Search Journal-type in search term and press enter
In Memoriam
Social Media-Follow Southwest Journal of Pulmonary and Critical Care on Facebook and Twitter


(Click on title to be directed to posting , most recent listed first)

Impact of Sleep and Dialysis Mode on Quality of Life in a Mexican Population
Out of Center Sleep Testing in Ostensibly Healthy Middle Aged to Older 
Sleep Related Breathing Disorders and Neurally Mediated Syncope (SRBD 
   and NMS)
Sleep Board Review Question: Restless Legs
Impact of Sleep Duration and Weekend Oversleep on Body Weight 
   and Blood Pressure in Adolescents
Role of Spousal Involvement in Continuous Positive Airway Pressure 
   (CPAP) Adherence in Patients with Obstructive Sleep Apnea (OSA)
The Impact of an Online Prematriculation Sleep Course (Sleep 101) on
   Sleep Knowledge and Behaviors in College Freshmen: A Pilot Study
Obstructive Sleep Apnea and Quality of Life: Comparison of the SAQLI,
   FOSQ, and SF-36 Questionnaires
Gender Differences in Real-Home Sleep of Young and Older Couples
Brief Review: Sleep Health and Safety for Transportation Workers
Lack of Impact of Mild Obstructive Sleep Apnea on Sleepiness, Mood and
   Quality of Life
Alpha Intrusion on Overnight Polysomnogram
Sleep Board Review Question: Insomnia in Obstructive Sleep Apnea
Long-Term Neurophysiologic Impact of Childhood Sleep Disordered 
   Breathing on Neurocognitive Performance
Sleep Board Review Question: Hyperarousal in Insomnia
Sleep Board Review Question: Epilepsy or Parasomnia?
Sleep Board Review Question: Nocturnal Hypoxemia in COPD
Sleep Board Review Questions: Medications and Their 
   Adverse Effects
Sleep Board Review Questions: The Restless Sleeper
Obstructive Sleep Apnea and Cardiovascular Disease:
   Back and Forward in Time Over the Last 25 Years
Sleep Board Review Questions: The Late Riser
Sleep Board Review Questions: CPAP Adherence in OSA
Sleep Board Review Questions: Sleep Disordered Breathing 
   That Improves in REM
The Impact Of Sleep-Disordered Breathing On Body
Mass Index (BMI): The Sleep Heart Health Study (SHHS)
Incidence and Remission of Parasomnias among Adolescent Children in the 
   Tucson Children’s Assessment of Sleep Apnea (TuCASA) Study 
A 45-Year Old Man with Excessive Daytime Somnolence, 
  and Witnessed Apnea at Altitude


The Southwest Journal of Pulmonary and Critical Care publishes articles related to those who treat sleep disorders in sleep medicine from a variety of primary backgrounds, including pulmonology, neurology, psychiatry, psychology, otolaryngology, and dentistry. Manuscripts may be either basic or clinical original investigations or review articles. Potential authors of review articles are encouraged to contact the editors before submission, however, unsolicited review articles will be considered.



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



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.


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


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.


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 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).


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).


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.  


