Chest ACCP Member Benefits
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

Guest Access | Sign In via User Name/Password
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF) Free
Right arrow Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Article Archive
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via ISI Web of Science (6)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Jankelowitz, L.
Right arrow Articles by Jain, M.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Jankelowitz, L.
Right arrow Articles by Jain, M.
(Chest. 2005;127:1593-1599.)
© 2005 American College of Chest Physicians

Cystic Fibrosis Patients Have Poor Sleep Quality Despite Normal Sleep Latency and Efficiency*

Larry Jankelowitz, MD; Kathryn J. Reid, MD; Lisa Wolfe, MD; Joanne Cullina, MD; Phyllis C. Zee, MD and Manu Jain, MD, FCCP

* From the Departments of Medicine (Drs. Jankelowitz, Wolfe, Cullina, and Jain) and Neurology (Drs. Reid and Zee), Division of Pulmonary and Critical Care Medicine, Northwestern University Medical School, Chicago, IL.

Correspondence to: Manu Jain, MD, Assistant Professor of Medicine, Division of Pulmonary and Critical Care Medicine, Northwestern University, 240 East Huron St, McGaw Pavilion second floor, Chicago, IL 60611; e-mail: m-jain{at}northwestern.edu


   Abstract
TOP
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Study objectives: Cystic fibrosis (CF) patients may be predisposed to poor sleep quality due to upper and lower airway abnormalities and impaired gas exchange. Previous sleep investigations of CF patients using single-night polysomnography have reported conflicting results. We hypothesized that sampling sleep for a prolonged period in a patient’s normal environment may give a more representative assessment of sleep quality than a single-night polysomnogram, and that impaired sleep quality would correlate with pulmonary disease severity and self-assessed sleep quality.

Design: Using wrist actigraphy, we measured sleep quality in clinically stable CF patients and age-matched control subjects. In addition, each CF patient and control subject completed the following three questionnaires: the Epworth sleepiness scale; the Pittsburgh sleep quality index (PSQI); and the Medical Outcomes Study 36-item short form.

Results: Twenty CF patients and control subjects were enrolled in the study, and were well-matched for age, sex, and body mass index. The mean (± SD) FEV1 for CF patients was 61.0 ± 20.1% predicted. CF patients and control subjects had similar sleep duration, sleep latency, and sleep efficiency. However, CF patients had higher PSQI scores (6.45 vs 4.55, respectively; p = .04), a higher fragmentation index (FI) [31.72 vs 18.02, respectively; p < 0.001], and less immobile time (88.87 vs 91.89, respectively; p = 0.02). There was a significant correlation of FI with FEV1 and PSQI scores.

Conclusions: Stable CF patients have disrupted sleep, and sleep disruption may in part be related to the severity of pulmonary disease. In addition, the PSQI may be useful in detecting CF patients with poor sleep quality.

Key Words: actigraphy • cystic fibrosis • fragmentation • Pittsburgh sleep quality index • sleep


   Introduction
TOP
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
Cystic fibrosis (CF) is the most common, life-shortening, genetic disorder in white individuals, with a median survival time of 33 years.12 Most CF patients develop bronchiectasis with airflow obstruction and air-trapping, which can lead to hypoxemia, hypercapnia, and cor pulmonale. Upper airway manifestations of CF include chronic sinusitis and nasal polyposis, which can increase upper airway resistance. Abnormalities of lung mechanics, gas exchange, and cardiac function may lead to sleep abnormalities that are similar to those seen in patients with asthma and COPD.3456789 Other contributors to CF sleep abnormalities may include the use of certain medications,1011 chronic anxiety, depression,12 and vitamin deficiencies.13

Sleep disruption may significantly impair daytime functioning and health-related quality of life.14 Health consequences may include impaired immune responses and metabolic abnormalities, as well as endocrine and cardiovascular dysfunction1516171819. Thus, sleep disruption could potentially exacerbate symptoms and worsen prognosis in an already compromised population such as CF patients. In order to optimize the complex medical management of CF patients, it is important to understand the magnitude of sleep disruption in this population and to identify the predictors of at-risk patients in whom further evaluation may be indicated.

