Journal of Gerontology: MEDICAL SCIENCES
2008, Vol. 63A, No. 10, 1069–1075
Copyright 2008 by The Gerontological Society of America
Sleep Problems and Associated Daytime Fatigue in
Community-Dwelling Older Individuals
Suzanne E. Goldman,1 Sonia Ancoli-Israel,2 Robert Boudreau,3 Jane A. Cauley,3
Martica Hall,4 Katie L. Stone,5 Susan M. Rubin,6 Suzanne Satterfield,7
Eleanor M. Simonsick,8 and Anne B. Newman,3 for the
Health, Aging and Body Composition Study
1
Department of Neurology, Sleep Disorders Program, Vanderbilt University Medical Center, Nashville, Tennessee.
2
Department of Psychiatry, University of California, San Diego.
Departments of 3Epidemiology and 4Psychiatry, University of Pittsburgh, Pennsylvania.
5
San Francisco Coordinating Center and California Pacific Medical Center Research Institute, San Francisco.
6
Department of Epidemiology and Biostatistics, University of California, San Francisco.
7
Department of Preventive Medicine, University of Tennessee, Memphis.
8
Clinical Research Branch, National Institute on Aging, Baltimore, Maryland.
Background. Reported fatigue has been identified as a component of frailty. The contribution of nighttime sleep
quality (duration and complaints) to fatigue symptoms in community-dwelling older adults has not been evaluated.
Methods. We studied 2264 men and women, aged 75–84 years (mean 77.5 years; standard deviation [SD] 2.9),
participating in the Year 5 (2001–2002) clinic visit of the Health, Aging, and Body Composition (Health ABC) study.
Fatigue was determined using a subscale of the Modified Piper Fatigue Scale (0–50; higher score indicating higher
fatigue). Hours of sleep per night, trouble falling asleep, waking up during the night, and waking up too early in the
morning were assessed using interviewer-administered questionnaires.
Results. The average fatigue score was 17.7 (SD 8.4). In multivariate models, women had a 3.8% higher fatigue score than
men did. Individuals who slept 6 hours/night had a 4.3% higher fatigue score than did those who slept 7 hours/night.
Individuals with complaints of awakening too early in the morning had a 5.5% higher fatigue score than did those without
these complaints. These associations remained significant after multivariate adjustment for multiple medical conditions.
Conclusion. The association between self-reported short sleep duration (6 hours), and waking up too early and
fatigue symptoms suggests that better and more effective management of sleep behaviors may help reduce fatigue in older
adults.
Key Words: Sleep—Fatigue—Aging.
F
ATIGUE, described as weariness, weakness, tiredness,
and depleted energy (1,2), is a multidimensional,
nonspecific syndrome (2–7) that occurs commonly in older
adults and can affect life quality (1). The estimated
prevalence of fatigue ranges from 6% to 45%, depending
on the population surveyed and the measure used (5,8).
Fatigue is often confused with daytime sleepiness. Although
fatigue and daytime sleepiness are separate constructs (9),
measures used to define fatigue and sleepiness may often
overlap. Questionnaires to determine fatigue do not
consistently evaluate sleepiness as a component of fatigue
(10,11). In older adults, tiredness and fatigue predict
development of disability in basic activities of daily living
(10–12), and higher levels of fatigue have been associated
with depression, poorer self-reported health, and higher
numbers of medical conditions (13,14).
Insomnia and other sleep disorders are also common in
older adults, with prevalence rates estimated as high as
50% (15,16). These also can result in decreased life quality
(17). Although sleep complaints and fatigue are thought
to be related, this association has not been evaluated
with consideration of the contribution of comorbidity in
community-dwelling older adults. This study assessed the
prevalence of fatigue in community-dwelling older adults
and hypothesized that fewer hours of sleep and/or difficulty
with initiating or maintaining sleep would be associated
with higher fatigue scores.
