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Daytime Sleepiness and Cognitive Impairment in the Elderly Population
Maurice M. Ohayon, MD, DSc, PhD;
Marie-Françoise Vecchierini, MD
Arch Intern Med. 2002;162:201-208.
ABSTRACT
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Background Recent findings suggest that there may be a relationship between excessive
daytime sleepiness (EDS) and cognitive deficits. This study aims to determine
to what extent EDS is predictive of cognitive impairment in an elderly population.
Methods A total of 1026 individuals 60 years or older representative of the
general population living in the metropolitan area of Paris, France, were
interviewed by telephone using the Sleep-EVAL expert system. To find these
individuals, 7010 randomly selected households were called: 1269 had at least
1 household member in this age range (participation rate, 80.9%). In addition
to Diagnostic and Statistical Manual of Mental Disorders,
Fourth Edition, and International Classification
of Sleep Disorders diagnoses, the system administered to participants
the Psychological General Well-being Schedule, the Cognitive Difficulties
Scale (MacNair-R), and an independent living scale.
Results Excessive daytime sleepiness was reported by 13.6% of the sample, with
no significant difference among age groups. Compared with nonsleepy participants,
those with EDS were at increased risk of cognitive impairment on all the dimensions
of the MacNair-R scale after controlling for age, sex, physical activity,
occupation, organic diseases, use of sleep or anxiety medication, sleep duration,
and psychological well-being. The odd ratios were 2.1 for attention-concentration
deficits, 1.7 for praxis, 2.0 for delayed recall, 2.5 for difficulties in
orientation for persons, 2.2 for difficulties in temporal orientation, and
1.8 for prospective memory.
Conclusions Among elderly individuals in the general population, EDS is an important
risk factor for cognitive impairment. A complaint of EDS by an elderly patient
should signal the possibility of an underlying cognitive impairment in need
of evaluation.
INTRODUCTION
IN INDUSTRIALIZED countries, the number of elderly people continues
to grow. Health care systems must be prepared to support this fast-growing
part of the population, whose needs are specific: the elderly population is
vulnerable to a high occurrence of organic diseases and to cognitive impairments
that will affect quality of life. The prevalence of mild to severe cognitive
deficits is 4% to 10% in the elderly population living in the community.1-3 Individuals with severe
cognitive disorders related to dementia are rarely found in the community
because they are quickly losing the autonomy necessary to live in the community.
Several longitudinal studies4-8
have shown an increased risk of mortality in nondemented elderly individuals
with cognitive impairments. The adjusted (for age, sex, and health status)
relative risk for mortality was 1.7 to 3.6 times higher in elderly individuals
with mild to severe cognitive impairments.4-12
Some longitudinal studies11, 13-14
showed that the mortality rate was not statistically different in any age
group in individuals with good cognitive performance and that the preservation
of cognitive functions was associated with better survival in older individuals.
Decline in cognitive performance has been associated with several factors,
including neurological diseases, vascular diseases, depression,15-17
and diabetes mellitus,18 but not always.19 However, other factors significantly accounted for
this decline in the aging process, namely, educational level20-21
and social disengagement.22 More recently,
excessive daytime sleepiness (EDS) has been associated with poor cognition
and dementia.23 Moreover, an increased mortality
risk of 1.73 was found in elderly individuals with cognitive impairments who
nap most of the time.24
This study aimed to verify that EDS is an independent predictive factor
of cognitive impairment in an elderly population living in the community after
controlling for possible confounders, such as obstructive sleep apnea syndrome,
organic diseases, anxiety, and depression.
PARTICIPANTS AND METHODS
STUDY DESIGN
An epidemiological survey of sleep habits and sleep disorders was conducted
by telephone in the metropolitan area of Paris, France, between October 13,
1999, and April 28, 2000. The targeted population consisted of noninstitutionalized
individuals 60 years or older. This age range represented approximately 4.5
million inhabitants in the metropolitan area of Paris. The sample was drawn
according to a 2-stage procedure. In the first stage, official census data
were used to divide the population according to its geographical distribution
in Paris. Subsequently, telephone numbers were randomly pulled according to
this stratification. In the second stage, the Kish method,25
a controlled selection method, was applied to maintain the representation
of the sample according to age and sex.
To find the participants, 11 650 telephone numbers were used; 7010
corresponded to households, and 4640 either were not a household or were not
in service. Of the households, 1269 included at least 1 resident 60 years
or older, and 1026 individuals agreed to be interviewed. The participation
rate (80.9%) was calculated based on the number of completed interviews (n
= 1026) divided by the number of eligible telephone numbers, which included
all residential numbers not meeting any of the exclusion criteria (N = 1269).
