Sleepiness in Patients with Moderate to Severe Sleep-Disordered Breathing

Sleepiness in Patients with Moderate to Severe Sleep-Disordered Breathing Vishesh K. Kapur, MD, MPH 1 ; Carol M. Baldwin, RN, PhD, HNC 2 ; Helaine E. Resnick, PhD, MPH 3 ; Daniel J. Gottlieb, MD, MPH 4 ; F. Javier Nieto, MD, PhD 5 1 Department of Medicine, University of Washington, Seattle, WA; 2 Arizona State University College of Nursing (Southwest Borderlands), Tempe, AZ; 3MedStar Research Institute, Hyattsville, MD; 4 Department of Medicine, Boston University and the VA Boston HCS, Boston, MA; 5 Department of Population Health Science, University of Wisconsin, Madison, WI Background: Population-based studies suggest that complaints of sleepiness are absent in many individuals with sleep-disordered breathing. We investigated the prevalence of sleepiness as well as factors associated with sleepiness in individuals with moderate to severe sleep-disordered breathing (apnea-hypopnea index > 15). Design: Cross-sectional study. Setting: The Sleep Heart Health Study. Participants: Sleep Heart Health Study participants (N = 6440). Measurements and Results: Sleepiness was defined as an Epworth Sleepiness Scale score >10 or a report of at least frequently feeling unrested or sleepy. Forty-six percent of participants with moderate to severe sleep-disordered breathing (n = 1149) reported sleepiness. Characteristics associated with sleepiness after adjustment for confounders included presence of respiratory disease, shorter self-reported weekday and weekend sleep, sleep durations, complaints of insufficient sleep, complaints of sleep maintenance insomnia, early morning awakening, habitual snoring, and complaints of awakening with leg cramps or leg jerks. Some respiratory polysomnography measures were associated with sleepiness, but sleepstage percentages and measures of sleep disruption were not. Conclusions: In this community-based cohort, subjective sleepiness is absent in many individuals with significant sleep-disordered breathing. Comorbid conditions, including respiratory disease, sleep restriction, insomnia, and nocturnal leg complaints, are important risk factors for sleepiness in individuals with moderate to severe sleep-disordered breathing. Key Words: Sleep, apnea, sleepiness, syndrome, subjective Citation: Kapur VK; Baldwin CM; Resnick HE et al. Sleepiness in patients with moderate to severe sleep-disordered breathing. SLEEP 2005;28(4):472-77. INTRODUCTION OBSTRUCTIVE SLEEP APNEA SYNDROME IS DEFINED AS THE PRESENCE OF FREQUENT EPISODES OF OBSTRUCT- ED BREATHING DURING SLEEP IN THE presence of complaints of sleepiness or insomnia. 1 In contrast, obstructive sleepdisordered breathing (SDB) refers to the physiologic abnormality (obstructed breathing) during sleep that is observed in individuals with obstructive sleep apnea syndrome as well as some individuals who do not have sleep-related complaints. Although SDB is often accompanied by sleepiness, population-based studies suggest that complaints of sleepiness are absent in most individuals with significant SDB. 2,3 In the Wisconsin Sleep Cohort Study, sleepiness was defined as the occurrence (> 2 days per week) of complaints of sleepiness, awakening unrefreshed, and uncontrollable sleepiness that interfered with daily living. In this study, only 22.6% of women and 15.5% of men with an apnea-hypopnea index (AHI) > 5 reported sleepiness. 2 A potential explanation for the absence of consistency between objective measure of SDB and subjective measures of sleepiness is that definitions of subjective sleepiness used in these studies were too restrictive. A study of patients with obstructive sleep Disclosure Statement This is not an industry-supported study. Dr. Kapur receives research support from the Washington Technology Center. Dr. Gottlieb has a consulting arrangement as an occasional medical monitor for Carestat, Inc. for hematology/oncology-related clinical trials. Drs. Baldwin, Resnick, and Nieto have indicated no financial conflicts of interest. Submitted for publication August 2004 Accepted for publication December 2004 Address correspondence to: Vishesh Kapur, MD, MPH, University of Washington Sleep Disorders Center, Box 359803, 325 Ninth Avenue, Seattle, WA 98104; Tel: (206) 731-4999; Fax: (206) 731-5657; E-mail: vkapur@u.washington.edu SLEEP, Vol. 28, No. 4, 2005 472 apnea syndrome found complaints of fatigue, tiredness, or lack of energy to be more common than complaints of sleepiness per se. 4 A broader definition of subjective sleepiness might result in sleepiness prevalence in subjects with SDB being significantly higher than has been reported in previous studies. Another potential explanation is that the low prevalence of sleepiness is related to the predominance of mild (5 > AHI < 15) SDB among the group classified as having SDB. Mild SDB may not cause sufficient physiologic burden to cause sleepiness in many individuals. In the Sleep Heart Health Study (SHHS) cohort, an increased score on the Epworth Sleepiness Scale (ESS) has been associated with severity of SDB, habitual snoring, male sex, shorter usual sleep duration, and complaints of awakenings due to leg cramps, leg jerks or both. 3 Clinic-based studies indicate that some polysomnography findings (sleep fragmentation, frequency of SDB, and hypoxemia) are predictors of sleepiness. 