METHODS
Participants
The Health, Aging, and Body Composition (Health ABC)
study, a prospective cohort study of 3075 well-functioning
black (42%) and white (58%) men (48.4%) and women
(51.6%), was designed to investigate changes in body
composition, health conditions, and social and behavioral
factors on physical and functional decline. Participants were
recruited from a random sample of white Medicare
beneficiaries and all community-dwelling black residents
in designated ZIP code areas around Pittsburgh, Pennsylvania, and Memphis, Tennessee. Eligible participants were
70–79 years old during the recruitment period from 1997
through 1998. By self-report they had no difficulty walking
1069
1070
GOLDMAN ET AL.
one-quarter of a mile, walking up 10 steps without resting,
or performing basic activities of daily living. Individuals
who required assistive devices or equipment to get around,
had a life-threatening illness, had a history of active
treatment for cancer in the past 3 years, were currently
enrolled in a lifestyle intervention treatment, or had plans to
move out of the area within 3 years were excluded.
Participants were re-examined annually. This report concerns 2264 participants who had an in-person clinic
examination in 2001–2002 (study year 5) and had data
collected on sleep and fatigue. The remaining portion of the
original cohort from 1997–1998 had telephone follow-up
only (N ¼ 337), a home visit (N ¼ 100), missed the visit (N ¼
70), had a mixed home-clinic visit (N ¼ 2), attended the
clinic visit with a proxy (N ¼ 15), withdrew from the study
(N ¼ 9), were deceased (N ¼ 263), or had incomplete fatigue
data (N ¼ 15). All participants gave informed written
consent, and the consent forms and protocol were approved
by the institutional review boards at each study site.
Assessment of Sleep Behaviors and Fatigue
At the year 5 clinic visit, standardized questionnaires that
included detailed questions about sleep, fatigue, medical
history, and physical activity were administered by trained
study personnel. Sleep and napping behavior were assessed
by the following questions: ‘‘How many hours of sleep do
you usually get at night during a usual week?’’ and ‘‘How
many times a week do you nap for 5 minutes or more?’’
Insomnia symptoms were evaluated with a series of
questions in which participants were asked how often they
experienced: (i) trouble falling asleep, (ii) waking up during
the night and having difficulty getting back to sleep, (iii)
waking up too early in the morning and being unable to get
back to sleep, and (iv) taking sleeping pills or other
medication to help sleep. Responses were categorized as:
never, rarely (1 time/month), sometimes (2–4 times/
month), often (5–15 times/month), or almost always (16–
30 times/month) (18,19).
Fatigue was measured with a subscale from the Revised
Piper Fatigue Scale, a scale originally developed for use in
breast cancer patients (12). This scale has also been used in
a group of older individuals residing in a long-term care
facility (20). Participants were asked the following questions
in reference to the past month: (i) How weak did you feel?
(ii) How sleepy did you feel during the day? (iii) How lively
did you feel? (iv) How tired did you feel? and (v) What was
your usual energy level? Responses could range from 0 to
10 with 10 indicating the most severe level. A total fatigue
score ranging from 0 (no fatigue) to 50 (highest fatigue) was
obtained by summing the scores for each item after
reversing the scoring for the questions on liveliness and
energy level as in the Revised Piper Fatigue Scale (12).
Assessment of Health Conditions
Information on health conditions was based on participant
self-report at the time of the year 5 clinic visit. Self-reported
health status was assessed with the question ‘‘In general,
how would you say your health is?’’ with response options
‘‘excellent,’’ ‘‘very good,’’ ‘‘fair,’’ ‘‘poor,’’ or ‘‘don’t
know.’’ Cardiovascular disease was assessed as self-report
of congestive heart failure, coronary heart disease, or stroke.
Self-reported dyspnea on exertion was classified separately.
Incident cancer was based on adjudicated events occurring
before the year 5 visit. Depression was assessed with the 10item Center for Epidemiologic Studies Depression Scale.
Physical activity was assessed by self-report and calculated
as the total kilocalories per week engaged in walking and
climbing stairs (21). Anthropometric measurements, including height measured with a stadiometer and weight
measured with a balance beam scale were also obtained.