Previous reports on sleep disturbances in CF patients have been conflicting. Some studies202122 have found CF patients to have reduced total sleep time, whereas others23 found no differences in sleep time when compared with healthy control subjects. Moreover, some reports found CF patients to have poor sleep efficiency2223 and increased sleep latency,22 while others found no differences2021 compared to control subjects. Thus, the presence and/or extent of sleep disruption in CF patients seems unclear.

Conflicting results in the past could be in part due to the use of a single overnight polysomnogram, which is considered to be the "gold standard" for assessing sleep quality. Pulmonary symptoms and exacerbations are episodic in the CF population. Polysomnography may thus be limited in its usefulness because a single-night study in a laboratory may not accurately reflect sleep quality in a home environment over a period of time. A commonly used alternative to polysomnography for recording sleep and wakefulness is activity monitoring. Sleep-wake activity determined by activity monitoring correlates relatively highly with polysomnography in many healthy and patient populations.242526 An advantage of activity monitoring is that it allows the continuous assessment of both wake and sleep behavior in the patient’s own environment over extended periods of time.

Using an activity monitor, we sought to determine whether sleep quality (eg, sleep duration, sleep efficiency, and fragmentation index [FI]) in CF patients with varying severities of lung disease is diminished compared to age-matched control subjects. We also wished to ascertain whether sleep parameters correlated with pulmonary disease severity, and validated questionnaires assessing quality of life and sleep.


   Materials and Methods
TOP
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
Subjects
CF patients who were ≥ 18 years of age were recruited from the adult CF clinic at Northwestern University from January 2002 to April 2003. CF diagnoses had been confirmed by an abnormal sweat chloride test result and/or the presence of two disease-causing mutations. Patients were ineligible if they had received oral or IV antibiotics for a pulmonary exacerbation in the 8 weeks prior to enrollment or had primary cardiac disease, psychiatric disorders, or a known sleep disorder. If a patient experienced a pulmonary exacerbation during the study, that patient was excluded from the study.

Healthy, age-matched control subjects were enrolled in the same study protocol. Control subjects were excluded if they had any history of medical or sleep disorders, or consumed excessive quantities of alcohol or caffeine. The study was approved by Northwestern University Institutional Review Board, and informed consent was obtained for all participants.

Study Design
Age, sex, height, and weight were obtained on the day of study enrollment. Each CF patient and control subject completed spirometry (max 20 C-series; SensorMedics; Yorba Linda, CA; and Morris-Polgar prediction equations) and three questionnaires, the Pittsburgh sleep quality index (PSQI), Epworth sleepiness scale (ESS), and the Medical Outcomes Study 36-item short form (SF-36), on the day of study enrollment. Patients and subjects were given a form for keeping a sleep log for 14 days.

The PSQI is a self-administered questionnaire that measures sleep quality during the previous month, and contains 19 self-rated questions yielding both a global score and seven "component" subscores. A global PSQI score of > 5 indicates that a person is a "poor sleeper."27

A subjective assessment of sleepiness was performed using the ESS questionnaire, which is a reliable and validated, self-administered questionnaire for measuring daytime sleepiness in adults. ESS scores significantly correlate with sleep latency (an indication of sleepiness) measured during multiple sleep latency testing and overnight polysomnography.2829 Patients are considered to be sleepy if they have an ESS score of > 10.

The SF-36 is a quality-of-life questionnaire, yielding scores for eight domains, as well as two summary scores, a mental component summary score and a physical component summary score.30 This measure is constructed to satisfy the minimum psychometric standards that are necessary for group comparisons involving generic health concepts that are not specific to any age, disease, or treatment group. The eight domains consist of physical functioning, role limitations-physical, bodily pain, general health perceptions, vitality, social functioning, role limitations-emotional, and mental health. Each of the eight domains is scored on a scale of 100 points, with higher scores indicating better functioning. The scores are standardized to a mean (SD) of 50 in the general US adult population and can therefore be used to determine whether a group or individual in question scores above or below the US average. Scores above and below 50 indicate above and below average functioning, respectively.30

