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Gender Differences in Nighttime Sleep Patterns and Variability Across the Adult Lifespan: A Global-Scale Wearables Study


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Study objectives: Previous research on sleep patterns across the lifespan have largely been limited to self-report measures and constrained to certain geographic regions. Using a global sleep dataset of in-situ observations from wearable activity trackers, we examine how sleep duration, timing, misalignment, and variability develop with age and vary by gender and BMI for non-shift workers. Methods: We analyze 11.14 million nights from 69,650 adult non-shift workers aged 19-67 from 47 countries. We use mixed effects models to examine age-related trends in naturalistic sleep patterns and assess gender and BMI differences in these trends while controlling for user and country-level variation. Results: Our results confirm that sleep duration decreases, the prevalence of nighttime awakenings increases, while sleep onset and offset advance to become earlier with age. Although men tend to sleep less than women across the lifespan, nighttime awakenings are more prevalent for women, with the greatest disparity found from early to middle adulthood, a life stage associated with child-rearing. Sleep onset and duration variability are nearly fixed across the lifespan with higher values on weekends than weekdays. Sleep offset variability declines relatively rapidly through early adulthood until age 35-39, then plateaus on weekdays, but continues to decrease on weekends. The weekend-weekday contrast in sleep patterns changes as people age with small to negligible differences between genders. Conclusion: A massive dataset generated by pervasive consumer wearable devices confirms age-related changes in sleep and affirms that there are both persistent and life-stage dependent differences in sleep patterns between genders.
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Gender Differences in Nighttime Sleep
Patterns and Variability Across the Adult
Lifespan: A Global-Scale Wearables Study
Sigga Svala Jonasdottir1, Kelton Minor2 and Sune Lehmann1,2*
1Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2Copenhagen
Center for Social Data Science, University of Copenhagen.
* To whom correspondence should be addressed. Email.
Study Objectives Previous research on sleep patterns across the lifespan has largely been limited to self-report measures and
constrained to certain geographic regions. Using a global sleep dataset of in-situ observations from wearable activity trackers,
we examine how sleep duration, timing, misalignment, and variability develop with age and vary by gender and BMI for non-
shift workers.
Methods We analyze 11.14 million nights from 69,650 adult non-shift workers aged 1967 from 47 countries. We use mixed
effects models to examine age-related trends in naturalistic sleep patterns and assess gender and BMI differences in these
trends while controlling for user and country-level variation.
Results Our results confirm that sleep duration decreases, the prevalence of nighttime awakenings increases, while sleep onset
and offset advance to become earlier with age. Although men tend to sleep less than women across the lifespan, nighttime
awakenings are more prevalent for women, with the greatest disparity found from early to middle adulthood, a life stage
associated with child-rearing. Sleep onset and duration variability are nearly fixed across the lifespan with higher values on
weekends than weekdays. Sleep offset variability declines relatively rapidly through early adulthood until age 35-39, then
plateaus on weekdays, but continues to decrease on weekends. The weekend-weekday contrast in sleep patterns changes as
people age with small to negligible differences between genders.
Conclusion A massive dataset generated by pervasive consumer wearable devices confirms age-related changes in sleep and
affirms that there are both persistent and life-stage dependent differences in sleep patterns between genders.
Sleep, Big Data, Aging, Gender, Sleep Variability, Sleep Misalignment, Sleep Timing and Duration
Statement of Significance
A global dataset from wearable devices enables a detailed understanding of age-related tendencies in sleep patterns, controlling
for country-level and within-individual variation. During early adulthood, we find elevated levels of variability in sleep offset
and duration along with high levels of weekend-weekday misalignment, suggesting that mismatches between internal timing
and external demands are pervasive during this phase of human development. In older adulthood, reduced sleep duration and
increased sleep disturbances may either contribute to, or correlate with, further age-related decline. Gender gaps in average
sleep duration, timing and nighttime awakenings are apparent, despite considerable heterogeneity in circadian preferences.
Information about parenting mobile application usage can be paired with big data from wearable devices to explore lifestage
gender inequality in sleep quality. Further research on person-centered behavioral interventions that promote regular sleep-
wake cycles is needed.
Sufficient sleep is fundamental to healthy human functioning.
Brief, irregular and/or disturbed sleep are risk factors for
infectious disease, cardiovascular disease, depression and all-
cause mortality1–3. Similar to other physiological functions, sleep
patterns vary between people across the human population and
change within individuals over the lifespan4–6. Meta-analyses and
cross-sectional research provide convergent evidence that the
ability to initiate and maintain sleep declines as people age,
independent of factors such as medical co-morbidities and
medication use7,8. However, large-scale in-situ data on changes in
sleep patterns across the lifespan remain scarce and
geographically constrained.
Age-associated changes in sleep include both decreases in total
time asleep, deep (slow wave) sleep and rapid eye movement
sleep, as well as increases in sleep latency, time awake after sleep
onset, stage 1 and stage 2 sleep7,9,10. Increases in nighttime
awakenings with age suggest a decline in the buildup of
homeostatic sleep pressure11. Evidence that healthy older adults
exhibit less objective and subjective sleepiness after selective slow
wave sleep deprivation indicates that sleep need may decline as
adults age12, while other research has shown that reduced sleep
still negatively impacts cognitive performance, irrespective of
age13,14. A recent review concluded that while there is still no
consensus, the current body of evidence largely supports the
hypothesis that older adults have an impaired ability to generate
sleep rather than a reduced sleep need15. Hence, additional insight
is needed to characterize structural changes in sleep patterns over
the lifespan and to better understand the underlying drivers.
Aside from changes in the duration and composition of sleep,
adult aging is further characterized by changes in circadian
regulatory processes, with phase advances and diminished
amplitudes in daily core body temperature, melatonin and
cortisol rhythms11. These changes are associated with, and often
proxied by, aging-related advances in sleep timing after
adolescence, with individuals going to bed and waking up earlier
with increasing age 1620.
Previous research has established the importance of measuring
sleep timing separately on both free and work days, since weekly
social schedules constrain daily rhythms and can induce a
misalignment with biological time21.
In a series of large scale survey-based studies conducted with
participants from four countries (Germany, Switzerland, the
Netherlands and Austria), both the difference between free and
work day sleep duration and midsleep timing (social jet lag) were
shown to decline with age22,23. Importantly, the authors did not
report how underlying differences in sleep onset and offset
timing between work and free days may contribute to observed
developments in misalignment. Since people exert more practical
influence over the beginning and end of their sleep period
compared to midsleep, an expectation of how onset and offset
change on weekdays and weekends with age would be useful.
Moreover, while a recent study found that inter-individual
variability in midsleep timing declines with age24, far less is known
about how intra-individual variability in sleep timing and duration
within work and free day periods changes across the life course25
27. In-situ sleep data collected over an extended period is needed
to inform expectations about age-related developments in sleep
timing, misalignment and variability.
Prior research has established that the extent of adult age-related
changes in sleep patterns is highly moderated by gender, with
women reaching both puberty and their peak eveningness earlier
than men in young adulthood and sleeping longer than men until
age 50-60, a period of adulthood that coincides with
menopause23,2830. Well-controlled laboratory studies have found
that women exhibit phase advanced core body temperature and
melatonin rhythms, as well as a shorter intrinsic circadian period
compared to men31,32. While some larger sleep surveys have
found that females are more likely to be morning types compared
to males from the end of adolescence to late adulthood17,33,
others have found no apparent gender differences34,35 and one
nationally representative study found the opposite trend36.
Notably, these cross-sectional studies were conducted within
different countries using slightly different methodologies,
highlighting the need for an integrated global assessment of
potential gender-related differences in sleep. Beyond long term
changes in sleep patterns, events occurring during certain stages
of adult development can impact women and men differently.
Pregnancy and the postpartum period are associated with
dynamic physiological changes and behavioral demands known
to disturb sleep quality for women, although limited in-situ
evidence exists comparing sleep disturbances for both women
and men during young child-rearing37,38.
Despite revealing salient age-related changes in sleep patterns,
there are a number of areas where previous research can be
extended and improved. Early lifespan meta-analyses favored
data from predominantly Western countries, averaged across
different sleep assessment methodologies and depended
extensively on short term polysomnography recordings that may
have disrupted participants habitual sleep cycles7,9. By
comparison, cross-sectional sleep survey research has primarily
relied on subjective measures prone to self-report, recall and
rounding biases17,24,39,40. Moreover, large scale sleep surveys
typically ask for single estimates of work and free day sleep onset
and offset times, and thus do not enable measurement of the
intra-individual variability of sleep patterns during work and free
day periods26. To address these limitations, recent research has
drawn upon behavioral data from mobile phones and self-
tracking apps to infer the dynamics of human activity and sleep
in everyday contexts across large populations of device users41
47. Differing from mobile phones, the activity trackers employed
in the current study were worn by users, enabling closely coupled
measurements of human sleep patterns. Similar to wrist-
actigraphs, wearable devices can automatically monitor sleep
measurements in situ over extended periods of use, making it
possible to study both average and time-varying sleep patterns in
daily contexts20,48,49. Drawing on a dataset of objective sleep
measurements and mobile application use statistics from a large
sample of n=69,650 wearable users over multiple years across 47
countries, we investigate the following questions:
1. After controlling for country-level and individual-level
variation with mixed effects models, does global data
from consumer wristband devices confirm gender
differences in age-related changes in sleep duration,
timing and circadian misalignment?
