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Sleep Complaints, Subjective and Objective Sleep Patterns, Health, Psychological Adjustment, and Daytime Functioning in Community-Dwelling Older Adults

¹ Center for Gerontological Studies and Department of Psychology
² College of Nursing
³ Department of Psychology, University of Florida, Gainesville.

Correspondence concerning this article should be addressed to Christina S. McCrae, Center for Gerontological Studies and Department of Psychology, University of Florida, McCarty C, Room 502, PO Box 115911, Gainesville, Florida, 32611-5911. E-mail: csmccrae{at}ufl.edu

	Abstract

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We examined sleep complaints, subjective and objective sleep patterns, health, psychological adjustment, and daytime functioning in 103 community-dwelling older adults to identify factors associated with sleep complaints. We collected 2 weeks of sleep diaries and actigraphy. Only health distinguished complaining from noncomplaining sleepers. Noncomplaining good sleepers had poorer objective sleep quantity than complaining poor sleepers. Actigraphy distinguished noncomplaining good and complaining poor sleepers only. Subjective and objective sleep quantities were related for noncomplainers only; this relationship was stronger for women. Implications include a need for research exploring: 1. sleep complaints, sleep perceptions, and health; 2. interventions focusing on older individuals with insomnia secondary to/comorbid with poor health; 3. gender differences in subjective sleep estimates and in "single-shot" versus longitudinal sleep measures.

Insomnia is the most common sleep disturbance in older adults with prevalence estimates ranging from 15–65% (Ohayon, 2002). Although quantitative sleep variables (e.g., total sleep time, sleep onset latency or the time required to fall asleep) can be measured subjectively (e.g., sleep diaries, retrospective questionnaires) or objectively (e.g, polysomnography [PSG], or sleep EEG, actigraphy), insomnia diagnosis is based on subjective estimates of poor sleep quantity and complaints of poor sleep quality. Interestingly, subjective estimates of poor sleep quantity are not always predictive of sleep complaints (McCrae et al., 2003; Fichten et al., 1995). Similarly, despite PSG evidence of age-related decreases in sleep duration and slow wave sleep (stages 3–4) and increases in awakenings and light sleep (stages 1–2; Buysse et al., 1992; Prinz et al. 1982), objective measurements of poor sleep quantity are not strongly related to either subjective estimates of poor sleep quantity or sleep complaints in healthy older adults (Buysse et al., 1991; Vitiello, Moe, & Prinz, 2002).

Recently, however, Vitiello, Larsen, and Moe (2004) found a strong subjective–objective sleep quantity relationship for older men compared to a considerably weaker one for older women, suggesting that gender influences individuals' perceptions of their sleep quantity. This is particularly interesting given that women are significantly more likely to complain of insomnia than men (Foley et al., 1995). Sleep complaints have also been linked to age-related risk factors, including poor health, medication, anxiety (Fichten et al., 1995), and depression (Foley et al., 1995; Foley et al., 1999; McCrae et al., 2003; Ohayon, 2002; Vitiello, Moe, & Prinz, 2002). Epidemiological evidence linking daytime sleepiness to nocturnal disturbances, depression, and medication usage in older adults (Whitney et al., 1997) suggests daytime functioning factors, such as sleepiness and fatigue, may also contribute to sleep complaints. Research exploring the complex relationships between subjective and objective sleep quantity, complaints, and the factors related to complaints is crucial for understanding and treating late-life insomnia.

Numerous researchers have studied late-life insomnia by comparing complaining poor sleepers to good sleepers (e.g., Morin & Gramling, 1989). Recently, researchers have become interested in a third group—noncomplaining poor sleepers (Fichten et al., 1995; McCrae et al., 2003). Because sleep complaints prompt treatment seeking, researchers hope to identify factors that distinguish individuals likely to seek treatment for insomnia from those unlikely to seek treatment by comparing noncomplaining and complaining poor sleepers. Research to date has produced interesting but equivocal results and warrants additional follow-up. For example, McCrae and colleagues (2003) found complaining poor sleepers generally reported worse subjective sleep quantity, poorer health, and more depressive symptoms than noncomplaining poor sleepers, while Fichten and colleagues (1995) found complaining poor sleepers reported more anxiety but small to no differences in subjective sleep quantity compared to noncomplaining poor sleepers.