Wrist Actigraphy
Wrist actigraphy (WA) detects physical motion, is a validated measure of sleep, and compares favorably to polysomnography.242526 CF patients and control subjects wore a wrist actigraph (Mini Motionloggers or Actillume; Ambulatory Monitoring Co; Ardsley, NY) for 14 days. Data stored in memory was downloaded and analyzed using a commercially available program (Action 3; Ambulatory Monitoring Co) with the actigraph worn on the nondominant arm. A sample actogram is shown in Figure 1 . The program can score activity/inactivity and derive from it periods of sleep or wakefulness using a computer-generated algorithm. Sleep log data are used in the computer algorithm to calculate sleep latency. It is also used in conjunction with activity monitoring to confirm bedtimes.25 WA data were analyzed cumulatively for the 2-week period during which subjects wore the actigraph and then separately for weekdays (ie, Sunday to Thursday) and weekends (ie, Friday and Saturday). There are several measures that can be derived from these algorithms, such as the FI, mean activity score (MAS), and immobile time (IT). The FI measures restlessness and is calculated by summing the percentage of minutes spent moving with the percentage time spent in the immobility phase per minute. MAS is the magnitude of activity on a per-epoch basis during sleep, and it is calculated by dividing the total activity score by the number of epochs during the assumed sleep period. IT is derived by dividing the number of minutes spent immobile by the assumed sleep time and multiplying by 100 (Action 3; Ambulatory Monitoring Co).



View larger version (100K):
[in this window]
[in a new window]
[Download PPT slide]
  		Figure 1. Sample actogram for a 20-day period demonstrating the change in activity from awake to sleep.

 
Statistical Analysis
Sample means and SDs were calculated for all variables. The independent sample Student t test for the comparison of two groups for all continuous variables was performed. (SPSS for Windows, version 11.5; SPSS; Chicago, IL). There are accepted cut points for the ESS and PSQI, indicating abnormal sleep parameters. For this reason, we performed {chi}2 analysis to compare groups for ESS and PSQI. A p value of < 0.05 was considered to be significant. A Pearson correlation coefficient was calculated to determine the relationship of continuous variables. We performed jackknife estimates of correlation coefficients (obtained by eliminating one x-y pair at a time), which provide a robust estimate of the underlying linear relationship (correlation) between variables of interest.31 A 95% confidence interval (CI) was calculated, which provides the most plausible range for jackknife correlation coefficient under assumption of repeated sampling.


   Results
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
Twenty-one CF patients and control subjects were initially enrolled into the study, of whom 20 individuals in each group completed the study. The CF and control groups were well-matched for age, sex, and body mass index (BMI) [Table 1 ]. The airflow of CF patients ranged from normal to severe obstruction, with a mean (± SD) FEV1 of 61.0 ± 20.1% predicted (95% CI, 31 to 91% predicted). None of the CF patients were receiving home oxygen therapy, and their mean oxygen saturation at the time of study enrollment was 96 ± 1% predicted.


View this table:
[in this window]
[in a new window]

  		Table 1. Demographics and Questionnaires*

 
Questionnaire Data
The mean PSQI for CF patients was 6.45 ± 3.31, which was significantly higher than that for control subjects (4.55 ± 2.21; p = .04). Furthermore, in the CF group there were 11 patients with a PSQI score of > 5, compared to only 5 subjects in the control group (p = .04). There was also a trend toward higher sleep disturbance (p = 0.07) and habitual sleep efficiency (p = 0.06) PSQI subscores in the CF group, although this did not reach statistical significance. The mean ESS scores for the CF and control groups were 6.75 ± 3.32 and 5.72 ± 3.63, respectively (p = 0.39), with four CF patients and three control subjects having scores of > 10. The SF-36 scores for the CF patients were significantly lower for both the physical and mental components compared to our healthy control subjects as well as the population means.32

WA Data
Control and CF patients had similar total sleep time, sleep latency, and sleep efficiency. Sleep onset and offset times were also comparable, meaning that CF patients and control subjects were going to bed and waking up at approximately the same times. Both CF patients and control subjects went to sleep an average of 1 h later on the weekends than during the week. The total activity score and MASs were also similar. In contrast, CF patients had a higher FI and less IT than control subjects (Table 2 ). There were no differences between weekdays and weekends for these parameters in either group (data not shown). Compared to control subjects, CF patients also displayed greater night-to-night variability in FI (p < 0.003) and IT (p < 0.02), as evidenced by greater SDs when each night was assessed separately (Table 2). These findings are consistent with the overall more disrupted sleep for CF patients.