  1. Punjabi NM. The epidemiology of adult obstructive sleep apnea. Proc Am Thorac Soc. 2008;5(2):136-143. [CrossRef] [PubMed]
  2. Peppard PE, Young T, Barnet JH, Palta M, Hagen EW, Hla KM. Increased prevalence of sleep-disordered breathing in adults. Am J Epidemiol. 2013;177(9):1006-1014. [CrossRef] [PubMed]
  3. Inge TH, King WC, Jenkins TM, et al. The effect of obesity in adolescence on adult health status. Pediatrics. 2013;132(6):1098-1104. [CrossRef] [PubMed]
  4. Peppard PE, Young T, Palta M, Skatrud J. Prospective study of the association between sleep-disordered breathing and hypertension. N Engl J Med. 2000;342(19):1378-84. [CrossRef] [PubMed]
  5. Young T, Finn L, Peppard PE, et al. Sleep disordered breathing and mortality: eighteen-year follow-up of the Wisconsin sleep cohort. Sleep. 2008;31(8):1071-1078. [PubMed]
  6. Gottlieb DJ, Yenokyan G, Newman AB, et al. Prospective study of obstructive sleep apnea and incident coronary heart disease and heart failure: the sleep heart health study. Circulation. 2010;122(4):352-360. [CrossRef] [PubMed]
  7. Marshall NS, Wong KK, Liu PY, Cullen SR, Knuiman MW, Grunstein RR. Sleep apnea as an independent risk factor for all-cause mortality: the Busselton Health Study. Sleep. 2008;31(8):1079-1085. [PubMed]
  8. Punjabi NM, Caffo BS, Goodwin JL, et al. Sleep-disordered breathing and mortality: a prospective cohort study. PLoS Med . 2009;6(8):e1000132. [CrossRef] [PubMed]
  9. Marin JM, Carrizo SJ, Vicente E, Agusti AG. Long-term cardiovascular outcomes in men with obstructive sleep apnoea-hypopnoea with or without treatment with continuous positive airway pressure: an observational study. Lancet. 2005;365(9464):1046-1053. [CrossRef] [PubMed]
  10. Schroder CM, O'Hara R. Depression and Obstructive Sleep Apnea (OSA). Ann Gen Psychiatry. 2005;4:13. [PubMed]
  11. Jordan AS, McSharry DG, Malhotra A. Adult obstructive sleep apnoea. Lancet. 2014;383(9918):736-747. [CrossRef] [PubMed]
  12. Lopes C, Esteves AM, Bittencourt LRA, Tufik S, Mello MT. Relationship between the quallity of life and the severity of obstructive sleep apnea syndrome. Braz J Med Biol Res. 2008;41(10):908-913. [CrossRef] [PubMed]
  13. Sateia MJ. International Classification of Sleep Disorders 2nd ed. Westchester, IL: American Academy of Sleep Medicine, 2005; 297.
  14. American Academy of Sleep Medicine Taskforce. Sleep-Related Breathing Disorders In Adults: Recommendations For Syndrome Definition And Measurement Techniques In Clinical Research. Sleep. 1999;22 (5): 667-689. [PubMed]
  15. Ayas NT, Hirsch AA, Laher I, et al. New frontiers in obstructive sleep apnoea. Clin Sci (Lond). 2014;127(4):209-216. [CrossRef] [PubMed]c
  16. Gottlieb DJ, Whitney CW, Bonekat WH, et al. Relation of sleepiness to respiratory disturbance index: the Sleep Heart Health Study. Am J Respir Crit Care Med . 1999;159(2):502-507. [CrossRef] [PubMed]
