SLEEP CHARACTERISTICS OF SELF-REPORTED LONG SLEEPERS
Sleep Characteristics of Self-Reported Long Sleepers
http://dx.doi.org/10.5665/sleep.1822
Sanjay R. Patel, MD, MS1; Terri Blackwell, MA2; Sonia Ancoli-Israel, PhD3; Katie L. Stone, PhD2; for the Osteoporotic Fractures in Men (MrOS) Research Group
1
3
Division of Sleep Medicine, Brigham and Women’s Hospital, Boston, MA; 2California Pacific Medical Center Research Institute, San Francisco CA;
Department of Psychiatry, University of California San Diego, San Diego, CA
Background: Self-reported long habitual sleep durations (≥ 9 h per night) consistently predict increased mortality. We compared objective sleep
parameters of self-reported long versus normal duration sleepers to determine whether long sleepers truly sleep more or have an underlying
sleep abnormality.
Methods: Older men participating in the Osteoporotic Fractures in Men Study (MrOS) were recruited for a comprehensive sleep assessment, which
included wrist actigraphy, overnight polysomnography (PSG), and a question about usual nocturnal sleep duration.
Results: Of the 3134 participants (mean age 76.4 ± 5.6; 89.9% Caucasian), 1888 (60.2%) reported sleeping 7-8 h (normal sleepers) and 174 (5.6%)
reported ≥ 9 h (long sleepers). On actigraphy, long sleepers spent on average 63.0 min more per night in bed (P < 0.001), slept 42.8 min longer
(P < 0.001), and spent 6.8 min more per day napping (P = 0.01). Based on PSG, the apnea hypopnea index, periodic limb movement index, arousal
index, and sleep stage distribution did not differ. After adjusting for differences in demographics, comorbidities, and medication usage, self-reported
long sleepers continued to spend more time in bed and sleep more, based on both actigraphy and PSG. Each additional 30 min in bed or asleep as
measured by actigraphy increased the odds of being a self-reported long-sleeper 1.74-fold and 1.33-fold, respectively (P < 0.001 for both).
Conclusions: On objective assessment, self-reported long sleepers spend more time in bed and more time asleep than normal duration sleepers.
This is not explained by differences in comorbidity or sleep disorders.
Keywords: Sleep duration, long sleeper, actigraphy, polysomnography
Citation: Patel SR; Blackwell T; Ancoli-Israel S; Stone KL. Sleep characteristics of self-reported long sleepers. SLEEP 2012;35(5):641-648.
INTRODUCTION
Epidemiologic studies have consistently reported a U-shaped
association between self-reported habitual sleep duration and
adverse health outcomes such as mortality, incident heart disease, diabetes, and obesity.1-6 While a large body of experimental work with short-term sleep deprivation has helped provide
understanding of the mechanisms by which short habitual sleep
times may adversely impact health,7-9 few experimental studies have provided insight into how long habitual sleep times
are related to disease. In fact, several studies using objective
measures of sleep quantity have failed to find an association between long sleep and adverse health,10,11 suggesting the adverse
effect attributed to self-reported long sleep may not be due to
increased sleep duration per se. The poor correlation between
self-reported sleep duration and objectively measured sleep has
lent further support to this contention.11,12 It is therefore unclear
what long self-reported sleep time represents. One possibility is
that it may represent the individual’s perception of an increased
time in bed secondary to sleep disorders such as sleep apnea
without an actual increase in sleep time. Another possibility is
that a report of long habitual sleep duration may reflect a general lack of well-being not specific to sleep.13 While prior work
has sought to identify demographic characteristics and comorbidities that predicted long self-reported sleep,14 little research
has been done to investigate whether there are abnormalities in
the objective measures of sleep in self-reported long sleepers.
In this work, we analyzed data from a large cohort of men par-
ticipating in the Osteoporotic Fractures in Men Study (MrOS)
who had undergone detailed sleep phenotyping to characterize
differences in the sleep characteristics of self-reported long and
normal duration sleepers.
METHODS
Study Population
During the baseline examination (2000-02) for the Osteoporotic Fractures in Men Study (MrOS), 5994 communitydwelling men ≥ 65 years were enrolled at 6 clinical centers in
the United States: Birmingham, Alabama; Minneapolis, Minnesota; Palo Alto, California; Pittsburgh, Pennsylvania; Portland,
Oregon; and San Diego, California. Men were not eligible to
participate if they reported bilateral hip replacement or required
the assistance of another person in ambulation at the baseline
examination. Further details on the MrOS cohort have been
previously published.15,16
An ancillary study, the MrOS Sleep Study, conducted between December 2003 and March 2005, recruited 3135 of the
MrOS participants for a comprehensive sleep assessment, of
whom 3058 underwent actigraphy and 2911 underwent inhome overnight polysomnography (PSG). Of the 2859 participants who did not participate in the sleep study; 1997 were
unwilling, 332 were not screened because recruitment goals
were met, 344 died before the sleep study visit, 150 were ineligible due to exclusion criteria, and 36 withdrew from the study
before the sleep visit. The protocols for the MrOS and MrOS
Sleep studies were approved by the institutional review boards
at all of the participating institutions. All participants provided
written informed consent.
Submitted for publication August, 2011
Submitted in final revised form November, 2011
Accepted for publication November, 2011
Address correspondence to: Sanjay R. Patel, MD, MS, 221 Longwood
Avenue, Room 225-C, Boston, MA 02115; Tel: (857) 307-0347; Fax: (617)
278-6946; E-mail: [email protected]
SLEEP, Vol. 35, No. 5, 2012
Self-Reported Sleep
Self-reported habitual sleep duration was obtained by response to the question, “On most nights, how many hours do
641
Sleep Characteristics of Long Sleepers—Patel et al
you sleep each night?” with responses rounded to the nearest
hour. Responses categorized men into short duration (≤ 6 h),
normal duration (7-8 h), and long duration (≥ 9 h) sleepers. Of
note, defining 7- to 8-h sleepers as normal was not meant to imply the other groups are abnormal. All but one of the 3135 men
responded to the question on sleep duration. Analyses were limited to a comparison between the 174 self-reported long duration and 1888 normal duration sleepers.
