The Impact of an Online Prematriculation Sleep Course (Sleep 101) on Sleep Knowledge and Behaviors in College Freshmen: A Pilot Study

Stuart F. Quan, M.D.

Pallas Snider Ziporyn, A.B.

Division of Sleep and Circadian Disorders

Brigham and Women’s Hospital

Boston, MA USA

Abstract

College students have a high prevalence of poor sleep quality and sleep deficiency which negatively impacts their academic, mental and physical performance. A prematriculation course focused on improving sleep knowledge and behaviors may reduce sleep problems. “Sleep 101” is an online prematriculation course developed to educate incoming college freshmen about the importance of sleep in their lives and to recommend behaviors that will improve their sleep health. In a pilot program, “Sleep 101” was administered to freshman at four universities. The results of a voluntary survey after completion of the course indicated that there was an improvement in knowledge about sleep and the effects of caffeine use, and that students were less likely to drive drowsy and pull “all-nighters,” These pilot data suggest that an internet administered prematriculation course on the importance of sleep and the adoption of healthy sleep behaviors will be effective in reducing sleep problems among college students.

Introduction

Poor sleep hygiene among college students is common (1). Not surprisingly, there is a high prevalence of sleep problems (2). Sleep deficiency in college students has been linked to poor academic and physical performance, depression, accident risk, excessive caffeine and stimulant medication use, impairment in social relationships and worse overall health (3-5). Unfortunately, unlike the efforts to reduce the use of alcohol and sexual misconduct on campuses, there has been relatively little attention paid to poor sleep health and its impact on individual health and performance.

Although there have been a few studies using in-person educational programs to improve sleep knowledge and behaviors, the impact of these have been inconsistent and in most cases limited to small numbers of students. Over the past 15 years, internet usage among college students has become ubiquitous (6). Thus, a sleep educational program delivered over the internet has the potential to reach large numbers of students. In a recent study, we demonstrated that an internet-based sleep learning module administered as component to an introductory college psychology course resulted in an improvement in sleep knowledge and changes in sleep habits (7). In an effort to provide a more comprehensive sleep educational intervention, we have developed an interactive internet-based sleep course, “Sleep 101.” The course is intended to be administered to matriculating freshmen in order improve their sleep knowledge and to prevent the development of poor sleep habits with their resultant adverse impacts on academic and physical performance, and personal health. This report describes the result of the “Sleep 101” pilot program at four universities.

Methods

In the fall of 2016, freshmen at four universities were asked to complete a pilot online educational course, “Sleep 101,” on the importance of obtaining sufficient sleep in their college lives. At two of the universities, the students were informed that completion of the course was required although there was no penalty for non-completion. At the other two universities, the students were required to take the course as part of a freshman seminar series. At the end of the course, a voluntary brief survey was administered to assess students’ opinion of the course, to obtain data regarding ease of course navigation and to identify any “software bugs.” One of the universities is located in the Midwest and has a total enrollment of approximately 6000 undergraduates. The other three universities are located on the East Coast. Two have undergraduate enrollments of approximately 4000 students and the other has an undergraduate enrollment of approximately 6700 students. All are private coeducational institutions.

The content of Sleep 101 includes material related to basic sleep physiology, the impact of sleep on mood, academic and physical performance, the impact of sleep deficiency on driving and personal health, the interactions among sleep and various substances including alcohol and caffeine and a review of common sleep disorders. The curriculum was developed in Articulate Storyline 2 and uses engaging video clips of actual students and sleep experts, interactive activities and text. Selected images from the course can be viewed by clicking the following link [Sleep 101 Slides].At the end of the course, colleges have the option of including custom links to health resources at their university. The program is designed to be completed in 45-60 minutes. A link to the course is available upon request to one of the authors.

Results

The Table shows aggregate and institutional response to four knowledge and behavior questions related to sleep:

• knowing more about sleep;
• knowing more about the effects of caffeine;
• the likelihood of “pulling an all-nighter”;
• the likelihood of driving drowsy.

In the aggregate results as well as for each institution, over three quarters of the students responded that they knew more about sleep and the effects of caffeine. In addition, nearly half indicated that they were less likely to stay up all night studying. Importantly, 60% of respondents indicated that they were less likely to drive when drowsy. When asked whether the course was easy to use, there were no major navigational issues.

Discussion

The results of this pilot study demonstrate that “Sleep 101” improved students’ knowledge about sleep and the effects of caffeine. In addition, they were less likely to “pull an all-nighter” and drive when drowsy. The data suggest that our course has the potential to improve the sleep of college students and ultimately their school performance and college experience.

Sleep in college students is notoriously poor. When deciding whether to sleep, study or socialize, most students will choose the latter two activities. The impact of poor sleep is broad. Sleep deficiency negatively affects academic and physical performance. There are impairments in mood and social relationships (8). Furthermore, reduced sleep is a risk factor for cardiac disease, hypertension, stroke and type 2 diabetes (9). To mitigate the effects of sleep deficiency, many students increase caffeine consumption and some use stimulating medications such as amphetamine and dextroamphetamine (Adderall) (10, 11). Both can potentially have an adverse impact on health. Thus, interventions to improve sleep health can potentially have a major impact on the health and well-being of college students.

Our pilot data indicate that a pre-matriculation curriculum focused on good sleep health can have a positive impact by improving knowledge concerning the importance of sleep and reducing behaviors that adversely affect sleep. Thus, the results are consistent with our previous study demonstrating a positive impact on sleep knowledge and behavior in a group of undergraduates enrolled in an introductory psychology course using an internet-based educational module (7).  In addition, Kloss et al reported improvements in sleep hygiene knowledge and sleep quality four weeks after an in-person sleep educational intervention (12). However, not all previous studies have been so encouraging. No difference in sleep hygiene knowledge was noted between sleep education and control groups after six weeks by Brown et al. (13). Similarly, no changes in sleep quality were reported by Clark et al and Lamberti et al. (14, 15). Explanations for these inconsistencies are unclear, but there were significant differences in the curriculum and the methods of content presentation, and the number of participating students was small in most of the studies.

“Sleep 101” was developed as an e-learning course to be taken online. Other sleep education programs in college students used in-person delivery of content (12-15). However, use of the internet will provide much greater scalability than in-person delivery. The latter will be logistically difficult and costly for universities with large enrollments.

Although promising, our data must be interpreted as preliminary. Not all students finished the course and completion of the survey was voluntary as well. Thus, a selection bias towards those who had an interest in improving their sleep was likely. In addition, the pilot universities had relatively small enrollments. Nevertheless, our feedback suggests that a sleep intervention for college students delivered through the internet such as “Sleep 101” is feasible and effective. The results provide an impetus for its dissemination to additional universities nationwide.

Acknowledgements

“Sleep 101” was developed as a collaboration between the Brigham Sleep Health Institute and the non profit Healthy Hours. Funding was provided by the Snider Family Fund.

References

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Cite as: Quan SF, Ziporyn PS. The impact of an online prematriculation sleep course (sleep 101) on sleep knowledge and behaviors in college freshmen: a pilot study. Southwest J Pulm Crit Care. 2017;14(4):159-63. doi: https://doi.org/10.13175/swjpcc028-17 PDF

Obstructive Sleep Apnea and Quality of Life: Comparison of the SAQLI, FOSQ, and SF-36 Questionnaires

Graciela E Silva PhD^a

James L Goodwin PhD^b

Kimberly D Vana, DNP, RN, FNP-BC, FNP- C^c

Stuart F Quan MD^b,d,e,f 

^aUniversity of Arizona College of Nursing, Tucson, AZ; ^bArizona Respiratory Center, University of Arizona, Tucson, AZ; ^cCollege of Nursing & Health Innovation, Arizona State University, Phoenix, AZ; ^dCollege of Medicine, University of Arizona, Tucson, AZ; ^eDivision of Sleep Medicine, Harvard Medical School, Boston, MA. ^fDivision of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA

Abstract 

Introduction: The impact of sleep on quality of life (QoL) has been well documented; however, there is a great need for reliable QoL measures for persons with obstructive sleep apnea (OSA). We compared the QoL scores between the 36-Item Short Form of the Medical Outcomes Survey (SF-36), Calgary Sleep Apnea Quality of Life Index (SAQLI), and Functional Outcomes Sleep Questionnaire (FOSQ) in persons with OSA.

Methods: A total of 884 participants from the Sleep Heart Health Study second examination, who completed the SF-36, FOSQ, and SAQLI, and in-home polysomnograms, were included. The apnea hypopnea index (AHI) at 4% desaturation was categorized as no OSA (<5 /hour), mild to moderate OSA (5-30 /hour) and severe OSA (>30 /hour). QoL scores for each questionnaire were determined and compared by OSA severity category and by gender.

Results: Participants were 47.6% male, 49.2% (n=435) had no OSA, 43.2% (n=382) had mild to moderate OSA, and 7.6% (n=67) had severe OSA. Participants with severe OSA were significantly older (mean age = 63.7 years, p <.0001), had higher BMI (mean = 34.3 kg/m², p <.0001) and had lower SF-36 Physical Component scores (PCS) (45.1) than participants with no OSA (48.5) or those with mild to moderate OSA (46.5, p= .006). When analyzed according to gender, no significant differences were found in males for QoL by OSA severity categories. However, females with severe OSA had significantly lower mean scores for the SAQLI (5.4, p= .006), FOSQ (10.9, p= .02), and SF-36 PCS (37.7, p<.0001) compared to females with no OSA (6.0, 11.5, 44.6) and those with mild to moderate OSA (5.9, 11.4, 48, respectively). Females with severe OSA also had significantly higher mean BMI (41.8 kg/m²,) than females with no OSA (26.5 kg/m²) or females with mild to moderate OSA (30.6 kg/m², p<.0001). The SF-36 PCS and Mental Component Scores (MCS) were correlated with the FOSQ and SAQLI (r=.37 PCS vs FOSQ; r=.31 MCS vs FOSQ; r=.42 PCS vs SAQLI; r=.52 MCS vs SAQLI; and r=.66 FOSQ vs SAQLI, p<.001 for all correlations). Linear regression analyses, adjusting for potential confounders, indicated that the impact of OSA severity on QoL is largely explained by the presence of daytime sleepiness. 

Conclusion: The impact of OSA on QoL differs between genders with a larger effect on females and is largely explained by the presence of daytime sleepiness. Correlations among QoL instruments are not high and various instruments may assess different aspects of QoL.

Introduction

Obstructive sleep apnea (OSA) is a highly prevalent condition occurring in as many as 17% and 9% of middle aged males and females, respectively (1). OSA is now recognized as an important risk factor for the development of hypertension and coronary heart disease as well as premature death (2). However, patients frequently present to health care providers with symptoms that are indicative of impairment in their quality of life (QoL). Improvement in QoL is an important determinant of whether patients adhere to continuous positive airway pressure (CPAP), the most commonly prescribed treatment for OSA. Additionally, measurement of QoL is one of the quality metrics recently developed for use in clinical practice (3) thus increasing the importance of evaluating tools used to assess QoL in OSA.

A variety of tools to measure QoL have been utilized in epidemiologic studies and clinical trials of OSA. The most common general QoL instrument used has been the Medical Outcomes Study Short-Form Health Survey SF-36 (4). More recently, two sleep specific QoL questionnaires have been developed, the Functional Outcomes of Sleep Questionnaire (FOSQ) (5) and the Sleep Apnea Quality of Life Inventory (SAQLI) (6). Whether these sleep specific QoL instruments are more sensitive in those with OSA than general QoL questionnaires is not clear. Furthermore, there have been few comparisons of the FOSQ to the SAQLI with respect to their sensitivity in those with OSA and whether QoL differs between males and females. Using data from a large cohort study, the purposes of these analyses were to compare these instruments to each other, to assess whether they were able to detect differences in QoL among groups with different severities of OSA and to determine whether there were differences between genders.

Methods

The Sleep Heart Health Study (SHHS) is a prospective multicenter cohort study designed to investigate the relationship between OSA and cardiovascular diseases in the United States. Details of the study design have been published elsewhere (7). Briefly, initial baseline recruitment began in 1995, enrolling 6,441 subjects, 40 years of age and older, from several ongoing geographically distinct cardiovascular and respiratory disease cohorts who were initially assembled between 1976 and 1995 (8). These cohorts included the Offspring Cohort and the Omni Cohort of the Framingham Heart Study in Massachusetts; the Hagerstown, MD, and Minneapolis, MN, sites of the Atherosclerosis Risk in Communities Study; the Hagerstown, MD, Pittsburgh, PA, and Sacramento, CA, sites of the Cardiovascular Health Study; 3 hypertension cohorts (Clinic, Worksite, and Menopause) in New York City; the Tucson Epidemiologic Study of Airways Obstructive Diseases and the Health and Environment Study; and the Strong Heart Study of American Indians in Oklahoma, Arizona, North Dakota, and South Dakota. A SHHS follow-up examination took place between February 2000 and May 2003, enrolling 4,586 of the original participants who had a repeat polysomnogram in addition to completing questionnaires and undergoing other measurements. The present study focused on 884 participants from the Tucson and Framingham sites of the Sleep Heart Health Study second examination in whom data were available from the sleep habits questionnaire, all quality of life questionnaires, and in-home polysomnograms. Data was limited to these sites because administration of the FOSQ was not done at the other field centers. 

The SHHS was approved by the respective institutional review boards for human subjects research, and informed written consent was obtained from all subjects at the time of their enrollment into each stage of the study.

Polysomnography

Participants underwent overnight in-home polysomnograms using the Compumedics Portable PS-2 System (Abbottsville, Victoria, Australia) administered by trained technicians (9). Briefly, after a home visit was scheduled, the Sleep Health Questionnaire, SF-36, SAQLI, and FOSQ questionnaires generally were mailed 1 to 2 weeks prior to the in-home polysomnography appointment. Each participant was asked to complete the questionnaire before the home visit, at which time the questionnaires were collected and verified for completeness. The home visits were performed by two-person, mixed-sex teams in visits that lasted 1.5 to 2 hours. There was emphasis on making the night of the polysomnographic assessment as representative as possible of a usual night of sleep. Participants were asked to schedule the visit so that it would occur approximately two hours prior to their usual bedtime. Participants’ weekday or weekend bedtime routines were encouraged to be consistent with the day of the week that the visits were made.

The SHHS recording montage consisted of electroencephalogram (C4/A1 and C3/A2), right and left electrooculogram, a bipolar submental electromyogram, thoracic and abdominal excursions (inductive plethysmography bands), airflow (detected by a nasal-oral thermocouple [Protec, Woodinville, WA]), oximetry (finger pulse oximetry [Nonin, Minneapolis, MN]), electrocardiogram and heart rate (using a bipolar electrocardiogram lead), body position (using a mercury gauge sensor), and ambient light (on/off, by a light sensor secured to the recording garment). Sensors were placed, and equipment was calibrated during an evening home visit by a certified technician. After technicians retrieved the equipment, the data, stored in real time on PCMCIA cards, were downloaded to the computers of each respective clinical site, locally reviewed, and forwarded to a central reading center (Case Western Reserve University, Cleveland, OH). Comprehensive descriptions of polysomnography scoring and quality-assurance procedures have been previously published (9, 10). In brief, sleep was scored according to guidelines developed by Rechtschaffen and Kales (11, 12). Strict protocols were maintained to ensure comparability among centers and technicians. Intra-scorer and inter-scorer reliabilities were high (10). As in previous analyses of SHHS data, an apnea was defined as a complete or almost complete cessation of airflow, as measured by the amplitude of the thermocouple signal, lasting at least 10 seconds. Hypopneas were identified if the amplitude of a measure of flow or volume (detected by the thermocouple or thorax or abdominal inductance band signals) was reduced discernibly (at least 25% lower than baseline breathing) for at least 10 seconds and did not meet the criteria for apnea. For this study, only apneas or hypopneas associated with a 4% or greater oxyhemoglobin desaturation were considered in the calculation of the apnea hypopnea index (AHI, apneas plus hypopneas per hour of total sleep time).

Sleep Habits Questionnaire and Covariates

Participants completed the SHHS Sleep Habits Questionnaire (13). The Sleep Habits Questionnaire contained questions regarding sleep habits. Height and weight were measured directly to determine body mass index (BMI, kg/m2). Sex and ethnicity were derived from data obtained from the SHHS parent cohorts. Participants answered yes or no to having a healthcare provider diagnosing them as having chronic obstructive pulmonary disease (COPD), chronic bronchitis, or asthma.

Sleepiness

The level of daytime sleepiness was determined using the Epworth Sleepiness Scale (ESS), a validated 8-item questionnaire that measures subjective sleepiness (14). Subjects were asked to rate how likely they are to fall asleep in different situations. Each question was answered on a scale of 0 to 3. ESS values ranged from 0 (unlikely to fall asleep in any situation) to 24 (high chance of falling asleep in all 8 situations). Mean ESS scores between 14 and 16 have been reported for patients with OSA (14, 15). Scores of 11 or greater are considered to represent an abnormal degree of daytime sleepiness (16). Sleepiness was defined as an ESS of at least 10.

Quality of Life Measures

Medical Outcomes Study Short-Form Health Survey (SF-36). Quality of life was evaluated using the Medical Outcomes Study Short-Form Health survey (SF-36) (4). The SF-36 is a multipurpose self-administered health survey consisting of 36 questions divided into 8 individual domains: (1) physical functioning (limitations in physical activity because of health problems), (2) role physical (limitations in usual role activities because of physical health problems), (3) bodily pain, (4) general health perceptions; (5) vitality (energy and fatigue), (6) social functioning (limitation in social activities because of physical or emotional problems), (7) role emotional (limitation in usual role activities because of emotional problems), and (8) general mental health. In addition, the 8 scales are used to form 2 distinct high-order summary scales: the physical component summary (PCS) and the mental component summary (MCS) (17). The PCS includes the physical functioning, role physical, bodily pain, and general health scales, and the MCS includes the vitality, social functioning, role emotional, and general mental health scales. The raw scores for each subscale and the 2 summary measures are standardized, weighted, and scored according to specific algorithms. The scores for the multifunction item scales and the summary measures range from 0 to 100, with higher scores indicating better quality of life. For the present study, we use only the PCS and MCS scales.

Functional Outcomes Sleep Questionnaire (FOSQ). The FOSQ was developed as a self-report instrument to assess the disorders of sleepiness on quality of life. It consists of 30 items with 5 factor-based subscales: activity level, vigilance, intimacy and sexual relationships, general productivity and social outcome. A mean weighted item score is obtained for each subscale. The subscales are summed to produce a global score (5). In SHHS, questions related to sexual intimacy were omitted because there were concerns that some participants would find these embarrassing or offensive.

Sleep Apnea Quality of Life Index (SAQLI). The SAQLI was developed as a sleep apnea specific quality of life instrument (6). It is a 35 item instrument that captures the adverse impact of sleep apnea on 4 domains: daily functioning, social interactions, emotional functioning, and symptoms. Items are scored on a 7-point scale with “all of the time” and “not at all” being the most extreme responses. Item and domain scores are averaged to yield a composite total score between 1 and 7. Higher scores represent better quality of life. In SHHS, the short form of the SAQLI was administered, because it allowed for self-completion by the participants (18).

Statistical Analysis

Differences in proportions for descriptive characteristics between OSA severity categories, and categorical variables were analyzed using Chi-square tests with 2 degrees of freedom. Fisher’s exact test was used when the expected frequency was less than 5 in any cell. One-way analyses of variance (ANOVA) were used to compare differences in mean values for continuous variables (BMI, total sleep time, SAQLI, FOSQ, SF-36 MCS, and SF-36 PCS) by OSA severity categories and by these categories separately for males and females. Pearson’s correlations were used to test for correlation coefficients between the four quality of life scales, SAQLI, FOSQ, SF-36 MCS, and SF-36 PCS.

Separate multivariate linear regression models were fitted to evaluate scores from each of the four QoL scales by OSA categories for males and females. Potential confounders (age, race, COPD, chronic bronchitis, ESS and asthma) were evaluated and adjusted for in the models; only those variables with significant coefficients were kept in the models. Thus, OSA severity, ESS, and asthma were the only variables retained in the final models. All statistical tests were performed using statistical software (Stata SE, version 13.0 for Windows; Stata Corp; College Station, TX) and a significance level of 0.05.

Results

Participants were 47.6% male and 52.4% female, 49.2% (n=435) had no OSA, 43.2% (n=382) had mild to moderate OSA, and 7.6% (n=67) had severe OSA. Approximately 21% of participants with mild to moderate OSA and 39% of those with severe OSA reported excessive daytime sleepiness (ESS >10) (Table 1).

Participants with severe OSA were significantly older (mean age = 63.7 years, p <.0001), had higher BMI (mean = 34.3 kg/m², p <.0001) and had lower SF-36 PCS scores (45.1, p= .006) than participants with no OSA or those with mild to moderate OSA. There was also a trend towards lower scores on the MCS of the SF-36, the SAQLI, and the FOSQ (Table 2).

When analyzed according to gender, no significant differences were found in males for QoL by OSA severity categories (Table 3).

Males with severe OSA had significantly higher BMI (mean 31.9, p<.0001) than males with no OSA or males with mild to moderate OSA. However, as shown in Table 4, females with severe OSA had significantly lower mean scores for the SAQLI (5.4, p= .006), FOSQ (10.9, p= .02), and SF-36 PCS (37.7, p<.0001) compared to females with no OSA and those with mild to moderate OSA.

Females with severe OSA also had significantly higher BMI (mean 41.8, p<.0001) than females with no OSA or females with mild to moderate OSA.

As shown in Table 5, comparisons between the QoL measures showed small correlations between the FOSQ and the SF-36 MCS (r=.31, p < .001) and the SF-36 PCS (r=.37, p <.001), and medium correlations between the SAQLI and the SF-36 MCS (r=0.52, p <.001) and the SF-36 PCS (r=.42, p < .001).

The correlation between the SAQLI and FOSQ was 0.66, p <.001, and the correlation between SF-36 MCS and SF-36 PCS was -.024, however this was not significant (p = .142). In addition, ESS scores were inversely correlated with the SAQLI (r = -.36), FOSQ (r = -.43), MCS (r = -.17), and PCS (r = -.16) (data not shown).

Because categorical analyses showed no difference for males in QoL scores, we, therefore, ran linear regression models separately for females and males (Table 6).