2. How does intra-individual variability in sleep duration
and timing develop across the life course separately on
weekdays and weekends, adjusting for country-level
3. Does child-rearing - as proxied by parenting mobile
application use - predict life-stage gender differences in
nighttime sleep disturbances?
The present study differs from prior self-report studies, most of
which featured self-report data from single countries or regions,
and - at the time of writing - represents the most geographically
extensive analysis of age-associated changes in sleep using
consistent, objectively recorded measures of sleep duration,
timing, misalignment and variability.
Data and demographics
The anonymized data set used in this study consists of sleep
observations collected using smart wristbands from 2015 to 2018
(see the section Data collection below for full details). In total,
we analyze 11.14 million nights of sleep observations arising
from 69,650 adult non-shift workers, about a third of them
women. In Table 1, we show the number of individuals and
nights broken out by the demographic variables age group, gender,
and BMI category and in Table S1 we list all the countries in our
sample and the ratio of users residing there. We compare age
statistics (median age) in our sample to information provided by
the United Nations Population Division (UN)50 for the five
countries with the most users in the data set in Table S2. The
median values in our sample and the overall population
correspond well: users from Japan are slightly younger (by 1 year)
while those from Taiwan and the United Kingdom match their
respective reference populations. By comparison users from
Germany are younger (by 7 years) as well as those from Russia
(by 5 years). We also compare age standardized BMI statistics of
the study sample to population estimates provided by the World
Health Organization (WHO) in Table S351,52. We find both men
and women from all countries fall within or near the 95 %
confidence intervals of the WHO reference values. Women from
the UK fall 0.5 points above the 95 % CI and women from Japan
average 0.5 points below the 95 % CI reference range.
Sleep duration, timing and variability
We use nine sleep metrics to assess how sleep patterns change
across the lifespan. Sleep duration specifies the total recorded time
a person spent asleep during a given night. To quantify sleep
timing, we use sleep onset (the registered point in time when a
person fell asleep) and sleep offset (the recorded time when a
person woke up). We measure the misalignment between an
individual's internal biological clock and external social clock by
applying a variant of the formula used to compute social jetlag22.
Specifically, instead of calculating social jetlag for midsleep (see
Figure S8 for a comparison) we estimate weekend-weekday
differences in sleep onset and offset. The weekend-weekday
misalignment of sleep timing can lead to the loss of sleep
duration on weekdays and partial compensation on weekends,
which is quantified by estimating the weekend-weekday sleep
duration difference. These metrics are calculated for each week
of data collection, resulting in weekly repeated measurements for
each user, which are then aggregated to produce user-level
averages. We also study the variability of sleep onset, offset and
duration in order to estimate the regularity of people's sleep
timing and duration. We quantify intra-individual variability as
the standard deviation of a person's corresponding
measurements for each sleep outcome, and compute this
separately for weekends and weekdays53.
The individual-level covariates for this study are gender
(female/male) and BMI category (underweight/normal
weight/overweight/obese) which was labelled according to the
World Health Organization classification54,55.
Additionally, we also include temporal variables for day category
(weekday/weekend) to account for likely differences in the social
structure over the course of the week. Since we do not directly
observe schedules, we assume the likelihood of work days is
highest on weekdays and work-free days is highest on weekends,
similar to others20,24.
# of Adult Users in Sample
# of Night Sleep
Age groups
Table 1: Overview of the data set with a focus on demographics: age, gender,
and BMI. The table provides statistics for both the number of adults in the
sample, as well as the number of nights analyzed. Note that the data set
contains more men than women and more people within the normal weight
range BMI category.
Data modeling
We analyzed the data set using R Version 3.5.156. Given the
longitudinal and hierarchical structure of the data with repeated
measurements within users, and users nested within their country
of residence, observations are likely highly correlated on both
levels (country and user). To account for this dependence within
the data set, we adopt a mixed effects modeling framework57.
Mixed effects models allow us to control for user and country-
level variation while examining age related trends in sleep
patterns and assessing the influence of demographic factors.
Concretely, the model can be specified in matrix form as
! " #$ %&' % ()* +,-.('/0𝒒
2* 3
(567()/0𝒏12* 84
representing the fixed effects parameters,
the random effects,
representing the
< = >(
design matrix for
the fixed-effects parameters, and
< = @(
design matrix
describing the random effects. The models for weekday-weekend
differences and sleep variability are defined without user random
effects since these measures were computed for each user as
single values. We use the lmerTest R package, the lmer
function to fit the data set and apply Satterthwaite's degrees of
freedom method to estimate the
-values for the significance of
fixed factors58,59.
We center the age variable around its mean to help improve
interpretability, prevent multi-collinearity, and lower the scale of
the variables to accommodate the inclusion of age squared in the
Data collection
The data was collected from 2015 to 2018 via SWR30 and
SWR12 connected wristbands, designed to track physical activity
and sleep behavior. The waterproof wristbands use proprietary,
internally validated algorithms based on movement registered by
an internal accelerometer to estimate sleeping and waking states
in 1-minute epochs. When connecting the wristband at first,
users received visual instruction on how and where (wrist) to
place the device and were advised to wear it on their dominant
side. All wearable data included in this study was wirelessly
transmitted via Bluetooth to an accompanying mobile phone
application, which also independently registered user mobile
application usage statistics. Similar to many other wearable
devices and wrist actigraphs, the devices used in the present study
detect sleep timing and total sleep time but do not detect time in
bed, preventing the further study of age-related changes in sleep
latency and sleep efficiency. Moreover, although the armbands
have been validated internally, we note that the wristbands have
not been publicly validated using the gold standard of
polysomnography as recommended in the Sleep Research Society
Workshop on wearable devices for the measurement of sleep.60
The wristbands employed in this study have been shown to
produce wake and sleep states that converge with objective
measures of user mobile phone use patterns46. However, this
global data-set offers unique methodological advantages: scale,
longitudinal coverage and ecologically valid observations. By
using it, we follow a growing trend of utilizing commercial
devices in sleep research to study sleep behavior in naturalistic
settings at large scales39,61,62. Further, we have performed an
extensive comparison of the findings here with multiple
independent global sleep datasets. We find that this world-wide
dataset externally converges with country-level sleep measures
from separate large-scale datasets, demonstrates consistency over
the period of observation and replicates age-related sleep trends
from previously published self-report studies, including changes
in sleep duration and timing. These full comparisons are
presented in the SI Sections Comparison of country-level statistics to
other publications and Consistency over time and Results.
Study participants consist of anonymized users who consented
to share their data for research purposes. Age group, BMI
category, gender and country of residence were pre-processed
from self-reported demographic information. All data analyses
were carried out in accordance with the EU’s General Data
Protection Regulation 2016/679 (GDPR) and the regulations set
out by the Danish Data Protection Agency. The GDPR describes
regulations for data protection and privacy in the European
Union and the European Economic Area. It also addresses the
transfer of personal data outside the EU and EEA areas.
Data processing and inclusion criteria
To reduce the risk of including artificially shortened sleep
observations due to users ceasing wristband use in the middle of
the resting period and observations from night-shift workers,
outliers from the sleep data were removed by applying inclusion
filters to sleep duration, onset, and offset. We adopt standard
filters for sleep duration (
), matching those
applied by Roenneberg et al.23. These filters are more inclusive
(by 2 hours) than those used by Walch et al.45 and Althoff62 (
). Furthermore, we apply the following
conservative sleep timing filters. First, we remove all sleep
observations with onset or offset times greater than one and a
half standard deviations away from the sample average computed
separately for weekdays and weekends and obtain the following
time filters:
onset weekends
onset weekdays
offset weekends
offset weekdays
This results in the removal of 12 % of sleep observations yielding
a final dataset consisting of 11.14 million nights from 69.650
users. The full data pre-processing procedure is described in the
section Data Filtering in the SI.
To help ensure that sleep estimates are representative of typical
sleeping behavior, we further require all participants to have a
minimum threshold of sleep observations. Specifically, each user
must have sleep observations extending over a minimum period
of 4 weeks, with at least one weekday and weekend night per
week, amounting to a minimum 8 nights per user (median 87
nights per user). We also limit our analysis to adults 19-67 years
of age due to limited across-country data for older age groups.
Each user is assigned a country of residence, defined as the
country in which the majority of their sleep entries occur.
