confirmed by the unconditional models: ‘Preferred bed time’
(couple level ICC=0.42; p<0.01), ‘Sleep latency’ (couple level ICC=0.25, p<0.001),
‘Light/Dark ratio’ (couple level ICC=0.28, p<0.001), ‘Wake bouts’ (couple level ICC=0.42,
p<0.001) and ‘Actual bed time’ (couple level ICC=0.52, p<0.001) all showed significant
clustering at the couple level.
9
However, as Table 1 illustrates, after including ‘age’ and ‘presence of children’ in the
models, only ‘Sleep latency’, ‘Light/Dark ratio’, ‘Wake bouts’ and ‘Actual bed time’,
remained showing significant variance at the couple level3. Further to this, the proportion of
variance at the couple level for ‘Sleep latency’ reduced from 0.25 to 0.18.
Insert Table 1 about here
Discussion
This paper has examined actigraphic data collected from 36 couples for one week. It has
investigated the partitioning of variance in twelve sleep/wake variables: ‘Preferred bed time’,
‘Actual bed time’, ‘Get up time’, ‘Difference between preferred and actual bed time’, ‘Actual
sleep time; ‘Light/Dark ratio’, ‘Sleep latency’, ‘Sleep efficiency’, ‘Fragmentation index’,
‘Number of wake bouts’, ‘Intradaily variability’ and ‘Interdaily stability’. ‘Get up time’,
‘Sleep efficiency’ and ‘Difference between preferred and actual bed time’ were not normally
distributed and no further analysis was performed on these variables.
After adding covariates to the models, four of the nine remaining variables showed
significant clustering at the couple level: ‘Actual bed time’, ‘Sleep latency’, ‘Light/Dark
ratio’ and ‘Wake bouts. These findings extend those of previous studies. Crossley (2004, p.
18) suggests that ‘In situations of co-presence the parties to that situation need to secure co-
operation from one another for their own sleep ritual, whether this means common bedtimes
and sleep conditions or different but complementary patterns, with each party respecting the
needs of the other.’ Within the present study a large proportion of the variance for ‘actual
bed time’ resided at the couple level; suggesting a couple dynamic in the timing of bed. Yet,
for ‘Preferred bed time’ the largest proportion of the variance was at the individual level,
suggesting that couples do not necessarily want to go to their shared bed at the same time.
The results for ‘Wake bouts’ confirm earlier research which suggests that about one third of
nocturnal awakenings are similar to bed partners. In two related studies, Pankhurst and Horne
(1994) examined actigraphically recorded concordance in movement in 46 pairs of bed
partners and the differences in nocturnal movement in those with and without bed partners.
The authors concluded that approximately one-third of measured movements were common
to both bed-partners, although couples did not seem aware of this concordance and reported
sleeping better when their partner was there.
10
Where this study differs is that it quantifies those variables which are susceptible to dyadic
influence, illustrating the strength of the clustering whilst isolating the potential effects of
potential cofounders, such as age. It illustrates how social factors impinge on the timing (and
quality) of sleep-wake cycles and that bed partners are an important part of these social
factors. Our findings exemplify how the dyadic nature of couples’ sleep needs to be given
much more attention. As Troxel et al (2007, p. 389) suggest, ‘recognizing the dyadic nature
of sleep and incorporating such knowledge into both clinical practice and research in sleep
medicine may elucidate key mechanisms in the etiology and maintenance of both sleep
disorders and relationship problems and may ultimately inform novel treatments’. Indeed, a
small scale study by Cartwright (2008) found that continuous positive airway pressure
(CPAP) compliance in married men is (positively) related to the frequency with which his
partner sleeps with him. Interdependence between partners within couples is not simply a
statistical nuisance or a logistical problem. Interdependence may be the defining feature of
relationships (Kenny and Cook 1999) and, in societies where it is common for adult partners
to share a bed (Hislop 2007), it is perhaps also the defining feature of sleep.