 

Discussion

In these analyses using a general (SF-36) and two sleep specific QoL assessment tools (FOSQ and SAQLI), we found that QoL was reduced in those with severe OSA; substantial differences were not apparent among participants with mild to moderate OSA and those with no OSA. However, there were significant gender disparities. Females with severe OSA demonstrated a substantial reduction in QoL with all instruments, but there was a lack of differences among males by OSA severity. The reductions in QoL were explained primarily by the presence of sleepiness. Furthermore, correlations among QoL questionnaires were modest at best, indicating that they assess different QoL domains.

When males and females were analyzed together in our study, only the PCS of the SF-36 showed a significant reduction in QoL in participants with OSA, but this was limited solely to participants with severe OSA. Additional studies also have found lower QoL only in those with severe OSA (19, 20). Moreover, other studies have failed to find any differences in QoL among participants with a broad spectrum of OSA severity (21-23). In one of these studies, Lee and colleagues (22) found that the AHI was not associated with differences in the PCS or MCS of the SF-36 in a large group of patients seen in a sleep clinic. In their study, other factors, such as age, gender, minimum oxygen saturation, sleepiness, and depression were associated with the PCS or MCS scores. Our study also found a strong trend between sleepiness and QoL scores for females and males. Similarly, in a smaller study, Lee et al. (22) did not find differences in the SAQLI among OSA patients of different severities. Our data also are consistent with a previous analysis from the first examination of SHHS in which severe OSA was associated with worse QoL on most subscales of the SF-36, but only the vitality subscale was reflective of poorer QoL in participants with OSA of less severity. In contrast, even mild OSA was associated with reduced QoL in comparison to no OSA among the middle-aged males and females of the Wisconsin Sleep Cohort (24). However, our cohort was older than participants in the Wisconsin Sleep Cohort and only a small sample from the SHHS was analyzed in the present study. Thus, age and other demographic differences among the cohorts may provide explanations for these discrepancies. Nevertheless, despite the absence of large cross-sectional differences in QoL as a function of OSA severity, in most studies, the SF-36, SAQLI, and FOSQ have been shown to be sensitive to changes in QoL after OSA treatment.

When analysis of our data was performed separately according to gender, we observed that the reduction in QoL with severe OSA was limited to females irrespective of the QoL instrument. Other studies (22) also have noted that QoL in participants with OSA is worse in women. However, in a study of a large cohort of males, Appleton et al.,(25) found that increasing AHI was associated with lower QoL on the SF-36, but only in those less than 69 years of age. The median age of the SHHS cohort is 60 years with substantial numbers of participants older than 70 years.  Thus, our results and those of Appleton et al. (25) may not be discrepant necessarily.

Excessive daytime sleepiness is one of the most common symptoms in OSA, and sleepiness can have a profound negative impact on QoL. Thus, not surprisingly, our multivariate analyses demonstrated that the negative impact of severe OSA was explained primarily by the presence of sleepiness. Our finding is consistent with the findings of some, (19, 22, 23, 26) but not all previous studies (27). The explanation for these inconsistent findings is not readily apparent, but possibilities include whether study populations were recruited from the general population or clinic, as well as whether the cohorts had other co-morbidities that would impact QoL. A differential perception of sleepiness between males and females offers a possible explanation of the greater impact of OSA on QoL in the latter. However, this assertion seems unlikely inasmuch as previous studies indicate females with OSA are more likely to report fatigue rather than sleepiness (28-30).

We observed that correlations among the SF-36, SAQLI, and FOSQ were relatively weak to moderate. Our results are consistent with the few studies that have done similar comparisons. In a Spanish multicenter study (21), correlations of the FOSQ and several scales of the SF-36 with the 4 domains of the SAQLI were poor to moderate. They ranged from r=.179 between the FOSQ and SAQLI Emotional Functioning domain to r=.579 for the SF-36 Vitality and SAQLI Daily Functioning domain. In a Polish study (31), the correlation between the SF-36 and the FOSQ was r=.46 and between the SF-36 and the SAQLI was r=.47. Other studies have compared the SF-36 to other general QoL instruments in patients with OSA, with some, but not all, demonstrating reasonable correspondence (32, 33).  Considering our results with other studies, various instruments may sample different aspects of QoL. Care should be exercised when selecting a tool to assess health outcomes in OSA.

There are several important limitations to our findings. First, the SHHS cohort was recruited from participants enrolled in other longitudinal studies, many of whom were long-time participants. These individuals may represent a group of survivors who would generally have better QoL regardless of OSA-severity status. Second, as a group, the SHHS cohort is older (mean age = 61.6 years) and may not be representative of the US adult population. Third, SHHS is a general population cohort, and thus, unlike a clinical cohort, some did not have symptoms of OSA. Finally, severity of OSA may not be best reflected by the AHI. Other markers of severity such as amount of oxygen desaturation or degree of sleep fragmentation may be better surrogates to show differences in QoL. Nevertheless, despite these limitations, our analyses have some unique qualities such as a well-characterized, racially and ethnically diverse cohort, use of home-based polysomnography to assess the severity of OSA, and data related to QoL derived from 3 different instruments.

In conclusion, in a middle-aged to elderly cohort, QoL is poorer only in females with severe OSA. To a large extent, these findings can be explained by the presence of daytime sleepiness. Correlations among 3 commonly used QoL instruments used in persons with OSA were weak to moderate, suggesting that each samples different aspects of QoL. Therefore, care should be exercised in selecting a QoL tool for documenting health care outcomes for research or clinical care.

Acknowledgments

The 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 authors and do not necessarily reflect the views of the Indian Health Service.

This work was supported by HL U01HL53940 (University of Washington), U01HL53941 (Boston University), U01HL53938 and U01HL53938-07S (University of Arizona), U01HL53916 (University of California, Davis), U01HL53934 (University of Minnesota), U01HL53931 (New York University), U01HL53937 and U01HL64360 (Johns Hopkins University), U01HL63463 (Case Western Reserve University), and U01HL63429 (Missouri Breaks Research).

Dr. Silva was supported by NHLBI grant HL 062373-05A2. 

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Cite as: Silva GE, Goodwin JL, Vana KD, Quan SF. Obstructive sleep apnea and quality of life: comparison of the SAQLI, FOSQ, and SF-36 questionnaires. Southwest J Pulm Crit Care. 2016;13(3):137-49. doi: http://dx.doi.org/10.13175/swjpcc082-16 PDF 

Gender Differences in Real-Home Sleep of Young and Older Couples

Maryam Butt, MSc¹

Stuart F. Quan, MD^3,4,5

Alex (Sandy) Pentland, PhD²

Inas Khayal, PhD^{1, 2}

¹Masdar Institute of Science and Technology, Abu Dhabi, UAE

²Massachusetts Institute of Technology, Cambridge, MA, USA

³Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA

⁴Arizona Respiratory Center, University of Arizona College of Medicine, Tucson, AZ, USA

⁵Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA

Abstract

Objectives: To understand gender differences in sleep quality, architecture and duration of young healthy couples in comparison to older couples in their natural sleep environment.

Design: Sleep was monitored in a naturalistic setting using a headband sleep monitoring device over a period of two weeks for young couples and home polysomnography for the older couples.

Participants: Ten heterosexual young couples (male mean age: 28.2 1.0[SD] years  /female mean age: 26.8 0.9 years) and 14 older couples (male mean age: 59.3+ 9.6 years/female mean age: 58.8+ 9.1 years).

Measurements and results: In the young couples, total sleep time (395+66 vs. 367+54 min., p<0.05), sleep efficiency (97.0+3.0 vs. 91.1+7.9, p<0.001), and % REM (31.1+4.8 vs. 23.6+5.5, p<0.001) in males was higher than in females. In contrast, % light sleep (51.7+7.1 vs. 59.7+6.7, p<0.001) and number of arousals (2.9+1.9 vs. 5.3+1.9, p<0.001) were lower.  These differences persisted after controlling for evening mood and various evening pre-sleep activities. In the older couples, there were no differences between genders. In addition, children in the household adversely impacted sleep.

Conclusions: In couples recorded in the home, young males slept longer and had better sleep quality than young females. This difference appears to dissipate with age. In-home assessment of couples can aid in understanding of gender differences in sleep and how they are affected by age and social environment.

Introduction

Sleep has a considerable public health impact and is needed to maintain optimal health and well-being. Impaired sleep has been shown to have adverse health effects from psychiatric illnesses such as depression (1) to physical health risks such as obesity and diabetes (2-4). Poor sleep has also been shown to lead to behavioral consequences such as sleepiness, impaired cognitive function, low job performance and motor vehicle accidents resulting in both health and financial losses (5). However, the prevalence of sleep disturbances varies according to both age and gender (6). In addition, objective assessments of sleep find that sleep architecture changes as a function of both these factors (7). This was confirmed in a study by Redline et al in which interactions between age and gender were an important factor in explaining variations in sleep architecture (8).

Studies investigating gender differences in sleep have mostly relied on laboratory polysomnography (PSG), wrist actigraphy and subjective survey instruments (9-11). These studies have not been able to capture sleep quality, architecture and duration from the subject’s natural sleep environment, which may be surrounded and affected by their bed partner, children or their routine sleep schedule. Furthermore, in many of these studies, the age spectrum of the participants was limited (9,12). With the recent availability of home sleep monitoring devices, it is now possible to objectively measure detailed sleep parameters in subjects’ real-home environment. This methodology attempts to minimize any disruptions to an individual’s naturalistic sleep setting.

The purpose of this study was to utilize a portable sleep monitoring device to measure detailed sleep parameters of young healthy couples in their real-home environment to study gender differences. In addition, we compared these results to home sleep recordings obtained from older adults to assess whether there were changes with age. We hypothesize that sleep parameters measured in a naturalistic setting will be affected by gender given the different social roles of young married men and women. In addition, we posited that these changes would evolve with age.

Methods

Study Populations

Graduate Student Cohort. This cohort consisted of 10 young healthy married heterosexual couples. The participants were residents of a vibrant married graduate-student community about half of whom had children. The mean age of male subjects was 28.2 years (SD 1.0) and the mean age for females was 26.8 years (SD 0.9). Fourteen of the 20 subjects were M.S. and Ph.D. students (10 males and 4 females). The remaining subjects were spouses that were not students. Four couples had children while the remaining did not. Flyers and e-mail messages were used to recruit participants. We recruited couples in which both members were willing to participate. The inclusion criteria also required the couples to share the sleep environment. Participants did not receive any financial compensation for their participation in this experiment. Data were collected over a period of two weeks in March and April 2011 in a naturalistic setting while participants underwent their normal routine activities.

Older Couples Cohort. This cohort was comprised of 14 married couples randomly selected from participants in the Sleep Heart Health Study (SHHS) none of whom were found to have obstructive sleep apnea (Apnea Hypopnea Index < 5 events/hour). The mean age of male participants was 59.3 years (SD + 9.6) and the mean age for females was 58.8 years (SD + 9.1). Overall recruitment in the SHHS has been previously reported (13,14). Briefly SHHS participants were recruited from several ongoing longitudinal cohort studies of cardiovascular or pulmonary disease. In addition to information obtained by their parent studies, they were asked to undergo an ambulatory polysomnogram and collection of data relevant to sleep. We used data from the first examination of SHHS (1995-1997) for this analysis.

Polysomnogram Data Collection

For the Graduate Student Cohort, detailed sleep parameters were recorded using an automated wireless system (ZEO Inc., Newton, MA) which includes an elastic head-band and a bed-side unit. It has been validated and found to be reliable and accurate for monitoring sleep in healthy adults (15,16). Sensors embedded on the headband detect single-channel frontal EEG (electroencephalographic) signals. The headband wirelessly transmits these signals to the bedside unit where the signals are then classified into the various sleep stages by an automated algorithm. The raw EEG data are not stored by the device. The bedside unit stores the sleep stage architecture (hypnogram) data onto the SD card located in the unit. The processed data can then be exported for analysis. The headband, unlike PSG electrodes, can be worn around the forehead without the use of any adhesive that makes it very simple and comfortable to use.

Each husband and wife couple were provided with the sleep monitoring device and were asked to use it for a minimum of 14 nights in their homes. The measured sleep parameters included: total sleep time (TST), rapid eye movement (Stage R, REM), time in non-slow wave NREM (Stages N1+N2, “Light Sleep”), and slow wave NREM (Stage N3, “Deep Sleep”) sleep, latency to first onset of sleep and number of awakenings. Sleep efficiency was calculated as the TST/(TST+Total Wake Time).

For the SHHS participants, as previously described, PSG was performed in an unattended setting at home (Compumedics PS-2 system; Compumedics Pty. Ltd, Abbotsville, Australia). The following channels were recorded: electroencephalogram (C3/A1 and C4/A2), right and left electrooculograms, submental electromyogram, nasal/oral airflow recorded by thermocouple (Protech, Woodenville, WA), rib cage and abdominal movement recorded by inductive plethysmography, oxyhemoglobin saturation (SpO2) by pulse oximetry (Nonin, Minneapolis, MN), and electrocardiogram. Leg movements were not recorded. Standardized techniques for sensor attachment and quality assurance were used and have been previously published (17).

Survey

Participants were asked to complete a questionnaire each morning about activities performed in the two hours prior to sleeping along with their happiness and stress levels before sleeping. Mood was measured on a scale of 1-7 (i.e., Happiness, 1: very unhappy 4: neither unhappy nor happy 7: very happy). Activities prior to sleeping included mental work (e.g., office work or studying for an exam), physical work (e.g., washing dishes, putting children to bed), heated arguments, etc. Food and beverage intake included caffeine and alcohol consumption. Activities prior to sleeping, and food and beverage consumption were measured on a scale of 1-5 (e.g., Physical activities 1: none at all 5: all the time, and caffeine intake 1: none at all, 5: a large amount). Subjects were also asked to report any cause of their sleep disturbances. Three options were provided which included disturbances by their spouse, children and other reasons. These were measured on a scale of 1-3, where 1: none at all, 3: a lot.

Data Analysis

For the Graduate Student Cohort, the few nights when subjects reported the headband falling off were eliminated from all analyses. Some participants provided recordings of less than 14 nights while others used the device for longer durations (up to 19 nights) giving a total of 281 recording nights for the sleep analysis. The mean number of nights per participant was 14 nights (SD:  0.82). In this cohort, the sleep of males was compared to females using mixed-effects linear regression models. The outcome variables were the parameters of sleep architecture and gender represented the sole fixed independent variable. Individual recordings for each participant were fitted as random effects to account for serial intraparticipant correlations. In preliminary analyses, the impact of repetitive recording nights was tested, and was not found to have any effect on the findings.

Multiple regression analysis also was performed in the Graduate Student Cohort to understand how pre-sleep mood and activities affected sleep parameters. The independent variables included the pre-sleep activities and mood variables that showed significant gender differences on univariate testing by analysis of variance. Activities and mood were coded as dummy variables (0: no activity and 1: when the activity was performed). Gender (0: Female, 1: Male) and children (0: without children, 1: with children) were also added as covariates. There were a total of 206 nights with both survey and sleep information. The analyses were performed for the following sleep parameters: Total Sleep Time, Wake Time, Sleep Latency, Sleep Efficiency, % Light Sleep %, Deep Sleep % and REM % as the dependent variables. The standardized coefficient β is reported as a measure of strength of the relationship. We considered p values less than or equal to 0.01 as indicating statistical significance.

For the SHHS cohort, there was only a single night of recording. Comparisons between males and females were performed using a one way analysis of variance with sleep architecture parameters as the dependent variables and gender as the independent variable.

In order to compare the two cohorts, the aggregated mean for each sleep architecture parameter was calculated for the Graduate Student Cohort. In the SHHS cohort, N1 and N2 sleep were combined as “Light Sleep” and N3 sleep was considered equivalent to “Deep Sleep” to provide comparability to the Graduate Student Cohort. Within each gender, differences in sleep parameters were contrasted using a one way analysis of variance.

Data are presented as mean + SD or as regression coefficients (β). In the case of the Graduate Student Cohort, the data represent the mean of all recording nights.

Results

In Table 1 is shown the sleep architecture for both cohorts stratified by gender.

Table 1. Sleep Architecture in Young and Older Couples

^ap<0.05          Male vs. Female

^bp<0.001        Male vs. Female

^cp<0.001        Graduate Student Males vs. Older Males

^dp<0.05          Graduate Student Males vs. Older Males

^ep<.01            Graduate Student Males vs. Older Males

^fp<.05             Graduate Student Females vs. Older Females

In the Graduate Student Cohort, total sleep time, sleep efficiency and %REM sleep were higher in males than females (Table 1 and Figure 1).

Figure 1. Panel A: Total sleep time in minutes. Panel B: Sleep efficiency (5). *p<0.05 graduate student males compared to females. ⁺p<0.001 graduate student males compared to females. ^#p<0.001 graduate student males compared to older males.

Light sleep and arousals were lower. In sensitivity analyses, we restricted the dataset only to nights where both couples wore the recording device and also only to nights where no caffeine was consumed. Our findings were substantially the same in either case. In contrast, there were no significant differences between males and females in the SHHS cohort. When the sleep of the Graduate Student Cohort was compared to the SHHS cohort, differences were generally confined to males. Males in the SHHS cohort had lower sleep efficiency, % REM and % Deep Sleep, and higher amounts of arousals and % Light Sleep. The only difference observed in female comparisons was the higher number of arousals in the SHHS cohort.

Table 2 illustrates the impact of children in the households of the Graduate Student Cohort. In those without children sleep efficiency was slightly better and the number of arousals was marginally less.

Table 2. Impact of Children on Sleep Architecture in Graduate Student Couple 

^a p<0.01         Without children vs. with children

^b p=0.088       Without children vs. with children

Males and females were also found to differ in their mood and activities prior sleeping. Females reported being happier prior sleeping than males (5.13+1.17 vs. 4.55+1.15, p<0.001). There were trends for males to be more involved with mental work (2.65+1.62 vs. 2.03+1.47, p=0.02) and to consume more caffeine (1.36+0.76 vs. 1.17+0.61, p<0.03) prior sleeping. In contrast, females did more physical work (1.23+0.55 vs. 1.92+1.15, p<0.001) and tended to eat more food (1.83+1.01 vs. 1.57+0.80, p=0.023).

The impact of evening activities on nighttime sleep is presented in Table 3. As shown by the model’s negative β coefficient, total sleep time was adversely impacted by female gender and mental work. In contrast, wake time was increased by gender, food intake and possibly physical work, but decreased by mood (happiness). The remaining sleep variables except for % Deep Sleep also were impacted by gender. In addition, as shown in Table 3, sleep latency, sleep efficiency, % Light Sleep, % Deep Sleep and % REM were variously affected by evening activities.

Table 3. Impact of Evening Pre-sleep Activities, Mood and Gender on Sleep in Graduate Students (n=206)

^a Variables analyzed in each model with their respective β and p values are shown vertically underneath each dependent sleep variable.

^b The overall R² for each model

Discussion        

In this study of couples sleeping together, we found that the naturalistic sleep of young males was better than females, but that these differences were not apparent in the sleep of older adults. Comparison of these groups indicated that the changes were a result of a decline in sleep quality in males. Including assessments of mood and pre-sleep activities in analyses did not substantially affect observed differences in sleep between genders in the younger couples. We also noted that children in the household had a negative effect on sleep quality. 

We observed that total sleep time, sleep efficiency and %REM sleep were higher in young males than young females. This finding is consistent with most previous studies observing that symptoms of sleep disturbances are less common in males (6,18-22). In contrast, previous polysomnographic recordings generally show better sleep quality among females (7,8,10,23-27), but many of these studies were conducted using only older populations (8,23,25,26). Nevertheless, in the few polysomnographic studies performed that have included younger individuals, there have been discordant results with no differences observed between genders (7) or females exhibiting better sleep (24, 27). However, only one of these was performed in a home environment and also analyzed the impact of bedpartners (27). In that study, sleep latency was longer in those sleeping with a bedpartner, but may have been confounded by age because older subjects were more likely to sleep by themselves (27). Although females in today’s society are more likely to have careers outside the home, they nonetheless still may shoulder a greater burden of the household chores as we found in our study. This may translate into a shorter duration of sleep and poorer sleep quality, and may represent the difference in social roles of married women relative to their partners. However, this is likely not the entire explanation because differences in total sleep time and sleep architecture persisted even after controlling for pre-sleep evening activities.

One explanation for our novel finding of better sleep in young males living with a bedpartner is our assessment of sleep in a naturalistic environment. Most previous studies that have recorded sleep have utilized laboratory PSG (7,8,10,23-27). Although it is considered the “gold standard” for objectively assessing sleep, the unfamiliar environment of a laboratory can disturb and change an individual’s usual sleep quality and quantity from that under habitual conditions (28-30). Laboratory PSG does not allow subjects to sleep in their naturalistic environment and follow their usual bedtime rituals. Hence, these studies are unable to capture the contribution of their routine behaviors on their sleep and may not be reflective of the subject’s home sleep. In-home studies have utilized methods such as actigraphy. However, it only indirectly detects sleep/wake patterns and is prone to inaccuracies by misinterpreting quiet wakefulness as sleep (31, 32). Furthermore, actigraphy cannot evaluate the different stages of sleep precluding studies to understand gender differences in these. Although survey collected information can assess sleep in a real-life environment, data can be incomplete, inaccurate and subject to recall bias (33). Our study overcomes the aforementioned limitations of PSG, actigraphy and surveys by capturing detailed sleep parameters in a real-home environment using a validated portable relatively unobtrusive sleep monitoring device and may be a model for future naturalistic sleep research.

The difference in sleep between genders we observed in our younger couples did not persist in the older couples. This appears to be related to disproportionate deterioration in sleep quantity and quality in males. Previous cross-sectional analyses of sleep in older persons have also found that sleep in males appears to be worse than in females (7,8,23,27). These previous observations in combination with our findings indicate as suggested by others (23), that over the lifespan, the sleep of males changes at a more rapid rate than in females.

Another interesting, but perhaps not surprising result was that couples without children had more and less interrupted sleep than those without children. Although parenthood is an important life event, very few studies have looked at sleep quality and architecture differences in people with and without children. An epidemiological study of sleep duration in United States found that parents with young children were more likely to get less sleep than those without children (34). Furthermore, the presence of children affects parents’ bedtimes and risetimes (35). However, these studies are based on self-reported data. Our results suggest that these differences should be further explored to understand how demographic and social factors impact our sleep quality and architecture.

One of the limitations of this study is its small sample size. Further larger studies should be performed to validate these results. Second, sleep staging by the sleep monitoring device is less accurate for distinguishing between wake and sleep in comparison to scoring by a sleep expert (16). However, scoring of other stages is more accurate. Third, although the heterogeneity of subjects in terms of profession and demographic factors, such as children allows comparison within the different groups, it prevents us from making strong conclusion of any one group due to limitations of the sample size. Further studies with similar sample size should try to maximize the homogeneity of the subjects. Finally, our comparison analysis should be interpreted cautiously. The cohorts were recruited separately and sleep was recorded using different instrumentation and under different protocols.