Self-reported information about sleepiness was assessed using
the Epworth Sleepiness Scale (ESS).17 An ESS score > 10 defined excessive daytime sleepiness.18 The Pittsburgh Sleep Quality Index (PSQI) assessed sleep quality, with a PSQI score > 5
defining poor quality sleep.19 The Functional Outcomes of Sleep
Questionnaire (FOSQ) assessed sleep-related quality of life.20
formed in 2911 MrOS participants. The recording montage
included C3/A2 and C4/A1 electroencephalography (EEG),
bilateral electrooculography, bipolar submental electromyography, thoracic and abdominal respiratory effort, airflow (nasaloral thermocouple and nasal pressure), finger pulse oximetry,
electrocardiography, body position, and bilateral leg movements. Centrally trained and certified staff performed home
visits to set up the unit, verify the values of the impedances
for each channel, confirm calibration of position sensors, and
note any problems encountered during set-up—similar to the
protocol used in the Sleep Heart Health Study.25 Staff collected
the equipment the next morning and transmitted the data to the
Central Sleep Reading Center (Cleveland, OH) to be scored by
certified research polysomnologists. PSG data quality was excellent, with a failure rate < 4%, and > 70% of studies graded as
being of excellent or outstanding quality.
Sleep period was the time from reported lights off to morning awakening. TST, TIB, SE, and WASO were defined similar to the actigraphy parameters. Sleep staging was performed
using standard criteria.26 Sleep stages were expressed as percentage of sleep time in each stage. SE was defined as the time
asleep divided by the sleep period time. Arousals were scored
according to American Sleep Disorder Association criteria.27
The arousal index was defined as the number of EEG arousals per hour of sleep. Apneas were defined as a complete or
almost complete cessation of airflow > 10 seconds. Hypopneas were defined as a > 30% reduction in amplitude of either
respiratory effort or airflow > 10 sec associated with ≥ 4%
oxygen desaturation. The apnea hypopnea index (AHI) was
computed as the average number of apneas and hypopneas per
hour of recorded sleep. Periodic leg movements were scored
according to AASM criteria (≥ 4 consecutive 0.5- to 5-sec
movements, each separated by 5-90 sec).28 Leg movements
that occurred at the termination of respiratory events were not
considered unless they were part of a cluster of ≥ 4 leg movements in which ≥ 2 leg movements occurred independently
of respiratory event termination. Periodic leg movement with
arousal (PLMA) was defined as a periodic leg movement in
which an EEG arousal occurred within 3 sec of termination of
the leg movement.
Actigraphy
Average nightly sleep duration was obtained using wrist actigraphy (Sleepwatch-O, Ambulatory Monitoring, Inc., Ardsley NY)
in 3058 participants. Subjects were asked to wear the actigraph ≥ 5
nights. Average use (SD) was 5.2 (0.9) nights. Data were collected
continuously and stored in 1-min epochs. The digital integration
mode of analysis, which has been validated against polysomnography in this cohort,21 was used to distinguish sleep from wake.
Action W-2 software (Ambulatory Monitoring, Inc.) was used to
analyze the raw data,22 and the University of California San Diego
(UCSD) scoring algorithm was used to determine sleep/wake status.23 Participants completed sleep diaries for the time period they
wore the actigraph. The diaries included time into and time out of
bed and times when the actigraph was removed. This information
was used in editing the actigraphy data files to set intervals for
when the participant was in bed trying to sleep (after “lights off”),
and to delete time when the actigraph was removed. Inter-scorer
reliability for editing the actigraphy data files has been previously
found to be high in our group (intra-class coefficient = 0.95), and
this measure has been shown to have good concordance with total
sleep time from polysomnography.24 Sleep as computed by the
automated UCSD sleep scoring algorithm that occurred outside
of time in bed was scored as nap time.
Variables estimated from actigraphy included: (1) total sleep
time (TST): the hours per night spent sleeping while in bed after
“lights off”; (2) time in bed (TIB): the time from “lights off”
to the time the participant got out of bed; (3) total nap time
(TNT): the minutes scored as sleeping between the last time
out of bed in the morning and time to bed at night; (4) sleep
efficiency (SE): the percentage of time in bed after “lights off”
spent sleeping; (5) wake after sleep onset (WASO): minutes of
wake after sleep onset during the time in bed interval; (6) time
of sleep onset defined as the first point at which the participant
achieved a 20-min continuous block of sleep after “lights off”;
(7) time of sleep offset: the time of the last minute scored as
sleep during the time in bed interval; and (8) sleep period midpoint: the point halfway from sleep onset to sleep offset. All
exposure variables from actigraphy reflect data averaged over
all nights they wore the device in order to obtain a more representative characterization of usual sleep patterns.