Sociocultural variation
This paper focuses on how sleep duration, timing, misalignment
and variability develop with age and how other demographic
factors such as gender and BMI may affect these trends. Hence,
it is important to note that users in the sample reside across a
wide range of countries around the world. Breaking the data set
out by country of residence yields cohorts from 47 distinct
countries with at least 150 users in each country. Table S1 lists
out all of the countries and the percentage of users residing in
each country, as well as the ratio of male users within each
country. Table 2 shows the development of sleep onset and
duration with age for men split up by the top 5 countries with the
most users. It is evident that there is substantial heterogeneity in
the amount and timing of sleep obtained between countries. The
summary statistics reveal, consistent with the literature, that there
are indeed large disparities in sleep patterns across
cultures20,45,63,64. Since the focus of the present research is to
assess and identify age and gender related changes, we control for
these baseline country-level differences through our mixed
effects modeling framework described in Data modeling.
N= 17231
(24.7 %)
(10.3 %)
(7.3 %)
(7.2 %)
(5.6 %)
Age groups
Average sleep onset (hh:mm)
Age groups
Average sleep duration (hours)
Table 2: Development of sleep onset and duration by age split up by the
top five countries with the most users in the data set. Note there are strong
differences between countries with a clear split between European and Asian
Sleep timing and duration over the lifespan
In order to summarize the development of sleep onset, offset,
and duration across the lifespan, we calculate each users average
value and then aggregate across our study sample by age, gender
and day type (weekday or weekend).
The resulting curves for sleep onset, offset and duration are
shown in Figure 1-3D.
Development of onset
The main panel on Figure 1 shows that, overall, sleep onset
becomes earlier as people age and that people tend to fall asleep
later on weekends (indicated by lighter colors); the difference
between weekday and weekend is roughly constant for both men
and women across all age-groups. There are large differences in
mean onset time between men and women (more than 30
minutes for the 19-24 young adult age group), which
progressively become smaller in magnitude across the lifespan,
eventually falling out of the range of statistical significance for
the 60-67 older adult age group. This eventual confluence of
sleep onset is driven by a steeper age-related advance in sleep
onset time for men than women. While the decline in sleep onset
time is consistent for men, the rate of decrease in onset time for
women nearly plateaus after the age 35-39 range. Even though
the 95 % confidence intervals for the mean are narrow, the actual
distribution of sleep onset is quite broad, as shown in Figure 1A-
C, which shows the distribution of onset time for the 19-24
group, the 40-44 age group and the 60-67 group. In order to
directly visualize the progression of sleep onset timing between
genders, in Figure 1E (weekends) and Figure 1F (weekdays), we
display the difference of male/female onset from the average
curve (genders weighted equally). The gender gap in onset time
appears to persist until around age 40, when the two curves begin
to converge.
Figure 1: Distributions for sleep onset on weekends split up by gender for different age groups: (A) age 1924, (B) age 4044, and (C) age 6067. The
development of average sleep onset by age group split up by gender and day type (weekend/weekday). The red/orange colors correspond to women, light/dark blue
colors correspond to men, darker colors represent weekdays and lighter colors signify weekends. The colored envelopes display 95% CIs around each age group
mean (D). The equally weighted, between gender sleep onset difference by age group with 95% CI on weekends (E) and weekdays (F).
Figure 1D plots the aggregated raw data from our sample; the
displayed trends in sleep onset are confirmed by our modeling
which adjusts for demographic covariates, and controls for
individual and country baseline behavior. Sleep onset has a
quadratic relationship with age (p <
(K = IM#$%*
Table S17). The
model estimates a
minute difference between
weekends and weekdays for women and
difference for men (age group 40-44), with a later average onset
on weekends. When we consider the rate of decrease of sleep
onset for men, we find the model results suggest an even steeper
rate of decrease than the raw data (see Figure S3 and Table S17).
Consequently, the difference between men and women at age 40-
44 on weekdays is estimated to be
minutes based on
the raw data but
minutes (95 % CI) by the model.
Furthermore, the model estimates the onset curves for men and
women to intersect slightly earlier (within the 50-54 age range)
than the raw data. From age 55 to 67 the model indicates that
men are expected to exhibit earlier onset than women. The mixed
effects model indicates that there is a larger range of country-
level random effect values for onset (1.76 hours) than offset (1.35
hours). This finding is in accordance with the results from a study
conducted in 2014 using surveys and smartphone data: country
of residence appears to exert a stronger influence on adult sleep
onset than offset45.
Development of offset
Turning to the development of sleep offset, Figure 2D shows
that the mean value of sleep offset mostly decreases with age, and
people tend to wake up earlier as they get older. On weekdays the
curve is nearly flat for women between ages 45-59, but there is
an increase for the age interval 60-67. Men consistently decrease
in wake-up time with age except the slight increase from 60-67
on weekdays. The contrast between weekends and weekdays is
nearly fixed across the lifespan with an hour difference resulting
in later wake-up time on weekends. The curves on Figure 2D
show roughly the opposite behavior of what we observed for
sleep onset (Figure 1D), with the 95 % CI of the mean values for
men and women overlapping until the middle of adulthood and
thereafter diverging with men rising earlier than women. Thus,
from age 19-39 women and men exhibit an average tendency to
go to bed at different times yet wake up at similar times. The
sleep offset curves for men and women diverge earlier on
weekends (40-44) where the separation occurs one age group
later (45-49) on weekdays. This can be seen even more clearly on
Figure 2E (weekends) and Figure 2F (weekdays) which shows the
difference of sleep offset by gender and age group from the
equally weighted average of the curves for men and women.
Similar to the case of onset, the plotted mean offset values have
small error bars (as indicated by the 95 % confidence bands),
while the actual distributions of sleep offset are quite broad. This
is depicted in Figure 2A-C, which shows the distribution of offset
time for the 19-24 group, the 40-44 group and the 60-67 group
respectively. We observe close agreement between the plots in
Figure 2D and the model results (Table S19). Age, gender and
type of day are the most influential factors on wake-up time,
which has a quadratic relationship with age (p <
, see Table
S19). For people aged 40-44, the model shows men to have the
same sleep offset time as women, whereas on weekends they are
expected to wake up
minutes earlier (Table S19). This
is displayed in Figure 2D, which shows that the sleep offset
curves for men and women diverge earlier in the lifespan on
weekends than weekdays.
Figure 2: Distributions for sleep offset on weekends split up by gender for different age groups: (A) age 1924, (B) age 40–44, and (C) age 6067. The
development of average sleep offset by age group split up by gender and day type (weekend/weekday). The red/orange colors correspond to women, light/dark blue
colors correspond to men, darker colors represent weekdays and lighter colors signify weekends. The colored envelopes display 95% Cis around each age group
mean (D). The equally weighted, between gender sleep offset difference by age group with 95% CI on weekends (E) and weekdays (F).
Development of duration
Figure 3D shows that sleep duration tends to decrease across the
lifespan. This development is nearly linear for weekends and less
so on weekdays, with a small increase in duration for the oldest
age group on weekdays. The difference between weekends and
weekdays remains similar throughout the lifespan with a slightly
smaller gap for men and women in the oldest group. This is
highlighted in Figure 3E (weekends) and Figure 3F (weekdays)
which show the difference of sleep duration by gender and age
group from the equally weighted average of the curves for men
and women. Although the average behavior shown in Figure 3D
exhibits statistically significant differences between men and
women across different age groups, each aggregated group mean
is derived from a broad range of underlying behavior as Figures
3A-C show; the distributions of sleep duration for the 19-24
group, the 40-44 group and the 60-67 group respectively.
The mixed effects model for sleep duration, which controls for
individual and country of residence variations, generally confirms
the trends observed in the aggregated raw data plots visible in
Figure 3 (for comparison of the raw data and model fit see Figure
S4). The weekend-weekday differences in duration are apparent
in the model results but the magnitude of gender differences turn
out smaller, due to different rates of change in sleep duration with
age. Consequently, the curves for men and women come close to
overlapping from age 55-67, see Figure S4. Adjusting for BMI,
the aggregated raw data estimates women at age 40-44 to sleep
minutes longer than men, whereas the model estimates
a difference of
minutes (95 % CI), see Table S21 for
estimates of fixed effects.
Figure 3: Distributions for sleep duration on weekends split up by gender for different age groups: (A) age 1924, (B) age 4044, and (C) age 6067. The
development of average sleep duration by age group with 95% CI split up by gender and day type (weekend/weekday). The red/orange colors correspond to
women, light/dark blue colors correspond to men, darker colors represent weekdays and lighter colors signify weekends. The colored envelopes display 95% CIs
around each age group mean (D). The equally weighted, between gender sleep duration difference by age group with 95% CI on weekends (E) and weekdays (F).
Development of nighttime awakenings with
Having considered the progression of sleep onset, offset and
duration, we now assess how the prevalence of nighttime
awakenings develops across adulthood. To quantify nighttime
awakenings, we use WASO (wake after sleep onset) which refers
to periods of wakefulness occurring after defined sleep onset and
reflects sleep fragmentation65. For each registered night WASO
is the total time an individual is recorded awake (after defined
sleep onset, but also occurring before defined sleep offset). Since
sleep was recorded in 1-minute epochs, only WASO
measurements greater than 60 seconds were registered by the
wristbands. We observe a large fraction of users with zero
instances of WASO (85 % of the users have a median WASO
value of zero). This is in part because accelerometer-based sleep
tracking bandssimilar to wrist actigraphy - may underestimate
sleep disruptions if individuals are awake but lying still in bed66.