Notes
1 Three different software packages were used for the analysis (SPSS, HLM and MLwiN).
HLM was used for the reliability measures, K-S test results were obtained from SPSS v14
and the variance component information was taken from MLwiN. No single package could
offer all the information required. For example, HLM is one of the few packages which
provides reliability measures, yet the p-values and confidence intervals produced by HLM
differ from those obtained by most other programs (Hox 2002, p. 42).
2- We are grateful to one of the reviewers who pointed out the importance of addressing this
point.
3- MCMC analysis confirmed the substantial interdependence in bed partner’s actual bed
time, sleep latency, light/dark ratio and wake bouts. MCMC is a conservative estimation
method in Hierarchical Linear Modelling.
11
Acknowledgements
The authors would like to offer sincere thanks to Vicky Vaughan for assisting with the data
collection and Ian Brunton-Smith and the reviewers for useful pointers and probing
questions. This study was funded by Economic and Social Research Council grant RES-000-
23-0268.
12
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Table 1: Hierarchical Linear Model Results for 36 couples (72 subjects) over one week
after removing outliers and including ‘age’ and ‘presence of children’ as predictors.
Interdaily
Stability
Intradaily
variability
Actual
Sleep
Time
Sleep
Latency
Light/
Dark
Ratio
Fragmentation
Index
Wake
Bouts
Descriptive
N
66
66
350
350
292
350
350
Mean
0.61
0.73
395.89
19.41
3.32
30.69
28.85
SD
0.11
0.18
59.58
15.24
3.33
12.83
10.40
Min
0.35
0.32
218
0
0.7
4.30
5
Max
0.82
1.16
565
109.82
37.7
71.20
65
Variance
[ICC]
Night
-
-
1685.23
159.10
0.04
62.91
30.58
Subject
0.010
0.026
1322.08
[0.38]
13.86
[0.07]
0.005
[0.08]
76.12
[0.47]
31.22
[0.29]
Couple
0
[0]
0.004
[0.13]
508.79
[0.14]
37.34
[0.18]
**
0.016
[0.26]
***
24.68
[0.15]
44.05
[0.42]
***
Reliability
Level 2
-
-
0.79
0.35
0.397
0.853
0.832
Level 3
0.302
0.368
0.4
0.7
0.683
0.368
0.708
Kolmogorov-
Smirnov
0.421
(p=0.994)
0.701
(p=0.710)
0.918
(p=0.369)
1.135
(p=0.152)
1.139
(p=0.149)
0.434
(p=0.992)
0.757
(p=0.615)
This table illustrates the results from the Unconditional Hierarchical Linear Models. The descriptives include the number of individuals (or
nights for three level data) in the data set (N) and the mean, standard deviation (SD), min and max for each variable. The variance columns
provide the actual variance and the proportion of variance [ICC] at each level. Actual Bed Time, actual sleep time, sleep latency,
fragmentation index, Light/Dark ratio and wake bouts all include weekdays only. Sleep Latency and Light/Dark ratio were log transformed.
* indicates significant differences between couples. *p<0.5, **p<0.01, ***p<0.001
16
Figure 1 Caterpillar plots for nine variables, showing each couple and whether they differ
significantly from the average at the 5% level.
The figures above plot the Couple Level residual (+/- 1.96 sd) against the ‘rank’. These figures are useful as they enable the identification
of the number of couples which differ significantly from the average at the 5% level (indicated on each graph by d=x). Where the error bars
do not cross 0 couples can be considered significantly different. Those above the 0 line have a significantly higher outcome on the
dependent variable and those below the 0 line have a significantly lower outcome on the dependent variable. There are fewer couples in the
‘preferred bed time’ graph, as residual plots do not include couples where the bed partners had exactly the same preferred bed time. That is,
because of the similarities in bed times across the data set as a whole, some couples are ranked exactly the same as other couples.
... Partner effects occur when one's own levels of an explanatory variable (e.g., one's sleep quality) are associated with one's partner's dependent variables (e.g., one's spouse's financial management behaviors) [29]. Because spouses' sleep quality is likely interdependent [30], one's spouse's quality of sleep might have actor and partner effects when predicting marital satisfaction. ...