Body mass index (BMI; kg/m2) was calculated from
measured height and weight. Sex and race (black or white)
were self-designated at baseline.
Statistical Analysis
Descriptive statistics were performed on all variables to
evaluate ranges, frequencies, normalities, and inconsistencies in the data. Differences between race and sex were tested
using chi-square tests and Student t tests. Age, race, BMI,
depression, self-reported health status, cardiopulmonary
disease, cancer, and physical activity were all considered
as possible confounders of the association between sleep and
fatigue and were treated as covariates (13,22–24).
For analysis purposes, based on the distribution of the
data, hours of sleep were categorized as 6, 7, 8, and .8
hours. Insomnia symptoms of trouble falling asleep, waking
up during the night, waking up too early in the morning, and
taking sleeping medicines were collapsed into two categories: ‘‘infrequent’’ consisting of never, rarely, or sometimes;
and ‘‘frequent’’ consisting of experiencing at least one
symptom often, or almost always (25,26).
Potential associations between the individual fatigue scale
questions, hours slept per night, and the individual insomnia
symptoms were examined using Spearman rank order correlation coefficients. The sum of the five fatigue questions
was then used in subsequent regression models.
The association between the total fatigue score and each
sleep variable was examined using a series of linear
regression models. Additional factors found significant in
univariate analysis were included using progressively
complex multivariable models. Model 1 included each
sleep variable adjusted for demographic factors, age, race,
and sex. Model 2 added health-related variables, BMI,
depressive symptom score, self-reported health status,
cardiopulmonary factors, and dyspnea to the variables in
Model 1. Results were similar to Model 1 and therefore are
not presented. Model 3 added kilocalories per kilogram per
week expended walking and climbing stairs to the variables
in Model 2. To express the strength of the associations,
percent differences were calculated from the regression
coefficients with the formula (b 3 unit / mean fatigue score)
(27). Factors found significantly associated with fatigue are
reported at p .05. Analyses were performed using SAS
(versions 8.2 and 9.0; SAS Institute, Cary, NC) and STATA
(version 8; STATA Corporation, College Station, TX).
RESULTS
Fatigue scores ranged from 0 to 50 with a mean (standard
deviation) score of 17.7 (8.4) (Table 1). Overall, men
SLEEP PROBLEMS AND DAYTIME FATIGUE
1071
Table 1. Participant Characteristics at the Health ABC Year 5 (2001–2002) Clinic Visit
Variable
Men
N ¼ 1078
(47.6%)
Women
N ¼ 1186
(52.4%)
Total
N ¼ 2264
77.7 (2.8)
77.3 (2.9)
77.5(2.9)
Demographics
Age (y), mean (SD)
Race
Black %
32.1
41.9
37.2
Average fatigue score*
16.8 (8.3)
18.5 (8.5)y
17.7 (8.4)
%
%
%
35.4
27.9
28.9
7.7
37.5
5.6
8.9
6.6
3.2
40.0
27.6
25.9
6.5
28.9y
8.8y
11.3z
9.7§
4.6z
37.9
27.7
27.3
7.1
33
14.4
20.2
16.3
7.8
27.2 (4.4)
27.3 (5.5)
27.3 (4.9)
11.7
33.3
36
19.1
9.6
31.6
40.3
18.5
10.6
32.4
38.2
18.7
4.0
17.5
7.8
29.1
10.7
3.2
10.7y
6.9
41.1y
5.1y
3.6
14
7.3
35.4
7.7
4.4 (4.0)
5.5 (4.6)y
5.0 (4.3)
16.8
23.3
27.3
32.6
26.2y
28.8
24.7
20.4
21.8
26.2
25.9
26.1
Self-reported sleep variables
Sleep duration
6 h (n ¼ 857)
7 h (n ¼ 628)
8 h (n ¼ 619)
.8 h (n ¼ 160)
Naps 7/wk, %
Trouble falling asleep (5 nights/mo), %
Wake up during night (5 nights/mo), %
Wake up too early (5 nights/mo), %
Sleep medications (5 nights/mo), %
Health variables
Body mass index (kg/m2), mean (SD)
Self-reported health status
Excellent (n ¼ 240)
Very good (n ¼ 733)
Good (n ¼ 865)
Fair–Poor (n ¼ 424)
Stroke (n ¼ 81)
Coronary heart disease (n ¼ 316)
Congestive heart failure (n ¼ 166)
Dyspnea (n ¼ 798)
Incident cancer (n ¼ 175)
Depression, mean (SD)
kcal/kg/wk walking þ climbing stairs
,0.03 (n ¼ 381)
0.03 1.23 (n ¼ 648)
.1.23 5.53 (n ¼ 617)
.5.53 (n ¼ 618)
Notes: *Total fatigue score ranged from 0 (no fatigue) to 50 (highest fatigue) and was made up of the total of scores for the answers to the following questions:
‘‘How weak did you feel?’’ ‘‘How lively did you feel?’’ ‘‘How tired did you feel?’’ and ‘‘Describe your usual energy level.’’