5,6 However, additional information is needed in subjects with SDB to clarify why sleepiness occurs in some individuals but not others. In this report, polysomnography and questionnaire data from the SHHS are used to investigate the prevalence of subjective sleepiness, as well as factors associated with sleepiness, in participants with moderate to severe SDB. We predict that a large proportion of participants with moderate to severe SDB do not have subjective sleepiness. We also predict that comorbid causes of sleepiness and objective measures of sleep disruption are predictors of sleepiness in these participants. METHODS Subjects in this report are 6440 participants in the SHHS, a multicenter study of the cardiovascular consequences of SDB, who had successful unattended polysomnography performed between December 1995 and February 1998. Subjects from all participating sites were provided with informed voluntary human subjects consent through their respective Institutional Review Boards. The design of the SHHS has been described. 7

Polysomnography Participants underwent a single night, unattended polysomnogram in the home, using a portable monitor (Compumedics P Series System, Abbotsford, Victoria, Australia). The following channels were recorded: electroencephalogram (C3/A2, C4/A1), electrooculogram (bilateral), electrocardiogram, chin electromyogram, oxyhemoglobin saturation (finger pulse oximetry), chest and abdominal excursion (inductance plethysmography), airflow (oronasal thermocouple), body position, and ambient light. Details of the hook-up procedure, failure rates, scoring of the studies, quality assurance, and quality control have been described elsewhere. 8,9 A qualifying abnormal respiratory event (an apnea or hypopnea) was associated with oxyhemoglobin desaturation of 4% or greater, compared with baseline. Medical History Information about chronic illnesses and other covariates was obtained from a technician-administered structured questionnaire. Participants reporting a yes response to any 1 or more questions on doctor-diagnosed angina, heart attack, stroke, or cardiac procedure (coronary artery bypass operation, coronary angioplasty, insertion of a pacemaker, or other cardiac operation) were classified as having cardiac disease. No cardiac disease was defined as no responses to all of the above questions. Similarly, participants were classified as to respiratory disease status based on their responses to questions on doctor-diagnosed emphysema, chronic bronchitis, chronic obstructive pulmonary disease, or asthma. A composite variable for chronic obstructive pulmonary disease was created by combining questions on emphysema, chronic bronchitis, and chronic obstructive pulmonary disease. The number of reported servings of wine, beer, and hard liquor per week were summed to obtain a measure of total alcohol use per week. Participants who were noted to be taking any medication in the categories of antipsychotics, benzodiazepines, antidepressants, or antidepressant combinations were classified as using a sedative medication. Sleep Complaints and Habits Participants also completed a Sleep Habits Questionnaire. 9 This instrument asked about trouble falling asleep, waking up during the night and having difficulty getting back to sleep (sleep maintenance insomnia), waking up too early in the morning, feeling unrested during the day no matter how many hours slept, feeling excessively sleepy during the day, not getting enough sleep, and awakenings due to leg cramps or leg jerks. Responses included never (0), rarely (1 per month or less), sometimes (2-4 per month), often (5-15 per month), and almost always (16-30 per month). These responses were recoded into binary variables that contrasted often and almost always responses versus the other responses ( often vs not often ). An ESS score was calculated for each participant (n = 6203) who had answered all 8 questions that comprise this measure of daytime sleepiness. 10 An ESS score greater than 10 was considered to be consistent with sleepiness. 