... The effect sizes were, again, small. In support of an interdependent perspective of couples' sleep quality [30], it appears that husbands' sleep quality might also be somewhat salient in longitudinally predicting wives' financial management behaviors through wives' marital satisfaction. We used additional post hoc analyses to test the robustness of our assertion of mediational pathways. ...
Article
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Research from the American Psychological Association suggests that approximately 67% of U.S. adults are getting more or less sleep than desired, and over 80% of U.S. adults ages 18-43 are stressed about finances. Cross-sectional evidence suggests that there may be a connection between the two. That is, some cross-sectional research suggests a contemporaneous association between sleep quality and finances. Using two waves of newlywed dyadic data (N = 1497 couples), we estimated a longitudinal structural equation model to test actor-partner associations between husbands' and wives' sleep quality and financial management behaviors. In these associations, we examined husbands' and wives' marital satisfaction as potential mediating variables. We found that both husbands' and wives' sleep quality longitudinally predicted their own and their partner's financial management behaviors. Additionally, husbands' and wives' sleep quality-through wives' marital satisfaction-indirectly and longitudinally predicted wives' financial management behaviors. As financial practitioners encourage newlywed couples to consistently experience quality sleep, their financial management behaviors may benefit. We suggest that for newlywed couples, both partners' bedtime may be longitudinally connected to both partners' management of their budget.
... However, smartphone sharing happens very rarely and those who report sharing their device do so mainly with their spouse/partner (11 out of the 21 (52%) respondents in Sample 2 who reported sharing their smartphone said that they share it with their spouse/partner). If we assume that people from the same household show similar sleep patterns (as shown by Gunn, Buysse, Hasler, Begley, &Troxel, 2015 andMeadows, Arber, Venn, Hislop, &Stanley, 2009), then the overall bias in estimates of sleep, even based on shared devices, should be negligible. ...
... However, smartphone sharing happens very rarely and those who report sharing their device do so mainly with their spouse/partner (11 out of the 21 (52%) respondents in Sample 2 who reported sharing their smartphone said that they share it with their spouse/partner). If we assume that people from the same household show similar sleep patterns (as shown by Gunn, Buysse, Hasler, Begley, &Troxel, 2015 andMeadows, Arber, Venn, Hislop, &Stanley, 2009), then the overall bias in estimates of sleep, even based on shared devices, should be negligible. ...
Article
Smartphones have become central to our daily lives and are often present in the same contexts as their users. Researchers take advantage of this phenomenon by using data from smartphone sensors to infer everyday activities, such as mobility, physical activity, and sleep. For example, that a person is sleeping might be inferred from the fact that their phone is idle and that there is no sound and light around the phone. The success of inference from raw smartphone sensor data to activity outcomes depends, among other factors, on how smartphone owners use their device. Not having the smartphone in close proximity throughout the day, turning the device off, or sharing the device with others can constitute barriers that interfere with accurately measuring everyday activity with data from the phone's native sensors. Against this background, we surveyed two independent, large-scale samples of German smartphone owners (n1 = 3956; n2 = 2525) on how they use their smartphones, with a focus on three everyday activities: mobility, physical activity, and sleep. We find that both sociodemographic as well as smartphone-related characteristics are associated with how people use their smartphones, and that this affects the suitability of smartphone data for measuring everyday activities.
... While most of the above-mentioned studies highlight the benefits of retirement on sleep, they conceptualize and measure sleep as a purely individual behavior, despite evidence that it is a shared process between couples. Couples often have interdependent sleep patterns and synchronized sleep stages, partially developed due to similar sleep schedules or movement patterns associated with bed sharing (Meadows et al., 2009;Richter et al., 2016). A few studies have indicated that due to the interdependent nature of sleep among couples, sleep problems may arise as a consequence of the quality of their relationship, a lack of concordance in activities and sleep schedules, and experiencing sleep disturbances (Gunn et al., 2015;Troxel et al., 2009). ...