y
p .001 men vs women.
z
p .01 men vs women.
§
p .05 men vs women.
Health ABC ¼ Health, Aging and Body Composition; SD ¼ standard deviation.
reported significantly lower fatigue than did women. There
was no difference in average fatigue levels between blacks
and whites. Mean hours of self-reported nightly sleep did
not differ by sex, although a higher percentage of men than
women napped at least 7 times a week. More than 34% of
participants reported at least 1 insomnia symptom, with
higher percentages of women reporting each insomnia
symptom relative to men.
Total fatigue, as well as answers to each question on the
fatigue scale, was slightly correlated with the individual
sleep behaviors (Table 2). Removal of the question ‘‘Over
the past month how sleepy did you feel during the day?’’
from the fatigue scale did not alter the associations. In
separate linear regression models for each nighttime sleep
variable adjusted for age, race, and sex, both short (6
hours) and long sleep duration (.8 hours) were associated
with higher levels of fatigue relative to sleep duration of
7 hours. Higher levels of fatigue were also associated with
trouble falling asleep, waking up during the night, waking
up too early, and use of sleep medications (Table 3). Results
were similar when all sleep variables were considered
together in the same model.
In the fully adjusted multivariable regression model,
women had a higher overall fatigue score than men did, and
blacks had a higher overall fatigue score than whites did.
Self-reported short sleep duration (6 hours) and waking up
too early in the morning remained associated with higher
fatigue scores, but were attenuated after adjustment. Also
associated with higher average fatigue scores were BMI,
poorer self-reported health status, high depressive symp-
GOLDMAN ET AL.
1072
Table 2. Correlation of Fatigue Scale Components With Total Fatigue Score and Symptoms of
Disturbed Nighttime Sleep (Spearman’s Rho [rs]) in Health ABC 2001–2002 (N ¼ 2264)
Nighttime Sleep Behaviors
Fatigue Scale Components
Trouble Falling
Asleep
Number of Times
Waking in the Night
Waking Up Too
Early in the Morning
Use of Medications
to Sleep
0.13y
0.07z
0.13y
0.15y
0.11y
0.15y
0.16y
0.19y
0.17y
0.14y
0.18y
0.18y
0.23y
0.23y
0.16y
0.14y
0.17y
0.17y
0.18y
0.22y
0.22y
0.12y
0.05z
0.13y
0.10y
0.10y
0.14y
0.15y
During the past month how:
Weak did you feel?*
Sleepy did you feel during day?*
Lively did you feel?*§
Tired did you feel?*
Describe your usual energy level*z§
Total fatigue score (5 item scale)k
Total fatigue score (4 item scale){
Notes: Questions were asked on an ordinal Scale 0¼ never, 1 ¼ once/month; 2 ¼ 2–4 times/month; 3 ¼ 5-15 times/month 4 ¼ 16–30 times/month.