In our research, we have found that a substantial number of older adults fall into a fourth sleep group—complaining good sleepers. The diagnostic label for this group is sleep state misperception (American Sleep Disorders Association, 1997). We believe inclusion of this group is essential for future research because it allows for comparisons of all possible sleep complaint—subjective sleep quantity combinations (noncomplaining good, complaining good, noncomplaining poor, complaining poor). Thus, the main goal of the present study is to identify the sleep, health, psychological, and daytime functioning factors that differentiate these 4 groups and to examine whether these factors vary by gender. Another goal is to examine whether complainers are less accurate in their subjective estimations of sleep quantity than noncomplainers. To accomplish these goals, we collected 2 weeks of subjective (sleep diary) and objective (actigraphy) sleep quantity data, as well as health, psychological adjustment, and daytime functioning information from relatively healthy community-dwelling older adults.

The present study advances previous research in several ways. First, we are the first to examine both subjective and objective sleep quantity in all four sleep groups. Previous research examined subjective sleep quantity only (Fichten et al., 1995; McCrae et al., 2003) or included only one subgroup (Vitiello et al., 2004). Second, we examine gender differences in all our comparisons. Third, our longitudinal actigraphic data provide unique objective information on older adults' usual sleep patterns, which is important, because: 1. sleep varies from day to day; 2. poor sleepers often sleep differently in the laboratory; and 3 laboratory-based studies typically rely on a single night or two of PSG (Buysee et al., 1991; Vitiello et al., 2004). Finally, we are the first to examine the role of daytime functioning in differentiating the four sleep groups.

	METHOD

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Sample
A convenience sample of 116 adults, aged 60 years and older, residing in North Central Florida was recruited through media advertisements, community groups, and flyers to participate in a study of sleep patterns in elderly people. Interested individuals were screened in two phases: a brief telephone interview (15–20 min) followed by a 1–1.5 hr in-person interview conducted either in the individual's home (76%) or at a local continuing care retirement center (24%). Exclusionary criteria included: a. younger than 60 years; b. self-report of sleep disorder diagnoses other than insomnia (e.g., sleep apnea, narcolepsy); c. self-report of symptoms suggestive of other sleep disorders (e.g., heavy snoring, gasping for breath, leg jerks, daytime sleep attacks); d. severe psychiatric disorders (e.g., thought disorders, depression); e. cognitive impairment (i.e., scoring in the impaired range on 3 or more subtests of the Cognistat); f. psychotropic or other medications (e.g., beta-blockers) known to alter sleep; and g. medical conditions that impaired ability to be completely independent in normal daily functions. Thirteen individuals were ineligible for reasons including age, dementia diagnosis, medication, sleep apnea diagnosis, and suspected sleep apnea. Thus, the final sample consisted of 103 participants (M = 72.81 years; SD = 7.12). All were living in their own homes during the study. The majority of the sample was Caucasian (96%) and had completed some college coursework (75%; M = 16.34 years, SD = 2.92).

We classified participants into 4 sleep groups based on subjective sleep quantity (good vs poor) and complaint status (complaining vs noncomplaining; see Table 1). Subjective sleep quantity was measured by sleep diaries, which are the recommended method for assessing insomnia and are more widely used in research and practice than actigraphy. Participants were categorized as poor sleepers if they reported at least 31 min of unwanted awake time (sleep latency and awakenings during sleep) at least 3 nights a week. We chose to use this 31-min cutoff rather than a 30-min cutoff (commonly used in research), because it is the most defensible quantitative criteria for identifying poor sleep (Lichstein et al., 2003).

Measures
Subjective sleep
Participants completed a sleep diary (Lichstein, Riedel, & Means, 1999) each morning for 14 days, providing subjective estimates of eight sleep–wake variables: 1. sleep onset latency_s (time from initial lights out until sleep onset); 2. number of nighttime awakenings (number of total awakenings during night); 3. wake time after sleep onset (time spent awake after initial sleep onset until last awakening); 4. total sleep time_s (computed by subtracting total wake time from time in bed); 5. sleep efficiency percentage_s (ratio of TST to total time spent in bed x 100); 6. total wake time_s (time spent awake from initial lights out until time out of bed in the morning); 7. sleep quality rating (scaled from 1 = very poor to 5 = excellent); and 8. total nap time (total amount of time spent sleeping prior to bedtime). We use "s" and "o" subscripts to distinguish the four sleep variables for which we have both subjective (s) and objective (o) measures. Means were computed for each variable for the 14 days. Compliance with diary completion was exceptionally high. Out of 11,536 possible data cells (8 sleep diary variables x 14 days x 103 participants), only 133 were missing (1.15%).