... Many older adults live with a partner and have interdependent lives, therefore examining retirement and associated health outcomes from an individual perspective provides only a partial understanding of the dynamics between retirement and health. Similarly, most research has regarded sleep as an individual process, and treatment therapies for adverse sleep outcomes predominantly focus on the individual, despite evidence that sleep is symbiotic in couples (Drews et al., 2017;Meadows et al., 2009;Richter et al., 2016). ...
Article
Background and Objectives Sleep problems are a public health burden and have adverse health consequences in older adults. Despite sleep being a shared biological process between couples, to date there have not been any studies that have assessed the association between retirement and sleep, in older couples. The objective of this study was to examine the impact of retirement on diagnosed sleep problems in older Dutch couples. Research Design and Methods This study used data from two waves of the NIDI Pension Panel Study for older Dutch adults living with a partner (n= 3,726). Logistic regression models examined the strength of association between retirement and sleep problems, while accounting for the moderating role of relationship characteristics. Results Retirement was associated with decreased odds of sleep problems at wave 2 (OR= 0.60, 95%CI=0.46-0.78). Lower relationship quality was associated with increased odds of sleep problems in the fully adjusted model (OR=1.81, 95%CI=1.32-2.49). Having a partner with sleep problems was associated with increased risk of sleep problems as well (OR=1.51, 95%CI=1.07-2.13). There was evidence of effect modification by relationship quality (OR= 1.87, 95%CI =1.05-3.31). Discission and Implications Retirement and sleep do not occur in a social vacuum and have implications beyond the individual level. More research is therefore needed to understand the impact of sleep and its health consequences on older coupled workers. Such research may provide valuable insights for management and treatment of sleep problems and may have implications for public health of aging communities.
... As all individuals exist on the morningness -eveningness continuum, it is logical to assume that a proportion of couples have chronotypes that do not align (i.e., where one partner is a morning type and the other is an evening type or vice versa) (Jocz et al. 2018;Richter et al. 2016). It has been suggested that relationships where both members tend to go to bed and wake up simultaneously (and therefore likely have matched chronotypes), are more likely to experience greater relationship harmony and less conflict (Meadows et al. 2008(Meadows et al. , 2009). Further, individuals may be more likely to couple with a partner with a similar chronotype Randler and Kretz 2011). ...
Article
Chronotype can be defined as an overt expression of circadian rhythmicity in an individual that dictates tendencies towards being a morning or evening person - also referred to as 'morningness' or 'eveningness.' Chronotypes generally impact preferred bed and wake times, in addition to a range of personal and social factors. This study examined how matching/mismatching chronotypes within relationships impact sexual satisfaction and sleep quality. A sample of 32 couples (52% females, 38.3 ± 11.7 years) each completed an online survey that assessed chronotype (reduced Morningness Eveningness Questionnaire), sleep (Pittsburgh Sleep Quality Index), and sexual satisfaction (Index of Sexual Satisfaction). Partner surveys were matched to identify whether chronotypes were matching or mismatching. Couples with matched chronotypes reported greater sexual satisfaction than those with mismatched chronotypes, F(1, 58) = 19.57, p < .001. Matched couples also reported better sleep quality than couples whose chronotypes were mismatched, F(1,62) = 48.02, p < .001. The individual chronotype did not seem to impact on sleep quality or sexual satisfaction. To improve sleep quality and sexual satisfaction, strategies (e.g., circadian phase advance or delay) could be used to increase circadian alignment between members of a couple.
... Seventy percent of American adults regularly sleep with a bed partner (National Sleep Foundation, 2013), making this a critical relationship context in which to explore social processes that affect sleep. Sleep behaviors are usually concordant among couples with parallel bed timing, wake timing, and the number of wakings (Meadows et al., 2009). Further, social interactions may impact sleep through their contribution to emotion or mood states that one experiences (Troxel et al., 2007). ...