  17. Cui R, Tanigawa T, Sakurai S, et al. Associations of sleep-disordered breathing with excessive daytime sleepiness and blood pressure in Japanese women. Hypertens Res. 2008;31(3):501-506. [CrossRef] [PubMed]
  18. Chen R, Xiong KP, Lian YX, et al. Daytime sleepiness and its determining factors in Chinese obstructive sleep apnea patients. Sleep Breath. 2011;15(1):129-135. [CrossRef] [PubMed]
  19. Kushida CA, Nichols DA, Quan SF, et al. The Apnea Positive Pressure Long-term Efficacy Study (APPLES): rationale, design, methods, and procedures. J Clin Sleep Med. 2006;2(3):288-300. [PubMed]
  20. Quan SF, Chan CS, Dement WC, et al. The association between obstructive sleep apnea and neurocognitive performance--the Apnea Positive Pressure Long-term Efficacy Study (APPLES). Sleep. 2011;34(3):303-314B. [PubMed]
  21. Johns MW. A new method for measuring daytime sleepiness: the Epworth sleepiness scale. Sleep. 1991;14(6):540-545. [PubMed]
  22. Hoddes E, Dement W, Zarcone V. The development and use of the Stanford Sleepiness Scale (SSS). Psychophysiol. 1972;9:150.
  23. McNair DM, Lorr M, Droppleman L. Manual for the Profile of Mood States. San Diego, CA: Educational and Industrial Testing Service, 1971;
  24. Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960;2356-62.
  25. Williams JB, Kobak KA, Bech P, et al. The GRID-HAMD: standardization of the Hamilton Depression Rating Scale. Int Clin Psychopharmacol. 2008;23(3):120-129. [CrossRef] [PubMed]
  26. Flemons WW, Reimer MA. Development of a disease-specific health-related quality of life questionnaire for sleep apnea. Am J Respir Crit Care Med. 1998;158(2):494-503. [CrossRef] [PubMed]
  27. Ishman SL, Cavey RM, Mettel TL, Gourin CG. Depression, sleepiness, and disease severity in patients with obstructive sleep apnea. Laryngoscope. 2010;120(11):2331-2335. [CrossRef] [PubMed]
  28. Minoguchi K, Yokoe T, Tazaki T, et al. Silent brain infarction and platelet activation in obstructive sleep apnea. Am J Respir Crit Care Med. 2007;175(6):612-617. [CrossRef] [PubMed]
  29. Yoshino A, Higuchi M, Kawana F, et al. Risk factors for traffic accidents in patients with obstructive sleep apnea syndrome. Sleep Biol Rhythms. 2006;4144-152.
  30. Back L, Palomaki M, Piilonen A, Ylikoski J. Sleep-disordered breathing: radiofrequency thermal ablation is a promising new treatment possibility. Laryngoscope. 2001;111(3):464-471. [CrossRef] [PubMed]
  31. Balsevicius T, Uloza V, Sakalauskas R, Miliauskas S. Peculiarities of clinical profile of snoring and mild to moderate obstructive sleep apnea-hypopnea syndrome patients. Sleep Breath. 2012;16(3):835-843. [CrossRef] [PubMed]