Objective sleep
Participants wore an Actiwatch-L with an integral ambient light sensor (Mini Mitter Co. Inc., 2001) on their nondominant wrist for 14 consecutive 24-hr periods concurrent with the sleep diary period. Actiwatch-L, which monitors ambient light exposure and gross motor activity, consists of an omnidirectional, piezoelectric accelerometer with a sensitivity of 0.01 g-force. The light sensor's recording range is 0.1 to 150,000 lux. For each 30 second epoch, Actiwatch-L samples data 32 times per second, recording the peak value for each second. The peak activity count for each epoch (i.e., the sum of the peak values) is then downloaded to a PC and analyzed by Actiware-Sleep vol. 3.3 (Mini Mitter) using a validated algorithm to identify the epoch as sleep or wake (Oakley, 1997). The software provides three default sensitivity settings (high, medium, low). We utilized high sensitivity because it provides excellent correlation with PSG for total sleep time (.95) in healthy older adults (Colling et al., 2000) and for total sleep time (.73) and sleep onset latency (.93) in individuals with insomnia (Cook et al., 2004). The threshold for high sensitivity is 20 activity counts. If the peak activity count for an epoch was 20, it was scored as wake. If the peak activity was < 20, the final activity count for the epoch was based on activity in the surrounding 2 min using the following equation:

where A = activity count for the epoch being scored and E_{A+/– 1-4} = activity count in adjacent epochs. If Total Activity Epoch A (i.e., weighted sum of activity counts) exceeded the threshold of 20, then Epoch A was scored as wake; otherwise, it was scored as sleep.

Bedtime and time out of bed in the morning were based on sleep diary entries as recommended in the software manual (Mini Mitter, 2001). Actiware-Sleep determined sleep start automatically by searching for the first 10 min during which no more than one epoch scored as wake. Likewise, sleep end was the last 10 min during which no more than one epoch scored as wake. Actiware-Sleep provides objective estimates for several sleep quantity variables also provided by sleep diaries. The variables we examined, and their definitions when measured objectively by actigraphy are: 1. sleep onset latency_o (interval between bedtime and sleep start); 2. total sleep time_o (sum of all sleep epochs within the sleep period); 3. sleep efficiency_o (ratio of total sleep time to total time spent in bed x 100); and 4. total wake time_o (sum of all wake epochs within the sleep period). Similar to the sleep diaries, these variables were averaged over the 14 days. Data loss was minimal. There were no equipment failures. Some participants (n = 3) took their Actiwatch-L off during the day for several hours (< 3 hours). However, in each case, the participant put the watch back on several hours before bedtime. Other participants (n = 3) left their watches off for an entire day (24 hours). To make up for this lost day, these participants wore their watches and completed their sleep diaries for an additional day immediately following the study period (e.g., day 15). One participant forgot to wear the watch during week 2. Thus, we have concurrent sleep diary and actigraphy data for 14 days for all but one participant.

Demographics and health survey
This two-page questionnaire contains 13 items on demographics, sleep disorders symptoms, physical health, and mental health (Lichstein et al., 2003). Sleep disorders items asked whether the participant had a sleep problem and if so, to describe it. Participants were asked if they or a bed partner noticed heavy snoring, difficulty breathing or gasping for breath, frequent leg jerks, restlessness before sleep onset, daytime sleep attacks, or paralysis at sleep onset. If they answered yes to any item, they were asked to provide an explanation and to indicate how often the symptoms occur.

Health
Two items were taken from the demographics and health survey–medical conditions and medications. Medical conditions were defined as the number reported from the following list: heart attack, other heart problems, cancer, AIDS, hypertension, neurological disorder (seizures, Parkinson's), breathing disorder (asthma, emphysema, allergies), urinary problems (kidney disease, prostate problems), diabetes, pain (arthritis, back pain, migraines), and gastrointestinal disorders (stomach, irritable bowels, ulcers, gastric reflux). Medications were defined as the number of prescription medications listed.

Psychological adjustment
Beck Depression Inventory–Second Edition (BDI-II; Beck, Steer, & Garbin, 1996) is a 21-item self-report questionnaire measuring severity of depressive symptomatology on a 3-point scale ranging from 0 (absence of symptoms) to 3 (most severe). Typically, respondents answer for the previous week, but the previous 2 weeks were used to match the study period. Total scores range from 0 to 63. Ranges for clinical levels of depression are 0–9 (none or minimal), 10–18 (mild to moderate), 19–29 (moderate to severe), and 30–63 (severe). BDI-II has demonstrated adequate reliability and validity. Higher scores indicate greater maladjustment.