Article
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To determine whether the association between perceived social support or strain in close relationships and sleep outcomes varies by gender. Participants were selected from the Biomarker projects of either the MIDUS II or MIDUS Refresher study if they were in a married-or married-like relationship and shared a bed with their partner ( N = 989). A subsample also participated in a seven-day sleep study ( n = 282). Perceived social support and strain from partner, family, and friends were examined by self-report questionnaires. We used the Pittsburgh Sleep Quality Index, sleep daily diary, and actigraphy to measure both subjective and objective sleep. Social support and strain were both associated with sleep outcomes. Specifically, higher social support was associated with fewer daily reports of light sleep and feeling more rested in the morning, while higher social strain was associated with higher clinical sleep disturbance. For women, but not men, social support was significantly associated with lower daily sleep disturbance while perceived social strain was significantly associated with higher daily sleep disturbance, lighter sleep, feeling less rested in the morning, lower sleep efficiency, and longer sleep onset latency. Mainly among women, social support and strain are associated with an important transdiagnostic health outcome–sleep–which may have implications for a wide range of health disparities. Interpersonal stressors may increase health risks differently for women compared to men and one mechanism that may link social relationships to long-term health outcomes is sleep.
... The absence of one's spouse may be felt most saliently during bedtime routines because many bereaved seniors spend decades co-regulating their sleep with their spouse. Dyadic sleep patterns significantly impact bedtime, sleep onset latency, and wake bouts [10]. In fact, compared to nonbereaved normal sleepers, widow(er)s experience significant sleep disruption (e.g., longer sleep onset latency, longer nighttime wakefulness) in the early months of grief [4]. ...
Article
Full-text available
Widow(er)s experience significant sleep disruption that may dysregulate immune functioning. This longitudinal study aimed to determine 1) whether changes in sleep quality were associated with changes in pro-inflammatory cytokine production during the first six months of bereavement and 2) whether these relationships depended on objective socioeconomic status (SES) and/or subjective social status. One hundred and six bereaved spouses (M = 68.49 years, SD = 9.35, 69 females) completed the following assessments at approximately three months post-death and six-month post-death: a venous blood draw and self-report questionnaires on sleep quality (Pittsburgh Sleep Quality Index), SES (MacArthur Sociodemographic Questionnaire), health, and demographic information. T-cell stimulated pro-inflammatory cytokines were assessed, including IL-6, TNF-α, IFN-γ, IL-17A, and IL-2. Worsening sleep quality was associated with increased levels of pro-inflammatory activity even after adjusting for confounding variables. The present study also identified SES as an important factor for understanding health following spousal bereavement: individuals with low SES were more susceptible to sleep-related changes in immune function. Compared to more educated widow(er)s, less educated widow(er)s showed greater increases and decreases in inflammation when sleep quality worsened or improved, respectively, over time. Findings provide evidence for a biobehavioral pathway linking bereavement to disease risk, highlight SES disparities in late adulthood, and identify individuals who may require tailored interventions to offset SES-related burden that impedes adaptive grief recovery.
... Couples who share a bed often experience similar sleep and wake times, whilst also demonstrating similar movement [10]. Previous research has found that sleeping concordantly among adult couples is associated with higher marital satisfaction [11] and reduced risk of cardiovascular disease [12], but not sleep quality [11,12]. ...
Article
Full-text available
Pet–owner co-sleeping is increasingly common in some parts of the world. Adult owners often subjectively report benefits of co-sleeping with pets, although objective actigraphy reports conversely indicate sleep disruptions due to the pet. Because limited research is available regarding pet–owner co-sleeping in non-adult samples, the aim of this two-part study was to explore whether co-sleeping improves sleep quality in adolescents, an age group in which poor sleep patterns are well documented. In Study One, an online survey with 265 pet-owning 13-to-17-year-old participants found that over 78% co-slept with their pet. Average sleep quality scores for co-sleepers and non-co-sleepers indicated generally poor sleep, with no differences in sleep quality depending on age, gender, or co-sleeping status. Study Two consisted of two preliminary case studies, using actigraphy on dog–adolescent co-sleepers. In both cases, high sleep concordance was observed, but owners again experienced generally poor sleep quality. Future actigraphy research is needed, including larger sample sizes and a control group of non-co-sleepers, to validate the preliminary findings from this study, but our limited evidence suggests that co-sleeping with a pet may not impact sleep quality in adolescents.