In conclusion, this study utilized a novel in-home sleep monitoring device to capture the sleep quality, architecture and duration of young couples from their natural sleep environment. The results suggest that young males have better sleep quality than females. Additionally, comparison of young couples sleep to older couples suggests that differences between genders evolve over time. Future studies including larger populations should perform in-home assessment of sleep parameters of couples of all ages to understand the effect of gender on these in a naturalistic setting.

Acknowledgements

This work has been presented at the 27th Annual Meeting of the Associated Professional Sleep Societies (APSS) in Baltimore, MD, June 2013. It was partially sponsored by Masdar Institute Fellowship, MIT/Masdar Collaborative Research Grant and MIT Media Lab Consortium as well as by HL53938 from the National Heart, Lung and Blood Institute. Dr. Quan is supported by AG009975 from the National Institute of Aging.

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Brief Review: Sleep Health and Safety for Transportation Workers

Stuart F. Quan, M.D.

Laura K. Barger, Ph.D.

Division of Sleep Medicine, Harvard Medical School, Division of Sleep and Circadian Disorders

Brigham and Women’s Hospital

Boston, MA

Abstract

Accidents related to sleepiness related fatigue are an important concern in transportation related industries. This brief review outlines the public safety concerns with sleepiness related fatigue in the railroad, aviation and motor vehicle transportation fields. In addition, the common causes of sleepiness related fatigue, and impact on operators and their families are highlighted. It is suggested that in addition to greater recognition and changes in duty hour regulations, there should be a greater emphasis on the education of operators on the importance of sleep and circadian factors in causing fatigue, as well as strategies to mitigate their impact.

Reports from the Field

The following are two of many potential examples from the National Transportation Safety Board that highlight “The Problem”.

Press Release: November 19, 2002

NTSB CITES CREW SLEEP APNEA IN 2001 MICHIGAN RAIL ACCIDENT (excerpt)

On November 15, 2001 Canadian National/Illinois Central Railway southbound train 533 and northbound train 243 collided near Clarkston, Michigan. The collision occurred at a switch at the south end of a siding designated as the Andersonville siding. Train 533 was traveling at 13 miles per hour when it struck train 243. The signal at the turnout for the siding displayed a stop indication, but train 533 did not stop before proceeding onto the mainline track. Train 243 was traveling about 25 miles per hour on a "proceed" signal on the single main track when the accident occurred. Both crewmembers on train 243 were fatally injured. The two crewmen on train 533 sustained serious injuries.

The Board found that both the conductor and the engineer of train 533 suffered from obstructive sleep apnea. Although the engineer was taking prescription medication for high blood pressure and diabetes and had been instructed by his private physician to seek further medical treatment for sleep apnea, his condition was not being treated at the time of the accident. The conductor's treatment was insufficient to successfully mitigate the affects of the condition, the Board found (1).

USA Today and National Transportation and Safety Board (AAR1402): September 9, 2014

NTSB: Fatigue a factor in fatal UPS crash

At approximately 4:47 am local time on August 14, 2013, UPS Flight 1354 crashed on approach to runway 18 at Birmingham-Shuttlesworth International Airport. The fuselage broke apart killing both the pilot and co-pilot. The accident was investigated by the National Transportation Safety Board and determined that the pilots failed to monitor their altitude and had descended below the minimum altitude resulting in the plane crashing into the ground below. The Board cited several procedural violations as factors causing the crash, but contributing to the accident were “the captain's performance deficiencies likely due to factors including, but not limited to, fatigue, …” and “the first officer's fatigue due to acute sleep loss resulting from her ineffective off-duty time management and circadian factors” (2,3). On the cockpit voice recorder, the pilots are heard to be complaining of being tired.

The Problem

Two fatal transportation industry accidents. One common root cause—sleepiness induced fatigue.

Although it is difficult to estimate the exact number of public transportation accidents that have fatigue as a causal or contributing factor, there is no doubt that operator fatigue is a critical issue. For rail accidents, this statement is supported by analyses from the Collision Avoidance Working Group determining that in 19 of 65 human factors-caused mainline track train collisions, 29.3% involved impaired alertness (4).Furthermore, in testimony before the Senate Subcommittee on Surface Transportation in 1998, the Administrator of the Federal Railroad Administration stated, “human factors account for about one-third of the rail equipment accidents/incidents as well as many personal injuries”. She went on to testify that fatigue was an important underlying factor in many of them (5).

Similar concerns were voiced by the Vice Chairman of the NTSB at an aviation fatigue symposium in 2008. In that address, he stated that there had been over 250 commercial aviation fatalities the 15 years prior to his speech as well as numerous general aviation fatalities (6). Since that time, pilot and/or crew fatigue has been cited by the NTSB as a contributing cause of several commercial airline crashes including that of the well publicized Colgan Air Flight 3407 over Buffalo, New York in 2009 (7).

Fatigue related accidents also are widespread in other transportation sectors. The deadly crash of a bus carrying 32 passengers returning from a casino in Connecticut in which the NTSB found that the driver was speeding and was “impaired by fatigue at the time of the accident due to sleep deprivation, poor sleep quality and circadian factors” has been widely publicized” (8). In another event that received national attention, police alleged that the truck driver who critically injured comedian Tracy Morgan and killed another passenger had been awake for more than 24 hours at the time of the crash (9). In Newton, MA, a subway train crashed because the operator failed to brake and was killed. She had untreated sleep apnea (10).

What We Know About the Problem

Why do transportation workers experience increased rates of fatigue? For some transportation industries, work hour regulations allow for prolonged and irregular schedules and schedules that  create circadian misalignment. According to The Rail Safety Improvement Act of 2008, railroad personnel may work no longer than 12 continuous hours and all shifts must be followed by a minimum of 10 hours off for undisturbed rest. In addition, they cannot exceed 276 hours of duty in one month and after 6 consecutive days of service they must be given a minimum of 48 hours off duty at their home terminal (11). Consequently, as an extreme example, an engineer could be assigned to work a schedule of 12 hours on and 10 hours off for 6 consecutive days. Although this is a significant improvement in comparison to work hours rules specified in previous regulations (no longer than 12 continuous hours followed by a minimum of 10 hours off duty, and that they be given at least 8 consecutive hours off duty in every 24-hour period), they nonetheless still allow very irregular working hours, unpredictability of scheduling and promote circadian misalignment. In comparison, a commercial airline pilot’s flight time is limited to 100 hours per month. However, depending on the number of flight segments and start time, their maximum duty period may be as long as 14 hours (12). Recently, new regulations incorporate variability in duty hours and rest periods to account for the impact of circadian factors on fatigue and sleepiness. Although the FAA encourages cargo airlines to voluntarily follow the new 2014 rule for flight, duty and rest requirements, it does not apply to cargo pilots, many of whom fly exclusively at night (13). A bus driver cannot drive more than 10 hours and not after having been on duty for 15 hours. Resumption of driving can only occur after 8 consecutive hours off duty. Furthermore, no driving is permitted after accumulating 60 hours on duty in 7 consecutive days (14). Truck drivers are limited to an 11 hour driving limit after 10 consecutive hours off duty, and cannot drive after the 14th consecutive hour on duty (14). Even these regulations for transportation workers allow for extended periods of continuous duty, much longer than that the traditional 8-hour work day. Furthermore, although all of these regulations specify rest periods, it is unclear whether operators actually obtain sufficient amounts of sleep.

In a survey of long haul (i.e., single long flight) and short haul (i.e., multiple flight segments per duty period) pilots, sleep deprivation was cited as a significant cause of fatigue and reduction in performance (15). In another study, the amount of sleep obtained by captains and first officers in the 24 hours prior to flight duty ranged from 3 to 13 hours with a mean of approximately 7 hours indicating that a significant proportion obtained insufficient sleep (16). Several studies have demonstrated that under current regulations, rail personnel also obtain inadequate amounts of sleep. In one study analyzing work/rest diary surveys of 200 locomotive engineers, although the average engineer obtained only slightly less sleep than a non railroader, those who started work late at night or in the very early morning slept only about five hours (17). In another study using simulated work schedules allowed by the current hours of service rules, subjects accumulated progressive sleep debt over time (18). Several older studies have documented that long haul truck drivers sleep inadequate amounts as well, with one study documenting less than 5 hours per 24 hour period (19-21). After implementation of new duty hour rules, there was some increase in the amount of sleep obtained, but it still averaged only approximately 6 hours per 24 hour period (22).

Apart from work hour rules, there are many other factors that contribute to sleep deficiency in the transportation sector. Often transportation workers are required to sleep away from home; accommodations might be in a hotel room or in the cab of a truck. Even sleeping at home may be challenging if that sleep occurs during daytime hours when noise, light and family obligations make it difficult. Additionally, the allotted rest time between shifts might be insufficient to accommodate long commutes and other tasks of daily living as well as sleep.

The health impact of sleepiness induced fatigue extends well beyond the obvious increase in human factors accidents. Accumulating data now implicate inadequate or short sleep duration as a risk factor for cardiovascular disease, hypertension, diabetes and obesity (23-25). Moreover, shift work is now considered by the World Health Organization as a probable risk factor for cancer (26). Thus, given their higher probability of experiencing chronically insufficient sleep, it is likely that transportation workers are at greater risk for these adverse health consequences of inadequate or short sleep duration than members of the general non-shift-working population.

There is also a link between insufficient sleep and behavioral health problems. Sleep deprivation is associated with acute worsening of mood, with complaints of irritability, depression, and decreased motivation (27-29). In the setting of a pre-existing mental illness, sleep deprivation may trigger a change in condition (30). There is no reason to suspect that transportation workers would be less susceptible to the behavioral consequences of sleep deprivation. Insufficient sleep is also known to adversely affect judgment (31). This can lead the person who has had insufficient sleep to underestimate its effect on his/her performance.

Fatigue is not the only issue adversely impacting the performance of transportation workers. Long hours and irregular schedules leading to chronic sleep deprivation can impact their personal lives which in turn can result in performance degradation. For example, the impact of fatigue on the family lives of train operators was extensively explored in study by Holland in 2004 (32). He found three general themes:

1. Emotional issues impacting the family such as mood swings and irritability, and the need to compensate in some way for these;
2. The need for family support and awareness;
3. Social implications of the erratic schedules leading to isolation and frustration because of the inability to have a normal social life.

The importance of social well-being (leisure time and marital relationships) was further emphasized in another study of 276 railroad engineers and conductors at a North American railroad. In this study, the investigators found that social-well being was a significant mediating factor in the causal pathway between organizational factors (i.e., scheduling) and fatigue (33). Such findings are not unique to railroad workers. In a study of airline pilots, mental health was associated with fatigue and lack of family social support (34). In a study of truck drivers, almost half of the drivers felt that their work interfered with their family responsibilities and those who drove more endorsed more issues with their family life (35).

Further exacerbating the impacts of chronic sleep deprivation and shift work is the specter of primary sleep disorders themselves. Obstructive sleep apnea syndrome is conservatively estimated to have a prevalence of 2 to 4% in middle-aged women and men respectively, but rates of polysomnographically defined obstructive sleep apnea may be as high as 9 and 24% in women and men from this same study (36). A more recent study conducted in Australia found the prevalence of OSA in middle-aged men to be 53% (37). It is generally accepted that obstructive sleep apnea is underdiagnosed and most afflicted individuals are either undiagnosed or inadequately treated (38). If one excludes the pervasiveness of chronic sleep deprivation, insomnia is one of the most common sleep disorders with a point prevalence rate of approximately 30% (39). Chronic insomnia is present in 10% of the general population, and tends to be an unremitting condition (40,41). Common complaints associated with insomnia are fatigue and sleepiness. Shift work as experienced by transportation workers is a cause of insomnia. Other sleep disorders such as restless legs syndrome, periodic limb movement disorder and narcolepsy also express themselves as causes of fatigue and/or sleepiness.

In general, workers in most transportation industries are hesitant to seek medical evaluation and treatment for sleep problems. Perceived or real concern about loss of employment tends to discourage those afflicted from seeking medical care. This results in large numbers of persons with untreated conditions working in potentially dangerous environments. For example, it is estimated that using a moderately conservative definition of obstructive sleep apnea, 46% of long-haul truck drivers have this condition (42). One can surmise that there are significant numbers of undiagnosed and hence untreated individuals with obstructive sleep apnea in other transportation industries as well.

What to do About the Problem

There are three components to addressing the issue of sleepiness related fatigue in the transportation industry. The first, admission that a problem exists, has been increasingly recognized by policy makers, the industry and workers as reflected by statements and presentations by these parties. The second is appropriate revision of duty hour regulations to make them consistent with scientific evidence related to the effects of sleep deprivation, circadian misalignment and their impact on performance. To some extent, this has resulted in revision of duty hour regulations in the railroad and the aviation industries. However, as evidenced by the exception given to cargo airlines, not all workers are covered. Moreover, a portion of the hours of service regulation for trucking that was enacted in 2011 has been recently rescinded, eliminating mandated rest. Additional changes are needed, but are difficult to implement because of the financial impacts they might have on employers. One of the reasons that cargo airlines were exempted from the new duty and rest regulations was that the calculated financial cost exceeded any benefit irrespective of the impact on the personal lives of the employees (13). The third component is focused on operator education. The importance of this was recognized in the Rail Safety Improvement Act of 2008 (43). In the statute, each railroad was mandated to develop a “fatigue management plan” that needed to incorporate “Employee education and training on the physiological and human factors that affect fatigue, as well as strategies to reduce or mitigate the effects of fatigue, based on the most current scientific and medical research and literature”, as well as “Opportunities for identification, diagnosis, and treatment of any medical condition that may affect alertness or fatigue, including sleep disorders.” Studies have demonstrated that operator educational programs decrease fatigue related accidents. For example, in a recent study of Australian truck drivers, crash rates were higher among those who had not completed a fatigue management program (44).

Although individual industries and employers are at liberty to develop their own fatigue management educational programs, such efforts are not necessarily comprehensive or viewed by employees as containing unbiased information. Thus, there is a need to provide a source of information pertaining to sleep and circadian science, sleep disorders, fatigue/sleep deprivation mitigation strategies, self-evaluation assessment and pathways to seek treatment that is both scientifically accurate and unbiased to assist transportation workers, their families as well as other interested parties. To achieve the most impact, education should be customized to the industry, using the specific industry “language” and fatigue-driven scenarios that apply to the workers in that industry. Consequently, there is an opportunity for disinterested third parties to develop educational fatigue management resources. An example is the educational website, http://www.railroadersleep.org, developed by Division of Sleep Medicine at Harvard Medical School under contract from the Volpe National Transportation Center and the Federal Rail Administration. Other resources can be found at websites sponsored by the American Academy of Sleep Medicine http://www.sleepeducation.com and the National Sleep Foundation (http://sleepfoundation.org).

Fatigue related to sleep deprivation remains commonplace in the transportation industries. Crashes caused by fatigue can have catastrophic consequences on both societal and personal levels. There needs to be greater action to eliminate these events including appropriate revision of duty hour regulations using the best available scientific evidence as well as individual operator education on ways to recognize and mitigate fatigue related to sleep deprivation.

Acknowledgements

Partially supported by Department of Transportation Contract #DTRT57-10-C-10030

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Lack of Impact of Mild Obstructive Sleep Apnea on Sleepiness, Mood and Quality of Life

Stuart F. Quan, M.D.^1,2,6

Rohit Budhiraja, M.D.³

Salma Batool-Anwar, M.D., M.P.H.²

Daniel J. Gottlieb, M.D., M.P.H.^1,2,4

Phillip Eichling, M.D., M.P.H.^7,8

Sanjay Patel, M.D., M.S.^1,2

Wei Shen, M.D.^6,9

James K. Walsh, Ph.D.⁵

Clete A. Kushida, M.D., Ph.D.¹⁰

¹Division of Sleep Medicine, Harvard Medical School, Boston, MA

²Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA

³Department of Medicine, Tufts University School of Medicine, Boston, MA

⁴VA Boston Healthcare System, Boston, MA

^S5leep Medicine and Research Center, St. Luke's Hospital, Chesterfield, MO

⁶Arizona Respiratory Center, University of Arizona, Tucson, AZ

⁷College of Medicine, University of Arizona, Tucson, AZ

⁸Comprehensive Sleep Solutions, Tucson, AZ

⁹Southern Arizona VA Health Care System, Tucson, AZ

¹⁰Stanford University Sleep Clinic and Center for Human Sleep Research, Redwood City, CA

Abstract

Background and Objectives: Obstructive sleep apnea (OSA) is associated with sleepiness, depression and reduced quality of life. However, it is unclear whether mild OSA has these negative impacts. Using data from the Apnea Positive Pressure Long-term Efficacy Study (APPLES), this study determined whether participants with mild OSA had greater sleepiness, more depressive symptoms and poorer quality of life in comparison to those without OSA.

Methods: 239 persons evaluated for participation in APPLES with a baseline apnea hypopnea index (AHI) < 15 /hour were assigned to 1 of 2 groups: No OSA (N=40, AHI < 5 /hour) or Mild OSA (N=199, 5 to <15 /hour) based on their screening polysomnogram. Scores on their Epworth Sleepiness Scale (ESS), Stanford Sleepiness Scale (SSS), Hamilton Rating Scale for Depression (HAM-D), Profile of Mood States (POMS) and Sleep Apnea Quality of Life Index (SAQLI) were compared between groups.

Results: There were no significant differences between the No OSA and Mild OSA groups on any of the 5 measures: ESS (No OSA, 9.8 + 3.5 vs Mild OSA, 10.6 + 4.3, p=0.26), SSS,(2.8 + 0.9 vs. 2.9 + 1.0, p=0.52), HAM-D (4.6 + 3.0 vs. 4.9 + 4.7, p=0.27), POMS (33.5 + 22.3 vs. 28.7 + 22.0, p=0.70), SAQLI (4.5 + 0.8 vs. 4.7 + 0.7, p=0.39).

Conclusion: Individuals with mild OSA in this cohort do not have worse sleepiness, mood or quality of life in comparison to those without OSA.

For accompanying editorial click here.

Abbreviations

AHI                Apnea Hypopnea Index

APPLES           Apnea Long-term Efficacy Study

BMI                Body Mass Index

HAM-D           Hamilton Rating Scale for Depression

IRB                Institutional Review Board

ESS                Epworth Sleepiness Scale

OSA               Obstructive Sleep Apnea

PSG                Polysomnogram

POMS              Profile of Mood States

RDI                 Respiratory Disturbance Index

SAQLI             Sleep Apnea Quality of Life Index

SSS                Stanford Sleepiness Scale

WAIS              Wechsler Adult Intelligence Scale

Introduction

Obstructive sleep apnea (OSA) is an important sleep related breathing disorder with prevalence rates between 3-17% in men and 3-9% in women (1,2). With the rising trend of obesity, it is becoming increasingly more common (2,3). In a number of longitudinal cohort studies, severe OSA is associated with an increased incidence of hypertension, cardiovascular disease and death (4-9). It also is adversely associated with a number of neurocognitive and behavioral outcomes including depression (10), sleepiness (11), and poor quality of life (12).

The most commonly used metric to classify severity of OSA is the apnea-hypopnea index (AHI) which is the number of apnea or hypopnea events per hour of sleep. Persons with an AHI < 5 are not considered to have OSA (13). In contrast, an AHI > 5 and < 15, AHI > 15 and <30, and an AHI > 30 are classified as mild, moderate, and severe respectively (14). It is generally accepted that OSA can negatively impact mood, wakefulness and quality of life. However, it is unclear whether mild OSA can have such effects (10, 11, 15). Epidemiological studies have generally shown that individuals with OSA are sleepier than those without OSA (16). Existing data in persons with mild OSA referred to sleep clinics are either limited primarily to assessments of sleepiness or have conflicting results (12, 17, 18).

The Apnea Positive Pressure Long-term Efficacy Study (APPLES) is a randomized, double-blinded, sham-controlled, multi-center trial of continuous positive airway pressure (CPAP) therapy designed to determine whether CPAP improves neu­rocognitive function over a 6-month test period (19). The present study is an analysis of the relationship between assessments of mood, sleepiness and quality of life in those without OSA versus mild OSA at the baseline visit (pre-randomization) in those screened for participation in APPLES. Our intent was to determine whether there was any association between mild OSA and these domains.

Methods

Participants and Study Design

The study design, recruitment procedures, and inclusion and exclusion criteria for APPLES have been described extensively (19). The institutional review board (IRB) at each site approved the study protocol. Briefly, APPLES was a multisite study conducted at 5 clinical centers: Stanford University, Stanford, CA; University of Arizona, Tucson, AZ; Providence St. Mary Medical Center, Walla Walla, WA; St. Luke’s Hospital, Chesterfield, MO; and Brigham and Women’s Hospital, Boston, MA. Participants were recruited into the study primarily from patients scheduled into a regular sleep clinic for evaluation of possible OSA, and from local adver­tising. Recruitment began in November 2003 and was completed in August 2008. Initial enrollment required age > 18 years and clinical symptoms of OSA, as defined by American Academy of Sleep Medicine (AASM) criteria (14). At enrollment, participants underwent a screening diagnostic polysomnogram (PSG) and baseline neurocognitive testing including the standardized assessments described below. Only participants with an apnea hypopnea index (AHI) > 10 events per hour continued to the clinical trial and were randomized subsequently to sham or active CPAP for 6 months as previously reported (19). Excluded were individuals who had 1) prior OSA treatment with CPAP or surgery, 2) household members with current/past CPAP use, 3) a sleepiness-related automobile accident within the year prior to potential enrollment, (4) oxygen saturations < 75% for > 10% of the diagnostic polysomnogram (PSG) total sleep time; or (5) conditions or use of medications that could potentially affect neurocognitive function and/or alertness. For the pres­ent analysis, data from both randomized and non randomized participants at the time of the screening polysomnography visit were utilized. In addition to new information, some of the material related to sleepiness reported herein represent reanalysis of data in a different format from what has been published in a previous paper (20).

Polysomnography

Polysomnography was conducted as previously described using signals from a nasal pressure cannula, nasal/oral thermistor, thoracic and abdominal piezo bands, and a pulse oximeter to classify apnea and hypopnea events. An apnea was identified by a > 90% amplitude decrease from baseline of the nasal pressure signal lasting > 10 sec. Hypopneas were scored if there was a > 50%, but < 90% decrease from baseline of the nasal pressure signal, or if there was a clear amplitude reduction of the nasal pressure signal that did not reach the above criterion but it was associ­ated with either an oxygen desaturation > 3% or an arousal, and the event duration was ≥ 10 seconds. Obstructive apneas were identified by persistence of chest or abdominal respiratory effort during flow cessation. Central apneas were noted if no displacement occurred on either the thoracic or abdominal chan­nels. All studies were scored at the central reading center located at Stanford University.