State–Trait Anxiety Inventory, Form Y1 (STAI-Y1; Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983) contains 20 self-descriptive statements indicating how often the statement is true on a 4-point scale ranging from 1 (not at all) to 4 (very much so). Total scores range from 20 to 80. STAI-Y1 demonstrates test–retest reliability exceeding.7 and reliably distinguishes patient and normal groups. STAI-Y1 was also based on the previous 2 weeks. Once again, higher scores indicate greater maladjustment.

Daytime functioning
Epworth Sleepiness Scale (ESS; Johns, 1991) measures trait daytime sleepiness. Respondents indicate how likely they are to fall asleep in eight common, quiet daytime activities (e.g., watching television, driving) during the 2 weeks. Ratings range from 0 (would never doze) to 3 (high chance of dozing). Total scores range from 0 to 24, with higher scores indicating greater daytime sleepiness. Adequate norms for ESS are not available. However, scores of 11 or greater are indicative of the severe daytime sleepiness reported by individuals with obstructive sleep apnea.

Fatigue Severity Scale (FSS; Krupp, LaRocca, Muir-Nash, & Steinberg, 1989) consists of nine items assessing the intrusion of fatigue in different aspects of living. Each item is rated from 1 (strongly disagree) to 7 (strongly agree). Responses are averaged across the nine items, yielding a possible score range of 1 to 7. Normative data on FSS are limited. Lichstein, Means, Noe, and Aguillard (1997) found individuals with insomnia seeking treatment at a sleep disorders center averaged 6.0 (SD = 0.5) on FSS.

Cognitive impairment
Cognistat (Mueller, Kiernan, & Langston, 2001) contains 10 subscales measuring five major ability areas: language, constructions, memory, calculations, and reasoning. In clinical trials, Cognistat has been shown to be more sensitive to detection of cognitive impairment than the Mini-Mental State Exam (Fields, Fulop, Sachs, Strain, & Fillit, 1992). Individuals who scored in the impaired range on 3 or more of the 10 subscales were not eligible to participate.

Procedures
During the 1–1.5 hr interview session, participants read and signed an informed consent form approved by the University of Florida's Institutional Review Board, and a research team member administered the Cognistat, conducted a sleep history interview, and explained how to complete the sleep diaries and other questionnaires. The team member also gave the participant an Actiwatch-L and explained how it works and how it should be worn. Participants were asked to complete the sleep diaries everyday and wear the Actiwatch-L continuously for 2 weeks. The demographics and health survey was completed at the start of the study, but the depression, anxiety, and daytime impairment measures were to be completed at the end of the 2 weeks. After the first week, a team member visited the participants again to determine how the study was going, answer any questions, and download the first week's data from the Actiwatch-L. At the end of the 2 weeks, a team member visited the participants a third time to collect the watches and questionnaires. All participants received $30 compensation.

Statistical analysis
Demographic differences among the 4 sleep groups were analyzed using analyses of variance (ANOVA; age, education, body mass index) and Chi-square analyses (gender). Because sleep diary variables (sleep onset latency_s and wake time after sleep onset_s) were used to classify participants as good or poor sleepers, two 2 (gender) x 2 (sleep group) multivariate analyses of variance (MANOVA) were performed to examine subjective sleep quantity by gender for the good and poor sleepers, separately. To examine objective sleep quantity, health, psychological adjustment, and daytime functioning by gender for all 4 sleep groups, four 2 (gender) x 4 (sleep group) MANOVAs were performed. Eight subjective sleep variables were analyzed—sleep onset latency_s, number of nighttime awakenings, wake time after sleep onset_s, total sleep time_s, sleep efficiency_s, total wake time_s, total nap time, and sleep quality rating. Four objective sleep variables were analyzed—sleep onset latency_o, total sleep time_o, total wake time_o, and sleep efficiency_o. Two health variables were analyzed—total number of medical conditions and total number of medications. Two psychological adjustment variables were analyzed—BDI-II and STAI. Two daytime functioning variables were analyzed—FSS and ESS.