Overall, 10 381 telephone numbers were rejected mainly because
(1) the household did not include an individual 60 years or older (51.9%);
(2) the telephone number did not correspond to a household (ie, it was a business
or fax number) (14.8%); (3) the telephone number was not in service (24.8%);
(4) the eligible respondent was ill, was deaf, or had a speech impairment
(1.8%); or (5) we could not communicate in French with the person who answered
the telephone (1.6%).
Interviewers explained the goals of the study to potential participants
before requesting verbal consent. Excluded from the study were individuals
younger than 60 years, those who did not speak sufficient French, and those
who had a hearing or speech impairment or an illness that precluded interview.
The ethical committee of the Xavier Bichat Hospital in Paris approved the
study.
Individuals who declined to participate were telephoned a second time
at least 3 weeks later and were asked again if they were willing to be part
of the study. Individuals were classified as refusers if they declined a second
time or when it was impossible to reach them a second time. Telephone numbers
were dropped and replaced only after a minimum of 10 unsuccessful dial attempts
were made at different times and on different days, including weekdays and
weekends. An added-digit technique, that is, increasing the last digit of
a telephone number by 1, was used to control for unlisted telephone numbers.26 The final sample consisted of 15.9% unlisted numbers.
INTERVIEWERS
Interviews were conducted by telephone using the Sleep-EVAL system.
The study was conducted at the Xavier Bichat Faculty of Medicine of the Paris
7 University. Interviews were performed by 16 university students who were
inexperienced in psychiatric assessment but who received special training
on how to use the Sleep-EVAL system. The average (SD) duration of the interviews
was 64.3 (25.6) minutes. An average of 5 telephone calls were made to complete
an interview. The team of interviewers was monitored daily by one supervisor
to ensure that questions were asked correctly and that data were entered properly.
INSTRUMENT
The Sleep-EVAL system was specifically designed to administer questionnaires
and conduct epidemiological studies on mental and sleep disorders in the general
population.27-29
It managed the telephone calls (generation of new numbers, management of appointments,
and management of numbers that had to be called back) and the Kish selection
procedure. It also kept track of all telephone calls made (date and time,
interviewer who made the call, issue of the call, duration of the call, elapsed
time between each call, number of questions asked during the call, and number
of times the number was dialed).
The Sleep-EVAL system includes a nonmonotonic, level-2 inference engine
endowed with a causal reasoning mode. These features enable the Sleep-EVAL
system to formulate a series of diagnostic hypotheses based on the responses
provided by a participant (causal reasoning). The nonmonotonic, level-2 inference
engine examines these hypotheses and confirms or rejects them through further
questions and deductions. Two classifications are implemented in the knowledge
base of Sleep-EVAL: the International Classification of
Sleep Disorders30 and the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition
(DSM-IV).31
The system formulates initial diagnostic hypotheses on the basis of
responses to a standard set of questions posed to all participants. Concurrent
mental diagnoses are allowed in accordance with the DSM-IV. The system terminates the interview once all International
Classification of Sleep Disorders and DSM-IV
diagnostic possibilities are exhausted. The system selects and phrases the
questions to be administered and provides examples and instructions on how
to ask them. The interviewer simply reads the questions as they appear on
the monitor and enters the responses. Questions can be close ended (eg, yes-no,
present-absent-unknown, or 5-point scale) or open ended (eg, name of illness
or duration of illness).
The system has been validated in various contexts and has been demonstrated
to be reliable and valid. Five validation studies have been conducted.27, 32-33
ASSESSMENT
The standard questionnaire of the Sleep-EVAL system covered (1) sociodemographic
information; (2) the sleep-wake schedule; (3) symptoms of sleep disorders;
(4) sleep hygiene; (5) current and past consumption of alcohol, tobacco, and
coffee; (6) current and past use of medication for sleep, anxiety, and depression;
(7) any other medication use; (8) medical information, including organic diseases,
hospitalizations, medical consultations, and blood pressure; (9) height and
weight; and (10) DSM-IV and International
Classification of Sleep Disorders diagnoses.
For the purposes of this study, validated assessment scales were added
to the knowledge base of the Sleep-EVAL system:
- Cognitive Difficulties Scale (MacNair-R) (26-item
French version).34 This scale assesses 6 dimensions
of cognitive difficulties: attention-concentration deficits, praxis, delayed
recall, difficulties in orientation for persons, difficulties in temporal
orientation, and difficulties in prospective memory. Higher scores correspond
to greater cognitive difficulties.
- Psychological General Well-being Schedule.35-36 This index measures subjective well-being.