For that reason, our measure of nighttime awakenings may be
conservative and correspond to relatively large sleep disruptions
detectable by the embedded accelerometer. The percentage of
users with non-zero median WASO is plotted by age group and
gender in Figure 4. The percentage of individuals with non-zero
median WASO increases with age; for the 19-24 age group, 4.5
% of men and 9.7 % of women have non-zero median WASO
compared to 33.4 % (men) and 35.6 % (women) for the 60-67
age group.
Figure 4 shows that a higher proportion of women have non-
zero WASO medians than men from age 19 to 39. Specifically,
17.5 % of women have non-zero WASO median values
compared to 13.5 % for men aged 19-39. The distribution of
within individual median WASO differs significantly for men and
women aged 19-39, estimated with two sample Kolmogorov-
Smirnov (KS) statistics where p=
. Interestingly,
this same stage of life marks a biological window where
childbirth, infant rearing and child caretaking are more likely67.
As a post-hoc analysis, we investigate the hypothesis that
increased prevalence of nighttime awakenings during early
adulthood may be linked to tending to infants and young children
which exhibit irregular sleep patterns for the first 0-2 years of
life68. For that reason, we analyze the age group 19-39, where the
difference between the two curves in Figure 4 diverges between
genders. As a proxy for information regarding parental status and
infant-rearing, we reference aggregated app-context information.
Specifically, we can anonymously identify users as probable
parents if they have apps installed on their phones intended for
parents with young children (parent apps). We describe how
we identify apps as parent appsin the SI: Identifying “parent
apps”. We find that women in the age range 19-39 with a parent
app installed on their devices have a significantly different
distribution of median (denoted
) WASO than age-matched
women without the application on their phone (estimated with
two sample Kolmogorov-Smirnov (KS) statistics,
> " NPTT =
) where
U'()*+,-.+/-012+/345+67.124+766 "IQJ(VWX
U'()*+,-.+/-012+/345-84+67.124+766 "TR(VWX
(see distribution
Figure S5). By comparison, the distribution of median WASO for
young adult men with parent apps does not differ significantly
from those without them (estimated with two sample KS
) where
U'()*+,-.+012+/345+67.124+766 "
U'()*+,-.+012+/345-84+67.124+766 "AS(VWX
distribution Figure S5).
Figure 4: The percentage of people with nonzero median
WASO, by age group and gender. The red color corresponds to
women and blue color to men.
Next, we examine the subset of sleep observations for users with
YWCGE<'()*+Z M
, but this choice of subset eliminates the skew
arising from a large fraction of users with zero WASO
measurements (see Figure S5).
After applying the same comparison, we find that women aged
19-39 with parent apps installed on their phones have a
significantly different distribution of mean values (denoted
) for
their WASO than similarly aged women without parent apps
(estimated with two-sample KS statistics,
> " IPAQ =IM#$9
['()*+,-.+/-012+/345+67.124 +766 "IIMR(VWX
['()*+,-.+/-012+/345+67.124+766 "QSJ(VWX
(see distribution on
Figure S6). In contrast, when we carry out the same comparison
for men, we find that their distributions do not differ significantly
between the group with and without parent apps (estimated with
two-sample KS statistics,
) and
['()*+,-.+012+/345+67.124+766 "NMR(VWX
['()*+,-.+012+/345-84+67.124+766 "QTS(VWX
(see distribution
Figure S6).
Development of circadian misalignment with
Many people (about 75% of the US and European labor force)
maintain a conventional five day work week from 9-5 which
constrains their weekly sleep behavior69,70. This recurrent
temporal pattern can lead to substantial sleep deprivation during
weekdays and sleep compensation during weekends, in addition
to a weekend-weekday contrast in sleep timing23. Figure 5D
illustrates the development of weekend-weekday sleep timing
differences over the lifespan (green/pink colors for onset and
blue/red colors for offset). From approximately age 19 to 55,
average sleep offset tends to be 55-70 minutes later on weekends
while onset tends to be 25-35 minutes later on weekends over the
same period. Thus, adults in our sample tend to sleep half an hour
more on weekends than weekdays. This result is confirmed in
Figure 6B which shows the development of sleep debt (the
difference between sleep duration on weekends and weekdays)
with age. We identify sleep duration to be 25-40 minutes longer
on weekends from age 19-55. After age 55, weekend-weekday
misalignment in onset timing declines to
minutes for
older adult men, alongside a marginally larger decrease in offset
misalignment to
minutes (95 % CI). The distributions
for sleep offset and onset split by weekends and weekdays for age
group 19-24 (offset Figure 5C and onset Figure 5F) and age
group 60-67 (offset Figure 5E and onset Figure 5G) illustrate this
contrast. For example, offset is on average
later on weekends for men age group 19-24 but
minutes for men in the 60-67 year old group (95 % CI).
Interestingly, average sleep offset misalignment remains greater
than sleep onset misalignment into older adulthood, despite an
overall convergence towards more similar weekend and weekday
schedules and a reduction in weekend-weekday sleep duration
Figures 5D and 6B indicate that misalignment in both sleep
timing and duration progress similarly for men and women
across the majority of the lifespan. A possible exception is visible
during the 35-49 age range, during which both sleep offset and
onset misalignment are slightly greater for women. Increased
offset misalignment in this period for women appears to be
driven by later weekend offset times compared to men, while
weekday offset times are similar for both genders (see section
Sleep timing and duration over the lifespan). This general
similarity between genders is confirmed when observing the
overlapping distribution for weekday-weekend differences of
sleep onset and offset times for men and women respectively, age
group 19-24 (offset on Figure 5A and onset on Figure 5H) and
age group 60-67 (offset Figure 5B and onset Figure 5I).
Figure 5: The development of weekendweekday differences for sleep onset and offset by age group split by gender. The red/pink colors correspond to women,
blue/ green blue colors correspond to men, darker colors represent weekendweekday offset difference and lighter colors signify weekendweekday onset difference.
The colored envelopes display 95% CIs around each age group mean (D). The distribution of weekendweekday differences in sleep onset for different age groups:
(A) age 1924 and (B) age 6067. The distribution for the sleep offset weekendweekday differences for different age groups: (H) age 1924 and (I) age 60
67. Distribution for sleep onset time on weekdays and weekends for different age groups: (C) age 1924 and (E) age 6067. Distribution for sleep offset time
on weekdays and weekends for different age groups: (F) age 1924 and (G) age 6067.
As before, we consider the potential biases in the data set when
drawing conclusions from the figures and compare the
aggregated empirical data to our mixed effects model. Our
primary inferences from Figures 5 and 6 are verified by our
modelling results presented in Tables S23, S25 and S27. After
controlling for country and adjusting for BMI in the mixed
effects model, the slight difference between middle-aged men
and women (age group 40-44, see Figure 5D), is no longer
evident or negligible due to small effect size (the model estimates
men to have a
minute higher weekend-weekday sleep
offset difference and
minute higher weekend-weekday
sleep onset difference than women (95 % CI)).
Figure 6: The development of weekendweekday sleep duration difference by age group split up by gender. The red color corresponds to women and blue color
to men. The colored envelopes display 95% CIs around each age group mean (B). Distribution of weekendweekday duration differences for different age groups:
(A) age 1924 and (C) age 6067.
Sleep variability over the lifespan
Figures 7A and B show the development of adult onset and
offset variability with age (green/purple colors correspond to
onset and blue/red to offset, while the darker shades represent
weekdays and lighter shades indicate weekends). Interestingly, we
find that onset variability, measured as the intra-individual
standard deviation of onset time, is nearly fixed across the
lifespan at 1.1 hours on weekdays and 1.3 hours on weekends. By
comparison, offset variability decreases relatively rapidly for age
group 19-24, both for men (weekdays
hours and
hours) and women (weekdays
hours and weekends
hour) up until age 35-
39, remaining around 0.9 hours on weekdays while continuing to
decrease on weekends at a gradual rate. Variability for all
measurements (onset, offset and duration) is always higher on
weekends than weekdays. We find that young adults have more
variable sleep offset times than onset times both on weekends
and weekdays. Figure 7C and D show that the difference between
offset variability and onset variability is positive and higher across
early adulthood (19-29) for men and women on both weekends
and weekdays. The weekend difference between offset and onset
variability is larger for men across the age 19-34 range, while the
weekday difference is larger for women in the 19-24 and 25-29
age groups.
Figure 7F illustrates the development of sleep duration variability
over the lifespan, which decreases gradually with age such that
the youngest group of men have only
minute higher
sleep variability than the oldest group on weekends. From
Figures 7A, B and F we observe small significant differences
between men and women; higher onset variability both on
weekends and weekdays after early adulthood and consistently
higher sleep offset variability on weekends for all age groups.