Multivariate results are reported using Roy's , a standard multivariate statistic that tests the first Eigen value derived from the ratio of the between group to within group matrices. Significant multivariate results were followed up using univariate and Tukey honestly significant difference testing. Partial eta squared values (_p²), a common measure of effect size in ANOVA, are provided for significant multivariate and univariate effects. Pearson r's were calculated to examine subjective–objective sleep relationships for sleep onset latency_s/o, total sleep time_s/o, sleep efficiency_s/o, and total wake time_s/o for the 4 sleep groups for the total sample and by gender.

	RESULTS

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Sleep Groups
Of the total sample, 48% were classified as noncomplaining good sleepers, 11% as complaining good sleepers, 23% as noncomplaining poor sleepers, and 18% as complaining poor sleepers. Table 1 provides sample characteristics for the 4 sleep groups. The sleep groups did not differ significantly in age (p =.60), education (p =.10), body mass index (p =.29), or gender (p =.93).

Group Differences in Sleep, Health, Psychological Adjustment, and Daytime Functioning
Subjective sleep–good sleepers
There was a significant main effect of sleep group, Roy's = 0.5, F (8, 48) = 2.97, p <.01, _p² =.33. Neither the main effect of gender nor the Gender x Sleep group interaction were significant. Univariate testing revealed significant differences between the complaining and noncomplaining good sleepers for number of awakenings, wake time after sleep onset_s, sleep efficiency_s, and total wake time_s (see Table 2). For all four variables, complaining good sleepers reported worse sleep than noncomplaining good sleepers. On average, complaining good sleepers awoke approximately one more time a night, were awake for approximately 14 min longer after sleep onset, slept approximately 5% less efficiently, and were awake approximately 24 min longer while in bed than noncomplaining good sleepers.

Psychological adjustment
The main effects of gender and sleep group as well as their interaction were not significant (see Table 1).

Daytime functioning
There was a significant main effect of sleep group, Roy's = 0.12, F (3, 95) = 3.65, p <.05, _p² =.10. Neither the main effect of gender nor the Gender x Sleep group interaction were significant. Univariate testing revealed significant differences between groups for fatigue severity, F (3, 99) = 3.55, p <.05, _p² =.10 (see Table 1). On average, complaining good sleepers reported more daytime fatigue than noncomplaining poor sleepers. Noncomplaining good sleepers and complaining poor sleepers did not significantly differ from each other or from the other 2 sleep groups.

Subjective–Objective Sleep Comparisons
In order to examine whether complainers were less accurate in their subjective estimates of sleep quantity than noncomplainers, sleep diary-actigraphy correlations were calculated for the 4 sleep groups (see Table 5). For noncomplaining good sleepers, the correlation was high for total sleep time only, while for noncomplaining poor sleepers, correlations were high for both sleep onset latency and total sleep time. None of the sleep diary/actigraphy correlations were significant for complaining good and poor sleepers.

For noncomplaining good and poor sleeping males, there were moderate and high correlations, respectively, for total sleep time only. None of the sleep diary–actigraphy correlations were significant for complaining good and poor sleeping males.

	DISCUSSION

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None of the sleep, health, psychological, or daytime functioning factors examined differentiated all 4 sleep groups. However, subjective sleep quantity distinguished complaining from noncomplaining good sleepers, while objective sleep quantity distinguished the 2 most extreme groups—noncomplaining good sleepers and complaining poor sleepers. In both cases, poorer sleep quantity was associated with complaints. Daytime fatigue was greater for complaining good sleepers than for noncomplaining poor sleepers. Consistent with previous research (Ohayon, 2002; Vitiello et al., 2002), complaints were linked to poorer health. Complaining good and poor sleepers reported 1–2 more chronic conditions on average than noncomplaining good and poor sleepers. The small effect size of this finding (_p² =.18) is not surprising as health likely interacts with a variety of other factors to produce sleep complaints.

Gender differences emerged for objectively measured sleep efficiency and for subjective–objective sleep quantity comparisons. For noncomplaining good sleepers and complaining poor sleepers, actigraphy revealed that women slept more efficiently than men. As anticipated, noncomplainers were more accurate in estimating their objective sleep quantity than noncomplainers. However, based on Vitiello, Larsen, and Moe's (2004) recent finding of stronger subjective–objective sleep quantity relationships for men compared to women, we were surprised to find strong subjective–objective sleep quantity relationships for total sleep time for both noncomplaining men and women. We were even more surprised to find no subjective–objective sleep quantity relationship for sleep onset latency for noncomplaining men and a moderate to high relationship for noncomplaining women. These contradictory results might be attributable to methodological differences between the studies—one night of PSG and a retrospective sleep questionnaire compared to actigraphy and sleep diary data for 14 days in the present study. Future research is needed to clarify the impact of gender on older individuals' subjective sleep estimates. Research exploring differences in "single-shot" versus longitudinal sleep measures and gender is warranted.