View this table:
[in this window]
[in a new window]

  		Table 2. WA Data*

 
CF patients’ lung function, as assessed by FEV1, had small but significant associations with both the FI and the IT (Fig 2 ). The confidence interval calculated by jackknife analysis31 suggests a robust relationship between decrement in lung function and the severity of sleep disturbance. In contrast, there were no significant associations between resting room air O2 saturation or BMI and any measured sleep parameter (data not shown).



View larger version (19K):
[in this window]
[in a new window]
[Download PPT slide]
  		Figure 2. Top, A: correlation coefficients and CIs for FI with FEV1. The Pearson correlation coefficient between the FI on WA and FEV1 was –0.26 (95% CI, –0.28 to –0.25). Bottom, B: correlation coefficients and CIs for IT with FEV1. The Pearson correlation coefficient between the FI on WA and FEV1 was 0.27 (95% CI, 0.24 to 0.28).

 
Comparison of Activity and Questionnaire Data
There were significant correlations for FI, IT, and MAS with the PSQI for CF subjects, but not for control subjects (Fig 3 ). In contrast, there were no significant correlations between the ESS and SF-36 instruments and any sleep parameter measured by WA (data not shown).



View larger version (16K):
[in this window]
[in a new window]
[Download PPT slide]
  		Figure 3. Top, A: correlation between MAS and PSQI. There was a statistically significant positive relationship between the MAS and PSQI. The Pearson correlation coefficient between the MAS on WA and the PSQI was 0.80. Middle, B: correlation between IT and PSQI. There was a statistically significant inverse relationship between the IT and PSQI. The Pearson correlation coefficient between the IT on WA and the PSQI was –0.64. Bottom, C: correlation between FI and PSQI. There was a statistically significant inverse relationship between the FI and PSQI. The Pearson correlation coefficient between the FI on WA and the PSQI was –0.48.

 

   Discussion
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
Total sleep time, sleep efficiency, and sleep latency were similar between CF patients and age-matched control subjects. However, sleep was significantly more fragmented, with more night-to-night variability. Self-perceived sleep quality was also poor in CF patients. In addition, there is evidence that the degree of sleep disruption may be related to pulmonary disease severity (as measured by FEV1).

Previous investigations20212223 in CF patients using polysomnography have reported conflicting results in sleep parameters including, sleep efficiency, sleep latency, and arousal index (AI). In the current study, using wrist activity monitoring, we found no differences in sleep efficiency and latency between control subjects and CF patients. In addition, sleep duration was 2 h longer in our CF patients than previously reported.2022 In contrast to control subjects, CF patients had a higher FI and a lower IT (ie, they spent less time lying still), which is consistent with more disrupted sleep. The FI, unlike sleep efficiency, measures the number of times an individual moves rather than the percentage of time that they spend awake during the sleep period. The FI may be a more useful measure to examine sleep disturbance because it may better detect repetitive short events that disrupt sleep.

The discrepancies between our results and previous findings may be due at least in part to methodological differences. WA is less cumbersome and can be performed in a subject’s natural sleep environment, as opposed to polysomnography, which is usually performed in a sleep laboratory. In addition, we sampled sleep for a 2-week period compared to a single night, which is typical for studies using polysomnography. By measuring sleep for 2 weeks in the home environment, we think that our study offers a more representative assessment of sleep-wake habits of CF patients and age-matched control subjects, especially in light of the greater night-to-night variability displayed by our CF subjects.

While CF patients have more disrupted sleep and rate their sleep as being worse than control subjects on the PSQI, they do not report greater sleepiness on the ESS. Self-assessed ratings such as the ESS, however, can have limited correlation to objective measures of daytime sleepiness.3334 Therefore, the increased sleep fragmentation observed in the CF patients may still have consequences for daytime function. Future studies of CF patients should evaluate the relationship between sleep fragmentation and objective measures of daytime sleepiness such as the multiple sleep latency test and/or neurocognitive performance.