3,10 An often response to the sleepy or the unrested questions was also classified as consistent with sleepiness. A composite variable for sleepiness was created that was used in most of the analyses. Participants who often complained of excessive sleepiness or feeling unrested or who had an SLEEP, Vol. 28, No. 4, 2005 473 Epworth score > 10 were classified as being sleepy. Participants in the not often groups for sleepy and rested questions and having an ESS score > 10 were classified as being not sleepy. Since the use of a criteria of greater than 10 to define sleepiness by the ESS score is an arbitrary one, we performed sensitivity analyses by varying the criteria to greater than 9, 8, or 7. As the criteria were relaxed, the percentages of subjects with moderate to severe SDB who met the ESS criteria for sleepiness were 33.3%, 39.8%, 49.3%, and 57.5%. The percentages of subjects with sleepiness in the subjects without significant SDB (AHI < 5) were 21.4%, 27.6%, 34.9%, and 42.3%. Responses to questions about feeling worn out and tired were available in a subset of our participants (n = 4789) who had completed the SF-36. Respondents reporting feeling worn out or tired at least a good bit of the time were added to the sleepy group for additional analyses in this subset of participants to determine if a broader vocabulary for describing sleepiness dramatically altered the prevalence of sleepiness. Data about typical weekday and weekend sleep hours were available. A variable for total sleep hours per week was calculated by adding 5 times weekday sleep time and 2 times weekend sleep hours. Statistical Analysis Statistical analyses were performed using SPSS. 11 All SHHS participants (N = 6440) were included in a comparison of prevalence of sleepiness using 4 different criteria by AHI category using the Pearson χ 2 test. All remaining analyses included only participants with moderate to severe SDB (AHI > 15: n = 1149; n = 1115 with sleepiness classification available). For analyses of sleep stage and arousal data, a subset of participants in whom acceptable study quality for these measures had been documented were used (n = 860). 19 Comparison of baseline and sleep-related measures between participants included and excluded (n = 255) in analyses of sleep stages and arousals revealed several differences. Participants who were excluded were more likely to be sleepy (53.3% vs 43.5%), have cardiac disease (29.7% vs 22.6%), and use sedative medication (13.2% vs 7.6%). They also had higher AHI (median 26.0 vs 23.8) and percentage of time below 90% oxygen saturation (median 6.9 vs 4.6). A smaller number of participants in whom the entire sleep period had been captured were included in analyses on total sleep time (n = 607) and sleep efficiency (n = 358; required accurate assessment of lights off time as well). In descriptive analyses, characteristics were compared between sleepy and nonsleepy participants (Pearson χ 2 test or the Mann Whitney U test). Multivariable logistic regression was performed to determine if findings persisted following adjustment for potential confounders. Confounders were selected for inclusion in multivariable models from variables that were significantly related (P <.05) to sleepiness in descriptive analyses. Colinearity between predictors was examined using correlation matrices and colinearity diagnostics. Additional analyses were performed that allowed for the possibility of nonlinear relationships between sleepiness and polysomnography variables. Polysomnography parameters were represented in quartiles in the logistic regression models. In order to explore if sleep disruption in a range typically seen in clinical populations evaluated for SDB was associated with sleepiness, several sleep-related polysomnography parameters, often used

clinically to assess degree of sleep disruption, were represented in logistic regression models as single binomial variables. The binomial variables divided the top or bottom tenth percentile (stage 1% > 12.8, stage 3/4 % < 0.78, stage rapid eye movement [REM] % < 10.4, arousal index > 46.3) of values from the rest. RESULTS Table 1 Prevalence of Sleepiness by Apnea-Hypopnea Index Category* The prevalence of sleepiness by category of AHI based on 4 different definitions of sleepiness is given in Table 1. The prevalence increases with increased severity of SDB for all definitions. Even among participants with moderate to severe SDB (AHI > 15), however, a minority (45.7%) reported being sleepy using a definition that includes the ESS score and reports of sleepiness and feeling unrested. A higher percentage (53.9%) reported being sleepy using a more inclusive definition (included feeling tired or worn