The individual self-reports on 22 items that are indicators of 6 affective
states: anxiety, depressed mood, sense of positive well-being, self-control,
general health, and vitality. Low scores on each of these states indicate
impairment.
- Mini-Mental State Examination.37-38
This evaluation scale assesses cognitive efficiency. Questions about orientation
and memory were asked. Praxis could not be assessed because it requires a
face-to-face meeting.
- Functional Assessment. Instrumental activities
of daily living39 that measures the older person's
abilities to perform simple everyday tasks.
ANALYSES
The recruited sample matched the official census data for the metropolitan
area of Paris in terms of age and sex distribution. Therefore, it was not
necessary to apply a weighting procedure that would correct for evident disparities.
Bivariate analyses were performed using the  2 test with
Yates correction or the Fisher exact test when n
values were smaller than 5. Reported differences were significant at P .05. Analysis of variance and independent samples t test were used to analyze continuous variables. When
basic assumptions for the use of these statistical methods were violated,
nonparametric tests were also performed (Kruskal-Wallis and Mann-Whitney tests).
Logistic regressions40 were used to compute
the odds ratios associated with each type of cognitive difficulty. Colinearity
between variables (ie, information redundancy) was verified beforehand.
RESULTS
Overall, 1026 individuals participated in the study: 28.7% were aged
60 to 64 years; 25.4% were aged 65 to 69 years; 21.2% were aged 70 to 74 years;
and 24.7% were 75 years and older. Women represented 59.8% of the sample.
The proportion of women increased with age: 51.6% of participants aged 60
to 69 years were women, and 66.9% of those 75 years and older were women.
More than half of the participants (52.0%) aged 60 to 64 years were active
(ie, working or doing an activity at least 3 days per week). This rate was
43.2% in those aged 65 to 69 years, 40.0% in those aged 70 to 74 years, and
28.2% in those 75 years and older.
SLEEP CHARACTERISTICS OF THE SAMPLE
Overall, the sleep-wake schedules of the different age groups were comparable
(Table 1). The only significant
difference was observed in bedtime between individuals 75 years and older
and those aged 70 to 74 years, the latter being in bed with the intention
of sleeping 35 minutes later than the older participants (Table 1).
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Table 1. Sleep Characteristics of 1026 Participants by Age Group*
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VARIABLES ASSOCIATED WITH COGNITIVE DIFFICULTIES
Age and Sex
Generally speaking, men and women had comparable scores on all dimensions
of the Cognitive Difficulties Scale; only praxis was higher in women than
in men, indicating a deterioration in manual dexterity and efficiency of movement
in women. Similarly, age groups had comparable scores on all the dimensions
of the Cognitive Difficulties Scale except praxis, which was higher in the
oldest participants compared with the other age groups (Table 2).
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Table 2. Results on the Dimensions of the Cognitive Difficulties Scale
by Sex and Age Group*
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Daytime Sleepiness
Overall, 5.3% of participants claimed that they fell asleep easily during
the day and almost everywhere at least weekly. This rate was comparable among
the age groups.
Almost 6% of participants reported feeling moderately sleepy during
the day, and 5.2% said they felt sleepy a lot during the day. Again, there
were no significant differences among the age groups. Therefore, 13.6% of
the sample reported having daytime sleepiness.
Individuals with daytime sleepiness had significantly higher scores
on all the dimensions of the Cognitive Difficulties Scale (Table 3). These differences were not present in all age groups:
for participants 75 years and older, the 6 dimensions of the MacNair-R scale
were all significantly higher in individuals with daytime sleepiness compared
with those without sleepiness. In individuals aged 60 to 64 years and those
aged 70 to 74 years, only praxis and prospective memory did not differ significantly
between individuals with and without daytime sleepiness. Individuals aged
65 to 69 years with daytime sleepiness differed significantly from those without
daytime sleepiness only on difficulties in temporal orientation (P = .02).
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Table 3. Results on the Dimensions of the Cognitive Difficulties Scale
by Daytime Sleepiness, Sleep Duration, and Naps*
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Napping
The proportion of participants taking a nap at least 2 days per week
was 30.1%. The percentage of nappers increased with age: 22.4% of individuals
aged 60 to 64 years; 29.3%, aged 65 to 69 years; 34.3%, aged 70 to 74 years;
and 33.8%, 75 years and older (23 = 11.05; P = .01). Most of them were taking only 1 nap per day (90.4%).
Among nappers, 14.6% had unintentional naps, that is, they were not planned.
The proportion of unintentional nappers was comparable in each age group.