When comparing these results to our mixed effects models which
control for the influence of country and demographic covariates,
we find that all of the general conclusions inferred from the
descriptive plots in Figure 7 are verified (see Tables S29, S31 and
S33). Taking the age 40-44 group as an example, the model
estimates a 3-minute higher onset variability for men than
women on weekdays, 2-minute greater onset variability on
weekends and 5-minute higher offset variability on weekends,
which are trends that can also be identified on Figures 7A and B.
Figure 7: The development of sleep onset and offset variability with 95% CI by age group and split by gender and type of day: (A) weekdays and (B) weekends.
Red/ orange curves is offset variability for women, light/dark blue is offset variability for men, light/dark purple is onset variability for women, and light/dark
green is onset variability for men. Darker colors represent weekdays and lighter colors weekends. The colored envelopes display 95% CIs around each age group
mean. The difference between sleep onset and offset variability with 95% CI by age group on weekdays (C) and weekends (D). The development of sleep duration
variability by age group split up by gender and day type (weekday/weekend). The red/orange colors correspond to women, light/dark blue colors correspond to
men, darker colors represent weekdays and lighter colors signify weekends. The colored envelopes display 95% CIs around each age group mean (F). Distribution
of sleep duration variability by gender for different age groups: (E) age 1924 and (G) age 60–67
hh:mm ± m
hh:mm ± m
hours ± hours
Normal weight
Table 3: Mixed effects model estimates of average sleep onset, offset and
duration for different BMI and gender groups on weekdays age 40-44 (95
% CI).
The effect of BMI
In Table 3 we list average sleep onset, offset and duration for
men and women within the four BMI groups estimated with the
mixed effects model (95 % CI) for age group 40-44 on weekdays.
Overall, differences in sleep timing between BMI groups are
statistically insignificant and/or small in effect. As one exception
to this trend, we find that men within the normal BMI range sleep
on average
minutes more than those in the obese BMI
category, and men in the underweight category sleep on average
minutes more than those in the obese category. We
carry out further discussion concerning these results in section
called “BMI Discussionin the SI.
Drawing on a massive global sleep data-set comprised of 11.14
million sleep observations from 69,650 adults spanning 47
countries, we confirm the presence of age-related changes in
sleep duration, timing, misalignment and variability. After
controlling for baseline country-level variation using mixed
effects models, we find that younger adulthood is marked by
both delayed sleep onset and offset, and higher intra-individual
sleep duration variability, offset variability, weekend-weekday
misalignment and weekend-weekday sleep duration difference
compared to older adulthood. Conversely, sleep duration is
shorter and nighttime awakenings are more prevalent during
older adulthood. Only sleep onset variability exhibits little to no
difference across the youngest and oldest age groups in our
sample. Certain changes in sleep behavior progress consistently
across most age groups observed, while others appear to be
highly life-stage and/or gender dependent. In contrast to studies
based on single or short-term observations, our unique data-set
of millions of multi-night sleep recordings enables a consistent,
detailed understanding of age-related tendencies in sleep patterns
including intra-individual variability within inferred working and
leisure periods as well as misalignment between them. We
confirm several recognized age-related changes in human sleep
and provide novel evidence of gender differences across key
phases of adult development. Further, in the SI we provide i) a
comparison of our data with multiple independent large scale and
global sleep datasets, ii) we explicitly compare our global
estimates of social jetlag to those from Roenneberg et al. (2012),
and perform a quantitative exploration of underlying regional
differences that appear to drive this disparity of our results
compared to Roenneberg et al., and lastly, iii) we perform an
exploratory analysis of the possible effect of retirement age,
which varies by country and demonstrates that different regional
policies appear to affect people’s sleep patterns.
Circadian misalignment has been found to be associated with
negative health outcomes such as obesity, metabolic risk factors
for diabetes and atherosclerotic cardiovascular disease, as well as
adverse behaviors such as drinking and smoking which can
negatively impact healthy human development23,71. Notably, we
find considerably lower levels of social jetlag in our sample across
all observed age groups compared to the values reported by
Roenneberg et al.23 (see Figure S9). In the Roenneberg et al.
(2012) study, the sample consisted of questionnaire respondents
from predominantly four European countries (Germany,
Switzerland, the Netherlands and Austria). Thus, the discrepancy
may reflect a mismatch between global and regional circadian
preferences, recall biases linked to the questionnaire and/or
other unobserved differences. Constraining our sample to only
include the same primary European countries as Roenneberg et
al. yields markedly higher values of social jetlag across the lifespan
than in our full global sample as well as altered age-related gender
differences during early and late adulthood, with men incurring
marginally more social jetlag than women - consistent with the
age-related dependencies identified by Roenneberg et al. By
contrast, in our global sample middle-aged women have
marginally more social jetlag than men, with negligible gender
differences in other age groups. Comparing social jetlag levels
between regional strata of our sample from Asia and Europe
suggests that social jetlag for young adults may be over twice as
large in the same European region sampled by Roenneberg et al.
(2012), and ~1.5 times larger for middle-aged and older adults
(Figure S10). This provides suggestive evidence that the gap
between the attenuated magnitude of social jetlag in our full
sample relative to Roenneberg et al.’s may not merely be due to
different means of data collection and associated measurement
error (objective multi-night recording vs. self-report
questionnaires). Rather, underlying regional differences appear to
play an important role. We contend that accounting for
underlying country-level variation is important to prevent biased
global estimates of salient sleep outcomes and age-related
In line with previous research, we find weekend-weekday
differences in sleep timing and duration to be more pronounced
among younger adults, with these elevated differences (Figure S8)
driven primarily by earlier sleep offset on weekdays and later
offset on weekends than weekdays. Weekend-weekday
misalignment in sleep timing and duration slightly decrease with
age and decline more rapidly around the age range of 55-59,
leading to near convergence of sleep onset timing on weekends
and weekdays for older adults aged 60-67. Reduced misalignment
in older adulthood may signal the social onset of exiting the labor
force for retirement. However, some misalignment in both sleep
offset, midsleep and duration persists across the age groups
observed, indicating that pervasive work schedules likely
continue to exert an influence on people's sleep-wake cycles
through most of the adult lifespan. Our results indicate that sleep
research involving adults should account for weekend-weekday
heterogeneity in sleep patterns, even in older populations where
nonstandard weekday schedules might otherwise be presumed.
A growing body of research indicates that irregular sleep is linked
to maladaptive responses adverse to human health25,53,80,7279
Outside of research on weekend-weekday misalignment, limited
evidence exists about age-related changes in sleep variability in
sleep patterns within individuals, particularly during weekdays
and within weekends81. Taken together, two recent cross-
sectional studies found that between-individual onset, offset,
duration and chronotype variability decrease with age24,45.
Similarly, a sleep diary-based study found that intraindividual
variability in sleep duration decreases with age27. By comparison,
our data set shows that intraindividual variability in sleep onset is
close to fixed over the lifespan - implying that young, middle-
aged and older adults may have persistently variable sleep onset
times, whereas variability in wake-up times decreases with age,
likely driving the observed decline in sleep duration variability.
We find that young adults tend to have more variable offset times
than onset times, a trend which inverts after age 35 due to a
decrease in sleep offset variability. One possible explanation is
that a concurrent rise in weekday alarm clock use to meet fixed
workplace, childcare and/or other social commitments might
drive this reduction in offset variability. Across our population,
variability measurements are consistently higher on weekends,
confirming that sleep patterns are more regulated on weekdays,
in line with the alarm clock hypothesis. However, the gradual
decline in both weekend sleep offset variability and duration
variability across most of the lifespan suggests that both
endogenous and exogenous factors may be involved.
Nighttime Awakenings
Previous research and reviews found that women are at a greater
risk than men to develop insomnia, and both insomnia and other
sleep disorders are more prevalent in women during pregnancy
and the postpartum period37,8284. Ours is the first study to use
the contextual information encoded in app usage as a proxy for
parental status to explore life stage gender inequality in sleep
quality. Importantly, the gender difference we observe in
nighttime awakenings is more pronounced from young to middle
adulthood than from middle to late adulthood. Supporting the
hypothesis that increased sleep disturbances for women during
early adulthood might be driven by childbirth and raising young
children, we find a significant difference in the median WASO
between women with parent apps installed on their phone and
age-matched women without such applications. When we
applied the same comparison to the two corresponding groups
of men, we found no significant difference, a finding which
suggests that the gap in prevalence of nighttime awakenings
between women and men aged 19-39 may be driven by the
presence of infants or young children - as well as gender-
associated caretaking norms - which disproportionately interrupt
the sleep of female parents. This finding is in agreement with a
panel study on changes in sleep satisfaction and sleep duration
after childbirth, which also found a less pronounced decrease in
sleep satisfaction for men than women85. Others have interpreted
disturbed sleep after childbirth as a contributor and/or symptom
of postpartum depression86,87.