Covariates
Participants completed questionnaires, which included items
about demographics, medical history, physical activity, smoking, and alcohol use. Caffeine consumption was estimated
based on self-report of the average daily number of cups of caffeinated coffee and tea or cans of caffeinated soda consumed.29
Participants were asked to bring in all current medications used
within the preceding 30 days. All prescription medications were
entered into an electronic database, and each medication was
matched to its ingredient(s) based on the Iowa Drug Information Service (IDIS) Drug Vocabulary (College of Pharmacy,
University of Iowa, Iowa City, IA).30 Cardiovascular disease
was defined as a self-reported history of myocardial infarction, angina, congestive heart failure, coronary artery bypass
surgery, coronary angioplasty, or cardiac pacemaker. The Geriatric Depression Scale (GDS) was used to assess depressive
symptoms, and the standard cutoff of ≥ 6 symptoms was used to
define depression.31 The level of physical activity was assessed
Polysomnography
In-home sleep studies, using unattended polysomnography
(Safiro unit; Compumedics, Melbourne, Australia), were perSLEEP, Vol. 35, No. 5, 2012
642
Sleep Characteristics of Long Sleepers—Patel et al
using the Physical Activity Scale for the Elderly (PASE).32 Activity level was also measured objectively using activity count
data from the actigraph. Objective activity level was defined as
the median activity (counts/min) during the out of bed interval,
averaged over all days the participant wore the actigraph. Cognitive functioning was assessed with the Modified Mini-Mental
State (3MS) examination, with higher scores representing better cognitive functioning.33 Cognitive impairment was defined
as 3MS < 80. This threshold has been previously demonstrated
to predict dementia in an elderly population.33 During the home
or clinic visits, body weight was measured with a standard balance beam or digital scale, height with a wall-mounted Harpenden stadiometer (Holtain, England); these measurements were
used to calculate body mass index (BMI).
more cardiovascular disease and depression among those reporting long sleep. Antidepressant use was nearly twice as
common in self-reported long sleepers. In contrast, there were
no significant differences in smoking, alcohol, caffeine, benzodiazepine, or opiate use.
Actigraphy data are presented in Table 2. On average, selfreported long sleepers spent 43 more min asleep per night and
63 more min in bed (P < 0.0001 for both). In addition, they spent
6.8 more min per day napping (P = 0.01). Although SE did not
substantially differ, WASO was 17 min greater (P < 0.0001).
The increased TIB was accomplished by going to sleep earlier
and awakening later, so that the midpoint of the sleep period
did not differ.
Data from the 2911 individuals with PSG are presented in
Table 3. Again, self-reported long sleepers were found to spend
more time asleep (P = 0.001) and more time in bed (P < 0.0001)
than those reporting sleeping 7-8 h. The differences as assessed
by PSG were 20 min for TST and 43 min for TIB, which were
slightly smaller than those measured by actigraphy. Similar
to the actigraphic assessment, WASO measured by PSG was
13 min greater in self-reported long sleepers (P = 0.02), while
unlike actigraphy, SE was slightly worse (72.2% vs. 74.9%,
P = 0.005). Sleep staging revealed no differences in the proportions of the various sleep stages between those reporting normal
and long sleep durations. Similarly, no differences were found
in terms of sleep disordered breathing, periodic limb movement
severity, or the arousal index between the 2 groups.
An interesting finding in comparing self-reported sleep duration with TST was that long sleepers tended to overestimate
their sleep duration to a greater extent than normal sleepers.
Relative to actigraphic TST, long sleepers overestimated sleep
duration by 2.0 h vs. 0.9 h for normal sleepers (P < 0.0001).
Similarly, the difference between self-reported sleep and PSG
TST was 2.9 h in long sleepers vs. 1.4 h in normal sleepers
(P < 0.0001).
Sleep quality as assessed by questionnaire is presented in
Table 4. No differences in sleepiness (assessed by the ESS) or
sleep-related quality of life (assessed by the FOSQ) were found
between the 2 groups. In contrast, self-reported long sleepers
reported a better sleep quality, as assessed by a lower score on
the PSQI (4.6 vs. 3.8, P < 0.0001), and a lower proportion with
poor sleep quality (20.1% vs. 31.9%, P = 0.001). As expected,
the majority of the difference in PSQI was due to better scores
on the sleep duration component of this index. However, selfreported long sleepers also scored significantly better on the
sleep quality and sleep efficiency components.
In analyses adjusted for age, activity level, history of depression, cardiovascular disease, diabetes, Mini-Mental Status
score, antidepressant, antiepileptic, and α-adrenergic blocker
use, self-reported long sleep continued to be associated with
actigraphically measured greater TST, TIB, and WASO, as well
as an earlier sleep onset and later sleep offset (Tables 5 and 6).
Among the PSG sleep measures, a greater TST and a greater
TIB were the only significant predictors of self-reported long
sleep in adjusted analyses.
For both actigraphy and PSG, TST and TIB were the strongest predictors of self-reported long sleep, and these 2 measures
were moderately correlated (Spearman correlation ρ = 0.55 for
actigraphy and ρ = 0.57 for PSG). Additional analyses were
Statistical Analyses
Correlations between self-reported sleep time, total sleep
time by actigraphy, and total sleep time by PSG were computed using Pearson correlation coefficients. Baseline characteristics were summarized by category of sleep duration at
night and differences between the sleepers of normal duration (7-8 h) and long duration (≥ 9 h) were compared using
t-tests for normally distributed continuous data, Wilcoxon
rank-sum tests for continuous skewed data, and χ2 tests for
categorical data.
Logistic regression models were used to identify independent predictors of reporting long sleep duration compared to
those with normal sleep duration. Multivariable adjustment was
done including all variables that were significantly associated
with self-reported long sleep duration at a P-value < 0.10 in bivariate analyses. Secondary analyses were done simultaneously
including time in bed and total sleep time as covariates in the
same model to identify the strongest predictor of self-reported
long sleep. In addition, linear regression was performed with
self-reported sleep as a categorical predictor (long duration
vs. normal duration) for each of the actigraphic and PSG sleep
measures, with and without adjustment for covariates.
All analyses were performed using SAS statistical software
(version 9.2, SAS Institute, Inc., Cary, North Carolina).