Intentional napping was associated with a higher score on difficulties
in orientation for persons compared with never napping. Participants who took
unintentional naps had higher scores on attention-concentration deficits,
delayed recall, difficulties in orientation for persons, and difficulties
in temporal orientation (Table 3).
Intentional nappers reported slightly but not significantly more frequently
feeling sleepy during the day (14.0% vs 9.5%; P =
.06). The association between intentional naps and EDS was significant only
in the 2 younger groups. In individuals aged 60 to 64 years, 19.5% of intentional
nappers reported EDS compared with 8.7% of nonnappers (P = .04). In participants aged 65 to 69 years, 15.8% of intentional
nappers also reported having EDS compared with 5.6% of nonnappers (P = .02).
Organic Diseases
Overall, 27.0% of the participants in the study had an organic disease.
The most frequently reported diseases were arthritic diseases (20.2%), hypertension
(17.3%), and heart diseases (10.5%). The prevalence of organic diseases was
higher in the 2 oldest groups (70-74 years, 34.6%;  75 years, 31.7%) compared
with the 2 younger groups (60-64 years, 18.7%; 65-69 years, 25.2%; 23 = 18.83; P<.001).
These participants achieved higher scores on 5 of the 6 dimensions of
the Cognitive Difficulties Scale: attention-concentration deficits, praxis,
difficulties in orientation for persons, difficulties in temporal orientation,
and prospective memory (Table 4).
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Table 4. Results on the Dimensions of the Cognitive Difficulties Scale
by Organic Disease and Use of Sleep or Anxiety Medication*
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Obstructive Sleep Apnea Syndrome
Obstructive sleep apnea syndrome was found in 2.6% of the sample, with
no significant difference among age groups. In general, participants with
obstructive sleep apnea syndrome obtained comparable cognitive difficulty
scores, except on prospective memory, where the scores were significantly
higher (Table 4).
Sleep, Anxiety, and Antidepressant Medications
Approximately 20% of the participants reported using a medication to
promote sleep, with no significant difference among age groups. The most frequently
used medications were lorazepam (22.1%), zolpidem tartrate (15.4%), zopiclone
(10.6%), and bromazepam (10.1%). Use of sleep medication was associated with
higher scores on attention-concentration deficits, praxis, and prospective
memory (Table 4).
The use of medication to reduce anxiety was reported by 7.3% of the
sample. The proportion of individuals using such medication was higher in
the 2 oldest groups (70-74 years, 11.2%; 75 years, 9.1%) compared with
the 2 younger age groups (60-64 years, 4.9%; 65-69 years, 5.6%; 23 = 8.85; P = .03).
The most frequently used medication was bromazepam (24.0%). Users of
anxiety medication had higher scores than nonusers on attention-concentration
deficits and praxis (Table 4).
Finally, 2.6% of the sample reported taking an antidepressant medication,
with no significant difference among age groups. This consumption, however,
was not associated with cognitive deficits.
Psychological General Well-being
The anxiety and general health dimensions of the Psychological General
Well-being Schedule were negatively correlated with scores on the Cognitive
Difficulties Scale for all dimensions except prospective memory (Table 5). This means that greater anxiety
and poor health perception were positively correlated with poor cognitive
performance. The depressed mood dimension was negatively correlated with attention-concentration
deficits and difficulties in temporal orientation (ie, greater depressive
mood was positively correlated with increased difficulties in attention-concentration
and temporal orientation). The self-control dimension was positively correlated
with 4 of the 6 dimensions of the Cognitive Difficulties Scale (attention-concentration
deficits, delayed recall, difficulties in temporal orientation, and prospective
memory). Positive well-being was correlated only with attention-concentration
deficits, and vitality was correlated with all the dimensions of the Cognitive
Difficulties Scale (Table 5).
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Table 5. Correlation Between the Dimensions of the Psychological General
Well-being Schedule (PGWB) and the Cognitive Difficulties Scale*
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PREDICTIVE FACTORS ASSOCIATED WITH COGNITIVE DIFFICULTIES
To verify the hypothesis that daytime sleepiness is an independent predictive
factor for cognitive difficulties, we used stepwise logistic regression analyses.
The models were adjusted for the possible confounding effects of age, sex,
physical activity, occupation, organic diseases, use of sleep or anxiety medications,
daytime sleepiness, napping, sleep duration, and the dimensions of the Psychological
General Well-being Scale. Each cognitive difficulty dimension was dichotomized
into presence vs absence of difficulty to meet the requirements of logistic
regression analysis. Furthermore, results obtained on the Mini-Mental State
Examination were also used as the dependent variable in a logistic regression
model. For all the cognitive difficulty dimensions and the Mini-Mental State
(memory), daytime sleepiness seemed to be a significant independent predictive
factor (Table 6).