In line with previous observational studies that suggest age-
related increases in WASO, we find that the prevalence of people
regularly experiencing sleep disturbances increases with age7,9. A
greater proportion of women than men regularly experience
some time awake after sleep onset across all age groups observed
in our study, indicating that more women may have difficulty
maintaining sleep even though women on average sleep longer
than men. Taken together, these findings contribute to the
nascent literature on the unequal burden of child rearing on
women's sleep quality88. The use of parenting apps to identify life
stage contextual factors - such as child-rearing status - illustrates
the promise of using contextual information related to app usage
as a novel way to understand the connection between sleep and
overall behavior.
Sleep timing and duration
Epidemiological studies have demonstrated that men are, on
average, later chronotypes than women until 40-50 years of age,
after which their circadian phase advances to overlap or become
earlier17,24. Our study both confirms (see S7) and expands on this
finding by documenting the underlying dynamics between sleep
onset and offset across these age groups that shape the full sleep
period and its relative position. We find that men tend to have a
later sleep onset than women up until 50-54 years of age, while
up until the age range of 35-39 there is no significant difference
in offset time between men and women. Thereafter, from middle
to late adulthood, women tend to rise later. Taken together, this
inversion may be indicative of gender-gaps in both domestic and
labor demands during this period from mid-late adulthood89. It
is possible that the general overlap in wake-up times for women
and men during young to middle adulthood may be due to
temporarily convergent external demands characteristic of this
phase of development, such as attending university, work,
tending to infants and/or raising young children, etc. By
choosing to focus our analysis on both the beginning and end of
the sleep period, rather than just its midpoint (see S7) as
commonly used in epidemiological sleep studies, we capture
these differences and changes which have not been consistently
described before at a global scale.
The finding that men sleep less than women on average across
age groups23,63, confirmed by our study, is believed to have both
a biological and social basis89,90. For instance, we find that the
sleep surplus for women relative to men is largest during young
to middle adulthood when sleep interruptions are considerably
more common for women than for men, likely due to the
differential burden of caregiving. Thus, a combination of
imbalanced nocturnal demands and socially imposed offset
timing due to labor schedules may drive the observed gender
differences in onset. Indeed, from middle to late adulthood
average onset times converge and average offset times diverge.
Furthermore, in line with previous research6,7,9, we find that
average sleep duration declines with age, with increasing portions
of the average sleep distributions for both men and women
falling below 7 hours until weekday sleep duration slightly
rebounds after age 60, a phase associated with attenuated
working demands due to retirement90. Interestingly, later
weekday wake up timing in late adulthood was apparent in
Germany and the United Kingdom, but was not evident for
Japan within the age range of our sample. Thus regional
heterogeneity in transitioning out of the labor force may be
reflected by differences in the manifestation of partial sleep
timing recovery (see SI: Analysis of effective retirement age with
three-way interaction of age, gender and country). However, such
recovery in sleep offset appears to be consistently more subdued
for older men than women. A recent global cross-sectional study
found that acute cognitive deficits in reasoning and verbal ability
can arise from sleeping less than 7-8 hours regardless of age13.
Importantly, average weekday sleep duration for men in our
sample was consistently under 7 hours across all age groups
Several considerations should be weighed when interpreting the
results of this study. First, the wearable fitness bands used rely
on in-built accelerometers and proprietary algorithms developed
and internally validated by a global mobile technology company.
Accelerometry-based consumer sleep trackers are known to
slightly overestimate sleep duration and underestimate sleep
disruptions66, suggesting that actual sleep duration may be slightly
lower than recorded in this study and that our estimates for the
prevalence of people with frequent nighttime awakenings may be
conservative. Second, age and BMI were self-reported. Despite
possible recall bias, we find good general agreement between the
World Health Organization's country-level estimates of median
age and age standardized BMI and the corresponding estimates
from our data set. Nevertheless, our sampled population of
wearable users may not be representative of the wider population
due to potential unobserved factors also associated with wearable
device ownership, such as post-secondary education
attainment91. Third, given the cross-sectional design of the
current study, we cannot statistically identify whether the
observed trends across age groups correspond to within-
individual changes over the lifespan or rather reflect generational
differences in normative sleep patterns. An exemplary
longitudinal study which analyzed the change in diurnal timing
preferences of 567 males in Finland across a 23 year period found
that sleep timing shifted to become earlier with age, supporting
the former intuition92. However, we cannot distinguish whether
the age-related sleep patterns we observe are primarily driven by
physiological or social developments associated with different
stages of adulthood. Fourth, similar to others, we use weekends
as a proxy for free days where individuals were not working to
help distinguish between endogenously and exogenously driven
changes20,24. This assumption does not hold for the subset of our
sample who might be unemployed or otherwise follow irregular
(ex. service industry) work schedules. Thus, our estimates of
misalignment may be slightly conservative. Despite these
limitations, our primary results converge with recognized age-
related trends in both sleep duration and timing9,28. Furthermore,
these trends appear to be generally consistent across multiple
geographic regions and sociocultural contexts.
Massive data sets generated by pervasive consumer wearable
devices can provide globally consistent measurements and thus
can contribute unique and confirmatory insights about the
development of human sleep patterns. Interestingly, the wide and
overlapping distributions of sleep times between genders across
the life course suggests that even though there are characteristic
differences in mean values, overgeneralization of gender
differences should be avoided. Underlying heterogeneity in sleep
duration and timing across the life course proves the rule rather
than the exception. Early weekday work schedules and norms
likely constrain the varied circadian preferences of individuals,
contributing to misalignment. Furthermore, given the pervasive
asymmetry between weekend and weekday sleep patterns as well
as variability in day-to-day sleep timing, research on behavioral
interventions that promote regular sleep wake cycles is needed.
Rather than impose standard morning start times, organizations
might explore and evaluate person-centered work schedules and
jobs that match the diverse circadian preferences of individuals,
evident in this study and others.
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... We have not been able to fnd studies that characterize (using the method of actigraphy) sleep with such a long-term (>ten years) distance from the completion of treatment or from diagnosis in this target group. Given that research studies in the healthy population also point to sex-related diferences in sleep parameters-women achieve longer sleep times and higher SE compared to men [27,28]-we are interested in whether these diferences also occur in adult survivors of childhood acute lymphoblastic leukemia (ALS). ...
... In terms of gender, the sleep parameters did not difer between the ALS and CG. Tese results do not correspond with the fndings of other studies dealing with healthy populations [28,39], which suggest that men sleep less on average than women across age categories. ...
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Background. Sleep problems are among the common late side effects of treatment that can occur in survivors of childhood acute lymphoblastic leukemia. At present, the objective evaluation of sleep in the natural environment using actigraphy rather than self-assessment of research participants or the more demanding polysomnography is increasingly coming to the forefront in population epidemiological studies. The main objective of this cross-sectional study is to objectively characterize selected sleep parameters with respect to gender in adult survivors of childhood acute lymphoblastic leukemia (ALS) in their natural environment and to compare them with a control group (CG) sampled from a healthy population. Another partial aim of the study is to determine the fulfillment of recommendations in the areas of sleep (SL) and sleep efficiency (SE). Methods. 20 ALS and 20 CGs aged 18–30 years participated in the survey. The ALS were diagnosed on average 15.5 years ago. Selected sleep parameters were measured instrumentally by means of an Axivity AX3 accelerometer worn on the wrist for seven days in a natural environment. Results. No significant differences were found between the ALS and CG groups for the selected sleep parameters. The total time in bed for the ALS was 405.5 min/day compared to 428.2 min/day for the CG (p = 0.37), sleep for the ALS was 372.7 min/day compared to 382.9 min/day for the CG (p = 0.34), and SE for the ALS was 88.0% compared to 88.5% for the CG (p = 0.99). No significant gender differences were found. The sleep recommendation of >420 min/day was met by 15% for the ALS and 19% for the CG; SE > 85% was achieved by 80% for the ALS and 80% for the CG. Conclusion. The results of our study suggest that ALS may achieve the same values as the healthy population in selected sleep parameters.
... Moreover, differences in weekend versus weekend sleep may need to be taken into account. Generally, individuals tend to sleep longer on weekends than weekdays, but this difference consistently decreases with increasing adult age (Åkerstedt et al., 2019;Jonasdottir et al., 2021). This would imply that adult age may moderate the effect of weekend vs. weekday on IIV of sleep. ...