  32. Lecube A, Sampol G, Lloberes P, et al. Asymptomatic sleep-disordered breathing in premenopausal women awaiting bariatric surgery. Obes Surg. 2010;20(4):454-461. [CrossRef] [PubMed]
  33. Saunamaki T, Jehkonen M. Depression and anxiety in obstructive sleep apnea syndrome: a review. Acta Neurol Scand. 2007;116(5):277-288. [CrossRef] [PubMed]
  34. Ohayon MM. The effects of breathing-related sleep disorders on mood disturbances in the general population. J Clin Psychiatry. 2003;64(10):1195-200; quiz, 1274-6. [CrossRef] [PubMed]
  35. Chandra RK, Epstein VA, Fishman AJ. Prevalence of depression and antidepressant use in an otolaryngology patient population. Otolaryngol Head Neck Surg. 2009;141(1):136-138. [CrossRef] [PubMed]
  36. Ju G, Yoon IY, Lee SD, Kim TH, Choe JY, Kim KW. Effects of sleep apnea syndrome on delayed memory and executive function in elderly adults. J Am Geriatr Soc. 2012;60(6):1099-1103. [CrossRef] [PubMed]
  37. Baldwin CM, Ervin AM, Mays MZ, et al. Sleep disturbances, quality of life, and ethnicity: the Sleep Heart Health Study. J Clin Sleep Med. 2010;6(2):176-183. [PubMed]
  38. Baldwin CM, Griffith KA, Nieto FJ, O'Connor GT, Walsleben JA, Redline S. The association of sleep-disordered breathing and sleep symptoms with quality of life in the Sleep Heart Health Study. Sleep. 2001;24(1):96-105.[PubMed]
  39. Isidoro SI, Salvaggio A, Bue AL, Romano S, Marrone O, Insalaco G. Quality of life in patients at first time visit for sleep disorders of breathing at a sleep centre. Health Qual Life Outcomes. 2013;11207.
  40. Stepnowsky C, Johnson S, Dimsdale J, Ancoli-Israel S. Sleep apnea and health-related quality of life in African-American elderly. Ann Behav Med. 2000;22(2):116-120. [CrossRef] [PubMed]
  41. Kezirian EJ, Harrison SL, Ancoli-Israel S, et al. Behavioral correlates of sleep-disordered breathing in older women. Sleep. 2007;30(9):1181-1188. [PubMed]
  42. Guilleminault C. Sleep and Breathing. In: Sleeping and Waking Disorders: Indications and Techiniques. Guilleminault C, ed. Menlo Park, CA: Addison-Wesley, 1982; 155-182
  43. Block AJ, Boysen PG, Wynne JW, Hunt LA. Sleep apnea, hypopnea and oxygen desaturation in normal subjects. N Engl J Med. 1979;300 (10):513-517. [CrossRef] [PubMed]
  44. Gould GA, Whyte KF, Rhind GB, et al. The sleep hypopnea syndrome. Am Rev Respir Dis .1988;137(4):895-898. [CrossRef] [PubMed]
  45. Pepin JL, Guillot M, Tamisier R, Levy P. The upper airway resistance syndrome. Respiration. 2012;83(6):559-566. [CrossRef] [PubMed]
  46. Flemons WW, Reimer MA. Measurement properties of the calgary sleep apnea quality of life index. Am J Respir Crit Care Med. 2002;165(2):159-164. [CrossRef]
  47. Quan SF, Griswold ME, Iber C, et al. Short-term variability of respiration and sleep during unattended nonlaboratory polysomnography--the Sleep Heart Health Study. Sleep. 2002;25(8):843-849. [PubMed]