Compared to previous studies of sleep in CF patients, our study included patients with milder disease. Our patients had an average FEV1 of 61% predicted and a BMI of 23, both of which are higher than those values in CF patients, in whom sleep data has been previously reported.20212223 Nearly two thirds of CF patients who are ≥ 18 years of age have an FEV1 of < 70% predicted.2 Our results need to be validated in a larger study but suggest that a significant proportion of CF patients may experience sleep disruption, which would put them at risk for reduced neurocognitive function, and potentially poorer health and sense of well being.22

There is limited information about sleep fragmentation in CF patients and how this relates to measures of pulmonary disease severity.2122 While daytime hypoxemia predicts nocturnal oxyhemoglobin desaturation in CF patients,2135 and nocturnal hypoxemia correlated with sleep efficiency in one study,22 oxygen supplementation has not resulted in improved sleep quality.203637 In another study, maximum expiratory strength correlated with rapid eye movement respiratory disturbance index scores, but spirometric function and arterial blood gas levels did not correlate with the respiratory disturbance index scores.21 This suggests that lung function and gas exchange are not good predictors of sleep fragmentation but respiratory muscle strength may be. However, in our study the FEV1 correlated with actigraphically recorded FI, a measure of disrupted sleep. One reason for this apparent discrepancy may be that prior studies did not assess the relationship between pulmonary function and the AI. The AI is the one polysomnography parameter that is most closely analogous to the FI in our study.

Previous studies3839 have shown inverse correlations between PSQI scores and polysomnography-measured sleep efficiency. When combined with our results, the PSQI appears to be a useful screening tool to identify CF patients who are at risk for sleep disturbance, especially if they have a decreased FEV1. Compared to polysomnography, the PSQI is simple to perform, inexpensive, could be followed over time, and does not involve a hospital stay. If the PSQI suggests poor sleep quality, then WA and/or polysomnography studies may be obtained for further evaluation of the underlying sleep disorders. Given the night-to-night variability observed in CF patients and the relative cost of polysomnography, it may be more accurate and cost-effective to obtain wrist activity monitoring.

That many CF patients experience poor sleep is clear from the present study, but the underlying etiology of the sleep disturbance is not. Possibilities include nocturnal coughing,4041 the use of ß2-agonists before sleep, increased work of breathing,4243 psychiatric disorders, and/or periodic leg movements. While our data do not allow us to distinguish between these possibilities, they do suggest avenues for future investigation. Future studies evaluating these potential mechanisms may suggest therapeutic interventions (eg, cough suppression, anxiolytics, bilevel positive airway pressure ventilation), which could be tested in clinical trials.

There are potential limitations of this study. The sample size was relatively small, which may limit the generalizability of these findings and should be confirmed in a larger study. A limitation of WA is the inability to detect differences between quiet wakefulness and sleep, resulting in an underestimation of sleep disruption or duration. However, in the current study CF patients had greater sleep disruption than control subjects. There is also the potential to overestimate total sleep time if subjects are lying quietly awake, which would lead to an overestimation of sleep efficiency. Based on these considerations, CF patients may actually have a greater number of sleep abnormalities than those measured in our study. While we do not have concomitant polysomnography data to confirm the WA data, several previous studies25 in other patient populations have successfully used activity monitoring to characterize sleep-wake characteristics.

Sleep fragmentation and decreased sleep quality are common in CF patients and likely to contribute to decreased daytime function and quality of life. It is not known whether improving sleep quality can reduce disease severity or associated health problems in this population. In light of the consequences of prolonged sleep loss/sleep disruption on health and well-being, the identification and treatment of sleep disorders in CF patients should be an integral part of disease management.


   Acknowledgements
 
This study is dedicated to the memory of Steven Baker, who originated the idea for this study and provided immeasurable support. We wish to thank Borko Jovanovic for statistical support.


   Footnotes
 
Abbreviations: AI = arousal index; BMI = body mass index; CF = cystic fibrosis; CI = confidence interval; ESS = Epworth sleepiness scale; FI = fragmentation index; IT = immobile time; MAS = mean activity score; PSQI = Pittsburgh sleep quality index; SF-36 = Medical Outcomes Study 36-item short form; WA = wrist actigraphy

Grant Support: Pulmonary Critical Care Divisional Funds.

Received for publication June 22, 2004. Accepted for publication November 22, 2004.