RESULTS
A total of 3134 men responded to the question on habitual
sleep duration. The distribution of responses was 13 (0.4%) reporting ≤ 3 h; 84 (2.7%) reporting 4 h; 269 (8.6%) reporting 5
h; 706 (22.5%) reporting 6 h; 1074 (34.3%) reporting 7 h; 814
(26.0%) reporting 8 h; 132 (4.2%) reporting 9 h; 34 (1.1%) reporting 10 h; 6 (0.2%) reporting 11 h; and 2 (0.1%) reporting 12
h. Overall, self-reported sleep duration was modestly correlated
with both total sleep time as assessed by actigraphy (r = 0.31,
P < 0.001) and PSG (r = 0.20, P < 0.001).
The analyses presented were limited to the 1888 men
(60.2% of the overall cohort) reporting a habitual sleep duration of 7-8 h, and the 174 men (5.6%) reporting ≥ 9 h. Demographic and medical data on each group are shown in Table
1. Self-reported long sleepers were older, more likely to have
diabetes, and had a lower score on the mental status examination. Although no difference was observed in self-reported
activity levels, based on actigraphy, self-reported long sleepers were less physically active. There was also a trend towards
SLEEP, Vol. 35, No. 5, 2012
643
Sleep Characteristics of Long Sleepers—Patel et al
Table 1—Demographic characteristics by self-reported sleep duration
Self-reported habitual sleep duration
Age (y)
Caucasian (%)
BMI (kg/m2)
PASE score
Median daytime activity (counts/min)
Good/excellent self-reported health (%)
Current smoker (%)
Alcohol (drinks/wk)
Caffeine (mg/day)
Depression (%)
Cardiovascular disease (%)
Stroke (%)
Diabetes (%)
Hypothyroidism (%)
Hyperthyroidism (%)
Lung disease (%)
3MS score (0-100)
Cognitive impairment (%)
Benzodiazepine use (%)
Opiate use (%)
Antidepressant use (%)
Antiepileptic use (%)
α-blocker use (%)
β-blocker use (%)
Diuretic use (%)
Thyroid Medication (%)
7-8 hours (n = 1888)
76.4 ± 5.6
92.6
27.0 ± 3.8
147 ± 69
3439 ± 1014
88.7
1.8
3.6 ± 4.3
236 ± 244
4.9
31.9
3.4
11.7
9.5
1.4
13.9
93.1 ± 6.3
3.1
3.7
3.9
7.5
3.5
19.7
26.9
25.4
9.0
9 or more hours (n = 174)
78.9 ± 6.0
89.7
27.3 ± 4.0
136 ± 85
3178 ± 1004
89.1
1.7
3.6 ± 4.5
217 ± 252
8.1
39.1
2.9
19.5
10.3
1.7
13.2
90.7 ± 7.5
4.6
4.1
4.1
14.5
1.2
25.4
32.4
23.7
9.3
P-value
< 0.0001
0.16
0.26
0.11
0.002
0.88
0.98
0.39
0.11
0.07
0.05
0.69
0.003
0.71
0.71
0.79
< 0.0001
0.29
0.82
0.91
0.001
0.10
0.07
0.12
0.63
0.91
Data presented as means ± SD or percentages. P-values derived from t-tests for continuous variables with normal distribution, from Wilcoxon rank-sum test
for continuous variables with skewed distribution, and from χ2 tests for categorical variables. BMI, body mass index; PASE, Physical Activity Scale for the
Elderly; 3MS, Modified Mini-Mental State examination.
Table 2—Actigraphic sleep characteristics by reported sleep duration
Self-reported habitual sleep duration
Total sleep time (min)
Time in bed (min)
Total nap time (min)
Sleep efficiency (%)
Wake after sleep onset (min)
Time of sleep onset (h)
Time of sleep offset (h)
Sleep period midpoint (h)
7-8 hours (n = 1838)
393.2 ± 67.7
498.3 ± 52.2
63.7 ± 56.4
79.0 ± 11.4
76.2 ± 42.2
23.3 ± 1.2
7.0 ± 1.0
3.1 ± 1.0
9 or more hours (n = 168)
435.9 ± 84.2
561.2 ± 55.8
70.5 ± 51.8
77.6 ± 12.4
93.1 ± 52.1
22.7 ± 1.5
7.3 ± 1.2
3.0 ± 1.2
Difference
42.7
62.9
6.8
-1.4
16.9
-0.6
0.3
-0.1
P-value
< 0.0001
< 0.0001
0.01
0.28
< 0.0001
< 0.0001
< 0.0001
0.29
Data presented as means ± SD or percentages. P-values derived from t-tests for variables with normal distribution and Wilcoxon rank-sum test for variables
with skewed distribution.
done modeling both variables simultaneously as predictors to
better understand which was most strongly associated with being a self-reported long sleeper. In the actigraphy analysis, TIB
remained significant (OR = 1.71 per 30-min increase; 95% CI
[1.53-1.91], P < 0.001) while TST was no longer significant
SLEEP, Vol. 35, No. 5, 2012
(OR = 1.03 per 30-min increase; 95% CI [0.94-1.11], P = 0.57).
Similarly, using the PSG measures, TIB remained a significant
predictor (OR = 1.21 per 30-min increase; 95% CI [1.11-1.31],
P < 0.001) but not TST (OR = 1.04 per 30-min increase; 95%
CI [0.96-1.14], P = 0.34).