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Since its inception four decades ago the two-process model introduced by Borbély has provided the conceptual framework to explain sleep wake regulation across many species, including humans. At its core, high level notions of circadian and homeostatic processes are modelled with a low dimensional description in the form of a one dimensional nonautonomous and nonsmooth flow, with the rate of change of homeostatic sleep pressure switching at specific times. These events in time can be described by an implicit map from one switching time to another and have given rise to an elegant mathematical description of periodic orbits and their instabilities using the theory of iterated maps. In this paper we show that an equivalent description can be obtained from a direct analysis of the underlying nonsmooth flow. We further show how to construct the Lyapunov exponent of the nonsmooth flow and use this to uncover a more detailed picture of the Arnol’d tongue structure of the model. Given the growing interest in studying networks of sleepers, where interactions may occur continuously throughout the day-night cycle and not just at event times, we advocate for the future use of techniques from nonsmooth dynamical systems in studying networks of the two-process model.
Chapter
Social factors influence sleep. Socioeconomic status may be the most important factor, with longer and better sleep among higher SES groups. The reasons may be related to income, education, healthier life styles and several other issues. Social support is a strong predictor of good sleep. Also, race is related to sleep, with better sleep among whites. Socio-economic status is of importance here, but also unfair treatment. Sleep also deteriorates with increasing age, but alertness increases. Women report more sleep problems than men but sleep physiologically better. Singles in general show more sleep complaints, which may be related to unhealthy life styles and to some extent to social isolation.
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Background Actigraphy is increasingly used in sleep research and the clinical care of patients with sleep and circadian rhythm abnormalities. The following practice parameters update the previous practice parameters published in 2003 for the use of actigraphy in the study of sleep and circadian rhythms. Methods Based upon a systematic grading of evidence, members of the Standards of Practice Committee, including those with expertise in the use of actigraphy, developed these practice parameters as a guide to the appropriate use of actigraphy, both as a diagnostic tool in the evaluation of sleep disorders and as an outcome measure of treatment efficacy in clinical settings with appropriate patient populations. Recommendations Actigraphy provides an acceptably accurate estimate of sleep patterns in normal, healthy adult populations and inpatients suspected of certain sleep disorders. More specifically, actigraphy is indicated to assist in the evaluation of patients with advanced sleep phase syndrome (ASPS), delayed sleep phase syndrome (DSPS), and shift work disorder. Additionally, there is some evidence to support the use of actigraphy in the evaluation of patients suspected of jet lag disorder and non-24hr sleep/wake syndrome (including that associated with blindness). When polysomnography is not available, actigraphy is indicated to estimate total sleep time in patients with obstructive sleep apnea. In patients with insomnia and hypersomnia, there is evidence to support the use of actigraphy in the characterization of circadian rhythms and sleep patterns/disturbances. In assessing response to therapy, actigraphy has proven useful as an outcome measure in patients with circadian rhythm disorders and insomnia. In older adults (including older nursing home residents), in whom traditional sleep monitoring can be difficult, actigraphy is indicated for characterizing sleep and circadian patterns and to document treatment responses. Similarly, in normal infants and children, as well as special pediatric populations, actigraphy has proven useful for delineating sleep patterns and documenting treatment responses. Conclusions Recent research utilizing actigraphy in the assessment and management of sleep disorders has allowed the development of evidence-based recommendations for the use of actigraphy in the clinical setting. Additional research is warranted to further refine and broaden its clinical value.
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This article discusses the conceptual meaning of partner effects, which occur when one person is affected by the behavior or characteristics of his or her partner. We show that partner effects can be used to validate the presence of a relationship and can elaborate the particular nature of that relationship. We discuss possible moderation of partner effects and show that many theoretical variables in relationship research (e.g., similarity) can be viewed as the interactions of partner effects with other variables. We present three extended examples that illustrate the importance of partner effects.