out) that was available in a subset of our participants. This more-inclusive definition resulted in a high prevalence of sleepiness (42.4%) in participants without significant SDB (AHI < 5). Participants with moderate to severe SDB (AHI > 15) with sleepiness were compared to those without sleepiness (Tables 2A and 2B). Among the demographic, medical, and subjective sleep characteristics evaluated, sleepy participants had a higher body mass index; more respiratory disease, sedative use, complaints of not getting enough sleep, insomnia symptoms, habitual snoring, awakening with leg cramps or jerks; and lower age and selfreported sleep hours (Table 2A). Among the sleep-study measures evaluated, sleepy participants had higher AHI and hypoxemic burden (percentage of sleep time spent below 90% oxygen saturation) and lower average oxygen saturation in REM and non-rem sleep (Table 2B). Sleep-stage distribution and arousal index were not different based on sleepiness status. Table 3 shows odds ratios derived from individual multivariable logistic regression models for each nonpolysomnography variable that differed significantly in univariate analyses. After adjustment for covariates, these variables remained statistically significant: respiratory disease, self-reported sleep hours, complaints of not getting enough sleep, sleep maintenance insomnia, early morning awakening, habitual snoring, and awakening with leg cramps or jerks. The relationship of greatest magnitude was for the symptom of often not get enough sleep (odds ratio 4.58 [3.19-6.58]). There was also a strong association of sleepiness with often awakening with leg cramps or jerks (odds ratio 2.36 [1.58-3.52]). Respiratory disease increased the risk of sleepiness (odds ratio 1.81 [1.22-2.67]). Longer usual sleep duration on weekdays (odds ratio 0.77 [0.69-0.86]; per 1-hour increase) and weekends [odds ratio 0.83 (0.76-0.92]; per 1-hour increase) was associated with less sleepiness. The logistic regression models were repeated after adding sedative use as a potential confounder, as it may represent a marker or confounder in the relationship to sleepiness for some variables. The odds ratios derived from these models did not differ significantly (< 10% change) from the original models. Table 4 shows odds ratios derived from individual multivariable logistic regression models for each polysomnography variable after adjusting for age, respiratory disease, sleep hours per week, and frequency of leg cramps. The associations found in univariate analyses persisted. In addition, obstructive apnea index and lowest saturation in non-rem sleep were also associated with sleepiness after adjusting for potential confounders. Sleep-stage distribution, sleep time, sleep efficiency, and arousal index were not associated with sleepiness. In analyses of polysomnography parameters using a quartile approach, the odds ratios showed a monotonic change with increasing quartiles for many of the respiratory parameters (AHI, AHI in REM, obstructive apnea index, and mean saturation in REM). For other respiratory parameters (hypoxemic burden and the lowest saturation in REM and non-rem), the trends did not support a monotonic relationship with sleepiness. Analyses of clinically relevant measures of sleep disruption using binomial variables to divide the top or bottom tenth percentile (stage 1% > 12.8, stage 3/4% < 0.78, stage REM% < 10.4, arousal index > 46.3) of values showed a significant positive association for arousal index (odds ratio 1.85 [1.14-2.99]). The magnitude and statistical significance of this relationship was reduced when AHI was included as a covariate in the model (1.46 [0.85-2.48]). DISCUSSION A majority of participants with moderate to severe SDB in this and previous epidemiologic studies do not report sleepiness. 2,3 In contrast to previous studies, this study shows that this is true even when a liberal definition is used for sleepiness that includes complaints of sleepiness, complaints consistent with fatigue (feeling unrested), and a self-report measure of propensity to doze in common situations (ESS score). Patients with sleepiness are in the majority in sleep-clinic populations, however. 4 Referral bias is the most likely explanation, since individuals with SDB who are sleepy are more likely to be referred for evaluation. Epidemiologic studies that have included mostly asymptomatic participants indicate that individuals with significant levels of SDB are at increased risk for developing hypertension and may have an increased risk for other adverse cardiovascular outcomes. 