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Table 6. Significant Predictive Factors of Cognitive Difficulties*
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The independent predictive factors for attention-concentration deficits
were having daytime sleepiness, taking sleep medication, sleeping 5 hours
or less per night, and being anxious. Odds ratios and associated 95% confidence
intervals are presented in Table 6.
Independent predictive factors for praxis were having daytime sleepiness,
being 75 years or older, being a woman, having an organic disease, taking
a sleep medication, and being physically inactive (ie, absence of physical
exercise 3 times per week for 15 minutes) (Table 6). For delayed recall, the independent predictive factors
were having daytime sleepiness, being a man, being depressed, and being anxious
(Table 6). Independent predictive
factors associated with difficulties in orientation for persons were having
daytime sleepiness, having an organic disease, sleeping 5 to 7 hours per night,
being anxious, and having an occupation (a job or participating in various
activities such as volunteer work, babysitting, or helping his or her children)
(Table 6). Independent predictive
factors associated with difficulties in temporal orientation were having daytime
sleepiness, having an organic disease, being a woman, being depressed, and
being anxious (Table 6). Independent
predictive factors associated with prospective memory were having daytime
sleepiness, having an organic disease, having a diagnosis of obstructive sleep
apnea syndrome, and having an occupation (Table 6). Finally, independent predictive factors associated with
the presence of memory deficits on the Mini-Mental State Examination were
having daytime sleepiness and being 70 years or older (Table 6).
COMMENT
To our knowledge, this is the first study that aimed to verify whether
EDS is an independent predictive factor for cognitive difficulties in elderly
individuals living in the community. Our results clearly show that EDS is
a strong predictive factor for cognitive difficulties in elderly individuals
even after controlling for possible confounding effects of age, sex, physical
activity, occupation, organic diseases, use of sleep or anxiety medication,
napping, and mental diseases.
In our sample, 14.1% of individuals 65 years and older had moderate
to severe daytime sleepiness. Napping at least 2 days per week was reported
by 32.6% of participants 65 years and older; 14.5% of elderly subjects were
taking a nap daily. Excessive daytime sleepiness should be distinguished from
intentional naps. The latter may not reflect the presence of a sleep disorder
and can be a healthy habit in the elderly. The increasing rate of napping
with age is probably because older people have little or no life constraints
preventing them from napping whenever they feel the urge, and rarely does
this constitute a social problem.
There was no association between intentional napping and EDS in the
elderly individuals in this study. The association was, however, significant
in younger individuals in our sample. These younger participants were mostly
still working and therefore could not nap when they felt the need. If intentional
napping is harmless, EDS can be the expression of a more important underlying
disorder, for example, a sleep-related breathing disorder or depression.16-18,41-44
LIMITS OF THE STUDY
Some may question the reliability of sleep data collected on elderly
people by telephone. However, the literature suggests that telephone interviews
in general are appropriate and yield results comparable to those of other
strategies.45-46 Rohde et al46 reported good interrater reliability between face-to-face
and telephone interviews assessing DSM-IV psychiatric
disorders. Another limitation is that such a method does not allow assessment
of the most impaired individuals, such as elderly individuals with important
cognitive deficits or those with speech or hearing impairments.
Another limitation is that we did not have objective data on cognitive
difficulties and daytime sleepiness apart from questions about orientation
and memory from the Mini-Mental State Examination. Some may argue that self-reporting
of cognitive difficulties may not be as good as objective measures of cognitive
functions. However, a study by Geerlings et al47
showed that self-reported memory difficulties was a strong predictor of incident
Alzheimer disease in older persons. Furthermore, 175 of our elderly participants
were called back by a physician to undergo a brief interview, and 10 have
been invited to come to the sleep laboratory at Xavier Bichat Hospital for
a complete examination.
SIGNIFICANCE OF EDS
In this study, we found that EDS is an independent predictive factor
of a variety of cognitive difficulties that may impair quality of life. Elderly
participants in this study were nondemented and had enough autonomy to still
live in the community. However, EDS and cognitive difficulties were related
in our study to a decreased ability to perform activities of daily living.
There was also the possibility that elderly individuals who were retired or
who had fewer domestic obligations received less stimulation and therefore
were more sleepy during the day. This could be deduced by the increase with
age in the number of individuals who took a nap during the day. However, from
the results of the logistic regressions that we performed, it seems that age
was not a major factor for the presence of cognitive difficulties and that
napping was not related at all. Older age was an independent predictive factor
on only 2 of the 7 cognitive measures: praxis and memory deficits on the Mini-Mental
State Examination. An independent predictive factor that may be surprising
at first for difficulties in orientation for persons and prospective memory
was the presence of an occupation. However, this is not surprising: the Cognitive
Difficulties Scale is based on a self-report of daily living difficulties.