*Scale of 0–10 (worst) over the past month.
y
p ,.0001.
z
p , .001.
§
Correlations reported for direction the question was asked.
k
Total fatigue score (see Methods section).
{
Total fatigue score made up of the following questions: How weak did you feel, how lively did you feel, how tired did you feel, and describe your usual energy
level.
Health ABC ¼ Health, Aging and Body Composition.
toms, prevalent cardiovascular disease, dyspnea, and
secondary primary cancer. These associations remained
significant, but were also partially attenuated in the fully
adjusted model. Individuals who engaged in walking or
climbing stairs equivalent to .5.3 kcal/kg/wk had lower
fatigue scores than individuals with 0.03 kcal/kg/wk of
activity. The final multivariable model explained 41% of the
overall variance in fatigue score. Finally, factors associated
with high levels of fatigue (score .30) were examined to
determine if any were specifically related to these higher
levels. Results were consistent with the results of the linear
model.
DISCUSSION
This study examined the independent contribution of
sleep to fatigue in a large cohort of predominately high
functioning, community-dwelling older adults. There was
a wide range of fatigue symptoms in this cohort, with
women having higher fatigue scores than men, and blacks
having higher fatigue scores than whites. Individuals who
reported sleeping ,6 hours a night had higher total fatigue
scores than those sleeping .8 hours. Both short and long
sleep durations were related to fatigue, and these associations were substantially attenuated when adjusting for health
conditions that are also associated with poor sleep and
fatigue. This finding suggests that poor health explains
a large part of these relationships; thus decreasing the
prevalence or the severity of these conditions could improve
sleep and fatigue. The finding that short sleep duration
remained independently associated with fatigue suggests
that increasing the time spent sleeping could alleviate
fatigue symptoms in this group.
Gender and race were both associated with fatigue. The
finding that women had higher fatigue scores than men
differed from the one other study of fatigue in older adults
where no difference was found in fatigue scores between
genders (20). However, the Health ABC cohort was younger
(77.5 [2.9] vs 87.8 [4.9] years of age), had a higher
percentage of men (47.7% vs 18%), and was considerably
larger (2264 vs 308 participants). In the general population,
women have been found to have higher levels of fatigue
than men (28–30). Furthermore, the gender association
would be consistent with similar syndromes such as daytime
sleepiness and insomnia, where women tend to report higher
levels of these symptoms than do men (15,22,31–34). The
association of blacks having lower fatigue scores than
whites was not expected. Previous research in other
populations has reported that blacks had higher fatigue
scores than whites (28,35,36). Whereas the differences
might be attributable to different population characteristics
such as age, or fatigue scale use, this association was found
after we adjusted for possible confounders. This finding is
interesting and warrants future research.
The definition and pathophysiology of fatigue are not
clear. Our study supports other research showing associations between fatigue, disturbed sleep, and medical
conditions (2,3,5,9,30,37). It is difficult to fully differentiate
fatigue from sleepiness because sleepiness is often used to
define fatigue (7). Removal of the question ‘‘How sleepy are
you during the day?’’ from the total fatigue score did not
change the associations found between disturbed sleep and
total fatigue in this study. Fatigue has been associated with
a wide range of sleep disorders and behaviors; however, the
causes of fatigue in sleep-disordered populations is
relatively unknown (3,22). In patients referred to sleep
clinics, subjective fatigue has been shown to be independent
of sleep disorder severity and daytime sleepiness (3,22).
Results of this study suggest an independent association of
self-reported sleep duration, and waking up too early and
fatigue. Long and short sleep durations have been associated
with increased mortality (38–41).