13-15 It is unknown whether increased cardiovascular risk is limited to those individuals with sleepiness. If it is not, criteria used to identify individuals with SDB should reflect this. The role of coexisting morbid conditions in the development and therapy of sleepiness in obstructive sleep apnea syndrome has not been emphasized in the literature. Our results suggest that insomnia, partial sleep deprivation, respiratory disease, and periodic limb movements are important predictors of sleepiness in individuals with moderate to severe SDB. Self-reported obstructive respiratory disease, particularly chronic obstructive pulmonary disease, AHI No. ESS score > 10 Sleepy Unrested Any of 3 < 5 3463 21.4% 11.5% 17.1% 34.8% 5-15 1831 26.7% 14.2% 17.1% 38.9% 15-30 749 29.6% 13.7% 19.0% 42.7% > 30 400 40.2% 22.1% 27.2% 51.4% *Different for all sleepiness definitions (P <.001, χ 2 test for trend) AHI refers to apnea-hypopnea index; ESS, Epworth Sleepiness Scale. SLEEP, Vol. 28, No. 4, 2005 474

Table 2A A Comparison of Demographics, Medical History, and Sleep Complaints by Sleepiness Report in Participants With Apnea- Hypopnea Index > 15 Variable Sleepy Not Sleepy (n = 510) (n = 605) Age, y*** 63.7 (62.0) 65.7 (66.0) Men, % 67.3 65.5 BMI, kg/m 2 ** 32.0 (31.1) 30.8 (29.9) Education, y 13.9 (13.0) 13.8 (13.0) Race, % White 72.9 76.0 African American 10.0 7.6 American Indian 12.0 12.6 Hispanic 4.1 3.3 Asian American 1.0 0.5 Cardiac disease, % 24.1 24.2 Respiratory disease, %*** 16.8 9.3 Asthma** 10.3 5.5 COPD** 10.5 5.1 Sedative use, %* 11.1 7.0 Alcohol use, servings/wk, % < 1 55.3 51.4 1-2 15.8 15.4 > 3 28.8 33.2 Weekday sleep, h*** 6.9 (7.0) 7.2 (7.0) Weekend sleep, h ** 7.3 (7.0) 7.5 (8.0) Not get enough sleep, %*** 35.6 8.8 Trouble falling asleep, %** 17.1 10.2 Wake up during night, %*** 26.2 13.1 Wake up too early, %*** 24.0 11.8 Habitual snorer, %*** 77.1 62.0 Awaken with leg cramps, %*** 17.7 8.1 Data are presented as mean (SD) unless otherwise specified. Mann-Whitney Test for continuous variables and Pearson χ 2 test for categorical variables. BMI refers to body mass index; COPD, chronic obstructive pulmonary disease. *P <.05 **P <.01 ***P <.001 Sleepiness is categorized based on the composite sleepiness definition (presence of Epworth Sleepiness Scale >10, often sleepy or often unrested). This classification was available for 1115 of 1149 participants with an apnea-hypopnea index (AHI) > 15. SLEEP, Vol. 28, No. 4, 2005 475 increased the likelihood of sleepiness. A previous study from the SHHS showed that participants with coexistent SDB and obstructive airway disease (based on pulmonary function tests) had higher ESS scores and more disrupted sleep (based on polysomnography) than did those with SDB alone. 16 One interpretation of these data is that the addition of lower airway obstruction to intermittent upper airway obstruction increases the negative impact on sleep quality. Partial sleep deprivation is a well-established and frequent cause of sleepiness in the general public, yet most studies of SDB-related sleepiness have not assessed the impact of partial sleep deprivation. 5,6 Complaints of not obtaining sufficient sleep were present in 35.6% of sleepy participants with moderate to severe SDB in this study, but in very few (8.8%) of nonsleepy participants. Although individuals who are sleepy for any reason may be more likely to report getting insufficient sleep, self-reported sleep duration was an important predictor of sleepiness in these participants, suggesting the importance of sleep deprivation per se. Sleep maintenance insomnia was twice as common in sleepy participants with moderate to severe SDB (26.2% vs 13.1%). This relationship was not explained by greater SDB-related sleep disruption in the sleepy group. Further, AHI was only very weakly correlated with frequency of sleep maintenance insomnia complaints (Spearman R = -0.03, P =.03). It appears likely that SDB and sleep maintenance insomnia are independent conditions in this population and that persons having both conditions may be at higher risk for sleepiness. Depression, which is associated with insomnia and sleepiness, is a potential confounder that was not measured in this study. A relationship between self-reported awakening from leg jerks or leg cramps and the ESS score has been reported previously in the SHHS population. 3 In this sample, frequent leg-related awakenings were reported in 17.7% of sleepy versus 8.1% of nonsleepy participants. This association remained highly significant after adjusting for AHI. Unfortunately this self-reported symptom lacks specificity. The perception of leg movements may indicate the presence of periodic limb movement disorder, a greater degree of SDB-related or spontaneous arousal not captured by conventional arousal scoring, or the presence of other neuropathologies (cramps or dysesthesias due to diabetic neuropathy or peripheral vascular disease). Studies with objective measurement of periodic limb movements during sleep are needed to clarify this issue. As would be expected based on previous studies, 3,6 the AHI was a predictor of sleepiness in our participants with moderate to severe SDB. Surprisingly, we did not find a relationship between sleep fragmentation and sleepiness. Studies in clinical populations have shown that decreased slow-wave and REM sleep and increased percentage stage 1 sleep are associated with sleepiness. 6 In general, the degree Table 2B A Comparison of Sleep Study Measures by Sleepiness Report of Participants with Apnea-Hypopnea Index > 15 Variable Sleepy Not Sleepy AHI, no./h** 31.6 (25.6) 28.6 (23.0) AHI during REM, no./h* 39.0 (38.0) 36.6 (34.9) AHI during NREM, no./h * 29.5 (24.3) 26.5 (21.6) Obstructive apnea index, no./h 12.2 (7.8) 10.0 (6.8) CT90, %** 13.0 (5.9) 9.5 (4.4) Avg O 2 during REM, %** 91.6 (92.5) 92.4 (92.9) Avg O 2 during NREM, %* 93.1 (93.4) 93.4 (93.6) Low O 2 during REM, % 78.9 (80.0) 79.9 (81.0) Low O 2 during NREM, % 80.5 (83.0) 81.5 (83.0) TST, min 341.7 (344.5) 341.9 (349.0) Arousal Index, no./h 28.2 (25.4) 27.4 (25.4) Sleep efficiency, % 79.5 (82.0) 79.2 (82.7) Percentage of time spent in each sleep stage, % 1 6.5 (5.6) 6.5 (5.5) 2 60.2 (60.9) 60.3 (60.6) 3 / 4 15.1 (12.7) 14.7 (13.2) REM 18.1 (18.5) 18.5 (18.6) Data are presented as mean (SD). Mann-Whitney Test for continuous variables and Pearson χ 2 test for categorical variables. * P <.05 **P<.01 ***P<.001 Sleepiness is categorized based on the composite sleepiness definition (presence of Epworth Sleepiness Scale score >10, often sleepy or often unrested). This classification was available for 1115 of 1149 participants with an apnea-hypopnea index (AHI) >15. REM refers to rapid eye movement sleep; NREM, non-rapid eye movement sleep; CT90, percentage of sleep time spent below 90% oxygen saturation; Avg O 2 : average oxygen saturation; Low O 2, lowest oxygen saturation; TST: total sleep time.

of sleep disruption in our population was low relative to what has been described in clinical populations. We did find a relationship with the arousal index when a more extreme classification (arousal index > 46) was used, but this was not independent of AHI. Previous analyses on the entire SHHS cohort have indicated that the effects of SDB on sleep architecture are small relative to differences by age and sex. 12 Differences between community-dwelling populations and clinic populations may explain the less dramatic sleep disruption seen in SHHS because clinic patients with SDB may be those individuals from the general public with SDB who are particularly susceptible to sleep disruption and sleepiness. The inability to detect a relationship between sleep disruption and sleepiness could also be a result of the composite measure of sleepiness we used or the subset of participants (AHI > 15) we included in this study. A number of participants (23%) were excluded from staging and arousal analyses based on study quality. These excluded participants were more likely to be sleepy and had slightly higher AHIs and percentage of time with an oxygen saturation below 90%. It is possible that excluding these participants biased toward not detecting a relationship between sleep fragmentation and sleepiness. This study has several limitations. This study represents a retrospective evaluation of an SHHS data set that was collected to Table 3 Relationship of Demographics, Medical History and Sleep Complaints to Sleepiness* in Separate Logistic Regression Models That Adjust for Potential Confounders Variable Adjusted odds ratio 95% confidence interval P value Age (5-year change) 0.95 0.89-1.01.115 BMI (5-unit change) 1.09 0.97-1.23.133 Respiratory Disease 1.81 1.22-2.67.003 Asthma 1.21 1.04-1.42.015 COPD 1.95 1.18-3.23.010 Sedative Use 1.48 0.94-2.34.091 Hours of sleep weekdays (1-h increase in nightly sleep) 0.77 0.69-0.86 <.001 Hours of sleep weekends (1-h increase in nightly sleep) 0.83 0.76-0.92 <.001 Not get enough sleep 4.58 3.19-6.58 <.001 Trouble falling asleep 1.48 0.99-2.21.055 Wake up during night 1.79 1.26-2.54.001 Wake up too early 1.81 1.26-2.61.002 Habitual Snorer 1.63 1.16-2.30.005 Awaken with leg cramps 2.36 1.58-3.52 <.001 Results of logistic regression with sleepiness as the dependent variable and the variable of interest plus possible confounders (age, body mass index [BMI], respiratory disease-not included when asthma or chronic obstructive pulmonary disease [COPD] dependent variables), sleep hours/week (not included when weekday or weekend sleep hours dependent variables), frequency of