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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.



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

Maryam Butt, MSc1

Stuart F. Quan, MD3,4,5

Alex (Sandy) Pentland, PhD2

Inas Khayal, PhD1, 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



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.


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.


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).


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.


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


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.


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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Reference as: Butt M, Quan SF, Pentland A, Khayal I. Gender differences in real-home sleep of young and older couples. Southwest J Pulm Crit Care. 2015;10(5):289-99. doi: PDF


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



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


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,, 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 and the National Sleep Foundation (

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.


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



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.  


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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]
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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