644
Sleep Characteristics of Long Sleepers—Patel et al
Table 3—Polysomnographic sleep characteristics by reported sleep duration
Self-reported habitual sleep duration
Total sleep time (min)
Time in bed (min)
Sleep efficiency (%)
Wake after sleep onset (min)
% Stage N1 sleep
% Stage N2 sleep
% Stage N3 sleep
% Stage REM sleep
Arousal index
Apnea hypopnea index
Periodic limb movement index
Periodic limb movement arousal index
7-8 hours (n = 1761)
362.9 ± 63.8
487.9 ± 71.3
74.9 ± 11.3
111.9 ± 63.5
6.7 ± 3.8
62.4 ± 9.3
11.5 ± 8.9
19.5 ± 6.5
23.4 ± 11.2
11.5 ± 12.8
36.5 ± 36.8
4.1 ± 5.6
9 or more hours (n = 162)
382.5 ± 73.7
530.5 ± 68.4
72.2 ± 11.7
124.5 ± 58.4
7.3 ± 4.2
63.3 ± 9.7
10.3 ± 8.8
19.1 ± 6.3
24.8 ± 11.4
11.8 ± 13.3
34.7 ± 36.8
4.1 ± 6.2
Difference
19.6
42.6
-2.7
12.6
0.6
0.9
-1.2
-0.4
1.4
0.3
-1.8
0.0
P-value
0.001
< 0.0001
0.005
0.02
0.11
0.26
0.12
0.51
0.14
0.76
0.52
0.45
Data presented as means ± SD or percentages. P-values derived from t-tests for variables with normal distribution and Wilcoxon rank-sum test for variables
with skewed distribution. REM, rapid eye movement.
Table 4—Subjective sleep characteristics by reported sleep duration
Self-reported habitual sleep duration
ESS score (0-24)
ESS > 10 (%)
FOSQ score (5-20)
PSQI score (0-21)
PSQI > 5 (%)
PSQI Components
1: Sleep Quality
2: Sleep Latency
3: Sleep Duration
4: Sleep Efficiency
5. Sleep Disturbances
6: Sleep Medication Usage
7: Daytime Dysfunction
7-8 hours (n = 1888)
6.0 ± 3.6
12.1
18.8 ± 1.4
4.6 ± 2.5
31.9
9 or more hours (n = 174)
6.0 ± 4.1
13.2
18.6 ± 1.7
3.8 ± 2.3
20.1
0.7 ± 0.6
0.7 ± 0.8
0.6 ± 0.5
0.3 ± 0.6
1.3 ± 0.5
0.4 ± 0.9
0.7 ± 0.7
0.6 ± 0.6
0.6 ± 0.6
0.0 ± 0.2
0.2 ± 0.5
1.3 ± 0.5
0.3 ± 0.8
0.8 ± 0.7
Difference
0.0
1.1
-0.2
-0.8
-11.8
P-value
0.91
0.66
0.17
< 0.0001
0.001
-0.1
-0.1
-0.6
-0.1
0.0
-0.1
0.1
0.005
0.47
< 0.0001
0.0006
0.69
0.17
0.11
Data presented as means ± SD or percentages. P-values derived from t-tests for continuous variables with normal distribution, from Wilcoxon rank-sum test
for continuous variables with skewed distribution, and from χ2 tests for categorical variables. ESS, Epworth Sleepiness Scale; FOSQ, Functional Outcomes
of Sleep Questionnaire; PSQI, Pittsburgh Sleep Quality Index.
DISCUSSION
In this cohort of older men who underwent both 5 days of actigraphy and 1 night of overnight polysomnography, individuals who reported sleeping 9 hours or more, a level of sleep that
has been consistently associated with adverse health outcomes,
did in fact sleep more than their counterparts who reported lesser amounts of sleep. In addition, these self-reported long sleepers spent more time in bed, resulting in no difference in sleep
efficiency as assessed by actigraphy but a slight reduction as
assessed by PSG, although the clinical significance of this difference (72.2% vs. 74.9%) is questionable. Furthermore, after
adjusting for differences in demographic and medical factors,
PSG-assessed sleep efficiency no longer predicted self-reported
long sleep duration. Similarly, other measures of sleep quality
Additional analyses assessed the independent effect of
WASO. In models including TST and WASO (either by actigraphy or PSG), both were independently associated with being a
self-reported long sleeper. In models with both TIB and WASO
(either by actigraphy or PSG), TIB remained an independent
predictor of self-reported long sleep. In contrast, greater WASO
was no longer associated with an increased likelihood of reporting being a long sleeper after adjusting for TIB.
In order to assess the robustness of findings, analyses were
repeated limiting self-reported long sleepers to the 42 men reporting ≥ 10 h of sleep. Overall, the results were very similar,
with TIB and TST from both actigraphy and PSG representing
the 2 strongest predictors of reporting sleeping ≥ 10 h per day
(P < 0.001 in adjusted analyses).
SLEEP, Vol. 35, No. 5, 2012
645
Sleep Characteristics of Long Sleepers—Patel et al
Table 5—Predictors of long reported sleep duration
Unit
OR for long sleep
P-value
Adjusted OR for
long sleep*
P-value
Actigraphy Variables
Total sleep time
Time in bed
WASO
Sleep onset
Sleep offset
30 min
30 min
1 SD (43.4 min)
1 SD (1.2 h)
1 SD (1.0 h)
1.36
1.80
1.39
0.59
1.43
< 0.0001
< 0.0001
< 0.0001
< 0.0001
< 0.0001
1.33
1.74
1.26
0.59
1.29
< 0.0001
< 0.0001
< 0.0001
< 0.0001
0.003
Polysomnography Variables
Total sleep time
Time in bed
Sleep efficiency
WASO
30 min
30 min
1 SD (11%)
1 SD (63.2 min)
1.16
1.28
0.81
1.20
0.0002
< 0.0001
0.005
0.02
1.16
1.23
0.90
1.08
0.0002
< 0.0001
0.19
0.36
*Adjusted for age, objective activity level, depression, cardiovascular disease, diabetes, Modified Mini-Mental State examination score, antidepressant use,
antiepileptic use, and α-blocker use. OR, odds ratio; WASO, wake after sleep onset.