The direct associations of chronic health conditions with
fatigue, as well as with sleep, in this population were
SLEEP PROBLEMS AND DAYTIME FATIGUE
1073
Table 3. Factors Associated With Fatigue in Multivariable Models Adjusted for Age, Race, and Sex and Fully Adjusted{
Percentage Difference in Fatigue per Unit (95% CI)
Variable
Age, y
Female gender
Black race
Mean (SD) or
Prevalence (%)
77.5 (2.9)
52.4%
37.2%
Unit/Referent*
1
Male
White
Adjusted Model
(Age, Race, and Sex)
0.7 (0.1, 1.3)
10.0 (6.1, 13.9)y
0.4 (3.6, 7.6)
Fully Adjusted
Model
0.3 (0.3, 0.9)
3.8 (0.4, 7.2)z
13.2 (16.8, 9.6)y
Sleep duration
6h
7h
8h
.8 h
Trouble falling asleep 5 nights/mo
Wake up during night 5 nights/mo
Wake up too early 5 nights/mo
Sleep medications 5 nights/mo
Body mass index (BMI)
37.9%
27.7%
27.3%
7.1%
15.4 (10.5, 20.2)y
4.3 (0.1, 6.8)z
Referent
3.6 (1.6, 8.8)
8.5 (3.7, 16.7)z
14.4
20.2
16.3
7.8
19.3
21.5
22.3
17.0
5.6
1 SD
(13.8, 24.8)y
(16.7, 26.3)y
(17.1, 27.6)y
(10.7, 23.2)y
(3.6, 7.6)y
2.4 (1.9, 6.8)
5.2 (1.9, 12.2)
1.7
2.3
5.5
1.4
3.8
(6.9, 3.6)
(2.5, 7.28)
(0.3, 10.7)z
(7.6, 4.9)
(2.0, 5.5)y
Self-reported health status
Excellent
Very good
Fair
Poor
10.6%
32.4%
38.2%
18.7%
Referent
17.0 (10.7, 23.2)y
37.8 (31.6, 44.5)y
66.5 (59.6, 73.5)y
11.0 (5.3, 16.8)y
23.8 (18.0, 29.6)y
38.4 (31.6, 45.3)y
13.7%
5.4%
12.5 (6.8, 18.2)y
23.7 (15.1, 32.3)y
3.7 (1.2, 8.7)
12.5 (4.8, 20.1)k
35.4%
5.6%
2.2%
5.0 (4.3)
31.2
3.1
17.9
23.3
No. of cardiovascular diseases§
1
2þ
Dyspnea (any symptoms)
Incident cancer
Recurrent or second primary cancer
Depression
1 SD
(27.3, 35.1)y
(5.5, 11.7)
(4.5, 31.6)k
(21.6, 25.1)y
14.0
2.7
15.9
16.6
(10.3, 17.7)y
(4.8, 10.2)
(4.5, 27.4)k
(14.8, 18.5)y
kcal/kg/wk walking þ climbing stairs
0.03 1.23 (n ¼ 648)
.1.23 5.53 (n ¼ 617)
.5.53 (n ¼ 618)
26.2%
25.9%
26.1%
,0.03
6.1 (11.9, 0.4)z
13.6 (19.4, 7.8)y
24.3 (30.2, 18.5)y
3.0 (1.7, 7.8)
0.4 (5.2, 4.5)
6.8 (11.8, 1.8)k
Model r2 0.41
Notes: *Referent group is group with none of the characteristic.
p .001.
z
p .05.
§
Cardiovascular disease includes congestive heart failure, coronary heart disease, and/or stroke.
k
p .01.
{
Fully adjusted model includes age, race, sex, BMI, self-reported number of hours of sleep at night, trouble falling asleep, waking up during the night, waking up
too early in the morning, using medication to help sleep, self-reported health status, cardiovascular disease, dyspnea on exertion, energy expended walking plus
climbing stairs, and depression.
CI ¼ confidence interval; SD ¼ standard deviation.
y
significant and cannot be disregarded. Fatigue in chronic
illness is pervasive and multidimensional, with different
perceived causes and implications (4). In this study, selfreported health status, cardiopulmonary disease, dyspnea,
depression, and recurrent or secondary cancer were strong
associates of fatigue, even after adjustment for other
variables. These associations were consistent with known
associations of fatigue and medical comorbidities reported
in other populations (2,13,20). To what extent chronic
disease might cause fatigue, or chronic disease might cause
disturbed sleep (which in turn causes fatigue), is not well
defined even in cancer or in sleep apnea patients. Results
from this study suggest that while chronic health conditions
are highly associated with fatigue, poor sleep may have an
independent contribution and warrants further investigation.