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Laboratory based sleep deprivation studies demonstrate that lack of sleep impairs well-being and performance ability, but suggest that these effects are mitigated in older adults. Yet, much less is known whether day-to-day variations of sleep have similar consequences in the context of everyday life. This project uses an intensive longitudinal design to investigate the occurrence of day-to-day variations in sleep and their impact on mood and performance in everyday life and to examine whether effects differ between young and older adults. We aim to include 160 young (18–30 years) and 160 older adults (55–75 years) to complete a 21-day experience sampling method (ESM) protocol. During the ESM period, participants are asked to fill in (i) a brief morning questionnaire, (ii) 8 short daytime questionnaires addressing momentary well-being, sleepiness, stress, and mind wandering, followed by a 1 min cognitive task and (iii) a brief evening questionnaire, all delivered via a mobile phone application. Sleep will be measured using self-reports (daily questions) and objectively with wrist actigraphy. The impact of adult age on mean levels and intraindividual variability of sleep will be analyzed using mixed-effects location scale models. The impact of sleep on daily cognitive performance will be analyzed using multilevel linear mixed models. The relationship of sleep to mean values and variability of positive and negative affect in young and older adults will be analyzed using mixed-effects location scale modeling. The overarching purpose of the project is improving the current knowledge on the occurrence of day-to-day variations in sleep and their relationship to performance as well as positive and negative affect in young and older adults.
... In contrast, heat sensitivity was highest during the rainiest and most humid month (August), which matches the findings that humans are affected more by heat when relative humidity is higher compared with higher air temperature alone [5]. On half of the nights evaluated, sleep duration was insufficient, and overall averages were up to an hour lower than those observed in studies conducted in other countries [60]. Our estimates of sleep duration did not provide insights into changes in sleep physiology. ...
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Background Extreme weather, including heat and extreme rainfall, is projected to increase owing to climate change, which can have adverse impacts on human health. In particular, rural populations in sub-Saharan Africa are at risk because of a high burden of climate-sensitive diseases and low adaptive capacities. However, there is a lack of data on the regions that are anticipated to be most exposed to climate change. Improved public health surveillance is essential for better decision-making and health prioritization and to identify risk groups and suitable adaptation measures. Digital technologies such as consumer-grade wearable devices (wearables) may generate objective measurements to guide data-driven decision-making. Objective The main objective of this observational study was to examine the impact of weather exposure on population health in rural Burkina Faso using wearables. Specifically, this study aimed to assess the relationship between individual daily activity (steps), sleep duration, and heart rate (HR), as estimated by wearables, and exposure to heat and heavy rainfall. Methods Overall, 143 participants from the Nouna health and demographic surveillance system in Burkina Faso wore the Withings Pulse HR wearable 24/7 for 11 months. We collected continuous weather data using 5 weather stations throughout the study region. The heat index and wet-bulb globe temperature (WBGT) were calculated as measures of heat. We used linear mixed-effects models to quantify the relationship between exposure to heat and rainfall and the wearable parameters. Participants kept activity journals and completed a questionnaire on their perception of and adaptation to heat and other weather exposure. Results Sleep duration decreased significantly (P<.001) with higher heat exposure, with approximately 15 minutes shorter sleep duration during heat stress nights with a heat index value of ≥25 °C. Many participants (55/137, 40.1%) reported that heat affected them the most at night. During the day, most participants (133/137, 97.1%) engaged in outdoor physical work such as farming, housework, or fetching water. During the rainy season, when WBGT was highest, daily activity was highest and increased when the daily maximum WBGT surpassed 30 °C during the rainiest month. In the hottest month, daily activity decreased per degree increase in WBGT for values >30 °C. Nighttime HR showed no significant correlation with heat exposure. Daytime HR data were insufficient for analysis. We found no negative health impact associated with heavy rainfall. With increasing rainfall, sleep duration increased, average nightly HR decreased, and activity decreased. Conclusions During the study period, participants were frequently exposed to heat and heavy rainfall. Heat was particularly associated with impaired sleep and daily activity. Essential tasks such as harvesting, fetching water, and caring for livestock expose this population to weather that likely has an adverse impact on their health. Further research is essential to guide interventions safeguarding vulnerable communities.
... In addition to highlighting the benefits and drawbacks of various methodologies, the study offers suggestions for future research areas. According to Jonasdottir S.S., et al. [10] (2021), AI has the ability to identify mental health conditions, which frequently coincide with sleep disturbances. Scientists created a prediction algorithm to track changes in suicidal thoughts by examining social media material. ...
... Circadian patterns are influenced by nonmodifiable risk factors such as age and gender. For example, healthy women typically have an earlier entrained circadian phase compared to healthy men of the same age, which may contribute to the observed increased prevalence of sleep disorders in women (29)(30)(31). Perhaps more pertinent to the scope of this review, due to the prevalence of older adults in our intensive care units, is the circadian misalignment seen with increasing age. ...
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Critical illness and stays in the Intensive Care Unit (ICU) have significant impact on sleep. Poor sleep is common in this setting, can persist beyond acute critical illness, and is associated with increased morbidity and mortality. In the past 5 years, intensive care clinical practice guidelines have directed more focus on sleep and circadian disruption, spurring new initiatives to study and improve sleep complications in the critically ill. The global SARS-COV-2 (COVID-19) pandemic and dramatic spikes in patients requiring ICU level care also brought augmented levels of sleep disruption, the understanding of which continues to evolve. This review aims to summarize existing literature on sleep and critical illness and briefly discuss future directions in the field.
Sleep deprivation, which is a decrease in duration and quality of sleep, is a common problem in today’s life. Epidemiological and interventional investigations have suggested a link between sleep deprivation and overweight/obesity. Sleep deprivation affects homeostatic and non-homeostatic regulation of appetite, with the food reward system playing a dominant role. Factors such as sex and weight status affect this regulation; men and individuals with excess weight seem to be more sensitive to reward-driven and hedonistic regulation of food intake. Sleep deprivation may also affect weight through affecting physical activity and energy expenditure. In addition, sleep deprivation influences food selection and eating behaviors, which are mainly managed by the food reward system. Sleep-deprived individuals mostly crave for palatable energy-dense foods and have low desire for fruit and vegetables. Consumption of meals may not change but energy intake from snacks increases. The individuals have more desire for snacks with high sugar and saturated fat content. The relationship between sleep and the diet is mutual, implying that diet and eating behaviors also affect sleep duration and quality. Consuming healthy diets containing fruit and vegetables and food sources of protein and unsaturated fats and low quantities of saturated fat and sugar may be used as a diet strategy to improve sleep. Since the effects of sleep deficiency differ between animals and humans, only evidence from human studies has been included, controversies are discussed, and the need for future investigations is highlighted.
Sleep disturbances have been associated with unemployment, but variation in sleep-wake patterns by labor force status has rarely been examined. With a population-based sample, we investigated differences in sleep-wake patterns by labor force status (employed, unemployed, and not-in-the-labor-force) and potential disparities by sociodemographic variables. The analysis included 130,602 adults aged 25-60 y, who participated in the American Time Use Survey between 2003 and 2019. Individual sleep-wake pattern was extracted from time use logs in a strict 24-h period (04:00 h-03:59 h). Functional nonparametric regression models based on dimensionality reduction and neighborhood matching were applied to model the relationship between sleep-wake patterns and labor force status. Specifically, we predicted changes in intra-person sleep-wake patterns under hypothetical changes of labor force status from employed to unemployed or not-in-the-labor-force. We then studied moderations of this association by gender, race/ethnicity and educational attainment. In comparison to the employed state, unemployed and not-in-the-labor-force states were predicted to have later wake-times, later bedtimes, and higher tendency for taking midday naps. Changes in labor force status led to more apparent shifts in wake-times than in bedtimes. Additionally, sleep schedules of Hispanics and those with higher education level were more vulnerable to the change of labor force status from employed to unemployed.
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Objectives: Research suggests strong associations between habitual sleep parameters (eg, mean duration, timing, efficiency), perceived stress, and insomnia symptoms. The associations between intraindividual variability (IIV; night-to-night within-person variation) in sleep, perceived stress, and insomnia have not been explored. This study examined associations between IIV in subjectively and objectively determined sleep parameters and to perceived stress in young adults with and without insomnia. Design: Prospective longitudinal. Setting and participants: Participants were 149 college students (mean age = 20.2 [SD = 2.4], 59% female) either with insomnia (n = 81; 54%) or without insomnia (n = 68; 46%). Measurements: Participants completed 1 week of daily sleep diaries and actigraphy (to assess total sleep time [TST], sleep efficiency [SE], and circadian midpoint [CM]), the Perceived Stress Scale, and a diagnostic interview for determination of insomnia as part of a parent study. Results: Greater IIV in actigraphy-determined TST (but not SE or CM) was independently associated with greater perceived stress, regardless of insomnia status. Greater IIV in sleep diary-determined TST, SE, or CM was not associated with perceived stress. Insomnia status was the most robust predictor of elevated perceived stress. There was a significant interaction between IIV in sleep diary-determined TST and insomnia status on perceived stress: Only in those without insomnia was greater IIV in sleep diary-determined TST associated with higher perceived stress. Conclusion: Maintaining a more consistent sleep duration may be associated with lower stress in college students. Future research is needed to clarify the directionality and implications of this association for treatment.