Table 6—Long reported sleep duration predicting sleep outcomes
Beta Coefficient
for Long Sleep
P-value
Adjusted Beta Coefficient
for Long Sleep*
P-value
Actigraphy Variables
Total sleep time (min)
Time in bed (min)
WASO (min)
Sleep onset (h)
Sleep offset (h)
42.7
63.0
16.9
-0.58
0.37
< 0.0001
< 0.0001
< 0.0001
< 0.0001
< 0.0001
41.6
56.2
11.9
-0.58
0.27
< 0.0001
< 0.0001
0.0008
< 0.0001
0.0008
Polysomnography Variables
Total sleep time (min)
Time in bed (min)
Sleep efficiency (%)
WASO (min)
19.6
42.6
-2.6
12.6
0.0002
< 0.0001
0.005
0.02
21.4
35.6
-1.3
6.4
0.0001
< 0.0001
0.17
0.21
*Adjusted for age, objective activity level, depression, cardiovascular disease, diabetes, Modified Mini-Mental State examination score, antidepressant use,
antiepileptic use, and α-blocker use. WASO, wake after sleep onset.
including arousal index and proportion of slow wave or REM
sleep time did not differ between those reporting normal and
long sleep durations. The diurnal phase of long sleepers also
did not substantially differ—the increased amount of sleep obtained was a result of going to sleep earlier and waking up later.
Finally, measures of sleep disorders such as sleep apnea and
periodic limb movement disorder did not differ.
This represents one of the first studies to compare the sleep of
reported long sleepers with reported normal duration sleepers.
However, prior work has compared the sleep of documented
long and short sleepers.34-36 In those studies, long sleepers were
also found to have a greater WASO and lower sleep efficiency.
In addition, the timing of sleep onset was earlier and sleep offset was later suggesting no consistent difference in circadian
phase. These results are similar to our findings comparing long
sleepers with 7-8 hour self-reported sleepers.
The amount of time in slow wave sleep has been found to be
similar between documented long and short sleepers suggesting
the percentage of slow wave sleep goes down with increased
sleep time in contrast to our findings that the percentage of
SLEEP, Vol. 35, No. 5, 2012
slow wave sleep is similar across self-reported sleep duration.
There are several possible reasons for this difference. First, our
analyses were based on self-reported sleep duration as opposed
to documented sleep duration. Second, the difference in PSGmeasured sleep between groups was much smaller in our study,
making it less likely to detect differences in sleep stages. Third,
our study, in the hopes of being generalizable included all
participants while prior studies only selected individuals with
proven long sleep durations who had regular sleep schedules,
no sleep disorders, no comorbidities, and no medication use. In
addition, our study focused on an older population, as opposed
to prior studies focusing on individuals in their late teens and
early twenties.
Overall, our results suggest that both more time in bed and
more time asleep characterize self-reported long sleepers as
compared to normal duration sleepers. In fact, in models where
both sleep duration and time in bed (as assessed by either actigraphy or PSG) were simultaneously included as predictors of
self-reported sleep, only time in bed was found to be an independent predictor. Thus, our data suggest that long sleepers do
646
Sleep Characteristics of Long Sleepers—Patel et al
outcomes. Further detailed analyses of how decisions regarding
time spent in bed are made and the extent to which these decisions are based on sleep need rather than non-sleep related cues
will be important in better understanding whether interventions
aimed at reducing time in bed might improve health outcomes,
and if so, how such interventions might be designed to be maximally effective.
It should be noted that our results do not imply that long
sleep duration or long time in bed are causally related to adverse health outcomes. These phenotypes may be a result of a
circadian rhythm-driven longer biological night, for example,
and this may be the true cause of the adverse health effects associated with long sleep. In such a case, interventions aimed
at shortening sleep duration without impacting the underlying
cause would not be expected to improve health outcomes and
may in fact worsen them.
Given that long sleepers also overestimate their sleep duration to a greater extent than normal sleepers, another possibility is that the tendency to overestimate sleep time may be the
underlying predictor of adverse health outcomes. However, the
mechanism for such an effect is not clear.
Our study has many strengths, including the large sample
size, objective measurement of sleep duration using both
actigraphy and polysomnography, and collection of important covariates such as medication usage. Several limitations
should also be recognized. Our cohort consisted exclusively
of older men, and thus the extent to which these findings
generalize to women or younger age groups is unknown. We
cannot exclude the potential for selection bias, as the frailest subjects in the parent cohort were probably less likely to
participate in the sleep assessments. In addition, we did not
distinguish between weekday and weekend nights. However,
given the age of the cohort, few participants were actively
employed so sleep habits likely did not vary much across the
week. It should be noted that actigraphic scoring of nap time
has not been validated and so the differences noted in regards
to napping may actually reflect differences in inactive wakefulness. Finally, most of the covariate data including medical history on comorbidities were obtained by self-report
without validation.
In summary, our findings suggest that individuals who report a long habitual sleep time do not importantly differ in their
sleep from those reporting normal sleep times other than the
increased amount of time spent in bed and asleep. Thus, associations linking self-reported long sleep with adverse health
outcomes are unlikely to be due to poor sleep quality, sleep disorders, or abnormal circadian phase. Instead, further research
should be focused on understanding the causes of an increased
time in bed.
not have grossly abnormal sleep. There are no discernible differences in sleep stage distribution, sleep fragmentation, sleepdisordered breathing, or periodic limb movement disorder
between those who report long versus normal sleep durations.