An individual who is still active is more likely to be confronted with a decline
in cognitive abilities than a person who has retired from active life. This
is confirmed by the fact that this factor was not predictive of memory deficits
when we used the results on memory from the Mini-Mental State Examination,
where memory is more objectively assessed. Furthermore, active individuals
at any age in our study less frequently reported being limited in their travels
and in their capacity to do their shopping, 2 activities that are narrowly
related to the items assessed in the prospective memory (need of a list when
shopping and forgetting the things that he or she was planning to buy).
What is the mechanism that explains the predictive value of daytime
sleepiness for cognitive difficulties? A possible explanation is that other
disorders, such as a mental or organic pathologic condition or an obstructive
sleep apnea syndrome, caused daytime sleepiness. Recently, obstructive sleep
apnea syndrome, for which daytime sleepiness is a cardinal symptom, has been
found to cause cognitive deficits because of the repeated anoxia provoked
by breathing pauses during sleep.41-42
However, this explanation does not fully explain our findings. Indeed, the
multivariate models controlled for the effects of these 3 types of pathologic
mechanisms, and daytime sleepiness still emerged as a strong independent predictor
of cognitive difficulties. Another possibility is that daytime sleepiness
may be due to a lack of cognitive or social stimulations. Several studies22, 48-49 have shown that when
elderly people receive cognitive stimulation and are kept socially active,
the likelihood of cognitive decline decreases. Another possible explanation
is that daytime sleepiness is an early indicator that may predict subsequent
cognitive decline. However, longitudinal studies are needed to confirm this
hypothesis.
In summary, these data from a community-based sample indicate that EDS
is a good predictor of cognitive difficulties. Physicians who treat elderly
patients with such complaints should be aware that these patients are at greater
risk to have cognitive deficits. As shown in longitudinal studies, it is possible
to delay or prevent these cognitive deficits by maintaining intellectual stimulation
and by promoting social engagement in these elderly individuals.
AUTHOR INFORMATION
Accepted for publication May 8, 2001.
This study was supported by an unrestricted grant from the Laboratoire
L. Lafon, Maisons Alfort, France.
We thank Serge Lubin, MD, for his help in this project and Kenza Ejbari,
PhD student, for her outstanding work in the monitoring of the study.
Corresponding author and reprints: Maurice M. Ohayon, MD, DSc, PhD,
Sleep Disorders Center, Stanford University School of Medicine, 401 Quarry
Rd, Suite 3301, Stanford, CA 94305 (e-mail: mrcohayon{at}aol.com).
From the Stanford Sleep Epidemiology Research Center, Stanford University
School of Medicine, Stanford, Calif (Dr Ohayon); and the Laboratoire d'Exploration
Fonctionnelle, Hôpital Bichat, Paris, France (Dr Vecchierini).
REFERENCES
| |
1. Cervilla JA, Prince M, Mann A. Smoking, drinking, and incident cognitive impairment: a cohort community
based study included in the Gospel Oak Project. J Neurol Neurosurg Psychiatry. 2000;68:622-626.
FREE FULL TEXT
2. Melzer D, McWilliams B, Brayne C, Johnson T, Bond J. Profile of disability in elderly people: estimates from a longitudinal
population study. BMJ. 1999;318:1108-1111.
FREE FULL TEXT
3. Worrall G, Moulton N. Cognitive function: survey of elderly persons living at home in rural
Newfoundland. Can Fam Physician. 1993;39:772-777.
ISI
| PUBMED
4. Bassuk SS, Wypij D, Berkman LF. Cognitive impairment and mortality in the community-dwelling elderly. Am J Epidemiol. 2000;151:676-688.
FREE FULL TEXT
5. Frisoni GB, Fratiglioni L, Fastbom J, Viitanen M, Winblad B. Mortality in nondemented subjects with cognitive impairment: the influence
of health-related factors. Am J Epidemiol. 1999;150:1031-1044.
FREE FULL TEXT
6. Ostbye T, Steenhuis R, Wolfson C, Walton R, Hill G. Predictors of five-year mortality in older Canadians: the Canadian
Study of Health and Aging. J Am Geriatr Soc. 1999;47:1249-1254.
ISI
| PUBMED
7. Arfken CL, Lichtenberg PA, Tancer ME. Cognitive impairment and depression predict mortality in medically
ill older adults. J Gerontol A Biol Sci Med Sci. 1999;54:M152-M156.