Consistent with the American Academy of Sleep Medicine's recent statement that actigraphy is not useful in diagnosing insomnia (Ancoli-Israel, et al., 2003; Littner et al., 2003), our actigraphic data distinguished noncomplaining good sleepers and complaining poor sleepers, but did not distinguish complaining from noncomplaining poor sleepers. Insomnia diagnosis relies on complaints and subjective sleep quantity estimates, so objective measurement devices, such as PSG and actigraphy, do not reliably diagnose insomnia. Nonetheless, the examination of group differences in subjective–objective sleep comparisons such as those reported here allows for closer examination of the complex relationships between objective sleep quantity and individuals' perceptions of their sleep (in terms of both sleep quantity estimates and complaints). Future research on the factors that influence these perceptions is needed. Specifically, because research to date has focused primarily on factors related to negative perceptions or complaints, research that identifies factors associated with positive perceptions is particularly important. Such factors (e.g., coping style, daily affect, nutrition, and exercise, as well as others) could lead to the development of new prevention and intervention strategies.

One limitation of the present study is the reliance on self-report screening for primary sleep disorders. Evidence from the Sleep Heart Health study (Young et al., 2002) indicates that the typical predictive factors for sleep-disordered breathing (SDB), such as snoring and obesity, may be inappropriate for identifying SDB in older people. The effect of SDB as well as other primary sleep disorders (e.g., periodic limb movements disorder) on the present study is difficult to estimate, because individuals with these disorders might fall into any of the 4 sleep groups as they might not be aware of these difficulties and therefore might not complain or report poor subjective sleep quantity. To determine whether participants suffer from insomnia due to primary sleep disorder, PSG screening should be employed in future research in this area.

Use of a convenience sample is another limitation. The recruitment announcements clearly stated that the study was about older adults' normal sleeping patterns and was not about poor sleep. Nonetheless, the sample might be biased toward individuals with poor sleep or those who were concerned that they might have poor sleep. The sample might also be biased toward individuals who were proud of their good sleep. The effect of these biases might be a sample that contains individuals who perceive their sleep to be either exceptionally good or exceptionally poor. However, it is also possible that individuals who are concerned about their sleep may perceive their sleep to be average. Thus, the impact of this limitation on the generalizability of the results is difficult to estimate. Random sampling can prevent this limitation in future research.

The advantages of actigraphy compared to PSG for naturalistic, longitudinal research were presented in the Introduction. Actigraphy also has some disadvantages. For example, it more accurately scores sleep than wake and therefore is better at estimating global sleep variables (e.g., total sleep time) than variables indicative of wakefulness (e.g., number of nighttime awakenings). Our actigraphic analyses focused on global variables with one exception. Sleep onset latency was also included because recent research suggests excellent actigraphy–PSG correlations for total sleep time and sleep onset latency in individuals with insomnia (Cook et al., 2004).

Conclusion
The present study indicates that both health and sleep perceptions warrant additional research as potential intervention targets. Older adults with poor health are often excluded from clinical outcome trials (notable exception, Lichstein, Wilson, & Johnson, 2000). Our results also support actigraphy's usefulness for estimating sleep onset latency (for women) and total sleep time (for women and men) for individuals unable to complete sleep diaries. Finally, our results indicate the need for future research using actigraphy because longitudinal, home-based, objective sleep measurement using actigraphy provides information that is distinctly different from that provided by "one-shot" laboratory-based sleep measurement using PSG.

	Acknowledgments

 
This research was supported in part by intramural grants from the College of Liberal Arts and Sciences and the College of Nursing, University of Florida. We thank Claydell Horne for her efforts in participant recruitment and data collection and Tom Tiegs and Linda Feldstein for their efforts in data collection and study coordination. Preliminary results from this study were presented at the November 2003 annual meeting of The Gerontological Society of America in San Diego, California.

	Footnotes

 
Decision Editor: Thomas M. Hess, PhD

Received for publication June 24, 2004. Accepted for publication January 28, 2005.

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