Assessments of Sleepiness

Epworth Sleepiness Scale (ESS): The ESS is a validated self-administered questionnaire that asks an individual to rate his or her probability of falling asleep on a scale of increasing probability from 0 to 3 in 8 different situations (21). The scores for the 8 questions are summed to obtain a single score from 0 to 24 that is indicative of self-reported sleep propensity. The ESS prior to randomization was administered at the time of the clinical evaluation and on the night of the diagnostic PSG. The value at the time of the diagnostic PSG was used, but if not available, then the value at the time of the clinical evaluation was substituted.

Stanford Sleepiness Scale (SSS): The SSS asks a person to rate current moment sleepiness on a scale of one to seven (22). Each numerical rating has an associated descriptor, for exam­ple a rating of 1 is described as “feeling active, vital, alert, or wide awake,” while a rating of 7 is described as “no longer fighting sleep, sleep onset soon; having dream-like thoughts.” For APPLES the SSS was administered at 10:00, 12:00, 14:00, and 16:00 on the day following the diagnostic PSG; the variable analyzed was the mean score from these 4 trials. 

Assessments of Mood

Profile of Mood States (POMS): The POMS assesses mood by asking respondents how they feel at that moment according to a series of 65 descriptors such as “unhappy, tense or cheerful” (23). Possible responses are not at all, a little, moderately; quite a lot, extremely. Six mood states are used in the POMS: tension, depression, anger, vigor, fatigue, and confusion, which can be combined to form the total POMS mood disturbance score. Higher scores represent more negative mood states. For this analysis, total mood disturbance score was used.

Hamilton Rating Scale for Depression (HAM-D): The HAM-D is a validated 21-item clinician-administered assess­ment of the severity of depression (24). APPLES used a modified version of this test, the GRID Hamilton Rating Scale for De­pression that was developed through a broad-based inter­national consensus process to both simplify and standardize administration and scoring in clinical practice and research (25). In this scale, 17 items (e.g., depressed mood, suicide, work and anhedonia, retardation, agitation, gastrointestinal or general somatic symptoms, hypochondriasis, loss of insight or weight) are scored using either a 3- or 5-point scale based on intensity and frequency, and are summed to provide a single score. Higher scores reflect more depressive symptoms.

Quality of Life Assessment

Calgary Sleep Apnea Quality of Life Index (SAQLI): The SAQLI was developed as a sleep apnea specific quality of life instrument (26). It is a 35 item instrument that captures the adverse impact of sleep apnea on 4 domains: daily functioning, social interactions, emotional functioning and symptoms. Items are scored on a 7- point scale with “all of the time” and “not at all” being the most extreme responses. Item and domain scores are averaged to yield a composite total score between 1 and 7. Higher scores represent better quality of life.

Statistical Analyses

For this analysis, participants who had an AHI < 5 were assigned to the No OSA group, and those who had an AHI > 5, but < 15 were assigned to the Mild OSA group. Body mass index (BMI) was computed as weight (kg)/height (m)². Participants’ race/ethnicity were classified as self-reported white or non-white. Marital status was categorized as married or not married. For continuous variables, unadjusted comparisons between the No OSA and Mild OSA groups were made using Student’s t-test. Differences in proportions were assessed using the χ² test. Analysis of covariance was performed to adjust for differences in study site, age and BMI. Data are expressed as mean + standard deviation (SD) or percentages. P < 0.05 was considered statistically significant. Analyses were performed using IBM SPSS Statistics Version 20 (Chicago, IL).

Results

In Table 1 are shown the demographic data for the No OSA and Mild OSA groups.

Table 1: Demographic Information

 

The groups were comparable with respect to gender, race, educational achievement, marital status and intelligence. By definition, the AHI for the Mild OSA group was significantly higher than for the No OSA group (10.9 + 2.5 vs. 3.1 + 1.4, p<0.01). However, participants in the No OSA were slightly younger than those in the Mild OSA group (42.1 + 15.1 vs. 47.1 + 13.1 years, p=0.03). There also was a slight trend for those in the Mild OSA group to have a higher BMI (27.3 + 4.5 vs. 29.0 + 5.9 kg/m², p=0.11). Some differences related to study site were noted as well. For the HAM-D, there was a trend for the mean score of both groups combined to be higher at the Brigham and Women’s Hospital site [N=51] in comparison to the University of Arizona site [N=59] (6.1 + 5.3 vs. 3.6 + 3.6, p=0.046). Similarly, there was a trend for the ESS to be lower at the University of Arizona site [N=61] comparison to the St. Luke’s Hospital Site [N=29] (9.2 + 33 vs. 11.1 + 3.3, p=0.051).

Table 2 shows the comparisons between the No OSA and Mild OSA groups for the sleepiness, mood and quality of life metrics.

Table 2: Sleepiness, Mood and Quality of Life in No OSA and OSA Groups

There were no statistically significant differences observed for any of these variables. The table also shows the power in this study to detect clinically significant differences in these metrics. As shown, there is 90% power to demonstrate a 1.92, 0.52, 12.72, 1.90 and 0.45 difference between groups in the ESS, SSS, POMS, HAM-D and SAQLI respectively. Furthermore, although the No OSA group was slightly younger, there were no significant correlations between age and the ESS, SSS, POMS, HAM-D, and the SAQLI (r values between 0.04 and 0.11).

Discussion

In this analysis, we show that using a commonly accepted definition of mild OSA, sleepiness and mood are not different in comparison to persons without significant OSA. Furthermore, there was no evidence that mild OSA negatively impacts quality of life. These data suggest that mild OSA as currently defined has little adverse impact on sleepiness, mood and quality of life.

We observed that there were no differences in the ESS between participants with No OSA in comparison to those with Mild OSA. Results from other large cohorts are conflicting. Our results are consistent with those of Lopes et al (12) who also did not find that the ESS was elevated in those with Mild OSA in a large population of patients undergoing PSG for suspected OSA. In contrast, a cohort of Chinese patients with mild OSA had a greater prevalence of subjective daytime sleepiness in comparison to those with primary snoring (18). However, the ESS was not higher in contrast to the Sleep Heart Health Study in which the ESS appeared to be greater in those with Mild OSA (16). Similarly, excessive daytime sleepiness was more commonly reported among a cohort of Japanese women participating in a cardiovascular risk study (17). In this latter study, OSA status was determined using pulse oximetry and not PSG. A number of other studies also have reported sleepiness data in subjects with mild OSA. However, small sample sizes, populations with specialized characteristics, and lack of specific comparisons between persons with mild OSA and no OSA limit their interpretability (27-32).

In this study, mood as assessed by the POMS and the HAM-D was not worse in the Mild OSA group. Although depressive symptoms and use of anti-depressants are commonly noted among patients with OSA (33-35), studies of whether mood is affected by mild OSA are few. In 2 studies performed in patients seen in an otolaryngology clinic (27, 31), the Beck's Depression Inventory (BDI) was not different in comparison to either a control group or primary snorers. Similarly, in a group of elderly Koreans referred to a sleep clinic, the BDI was not elevated in comparison to an age-matched control group (36). Our findings extend these previous reports by showing that using two different assessments of mood, there was no adverse impact of mild OSA.

Quality of life in this study was not affected by mild OSA. In contrast, in a number of studies, quality of life assessed with various instruments is impaired in persons with OSA (37-40). However, there are few studies in which the potential impact of mild OSA has been examined. In a relatively small study performed in patients from an otolaryngology clinic, scores on the SAQLI in patients with mild OSA were the same as a group of primary snorers (31). Similarly, in an analysis of 461 elderly women who underwent PSG in the Study of Osteoporotic Fractures cohort, scores on the Functional Outcomes of Sleep Questionnaire were the same across tertiles of OSA severity (41). Thus, our findings demonstrating a lack of association between mild OSA and quality of life are consistent with these previous studies.

Our failure to demonstrate an association between mild OSA and sleepiness, mood and quality of life provides additional data challenging the commonly used threshold for “defining disease” in the assessment of OSA. The traditional cutpoint of 5 originated more than 30 years ago when only apneic events were scored (42, 43). In the intervening years, it has been accepted that hypopneas have pathophysiologic significance and are now incorporated into the AHI (44). Additionally, some clinicians advocate including the more subtle respiratory effort related arousals into a broader respiratory disturbance index (RDI) (45). The data in this study suggest that at least for some domains of OSA symptomatology, mild OSA based on the application of current scoring criteria to older thresholds may in fact be part of a normal population.

Despite our findings, clinicians, insurers and policy makers should be cautioned about using the AHI as the sole metric in determining whether or not to treat an individual patient. The impact of OSA insofar as behavioral and neurocognitive domains are concerned appears to be quite heterogeneous. For example, 54% of individuals in the Sleep Heart Health Study with moderate to severe OSA were not sleepy on any one of 3 measures of sleepiness. Conversely, some individuals with less severe OSA may be sleepy (16). In our study, the mean ESS in both the No OSA and Mild OSA groups was above what would be expected for an unselected general population suggesting that other causes of sleepiness were present in the cohort (16). Thus, before deciding to initiate OSA specific treatment for Mild OSA, clinicians should consider whether there are other explanations for the patient’s symptoms, and not just treat the AHI.

This study does have three major limitations. First, it might be argued that our study was underpowered to detect small differences between the No OSA and Mild OSA groups. However, sufficient statistical power was present to detect clinically important differences (Table 2). For example, it has been proposed that the minimally important difference on repeated administrations of the SAQLI is approximately 1 (46). Our results demonstrated that we had 90% power to detect a change of 0.5. Moreover, our findings are consistent with the limited number of studies previously performed. Second, our participants were a mixture of individuals recruited from sleep clinics and those responding to advertisements. Thus, they may not be representative of the general populace. Third, it is possible that the No OSA group included some individuals who actually had mild OSA. Inasmuch as all participants were considered by clinicians to have symptoms consistent with OSA, some individuals in the No OSA group may have had falsely “negative” PSGs. Such misclassification would bias towards a null effect. The extent to which this occurred is not known, but night to night variability of the AHI is relatively low (47). Thus, we suspect this potential bias is small. Despite these limitations, however, the APPLES cohort was geographically and ethnically diverse, and had a representative gender distribution.

In conclusion, evidence from this analysis does not indicate that mild OSA has any impact on sleepiness, mood or quality of life. This raises concerns whether the current AHI criteria for distinguishing mild OSA from no clinically significant OSA needs to be reassessed. Nevertheless, additional comparisons between individuals who are truly without OSA symptoms and those with mild OSA as currently defined need to be performed before a final conclusion can be determined.  

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Acknowledgements

APPLES was funded by contract 5UO1-HL-068060 from the National Heart, Lung and Blood Institute. The APPLES pilot studies were supported by grants from the American Academy of Sleep Medicine and the Sleep Medicine Education and Research Foundation to Stanford University and by the National Institute of Neurological Disorders and Stroke (N44-NS-002394) to SAM Technology. In addition, APPLES investigators gratefully recognize the vital input and support of Dr. Sylvan Green who died before the results of this trial were analyzed, but was instrumental in its design and conduct.

Administrative Core

Clete A. Kushida, MD, PhD; Deborah A. Nichols, MS; Eileen B. Leary, BA, RPSGT; Pamela R. Hyde, MA; Tyson H. Holmes, PhD; Daniel A. Bloch, PhD; William C. Dement, MD, PhD

Data Coordinating Center

Daniel A. Bloch, PhD; Tyson H. Holmes, PhD; Deborah A. Nichols, MS; Rik Jadrnicek, Microflow, Ric Miller, Microflow Usman Aijaz, MS; Aamir Farooq, PhD; Darryl Thomander, PhD; Chia-Yu Cardell, RPSGT; Emily Kees, Michael E. Sorel, MPH; Oscar Carrillo, RPSGT; Tami Crabtree, MS; Booil Jo, PhD; Ray Balise, PhD; Tracy Kuo, PhD

Clinical Coordinating Center

Clete A. Kushida, MD, PhD, William C. Dement, MD, PhD, Pamela R. Hyde, MA, Rhonda M. Wong, BA, Pete Silva, Max Hirshkowitz, PhD, Alan Gevins, DSc, Gary Kay, PhD, Linda K. McEvoy, PhD, Cynthia S. Chan, BS, Sylvan Green, MD

Clinical Centers

Stanford University

Christian Guilleminault, MD; Eileen B. Leary, BA, RPSGT; David Claman, MD; Stephen Brooks, MD; Julianne Blythe, PA-C, RPSGT; Jennifer Blair, BA; Pam Simi, Ronelle Broussard, BA; Emily Greenberg, MPH; Bethany Franklin, MS; Amirah Khouzam, MA; Sanjana Behari Black, BS, RPSGT; Viola Arias, RPSGT; Romelyn Delos Santos, BS; Tara Tanaka, PhD

University of Arizona

Stuart F. Quan, MD; James L. Goodwin, PhD; Wei Shen, MD; Phillip Eichling, MD; Rohit Budhiraja, MD; Charles Wynstra, MBA; Cathy Ward, Colleen Dunn, BS; Terry Smith, BS; Dane Holderman, Michael Robinson, BS; Osmara Molina, BS; Aaron Ostrovsky, Jesus Wences, Sean Priefert, Julia Rogers, BS; Megan Ruiter, BS; Leslie Crosby, BS, RN

St. Mary Medical Center

Richard D. Simon Jr., MD; Kevin Hurlburt, RPSGT; Michael Bernstein, MD; Timothy Davidson, MD; Jeannine Orock-Takele, RPSGT; Shelly Rubin, MA; Phillip Smith, RPSGT; Erica Roth, RPSGT; Julie Flaa, RPSGT; Jennifer Blair, BA; Jennifer Schwartz, BA; Anna Simon, BA; Amber Randall, BA

St. Luke’s Hospital

James K. Walsh, PhD, Paula K. Schweitzer, PhD, Anup Katyal, MD, Rhody Eisenstein, MD, Stephen Feren, MD, Nancy Cline, Dena Robertson, RN, Sheri Compton, RN, Susan Greene, Kara Griffin, MS, Janine Hall, PhD

Brigham and Women’s Hospital

Daniel J. Gottlieb, MD, MPH, David P. White, MD, Denise Clarke, BSc, RPSGT, Kevin Moore, BA, Grace Brown, BA, Paige Hardy, MS, Kerry Eudy, PhD, Lawrence Epstein, MD, Sanjay Patel, MD

*Sleep HealthCenterscfor the use of their clinical facilities to conduct this research

Consultant Teams

Methodology Team: Daniel A. Bloch, PhD, Sylvan Green, MD, Tyson H. Holmes, PhD, Maurice M. Ohayon, MD, DSc, David White, MD, Terry Young, PhD

Sleep-Disordered Breathing Protocol Team: Christian Guilleminault, MD, Stuart Quan, MD, David White, MD

EEG/Neurocognitive Function Team: Jed Black, MD, Alan Gevins, DSc, Max Hirshkowitz, PhD, Gary Kay, PhD, Tracy Kuo, PhD

Mood and Sleepiness Assessment Team: Ruth Benca, MD, PhD, William C. Dement, MD, PhD, Karl Doghramji, MD, Tracy Kuo, PhD, James K. Walsh, PhD

Quality of Life Assessment Team: W. Ward Flemons, MD, Robert M. Kaplan, PhD

APPLES Secondary Analysis-Neurocognitive (ASA-NC) Team: Dean Beebe, PhD, Robert Heaton, PhD, Joel Kramer, PsyD, Ronald Lazar, PhD, David Loewenstein, PhD, Frederick Schmitt, PhD

National Heart, Lung, and Blood Institute (NHLBI)

Michael J. Twery, PhD, Gail G. Weinmann, MD, Colin O. Wu, PhD

Data and Safety Monitoring Board (DSMB)

Seven year term: Richard J. Martin, MD (Chair), David F. Dinges, PhD, Charles F. Emery, PhD, Susan M. Harding MD, John M. Lachin, ScD, Phyllis C. Zee, MD, PhD

Other term: Xihong Lin, PhD (2 yrs), Thomas H. Murray, PhD (1 yr).

None of the authors claim any conflicts of interest relevant to the article.

Reference as: Quan SF, Budhiraja R, Batool-Anwar S, Gottlieb DJ, Eichling P, Patel S, Shen W, Walsh JK, Kushida CA. Lack of impact of mild obstructive sleep apnea on sleepiness, mood and quality of life. Southwest J Pulm Crit Care. 2014;9(1):44-56. doi: http://dx.doi.org/10.13175/swjpcc082-14 PDF

Alpha Intrusion on Overnight Polysomnogram

Ryan Nahapetian, MD, MPH^a and John Roehrs, MD^b

^aPulmonary, Allergy, Critical Care, & Sleep Medicine, University of Arizona, Tucson, AZ

^bSouthern Arizona Veterans Administration Health Care System, Tucson, AZ

Figure 1. Thirty second polysomnogram epoch showing stage N2 non-REM sleep with frequent bursts of alpha frequency waves (black arrows).

Figure 2. Thirty second polysomnogram epoch showing stage N3 delta sleep (black arrows) with overriding alpha frequency (red arrows)

A 30 year-old Army veteran with a past medical history significant for chronic lumbar back pain stemming from a fall-from-height injury sustained in 2006 was referred to the sleep laboratory for evaluation of chronic fatigue and excessive daytime hypersomnolence. His Epworth sleepiness scale score was 16. He denied a history of snoring and witnessed apnea. Body Mass Index (BMI) was 25.7 kg/m². His main sleep related complaints were frequent nocturnal arousals, poor sleep quality, un-refreshing sleep, prolonged latency to sleep onset, and nightmares. An In-lab attended diagnostic polysomnogram was performed. Sleep efficiency was reduced  (73%) and overall arousal index was not significantly elevated (3.2 events/hour). The sleep study showed rapid eye movement (REM) related sleep disordered breathing that did not meet diagnostic criteria for sleep apnea. There was no evidence for period limb movement disorder. However, the study was significant for alpha wave intrusion in stage N2 non-REM and stage N3 delta sleep. Example epochs are shown in figures 1 and 2.

Alpha wave activity is characteristic of drowsy wakefulness and represents the background electro-encephalographic (EEG) pattern of the occipital region of the brain. Alpha activity occurs when individuals close their eyes and the occipital region loses visual stimulus. Alpha-Delta sleep is defined by a mixture of 5-20% delta waves combined with alpha-like rhythms that are interspersed among the delta waves and was first described in 1973 by Hauri & Hawkins (1). Alpha-Delta sleep has been associated with various neuro-psychiatric conditions including schizophrenia, depression, schizoaffective disorder, narcotic addiction, temporal epilepsy, fibromyalgia, chronic fatigue syndrome, and chronic pain syndrome (1,2). Alpha wave intrusion has also been shown to occur in stage N2 non-REM sleep in individuals with fibromyalgia and chronic pain. Poor sleep quality is often reported in individuals with complaints of chronic pain. It is suggested that alpha wave intrusion correlates with pain severity and can be used as a monitor to assess response to therapy (3).

References

1. Hauri P, Hawkins D. Alpha-delta sleep. Electroencephalogr and Clin Neurophysiol. 1973; 34(3): 233-7. [CrossRef] [PubMed]
2. Manu P, Lane TJ, Matthews DA, Castriotta RJ, Watson RK, Abeles M. Alpha-delta sleep in patients with a chief complaint of chronic fatigue. South Med J. 1994; 87(4): 465-70. [CrossRef] [PubMed]
3. Roizenblatt S, Molodofsky H, Benedito-Silva AA, Tufik S. Alpha sleep characteristics in fibromyalgia. Arthritis Rheum. 2001; 44(1): 222-30. [CrossRef] [PubMed]

Reference as: Nahapetian R, Roehrs JD. Alpha intrusion on ovenight polysomnogram. Southwest J Pulm Crit Care. 2014;8(6):334-5. doi: http://dx.doi.org/10.13175/swjpcc075-14 PDF

Sleep Board Review Question: Insomnia in Obstructive Sleep Apnea

Rohit Budhiraja, MD

Department of Medicine, Southern Arizona Veterans Affairs Health Care System (SAVAHCS) and University of Arizona, Tucson, AZ.

What is the estimated prevalence of insomnia symptoms in patients with obstructive sleep apnea?

1. Less than 1%
2. 5%-10%
3. 20-30%
4. 40%-60%
5. Greater than 80% 

Reference as: Budhiraja R. Sleep board review question: insomnia in obstructive sleep apnea. Southwest J Pulm Crit Care. 2013;7(5):302-3. doi: http://dx.doi.org/10.13175/swjpcc150-13 PDF

Long-Term Neurophysiologic Impact of Childhood Sleep Disordered Breathing on Neurocognitive Performance

Stuart F. Quan, M.D.^ab

Stuart_Quan@hms.harvard.edu

Kristen Archbold, Ph.D.^c

Karchbold@nursing.arizona.edu

Alan S. Gevins, D.Sc.^d

Alan@eeg.com

James L. Goodwin, Ph.D.^a

jamieg@arc.arizona.edu

aArizona Respiratory Center, University of Arizona College of Medicine, Tucson, AZ, bDivision of Sleep Medicine, Harvard Medical School, Boston, MA, cPractice Division, University of Arizona College of Nursing, Tucson, AZ, dSAM Technology & San Francisco Brain Research Institute, San Francisco, CA

Abstract

Study Objective. To determine the impact of sleep disordered breathing (SDB) in children on neurocognitive function 5 years later.

Design, Setting, and Participants. A subgroup of 43 children from the Tucson Children’s Assessment of Sleep Apnea Study (TuCASA) who had SDB (RDI > 6 events/hour) at their initial exam (ages 6-11 years) were matched on the basis of age (within 1 year), gender and ethnicity (Anglo/Hispanic) to 43 children without SDB (Control, RDI < 4 events/hour). The Sustained Working Memory Task (SWMT) which combines tests of working memory (1-Back Task), reaction time (Simple Reaction Time) and attention (Multiplexing Task) with concurrent electroencephalographic monitoring was administered approximately 5 years later.

Results. There were no differences in performance on the working memory, reaction time and attention tests between the SDB and Control groups. However, the SDB group exhibited lower P300 evoked potential amplitudes during the Simple Reaction Time and Multiplexing Tasks. Additionally, peak alpha power during the Multiplexing Task was lower in the SDB Group with a similar trend in the Simple Reaction Time Task (p=0.08).