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Prior reports on geographical differences in sleep duration have relied on samples collected at different time points with a variety of subjective instruments. Using sleep data from a total of 553,559 nights from 23,680 Fitbit users (aged 15–80y), we found objective evidence for regional disparities in sleep duration of 32–43 min between Oceanian and East Asian users on weekdays. This was primarily driven by later bedtimes in East Asians. Although users in all countries extended sleep on weekends, East Asians continued to sleep less than their Oceanian counterparts. Women generally slept more than men, and older users slept less than younger users. Reasons for shorter sleep duration in East Asians on both weekdays and weekends, across the lifespan and in both sexes remain to be investigated.
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Study Objectives: Intraindividual variability (IIV) in sleep may be a risk factor for disease above the influence of mean sleep. Associations between IIV in sleep and risk for a comprehensive set of common medical and mental health conditions have not been assessed in a representative sample. Methods: The current study examined mean and IIV in total sleep time (TST), sleep quality (SQ), sleep efficiency (SE), and circadian midpoint (CM) in 771 adults recruited for an epidemiological study. Participants completed 14 days of sleep diaries to assess TST, SQ, SE, and CM, after which they reported on medical conditions and mental health symptoms. Data were analyzed using logistic regression, and models controlled for gender, body mass index, age, and race. Results: Lower mean TST, SQ, and SE were related to increased odds of having gastrointestinal problems, depression, and anxiety. IIV in TST was related to increased odds of having neurological, breathing, and gastrointestinal problems, as well as pain and depression; all results held controlling for mean sleep and adjusting for false discovery rate. IIV in SQ and SE was not associated with odds of having any medical or mental health conditions after adjusting for false discovery rate, nor was IIV in CM or mean CM. Conclusion: Confirming previous research, mean TST, SQ, and SE are related to risk for gastrointestinal problems, depression, and anxiety. IIV in TST may be a unique facet of disturbed sleep that is associated with increased risk for a diverse cluster of medical and mental health conditions.
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Study Objectives To examine the changes in mothers’ and fathers’ sleep satisfaction and sleep duration across pre-pregnancy, pregnancy, and the postpartum period of up to six years after birth; it also sought to determine potential protective and risk factors for sleep during that time. Methods Participants in a large population-representative panel study from Germany reported sleep satisfaction and sleep duration in yearly interviews. During the observation period (2008–2015), 2,541 women and 2,118 men reported the birth of their first, second, or third child and provided longitudinal data for analysis. Fixed-effects regression models were used to analyze changes in sleep associated with childbirth. Results Sleep satisfaction and duration sharply declined with childbirth and reached a nadir during the first three months postpartum, with women more strongly affected (sleep satisfaction reduction compared with pre-pregnancy: women, 1.81 points on a 0 to 10 scale, d = 0.79 vs. men, 0.37 points, d = 0.16; sleep duration reduction compared with pre-pregnancy: women, 62 min, d = 0.90 vs. men, 13 min, d = 0.19). In both women and men, sleep satisfaction and duration did not fully recover for up to six years after the birth of their first child. Breastfeeding was associated with a slight decrease in maternal sleep satisfaction (0.72 points, d = 0.32) and duration (14 min, d = 0.21). Parental age, household income, and dual vs. single parenting were unrelated, or only very weakly related, to improved sleep. Conclusion Following the sharp decline in sleep satisfaction and duration in the first months postpartum, neither mothers’ nor fathers’ sleep fully recovers to pre-pregnancy levels up to six years after the birth of their first child.
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Background: Sleep duration and quality have been associated with increased cardiovascular risk. However, large studies linking objectively measured sleep and subclinical atherosclerosis assessed in multiple vascular sites are lacking. Objectives: The purpose of this study was to evaluate the association of actigraphy-measured sleep parameters with subclinical atherosclerosis in an asymptomatic middle-aged population, and investigate interactions among sleep, conventional risk factors, psychosocial factors, dietary habits, and inflammation. Methods: Seven-day actigraphic recording was performed in 3,974 participants (age 45.8 ± 4.3 years; 62.6% men) from the PESA (Progression of Early Subclinical Atherosclerosis) study. Four groups were defined: very short sleep duration <6 h, short sleep duration 6 to 7 h, reference sleep duration 7 to 8 h, and long sleep duration >8 h. Sleep fragmentation index was defined as the sum of the movement index and fragmentation index. Carotid and femoral 3-dimensional vascular ultrasound and cardiac computed tomography were performed to quantify noncoronary atherosclerosis and coronary calcification. Results: When adjusted for conventional risk factors, very short sleep duration was independently associated with a higher atherosclerotic burden with 3-dimensional vascular ultrasound compared to the reference group (odds ratio: 1.27; 95% confidence interval: 1.06 to 1.52; p = 0.008). Participants within the highest quintile of sleep fragmentation presented a higher prevalence of multiple affected noncoronary territories (odds ratio: 1.34; 95% confidence interval: 1.09 to 1.64; p = 0.006). No differences were observed regarding coronary artery calcification score in the different sleep groups. Conclusions: Lower sleeping times and fragmented sleep are independently associated with an increased risk of subclinical multiterritory atherosclerosis. These results highlight the importance of healthy sleep habits for the prevention of cardiovascular disease.
The ‘International Biomarkers Workshop on Wearables in Sleep and Circadian Science’ was held at the 2018 SLEEP Meeting of the Associated Professional Sleep Societies. The workshop brought together experts in consumer sleep technologies and medical devices, sleep and circadian physiology, clinical translational research, and clinical practice. The goals of the workshop were: 1) characterize the term “wearable” for use in sleep and circadian science, and identify relevant sleep and circadian metrics for wearables to measure; 2) assess the current use of wearables in sleep and circadian science; 3) identify current barriers for applying wearables to sleep and circadian science; and 4) identify goals and opportunities for wearables to advance sleep and circadian science. For the purposes of biomarker development in the sleep and circadian fields, the workshop included the terms “wearables”, “nearables”, and “ingestibles”. Given the state of the current science and technology, the limited validation of wearable devices against gold standard measurements is the primary factor limiting large-scale use of wearable technologies for sleep and circadian research. As such, the workshop committee proposed a set of best practices for validation studies and guidelines regarding how to choose a wearable device for research and clinical use. To complement validation studies, the workshop committee recommends the development of a public data repository for wearable data. Finally, sleep and circadian scientists must actively engage in the development and use of wearable devices to maintain the rigor of scientific findings and public health messages based on wearable technology.
Background: Development induces changes in sleep, and its duration has been reported to change as a function of aging. Additionally, sleep timing is a marker of pubertal maturation, where during adolescence, the circadian rhythm shifts later. Typically, this is manifested in a later sleep onset in the evening and later awakening in the morning. These changes across development seem to be universal around the world but are unlikely to persist into adulthood. Methods: This study utilized accelerometer data from 17,355 participants aged 16-30 years (56% female) measured by validated Polar wearables over a 14-day period. We compared sleep duration, chronotype (sleep midpoint) and weekend catch-up (ie, social jetlag) sleep across ages and regions over 242,948 nights. Results: The data indicate a decline in sleep duration as well as a dramatic shift in sleep onset times throughout adolescence. This continues well into early adulthood and stabilizes nearer age 30. Differences in sleep duration across ages were significant, and ranged from 7:53 h at age 16 to 7:29 h at age 30 in the sample. Additionally, there was a clear difference between females and males throughout adolescence and young adulthood: girls had longer sleep duration and earlier timed sleep in the current study. Differences in sleep were found between regions across the world, and across European areas. Conclusions: Both sleep duration and sleep timing go through a clear developmental pattern, particularly in early adulthood. Females had an earlier sleep midpoint and obtained more sleep. Regional differences in sleep occurred across the world.
Background In healthy populations, irregular sleep patterns are associated with delayed sleep and poor functional/mood outcomes. It is unknown whether irregular sleep contributes to poor functional/mood outcomes in individuals with Delayed Sleep-Wake Phase Disorder (DSWPD). Methods In 170 patients with DSWPD, we collected sleep-wake patterns, dim light melatonin onset (DLMO), and functional/mood outcomes. The Sleep Regularity Index (SRI) and other sleep timing metrics were computed. Correlations of SRI were computed with phase angle (difference between DLMO and desired bedtime), sleep timing and quality variables, daytime function, sleep-related daytime impairment, mood, and insomnia symptom severity. Path analyses assessed whether SRI or total sleep time mediated the associations between sleep onset time and phase angle with daytime functioning, sleep-related impairment, and mood outcomes. Results Higher SRI was associated with earlier sleep and longer total sleep time, but did not relate to sleep quality, daytime function, or mood outcomes. Path analysis showed that phase angle was directly associated with all outcome variables, whereas sleep onset time was not directly associated with any. SRI mediated the effects of sleep onset time and phase angle on daytime function. Total sleep time mediated the effects of sleep onset time and phase angle on sleep-related impairment. Conclusion Individuals with DSWPD who have more delayed sleep and a greater phase angle also have more irregular sleep. This suggests that it is not delayed sleep timing per se that drives poor functional outcomes in DSWPD, but rather the timing of sleep relative to circadian phase and resultant irregular sleep patterns.