8. Hays JC, Blazer DG, Foley DJ. Risk of napping: excessive daytime sleepiness and mortality in an older
community population. J Am Geriatr Soc. 1996;44:693-698.
ISI
| PUBMED
9. Gussekloo J, Westendorp RG, Remarque EJ, Lagaay AM, Heeren TJ, Knook DL. Impact of mild cognitive impairment on survival in very elderly people:
cohort study. BMJ. 1997;315:1053-1054.
FREE FULL TEXT
10. Rozzini R, Franzoni S, Frisoni G, Trabucchi M. Cognitive impairment and survival in very elderly people: decreased
survival with cognitive impairment seems not be related to comorbidity [letter]. BMJ. 1998;316:1674.
FREE FULL TEXT
11. Perls TT, Morris JN, Ooi WL, Lipsitz LA. The relationship between age, gender and cognitive performance in the
very old: the effect of selective survival. J Am Geriatr Soc. 1993;41:1193-1201.
ISI
| PUBMED
12. Korten AE, Jorm AF, Jiao Z, et al. Health, cognitive, and psychosocial factors as predictors of mortality
in an elderly community sample. J Epidemiol Community Health. 1999;53:83-88.
ABSTRACT
13. Deeg DJ, Hofman A, van Zonneveld RJ. The association between change in cognitive function and longevity
in Dutch elderly. Am J Epidemiol. 1990;132:973-982.
FREE FULL TEXT
14. Starr JM, Deary IJ, Inch S, Cross S, MacLennan WJ. Age-associated cognitive decline in healthy old people. Age Ageing. 1997;26:295-300.
FREE FULL TEXT
15. Fichter MM, Bruce ML, Schroppel H, Meller I, Merikangas K. Cognitive impairment and depression in the oldest old in a German and
in US communities. Eur Arch Psychiatry Clin Neurosci. 1995;245:319-325.
FULL TEXT
|
ISI
| PUBMED
16. Arfken CL, Lichtenberg PA, Tancer ME. Cognitive impairment and depression predict mortality in medically
ill older adults. J Gerontol A Biol Sci Med Sci. 1999;54:M152-M156.
17. Yaffe K, Blackwell T, Gore R, Sands L, Reus V, Browner WS. Depressive symptoms and cognitive decline in nondemented elderly women:
a prospective study. Arch Gen Psychiatry. 1999;56:425-430.
FREE FULL TEXT
18. Croxson SC, Jagger C. Diabetes and cognitive impairment: a community-based study of elderly
subjects. Age Ageing. 1995;24:421-424.
FREE FULL TEXT
19. Tariot PN, Ogden MA, Cox C, Williams TF. Diabetes and dementia in long-term care. J Am Geriatr Soc. 1999;47:423-429.
ISI
| PUBMED
20. Lyketsos CG, Chen LS, Anthony JC. Cognitive decline in adulthood: an 11.5-year follow-up of the Baltimore
Epidemiologic Catchment Area Study. Am J Psychiatry. 1999;156:58-65.
FREE FULL TEXT
21. Leibovici D, Ritchie K, Ledesert B, Touchon J. Does education level determine the course of cognitive decline? Age Ageing. 1996;25:392-397.
FREE FULL TEXT
22. Bassuk SS, Glass TA, Berkman LF. Social disengagement and incident cognitive decline in community-dwelling
elderly persons. Ann Intern Med. 1999;131:165-173.
FREE FULL TEXT
23. Dealberto MJ, Pajot N, Courbon D, Alperovitch A. Breathing disorders during sleep and cognitive performance in an older
community sample: the EVA Study. J Am Geriatr Soc. 1996;44:1287-1294.
ISI
| PUBMED
24. Foley DJ, Monjan AA, Masaki KH, Enright PL, Quan SF, White LR. Associations of symptoms of sleep apnea with cardiovascular disease,
cognitive impairment, and mortality among older Japanese-American men. J Am Geriatr Soc. 1999;47:524-528.
ISI
| PUBMED
25. Kish L. Survey Sampling. New York, NY: John Wiley & Sons Inc; 1965.
26. Landon EL, Banks SK. Relative efficiency and bias of plus-one telephone sampling. J Marketing Res. 1977;14:294-299.
FULL TEXT
27. Ohayon MM, Guilleminault C, Paiva T, et al. An international study on sleep disorders in the general population:
methodological aspects. Sleep. 1997;20:1086-1092.
ISI
| PUBMED
28. Ohayon M. Knowledge-Based System EVAL Decisional Trees and
Questionnaires. Ottawa, Ontario: National Library of Canada; 1995.