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Study Objectives: To analyze relationships between spouses' sleep problems and their partners' physical health, mental health, well-being, social involvement, and marital quality in a sample of older persons. Design: The Alameda County Study is a population-based longitudinal study focusing on behavioral factors associated with health and mortality. Setting: Participants completed questionnaires for the sixth wave of data collection (1999). Participants: 405 couples (810 husbands and wives aged 51 to 94 years). Measurements and Results: Participants were asked how often they had experienced difficulty falling asleep, waking up in the middle of the night, and waking up very early in the morning over the past month. Response sets ranged from "never" to "often." Scores were summed. Analyses included multivariate statistical models using generalized estimating equations to adjust for paired data as well as partner age, sex, chronic conditions, financial problems, and own sleep problems. Although partners' associations with negative outcomes were stronger for their own sleep problems, spouses' sleep problems were associated with partners' poor health, depressed mood, poor mental health, unhappiness, low optimism, feeling left out, not satisfied with relationships, and unhappy marriage, even after adjusting for the partners' sleep problems. We found no sex differences in associations between spouses' sleep problems and partners' outcomes. Conclusions: Although data are cross-sectional, findings suggest that spouses' sleep problems negatively impact partners' health and wellbeing. Our analyses emphasize the importance of treating sleep problems to promote the health and well-being of both affected individuals and their partners.
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Millions of adults sleep with another adult, but what does it mean to share a bed with someone else, and how does it affect a couple's relationship? What happens when one partner snores? Steals the sheets? Prefers to sleep in the nude? To address these and other questions, Paul C. Rosenblatt asked couples to describe the struggles, challenges, and achievements of their bed-sharing experiences. Two in a Bed includes interviews with more than forty bed-sharing couples as they candidly discuss winding down and waking up, cold feet and tucked sheets, who sleeps near the door and who gets pushed to the edge, snoring, spooning, sleep talking, sleep walking, and the myriad other behaviors we negotiate in falling asleep, staying asleep, and waking up each morning beside a partner. In addition to exploring the routines and realities of sharing a bed with another person, these interviews reveal important information about sleep, relationships, and American society. Stressing the intricacy and importance of a previously unremarked activity, Rosenblatt's Two in a Bed shows that sleep should no longer be viewed solely as an individual phenomenon.
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SAS PROC MIXED is a flexible program suitable for fitting multilevel models, hierarchical linear models, and individual growth models. Its position as an integrated program within the SAS statistical package makes it an ideal choice for empirical researchers and applied statisticians seeking to do data reduction, management, and analysis within a single statistical package. Because the program was developed from the perspective of a "mixed" statistical model with both random and fixed effects, its syntax and programming logic may appear unfamiliar to users in education and the social and behavioral sciences who tend to express these models as multilevel or hierarchical models. The purpose of this paper is to help users familiar with fitting multilevel models using other statistical packages (e.g., HLM, MLwiN, MIXREG) add SAS PROC MIXED to their array of analytic options. The paper is written as a step-by-step tutorial that shows how to fit the two most common multilevel models: (a) school effects models, designed for data on individuals nested within naturally occurring hierarchies (e.g., students within classes); and (b) individual growth models, designed for exploring longitudinal data (on individuals) over time. The conclusion discusses how these ideas can be extended straighforwardly to the case of three level models. An appendix presents general strategies for working with multilevel data in SAS and for creating data sets at several levels.
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This paper is a preliminary statement about people who wake up easily and are most active in the morning, and those who wake up slowly and are most active during the evening and night-time hours. In order to examine "morningness" and "nightness" as individual and familial factors, the authors collected and analyzed married student responses to open-ended questions concerning these phenomena. These summer school class members were able to define morning and night people according to the following characteristics: (a) patterns of arising and bedtime; (b) timing of energy/efficiency peaks; (c) type and times of preferred shared activities; and (d) personal values. In addition, they were able to relate matching or mismatching in morningness or nightness to various aspects of martial and familial adjustment. The authors conclude that this issue has both theoretical and practical significance for marriage and the family, and they are pursuing this conviction with a program of empirical research.