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: PDF


Alpha Intrusion on Overnight Polysomnogram

Ryan Nahapetian, MD, MPHa and John Roehrs, MDb

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).


  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: 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?

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


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

Stuart F. Quan, M.D.ab


Kristen Archbold, Ph.D.c


Alan S. Gevins, D.Sc.d


James L. Goodwin, Ph.D.a


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


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.


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 ( ).

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 ( and are presented as mean + SE.


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 1st PSG, and only 2 had this procedure during the time interval before the 2nd 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 2nd PSG. No differences were observed between children who had SDB on the 2nd PSG and those who did not.


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.


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.


  1. Owens JA. Neurocognitive and behavioral impact of sleep disordered breathing in children. Pediatr Pulmonol. 2009;44(5):417-422.  [CrossRef] [PubMed]
  2. Grigg-Damberger M, Ralls F. Cognitive dysfunction and obstructive sleep apnea: from cradle to tomb. Curr Opin Pulm Med. 2012;18(6):580-587.  [CrossRef] [PubMed]
  3. Beebe DW. Neurobehavioral morbidity associated with disordered breathing during sleep in children: a comprehensive review. Sleep. 2006;29(9):1115-1134. [PubMed]
  4. Giordani B, Hodges EK, Guire KE, et al. Changes in neuropsychological and behavioral functioning in children with and without obstructive sleep apnea following Tonsillectomy. J Int Neuropsychol Soc. 2012;18(2):212-222.  [CrossRef] [PubMed]
  5. Ikeda FH, Horta PA, Bruscato WL, Dolci JE. Intellectual and school performance evaluation of children submitted to tonsillectomy and adenotonsillectomy before and after surgery. Braz J Otorhinolaryngol. 2012;78(4):17-23. [PubMed]
  6. Chervin RD, Ruzicka DL, Archbold KH, Dillon JE. Snoring predicts hyperactivity four years later. Sleep. 2005;28(7):885-890. [PubMed]
  7. Ali NJ, Pitson D, Stradling JR. Natural history of snoring and related behaviour problems between the ages of 4 and 7 years. Arch Dis Child. 1994;71(1):74-76. [PubMed]
  8. Urschitz MS, Eitner S, Guenther A, et al. Habitual snoring, intermittent hypoxia, and impaired behavior in primary school children. Pediatrics. 2004;114(4):1041-1048.  [CrossRef] [PubMed]
  9. Gozal D, Pope DW,Jr. Snoring during early childhood and academic performance at ages thirteen to fourteen years. Pediatrics. 2001;107(6):1394-1399. [CrossRef]  [PubMed]
  10. 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. [PubMed]
  11. Goodwin JL, Vasquez MM, Silva GE, Quan SF. Incidence and Remission of Sleep-Disordered Breathing and Related Symptoms in 6- to 17-Year Old Children-The Tucson Children's Assessment of Sleep Apnea Study. J Pediatr. 2010;  [CrossRef] [PubMed]
  12. Gevins A, McEvoy LK, Smith ME, et al. Long-term and within-day variability of working memory performance and EEG in individuals. Clin Neurophysiol. 2012;123(7):1291-1299.  [CrossRef] [PubMed]
  13. Smith ME, McEvoy LK, Gevins A. The impact of moderate sleep loss on neurophysiologic signals during working-memory task performance. Sleep. 2002;25(7):784-794. [PubMed]
  14. Rechtschaffen A, Kales A. A Manual of Standardized Terminology,
  15. Techniques and Scoring System for Sleep Stages of Human Subject. Washington, D.C.: US Government Printing Office, National Institute of Health Publication, 1968
  16. Kaemingk KL, Pasvogel AE, Goodwin JL, et al. Learning in children and sleep disordered breathing: findings of the Tucson Children's Assessment of Sleep Apnea (tuCASA) prospective cohort study. J Int Neuropsychol Soc. 2003;9(7):1016-1026.  [CrossRef] [PubMed]
  17. Wechsler D. Wechsler Abbreviated Scale of Intelligence. San Antonio, TX: Psychological Corp., 1999
  18. Gevins A, Smith ME, McEvoy LK, et al. A cognitive and neurophysiological test of change from an individual's baseline. Clin Neurophysiol. 2011;122(1):114-120.  [CrossRef] [PubMed]
  19. Gevins A, Smith ME, McEvoy L, Yu D. High-resolution EEG mapping of cortical activation related to working memory: effects of task difficulty, type of processing, and practice. Cereb Cortex. 1997;7(4):374-385. [CrossRef] [PubMed]
  20. Gevins A, Cutillo B. Spatiotemporal dynamics of component processes in human working memory. Electroencephalogr Clin Neurophysiol. 1993;87(3):128-143. [CrossRef] [PubMed]
  21. McEvoy LK, Smith ME, Gevins A. Dynamic cortical networks of verbal and spatial working memory: effects of memory load and task practice. Cereb Cortex. 1998;8(7):563-574. [CrossRef] [PubMed]
  22. Gevins A, Smith ME, McEvoy LK. Tracking the cognitive pharmacodynamics of psychoactive substances with combinations of behavioral and neurophysiological measures. Neuropsychopharmacology. 2002;26(1):27-39. [PubMed] [PubMed]
  23. Hart CL, Ilan AB, Gevins A, et al. Neurophysiological and cognitive effects of smoked marijuana in frequent users. Pharmacol Biochem Behav. 2010;96(3):333-341.  [CrossRef] [PubMed]
  24. Polich J. Updating P300: an integrative theory of P3a and P3b. Clin Neurophysiol. 2007;118(10):2128-2148.  [CrossRef] [PubMed]
  25. Stern Y. Cognitive reserve. Neuropsychologia. 2009;47(10):2015-2028.  [CrossRef] [PubMed]
  26. Liang J, Matheson BE, Kaye WH, Boutelle KN. Neurocognitive correlates of obesity and obesity-related behaviors in children and adolescents. Int J Obes (Lond). 2013 Aug 5.  [Epub ahead of print] [CrossRef] [PubMed]

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: 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?

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