   References
TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

  1. FitzSimmons, SC (1993) The changing epidemiology of cystic fibrosis. J Pediatr 122,1-9[ISI][Medline]
  2. Foundation, CF Patient registry 2001 annual data report. 2001 Cystic Fibrosis Foundation. Bethesda, MD:
  3. Guilleminault, C, Cummiskey, J, Motta, J Chronic obstructive airflow disease and sleep studies. Am Rev Respir Dis 1980;122,397-406[Medline]
  4. Fleetham, J, West, P, Mezon, B, et al Sleep, arousals, and oxygen desaturation in chronic obstructive pulmonary disease: the effect of oxygen therapy. Am Rev Respir Dis 1982;126,429-433[ISI][Medline]
  5. Wynne, JW, Block, AJ, Hemenway, J, et al Disordered breathing and oxygen desaturation during sleep in patients with chronic obstructive lung disease (COLD). Am J Med 1979;66,573-579[CrossRef][ISI][Medline]
  6. Calverley, PM, Brezinova, V, Douglas, NJ, et al The effect of oxygenation on sleep quality in chronic bronchitis and emphysema. Am Rev Respir Dis 1982;126,206-210[Medline]
  7. Cormick, W, Olson, LG, Hensley, MJ, et al Nocturnal hypoxaemia and quality of sleep in patients with chronic obstructive lung disease. Thorax 1986;41,846-854[Abstract]
  8. Fitzpatrick, MF, Martin, K, Fossey, E, et al Snoring, asthma and sleep disturbance in Britain: a community-based survey. Eur Respir J 1993;6,531-535[Abstract]
  9. Janson, C, De Backer, W, Gislason, T, et al Increased prevalence of sleep disturbances and daytime sleepiness in subjects with bronchial asthma: a population study of young adults in three European countries. Eur Respir J 1996;9,2132-2138[Abstract]
  10. Avital, A, Sanchez, I, Holbrow, J, et al Effect of theophylline on lung function tests, sleep quality, and nighttime SaO2 in children with cystic fibrosis. Am Rev Respir Dis 1991;144,1245-1249[Medline]
  11. Lipsky, BA, Baker, CA Fluoroquinolone toxicity profiles: a review focusing on newer agents. Clin Infect Dis 1999;28,352-364[ISI][Medline]
  12. Thase, ME Depression, sleep, and antidepressants. J Clin Psychiatry 1998;59(suppl),55-65
  13. Williams, DC Periodic limb movements of sleep and the restless legs syndrome. Va Med Q 1996;123,260-265[Medline]
  14. Moyer, CA, Sonnad, SS, Garetz, SL, et al Quality of life in obstructive sleep apnea: a systematic review of the literature. Sleep Med 2001;2,477-491[Medline]
  15. Spiegel, K, Sheridan, JF, Van Cauter, E Effect of sleep deprivation on response to immunization. JAMA 2002;288,1471-1472[Free Full Text]
  16. Tasali, E, Van Cauter, E Sleep-disordered breathing and the current epidemic of obesity: consequence or contributing factor? Am J Respir Crit Care Med 2002;165,562-563[Free Full Text]
  17. Van Cauter, E, Copinschi, G Interrelationships between growth hormone and sleep. Growth Horm IGF Res 2000;10(suppl),S57-S62
  18. Spiegel, K, Leproult, R, Van Cauter, E Impact of sleep debt on metabolic and endocrine function. Lancet 1999;354,1435-1439[CrossRef][ISI][Medline]
  19. Lanfranchi, PA, Somers, VK Sleep-disordered breathing in heart failure: characteristics and implications. Respir Physiol Neurobiol 2003;136,153-165[CrossRef][ISI][Medline]
  20. Milross, MA, Piper, AJ, Norman, M, et al Low-flow oxygen and bilevel ventilatory support: effects on ventilation during sleep in cystic fibrosis. Am J Respir Crit Care Med 2001;163,129-134[Abstract/Free Full Text]
  21. Milross, MA, Piper, AJ, Norman, M, et al Predicting sleep-disordered breathing in patients with cystic fibrosis. Chest 2001;120,1239-1245[Abstract/Free Full Text]
  22. Dancey, DR, Tullis, ED, Heslegrave, R, et al Sleep quality and daytime function in adults with cystic fibrosis and severe lung disease. Eur Respir J 2002;19,504-510[Abstract/Free Full Text]
  23. Bradley, S, Solin, P, Wilson, J, et al Hypoxemia and hypercapnia during exercise and sleep in patients with cystic fibrosis. Chest 1999;116,647-654[Abstract/Free Full Text]
  24. Sadeh, A, Hauri, PJ, Kripke, DF, et al The role of actigraphy in the evaluation of sleep disorders. Sleep 1995;18,288-302[ISI][Medline]