leg cramps, apnea-hypopnea index, CT90, percentage of sleep time spent below 90% oxygen saturation). Statistically significant variables (P <.05) shown in italics *Sleepiness is categorized based on the composite sleepiness definition (presence of score on the Epworth Sleepiness Scale > 10, often sleepy or often unrested). n = 1018-1060 for each analysis except habitual snorer (n = 787) Table 4 Relationship of Sleep-Study Measures To Sleepiness* in Separate Logistic Regression Models That Adjust for Potential Confounders Variable Adjusted odds ratio 95% confidence interval P value AHI (per 10-unit change) 1.12 1.03-1.21.006 AHI during REM (per 10-unit change) 1.07 1.00-1.14.040 AHI during NREM (per 10-unit change) 1.10 1.02-1.18.010 Obstructive apnea index (per 10-unit change) 1.19 1.06-1.33.003 CT90 (per 1% change) 1.01 1.01-1.02.002 Avg O 2 during REM (per 1% change) 0.94 0.91-0.98.003 Avg O 2 during NREM (per 1% change) 0.92 0.87-0.98.006 Low O 2 during REM (per 1% change) 0.99 0.97-1.00.143 Low O 2 during NREM (per 1% change) 0.98 0.96-1.00.040 TST (per 60-minute change) 0.97 0.83-1.14.720 Arousal Index (per 10-unit change) 1.09 0.97-1.21.145 Sleep efficiency (per 10-unit change) 0.98 0.81-1.19.847 Stage 1 % (per 10-unit change) 1.03 0.77-1.39 835 Stage 2% (per 10-unit change) 1.04 0.92-1.19.511 Stage 3/4% (per 10-unit change) 1.01 0.89-1.15.880 Stage REM % (per 10-unit change) 0.82 0.65-1.04.103 Results of logistic regression with sleepiness as the dependent variable and the variable of interest plus possible confounders (age, respiratory disease, sleep hours/week, frequency of leg cramps). Statistically significant variables (P <.05) shown in italics. AHI refers to apnea-hypopnea index (apneas + hypopneas / total sleep time); REM rapid eye movement sleep; NREM, non-rapid eye movement sleep; CT90, percentage of sleep time spent below 90% oxygen saturation; Avg O 2 : average oxygen saturation; Low O 2, lowest oxygen saturation; TST: total sleep time. *Sleepiness is categorized based on the composite sleepiness definition (presence of Epworth Sleepiness Scale score > 10, often sleepy or often unrested). Range of valid numbers (909-1040) for respiratory parameters, for sleep parameters n = 798 with the exception of sleep efficiency (n = 333), and total sleep time (TST) (n =565) SLEEP, Vol. 28, No. 4, 2005 476

answer questions other than the ones asked in this manuscript. As such, we were limited by the measures of sleepiness and covariate data that had been collected. Although we included several descriptions of sleepiness (sleepy, unrested, tired, and worn out) and the ESS score in our most inclusive definition of sleepiness, there is an expansive vocabulary for sleepiness (ie, fatigued, drowsy, etc.) that was not used in the sleep habits questionnaire. In addition, we did not have data on objective sleepiness such as could have been provided by the Multiple Sleep Latency Test. Subjective measures of sleepiness have only weak to moderate correlation with objective sleepiness measures; however, even objective measure of sleepiness, such as the Multiple Sleep Latency Test and Maintenance of Wakefulness Test, show only moderate correlation. 17 While some participants without subjective sleepiness would be expected to have objective sleepiness, the converse is also true. Our study population is not necessarily representative of the general population above the age of 40 years. Participants were recruited from preexisting epidemiologic cohorts with oversampling of snorers at younger ages. The final SHHS cohort reflected the selection biases inherent in the parent cohorts, in addition to the selection criteria used for SHHS, and is not necessarily representative of the general population. 18 Nevertheless, there is no reason to believe that the recruitment process led to bias that would affect our analyses of the relationship between SDB and sleepiness. The relationship between sleep architecture and sleepiness may be better defined using multiple-night data rather than a single polysomnogram. Repeated measures of polysomnography data are less prone to the effects of measurement error and night-to-night variability in sleep quality. Polysomnography did not include measurement of limb movements. We used a subjective measure (report of leg jerks or cramps) that has unknown sensitivity or specificity in the evaluation of periodic limb movements. We did not evaluate whether a broad range of chronic illnesses might be associated with sleepiness. Therefore, we can not say with certainty that there is something unique about how respiratory disease interacts with SDB or whether there is a more general relationship with chronic illnesses or tendency