Fatigue has been considered in the context of cancer
(5,6,12), or other chronic diseases (42,43), although it
impacts other populations as well. Inclusion of incident
cancer in the models in this study slightly attenuated the
associations between fatigue and sleep. However, it did not
change the overall significance of the associations. Although
fatigue is a common complaint in older adults, it has rarely
been addressed as a specific outcome in this population.
Studies performed in younger cohorts have reported fatigue
symptoms in 12%–25% of the population (8,36). In one
group of older adults living in an assisted living facility,
more than 50% of these individuals exhibited at least some
complaint of mild fatigue (20). In addition to the dissimilar
population groups assessed, the use of multiple, nonstandardized fatigue scales makes it difficult to compare
the fatigue rates found in the Health ABC cohort with those
of these other studies. However, more than 37% of the
Health ABC cohort had a fatigue score 20 that would
indicate a mild-moderate fatigue level.
1074
GOLDMAN ET AL.
This study had several limitations. The cross-sectional
nature of the data limits evaluation of the temporal
relationship between sleep and fatigue. Fatigue is a subjective syndrome, and there is no gold standard to assess it.
Various other fatigue scales, or components of scales,
currently in use may provide different correlates of fatigue
(22). Severity of disease and presence of other clinical
conditions that might be associated with fatigue were not
evaluated. Usual sleep time and sleep behaviors were
obtained by self-report. Although the validity of self-report
sleep data has been demonstrated with actigraphy, some
variability between actual and self-report exists. Use of selfreport data also precluded the measurement of sleep
disorders obtained through polysomnography such as
apneas, hypopneas, and periodic limb movement disorders
that might also be related to complaints of poor sleep.
Finally, it is important to keep in mind that the Health ABC
cohort members were well-functioning at baseline, so they
may be healthier than other older populations.
Conclusion
The results of this study of well-functioning, communitydwelling older adults suggest that fatigue may be associated
with several dimensions of sleep including short sleep
duration and disrupted sleep. Although part of this
association may be explained by poorer self-reported health
status, these observed relationships were independent of
other health-related conditions commonly associated with
fatigue. Additional studies are needed to help understand the
direction of these relationships.
ACKNOWLEDGMENTS
This work was supported by National Institutes of Health (NIH) contracts
N01-AG-6-2101, 2103, and 2106; National Institute on Aging (NIA) grant
AG08415; and National Cancer Institute grants CA85264 and CA112035.
This research was also supported in part by the Intramural Research
program of the NIH, NIA (Aging Training Grant 2, T32, AG000181-16)
and by funds from the California Breast Cancer Research Program of the
University of California (Grant 11IB-0034).
CONFLICTS OF INTEREST (FINANCIAL DISCLOSURES)
Sonia Ancoli-Israel is a member of the Advisory Board and/or has
participated in speaking engagements supported by Sepracor, Takeda
Pharmaceuticals, King, Sanofi-Aventis, Cephalon, Merck, and Neurocrine
Biosciences.
Jane A. Cauley receives research funds from Merck & Co, Eli Lilly &
Co, Pfizer Pharmaceuticals, and Novartis Pharmaceuticals. She receives
honoraria from Merck & Co, and Eli Lilly & Co, and has participated in the
speaker’s bureau of Merck & Co.
CORRESPONDENCE
Address correspondence to Suzanne E. Goldman, PhD, Vanderbilt
University Medical Center, Department of Neurology, Sleep Disorders
Program, 1301 Medical Center Drive, Room B-727, Nashville, TN 37232.
E-mail: [email protected]
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Received October 23, 2007
Accepted January 14, 2008
Decision Editor: Darryl Wieland, PhD, MPH