  25. Ancoli-Israel, S, Cole, R, Alessi, C, et al The role of actigraphy in the study of sleep and circadian rhythms. Sleep 2003;26,342-392[ISI][Medline]
  26. Littner, M, Kushida, CA, Anderson, WM, et al Practice parameters for the role of actigraphy in the study of sleep and circadian rhythms: an update for 2002. Sleep 2003;26,337-341[ISI][Medline]
  27. Buysse, DJ, Reynolds, CF, III, Monk, TH, et al The Pittsburgh sleep quality index: a new instrument for psychiatric practice and research. Psychiatry Res 1989;28,193-213[CrossRef][ISI][Medline]
  28. Johns, MW Reliability and factor analysis of the Epworth sleepiness scale. Sleep 1992;15,376-381[ISI][Medline]
  29. Johns, MW A new method for measuring daytime sleepiness: the Epworth sleepiness scale. Sleep 1991;14,540-545[ISI][Medline]
  30. Ware, JE, Jr, Gandek, B Overview of the SF-36 Health Survey and the International Quality of Life Assessment (IQOLA) Project. J Clin Epidemiol 1998;51,903-912[CrossRef][ISI][Medline]
  31. Efron, B The jackknife, the bootstrap and other resampling plans. 1982 Society for Industrial and Applied Mathematics. Philadelphia, PA:
  32. Ware, JE, Jr, Kosinski, M, Bayliss, MS, et al Comparison of methods for the scoring and statistical analysis of SF-36 health profile and summary measures: summary of results from the Medical Outcomes Study. Med Care 1995;33,AS264-A279[ISI][Medline]
  33. Sangal, RB, Sangal, JM Rating scales for inattention and sleepiness are correlated in adults with symptoms of sleep disordered breathing syndrome, but not in adults with symptoms of attention-deficit/hyperactivity disorder. Sleep Med 2004;5,133-135[Medline]
  34. Pelin, Z, Karadeniz, D, Ozturk, L, et al The role of mean inspiratory effort on daytime sleepiness. Eur Respir J 2003;21,688-694[Abstract/Free Full Text]
  35. Versteegh, FG, Bogaard, JM, Raatgever, JW, et al Relationship between airway obstruction, desaturation during exercise and nocturnal hypoxaemia in cystic fibrosis patients. Eur Respir J 1990;3,68-73[Abstract]
  36. Tepper, RS, Skatrud, JB, Dempsey, JA Ventilation and oxygenation changes during sleep in cystic fibrosis. Chest 1983;84,388-393[Abstract/Free Full Text]
  37. Spier, S, Rivlin, J, Hughes, D, et al The effect of oxygen on sleep, blood gases, and ventilation in cystic fibrosis. Am Rev Respir Dis 1984;129,712-718[Medline]
  38. Backhaus, J, Junghanns, K, Broocks, A, et al Test-retest reliability and validity of the Pittsburgh Sleep Quality Index in primary insomnia. J Psychosom Res 2002;53,737-740[CrossRef][ISI][Medline]
  39. Milross, MA, Piper, AJ, Norman, M, et al Subjective sleep quality in cystic fibrosis. Sleep Med 2002;3,205-212[CrossRef][Medline]
  40. Stokes, DC, McBride, JT, Wall, MA, et al Sleep hypoxemia in young adults with cystic fibrosis. Am J Dis Child 1980;134,741-743[Abstract]
  41. Francis, PW, Muller, NL, Gurwitz, D, et al Hemoglobin desaturation: its occurrence during sleep in patients with cystic fibrosis. Am J Dis Child 1980;134,734-740[Abstract]
  42. O’Neill, S, Leahy, F, Pasterkamp, H, et al The effects of chronic hyperinflation, nutritional status, and posture on respiratory muscle strength in cystic fibrosis. Am Rev Respir Dis 1983;128,1051-1054[ISI][Medline]
  43. Marks, J, Pasterkamp, H, Tal, A, et al Relationship between respiratory muscle strength, nutritional status, and lung volume in cystic fibrosis and asthma. Am Rev Respir Dis 1986;133,414-417[ISI][Medline]




This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF) Free
Right arrow Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Article Archive
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via ISI Web of Science (6)
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Jankelowitz, L.
Right arrow Articles by Jain, M.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Jankelowitz, L.
Right arrow Articles by Jain, M.

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS