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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. sljo@dtu.dk
Abstract
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 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.
Keywords
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.
Introduction
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 16–20.
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
menopause23,28–30. 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
variation?
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.
Methods
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
outcomes
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
Observations
All
Male
Female
All
Male
Female
Total
69,650
47,656
21,993
11,144,539
7,673,495
3,471,044
Age groups
19-24
5,466
3,745
1,721
579,315
383,761
195,554
25-29
8,976
5,813
3.163
1,105,037
698,471
406,566
30-34
11,224
7,414
3,810
1,559,445
1,022,033
537,412
35-39
9,796
6,584
3,212
1,520,749
1,024,874
495,875
40-44
9,315
6,435
2,880
1,591,014
1,092,829
498,185
45-49
9,934
6,994
2,940
1,844,717
1,308,604
536,113
50-54
7,164
5,059
2,105
1,395,210
999,842
395,368
55-59
4,445
3,180
1,265
879,907
646,560
233,347
60-67
3,330
2,433
897
669,145
496,521
172,624
BMI
categories
Underweight
2,272
1,197
1,075
350,954
173,220
177,734
Normal
weight
34,063
22,101
11,962
5,773,876
3,843,837
1,930,039
Overweight
22,936
17,371
5,565
3,558,454
2,687,277
871,177
Obese
10,379
6,988
3,391
1,461,255
969,161
492,094
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
1
2* 3
4
(567()/0𝒏12* 84
with
9
representing the fixed effects parameters,
:
representing
the random effects,
;(
representing the
< = >(
design matrix for
the fixed-effects parameters, and
?(
the
< = @(
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
model.
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 (
A B C:DEFGH< BIA
), 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 (
J B
C:DEFGH< BIK
). 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:
• 20:24
L
onset weekends
L
04:52
• 20:28
L
onset weekdays
L
03:59
• 03:59
L
offset weekends
L
12:52
• 03:21
L
offset weekdays
L
11:25
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.
Japan
N= 17231
(24.7 %)
Germany
N=7140
(10.3 %)
Russia
N=5095
(7.3 %)
Taiwan
N=5028
(7.2 %)
UK
N=3900
(5.6 %)
Age groups
Average sleep onset (hh:mm)
19-24
00:53
23:55
00:39
01:12
00:24
25-29
00:44
23:52
00:29
00:59
00:07
30-34
00:41
23:40
00:21
00:51
23:52
35-39
00:27
23:36
00:08
00:42
23:46
40-44
00:21
23:37
00:04
00:32
23:41
45-49
00:15
23:30
00:03
00:30
23:45
50-54
00:06
23:31
23:58
00:16
23:36
55-59
23:54
23:27
23:55
23:52
23:45
60-67
23:42
23:26
23:50
23:50
23:42
Age groups
Average sleep duration (hours)
19-24
6.6
7.3
7.0
6.7
7.3
25-29
6.4
7.1
7.0
6.7
7.3
30-34
6.4
7.1
7.0
6.6
7.2
35-39
6.3
7.0
7.0
6.4
7.1
40-44
6.3
6.9
7.0
6.5
7.1
45-49
6.2
7.0
7.0
6.4
7.0
50-54
6.2
7.0
7.0
6.6
7.0
55-59
6.3
6.9
7.0
6.6
7.0
60-67
6.4
7.2
7.1
6.5
7.1
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
countries.
Results
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 user’s 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 19–24, (B) age 40–44, and (C) age 60–67. 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
KN( O MPKM
minute difference between
weekends and weekdays for women and
KQ( O MPKM
minute
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
KJ O IPR
minutes based on
the raw data but
IR O IPR
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 <
IM#$%
, 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
KPS( O MPKM
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 19–24, (B) age 40–44, and (C) age 60–67. 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
KA( O IPS
minutes longer than men, whereas the model estimates
a difference of
II( O IPM
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 19–24, (B) age 40–44, and (C) age 60–67. 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
age
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 bands – similar 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=
APMT =IM#&$
. 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 apps” in 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
U
) WASO than age-matched
women without the application on their phone (estimated with
two sample Kolmogorov-Smirnov (KS) statistics,
> " NPTT =
IM#&$
) where
U'()*+,-.+/-012+/345+67.124+766 "IQJ(VWX
and
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
statistics,
> " MPKKQ
) where
U'()*+,-.+012+/345+67.124+766 "
RK(VWX
and
U'()*+,-.+012+/345-84+67.124+766 "AS(VWX
(see
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
)
where
['()*+,-.+/-012+/345+67.124 +766 "IIMR(VWX
and
['()*+,-.+/-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,
> " MPKMS
) and
['()*+,-.+012+/345+67.124+766 "NMR(VWX
and
['()*+,-.+012+/345-84+67.124+766 "QTS(VWX
(see distribution
Figure S6).
Development of circadian misalignment with
age
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
KM O KPT
minutes for
older adult men, alongside a marginally larger decrease in offset
misalignment to
AQ O KPA
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
TS O KPK
minutes
later on weekends for men age group 19-24 but
AQ( O KPA
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
difference.
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 weekend–weekday 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 weekend–weekday offset difference and lighter colors signify weekend–weekday onset difference.
The colored envelopes display 95% CIs around each age group mean (D). The distribution of weekend–weekday differences in sleep onset for different age groups:
(A) age 19–24 and (B) age 60–67. The distribution for the sleep offset weekend–weekday differences for different age groups: (H) age 19–24 and (I) age 60–
67. Distribution for sleep onset time on weekdays and weekends for different age groups: (C) age 19–24 and (E) age 60–67. Distribution for sleep offset time
on weekdays and weekends for different age groups: (F) age 19–24 and (G) age 60–67.
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
IPN O IPM
minute higher weekend-weekday sleep
offset difference and
APM O MPQ
minute higher weekend-weekday
sleep onset difference than women (95 % CI)).
Figure 6: The development of weekend–weekday 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 weekend–weekday duration differences for different age groups:
(A) age 19–24 and (C) age 60–67.
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
IPK O MPMIR
hours and
weekends
IPR O MPMIT
hours) and women (weekdays
IPA O
MPMKI
hours and weekends
IPJ O MPMKI
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
IJ( O IPK
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 19–24 and (G) age 60–67
Onset
hh:mm ± m
Offset
hh:mm ± m
Duration
hours ± hours
Women
Men
Women
Men
Women
Men
Underweight
23:55±8
00:10±8
07:05±7
07:11±8
7.03±0.0938
6.95±0.114
Normal weight
23:45±7
00:01±7
06:58±6
06:58±6
7.03±0.0938
6.84±0.0952
Overweight
23:45±7
00:01±7
06:55±6
06:55±6
6.98±0.0972
6.72±0.102
Obese
23:52±8
00:10±8
06:54±6
06:54±6
6.86±0.0988
6.56±0.106
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
IS( O KPQ
minutes more than those in the obese BMI
category, and men in the underweight category sleep on average
KJ( O JPS
minutes more than those in the obese category. We
carry out further discussion concerning these results in section
called “BMI Discussion” in the SI.
Discussion
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.
Misalignment
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
developments.
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.
Variability
A growing body of research indicates that irregular sleep is linked
to maladaptive responses adverse to human health25,53,80,72–79
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 period37,82–84. 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
observed.
Limitations
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.
Implications
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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