Conclusions. SDB in children may cause subtle long-term changes in executive function that are not detectable with conventional neurocognitive testing and are only evident during neuroelectrophysiologic monitoring.

Introduction

There is increasing evidence that childhood sleep disordered breathing (SDB) is associated with neurobehavioral morbidity (1-3). In cross-sectional studies, children with SDB are found to have deficits in a variety of neurobehavioral domains including attention, executive function, behavior regulation, alertness, learning and academic performance (3). Treatment of OSA with either tonsillectomy or adenoidectomy often results in resolution or improvement in many of these domains (4, 5).

Despite the large amount of data implicating SDB as a causative factor in producing deficits in neurocognition in children, there have been few studies implicating SDB in children as a risk for long-term neurobehavioral morbidity. Several studies have reported that snoring as a surrogate for SDB predicted increased risk for hyperactive behavior (6-8). In addition, in a retrospective analysis, Gozal and Pope (9) reported that low performing middle school students had a greater likelihood of snoring during childhood than their high performing classmates. However, there have been no studies of long-term neurobehavioral morbidity that have used polysomnography (PSG) to document the presence of SDB. Determining whether long-term or permanent deficits in neurocognition occur as a result of SDB will be important in timing of treatment intervention in these children.

In the present study, a subset of the Tucson Children's Assessment of Sleep Apnea Study (TuCASA) underwent additional cognitive neurophysiological testing to determine whether SDB documented during childhood was a risk factor for deficits 5 years later. We hypothesized that children with SDB would exhibit subtle abnormalities during these neurophysiologic tests.

Materials & Methods

Subjects. The Tucson Children’s Assessment of Sleep Apnea study (TuCASA) was a longitudinal cohort established to investigate the correlates and natural history of childhood sleep disordered breathing. Recruitment and overall study methods have been previously described (10, 11). In brief, the TuCASA cohort consisted of healthy school-aged children that were enrolled in a large urban school district in the Southwest United States. With the cooperation of their respective elementary schools, parents of the students were asked to complete a brief screening questionnaire and to provide contact information if they wanted to see if their child was eligible for the study. Those who qualified were then studied using a single overnight unattended in-home PSG along with completion of a questionnaire regarding their sleep habits. TuCASA initially recruited 503 participants (ages 6 – 11) who had their PSGs recorded between the years of 2000 and 2004. Approximately 5 years later, the study attempted to contact the same participants and was successful in restudying 319 children who had valid PSGs for both the baseline and follow-up time points. From this group, we selected 2 groups of children based on their respiratory disturbance index measured from the PSG performed during their baseline exam cycle. No children had received continuous positive airway pressure treatment for SDB. The first group (SDB) consisted of children with SDB as defined by a respiratory disturbance index (RDI) > 6 /hour. The second group (Control) was children without SDB as defined by a RDI < 4/hour. Each child in the Control group was matched to a child in the SDB group on the basis of age (within 1 year), gender and ethnicity. This resulted in a study cohort of 43 pairs of children.

All methods used to recruit subjects and to collect the present data set were approved both by the University of Arizona Human Subjects Committee and the Tucson Unified School District Research Committee. In all cases, we obtained written informed consent from the parents, and assent from the children.

Study Design. Children from both groups were asked to return to the TuCASA sleep laboratory to undergo the Sustained Working Memory Test (SWMT) which was adapted for use in children. The SWMT is an automated cognitive neurophysiological test that combines cognitive test performance measures with electroencephalograhic (EEG) measures. It has been validated in distinguishing cognitive performance in subjects who have ingested alcohol, caffeine, diphenhydramine and who have been sleep deprived (12, 13). These studies were performed on a day separate from any other testing done for TuCASA.

Polysomnography. In both the baseline and follow-up examinations, children underwent unattended, nocturnal home PSG using the Compumedics PS-2 system (Abbotsford, Victoria, Australia) (10, 11). The following signals were obtained: C3/A2 and C4/A1 EEG, right and left electrooculogram, a bipolar submental electromyogram, thoracic and abdominal displacement (inductive plethysmography bands), airflow (nasal/oral thermocouple), nasal pressure, electrocardiogram (single bipolar lead), snoring (microphone attached to a vest), body position (Hg gauge sensor), pulse oximetry (Nonin, Plymouth, MN) and ambient light (sensor attached to the vest to record on/off). Using Compumedics W-Series Replay, v 2.0, release 22, sleep stages were scored according to Rechtschaffen and Kales criteria (14). The RDI was defined as the number of respiratory events (apneas and hypopneas) per hour of the total sleep time irrespective of any associated oxygen desaturation or arousal. Studies with less than 4 hours of scorable oximetry were classified as failed studies and were repeated if the participant consented. Central apneas were scored if both airflow and thoracoabdominal effort were absent. However, central events that occurred after movement were not included. Obstructive apneas were identified if the airflow signal decreased to below 25% of the “baseline amplitude”. Hypopneas were scored if the magnitude of any ventilation signal decreased below approximately 70% of the “baseline” amplitude, as described previously (15).

Body Mass Index Computation. Height and weight were collected on a platform scale. BMI was calculated kg/m², and percentile of BMI adjusted for age, sex and ethnicity was calculated with a standardized data-analysis program from the Centers for Disease Control (http://www.cdc.gov/nccdphp/dnpao/growthcharts/resources/sas.htm ).

Wechsler Abbreviated Scale of Intelligence (WASI). The WASI (16) is nationally standardized intelligence test which is linked to the Wechsler Intelligence Scale for Children®—Fourth Edition (WISC–IV®). It was administered in TuCASA as part of an overall neurocognitive test battery within several weeks of the SWMT.

Sustained Working Memory Test. The SWMT (17) consists of a brief 25 minute computerized test consisting of two blocks of an attentional multiplexing task, an easy and a more difficult version of a spatial n-back working memory task, and eyes open and eyes closed resting tasks. The test is designed for concurrent EEG recording. Data collected included EEG and evoked potential (EP) signals, as well as task performance measures. All subjects were trained how to perform each task the same day the test was administered.

In the attentional multiplexing task (MT), the participants were required to monitor multiple stimuli as they changed shape, color, and pattern, and to sort each object into a bin based on its relevant features. This task adapted to an individual’s ability level; task difficulty increases if performance exceeds a pre-defined threshold, and decreases if performance falls below the threshold. Each MT block lasted approximately 3.5 minutes. The working memory (WM) test consisted of a 3.5 minute spatial 1-back task, in which participants compared the location of the dot stimulus on each trial to that on the immediately preceding trial. A simple reaction time (SRT) test with the same stimulus and response characteristics also was administered as a control task. Resting EEG was also recorded for 1.5 min with eyes open and 1.5 min with eyes closed.

EEGs were recorded from seven scalp locations (Fz, F3, F4, Cz, Pz, P3, P4) positioned via a nylon electrode cap and referenced to linked mastoids. This montage was designed to include adequate spatial representation of the signal features of primary interest as defined by prior high-resolution EEG studies of the working memory tasks used herein (18). Potentials generated by eye movements and blinks were recorded by electrodes positioned above and at the outer canthus and superior orbital ridge of each eye. The resulting data were digitally high-pass filtered at 0.5 Hz. EEG was recorded continuously during task performance and during passive resting conditions. Electrode impedances were kept < 5 KΩ for the references and <20 KΩ on all other channels.

Automated artifact-detection and artifact-decontamination filters were used to minimize contaminants induced by eye movement and other physiologic and instrumental sources. All data were then visually inspected, and any residual contaminants were excluded from further analysis.

SWMT Data Analysis. To assess neurophysiological measures between the SDB and Control groups, EEG power spectra and EPs were calculated. Power spectra were computed from all artifact-free EEG for each task block and converted to dB power with a log10 transformation. To calculate EPs, trials were averaged in 1.2 s epochs beginning 0.2 s before stimulus onset. EP peak amplitudes were measured relative to the mean amplitude in the prestimulus interval.

A number of prior studies have served to identify spectral features of the EEG that are sensitive to task-difficulty manipulations in the types of working memory tasks used in the SWMT Exam (18-20). Based on such previous findings, a number of such sensitive EEG and EP signals were compared between the SDB and Control groups. EEG alpha power was measured as the maximum power in a 2 Hz band between 8- to 12-Hz in all tasks. Amplitude of the P300 (measured in a 100 ms window centered on the largest positive peak between 250 and 520 ms at Pz), and slow-wave EPs (measured in a 250 ms window centered on the largest positive peak between 250 and 650 ms) were computed in the SRT and 1-back WM tasks. Because of the nature of the MT task, EPs were calculated relative to the onset of the visual feedback that immediately follows a correct or incorrect response. A P300 was measured in the MT as the largest positive peak occurring 200-450 ms after the feedback.

Statistical Methods. Potential differences in the characteristics of the study population between the SDB and Control groups were evaluated using Students’ unpaired t-test or linear correlation. Inasmuch as intelligence is a significant factor in determining performance on neurocognitive tests, analysis of covariance was used to compare performance on the various components of the SWMT between groups while controlling for intelligence as assessed by the WASI. Other covariates were included in the models if significant on univariate analyses. Data were analyzed with IBM SPSS Version 20 (http://www-01.ibm.com/software/analytics/spss/) and are presented as mean + SE.

Results

In Table 1 is shown the characteristics of the children in this study.

Boys and Anglos comprised the majority of the study cohort. No children had undergone an adenotonsillectomy at the time of the 1^st PSG, and only 2 had this procedure during the time interval before the 2^nd PSG. As defined by the study design, there were no differences between the SDB and Control groups with respect to age, the time of the 1st or 2nd PSG, or at time of SWMT. Additionally, as dictated by the study design, RDI at the 1st PSG was significantly greater in the SDB group as was the BMI and the standardized BMI (sBMI). The RDI at the 2nd PSG also was higher in the SDB group. However, RDI decreased in both groups over the time period from the 1st to the 2nd PSG. Seven children had SDB on both PSGs and 2 children developed SDB over the study interval. In the SDB group, the mean RDI at the 2nd PSG was below the RDI threshold used to define the Control group at the baseline examination (1st PSG). Significant, but weak negative correlations were observed between sBMI and some of the EEG and evoked potential components of the SWMT [1 back peak alpha, r=-.23, p=0.03; Multiplex Block 1 Alpha Power, r=-.25, p=0.02; Multiplex Block 2 Alpha Power, r=-.21, p=0.05; eyes closed peak alpha, r=-.29, p<0.01; eyes open delta theta power, r=-.25, p=0.02]. Overall, the WASI indicated that the cohort was above average in intelligence and there were no differences between the 2 groups. However, there was considerable heterogeneity within the overall cohort (Minimum WASI: 77; Maximum WASI: 138).

The results of various components of the SWMT are shown in Table 2.

Increased slow eye movement, increased delta/theta band power, and decreased eye closed to eyes open alpha power ratio are neurophysiological indicators of decreased alertness. No differences between the SDB and Control groups were observed for any of these alertness measures.  Similarly, there were no differences with respect to either % items correct or reaction time for the Simple Reaction Time, 1 Back or Multiplexing Tasks. However, the 1 back slow wave amplitude was lower in the SDB Group, and there was a strong trend for the P300 evoked potential amplitude during the Multiplexing Task (p=0.06) and peak alpha power during the Simple Reaction Time Task (p=0.08) to be lower as well. In addition, peak alpha power during the both blocks of the Multiplexing Task was lower in the SDB Group.

Additional analyses were performed to determine whether performance on the SWMT was related to the presence of SDB at the time of the 2^nd PSG. No differences were observed between children who had SDB on the 2^nd PSG and those who did not.

Discussion

In this study, we have demonstrated that after approximately 5 years, several conventional measures of executive function were not different between children with and without SDB. However, neuroelectrophysiologic assessments recorded during task performance were able to distinguish between these 2 groups. These data suggest that SDB in children can have a long-term, albeit subtle impact on neurocognition in children.

Two important domains of executive function are attention and working memory. In our study these were assessed using a simple reaction time task, a multiplexing task and a 1 back working memory task. Although we did not observe that children with SDB had worse performance in either of these domains, previous cross-sectional studies in children have found deficits using a variety of instruments (3). However, many of these studies assessed children derived from clinic populations. In addition, none determined if there was any impact on long-term performance.

The principal finding from our study is that peak alpha power during the multiplexing and simple reaction time tasks was lower in the SDB group. Alpha power reduction is generally considered a marker of cortical activation. Thus, during task performance, peak alpha power should decline as a function of the amount of effort needed to accomplish a given task (21). It is possible that SDB children in this study may have expended more effort to maintain task performance, as evidenced by a lower alpha power. Using the SWMT, similar findings have been observed after marijuana smoking (22).

Similar to the differences in peak alpha power between our SDB and Control groups, we also observed that the P300 evoked potential amplitude during the multiplexing task and the slow wave evoked potential amplitudes during the 1 back task were lower in the SDB group. These evoked potential components are thought to represent aspects of memory encoding, manipulation and retrieval (23). Thus, these data suggest that children with SDB may experience subtle long-term impairment in memory function.

There are several possible explanations of why we did not observe any overt deficits in executive function in children with SDB. First, there was a significant improvement in the RDI in the approximately 5 year interval between the 1st PSG and the testing of these children. Thus, in many of the children, remission of their SDB occurred leading to a reduction in any possible impact of SDB on neurocognition. This would support the contention that overt neurocognitive deficits produced by SDB in school-aged children resolve if SDB improves. Second, the cohort overall had above average intelligence. It is plausible that any impact of SDB would be more evident in those who have less cognitive abilities. Third, it is possible that the 1-back working memory task used in the study was not sufficiently difficult to expose any underlying impairments in executive function. Finally, there is the possibility that inherent cognitive reserve is mitigating the impact of SDB. The cognitive reserve theory postulates that individual differences in how the brain processes tasks may prevent greater insult by using preexisting cognitive processes or by recruiting compensatory ones before there is a detrimental impact on performance (24). Inasmuch as children have the potential for a high amount of neural plasticity, this explanation may be highly relevant in children with SDB.

Our study is not without some important limitations. First, classification of these children into Control and SDB groups was done without regards to desaturation during apnea and hypopnea events. Thus, the impact of oxygen desaturation cannot be assessed. It is unlikely, however, that this is a major confounder because significant oxygen desaturation below 90% was uncommonly observed in these children. Second, there may have been misclassification of children into the SDB and Control groups especially at the cutpoint boundary. We believe this is less likely because by setting the Control cutpoint at < 4 events per hour and the SDB cutpoint at > 6 events per hour, there would have been less risk of misclassification. Third, we did not use intelligence as a factor in assigning children to the 2 groups. However, we believe this had little impact on our results because there was no difference between the groups on the WASI, and we controlled for intelligence in the analyses. Finally, we also observed that body mass index was negatively correlated with performance on some of the SWMT components. Others have found that neurocognitive performance may be negatively associated with obesity (25). However, most studies have been cross-sectional and thus the directionality and causal mechanisms of this association are unclear. Nevertheless, it is unlikely that our results can be explained by this association inasmuch as we controlled for BMI in our analyses. Despite these aforementioned limitations, our study is the only one to our knowledge that has simultaneously assessed neuroelectrophysiologic function during performance of executive function testing in children thus demonstrating its feasibility and potential for acquiring unique information.

In conclusion, SDB in children has the potential to result in subtle long-term detrimental effects in executive function that are not detectable with conventional neurocognitive testing, but may be evident with simultaneous neuroelectrophysiologic monitoring. These data emphasize the importance of recognizing and treating SDB in children in order to prevent possible long-term consequences in neurocognitive function.

Acknowledgements

TuCASA was supported by HL62373. In addition, development of the SWMT was supported by grants from the National Institute of Neurological Diseases and Stroke, The National Institute of Mental Health, The Air Force Research Laboratory and The Office of Naval Research.

Conflicts of Interests: The authors do not have any conflicts of interest to disclose.

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Reference as: Quan SF, Archbold K, Gevins AS, Goodwin JL. Long-term neurophysiologic impact of childhood sleep disordered breathing on neurocognitive performance. Southwest J Pulm Crit Care. 2013;7(3):165-75. doi: http://dx.doi.org/10.13175/swjpcc110-13 PDF

Sleep Board Review Question: Hyperarousal in Insomnia

Rohit Budhiraja, MD

Department of Medicine, Southern Arizona Veterans Affairs Health Care System (SAVAHCS) and University of Arizona, Tucson, AZ.

Insomnia is characterized by which of the following?

1. Increased brain metabolism compared to controls only during awake.
2. Increased brain metabolism compared to controls only during sleep.
3. Increased brain metabolism during both asleep and awake compared to controls.
4. No increase in brain metabolism compared to controls.

Reference as: Budhiraja R. Sleep board review question: hyperarounsal in insomnia. Southwest J Pulm Crit Care. 2013;7(1):38-9. doi: http://dx.doi.org/10.13175/swjpcc091-13 PDF

Sleep Board Review Question: Epilepsy or Parasomnia?

Rohit Budhiraja, MD

Department of Medicine, Southern Arizona Veterans Affairs Health Care System (SAVAHCS) and the University of Arizona, Tucson, AZ.

Which of the following is the most helpful in differentiating nocturnal frontal lobe epilepsy (NFLE) from non-rapid eye movement (NREM) arousal parasomnias?

1. Onset during rapid eye movement (REM) sleep.
2. Arousal preceding the event.
3. Stereotypy.
4. Concomitant presence of sleep apnea.

Reference as: Budhiraja R. Sleep board review question: epilepsy or parasomnia? Southwest J Pulm Crit Care 2013;6(2):87-8. PDF

Sleep Board Review Question: Nocturnal Hypoxemia in COPD

Chithra Poongkunran¹

Rohit Budhiraja, MD^1,2

¹ Department of Medicine, The University of Arizona, Tucson, AZ, 85724, USA.

² Department of Medicine, Southern Arizona Veterans Affairs Health Care System (SAVAHCS), Tucson, AZ 85723, USA.

Question: Which of the following is the strongest predictor of nocturnal hypoxemia in patients with chronic obstructive pulmonary disease (COPD)?

1. Forced expiratory volume in 1 second (FEV1)
2. Age
3. Daytime Oxygen Saturation
4. Radiological severity of COPD

Reference as: Poongkunran C, Budhiraja R. Sleep board review question: nocturnal hypoxemia in COPD. Southwest J Pulm Crit Care. 2013;6(1):12-14. PDF

Sleep Board Review Questions: Medications and Their Adverse Effects

Rohit Budhiraja, MD^1,2

¹ Department of Medicine, Southern Arizona Veterans Affairs Health Care System (SAVAHCS), Tucson, AZ 85723, USA.

² Department of Medicine, Section of Pulmonary, Allergy, Critical Care and Sleep Medicine, The University of Arizona, Tucson, AZ, 85724, USA.

Question: Which of the following medications is not matched with a characteristic side effect?

1. Pramipexole -Pathological gambling
2. Eszopiclone - Unpleasant taste
3. Modafinil - Headache
4. Mirtazapine - Weight Loss

Reference as: Budhiraja R. Sleep board review questions: medications and their adverse effects. Southwest J Pulm Crit Care 2012:5;297-9. PDF

Sleep Board Review Questions: The Restless Sleeper

Tauseef Afaq, MD¹

Rohit Budhiraja, MD^1,2

¹ Department of Medicine, Section of Pulmonary, Allergy, Critical Care and Sleep Medicine, The University of Arizona, Tucson, AZ, 85724, USA. tafaq@deptofmed.arizona.edu

² Department of Medicine, Southern Arizona Veterans Affairs Health Care System (SAVAHCS), Tucson, AZ 85723, USA. rohit.budhiraja@va.gov

A 50 year old female is being evaluated at the Sleep Clinic for difficulty falling asleep.  She reports having unpleasant sensation in her legs causing an urge to move when she lies down to go to sleep. She feels better when she gets up and walks around. She is a restless sleeper and usually finds her bed-sheets on the floor in the morning.  She does not have any other medical history. Her physical exam is normal. 

Which of the following studies would be most helpful in diagnosis and management of this patient?

1. Nerve conduction study
2. Nocturnal polysomnography (PSG)
3. Serum ferritin level
4. Suggested immobilization test (SIT)

Reference as: Afaq T, Budhiraja R. Sleep board review questions: the restless sleeper. Southwest J Pulm Crit Care 2012;5:262-5. PDF

Obstructive Sleep Apnea and Cardiovascular Disease: Back and Forward in Time Over the Last 25 Years

Stuart F. Quan, M.D.

Division of Sleep Medicine, Harvard Medical School and Brigham Womens Hospital, Boston, MA; Arizona Respiratory Center, University of Arizona College of Medicine, Tucson, AZ

Abstract

Over the past 25 years, there have been significant advances made in understanding the pathophysiology and cardiovascular consequences of obstructive sleep apnea (OSA). Substantial evidence now implicates OSA as an independent risk factor for the development of hypertension, coronary artery disease, congestive heart failure and stroke, as well as increased risk of death. Pathophysiologic mechanisms include release of inflammatory mediators, oxidative stress, metabolic dysfunction, hypercoagulability and endothelial dysfunction. Although non-randomized intervention studies suggest that treatment of OSA with continuous positive airway pressure may mitigate its impact of the development of cardiovascular disease, randomized clinical trials are lacking.

Introduction

The first report of the physiologic events occurring in obstructive sleep apnea (OSA) was published in 1965 by Gastaut and colleagues (1). However, literary and historical accounts of what most likely was OSA have existed since antiquity (2). Most well-known of these is the description of Burwell’s case of a man falling asleep while playing poker. This led to the widespread use of the “Pickwickian Syndrome” because of the similarity of the patient’s symptoms to the literary imagery of “Joe, the fat boy”, a character in Charles Dicken’s Posthumous Papers of the Pickwick Club (3). In the 50 years since Gastaut’s original description of OSA, there has been an exponential growth in the recognition that it is a clinical condition with a high prevalence, subtle to disabling clinical symptoms, and more recently, substantial cardiovascular morbidity and mortality. However, much of the progress in our understanding of OSA with respect to cardiovascular disease has occurred in the past 25 years. Thus, it is appropriate to look back to where we were 25 years ago, our current state of knowledge and identify avenues for future research. Before doing so, however, one must examine whether there is sufficient evidence suggest that a biologic association between OSA and cardiovascular disease is plausible.           