In addition, long sleepers had better sleep quality as assessed by
the PSQI. These findings are supported by results from Aeschbach et al. which have found no underlying differences in homeostatic sleep drive between long and short sleepers.36 Their
finding that long sleepers may have a circadian rhythm-driven
longer biological night without difference in phase is also consistent with our results.37
Although our analyses demonstrate that self-reported sleep
time is predictive of actual sleep time, our data do indicate substantial measurement error. While one would expect the difference in total sleep time between those reporting 9 or more hours
of sleep and those reporting 7-8 hours to be at least an hour, the
measured differences were only 43 minutes by actigraphy and
20 minutes by PSG. This overestimate of true sleep differences
would tend to cause studies that relied on self-reported sleep
measures to underestimate the true impact of long sleep duration on adverse outcomes. Thus, a 20-minute difference in PSG
sleep duration may have important clinical ramifications on risk
of obesity, diabetes, and other health outcomes associated with
self-reported long sleep. Further research is needed to confirm
this finding.
One explanation for the underestimate is that individuals are
actually basing their self-reported sleep on time in bed rather
than time asleep. In fact, the measured differences in time in
bed—63 minutes as assessed by actigraphy and 43 minutes as
assessed by PSG—are much more similar to the magnitude of
differences in self-reported sleep time.
Another issue apparent from our findings is that self-reported
sleep duration is poorly calibrated with sleep time measured by
either actigraphy or PSG. Our 7-8 hour sleepers had a mean
sleep duration of only 6.0 h by PSG and 6.6 h by actigraphy,
and this systematic difference has been reported in other studies.12,38 Thus, the amount of sleep that should be considered a
normal or long duration may need to vary depending on the
measurement technique used.
Overall, our results support the contention that the adverse
health impact associated with long self-reported sleep duration
likely reflects an association with altered sleep habits—either
an increased time spent asleep or an increased time spent in
bed. Because these variables are highly correlated, it is difficult
to definitively identify the most important factor with an observational study design. However, to the extent that conclusions
can be made based on our multivariable models simultaneously
including both variables as predictors, time in bed appeared to
be the strongest predictor of self-reported sleep duration. This
is supported by the finding that increased WASO predicted being a self-reported long sleeper independent of total sleep time.
An increased time in bed may be a reflection of depression
or social isolation limiting opportunities to exchange sleep for
other activities. In fact, each of these factors has been associated with being a long self-reported sleeper and in addition, has
been associated with adverse health outcomes.14,39,40 Another
possibility is that an increased time in bed may itself be the
cause of behaviors such as reduced physical activity or limited
socialization with others that may directly lead to poor health
SLEEP, Vol. 35, No. 5, 2012
ACKNOWLEDGMENTS
In addition to the support below, this work was supported by
National Institutes of Health grant HL081385 and AG08415.
The Osteoporotic Fractures in Men (MrOS) Study is supported
by National Institutes of Health funding. The following institutes provide support: the National Institute of Arthritis and
Musculoskeletal and Skin Diseases (NIAMS), the National
Institute on Aging (NIA), the National Center for Research
Resources (NCRR), and NIH Roadmap for Medical Research
647
Sleep Characteristics of Long Sleepers—Patel et al
under the following grant numbers: AR045580, AR045614,
AR045632, AR045647, AR045654, AR045583, AG018197,
AG027810, and RR024140. The National Heart, Lung, and
Blood Institute (NHLBI) provides funding for the MrOS Sleep
ancillary study “Outcomes of Sleep Disorders in Older Men”
under the following grant numbers: HL071194, HL070848,
HL070847, HL070842, HL070841, HL070837, HL070838,
and HL070839.
17. Johns MW. A new method for measuring daytime sleepiness: the Epworth
sleepiness scale. Sleep 1991;14:540-5.
18. Johns MW. Sensitivity and specificity of the multiple sleep latency test
(MSLT), the maintenance of wakefulness test and the Epworth sleepiness
scale: Failure of the MSLT as a gold standard. J Sleep Res 2000;9:5-11.
19. Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and
research. Psychiatry Res 1989;28:193-213.
20. Weaver TE, Laizner AM, Evans LK, et al. An instrument to measure functional status outcomes for disorders of excessive sleepiness. Sleep 1997;
20:835-43.
21. Blackwell T, Redline S, Ancoli-Israel S, Stone K. Comparison of total
sleep time from actigraphy and polysomnography in older men: The
MrOS Sleep Study. Sleep 2007;30:A346-A7.
22. Action-W User’s Guide, Version 2.0. Ardsley, NY: Ambulatory Monitoring, Inc.
23. Jean-Louis G, Kripke DF, Mason WJ, Elliot JA, Youngstedt SD. Sleep
estimation from wrist movement quantified by different actigraphic modalities. J Neurosci Methods 2001;105:185-91.
24. Blackwell T, Ancoli-Israel S, Gehrman PR, Schneider JL, Pedula KL,
Stone KL. Actigraphy scoring reliability in the study of osteoporotic fractures. Sleep 2005;28:1599-605.
25. Redline S, Sanders MH, Lind BK, et al. Methods for obtaining and analyzing unattended polysomnography data for a multicenter study. Sleep
Heart Health Research Group. Sleep 1998;21:759-67.
26. Rechtschaffen A, Kales A. A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. Washington, DC: US Government Printing Office, 1968.
27. American Sleep Disorders Association. EEG arousals: scoring rules and
examples: a preliminary report from the Sleep Disorders Atlas Task Force
of the American Sleep Disorders Association. Sleep 1992;15:173-84.
28. American Academy of Sleep Medicine. Recording and scoring leg movements. The Atlas Task Force. Sleep 1993;16:748-59.
29. Barone JJ, Roberts HR. Caffeine consumption. Food Chem Toxicol
1996;34:119-29.