29. Ohayon M. Improving decision making processes with the fuzzy logic approach in
the epidemiology of sleep disorders. J Psychosom Res. 1999;47:297-311.
FULL TEXT
|
ISI
| PUBMED
30. ICSD Diagnostic Classification Steering Committee. International Classification of Sleep Disorders:
Diagnostic and Coding Manual (ICSD). Rochester, Minn: American Sleep Disorders Association; 1990.
31. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition. Washington, DC: American Psychiatric Association; 1994.
32. Ohayon MM, Guilleminault C, Zulley J, et al. Validation of the Sleep-EVAL system against clinical assessments of
sleep disorders and polysomnographic data. Sleep. 1999;22:925-930.
ISI
| PUBMED
33. Hosn R, Shapiro CM, Ohayon MM. Diagnostic concordance between sleep specialists and the sleep-EVAL
system in routine clinical evaluations. J Sleep Res. 2000;9(suppl 1):86.
34. Derouesne C, Dealberto MJ, Boyer P, et al. Empirical evaluation of the cognitive difficulties scale for assessment
of memory complaints in general practice: a study of 1628 cognitively normal
subjects aged 45-75 years. Int J Geriatr Psychiatr. 1993;8:599-607.
FULL TEXT
35. Dupuy HJ. The Psychological General Well-being (PGWB) Index and Appendix I: selected
test instruments. In: Wenger NK, Mattson ME, Furberg C, Elinson J. Assessment of Quality of Life in Clinical Trials of Cardiovascular Therapies. New York, NY: Le Jacq Publishing Inc; 1984:170-183, 353-356.
36. Psychological Well-Being. In: McDowel I, Newell C. Measuring Health: A Guide
to Rating Scales and Questionnaires. 2nd ed. New York, NY: Oxford University
Press Inc; 1996:206-213.
37. Folstein MF, Folstein SE, McHugh PR. "Mini-Mental State": a practical method for grading the cognitive state
of patient for the clinician. J Psychiatr Res. 1975;12:189-198.
FULL TEXT
|
ISI
| PUBMED
38. Crum RM, Anthony JC, Bassett SS, Folstein MF. Population-based norms for the Mini-Mental State Examination by age
and educational level. JAMA. 1993;269:2386-2391.
FREE FULL TEXT
39. Lawton MP, Brody EM. Assessment of older people: self-maintaining and instrumental activities
of daily living. Gerontologist. 1969;9:179-186.
ISI
| PUBMED
40. Hosmer DW, Lemeshow S. Applied Logistic Regression. New York, NY: John Wiley & Sons Inc; 1989.
41. Naegele B, Thouvard V, Pepin JL, et al. Deficits of cognitive executive functions in patients with sleep apnea
syndrome. Sleep. 1995;18:43-52.
ISI
| PUBMED
42. Cheshire K, Engleman H, Deary I, Shapiro C, Douglas NJ. Factors impairing daytime performance in patients with sleep apnea/hypopnea
syndrome. Arch Intern Med. 1992;152:538-541.
FREE FULL TEXT
43. Ohayon MM, Zulley J. Prevalence of naps in the general population. Sleep Hypnosis. 1999;1:88-97.
44. Ohayon MM, Caulet M, Philip P, Guilleminault C, Priest RG. How sleep and mental disorders are related to complaints of daytime
sleepiness. Arch Intern Med. 1997;157:2645-2652.
FREE FULL TEXT
45. Slutske WS, True WR, Scherrer JF, et al. Long-term reliability and validity of alcoholism diagnoses and symptoms
in a large national telephone interview survey. Alcohol Clin Exp Res. 1998;22:553-558.
FULL TEXT
|
ISI
| PUBMED
46. Rohde P, Lewinsohn PM, Seeley JR. Comparability of telephone and face-to-face interviews in assessing
axis I and II disorders. Am J Psychiatry. 1997;154:1593-1598.
FREE FULL TEXT
47. Geerlings MI, Jonker C, Bouter LM, Ader HJ, Schmand B. Association between memory complaints and incident Alzheimer's disease
in elderly people with normal baseline cognition. Am J Psychiatry. 1999;156:531-537.
FREE FULL TEXT
48. Hultsch DF, Hammer M, Small BJ. Age differences in cognitive performance in later life: relationships
to self-reported health and activity life style. J Gerontol. 1993;48:P1-P11.
49. Christensen H, Korten A, Jorm AF, Henderson AS, Scott R, Mackinnon AJ. Activity levels and cognitive functioning in an elderly community sample. Age Ageing. 1996;25:72-80.
FREE FULL TEXT
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