to self-report chronic illness. Finally, we acknowledge the potential role of unmeasured confounders and residual confounding on our results. Our study highlights that, although subjective sleepiness is a common correlate of SDB, it is present in only a minority of individuals with moderate to severe SDB. In addition to severity of SDB, coexisting conditions, particularly partial sleep deprivation, insomnia, obstructive lung disease, and awakening with leg cramps and movements, are important correlates of sleepiness. The role of modification of these conditions in the treatment of obstructive sleep apnea syndrome warrants further study. ACKNOWLEDGMENTS Sleep Heart Health Study (SHHS) acknowledges the Atherosclerosis Risk in Communities Study (ARIC), the Cardiovascular Health Study (CHS), the Framingham Heart Study (FHS), the Cornell/Mt. Sinai Worksite and Hypertension Studies, the Strong Heart Study (SHS), the Tucson Epidemiologic Study of Airways Obstructive Diseases (TES), and the Tucson Health and Environment Study (H&E) for allowing their cohort members to be part of the SHHS and for permitting data acquired by them to be used in the study. SHHS is particularly grateful to the members of these cohorts who agreed to participate in SHHS as well. SHHS further recognizes all of the investigators and staff who have contributed to its success. A list of SHHS investigators, staff and their participating institutions is available on the SHHS website, http://www.jhucct.com/shhs/. The opinions expressed in the paper are those of the author(s) and do not necessarily reflect the views of the IHS. REFERENCES 1. International Classification of Sleep Disorders, Revised: Diagnostic and Coding Manual. Rochester, MN: American Academy of Sleep Medicine; 2001:57-8. 2. Young T, Palta M, Dempsey J, Skatrud J, Weber S, Badr S. The occurrence of sleep-disordered breathing among middle-aged adults. N. Engl J Med 1993;328:1230-5. 3. Gottlieb DJ, Whitney CW, Bonekat WH, et al. Relation of sleepiness to respiratory disturbance index: the Sleep Heart Health Study. Am J Respir Crit Care Med 1999;159:502-7. 4. Chervin RD. Sleepiness, fatigue, and lack of energy in obstructive sleep apnea. Chest 2000;118:372-9. 5. Chervin RD, Aldrich MS. Characteristics of apneas and hypopneas during sleep and relation to excessive daytime sleepiness. Sleep 1998;21:799-806. 6. Punjabi NM, O'Hearn DJ, Neubauer DN, Nieto FJ, Smith PL, Bandeen-Roche K. Modeling hypersomnolence in sleep-disordered breathing. A novel approach using survival analysis. Am J Respir Crit Care Med 1999;159:1703-9. 7. Quan SF, Howard BV, Iber C, et al. The Sleep Heart Health Study: design, rationale, and methods. Sleep 1997;20:1077-85. 8. Redline S, Sander MH, Lind BK, et al. Methods for obtaining and analyzing unattended polysomnography data for a multicenter study. Sleep 1998;21:759-67. 9. Sleep Heart Health Study Research Group. Sleep Heart Health Study Manual of Operation. Seattle, WA: SHHS Coordinating Center; 1996. Available at http://www.jhucct.com/shhs/. Accessed December 22, 2004. 10. Johns MW. A new method for measuring daytime sleepiness: the Epworth sleepiness scale. Sleep 1991;14:540-5. 11. SPSS for Windows: Base System User's Guide, Release 6.0, SPSS Inc; 1993. 12. Redline S, Kirchner HL, Quan SF, Gottlieb DJ, Kapur V, Newman A. The effects of age, sex, ethnicity, and sleep-disordered breathing on sleep architecture. Arch Intern Med 2004;164:406-18. 13. Peppard PE, Young T, Palta M, Skatrud J. Prospective study of the association between sleep-disordered breathing and hypertension. N Engl J Med 2000;342:1378-84. 14. Nieto FJ, Young TB, Lind BK, et al. Association of sleep-disordered breathing, sleep apnea, and hypertension in a large communitybased study. Sleep Heart Health Study. JAMA 2000;283:1829-36. 15. Shahar E, Whitney CW, Redline S, et al. Sleep-disordered breathing and cardiovascular disease: cross-sectional results of the Sleep Heart Health Study. Am J Respir Crit Care Med 2001;163:19-25. 16. Sanders MH, Newman AB, Haggerty CL, et al. Sleep and sleep-disordered breathing in adults with predominantly mild obstructive airway disease. Am J Respir Crit Care Med 2003;167:7-14. 17. Johns MW. Sensitivity and specificity of the multiple sleep latency test (MSLT), the Maintenance of Wakefulness Test and the Epworth Sleepiness Scale: failure of the MSLT as a gold standard. J Sleep Res 2000;9:5-11. 18. Lind BK, Goodwin JL, Hill JG, Ali T, Redline S, Quan SF. Recruitment of healthy adults into a study of overnight sleep monitoring in the home: experience of the Sleep Heart Health Study. Sleep Breath 2003;7:13-24. SLEEP, Vol. 28, No. 4, 2005 477

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