30. Pahor M, Chrischilles EA, Guralnik JM, Brown SL, Wallace RB, Carbonin P. Drug data coding and analysis in epidemiologic studies. Eur J
Epidemiol 1994;10:405-11.
31. Sheikh JI, Yesavage JA. Geriatric Depression Scale (GDS): recent evidence and development of a shorter version. Clin Gerontol 1986;5:165-73.
32. Washburn RA, Smith KW, Jette AM, Janney CA. The Physical Activity
Scale for the Elderly (PASE): development and evaluation. J Clin Epidemiol 1993;46:153-62.
33. Teng EL, Chui HC. The Modified Mini-Mental State (3MS) examination.
J Clin Psychiatry 1987;48:314-8.
34. Webb WB, Friel J. Sleep stage and personality characteristics of “natural”
long and short sleepers. Science 1971;171:587-8.
35. Webb WB, Agnew HW Jr. Sleep stage characteristics of long and short
sleepers. Science 1970;168:146-7.
36. Aeschbach D, Cajochen C, Landolt H, Borbely AA. Homeostatic sleep
regulation in habitual short sleepers and long sleepers. Am J Physiol
1996;270:R41-53.
37. Aeschbach D, Sher L, Postolache TT, Matthews JR, Jackson MA, Wehr
TA. A longer biological night in long sleepers than in short sleepers. J Clin
Endocrinol Metab 2003;88:26-30.
38. Silva GE, Goodwin JL, Sherrill DL, et al. Relationship between reported
and measured sleep times: the Sleep Heart Health Study (SHHS). J Clin
Sleep Med 2007;3:622-30.
39. Ferketich AK, Schwartzbaum JA, Frid DJ, Moeschberger ML. Depression as an antecedent to heart disease among women and men in the
NHANES I study. National Health and Nutrition Examination Survey.
Arch Intern Med 2000;160:1261-8.
40. Berkman LF, Syme SL. Social networks, host resistance, and mortality: a
nine-year follow-up study of Alameda County residents. Am J Epidemiol
1979;109:186-204.
DISCLOSURE STATEMENT
This was not an industry supported study. Dr. Patel has accepted research support from HealthRight Products and Philips Respironics. He has been a paid consultant for SleepHealth
Centers. Dr. Ancoli-Israel has been a paid consultant for Ferring
Pharmaceuticals Inc., GlaxoSmithKline, Johnson & Johnson,
Merck, NeuroVigil, Inc., Pfizer, Philips Respironics, and Purdue Pharma LP. The other authors have indicated no financial
conflicts of interest.
REFERENCES
1. Kripke DF, Garfinkel L, Wingard DL, Klauber MR, Marler MR. Mortality
associated with sleep duration and insomnia. Arch Gen Psychiatry 2002;
59:131-6.
2. Patel SR, Ayas NT, Malhotra MR, et al. A prospective study of sleep duration and mortality risk in women. Sleep 2004;27:440-4.
3. Ayas NT, White DP, Al-Delaimy WK, et al. A prospective study of selfreported sleep duration and incident diabetes in women. Diabetes Care
2003;26:380-4.
4. Ayas NT, White DP, Manson JE, et al. A prospective study of sleep duration
and coronary heart disease in women. Arch Intern Med 2003;163:205-9.
5. Patel SR, Malhotra A, White DP, Gottlieb DJ, Hu FB. Association between reduced sleep and weight gain in women. Am J Epidemiol
2006;164:947-54.
6. Youngstedt SD, Kripke DF. Long sleep and mortality: rationale for sleep
restriction. Sleep Med Rev 2004;8:159-74.
7. Spiegel K, Leproult R, Van Cauter E. Impact of sleep debt on metabolic
and endocrine function. Lancet 1999;354:1435-9.
8. Spiegel K, Tasali E, Penev P, Van Cauter E. Sleep curtailment in healthy
young men is associated with decreased leptin levels, elevated ghrelin levels, and increased hunger and appetite. Ann Intern Med 2004;141:846-50.
9. Meier-Ewert HK, Ridker PM, Rifai N, et al. Effect of sleep loss on Creactive protein, an inflammatory marker of cardiovascular risk. J Am
Coll Cardiol 2004;43:678-83.
10. Lauderdale DS, Knutson KL, Rathouz PJ, Yan LL, Hulley SB, Liu K.
Cross-sectional and longitudinal associations between objectively measured sleep duration and body mass index: The CARDIA Sleep Study. Am
J Epidemiol 2009;170:805-13.
11. van den Berg JF, Knvistingh Neven A, Tulen JH, et al. Actigraphic sleep
duration and fragmentation are related to obesity in the elderly: the Rotterdam Study. Int J Obes (Lond) 2008.
12. Lauderdale DS, Knutson KL, Yan LL, Liu K, Rathouz PJ. Self-reported and measured sleep duration: how similar are they? Epidemiology
2008;19:838-45.
13. Bliwise DL, Young TB. The parable of parabola: what the U-shaped curve
can and cannot tell us about sleep. Sleep 2007;30:1614-5.
14. Patel SR, Malhotra A, Gottlieb DJ, White DP, Hu FB. Correlates of long
sleep duration. Sleep 2006;29:881-9.
15. Blank JB, Cawthon PM, Carrion-Petersen ML, et al. Overview of recruitment for the osteoporotic fractures in men study (MrOS). Contemp Clin
Trials 2005;26:557-68.
16. Orwoll E, Blank JB, Barrett-Connor E, et al. Design and baseline characteristics of the osteoporotic fractures in men (MrOS) study--a large observational study of the determinants of fracture in older men. Contemp Clin
Trials 2005;26:569-85.
SLEEP, Vol. 35, No. 5, 2012
648
Sleep Characteristics of Long Sleepers—Patel et al