Obstructive Sleep Apnea and Cardiovascular Disease: Biologic Plausibility

Obstructive sleep apnea is characterized by repetitive episodes of occlusion or near occlusion of the upper airway at the level of the pharynx despite increasing inspiratory efforts (4). These episodes are terminated by brief arousals from sleep resulting in sleep fragmentation. There are a number of physiologic consequences to what essentially are repetitive involuntary Müeller maneuvers (5,6). With cessation or near cessation of airflow, transient oxygen desaturation and hypercarbia occur (4,6). Inspiratory efforts against an occluded airway result in large intrathoracic pressure swings (5,6). As a result, there is increased sympathetic nervous system activity (7), fluctuations in parasympathetic tone (8), large cyclical changes in heart rate, arterial and pulmonary vasoconstriction and hypertension, and increases in cardiac preload and afterload (9-11). Given the chronicity of OSA, it is not difficulty project that such physiologic changes might lead to daytime cardiovascular dysfunction. Indeed, daytime hypertension has been induced in a canine model of OSA (12) as well as in rodent models of intermittent hypoxia (13). Thus, there are potential biologic mechanisms to explain why OSA might be an independent risk factor for cardiovascular disease.

Obstructive Sleep Apnea and Cardiovascular Disease: Circa 1970s-1980s

In the 1980’s, studies in Europe found cross-sectional associations between snoring as a surrogate for OSA and both hypertension and cardiovascular disease (14,15). Slightly earlier, evidence also was emerging of an association between obstructive sleep apnea and hypertension. These studies noted a greater than 50% prevalence of hypertension among patients with OSA (16,17) and conversely, a 20-30% prevalence of OSA was observed in patients with hypertension (18,19). Furthermore, several case control and cohort studies indicated that the risk of stroke or heart disease was 2.1 to 10.3 fold greater for those with snoring (20-23). Cardiac arrhythmias were frequently noted to occur in OSA patients as well (24).

Later during this time period, there were 3 retrospective studies analyzing the relationship between OSA and mortality. From the Henry Ford Sleep Disorders Center, the vital status of 385 male patients with OSA studied with polysomnography between 1978 and 1986 was determined. Mortality was significantly greater in those with an apnea index (not apnea hypopnea index [AHI]) greater than 20 events/hour. Uvulopalatopharyngoplasty did not attenuate the mortality rate, but better survival was reported in those who had a tracheostomy or were prescribed nasal continuous positive airway pressure (CPAP) (25). At the same time, the Stanford Sleep Disorders Center analyzed their experience in 198 patients who had either a tracheostomy (n=71) or were treated conservatively with a recommendation for weight loss (n=127).  There were 14 deaths of which 8 were from stroke or myocardial infarction over a 5 year follow-up period.  All occurred in the conservatively treated group (26). In contrast, another series from the University of Florida failed to find any mortality differences between 91 treated and untreated OSA patients and 35 patients with symptoms consistent with OSA, but negative findings on polysomnography over a 7-98 month follow-up (27).

Given the known acute effects of repetitive obstruction of the upper airway on systemic and pulmonary blood pressure, and heart rate, as well as the recurrent episodes of hypoxemia (11), linkages between these physiologic findings and the development of cardiovascular disease were proposed (28). One such pathophysiologic pathway highlighted important roles for hypoxemia and increased sympathetic activity, both of which are currently considered important mechanistic factors for the development of cardiovascular disease (28).

Obstructive Sleep Apnea And Cardiovascular Disease 2012: Epidemiology

Starting in the mid 1990’s, important observations were made that solidified linkages between OSA and cardiovascular disease. In the Wisconsin Sleep Cohort, the first large prospective population-based cohort study to use polysomnography to confirm the presence of OSA, Peppard et al. (29) demonstrated that OSA was an independent risk factor for the development of hypertension. Furthermore, the risk progressively increased with greater levels of OSA severity (OR: 1.42, 2.03, 2.89 [AHI: <5, 5-<15, >15 /hour vs. referent=0]) (29). These findings were subsequently confirmed in the Sleep Heart Health Study (OR: 1.13, 1.54, 2.119 [AHI: 5-<15, 15-<30, >30 /hour vs. referent=<5], although they were significantly attenuated when body mass index (BMI) was included in the analytic models (30). In addition, the development of hypertension was found to be associated with the presence of nocturnal hypoxemia. A more recent prospective study from a clinical cohort showed similar findings (31). Moreover, a number of studies have shown that blood pressure will decrease after treatment of OSA with continuous positive airway pressure (32). Although the magnitude of improvement in large clinical trials is only 2-3 mm Hg, such changes are large from an epidemiologic and public health perspective, and may be greater in individual patients and those with resistant hypertension (33). Given this evidence, the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure has concluded that OSA is an identifiable cause for the development of hypertension (34).

Evidence now indicates that OSA is an independent risk factor for the development of coronary heart disease (CHD). In a Spanish clinical cohort of only men, there was an increased risk of incident CHD over a 12 year follow-up period (35). Subsequently, this observation was confirmed in the Sleep Heart Health Study (36). In this latter study, however, increased risk (Hazard Ratio: 1.75 for AHI > 30 /hour vs. AHI < 5) was only observed in men less than 70 years of age. It is possible that the absence of an impact of OSA on risk of CHD in older men is related to a “healthy survivor” effect (37). Thus, whether OSA increases the likelihood of developing CHD in older men and women remains unclear.

In addition to findings linking OSA to greater risk of incident CHD, OSA appears to enhance the likelihood of new events in those with prevalent CHD. In a study of 407 consecutive patients with CHD, those with an oxygen desaturation index greater than 5 per hour had a 70% relative increase and a 10.7% absolute increase in the composite endpoint (cerebrovascular event, myocardial infarction or death) (38). In another study, 89 patients who had undergone percutaneous coronary intervention had polysomnography with OSA found in 51. In comparison to those who did not have OSA, 23.5% vs. 5.3% had a major adverse coronary event (cardiac death, reinfarction, and target vessel revascularization) in the ensuing follow-up period (mean=227 days) (39).

Sleep apnea, in particular central sleep apnea, frequently is observed in association with congestive heart failure (CHF). However, recent data from the Sleep Heart Health Study suggest that severe OSA is a risk factor for incident CHF in men (Hazard Ratio: 1.71 for AHI > 30 /hour vs. AHI < 5), but not women (36).

Recent observations indicate that stroke incidence also may be increased in those with OSA. In the Wisconsin Sleep Cohort over a 4 year follow-up, a significantly increased odds ratio for incident stroke after age and sex adjustment of 4.48 was observed in those with an AHI > 20 /hour (40). This was attenuated to 3.08 after controlling for body mass index. In the Sleep Heart Health Study, a significant increase in incident stroke risk also was found in those with an AHI > 20 /hour, but only in men (41).

As initially reported over 25 years ago (24), more recent observations have confirmed an association between OSA and cardiac arrhythmias. In the Sleep Heart Health Study, those with severe OSA were more likely to have both ventricular and atrial ectopy (42). Furthermore, those with severe OSA were 4.5 and 1.8 times more likely to have episodes of atrial fibrillation, and complex ventricular ectopy or non-sustained ventricular tachycardia.42 Additional analyses reveal that the relative risk of having an episodes of atrial fibrillation is 17 times greater after an apnea or hypopnea episode and there is 1 excess episode of paroxysmal atrial fibrillation or nonsustained ventricular tachycardia for every 1000 hours of sleep or 40000 respiratory disturbances (43). Obstructive sleep apnea with attendant hypoxemia also may be a risk factor for recurrence of atrial fibrillation after cardioversion (44).

Finally, there is now relatively conclusive evidence from 3 longitudinal cohort studies demonstrating that OSA contributes to excess mortality. In the Busselton Health Study of 380 individuals followed for a mean of 13.4 years, a respiratory disturbance index of greater than 15 /hour yielded a hazard ratio of 6.24 for excess mortality (45). Subsequently, in an 18 year follow-up of 1496 participants in the Wisconsin Sleep Cohort, the adjusted hazard ratio for excess all cause mortality related to severe OSA was 3.0 in comparison to no OSA (46). Furthermore, the hazard ratio related to cardiovascular disease mortality was 5.2 (46). More recently, the Sleep Heart Health Study reported a hazard ratio of 1.46 for all cause mortality over an 8.2 year average follow-up. Similar to the Wisconsin Sleep Cohort, it appeared that cardiovascular deaths accounted for much of the excess risk (47). In this latter study, indices of nocturnal hypoxemia also were associated with excess all cause mortality. Although sudden cardiac death in the general population usually occurs during the 6 am to 12 noon time frame, in those with OSA, it is shifted to night-time hours, 12 midnight to 6 am providing further evidence of the adverse impact of OSA on the heart (48).

Obstructive Sleep Apnea and Cardiovascular Disease: Mechanistic Observations

Since the initial observations of the physiologic events that might be operative in the pathogenesis of cardiovascular sequelae of OSA over 25 years ago, substantial progress has been made towards understanding how OSA is a risk factor for cardiovascular disease. These findings include OSA induced changes in cardiac structure and function, abnormalities in metabolic function, and increases in inflammation, coagulability and sympathetic nervous system activity. These latter issues then interact to enhance atherogenesis and cardiac dysfunction.

Obstructive sleep apnea is associated with increases in left ventricular mass. In the Sleep Heart Health Study, those with an AHI > 30 /hour in comparison to those with an AHI < 5 were more likely to have left ventricular hypertrophy on echocardiography, and estimates of left ventricular mass were higher (49). These associations were even stronger when indices of nocturnal hypoxemia were used instead of the AHI. This further highlights the potential role of hypoxemia in the pathogenesis of cardiovascular disease attributable to OSA. Given the presence of left ventricular hypertrophy related to OSA, it is not surprising that diastolic dysfunction is more common among individuals with OSA (50). However, use of CPAP may improve left ventricular function (50). This raises the possibility that early intervention to treat OSA may reduce cardiac morbidity and mortality.    

A number of studies have demonstrated that OSA is associated with metabolic abnormalities. For example, in the Sleep Heart Health Study, levels of cholesterol and triglycerides increased as a function of increasing OSA severity (51). These findings may be related to OSA induced intermittent hypoxia (52). Furthermore, the prevalence of metabolic syndrome is higher among persons with OSA in comparison to those without OSA (53). This finding appears to be driven primarily by the higher frequency of hypertension in persons with OSA. Whether these findings are causally related to OSA remains to be determined. However, OSA might potentially increase the risk of CHD by promoting dyslipidemia.

The prevalence of OSA among persons with type 2 diabetes mellitus is high with one study observing that 86% of obese type 2 diabetics had an AHI >5 /hour, indicative of mild OSA (54). Therefore, it is not surprising that considerable evidence now implicates OSA as a determinant of glucose regulation. Both cross-sectional and prospective studies have demonstrated that OSA is a risk factor for glucose dysregulation and in some studies incident diabetes mellitus (55). Furthermore, some studies have demonstrated that treatment of OSA with CPAP results in improved glucose control (55). Data suggest that OSA induced hypoxemia may be a causative mechanism (56). The close association between OSA and type 2 diabetes mellitus raises the distinct possibility that a positive interaction exists to increase the risk of CHD in persons with both conditions.

It is generally accepted that obesity is a risk factor for the development of OSA (57). However, a few studies have reported that reverse causality may be present such that OSA promotes weight gain (58,59). Recent data from the Sleep Heart Health Study support this hypothesis. Over an approximate 5 year follow-up, weight gain was greater among those with an AHI > 15 /hour in comparison to those with an AHI < 5 /hour (60). Thus, promotion of weight gain may be another mechanism by which OSA increases cardiovascular risk.

Especially in those with severe OSA, recurring apneas and hypopneas result in repetitive episodes of hypoxia and reoxygenation. This produces oxidative stress leading to an increase in the flux of free radicals, induction of endothelin expression, suppression of nitric oxide generation, local vasoconstriction and changes in vascular permeability (61). All of these effects have the potential of enhancing the development of cardiovascular disease.

Substantial data now is available demonstrating that OSA is associated with release of a number of inflammatory mediators such as IL6, sIL6R, IL-8, TNFα, CRP and NF-Kappa β (62). In addition, there is evidence for elevated levels of pro-thrombotic factors such as PAI-1, P-selectin, fibrinogen and VEGF in persons with OSA (62). With the recent findings of the importance of inflammation and thrombosis in the pathogenesis of cardiovascular disease, these observations may be important causal mechanistic links that lead OSA to cardiovascular disease.

Using observations from 25 years ago in combination with current data, a plausible pathogenic pathway from OSA to cardiovascular can be summarized as follows (Figure 1). Recurrent episodes of apnea and hypopnea lead to intermittent hypoxia, increased sympathetic activity, hypercapnia, sleep fragmentation with arousals and large swings in intrathoracic pressure. These physiologic perturbations result in increased oxidative stress, release of inflammatory mediators, metabolic dysfunction, weight gain, hypercoagulability, glucose dysregulation and endothelial dysfunction All of these mechanisms can lead to the development of hypertension, diabetes mellitus, CHD, CHF, stroke and increased risk of death.

Figure 1. Proposed mechanisms leading from physiologic alterations occurring during obstructive sleep apnea and hypopnea to the development of cardiovascular disease.

Obstructive Sleep Apnea and Cardiovascular Disease: Knowledge Gaps

Although significant advances have been made in our understanding of the relationship between OSA and CVD in the past 25 years, there are a number of important areas which require further investigation. With respect to OSA and hypertension, it remains unclear whether treatment of OSA reduces the risk of developing hypertension. Most studies to date have used non-randomized cohorts. In the most recently published randomized controlled trial, CPAP treatment did not decrease the incidence of hypertension in nonsleepy subjects over a median 4 year follow-up (63). However, post-hoc analyses did suggest an effect in subjects who were compliant with CPAP for more than 4 hours per night (63). Furthermore, if CPAP or other treatments for OSA are beneficial in reducing CVD risk, are there subsets of the population for whom it is more advantageous?

As to the impact of OSA on CVD and stroke, there also have not been any published large scale clinical trials demonstrating an impact of treatment on changing the incidence of disease. Similar to hypertension, published studies are from non-randomized cohorts. However, there are several large clinical trials such as Randomized Intervention with CPAP in Coronary Artery Disease and Sleep Apnoea (RICCADSA), Sleep Apnea Cardiovascular Endpoints Study (SAVE), and Heart Biomarker Evaluation in Apnea Treatment study (HeartBEAT). In the RICCADSA study, 400 CAD participants will be randomized to one of 4 groups: 1) non-sleepy with OSA treated with CPAP, 2) non-sleepy with OSA and no CPAP treatment, 3) sleepy with OSA treated with CPAP, 4) CAD but no OSA. The participants will be followed for 3 years for CVD morbidity and mortality (64).  In the multinational SAVE trial, participants with OSA at high risk for CVD will be randomized to CPAP or conventional medical therapy, and followed for 3-5 years (65). Because of the length of follow-up required and expense, and some would argue the ethical dilemmas in performing a long-term interventional trial, studies such as the recently completed HeartBEAT have attempted to assess intermediate outcomes. In the HeartBEAT trial, 270 subjects with CHD or at high risk for CHD were randomized to healthy lifestyle instruction, CPAP or nocturnal oxygen with a primary endpoint of 24 hour blood pressure (66). Whether findings from trials using intermediate endpoints will be predictive of an impact on “hard” endpoints such as incident myocardial infarction or stroke remains to be determined.

Conclusions

Substantial progress has been made in the past 25-30 years in our understanding of the relationship between OSA and CVD. Accumulating evidence implicates OSA as an independent risk factor for hypertension, CHD and stroke. However, the risk may not be the same for all segments of the population. A variety of mechanisms may be operative. Non-randomized trials suggest that treatment appears to mitigate the risk in some clinical populations, but it is unclear whether treatment is beneficial in patients without symptoms. Large scale randomized clinical trials are needed to clearly demonstrate that current treatment modalities for OSA can mitigate CVD risk and to delineate which populations will accrue the most benefit.

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49. Chami HA, Devereux RB, Gottdiener JS, et al. Left ventricular morphology and systolic function in sleep-disordered breathing: the Sleep Heart Health Study. Circulation 2008; 117(20):2599-2607.
50. Arias MA, Garcia-Rio F, Alonso-Fernandez A, Mediano O, Martinez I, Villamor J. Obstructive sleep apnea syndrome affects left ventricular diastolic function: effects of nasal continuous positive airway pressure in men. Circulation 2005; 112(3):375-383.
51. Newman AB, Nieto FJ, Guidry U, et al. Relation of sleep-disordered breathing to cardiovascular disease risk factors: the Sleep Heart Health Study. Am J Epidemiol 2001; 154(1):50-59.
52. Adedayo AM, Olafiranye O, Smith D, et al. Obstructive sleep apnea and dyslipidemia: evidence and underlying mechanism. Sleep Breath 2012;
53. Parish JM, Adam T, Facchiano L. Relationship of metabolic syndrome and obstructive sleep apnea. J Clin Sleep Med 2007; 3(5):467-472.
54. Foster GD, Sanders MH, Millman R, et al. Obstructive sleep apnea among obese patients with type 2 diabetes. Diabetes Care 2009; 32(6):1017-1019.
55. Clarenbach CF, West SD, Kohler M. Is obstructive sleep apnea a risk factor for diabetes? Discov Med 2011; 12(62):17-24.
56. Punjabi NM, Shahar E, Redline S, et al. Sleep-disordered breathing, glucose intolerance, and insulin resistance: the Sleep Heart Health Study. Am J Epidemiol 2004; 160(6):521-530.
57. Young T, Peppard PE, Taheri S. Excess weight and sleep-disordered breathing. J Appl Physiol 2005; 99(4):1592-1599.
58. Traviss KA, Barr SI, Fleming JA, Ryan CF. Lifestyle-related weight gain in obese men with newly diagnosed obstructive sleep apnea. J Am Diet Assoc 2002; 102(5):703-706.
59. Phillips BG, Hisel TM, Kato M, et al. Recent weight gain in patients with newly diagnosed obstructive sleep apnea. J Hypertens 1999; 17(9):1297-1300.
60. Brown MA, Goodwin JL, Silva GE, et al. The Impact of Sleep-Disordered Breathing on Body Mass Index (BMI): The Sleep Heart Health Study (SHHS). Southwest J Pulm Crit Care 2011; 3:159-168.
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62. Kent BD, Ryan S, McNicholas WT. Obstructive sleep apnea and inflammation: relationship to cardiovascular co-morbidity. Respir Physiol Neurobiol 2011; 178(3):475-481.
63. Barbe F, Duran-Cantolla J, Sanchez-de-la-Torre M, et al. Effect of continuous positive airway pressure on the incidence of hypertension and cardiovascular events in nonsleepy patients with obstructive sleep apnea: a randomized controlled trial. JAMA 2012; 307(20):2161-2168.
64. Peker Y, Glantz H, Thunstrom E, Kallryd A, Herlitz J, Ejdeback J. Rationale and design of the Randomized Intervention with CPAP in Coronary Artery Disease and Sleep Apnoea--RICCADSA trial. Scand Cardiovasc J 2009; 43(1):24-31.
65. Anonymous . Continuous Positive Airway Pressure Treatment of Obstructive Sleep Apnea to Prevent Cardiovascular Disease (SAVE). Last Updated: 2011. Accessed: October 1, 2012. http://www.clinicaltrials.gov/ct2/show/NCT00738179.
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Contact Information:

Stuart F. Quan, M.D.

Division of Sleep Medicine

Harvard Medical School

401 Park Dr., 2^nd Floor East

Boston, MA 02215

Voice: 617-998-8842

Fax: 617-998-8823

Email: Stuart_Quan@hms.harvard.edu

Conflicts of Interest: The author does not have any conflict of interests pertinent to the subject matter of this review.

Reference as: Quan SF. Obstructive sleep apnea and cardiovascular disease: back and forward in time over the last 25 years. Southwest J Pulm Crit Care 2012;5:206-17. PDF

Sleep Board Review Questions: The Late Riser

Tauseef Afaq, MD¹

Rohit Budhiraja, MD^1,2

¹ Department of Medicine, Section of Pulmonary, Allergy, Critical Care and Sleep Medicine, The University of Arizona, Tucson, AZ, 85724, USA. tafaq@deptofmed.arizona.edu

² Department of Medicine, Southern Arizona Veterans Affairs Health Care System (SAVAHCS), Tucson, AZ 85723, USA. rohit.budhiraja@va.gov

A 22-year-old male presents to Sleep Clinic for sleep onset insomnia and difficulty waking up in the morning.  He plans to begin a new job in a few weeks, which would require him to wake up at 6 AM. He usually goes to sleep at 2 AM and wakes up at 10 AM.  He remembers having this problem through high school and college. He admits to being unable to sleep even if he goes to bed at an earlier time.  He reports sleeping through alarms in the morning.  His sleep log and actigraphy (non-invasive method of monitoring activity) are consistent with delayed sleep phase disorder (DSPD). 

In order to maximally advance the sleep-wake phase in this patient, when should the administration of bright light take place? 

1. Between 5 AM to 6 AM.
2. Between 8 AM to 9 AM.
3. Between 2 AM to 3 AM.
4. Between 10 PM to 11 PM.

Reference as: Afaq T, Burhiraja R. Sleep board review questions: the late riser. Southwest J Pulm Crit Care 2012;5:176-8. PDF

Sleep Board Review Questions: CPAP Adherence in OSA

Carmen Luraschi-Monjagatta, MD¹

Rohit Budhiraja, MD^1,2

¹ Department of Medicine^, Section of Pulmonary, Allergy, Critical Care and Sleep Medicine, University of Arizona, Tucson, AZ, 85724, USA. mdelcarmen@deptofmed.arizona.edu

² Department of Medicine, Southern Arizona Veterans Affairs Health Care System (SAVAHCS), Tucson, AZ 85723, USA. rohit.budhiraja@va.gov

Which of the following has been shown to be associated with a better adherence to positive airway pressure (PAP) therapy in adults with obstructive sleep apnea (OSA)?

1. Use of auto-titrating positive airway pressure (autoPAP) instead of CPAP.
2. Cognitive behavioral therapy prior to initiation of PAP therapy.
3. Severe sleep apnea on diagnostic polysomnography.
4. Epworth Sleepiness Scale (ESS) score at initiation of PAP therapy.

Reference as: Luraschi-Monjagatta C, Budhiraja R. Sleep board review questions: CPAP adherence in OSA. Southwest J Pulm Crit Care 2012;5:135-7. (Click here for a PDF version)

Sleep Board Review Questions: Sleep Disordered Breathing That Improves in REM

Rohit Budhiraja, MD

Pulmonary, Allergy, Critical Care and Sleep Medicine

University of Arizona

Which of the following breathing disorders is usually less severe in rapid eye movement (REM) sleep compared to non-rapid eye movement (NREM) sleep?

1. Sleep-related hypoxemia in COPD
2. Obstructive Sleep Apnea
3. Cheyne Stokes Breathing
4. Hypoxemia in Pulmonary Hypertension

Reference as: Budhiraja R. Sleep board review questions: sleep disordered breathing that improves in REM. Southwest J Pulm Crit Care;2012:106-7. (Click here for a PDF version)

The Impact of Sleep-Disordered Breathing on Body Mass Index (BMI): The Sleep Heart Health Study (SHHS)

Mark A. Brown, M.D. ¹

James L. Goodwin, Ph.D.²

Graciela E. Silva, Ph.D, MPH.³

Ajay Behari, M.D.⁴

Anne B. Newman, M.D., M.P.H^5,6

Naresh M. Punjabi, M.D., Ph.D.⁷

Helaine E. Resnick, Ph.D., M.P.H.⁸

John A. Robbins, M.D., M.S.H.⁹

Stuart F. Quan, M.D.^2,10

¹Department of Psychiatry, Kaiser Permanente, Portland, OR (markbrownmd@gmail.com); 

²Sleep and Arizona Respiratory Centers, University of Arizona College of Medicine, Tucson, AZ(jamieg@arc.arizona.edu);

³College of Nursing & Health Innovation, Arizona State University, Tempe, AZ (Graciela.Silva@asu.edu); 

⁴Pulmonary and Critical Care Associates of Baltimore, Baltimore, MD (ajaybehari@yahoo.com);

⁵Graduate School of Public Health, Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA

⁶Division of Geriatric Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA (NewmanA@edc.pitt.edu);

⁷Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (npunjabi@jhmi.edu);

⁸American Association of Homes and Services for the Aging, Washington, DC (heresnick@gmail.com); 

⁹Center for HealthCare Policy and Research, University of California, Davis, Sacramento, CA (jarobbins@ucdavis.edu);

¹⁰Division of Sleep Medicine, Harvard Medical School, Boston, MA (squan@arc.arizona.edu)

Address for correspondence and reprint requests: Stuart F. Quan, M.D., Division of Sleep Medicine, Harvard Medical School, 401 Park Dr., 2^nd Floor East, Boston, MA 02215, Tel (617) 998-8842, Fax (617) 998-8823, Email: squan@arc.arizona.edu

Conflict of Interest Statement: None of the authors have conflicts of interest pertinent to the subject matter of this manuscript. 

Reference as: Brown MA, Goodwin JL, Silva GE, Behari A, Newman AB, Punjabi NM, Resnick HE, Robbins JA, Quan SF. The impact of sleep-disordered breathing on body mass index (BMI): the sleep heart health study (SHHS). Southwest J Pulm Crit Care 2011;3:159-68. (Click here for PDF version of the manuscript)

Abstract

Introduction: It is well known that obesity is a risk factor for sleep-disordered breathing (SDB). However, whether SDB predicts increase in BMI is not well defined. Data from the Sleep Heart Health Study (SHHS) were analyzed to determine whether SDB predicts longitudinal increase in BMI, adjusted for confounding factors.

Methods: A full-montage unattended home polysomnogram (PSG) and body anthropometric measurements were obtained approximately five years apart in 3001 participants. Apnea-hypopnea index (AHI) was categorized using clinical thresholds: < 5 (normal), ≥ 5 to <15 (mild sleep apnea), and ³ 15 (moderate to severe sleep apnea). Linear regression was used to examine the association between the three AHI groups and increased BMI. The model included age, gender, race, baseline BMI, and change in AHI as covariates.

Results: Mean (SD) age was 62.2 years (10.14), 55.2% were female and 76.1% were Caucasian. Five-year increase in BMI was modest with a mean (SD) change of 0.53 (2.62) kg/m² (p=0.071). A multivariate regression model showed that subjects with a baseline AHI between 5-15 had a mean increase in BMI of 0.22 kg/m² (p=0.055) and those with baseline AHI ≥ 15 had a BMI increase of 0.51 kg/m² (p<0.001) compared to those with baseline AHI of <5.

Conclusion: Our findings suggest that there is a positive association between severity of SDB and subsequent increased BMI over approximately 5 years. This observation may help explain why persons with SDB have difficulty losing weight.

Key Words: Sleep Apnea, Weight Gain, Obesity

Abbreviation List: PSG-polysomnogram, SDB-sleep disordered breathing, AHI-apnea hypopnea index, SHHS-Sleep Heart Health Study, BMI-body mass index, SD-standard deviation, SEM-standard error of the mean, ANOVA-analysis of variance

Introduction

There is overwhelming epidemiological and clinical data indicating that obesity is a risk factor for sleep disordered breathing (SDB).^1-8 The association between obesity and SDB is substantial, with high body mass index (BMI) contributing to moderate to severe SDB in 58% of affected persons.⁹ The effect of obesity is greater in men than women^1,10-12 although it decreases with increasing age.^6,7 In addition, weight loss has been demonstrated to decrease the severity of SDB.^10,13,14 Longitudinal data from population studies including the Sleep Heart Health Study (SHHS),¹⁰ the Wisconsin Sleep Cohort,¹⁵ and the Cleveland Family Study⁶ have initially focused on the impact of increased weight on SDB severity. However, examination of the opposite causal pathway has yet to be prospectively addressed.

Anecdotally, patients with SDB appear to have more difficulty losing weight than obese patients without SDB. They also report marked weight gain prior to confirmation of their diagnosis. Two small studies support these empiric observations.^5,16  Given this limited information on the impact of SDB on BMI, data from SHHS was analyzed to examine the impact of SDB on BMI after controlling for change in AHI and severity of SDB.

Methods

Study Design and Population. The SHHS is a multi-center, community-based prospective cohort study of the natural history and cardiovascular consequences of SDB. Details of the study design, sampling, and procedures have been reported.¹⁷ Briefly, between November 1995 and January 1998 participants were recruited from several ongoing prospective cohort studies--the Framingham Offspring and Omni Studies, the Atherosclerosis Risk in Communities Study, the Cardiovascular Health Study, the Strong Heart Study, and the cohort studies of respiratory disease in Tucson and of hypertension in New York. Participants were eligible if they were ≥ 40 years of age and were not being treated for sleep apnea with positive pressure therapy, an oral appliance, oxygen, or a tracheostomy. Habitual snorers < 65 years were over sampled to increase the prevalence of obstructive sleep apnea. Subjects were required to provide written consent and the protocol was approved by the institutional review boards of each of the eight investigative sites. 

Data Collection. A total of 6,441 subjects completed the baseline polysomnogram (PSG), and 4,586 consented to have a second evaluation approximately five years later. This analysis focuses on the 3,040 participants who had PSG and BMI data at both time points. Data from all 215 participants who had a follow-up PSG from the New York center were excluded because they did not meet quality standards for the follow-up examination. The remaining participants died, were too ill to participate, refused to participate, were lost to follow-up or had incomplete covariate data such as weight. This latter group had a higher percentage of Whites (85%) compared with the study group (75.5%) (p-value <0.001). There also were statistically significant differences in baseline BMI, baseline AHI, and age between the study group compared with the excluded group, however, these differences were very small and were not clinically significant. There was no gender difference between the two groups.

Weight was measured on the night of the PSG examination with the participant in light clothes on a calibrated portable scale. Height was obtained at the baseline home visit if not already measured within + 3 months of the parent study. BMI was calculated as weight in kilograms divided by the square of height in meters. Baseline height was used for baseline and follow-up BMI calculations. Age, sex, and ethnicity were self-reported. 

The PSG was conducted using a portable monitor (PS-2 System; Compumedics Limited, Abbotsford, Victoria, Australia), using methods previously described.¹⁸ Apnea was present if there was an absence or near absence of airflow or thoracoabdominal movement (at least < 25% of baseline) for > 10 seconds. Hypopnea was defined as a decrease in the amplitude of the airflow or thoracoabdominal movement below 70% of baseline for > 10 seconds. The apnea-hypopnea index (AHI) was calculated as the number of apnea and hypopnea events, each associated with at least a 4% decrease in oxygen saturation, divided by total sleep time in hours.

Results

Participant characteristics are provided in Table 1. 

Table 1: Characteristics of participants of the Sleep Heart Health Study cohort with complete baseline and follow-up polysomnography and weight measurements as a function of sleep apnea severity.

As expected, women were over-represented in the baseline AHI < 5 group (64.9%) and men were over-represented in the AHI ³ 15 group (63.5%) (p<0.001). Baseline BMI increased as baseline AHI severity increased.  Overall unadjusted five-year increase in BMI was modest with a mean (SD) BMI change of 0.53 (2.61) kg/m². The unadjusted five-year increase in BMI was 0.63 (2.54) kg/m² for those with baseline AHI < 5, 0.43 (2.48) kg/m² among those with AHI ≥ 5 to < 15 and 0.37 (3.05) kg/m² for the AHI group ≥ 15. These values were not statistically different from each other.

A multivariate regression model was constructed predicting five-year change in BMI by baseline AHI category adjusted for age, gender, race, baseline BMI, and AHI change (Table 2).

Table 2: Adjusted β coefficients of BMI change according to AHI and continuous variables in the Sleep Heart Health Study*.

Compared to baseline AHI group of < 5, those with AHI between ≥ 5 to < 15 had a mean adjusted increase in BMI of 0.21 that approached statistical significance (p=0.055). However, those with AHI ≥ 15 had a statistically significant adjusted BMI increase of 0.51 (p<0.001).  Younger age, lower baseline BMI and greater AHI change also were associated with a larger BMI increase. There was a trend for women to have a greater increase in BMI, but no effect of race was observed. However, the model only accounted for 7% of the total variance. Adjusted means by baseline AHI group are displayed graphically in Figure 1.

Figure 1: Estimated Adjusted Means of BMI increase according to AHI in the Sleep Heart Health Study. Data are adjusted for baseline age (continuous), race (categorical), gender (categorical), baseline BMI (continuous), change in AHI (continuous). Covariates fixed at: baseline BMI = 28.7, baseline age 62.1, change in RDI = 2.7. Bars represent 95% confidence intervals.

Discussion

Our findings indicate that there is a positive association between severity of SDB and five-year increase in BMI. The finding was demonstrated after controlling for key covariates including age, gender, race, baseline BMI, and AHI change. This observation may help explain the difficulty patients with SDB have in trying to lose weight.

Two previous small studies have demonstrated a positive association between newly diagnosed SDB and weight gain. A retrospective study by Phillips et al. compared one-year weight histories of 53 men and women patients who were recently diagnosed with SDB with 24 control subjects matched for gender, age, BMI and percent body fat.⁵ Subjects in that study were somewhat younger than the SHHS cohort with an age difference of approximately 10 years. The SDB among subjects in the previous study tended to be moderate to severe with mean ± SEM AHI 33 ± 5 /h for men and 37 ± 10 /h for women. Mean ± SEM of BMI at time of diagnosis was somewhat higher than in the SHHS with 35 ± 1 kg/m² for men and 44 ± 2 kg/m² for women.  Men and women patients with SDB had reported a recent weight gain of 7.4 ± 1.5 kg compared with a weight loss of 0.5 ± 1.7 kg (p=0.001) in obese controls without SDB. However, given the design of this study it is not possible to determine whether weight gain contributed to the onset of SDB or was a result of SDB. The study was also limited by reliance on self-report of weight gain history.

Another study by Traviss et al. prospectively evaluated 49 obese patients with newly diagnosed SDB.¹⁶ Mean ± SD of AHI at diagnosis was severe at 45 ± 27 /h.  BMI at diagnosis was elevated at 36.5 ± 6.2 kg/m². Of the 49 subjects, 43 could estimate the duration of their symptoms with 84% reporting weight gain since becoming symptomatic.  Weight gain was relatively large, with a reported 17 ± 15 kg over 5.3 ± 4.8 years. However, this study was limited by the lack of a control group and reliance on self-report of weight history.  These two small studies, in addition to our findings, suggest that there is an association between SDB and increased BMI.

Interestingly, unadjusted BMI change in our study was quite modest and not statistically different as a function of SDB severity. However, BMI change over time is a complex phenomenon influenced by several variables. A large (29,799 subjects) prospective study examining 5-year change in weight in a multi-ethnic cohort of men and women explored several of these relationships.¹⁹ In that study, younger men and to a greater degree, younger women were at greater risk for weight gain compared to older adults. This is consistent with our initial findings. In addition, there was a trend for women in the higher baseline BMI categories of ‘overweight’ (BMI >25– 30 kg/m²) and ‘obese’ (BMI >30 kg/m²) in the aforementioned cohort to gain more weight than men in the higher baseline BMI categories. In order to more precisely examine the effect of AHI on weight change, we controlled for these confounders in our final multivariate model thus resulting in the finding of an increase in BMI as a function of SDB severity in this study. 

Several mechanisms could explain why SDB contributes to increased BMI. First, persons with SDB may have a reduction in the quantity and quality of their sleep. Recent data indicate that insufficient sleep may be a risk factor for obesity.²⁰ Experimental sleep restriction increases ghrelin and reduces leptin production favoring appetite enhancement,²¹ a finding that also has been observed in a large population cohort.²² Second, those with SDB may eat a diet that favors weight gain. In support of this hypothesis, sleep restriction has been shown to increase craving for calorie dense food with high carbohydrate content. The Apnea Positive Pressure Long-Term Efficacy Study (APPLES) demonstrated that those with severe SDB consumed a diet higher in cholesterol, protein, total fat and total saturated fatty acids, even after adjusting for BMI, age, and daytime sleepiness.²³ Third, a cardinal symptom of SDB is excessive daytime sleepiness. Thus, it is possible that persons with SDB engage in less physical activity because they are too fatigued to exercise. Data from APPLES indicate that recreational physical activity is less in those with SDB. However, this finding appears to be principally explained by concomitant obesity. 

Weight loss frequently results in an improvement and sometimes resolution in SDB. This is most evident in those who undergo bariatric surgical procedures.^24,25 Persons with SDB are frequently counseled to treat their SDB by losing weight through diet and exercise,²⁶ an approach that is usually unsuccessful.²⁵ Failure to primarily address SDB in conjunction with a weight reduction program may diminish the latter’s success. However, evidence to date indicates that treatment of SDB does not consistently result in weight loss. In a sample of clinical patients with SDB, treatment with CPAP did not result in weight loss. Moreover, in female patients, there was actually an increase in weight.²⁷ In addition, consistent weight reduction was not observed in a small number of patients with severe OSA who underwent tracheostomy.²⁸ Thus, it appears that weight gain engendered by the presence of OSA is not easily reversed despite therapy. Prospective studies will be required to determine whether primary treatment for OSA enhances weight loss programs in those with OSA.

Although this analysis demonstrated a positive association of severity of SDB on five-year increase in BMI, there are several caveats that deserve consideration. The BMI of participants tended to be lower than that seen in clinical SDB populations and a relatively small number of subjects had large changes in BMI. As previously noted, the mean BMI increase was, at best, quite modest. When converted for illustrative purposes to weight using an average height of 167 cm of the participants, those with an AHI between ≥ 5 to < 15 had a mean adjusted increase in BMI of 0.21 kg/m² equal to 0.59 kg or 1.30 lbs. Similarly, those with AHI ≥ 15 had an adjusted BMI increase of 0.51 kg/m² equal to 1.42 kg or 3.13 lbs. Thus, the magnitude of the changes we observed may not be applicable to clinical populations where patients with SDB may have a higher BMI. In addition, it is not known when the participants developed SDB, thus definitive inference of causality cannot be made. However, following a large undiagnosed cohort over an extended period of time to determine incidence of SDB onset and subsequent change in weight would be exceedingly difficult and costly.  Additionally, the model only accounted for a small amount of the total variance in five-year BMI increase, suggesting that there are likely other unmeasured variables influencing the amount of BMI increase over time in this cohort. Finally, while not statistically significant, the unadjusted mean change in BMI was slightly less in the high RDI group in comparison to the lower RDI groups. This observation underscores the biological complexity of the interactions among weight change, SDB, age, gender and other factors.

In conclusion, our findings suggest that although weight gain is a risk factor for developing or worsening SDB, SDB may, in a reciprocal fashion, lead to increased weight gain. This may help explain why patients with SDB find it difficult to lose weight.

Acknowledgements

This work was supported by National Heart, Lung and Blood Institute cooperative agreements U01HL53940 (University of Washington), U01HL53941 (Boston University), U01HL53938 (University of Arizona), U01HL53916 (University of California, Davis), U01HL53934 (University of Minnesota), U01HL53931 (New York University), U01HL53937 and U01HL64360  (Johns Hopkins University), U01HL63463 (Case Western Reserve University), and U01HL63429 (Missouri Breaks Research).

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, 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 Indian Health Service.

These data have been presented in part at the Annual Meeting of the Associated Professional Sleep Societies, June 11, 2009, Seattle, WA.

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28. Haapaniemi JJ, Laurikainen EA, Halme P, Antila J. Long-term results of tracheostomy for severe obstructive sleep apnea syndrome. ORL J Otorhinolaryngol Relat Spec 2001;63:131-6.

Incidence and Remission of Parasomnias among Adolescent Children in the Tucson Children’s Assessment of Sleep Apnea (TuCASA) Study

Oscar Furet, RN M.P.H.

Arizona Arthritis Center, University of Arizona, Tucson, AZ

oscarf@arthritis.arizona.edu

James L. Goodwin, Ph.D.

Arizona Respiratory Center, University of Arizona, Tucson, AZ

jgoodwin@email.arizona.edu

Stuart F. Quan, M.D.

Arizona Respiratory Center, University of Arizona, Tucson, AZ. Division of Sleep Medicine, Brigham and Womens Hospital and Harvard Medical School, Boston, MA

Correspondent:             Stuart F. Quan, M.D.

                                    Division of Sleep Medicine

                                    Harvard Medical School

                                    401 Park Dr., 2^nd Floor East

                                    Boston, MA 02215

                                    Email: squan@arc.arizona.edu

                                    Voice: 617-998-8842

                                    Fax:    617-998-8823

Reference as: Furet O, Goodwin JL, Quan SF. Incidence and remission of parasomnias among adolescent children in the Tucson Children’s Assessment of Sleep Apnea (TuCASA) Study. Southwest J Pulm Crit Care 2011;2:93-101. (Click here for PDF version)

Abstract

Background: Longitudinal assessments of parasomnias in the adolescent population are scarce. This analysis aims to identify the incidence and remission of parasomnias in the adolescent age group.

Methods: The TuCASA study is a prospective cohort study that initially enrolled children between the ages of 6 and 11 years (Time 1) and subsequently re-studied them approximately 5 years later (Time 2). At both time points parents were asked to complete a comprehensive sleep habits questionnaire designed to assess the severity of sleep-related symptoms that included questions about enuresis (EN), sleep terrors (TR), sleep walking (SW) and sleep talking (ST).

Results: There were 350 children participating at Time 1 who were studied as adolescents at time 2. The mean interval between measurements was (4.6 years). The incidence of EN, TR, ST, and SW in these 10-18 year old children was 0.3%, 0.6%, 6.0% and 1.1% respectively. Remission rates were 70.8%, 100%, 64.8% and 50.0% respectively.

Conclusions:  The incidence rates of EN, TR, and SW were relatively low moving from childhood to adolescence while remission rates were high across all parasomnias.

Introduction

Parasomnias are unpleasant or undesirable behavioral or experiential phenomena which occur predominantly or exclusively during sleep.¹ When occurring during childhood, they can result in substantial parental sleep disruption, anxiety and concern. In addition, there may be adverse consequences on the child's behavior and self-esteem.^2-4 There are 4 parasomnias that are commonly observed during childhood. Sleepwalking (SW) and sleep terrors (NT) are parasomnias associated with arousal that usually occur during slow wave sleep.⁵  Sleepwalking is semi-purposeful ambulatory behavior without awareness. Night terrors (also called sleep terrors) are recurrent episodes of abrupt awakening from deep non-REM sleep, usually with a scream and signs of intense fear and autonomic arousal.⁵ Sleep talking (somniloquy) (ST) consists of vocalizations, frequently nonsensical, during both REM and non-REM sleep.⁶  Enuresis (EN) is characterized by recurrent involuntary micturition that occurs during sleep.⁷ In contrast to SW, and NT, enuresis may occur during non-rapid eye movement (NREM) or rapid eye movement (REM) sleep.⁸

Epidemiological surveys investigating parasomnias in the general population are uncommon, perhaps because these parasomnias are usually considered harmless childhood occurrences. However, the prevalence of parasomnias in the general population of children has been estimated at approximately 3%–17% for SW,^{5, 9, 10} 1%–7% for NT,^{5, 9} 2%–18% for EN^9-12 and 5%–27% for ST. ^9-12 These estimates vary greatly because rarely are the same definitions for the frequency of events used. Although it is generally accepted that childhood parasomnias remit with age, virtually all studies have been cross-sectional. ^{4, 10, 11, 13-15} To our knowledge, there have been no studies investigating the remission and incidence of parasomnias in a community-based adolescent population. Therefore, it is the purpose of this analysis to describe the incidence and remission of parasomnias in such a cohort using data from the Tucson Children’s Assessment of Sleep Apnea (TuCASA) study.

Methods

TuCASA was designed to investigate the incidence, prevalence and correlates of objectively measured sleep-related breathing disorders (SRBD) in a prospective cohort study of preadolescent Hispanic and Caucasian children ages 6 to 12 years. Detailed recruitment methods have been described previously.¹⁶ Briefly, Hispanic and Caucasian children ages 6 to 12 years were recruited through the Tucson Unified School District (TUSD), a very large district with a substantial elementary school population. Parents were asked to complete a short screening questionnaire and to provide their contact information if they were willing to allow study personnel to contact them to determine if their child was eligible for the study. A total of 7,055 screening questionnaires were sent home with children in a “notes home” folder. Of these, 2,327 (33%) were returned. Recruitment information was supplied on 52% of the returned questionnaires. From these questionnaires, children were selected for potential participation based on pre-established inclusion and exclusion criteria, and after parents gave informed consent and the child gave assent using language-appropriate IRB approved forms. The TuCASA protocol was approved by both the University of Arizona Human Subjects Committee and the TUSD Research Committee.

Initially from 1999-2003, 503 children were enrolled (Time 1) and subsequently 350 were re-studied approximately 5 years later (Time 2). In addition to undergoing home polysomnography at both time points, parents were asked to complete a comprehensive sleep habits questionnaire (SHQ) that recorded the characteristics of their child’s sleep history including questions about EN, NT, SW and ST.

Specific questions were the following: "Does this child sleepwalk?", and "Does this child talk in his or her sleep? (Talk without being fully awake?)". For these 2 questions, possible responses were "Never", "less than three times per month", "three to five times per month", or "more than five times per month". The occurrence of these parasomnias was defined as follows: SW was present if it was reported more than three times per month, and ST was present if it was reported more than five times per month. Additionally, the parent was asked "How often does this child awaken at night afraid or appearing tearful?” If the parent answered that the child had more than five fearful awakenings per month then the child was classified as having NT. EN was present if it was reported as occurring more than five times per month. These definitions were chosen to be consistent with our previous analyses of parasomnias in this cohort and were thought to be clinically meaningful when these children were preadolecents.⁹

The SHQ was also used to define the occurrence of habitual snoring (SN), excessive daytime sleepiness (EDS), witnessed apnea (WITAP), difficulty initiating and maintaining sleep (INSOM), and learning problems (LP).  These sleep problems were considered present if they were reported 'frequently' or more (5 or more times per week). Although the specific range and order of questions used on the TuCASA SHQ and screening questionnaires have not been previously validated, key questions in the questionnaire have face validity and were taken from those used by Carroll and colleagues.¹⁷

As described in previous analyses from the TuCASA cohort,^9,16 we computed a respiratory disturbance index (RDI) as the total number of apneas and hypopneas/total sleep time (TST). Hypopneas were required to have an associated oxygen desaturation of 3%. Sleep disordered breathing (SDB) was considered present if the RDI was > 1 event/hour TST.

Statistical analysis of the data was performed using Stata 10 (StataCorp LP, College Station, TX)  and IBM SPSS Statistics 18 (New York, NY). As appropriate, comparisons of means and proportions were performed using two-sample t-tests for continuous data, and chi-squared tests and the exact binomial test for categorical data.  Data are expressed as means + SD and percentages.

Results 

There were a total of 503 children participating at Time 1 and 350 adolescents at time 2. Characteristics for the study group are shown in Table 1.

Table 1: Description of the TuCASA Cohort at Time 1 and Time 2

	Time 1	Time 2	p
Number in Cohort	503	350	-
Age (Mean + SD)	8.8 ± 1.6	13.3 ±1.7	-
Age (Min-Max)	[6,12.6]	[9.9,17.5]	-
Gender (% Male)	50	51	0.7643
Ethnicity (% Caucasian)	58	63	0.1283
Standardized BMI (Mean + SD)	.30 ± 1.2	.50 ± 1.1	0.0249
Obesity (%)	13	24	0.017

The mean age at first assessment was 8.8 years (min/max: 6-12.6 years) while mean age at second assessment was 13.3 years (min-max: 9.9-17.5 years). The mean time between assessments was 4.6 years (range: 2.9-7.3 years). There were 51% males and 49% females at the Time 2, approximately the same ratio as Time1. The gender ratio remained approximately the same at both measurements. Notably, standardized BMI increased and the % of the cohort classified as obese increased over the time interval. However, standardized BMI was not significantly higher in those children with parasomnias (data not shown).

As shown in Table 2, at Time 1 there were no differences in the prevalence rates of all 4 parasomnias using the entire cohort in comparison to a cohort restricted only to those children who had assessments made at both time points (Restricted Cohort). In addition, the prevalence of parasomnias at Time 2 remained similar to the prevalence at Time 1 with the exception of EN which declined markedly from 7% to 2%. Also shown in Table 2 are the prevalence, remission and incidence rates of the 4 parasomnias at Time 2. The incidence of EN, TR, and SW in our 10-17 year old children were approximately 1% for all 3 contrasting with ST with an incidence rate of approximately 6%. Furthermore, remission rates were high for all the parasomnias. All 9 adolescents had remission from NT. 17 of 24 subjects, approximately 71%, had remission from EN. 24 of 37 participants had remission from ST, approximately 65%. 1 out of 2 subjects (50%) with SW had remission. Incidence and remission of all parasomnias were not related to SN, WITAP, INSOM, or LP although limited incidence and remission numbers precluded extensive meaningful analyses. At Time 1, the prevalence of SDB was  27.8% (89/320). At Time 2, 14.4% (46/320)  Children with SDB on both occasions were 25/320 or 7.8%. There were 15 boys and 10 girls with persistent SDB with no ethnic differences. Because of the relatively small numbers of children with parasomnias and SDB at Time 2, we were unable to determine whether persistent SDB was a risk for prevalent or incident parasomnias .

In Table 3 is shown the number and percent of parasomnias that occurred in association with other parasomnias at Time 2. Except for an association between sleep walking and sleep talking, parasomnias occurred independent of each other.  

Discussion 

The TuCASA study has documented the prevalence, incidence and remission of parasomnias in a population-based sample of 10-17 year old subjects. We found that incidence rates for parasomnias were very low for EN NT and SW and remission rates were high for all parasomnias. Furthermore, incidence and remission rates did not appear to be related to symptoms of sleep disturbances or learning problems, and except for sleep walking and sleep talking, they generally were not co-prevalent.

In this study, except for ST, prevalence rates for parasomnias in our cohort of adolescents were relatively low. Available data documenting the prevalence rates of various parasomnias in this age group are relatively sparse with previous reports largely restricted to preadolescents. ^{4, 9-12, 15} However, with respect to SW, 3 previous studies in adolescents have observed prevalence rates ranging from 3 to 15% which are higher than the 1.4% noted in our study.^18-20 Inconsistent prevalence rates have been noted for NT with one study reporting rates <4%,²⁰  but another reporting 10.2%.¹⁹ In contrast, the prevalence of enuresis ^{7, 18, 20} and the prevalence of ST ^{6,13, 20} in adolescents have been reported to be consistently low and high respectively. Our data are concordant with these previous reports. However, the relevance of these comparisons is unclear since no standard method of assessing the frequency of parasomnias exists. Our requirement that these events occurred more than three to five times per month are more stringent than those employed in most studies. Thus, it not surprising that our prevalence rates for SW and NT in adolescence are discordant with previous observations.

There is general consensus that childhood parasomnias remit as children develop from childhood to adolescence and that few adolescents develop them.^{5, 21} However, this impression is based primarily on empiric observations because there are few longitudinal studies.^{20, 22, 23} The largest longitudinal study prospectively interrogated parents of children at age 10 through age 13 years, but retrospectively questioned parents to determine if a parasomnia was present between ages 3 and 9 years.²⁰ In this study, prevalence rates were noted to decline markedly by age 13 years to 3.3%, 1.2% and 2.0% for SW, NT and EN, respectively. In contrast, there was only a slight nonsignificant decrease in ST with 29.2% of children still having this condition at age 13 years. Our data are generally consistent with the results of this previous study although our prevalence of ST is somewhat lower. However, we extend these foregoing findings by documenting incidence and remission rates. Except for ST, very few adolescents developed new parasomnias. Nonetheless, although remission rates were high, some participants in this study had persistent symptoms which likely continue through into adulthood.^{24, 25}

We previously examined the prevalence of parasomnias in the TuCASA cohort when the children were preadolescents.⁹ We found an associations between parasomnias, and SDB, symptoms of other sleep disturbances and learning problems. Unfortunately, because of the small numbers of children with parasomnias in this follow-up cohort, we were unable to determine whether these latter findings are still present.

In this study, standardized BMI increased as did the % of the cohort classified as obese. While these observations most likely are a reflection of the ongoing obesity epidemic in the United States, those with parasomnias did not have significantly higher standardized BMI.

Some evidence indicates that individuals with one parasomnia have a greater likelihood of having another one.^{20, 25} Consistent with these previous studies, we found a modest association between ST and SW, both of which are disorders of arousal. Otherwise, we found little evidence to support the contention that parasomnias are more likely to be co-prevalent.

Although our study is the first to prospectively document the incidence and remission of parasomnias in a large general population of children, it is not without some limitations. First, it is possible that parents underestimated the actual number of parasomnias that occurred. Depending on the bedtime of the child and the severity of the event, it is probable that parents are not awake during every occurrence. Second, recruitment may have incurred a selection bias so that parents who agreed to have their children participate might be more likely to have symptomatic children than those who did not. We think this unlikely because the focus of the study was SDB and it is unlikely that parents agreed to have their children participate based on the presence of parasomnias. Lastly, there were 153 subjects that participated at Time 1 but that did not participate at Time 2. There was no difference related to gender between those that participated at Time 2 and those that did not although slightly more Hispanic adolescents were lost to follow-up than Caucasians. In addition, the prevalence rates of parasomnias in the entire cohort and the restricted were the same. Thus, we do not believe that the restricted cohort of children who had data at both time points was markedly different than the larger group of children recruited at Time 1.

In conclusion, although parasomnias are relatively common in childhood, our study demonstrates that most remit, and that the development of parasomnias in older children is uncommon. Our findings provide objective data supporting the generally accepted perception that most parasomnias in children will resolve over time.

Acknowledgements         

This study was supported by Grant HL 62373 from the National Heart Lung and Blood Institute. All authors contributed to the writing and analyses contained in the study and had full access to the data. Dr. Quan is the Principal Investigator of the TuCASA study. Drs. Quan and Goodwin supervised the recruitment of participants and the operations of TuCASA.

References

1 . Matwiyoff G, Lee-Chiong T. Parasomnias: an overview. Indian J Med Res 2010; 131:333-337.

2 . Heussler HS. 9 Common causes of sleep disruption and daytime sleepiness: childhood sleep disorders II. Med J Aust 2005; 182(9):484-489.

3 . Chokroverty S. Overview of sleep & sleep disorders. Indian J Med Res 2010; 131:126-140.

4 . Agargun MY, Cilli AS, Sener S, et al. The prevalence of parasomnias in preadolescent school-aged children: a Turkish sample. Sleep 2004; 27(4):701-705.

5 . Avidan AY, Kaplish N. The parasomnias: epidemiology, clinical features, and diagnostic approach. Clin Chest Med 2010; 31(2):353-370.

6 . Arkin AM. Sleep-talking: a review. J Nerv Ment Dis 1966; 143(2):101-122.

7 . Hansakunachai T, Ruangdaraganon N, Udomsubpayakul U, Sombuntham T, Kotchabhakdi N. Epidemiology of enuresis among school-age children in Thailand. J Dev Behav Pediatr 2005; 26(5):356-360.

8 . Mikkelsen EJ, Rapoport JL, Nee L, Gruenau C, Mendelson W, Gillin JC. Childhood enuresis. I. Sleep patterns and psychopathology. Arch Gen Psychiatry 1980; 37(10):1139-1144.

9 . Goodwin JL, Kaemingk KL, Fregosi RF, et al. Parasomnias and sleep disordered breathing in Caucasian and Hispanic children - the Tucson children's assessment of sleep apnea study. BMC Med 2004; 2:14.

10 . Arruda MA, Guidetti V, Galli F, Albuquerque RC, Bigal ME. Childhood periodic syndromes: a population-based study. Pediatr Neurol 2010; 43(6):420-424.

11 . Liu X, Ma Y, Wang Y, et al. Brief report: An epidemiologic survey of the prevalence of sleep disorders among children 2 to 12 years old in Beijing, China. Pediatrics 2005; 115(1 Suppl):266-268.

12 . Simola P, Niskakangas M, Liukkonen K, et al. Sleep problems and daytime tiredness in Finnish preschool-aged children-a community survey. Child Care Health Dev 2010; 36(6):805-811.

13 . Garcia-Jimenez MA, Salcedo-Aguilar F, Rodriguez-Almonacid FM, et al. The prevalence of sleep disorders among adolescents in Cuenca, Spain. Rev Neurol 2004; 39(1):18-24.

14 . Ipsiroglu OS, Fatemi A, Werner I, Tiefenthaler M, Urschitz MS, Schwarz B. Prevalence of sleep disorders in school children between 11 and 15 years of age. Wien Klin Wochenschr 2001; 113(7-8):235-244.

15 . Petit D, Touchette E, Tremblay RE, Boivin M, Montplaisir J. Dyssomnias and parasomnias in early childhood. Pediatrics 2007; 119(5):e1016-25.

16 . Goodwin JL, Enright PL, Kaemingk KL, et al. Feasibility of using unattended polysomnography in children for research--report of the Tucson Children's Assessment of Sleep Apnea study (TuCASA). Sleep 2001; 24(8):937-944.

17 . Carroll JL, McColley SA, Marcus CL, Curtis S, Loughlin GM. Inability of clinical history to distinguish primary snoring from obstructive sleep apnea syndrome in children. Chest 1995; 108(3):610-618.

18 . Klackenberg G. Incidence of parasomnias in children in a general population. In: Sleep and its Disorders in Children. Guilleminault C, ed. New York: Raven Press, 1987; 99-113

19 . Ipsiroglu OS, Fatemi A, Werner I, Paditz E, Schwarz B. Self-reported organic and nonorganic sleep problems in schoolchildren aged 11 to 15 years in Vienna. J Adolesc Health 2002; 31(5):436-442.

20 . Laberge L, Tremblay RE, Vitaro F, Montplaisir J. Development of parasomnias from childhood to early adolescence. Pediatrics 2000; 106(1 Pt 1):67-74.

21 . Guilleminault G. Disorders of arousal in children: Somnambulism and night terros. In: Guilleminault C, ed. New York: Raven Press, 1987; 243-252

22 . DiMario FJ,Jr, Emery ES,3rd. The natural history of night terrors. Clin Pediatr (Phila) 1987; 26(10):505-511.

23 . Klackenberg G. Somnambulism in childhood--prevalence, course and behavioral correlations. A prospective longitudinal study (6-16 years). Acta Paediatr Scand 1982; 71(3):495-499.

24 . Oluwole OS. Lifetime prevalence and incidence of parasomnias in a population of young adult Nigerians. J Neurol 2010; 257(7):1141-1147.

25 . Bjorvatn B, Gronli J, Pallesen S. Prevalence of different parasomnias in the general population. Sleep Med 2010; 11(10):1031-1034.

A 45-YEAR-OLD MAN WITH EXCESSIVE DAYTIME SOMNOLENCE, AND WITNESSED APNEA AT ALTITUDE

Rebecca Keith MD

University of Colorado Denver, Denver, CO

rebecca.keith@ucdenver.edu

Carolyn H. Welsh MD

Veterans Affairs Medical Center, Denver, CO

Professor of Medicine

Division of Pulmonary Sciences and Critical Care Medicine, University of Colorado Denver, Denver, CO

carolyn.welsh@va.gov

Study performed at Denver Veterans Affairs Medical Center

Reference as: Keith R, Welsh CH. A 45-year-old man with excessive daytime somnolence, and witnessed apnea at altitude. Southwest J Pulm Crit Care 2011;2:53-57. (Click here for PDF version)

Abstract

A sleepy man without sleep apnea at 1609m (5280 feet) had disturbed sleep at his home altitude of 3200m (10500 feet).  In addition to common disruptors of sleep such as psychophysiologic insomnia, restless leg syndrome, alcohol and excessive caffeine use, central sleep apnea with periodic breathing can be a significant cause of disturbed sleep at altitude.  In symptomatic patients living at altitude, a sleep study at their home altitude should be considered to accurately diagnose the presence and magnitude of sleep disordered breathing as sleep studies performed at lower altitudes may miss this diagnosis. Treatments options differ from those to treat obstructive apnea.  Supplemental oxygen is considered by many to be first-line therapy.

Case Report 

A 45 year old man presents for evaluation of poor sleep, daytime somnolence, and morning headaches.  His bed partner reports that he has loud snoring, gasping arousals, and frequent episodes of apnea.  He is an obese man with diabetes, hypertension, rhinosinusitis, and gastroesophageal reflux disease who is a long time resident at 3200m (10500 feet).  Polysomnography performed at 1609m (5280 feet) showed a normal apnea-hypopnea index (AHI) of 1.4.  He spent less than one minute with an oxygen saturation (SaO2) less than 88%. 

Given the severity of his symptoms, a home cardiopulmonary sleep study was performed at 3200m (10500 feet).  AHI was 39 non-supine and 56 supine. A representative portion of his study is shown in Figure 1.  It demonstrates the absence of respiratory effort during apnea events, consistent with central sleep apnea from periodic breathing at altitude.

Figure 1. This is a representative example of breathing during sleep. The upper two channels show nasal (nasal flow) and oral air flow (thermistor).  Respiratory effort signals in the abdomen and thorax are shown in the next two channels and oxygen saturation in the bottom tracing. A ten minute period of recording is displayed. (Click here for JPEG image)

Discussion

Central apnea is characterized by recurrent episodes of apnea resulting from temporary loss of ventilatory effort.  These episodes generally result from a strong dependence on the metabolic control of ventilation during sleep.¹   Conditions inducing a reduced partial pressure of carbon dioxide in arterial blood (PaCO2) can precipitate a central apnea, often associated with an arousal and leading to daytime somnolence.    

At higher altitudes, the fraction of inspired oxygen (FiO2) is the same as it is at sea level, but the barometric pressure and oxygen tension are less, so the partial pressure of oxygen in arterial blood (PaO2) and SaO2 tend to be reduced.  This tendency toward hypoxemia elicits a series of physiologic responses including increased alveolar ventilation, lowering the resting PaCO2, shrinking the gap between the resting PaO2 and the PaCO2 associated with apnea (the apnea threshold).  People with sensitive chemoreceptors are more likely to develop periodic breathing at altitude.  An increased ventilatory response to hypoxemia may reduce PaCO2 below the apnea threshold.  The resultant apnea will eventually result in worsened hypoxemia, which will trigger hyperventilation setting up the oscillating pattern of apnea and hyperventilation typical of periodic breathing.¹  Of note, these responses seem primarily related to hypoxemia rather than low barometric pressure as they are present in simulated normobaric hypoxia equivalent to 2000m (6562 feet) altitude .² 

Central sleep apnea encompasses multiple disorders including narcotic-induced central apnea, obesity-hypoventilation syndrome, Cheyne-Stokes breathing often seen with heart failure or cerebrovascular disease, and high altitude-induced periodic breathing.  Periodic breathing at altitude has the pattern of apneic episodes with cycles of hypoxemia, subsequent hyperventilation, hypocapnia, and resulting apnea described above.  The skeletal muscle atonia and reduced respiratory drive³ that normally occurs during rapid eye movement (REM) sleep typically terminates the periodicity by eliminating hyperventilation and restoring regular breathing.  Thus, periodic breathing at altitude is seen primarily during non-REM sleep.⁴   This aberrant respiratory pattern may initially be beneficial at high altitude since the hyperventilation increases SaO2 by 2.9±1.5%, thus improving hypoxemia.⁴  In our patient, this potential benefit was overcome by a significant increase in sleep disturbance leading to sleep deprivation as well as mental and physical impairment.

Treatment options for periodic breathing at altitude include nighttime oxygen therapy, acetazolamide, and sedative-hypnotics.  Supplemental oxygen breaks the apnea cycle by eliminating the hypoxemia that results in hyperventilation, hypocapnia, and apnea.  Studies of supplemental oxygen at altitude have shown that subjects spend more time in deep sleep and have improvements in SaO2, tidal volume and AHI.⁵  

Acetazolamide, a carbonic anhydrase inhibitor, induces a metabolic acidosis by urinary elimination of bicarbonate. This is thought to increase ventilation, left-shift the hypercapnic ventilatory response and thus reduce apnea events.  Acetazolamide can significantly improve sleep disordered breathing and oxygen saturation during sleep.⁶ Sedative-hypnotic agents have also improved slow wave sleep at altitude without adversely affecting respiration or performance.  This improvement is thought to be secondary to a decrease in wakefulness after sleep onset and improved sleep efficiency.⁷

This patient presented with classic symptoms of sleep apnea with a negative sleep study at 1609m (5280 feet), arousing clinical suspicion for periodic breathing at altitude.  A repeat home sleep study revealed moderate to severe central sleep apnea.  After treatment with oxygen and acetazolamide his symptoms significantly improved and repeat home monitoring demonstrated an AHI of 6.  Treatment with oxygen alone resulted in improvement as well with an AHI of 13.

Central sleep apnea and periodic breathing are common causes of disturbed sleep at altitude.  Symptomatic patients living at altitude should have a sleep study at their home altitude to accurately diagnose the presence and magnitude of sleep disordered breathing.  Oxygen is considered by many to be first line therapy.

References

1.  Eckert DJ, Jordan AS, Merchia P, Malhotra A. Central sleep apnea: Pathophysiology and Treatment. Chest 2007; 131(2): 595-607.

2. Kinsman TA, Hahn AG, Gore CJ, Wilsmore BR, Martin DT, Chow CM. Respiratory events and periodic breathing in cyclists sleeping at 2,650-m simulated altitude. Journal of Applied Physiology 2002; 92 (5): 2114-8.

3. Schafer, T, Schlafke ME, Respiratory changes associated with rapid eye movements in normo- and hypercapnia during sleep. Journal of Applied Physiology 1998; 85: 2213-2219.

4. Salvaggio A, Insalaco G, Marrone O, et al. Effects of high-altitude periodic breathing on sleep and arterial oxyhaemoglobin saturation. European Respiratory Journal 1998; 12 (2): 408-13.

5. Windsor JS, Rodway GW. Supplemental oxygen and sleep at altitude. High Altitude Medicine & Biology 2006; 7 (4): 307-11.

6. Fischer R, Lang SM, Leitl M, Thiere M, Steiner U, Huber RM. Theophylline and acetazolamide reduce sleep-disordered breathing at high altitude. European Respiratory Journal 2004; 23 (1): 47-52.

7. Beaumont M, Batejat D, Pierard C, et al. Zaleplon and zolpidem objectively alleviate sleep disturbances in mountaineers at a 3,613 meter altitude. Sleep 2007; 30 (11): 1527-33.

Acknowledgments:

Rebecca Keith was the primary author writing the manuscript. Carolyn Welsh also contributed to content and manuscript preparation and revision.

Corresponding author: Rebecca Keith MD

                                     National Jewish Health

                                     1400 Jackson Street

                                     Denver, CO 80206

                                     Phone: 303-398-1511

                                     FAX: 303-398-1381

No financial support was provided for this manuscript and there is no off-label or investigational use of medications or devices.

Dr Keith has no conflict of interest to disclose

Dr. Welsh has no conflict of interest to disclose

Abbreviation List:  

AHI - Apnea hypopnea index, the number of apneas or hypopneas per hour of sleep or recording

m – Meters

FiO2 – Fraction of inspired oxygen

PaCO2 - Partial pressure of carbon dioxide in arterial blood

PaO2 – Partial pressure of oxygen in arterial blood

REM – Rapid eye movement

SaO2 – Percentage of available hemoglobin that is saturated with oxygen